[r-cran-zelig] 77/102: Import Upstream version 3.5
Andreas Tille
tille at debian.org
Sun Jan 8 17:00:07 UTC 2017
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-zelig.
commit a0fac48f5c261a59cc5c6a8dcf4a99aefb5dce2a
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:40:26 2017 +0100
Import Upstream version 3.5
---
DESCRIPTION | 8 +-
R/summarize.default.R | 2 +-
data/MatchIt.url.tab | 4 -
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data/PErisk.txt | 63 -
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data/Zelig.url.tab | 50 -
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inst/doc/vigpics/poisson_gee-ExamplePlot.pdf | 708 -
inst/doc/vigpics/poissonsurvey-ExistingPlot.eps | 1487 +-
inst/doc/vigpics/poissonsurvey-ExistingPlot.pdf | 2382 +-
inst/doc/vigpics/poissonsurvey-ReplicatePlot.eps | 1550 +-
inst/doc/vigpics/poissonsurvey-ReplicatePlot.pdf | 2456 +-
inst/doc/vigpics/probit_gee-ExamplePlot.eps | 706 -
inst/doc/vigpics/probit_gee-ExamplePlot.pdf | 696 -
.../doc/vigpics/probit_gee-FirstDifferencePlot.eps | 1947 -
.../doc/vigpics/probit_gee-FirstDifferencePlot.pdf | 1880 -
inst/doc/vigpics/probitnet-007.eps | 777 -
inst/doc/vigpics/probitnet-007.pdf | 757 -
inst/doc/vigpics/probitnet-009.eps | 777 -
inst/doc/vigpics/probitnet-009.pdf | 757 -
inst/doc/vigpics/probitsurvey-ExistingPlot.eps | 2080 +-
inst/doc/vigpics/probitsurvey-ExistingPlot.pdf | 3357 +-
inst/doc/vigpics/probitsurvey-ReplicatePlot.eps | 25 +-
inst/doc/vigpics/probitsurvey-ReplicatePlot.pdf | 302 +-
inst/doc/vigpics/quantile-DummyPlot.eps | 1946 -
inst/doc/vigpics/quantile-DummyPlot.pdf | 1863 -
inst/doc/vigpics/quantile-ExamplesPlot.eps | 1939 -
inst/doc/vigpics/quantile-ExamplesPlot.pdf | 1864 -
inst/doc/vigpics/quantile-MultiplePlot.eps | 418 -
inst/doc/vigpics/quantile-MultiplePlot.pdf | 354 -
inst/doc/vigpics/relogit-Example1Plot.eps | 776 -
inst/doc/vigpics/relogit-Example1Plot.pdf | 754 -
inst/doc/vigpics/relogit-Example3Plot.eps | 779 -
inst/doc/vigpics/relogit-Example3Plot.pdf | 752 -
inst/doc/vigpics/roc.pdf | 401 -
inst/doc/vigpics/rq-DummyPlot.eps | 1963 -
inst/doc/vigpics/rq-DummyPlot.pdf | 1881 -
inst/doc/vigpics/rq-ExamplesPlot.eps | 1921 -
inst/doc/vigpics/rq-ExamplesPlot.pdf | 1854 -
inst/doc/vigpics/rq-MultiplePlot.eps | 418 -
inst/doc/vigpics/rq-MultiplePlot.pdf | 354 -
inst/doc/vigpics/threesls-Examplesthreesls.eps | 3269 +-
inst/doc/vigpics/threesls-Examplesthreesls.pdf | 4560 +-
inst/doc/vigpics/twosls-Examplestwosls.eps | 3802 -
inst/doc/vigpics/twosls-Examplestwosls.pdf | 3633 -
inst/doc/vigpics/weibull-ExamplePlot.eps | 2197 +-
inst/doc/vigpics/weibull-ExamplePlot.pdf | 3298 +-
inst/doc/weibull.log | 695 -
inst/doc/weibull.pdf | Bin 135232 -> 208997 bytes
inst/doc/whatsnew.tex | 455 -
inst/doc/zelig.html | 371 -
inst/doc/zelig.log | 1604 -
inst/doc/zelig.pdf | Bin 370675 -> 0 bytes
inst/doc/zelig.tex | 81 -
inst/doc/zeligfooter.html | 34 -
inst/doc/zeligheader.html | 236 -
inst/doc/zeligmodels.html | 101 -
inst/doc/zinput.tex | 7 +-
500 files changed, 82919 insertions(+), 468661 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 06b1c02..3a8090b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,11 +1,11 @@
Package: Zelig
-Version: 3.4-8
+Version: 3.5
Date: 2010-01-20
Title: Everyone's Statistical Software
Author: Kosuke Imai <kimai at Princeton.Edu>, Gary King
<king at harvard.edu>, Olivia Lau <olivia.lau at post.harvard.edu>
Maintainer: Kosuke Imai <kimai at Princeton.Edu>
-Depends: R (>= 2.6.0), MASS, boot
+Depends: R (>= 2.10), MASS, boot
Description: Zelig is an easy-to-use program that can estimate, and
help interpret the results of, an enormous range of statistical
models. It literally is ``everyone's statistical software''
@@ -24,6 +24,6 @@ URL: http://gking.harvard.edu/zelig
Suggests: VGAM (>= 0.7-5), MCMCpack (>= 0.8-2), mvtnorm, survival,
sandwich (>= 2.1-0), zoo (>= 1.5-0), coda, nnet, sna, gee,
systemfit, mgcv, lme4, anchors (>= 2.0), survey, quantreg
-Packaged: 2010-01-21 01:48:21 UTC; rbuild
Repository: CRAN
-Date/Publication: 2010-01-21 07:13:07
+Date/Publication: 2011-02-14 19:44:16
+Packaged: 2011-02-14 18:45:18 UTC; matt
diff --git a/R/summarize.default.R b/R/summarize.default.R
index 507fc98..8ec79fd 100644
--- a/R/summarize.default.R
+++ b/R/summarize.default.R
@@ -3,7 +3,7 @@ summarize.default <- function(x, rows = NULL, cip, stats, subset = NULL) {
if (is.numeric(x)) {
for (i in 1:length(stats))
res <- c(res, do.call(stats[i], list(x)))
- res <- c(res, quantile(x, cip))
+ res <- c(res, quantile(x, cip, na.rm=TRUE))
names(res) <- c(stats, paste(cip*100, "%", sep = ""))
}
else if (is.character(x)) {
diff --git a/data/MatchIt.url.tab b/data/MatchIt.url.tab
deleted file mode 100644
index f647e0c..0000000
--- a/data/MatchIt.url.tab
+++ /dev/null
@@ -1,4 +0,0 @@
-MatchIt http://gking.harvard.edu/matchit
-matchit http://gking.harvard.edu/matchit/docs/_TT_matchit_TT__Implem.html
-match.data http://gking.harvard.edu/matchit/docs/_TT_match_data_TT__Ext.html
-
diff --git a/data/MatchIt.url.tab.gz b/data/MatchIt.url.tab.gz
new file mode 100644
index 0000000..a0e3c33
Binary files /dev/null and b/data/MatchIt.url.tab.gz differ
diff --git a/data/PErisk.txt b/data/PErisk.txt
deleted file mode 100644
index 2e556ca..0000000
--- a/data/PErisk.txt
+++ /dev/null
@@ -1,63 +0,0 @@
-"country" "courts" "barb2" "prsexp2" "prscorr2" "gdpw2"
-"Argentina" "Argentina" "0" -0.7207754 "1" "3" 9.69017
-"Australia" "Australia" "1" -6.907755 "5" "4" 10.30484
-"Austria" "Austria" "1" -4.910337 "5" "4" 10.10094
-"Bangladesh" "Bangladesh" "0" 0.7759748 "1" "0" 8.379768
-"Belgium" "Belgium" "1" -4.617344 "5" "4" 10.25012
-"Bolivia" "Bolivia" "0" -2.46144 "0" "0" 8.583543
-"Botswana" "Botswana" "1" -1.244868 "4" "3" 8.77771
-"Brazil" "Brazil" "1" -0.4570337 "4" "3" 9.375601
-"Burma" "Burma" "0" 1.604343 "3" "1" 7.096721
-"Cameroon" "Cameroon" "0" -4.229065 "3" "1" 8.120886
-"Canada" "Canada" "1" -6.907755 "5" "5" 10.41018
-"Chile" "Chile" "1" -1.542761 "3" "2" 9.261224
-"Colombia" "Colombia" "0" -2.057821 "3" "2" 9.191973
-"Congo-Kinshasa" "Congo-Kinshasa" "0" -2.323288 "1" "0" 7.095064
-"Costa Rica" "Costa Rica" "1" -5.090003 "3" "4" 9.167329
-"Cote d'Ivoire" "Cote d'Ivoire" "1" -4.229065 "4" "2" 8.228711
-"Denmark" "Denmark" "1" -6.907755 "5" "5" 10.10651
-"Dominican Republic" "Dominican Republic" "0" -2.378862 "2" "2" 8.899731
-"Ecuador" "Ecuador" "1" -1.845337 "3" "2" 9.117786
-"Finland" "Finland" "1" -6.907755 "5" "5" 10.12367
-"Gambia, The" "Gambia, The" "0" -1.543332 "4" "2" 7.501082
-"Ghana" "Ghana" "0" -1.011517 "2" "1" 7.597396
-"Greece" "Greece" "0" -2.073237 "3" "3" 9.701494
-"Hungary" "Hungary" "1" -0.9041942 "4" "3" 9.35184
-"India" "India" "0" -2.105104 "4" "2" 7.970049
-"Indonesia" "Indonesia" "0" -2.100232 "3" "0" 8.39231
-"Iran" "Iran" "0" 2.337425 "0" "2" 9.368114
-"Ireland" "Ireland" "1" -6.907755 "5" "4" 9.891465
-"Israel" "Israel" "0" -2.319996 "4" "4" 10.06777
-"Italy" "Italy" "1" -6.907755 "4" "3" 10.26078
-"Japan" "Japan" "1" -6.907755 "5" "4" 9.892022
-"Kenya" "Kenya" "0" -2.327605 "2" "2" 7.619724
-"Korea, South" "Korea, South" "0" -2.655795 "4" "1" 9.422787
-"Malawi" "Malawi" "0" -1.469424 "3" "3" 7.029973
-"Malaysia" "Malaysia" "1" -3.927949 "4" "3" 9.178953
-"Mexico" "Mexico" "0" -1.657935 "2" "2" 9.661735
-"Morocco" "Morocco" "0" -3.156958 "3" "1" 8.78048
-"New Zealand" "New Zealand" "1" -6.907755 "5" "5" 10.17626
-"Nigeria" "Nigeria" "0" 0.3001068 "1" "1" 7.68708
-"Norway" "Norway" "1" -6.907755 "5" "5" 10.29833
-"Papua New Guinea" "Papua New Guinea" "1" -2.636158 "4" "2" 8.126518
-"Paraguay" "Paraguay" "0" -0.9707628 "3" "0" 8.727616
-"Philippines" "Philippines" "0" -2.964776 "1" "1" 8.384804
-"Poland" "Poland" "1" 1.317021 "3" "3" 9.0524
-"Portugal" "Portugal" "1" -2.459625 "4" "3" 9.444543
-"Sierra Leone" "Sierra Leone" "0" 1.406406 "3" "1" 7.759614
-"Singapore" "Singapore" "1" -4.848516 "5" "5" 9.882724
-"South Africa" "South Africa" "0" -2.17582 "3" "4" 9.191871
-"Spain" "Spain" "1" -6.907755 "5" "3" 10.04733
-"Sri Lanka" "Sri Lanka" "0" -1.864343 "2" "2" 8.627661
-"Sweden" "Sweden" "1" -6.907755 "4" "5" 10.22434
-"Switzerland" "Switzerland" "1" -6.907755 "5" "5" 10.3411
-"Syria" "Syria" "0" 1.725166 "1" "1" 9.664151
-"Thailand" "Thailand" "0" -6.907755 "3" "2" 8.548692
-"Togo" "Togo" "0" -4.229065 "4" "1" 7.331715
-"Tunisia" "Tunisia" "0" -2.585399 "2" "2" 9.047586
-"Turkey" "Turkey" "0" -2.673243 "3" "2" 8.978912
-"United Kingdom" "United Kingdom" "1" -6.907755 "5" "5" 10.12727
-"Uruguay" "Uruguay" "0" -2.127775 "2" "2" 9.414342
-"Venezuela" "Venezuela" "1" 0.428845 "3" "2" 9.84882
-"Zambia" "Zambia" "0" 0.9658105 "3" "1" 7.726213
-"Zimbabwe" "Zimbabwe" "0" -0.6403214 "3" "2" 7.965893
diff --git a/data/PErisk.txt.bz2 b/data/PErisk.txt.bz2
new file mode 100644
index 0000000..056a8ed
Binary files /dev/null and b/data/PErisk.txt.bz2 differ
diff --git a/data/SupremeCourt.txt b/data/SupremeCourt.txt
deleted file mode 100644
index 07b11a7..0000000
--- a/data/SupremeCourt.txt
+++ /dev/null
@@ -1,44 +0,0 @@
-"Rehnquist" "Stevens" "OConnor" "Scalia" "Kennedy" "Souter" "Thomas" "Ginsburg" "Breyer"
-"1" 0 1 1 0 1 1 0 1 1
-"2" 0 1 0 0 0 1 0 1 1
-"3" 0 1 0 0 0 1 0 1 1
-"4" 0 0 0 0 0 0 0 0 1
-"5" 1 1 0 0 1 0 0 0 0
-"6" 0 1 0 0 0 0 0 0 0
-"7" 0 1 1 0 0 1 0 1 1
-"8" 0 1 0 0 0 0 0 0 0
-"9" 0 1 0 0 1 1 0 1 1
-"10" 1 1 1 0 1 1 0 1 1
-"11" 0 1 1 0 1 1 0 1 1
-"12" 0 1 0 0 0 1 0 1 1
-"13" 1 0 1 1 1 1 1 1 0
-"14" 0 1 0 0 0 1 0 1 1
-"15" 0 1 1 0 0 1 0 1 1
-"16" 0 1 0 0 0 1 0 1 1
-"17" 0 1 1 0 0 0 0 1 1
-"18" 0 1 0 0 0 1 0 1 1
-"19" 0 1 0 0 0 1 0 1 1
-"20" 0 0 0 0 0 0 0 1 0
-"21" 0 1 0 1 0 0 1 0 1
-"22" 0 1 1 0 1 1 0 1 1
-"23" 1 NA NA 0 1 1 0 1 1
-"24" 0 1 0 0 0 1 0 1 1
-"25" 1 1 1 0 1 1 0 1 1
-"26" 0 1 0 0 0 1 0 1 1
-"27" 0 1 1 0 1 1 0 1 1
-"28" 0 1 0 0 0 0 0 0 0
-"29" 0 0 0 1 0 1 1 1 1
-"30" 0 0 1 0 0 1 0 1 1
-"31" 0 1 0 0 0 1 0 1 0
-"32" 0 0 0 0 0 0 0 1 1
-"33" 1 1 1 0 1 1 1 1 1
-"34" 0 1 1 0 0 1 0 1 1
-"35" 0 1 0 0 1 1 0 1 1
-"36" 0 1 0 0 1 1 0 1 1
-"37" 1 1 0 1 1 1 1 0 0
-"38" 0 1 1 0 0 1 0 1 1
-"39" 1 0 1 1 1 1 1 1 0
-"40" 1 0 1 1 1 0 1 0 0
-"41" 0 1 0 0 0 1 0 1 1
-"42" 0 1 0 0 0 1 0 1 1
-"43" 0 1 1 0 0 1 0 1 1
diff --git a/data/SupremeCourt.txt.gz b/data/SupremeCourt.txt.gz
new file mode 100644
index 0000000..5130495
Binary files /dev/null and b/data/SupremeCourt.txt.gz differ
diff --git a/data/Weimar.txt b/data/Weimar.txt
deleted file mode 100644
index 060013d..0000000
--- a/data/Weimar.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-Nazi Government Communists FarRight Other shareunemployed shareblue sharewhite shareself sharedomestic shareprotestants
-6710 6854 1747 1440 4024 0.075565 0.4667877 0.0751894 0.1452451 0.2372128 0.9708675
-14006 26892 3904 5046 12117 0.0841387 0.37847 0.0886076 0.1776995 0.2710842 0.4913195
-4411 15359 1620 2901 7418 0.0685777 0.2878295 0.061648 0.21005 0.3718949 0.0613757
-26095 16502 10764 7174 17918 0.0887963 0.4592575 0.0911449 0.1498591 0.2109423 0.9626554
-75760 42647 33878 10478 51069 0.234548 0.2782162 0.2976835 0.1146592 0.0748931 0.8933946
-11339 3936 4404 1948 9413 0.0690353 0.3040565 0.0573774 0.2145604 0.3549705 0.9769614
-12468 6386 1957 4274 7844 0.0699124 0.3878074 0.0623398 0.1842659 0.2956744 0.9476545
-18987 12960 6048 6074 12977 0.0829753 0.4434049 0.0756757 0.1596905 0.2382536 0.9487126
-10866 4226 1427 2357 4526 0.0513157 0.4367858 0.0648391 0.1704057 0.2766537 0.9395571
-10904 7230 3472 3475 5015 0.098927 0.4601309 0.1309826 0.1285784 0.1813811 0.9099904
diff --git a/data/Weimar.txt.gz b/data/Weimar.txt.gz
new file mode 100644
index 0000000..3c65b88
Binary files /dev/null and b/data/Weimar.txt.gz differ
diff --git a/data/Zelig.url.tab b/data/Zelig.url.tab
deleted file mode 100644
index a424e56..0000000
--- a/data/Zelig.url.tab
+++ /dev/null
@@ -1,50 +0,0 @@
-command http://gking.harvard.edu/zelig/docs/Main_Commands.html
-commands http://gking.harvard.edu/zelig/docs/Main_Commands.html
-model http://gking.harvard.edu/zelig/docs/Models_Zelig_Can.html
-models http://gking.harvard.edu/zelig/docs/Models_Zelig_Can.html
-zelig http://gking.harvard.edu/zelig/docs/_TT_zelig_TT__Estimati.html
-setx http://gking.harvard.edu/zelig/docs/_TT_setx_TT__Setting_E.html
-sim http://gking.harvard.edu/zelig/docs/_TT_sim_TT__Simulating.html
-matchit http://gking.harvard.edu/zelig/docs/_TT_matchit_TT__Create.html
-Matchit http://gking.harvard.edu/zelig/docs/_TT_matchit_TT__Create.html
-MatchIt http://gking.harvard.edu/zelig/docs/_TT_matchit_TT__Create.html
-summary http://gking.harvard.edu/zelig/docs/_TT_summary_TT__Summar.html
-print http://gking.harvard.edu/zelig/docs/_TT_print_TT__Printing.html
-plot http://gking.harvard.edu/zelig/docs/_TT_plot_TT__Graphing.html
-plot.ci http://gking.harvard.edu/zelig/docs/_TT_plot_ci_TT__Plotti.html
-ROC http://gking.harvard.edu/zelig/docs/_TT_rocplot_TT__Receiv.html
-roc http://gking.harvard.edu/zelig/docs/_TT_rocplot_TT__Receiv.html
-rocplot http://gking.harvard.edu/zelig/docs/_TT_rocplot_TT__Receiv.html
-ternarypoints http://gking.harvard.edu/zelig/docs/_TT_ternarypoints_TT.html
-repl http://gking.harvard.edu/zelig/docs/_TT_repl_TT__Replicati.html
-blogit http://gking.harvard.edu/zelig/docs/_TT_blogit_TT__Bivaria.html
-bprobit http://gking.harvard.edu/zelig/docs/_TT_bprobit_TT__Bivari.html
-exp http://gking.harvard.edu/zelig/docs/_TT_exp_TT__Exponentia.html
-ei.dynamic http://gking.harvard.edu/zelig/docs/_TT_ei_dynamic_TT__Qui.html
-ei.hier http://gking.harvard.edu/zelig/docs/_TT_ei_hier_TT__Hierar.html
-factor.bayes http://gking.harvard.edu/zelig/docs/_TT_factor_bayes_TT__B.html
-factor.mix http://gking.harvard.edu/zelig/docs/_TT_factor_mix_TT__Mix.html
-factor.ord http://gking.harvard.edu/zelig/docs/_TT_factor_ord_TT__Ord.html
-gamma http://gking.harvard.edu/zelig/docs/_TT_gamma_TT__Gamma_Re.html
-irt1d http://gking.harvard.edu/zelig/docs/_TT_irt1d_TT__One_Dime.html
-irtkd http://gking.harvard.edu/zelig/docs/_TT_irtkd_TT__tex2htm.html
-ls http://gking.harvard.edu/zelig/docs/_TT_ls_TT__Least_Squar.html
-logit http://gking.harvard.edu/zelig/docs/_TT_logit_TT__Logistic.html
-logit.bayes http://gking.harvard.edu/zelig/docs/_TT_logit_bayes_TT__Ba.html
-lognorm http://gking.harvard.edu/zelig/docs/_TT_lognorm_TT__Log_No.html
-mlogit http://gking.harvard.edu/zelig/docs/_TT_mlogit_TT__Multino.html
-mlogit.bayes http://gking.harvard.edu/zelig/docs/_TT_mlogit_bayes_TT__B.html
-negbin http://gking.harvard.edu/zelig/docs/_TT_negbin_TT__Negativ.html
-normal http://gking.harvard.edu/zelig/docs/_TT_normal_TT__Normal.html
-normal.bayes http://gking.harvard.edu/zelig/docs/_TT_normal_bayes_TT__B.html
-ologit http://gking.harvard.edu/zelig/docs/_TT_ologit_TT__Ordinal.html
-oprobit http://gking.harvard.edu/zelig/docs/_TT_oprobit_TT__Ordina.html
-oprobit.bayes http://gking.harvard.edu/zelig/docs/_TT_oprobit_bayes_TT.html
-poisson http://gking.harvard.edu/zelig/docs/_TT_poisson_TT__Poisso.html
-poisson.bayes http://gking.harvard.edu/zelig/docs/_TT_poisson_bayes_TT.html
-probit http://gking.harvard.edu/zelig/docs/_TT_probit_TT__Probit.html
-probit.bayes http://gking.harvard.edu/zelig/docs/_TT_probit_bayes_TT__B.html
-relogit http://gking.harvard.edu/zelig/docs/_TT_relogit_TT__Rare_E.html
-tobit http://gking.harvard.edu/zelig/docs/_TT_tobit_TT__Regress.html
-tobit.bayes http://gking.harvard.edu/zelig/docs/_TT_tobit_bayes_TT.html
-weibull http://gking.harvard.edu/zelig/docs/_TT_weibull_TT__Weibul.html
diff --git a/data/Zelig.url.tab.gz b/data/Zelig.url.tab.gz
new file mode 100644
index 0000000..f3377f9
Binary files /dev/null and b/data/Zelig.url.tab.gz differ
diff --git a/data/approval.tab b/data/approval.tab
deleted file mode 100644
index abc2ea1..0000000
--- a/data/approval.tab
+++ /dev/null
@@ -1,66 +0,0 @@
-"month" "year" "approve" "disapprove" "unsure" "sept.oct.2001" "iraq.war" "avg.price"
-"1" 2 2001 58.67 23.67 17.67 0 0 144.975
-"2" 3 2001 58 26.67 15.33 0 0 140.925
-"3" 4 2001 60.5 29.5 10 0 0 155.16
-"4" 5 2001 55 33.33 11.67 0 0 170.175
-"5" 6 2001 54 34 12 0 0 161.625
-"6" 7 2001 56.5 34 9.5 0 0 142.06
-"7" 8 2001 56 35 9 0 0 142.075
-"8" 9 2001 75.67 18.33 6 1 0 152.15
-"9" 10 2001 88 9 3 1 0 131.54
-"10" 11 2001 87 8.67 4.33 0 0 117.05
-"11" 12 2001 86 10.5 3.5 0 0 108.6
-"12" 1 2002 83.67 12.67 3.67 0 0 110.725
-"13" 2 2002 82 14 4 0 0 111.375
-"14" 3 2002 79.25 15.75 5 0 0 124.925
-"15" 4 2002 76.25 19 4.75 0 0 139.7
-"16" 5 2002 76.33 17.67 6 0 0 139.175
-"17" 6 2002 73.4 20.2 6.4 0 0 138.225
-"18" 7 2002 70.5 23.5 6 0 0 139.7
-"19" 8 2002 66.5 27 6.5 0 0 139.575
-"20" 9 2002 67.2 28.2 4.6 0 0 139.96
-"21" 10 2002 64.75 29 6.25 0 0 144.525
-"22" 11 2002 66.33 27 6.67 0 0 141.9
-"23" 12 2002 62.75 31.5 5.75 0 0 138.58
-"24" 1 2003 60.17 35 4.83 0 0 145.75
-"25" 2 2003 58.75 35.75 5.5 0 0 161.3
-"26" 3 2003 65.2 30.6 4.2 0 1 169.3
-"27" 4 2003 70 25.75 4.25 0 1 158.9
-"28" 5 2003 66.33 30 3.67 0 1 149.725
-"29" 6 2003 62 34.33 3.67 0 0 149.28
-"30" 7 2003 59.67 36.67 3.67 0 0 151.25
-"31" 8 2003 59.5 36.5 4 0 0 162.025
-"32" 9 2003 51 45 4 0 0 167.88
-"33" 10 2003 54.67 41.33 4 0 0 156.35
-"34" 11 2003 51.67 45 3.33 0 0 151.2
-"35" 12 2003 58.5 38.25 3.25 0 0 147.88
-"36" 1 2004 55.25 41.25 3.5 0 0 157.175
-"37" 2 2004 51.33 45.33 3.33 0 0 164.75
-"38" 3 2004 50.67 46.33 3 0 0 173.6
-"39" 4 2004 52 45 3 0 0 179.775
-"40" 5 2004 47.33 49.33 3.33 0 0 198.34
-"41" 6 2004 48.5 49 2.5 0 0 196.925
-"42" 7 2004 47.75 48.5 3.75 0 0 191.125
-"43" 8 2004 50 46.5 3.5 0 0 187.8
-"44" 9 2004 52.67 45 2.33 0 0 186.975
-"45" 10 2004 49.17 47.67 3.17 0 0 199.95
-"46" 11 2004 54 43 3 0 0 197.94
-"47" 12 2004 51 45 4 0 0 184.1
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diff --git a/data/coalition.tab.xz b/data/coalition.tab.xz
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diff --git a/data/coalition2.txt b/data/coalition2.txt
deleted file mode 100644
index 310ceb2..0000000
--- a/data/coalition2.txt
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-"144" 13 1 0 761 23 1 65 "iceland"
-"145" 2 1 0 761 23 0 3 "iceland"
-"146" 39 0 0 736 18 1 67 "iceland"
-"147" 48 0 0 754 17 1 28 "iceland"
-"148" 40 1 1 724 0 0 0 "ireland"
-"149" 35 1 1 692 0 0 0 "ireland"
-"150" 34 1 1 671 0 1 13 "ireland"
-"151" 27 1 1 636 3 1 0 "ireland"
-"152" 28 1 1 636 3 1 0 "ireland"
-"153" 42 1 1 645 0 0 0 "ireland"
-"154" 19 1 1 620 0 1 0 "ireland"
-"155" 32 1 1 620 0 1 2 "ireland"
-"156" 44 1 1 593 0 1 0 "ireland"
-"157" 52 0 1 612 0 1 0 "ireland"
-"158" 29 1 1 580 0 1 0 "ireland"
-"159" 18 1 1 580 0 1 6 "ireland"
-"160" 7 1 1 582 0 0 0 "ireland"
-"161" 8 1 1 609 0 0 19 "ireland"
-"162" 48 1 1 608 0 1 20 "ireland"
-"163" 11 1 1 802 11 1 71 "israel"
-"164" 3 1 1 802 11 1 0 "israel"
-"165" 12 1 1 802 11 1 4 "israel"
-"166" 18 1 1 802 11 1 28 "israel"
-"167" 4 1 1 802 11 1 0 "israel"
-"168" 6 1 1 834 18 1 7 "israel"
-"169" 16 1 1 834 18 1 0 "israel"
-"170" 13 1 1 797 16 1 44 "israel"
-"171" 17 1 1 814 18 1 274 "israel"
-"172" 18 1 1 814 18 1 10 "israel"
-"173" 10 1 1 814 18 1 8 "israel"
-"174" 17 1 1 789 5 1 70 "israel"
-"175" 21 1 1 789 5 1 0 "israel"
-"176" 8 1 1 789 5 1 19 "israel"
-"177" 8 1 1 721 5 1 31 "israel"
-"178" 41 0 1 721 5 1 0 "israel"
-"179" 1 1 1 702 8 1 68 "israel"
-"180" 5 1 1 702 8 1 53 "israel"
-"181" 26 1 1 702 8 1 0 "israel"
-"182" 4 1 1 771 11 0 183 "israel"
-"183" 44 0 1 771 11 1 0 "israel"
-"184" 25 1 1 680 5 0 36 "israel"
-"185" 9 1 1 680 5 0 35 "israel"
-"186" 25 1 1 741 6 1 52 "israel"
-"187" 2 1 1 652 36 1 7 "italy"
-"188" 14 1 1 652 36 1 15 "italy"
-"189" 3 1 1 652 36 1 1 "italy"
-"190" 23 1 1 652 36 1 10 "italy"
-"191" 0.5 1 1 718 36 0 17 "italy"
-"192" 5 1 1 718 36 0 20 "italy"
-"193" 0.5 1 1 718 36 0 13 "italy"
-"194" 16 1 1 718 36 1 11 "italy"
-"195" 22 1 1 718 36 1 14 "italy"
-"196" 13 1 1 718 36 0 13 "italy"
-"197" 7 1 1 710 32 0 12 "italy"
-"198" 12 1 1 710 32 0 20 "italy"
-"199" 4 1 1 710 32 0 30 "italy"
-"200" 19 1 1 710 32 0 7 "italy"
-"201" 15 1 1 710 32 1 19 "italy"
-"202" 5 1 1 733 32 0 36 "italy"
-"203" 7 1 1 733 32 1 29 "italy"
-"204" 18 1 1 733 32 1 26 "italy"
-"205" 27 1 1 733 32 1 33 "italy"
-"206" 5 1 1 717 37 0 19 "italy"
-"207" 7 1 1 717 37 1 23 "italy"
-"208" 6 1 1 717 37 0 31 "italy"
-"209" 3 1 1 717 37 1 48 "italy"
-"210" 7 1 1 717 37 1 31 "italy"
-"211" 10 1 1 717 37 1 0 "italy"
-"212" 0.5 1 1 717 37 0 33 "italy"
-"213" 12 1 1 719 37 1 121 "italy"
-"214" 8 1 1 719 37 1 25 "italy"
-"215" 7 1 1 719 37 1 12 "italy"
-"216" 14 1 1 719 37 0 51 "italy"
-"217" 3 1 1 719 37 0 36 "italy"
-"218" 18 1 1 683 43 0 90 "italy"
-"219" 11 1 1 683 43 0 55 "italy"
-"220" 0.5 1 1 683 43 0 47 "italy"
-"221" 8 1 1 710 38 0 126 "italy"
-"222" 6 1 1 710 38 1 15 "italy"
-"223" 7 1 1 710 38 1 22 "italy"
-"224" 13 1 1 710 38 1 33 "italy"
-"225" 3 1 1 710 38 1 16 "italy"
-"226" 5 1 1 710 38 1 17 "italy"
-"227" 45 1 1 751 39 1 98 "italy"
-"228" 30 1 0 786 8 1 30 "netherlands"
-"229" 15 1 0 786 8 1 50 "netherlands"
-"230" 46 0 0 785 6 1 69 "netherlands"
-"231" 26 1 0 754 5 1 12 "netherlands"
-"232" 3 1 0 754 5 1 12 "netherlands"
-"233" 48 0 0 759 3 1 63 "netherlands"
-"234" 19 1 0 778 8 1 70 "netherlands"
-"235" 18 1 0 778 8 1 46 "netherlands"
-"236" 3 1 0 778 8 0 39 "netherlands"
-"237" 49 0 0 825 16 1 49 "netherlands"
-"238" 13 1 0 844 15 1 69 "netherlands"
-"239" 4 1 0 844 15 0 23 "netherlands"
-"240" 46 0 0 844 9 1 163 "netherlands"
-"241" 41 0 0 730 3 1 270 "netherlands"
-"242" 8 1 0 767 5 1 108 "netherlands"
-"243" 3 1 0 767 5 0 17 "netherlands"
-"244" 43 0 0 751 5 1 57 "netherlands"
-"245" 47 0 0 685 7 1 0 "norway"
-"246" 25 1 0 626 0 1 0 "norway"
-"247" 23 1 0 626 0 1 2 "norway"
-"248" 15 1 0 677 2 1 0 "norway"
-"249" 33 1 0 677 2 1 7 "norway"
-"250" 47 0 0 666 1 1 0 "norway"
-"251" 23 1 0 689 1 0 0 "norway"
-"252" 1 1 0 689 1 0 4 "norway"
-"253" 25 1 0 689 1 0 4 "norway"
-"254" 47 0 0 715 1 1 1 "norway"
-"255" 18 1 0 680 0 1 0 "norway"
-"256" 19 1 0 680 0 0 11 "norway"
-"257" 12 1 0 680 0 0 11 "norway"
-"258" 27 1 0 758 13 0 4 "norway"
-"259" 20 1 0 758 13 0 3 "norway"
-"260" 41 0 0 663 1 0 0 "norway"
-"261" 8 1 0 663 1 0 5 "norway"
-"262" 19 1 0 687 5 0 2 "norway"
-"263" 28 1 0 687 5 1 0 "norway"
-"264" 7 1 0 676 5 0 0 "norway"
-"265" 3 1 0 709 16 1 0 NA
-"266" 17 1 0 709 16 0 9 NA
-"267" 6 1 0 709 16 1 46 NA
-"268" 9 1 0 762 19 1 7 NA
-"269" 2 1 0 763 16 1 0 NA
-"270" 23 1 0 763 16 1 36 NA
-"271" 3 1 0 763 16 1 35 NA
-"272" 24 1 0 701 18 1 47 NA
-"273" 3 1 0 701 18 1 9 NA
-"274" 17 1 0 761 15 0 30 NA
-"275" 22 1 1 644 13 0 3 "spain"
-"276" 22 1 1 644 13 0 28 "spain"
-"277" 43 0 1 575 5 1 35 "spain"
-"278" 14 1 0 648 7 1 0 "sweden"
-"279" 23 1 0 648 7 1 5 "sweden"
-"280" 36 1 0 673 3 0 0 "sweden"
-"281" 12 1 0 673 3 1 1 "sweden"
-"282" 48 0 0 677 2 1 0 "sweden"
-"283" 13 1 0 686 3 1 0 "sweden"
-"284" 7 1 0 686 3 0 4 "sweden"
-"285" 28 1 0 684 2 0 0 "sweden"
-"286" 48 0 0 679 2 0 0 "sweden"
-"287" 48 0 0 692 3 0 0 "sweden"
-"288" 12 1 0 653 1 1 0 "sweden"
-"289" 11 1 0 653 1 1 18 "sweden"
-"290" 36 0 0 698 5 0 0 "sweden"
-"291" 36 0 0 702 5 0 0 "sweden"
-"292" 24 1 0 710 5 1 18 "sweden"
-"293" 12 1 0 710 5 0 8 "sweden"
-"294" 19 1 0 713 6 1 1 "sweden"
-"295" 16 1 0 713 6 0 14 "sweden"
-"296" 37 0 0 681 6 0 18 "sweden"
-"297" 5 1 0 705 5 0 19 "sweden"
-"298" 55 0 0 511 0 1 21 "uk"
-"299" 20 1 0 518 0 1 0 "uk"
-"300" 41 1 0 513 0 1 1 "uk"
-"301" 2 1 0 513 0 1 1 "uk"
-"302" 19 1 0 508 0 1 1 "uk"
-"303" 33 1 0 508 0 1 1 "uk"
-"304" 48 1 0 497 0 1 0 "uk"
-"305" 12 1 0 497 0 1 5 "uk"
-"306" 16 1 0 514 0 1 0 "uk"
-"307" 51 0 0 506 0 1 0 "uk"
-"308" 44 1 0 518 0 1 0 "uk"
-"309" 7 1 0 556 3 0 4 "uk"
-"310" 18 1 0 556 2 1 0 "uk"
-"311" 7 1 0 556 2 1 0 "uk"
-"312" 30 1 0 556 2 0 0 "uk"
-"313" 50 0 0 536 1 1 0 "uk"
-"314" 49 0 0 522 1 1 6 "uk"
diff --git a/data/coalition2.txt.xz b/data/coalition2.txt.xz
new file mode 100644
index 0000000..7e03045
Binary files /dev/null and b/data/coalition2.txt.xz differ
diff --git a/data/eidat.txt b/data/eidat.txt
deleted file mode 100644
index dba9426..0000000
--- a/data/eidat.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-"x0" "x1" "t0" "t1"
-"1" 200 3911 2850 1261
-"2" 162 2636 1541 1257
-"3" 206 2460 1091 1575
-"4" 213 1654 517 1350
-"5" 209 637 163 683
-"6" 190 1911 216 1885
-"7" 206 3460 226 3440
-"8" 190 715 102 803
-"9" 183 2058 126 2115
-"10" 189 2658 138 2709
diff --git a/data/eidat.txt.gz b/data/eidat.txt.gz
new file mode 100644
index 0000000..b0c55e6
Binary files /dev/null and b/data/eidat.txt.gz differ
diff --git a/data/free1.tab b/data/free1.tab
deleted file mode 100644
index c8b54dd..0000000
--- a/data/free1.tab
+++ /dev/null
@@ -1,451 +0,0 @@
-"sex" "age" "educ" "country" "y" "v1" "v2" "v3" "v4" "v5" "v6"
-"109276" 0 20 4 "Eurasia" 1 4 3 3 5 3 4
-"88178" 1 25 4 "Oceana" 2 3 3 5 5 5 5
-"111063" 1 56 2 "Eastasia" 2 3 2 4 5 5 4
-"161488" 0 65 6 "Eastasia" 2 3 3 5 5 5 5
-"44532" 1 50 5 "Oceana" 1 5 3 5 5 3 5
-"95503" 0 20 5 "Eastasia" 1 4 4 3 3 4 4
-"26276" 1 63 2 "Oceana" 2 3 3 4 4 4 5
-"26299" 0 39 3 "Oceana" 2 4 3 3 4 3 3
-"89206" 0 58 2 "Oceana" 2 3 3 5 5 5 5
-"24342" 0 40 3 "Oceana" 1 2 2 3 5 4 5
-"109960" 1 37 4 "Eurasia" 1 4 4 3 5 1 3
-"117509" 1 22 5 "Eurasia" 2 4 4 5 4 5 4
-"11633" 1 37 6 "Eastasia" 3 5 1 4 5 4 5
-"57867" 0 46 1 "Oceana" 1 5 5 5 5 5 5
-"30513" 0 49 5 "Oceana" 1 3 4 4 4 5 5
-"91576" 1 28 3 "Oceana" 2 4 3 4 3 3 3
-"42161" 0 59 4 "Oceana" 1 5 2 5 5 3 5
-"74317" 0 26 3 "Oceana" 1 2 1 4 5 3 5
-"661" 1 26 3 "Oceana" 2 5 3 4 5 5 5
-"41371" 1 41 5 "Oceana" 1 2 1 4 4 3 5
-"60279" 0 54 1 "Oceana" 1 5 4 5 5 4 5
-"110851" 1 37 6 "Eurasia" 2 5 1 5 5 5 5
-"22764" 0 51 4 "Oceana" 1 3 1 3 4 4 5
-"92556" 0 30 5 "Oceana" 1 2 1 4 5 4 5
-"38858" 0 22 5 "Eastasia" 4 5 5 5 5 5 5
-"71819" 0 21 1 "Eastasia" 4 5 3 5 5 5 5
-"108399" 0 24 6 "Eurasia" 2 4 5 4 4 5 4
-"27707" 1 81 3 "Oceana" 4 5 5 5 5 5 5
-"166386" 0 34 4 "Eastasia" 1 4 2 4 5 2 5
-"8524" 1 32 1 "Eastasia" 4 5 4 5 5 5 5
-"25230" 1 84 4 "Oceana" 1 4 4 4 4 4 4
-"106816" 0 18 2 "Eastasia" 1 2 1 5 5 4 5
-"21996" 1 60 3 "Oceana" 1 2 1 4 4 4 5
-"27273" 0 71 3 "Oceana" 1 5 5 5 5 5 5
-"25441" 0 42 7 "Oceana" 2 4 4 4 4 4 5
-"27977" 0 82 3 "Oceana" 1 4 4 4 4 4 5
-"52125" 1 52 5 "Eastasia" 4 5 5 5 5 5 5
-"108539" 1 46 2 "Eurasia" 1 5 5 5 5 5 5
-"41496" 0 54 4 "Oceana" 1 5 2 3 4 4 5
-"29648" 0 55 4 "Oceana" 3 4 3 5 5 5 5
-"108418" 0 27 6 "Eurasia" 2 4 2 4 4 3 3
-"30295" 1 68 3 "Oceana" 1 3 2 4 5 5 5
-"74586" 1 34 3 "Oceana" 2 4 2 5 5 4 3
-"31052" 0 22 5 "Oceana" 1 4 3 4 5 4 5
-"76803" 1 71 1 "Oceana" 2 4 4 4 5 4 5
-"23208" 0 18 4 "Oceana" 2 3 1 3 3 3 3
-"119800" 0 60 2 "Eurasia" 2 4 3 3 5 5 4
-"50886" 1 64 6 "Eastasia" 1 4 1 5 1 4 5
-"60488" 1 63 1 "Oceana" 4 5 4 5 5 5 5
-"109296" 1 27 2 "Eurasia" 2 4 3 5 5 5 5
-"42602" 1 61 3 "Oceana" 1 2 2 4 4 4 5
-"26072" 1 70 2 "Oceana" 1 5 5 5 5 5 5
-"30418" 1 53 3 "Oceana" 3 5 5 5 5 5 5
-"108736" 0 43 2 "Eurasia" 2 5 5 5 5 5 5
-"116362" 1 28 1 "Eurasia" 3 4 4 5 4 4 4
-"2792" 1 37 2 "Oceana" 3 5 1 4 3 3 5
-"119468" 0 27 4 "Eurasia" 1 3 3 3 4 5 5
-"43574" 0 74 3 "Oceana" 1 2 2 2 5 3 5
-"60125" 0 41 NA "Oceana" 1 5 2 4 5 5 5
-"157865" 1 60 1 "Eastasia" 2 4 3 5 5 3 5
-"30266" 0 19 4 "Oceana" 1 3 4 5 5 5 5
-"4949" 1 26 4 "Oceana" 2 5 5 5 4 4 4
-"30485" 0 68 7 "Oceana" 2 3 4 3 4 5 5
-"90418" 1 24 5 "Oceana" 2 3 2 5 5 3 5
-"41906" 1 34 3 "Oceana" 2 3 2 5 4 5 5
-"26469" 1 71 3 "Oceana" 2 3 3 3 5 5 5
-"31119" 1 56 4 "Oceana" 1 5 2 5 5 4 5
-"73821" 1 45 1 "Oceana" 2 4 2 5 3 4 5
-"120523" 1 36 1 "Eurasia" 2 3 3 4 4 4 5
-"109579" 1 20 6 "Eurasia" 1 2 5 4 5 5 5
-"65315" 0 54 1 "Eastasia" 1 2 2 5 5 5 5
-"115859" 1 48 1 "Eurasia" 2 3 3 5 5 3 5
-"30741" 1 55 5 "Oceana" 4 5 5 5 5 5 5
-"87974" 0 30 2 "Oceana" 2 3 2 4 5 5 5
-"140966" 0 35 1 "Eastasia" 1 3 4 5 5 3 5
-"46293" 1 71 3 "Eastasia" 1 2 1 3 5 3 5
-"163463" 1 27 3 "Eastasia" 3 3 2 5 5 5 5
-"3946" 0 78 2 "Oceana" 1 1 3 2 4 3 1
-"42315" 1 67 4 "Oceana" 3 3 2 3 5 5 5
-"2157" 1 47 2 "Oceana" 1 2 2 1 2 4 2
-"79340" 0 29 4 "Oceana" 3 3 4 3 3 4 5
-"119525" 1 51 5 "Eurasia" 2 2 5 4 5 5 2
-"26301" 0 63 3 "Oceana" 3 3 5 5 5 5 5
-"167550" 0 53 3 "Eastasia" 4 4 3 5 5 5 5
-"42090" 1 84 2 "Oceana" 1 1 1 5 4 4 5
-"25324" 0 43 4 "Oceana" 3 5 4 5 4 5 3
-"117374" 1 48 3 "Eurasia" 3 3 3 4 5 5 5
-"120366" 0 50 1 "Eurasia" 3 3 3 4 4 4 4
-"2806" 0 28 3 "Oceana" 1 2 1 1 3 5 2
-"76784" 1 55 1 "Oceana" 1 1 1 5 5 5 5
-"85193" 1 20 4 "Eastasia" 5 5 5 5 5 5 5
-"109963" 0 56 1 "Eurasia" 4 5 4 4 4 5 5
-"120397" 0 70 1 "Eurasia" 2 2 3 3 4 5 5
-"60470" 0 37 1 "Oceana" 2 2 2 3 5 5 5
-"98636" 0 21 4 "Eastasia" 3 3 2 4 2 2 2
-"110047" 1 28 5 "Eurasia" 3 3 3 2 2 1 2
-"28150" 0 42 4 "Oceana" 2 2 3 4 4 4 5
-"84649" 0 62 4 "Eastasia" 5 5 5 5 5 5 5
-"118196" 0 36 3 "Eurasia" 5 5 5 5 5 5 5
-"57136" 1 28 1 "Oceana" 5 5 5 5 5 5 5
-"71010" 1 51 1 "Eastasia" 5 5 5 5 5 5 5
-"145298" 1 20 2 "Eastasia" 2 2 1 3 4 5 3
-"162131" 1 43 5 "Eastasia" 3 3 3 3 3 3 3
-"81406" 0 45 1 "Eastasia" 1 1 1 1 1 1 1
-"164869" 1 61 6 "Eastasia" 3 3 2 4 5 5 5
-"110303" 0 37 6 "Eurasia" 3 3 2 3 5 3 3
-"78048" 1 28 1 "Oceana" 2 2 2 4 5 4 5
-"118281" 1 23 3 "Eurasia" 3 3 4 3 3 3 3
-"24024" 1 75 3 "Oceana" 2 2 2 3 4 3 5
-"101158" 1 25 3 "Eastasia" 2 5 4 3 4 5 2
-"113030" 0 35 4 "Eastasia" 2 2 2 2 3 2 3
-"61465" 0 34 1 "Oceana" 2 2 2 2 4 4 5
-"119390" 0 29 4 "Eurasia" 3 4 1 3 5 3 5
-"950" 0 19 3 "Oceana" 2 2 2 5 5 5 5
-"119084" 1 63 1 "Eurasia" 3 3 2 2 2 1 3
-"24643" 0 59 6 "Oceana" 3 3 2 4 4 3 5
-"56613" 0 35 1 "Oceana" 5 5 4 4 5 5 5
-"127032" 1 24 1 "Eastasia" 5 5 5 5 5 5 5
-"78703" 1 40 1 "Oceana" 3 4 2 5 5 4 3
-"150796" 1 53 4 "Eastasia" 2 2 4 3 4 4 5
-"41762" 1 35 4 "Oceana" 2 2 2 5 4 5 5
-"108599" 1 50 3 "Eurasia" 1 1 1 4 5 5 4
-"166412" 0 31 1 "Eastasia" 3 3 3 4 3 4 3
-"50610" 0 47 5 "Eastasia" 4 4 4 4 4 4 3
-"170400" 0 20 3 "Eastasia" 3 3 2 2 4 3 4
-"142" 0 44 3 "Oceana" 2 2 2 2 2 2 2
-"110622" 0 37 3 "Eurasia" 3 3 3 3 3 3 3
-"78321" 1 NA 1 "Oceana" 3 3 4 4 5 5 5
-"150486" 1 69 4 "Eastasia" 3 3 2 3 4 4 5
-"27969" 0 44 4 "Oceana" 3 3 3 4 4 4 4
-"154341" 1 19 3 "Eastasia" 2 2 2 4 4 4 5
-"843" 0 51 2 "Oceana" 4 4 4 4 4 4 4
-"61135" 0 45 1 "Oceana" 4 4 4 4 4 4 4
-"88111" 0 30 5 "Oceana" 3 3 2 4 5 5 5
-"77442" 1 20 3 "Oceana" 2 2 3 3 3 3 4
-"119647" 1 80 1 "Eurasia" 3 3 2 3 4 3 5
-"61252" 0 62 1 "Oceana" 5 5 5 5 5 5 5
-"115907" 0 90 1 "Eurasia" 5 5 5 5 5 5 5
-"17437" 1 43 4 "Eastasia" 3 3 2 4 5 4 5
-"110230" 0 28 4 "Eurasia" 3 3 2 4 5 5 5
-"90873" 1 53 1 "Oceana" 2 2 2 3 4 4 4
-"132505" 0 36 6 "Eastasia" 3 3 1 5 5 3 5
-"463" 0 55 1 "Oceana" 5 5 5 5 5 5 4
-"24061" 0 65 3 "Oceana" 2 2 2 3 4 4 5
-"155330" 0 26 NA "Eastasia" 2 2 2 3 5 1 5
-"603" 1 61 1 "Oceana" 2 2 2 5 5 5 5
-"42669" 1 40 4 "Oceana" 2 2 2 3 5 4 5
-"128180" 0 68 1 "Eastasia" 3 2 2 5 3 3 5
-"26881" 1 77 2 "Oceana" 3 2 3 4 4 4 4
-"4360" 1 23 4 "Oceana" 3 2 1 4 5 4 5
-"128252" 0 48 3 "Eastasia" 3 2 3 5 3 3 5
-"88856" 0 30 7 "Oceana" 3 2 2 5 4 4 5
-"59641" 1 28 1 "Oceana" 3 1 2 5 5 5 5
-"125667" 1 57 1 "Eastasia" 3 2 1 4 5 3 5
-"78538" 1 52 1 "Oceana" 3 1 1 4 4 3 5
-"89971" 1 28 4 "Oceana" 2 1 1 5 3 5 5
-"74774" 0 34 3 "Oceana" 3 2 2 4 5 5 5
-"98190" 1 20 3 "Eastasia" 4 2 2 5 5 5 5
-"76998" 0 18 1 "Oceana" 3 2 2 5 5 5 5
-"37771" 0 21 5 "Eastasia" 2 1 3 4 4 4 5
-"1379" 0 72 2 "Oceana" 3 1 1 5 5 5 5
-"47175" 1 34 5 "Eastasia" 3 2 1 4 4 3 5
-"82247" 1 30 2 "Eastasia" 2 1 2 3 4 5 4
-"4028" 0 50 1 "Oceana" 3 1 1 4 5 5 5
-"83918" 0 20 1 "Eastasia" 3 1 2 4 3 3 5
-"89999" 1 30 3 "Oceana" 3 2 2 5 5 5 5
-"42641" 1 35 4 "Oceana" 3 1 3 4 4 5 5
-"78558" 1 47 1 "Oceana" 3 2 1 4 5 4 5
-"116872" 1 18 4 "Eurasia" 2 1 1 4 3 5 5
-"155287" 0 37 3 "Eastasia" 3 2 1 5 4 4 5
-"78731" 1 38 1 "Oceana" 3 2 2 4 5 4 5
-"42755" 1 60 1 "Oceana" 3 2 2 5 5 4 5
-"116552" 1 68 1 "Eurasia" 4 3 3 5 5 4 5
-"119774" 1 39 1 "Eurasia" 3 2 4 4 5 3 5
-"44669" 0 25 4 "Oceana" 3 1 1 4 3 4 4
-"23450" 1 30 5 "Oceana" 3 1 1 4 4 4 5
-"103279" 1 54 2 "Eastasia" 2 1 1 3 5 3 5
-"51748" 1 54 5 "Eastasia" 3 2 2 4 5 4 5
-"117889" 0 20 1 "Eurasia" 4 3 1 5 5 5 5
-"148657" 0 55 5 "Eastasia" 3 2 2 4 4 5 5
-"170806" 1 19 4 "Eastasia" 3 1 1 5 3 3 5
-"22825" 0 43 3 "Oceana" 2 1 1 5 4 4 5
-"119789" 0 21 4 "Eurasia" 3 2 4 5 3 3 4
-"99700" 1 37 3 "Eastasia" 2 1 1 3 4 3 5
-"119681" 1 20 3 "Eurasia" 3 2 4 4 3 1 5
-"23655" 1 42 3 "Oceana" 3 1 3 4 4 5 5
-"101948" 0 31 3 "Eastasia" 4 2 1 5 5 4 5
-"37928" 1 34 4 "Eastasia" 2 1 3 3 4 4 5
-"97315" 1 22 1 "Eastasia" 3 1 1 5 5 5 5
-"90645" 0 22 5 "Oceana" 3 2 1 4 4 4 5
-"131352" 1 52 5 "Eastasia" 3 2 2 4 5 5 5
-"3392" 1 42 2 "Oceana" 3 1 1 5 5 5 5
-"23288" 1 62 5 "Oceana" 3 2 2 4 3 3 5
-"81480" 0 18 1 "Eastasia" 2 1 1 3 5 5 5
-"47907" 0 36 5 "Eastasia" 3 1 2 4 4 4 4
-"155209" 1 32 4 "Eastasia" 2 1 2 5 5 4 5
-"23272" 0 37 6 "Oceana" 3 2 2 5 5 4 5
-"117321" 1 66 1 "Eurasia" 4 2 1 5 4 4 5
-"125327" 1 28 1 "Eastasia" 4 3 2 5 5 3 5
-"156041" 1 40 1 "Eastasia" 4 2 4 4 4 3 5
-"20166" 1 83 2 "Eastasia" 3 2 2 3 3 3 3
-"3085" 0 24 4 "Oceana" 3 1 2 3 5 3 5
-"115861" 0 22 4 "Eurasia" 5 3 1 5 1 5 5
-"7578" 1 59 1 "Eastasia" 5 2 2 5 5 3 5
-"8296" 0 32 1 "Eastasia" 5 3 3 5 5 5 5
-"43044" 1 50 4 "Oceana" 4 3 2 4 4 5 5
-"53027" 0 34 6 "Eastasia" 3 2 2 3 5 5 5
-"9857" 1 48 NA "Eastasia" 5 4 4 5 5 5 5
-"116053" 1 38 7 "Eurasia" 5 4 3 5 5 4 5
-"117837" 0 25 3 "Eurasia" 5 3 1 5 5 5 5
-"119139" 0 25 3 "Eurasia" 4 3 3 4 5 4 5
-"28144" 1 59 3 "Oceana" 4 2 3 5 4 4 4
-"118227" 0 28 3 "Eurasia" 4 2 1 4 5 3 5
-"72478" 1 23 2 "Eastasia" 5 2 3 5 2 5 5
-"59565" 0 NA 3 "Oceana" 4 3 1 4 5 5 5
-"108562" 0 57 4 "Eurasia" 3 2 1 3 4 3 5
-"132225" 1 65 5 "Eastasia" 4 1 5 5 5 4 3
-"45188" 1 69 3 "Eastasia" 4 2 2 4 3 3 5
-"144391" 1 32 3 "Eastasia" 3 1 3 3 4 5 5
-"48235" 1 76 3 "Eastasia" 3 2 2 3 3 4 5
-"125656" 0 27 1 "Eastasia" 3 1 1 3 3 3 4
-"41348" 1 27 3 "Oceana" 4 3 1 4 3 3 5
-"162548" 1 42 4 "Eastasia" 3 2 1 3 3 3 5
-"127868" 1 46 1 "Eastasia" 5 1 1 5 5 5 1
-"115772" 0 66 2 "Eurasia" 5 3 2 5 5 5 5
-"117954" 1 70 1 "Eurasia" 4 2 2 4 5 3 5
-"194" 0 21 2 "Oceana" 5 2 4 5 3 5 4
-"40477" 1 22 5 "Eastasia" 3 2 2 3 5 3 5
-"108363" 1 37 5 "Eurasia" 5 4 4 5 5 5 5
-"43862" 0 53 3 "Oceana" 3 2 2 3 5 5 5
-"118327" 1 33 4 "Eurasia" 4 3 2 4 5 5 5
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diff --git a/data/free2.tab b/data/free2.tab
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-"4028" 0 50 1 "Oceana" 3 1 1 4 5 5 5
-"83918" 0 20 1 "Eastasia" 3 1 2 4 3 3 5
-"89999" 1 30 3 "Oceana" 3 2 2 5 5 5 5
-"42641" 1 35 4 "Oceana" 3 1 3 4 4 5 5
-"78558" 1 47 1 "Oceana" 3 2 1 4 5 4 5
-"116872" 1 18 4 "Eurasia" 2 1 1 4 3 5 5
-"155287" 0 37 3 "Eastasia" 3 2 1 5 4 4 5
-"78731" 1 38 1 "Oceana" 3 2 2 4 5 4 5
-"42755" 1 60 1 "Oceana" 3 2 2 5 5 4 5
-"116552" 1 68 1 "Eurasia" 4 3 3 5 5 4 5
-"119774" 1 39 1 "Eurasia" 3 2 4 4 5 3 5
-"44669" 0 25 4 "Oceana" 3 1 1 4 3 4 4
-"23450" 1 30 5 "Oceana" 3 1 1 4 4 4 5
-"103279" 1 54 2 "Eastasia" 2 1 1 3 5 3 5
-"51748" 1 54 5 "Eastasia" 3 2 2 4 5 4 5
-"117889" 0 20 1 "Eurasia" 4 3 1 5 5 5 5
-"148657" 0 55 5 "Eastasia" 3 2 2 4 4 5 5
-"170806" 1 19 4 "Eastasia" 3 1 1 5 3 3 5
-"22825" 0 43 3 "Oceana" 2 1 1 5 4 4 5
-"119789" 0 21 4 "Eurasia" 3 2 4 5 3 3 4
-"99700" 1 37 3 "Eastasia" 2 1 1 3 4 3 5
-"119681" 1 20 3 "Eurasia" 3 2 4 4 3 1 5
-"23655" 1 42 3 "Oceana" 3 1 3 4 4 5 5
-"101948" 0 31 3 "Eastasia" 4 2 1 5 5 4 5
-"37928" 1 34 4 "Eastasia" 2 1 3 3 4 4 5
-"97315" 1 22 1 "Eastasia" 3 1 1 5 5 5 5
-"90645" 0 22 5 "Oceana" 3 2 1 4 4 4 5
-"131352" 1 52 5 "Eastasia" 3 2 2 4 5 5 5
-"3392" 1 42 2 "Oceana" 3 1 1 5 5 5 5
-"23288" 1 62 5 "Oceana" 3 2 2 4 3 3 5
-"81480" 0 18 1 "Eastasia" 2 1 1 3 5 5 5
-"47907" 0 36 5 "Eastasia" 3 1 2 4 4 4 4
-"155209" 1 32 4 "Eastasia" 2 1 2 5 5 4 5
-"23272" 0 37 6 "Oceana" 3 2 2 5 5 4 5
-"117321" 1 66 1 "Eurasia" 4 2 1 5 4 4 5
-"125327" 1 28 1 "Eastasia" 4 3 2 5 5 3 5
-"156041" 1 40 1 "Eastasia" 4 2 4 4 4 3 5
-"20166" 1 83 2 "Eastasia" 3 2 2 3 3 3 3
-"3085" 0 24 4 "Oceana" 3 1 2 3 5 3 5
-"115861" 0 22 4 "Eurasia" 5 3 1 5 1 5 5
-"7578" 1 59 1 "Eastasia" 5 2 2 5 5 3 5
-"8296" 0 32 1 "Eastasia" 5 3 3 5 5 5 5
-"43044" 1 50 4 "Oceana" 4 3 2 4 4 5 5
-"53027" 0 34 6 "Eastasia" 3 2 2 3 5 5 5
-"9857" 1 48 NA "Eastasia" 5 4 4 5 5 5 5
-"116053" 1 38 7 "Eurasia" 5 4 3 5 5 4 5
-"117837" 0 25 3 "Eurasia" 5 3 1 5 5 5 5
-"119139" 0 25 3 "Eurasia" 4 3 3 4 5 4 5
-"28144" 1 59 3 "Oceana" 4 2 3 5 4 4 4
-"118227" 0 28 3 "Eurasia" 4 2 1 4 5 3 5
-"72478" 1 23 2 "Eastasia" 5 2 3 5 2 5 5
-"59565" 0 NA 3 "Oceana" 4 3 1 4 5 5 5
-"108562" 0 57 4 "Eurasia" 3 2 1 3 4 3 5
-"132225" 1 65 5 "Eastasia" 4 1 5 5 5 4 3
-"45188" 1 69 3 "Eastasia" 4 2 2 4 3 3 5
-"144391" 1 32 3 "Eastasia" 3 1 3 3 4 5 5
-"48235" 1 76 3 "Eastasia" 3 2 2 3 3 4 5
-"125656" 0 27 1 "Eastasia" 3 1 1 3 3 3 4
-"41348" 1 27 3 "Oceana" 4 3 1 4 3 3 5
-"162548" 1 42 4 "Eastasia" 3 2 1 3 3 3 5
-"127868" 1 46 1 "Eastasia" 5 1 1 5 5 5 1
-"115772" 0 66 2 "Eurasia" 5 3 2 5 5 5 5
-"117954" 1 70 1 "Eurasia" 4 2 2 4 5 3 5
-"194" 0 21 2 "Oceana" 5 2 4 5 3 5 4
-"40477" 1 22 5 "Eastasia" 3 2 2 3 5 3 5
-"108363" 1 37 5 "Eurasia" 5 4 4 5 5 5 5
-"43862" 0 53 3 "Oceana" 3 2 2 3 5 5 5
-"118327" 1 33 4 "Eurasia" 4 3 2 4 5 5 5
-"49882" 1 51 6 "Eastasia" 4 3 1 4 5 4 5
-"58633" 1 18 1 "Oceana" 5 4 3 5 3 3 5
-"115753" 1 52 3 "Eurasia" 5 2 2 5 5 5 5
-"171662" 1 22 6 "Eastasia" 3 2 1 3 4 4 5
-"75442" 0 30 3 "Oceana" 4 3 3 4 3 4 4
-"152882" 1 21 4 "Eastasia" 4 3 1 4 3 4 5
-"118389" 1 50 3 "Eurasia" 4 1 2 4 4 3 3
-"92750" 1 19 5 "Oceana" 4 3 3 4 4 5 5
-"63117" 1 31 3 "Eastasia" 5 3 1 5 5 5 5
-"120054" 0 54 2 "Eurasia" 3 2 2 3 3 3 4
-"42743" 0 30 4 "Oceana" 5 2 2 5 5 4 5
-"146950" 0 38 2 "Eastasia" 5 3 2 5 5 4 5
-"160339" 1 39 1 "Eastasia" 5 4 5 5 3 4 5
-"101154" 1 52 2 "Eastasia" 4 2 1 4 5 4 5
-"78405" 0 40 1 "Oceana" 4 3 3 4 4 4 5
-"5290" 1 29 2 "Oceana" 5 1 5 5 5 5 5
-"78281" 1 22 4 "Oceana" 3 1 1 3 5 5 5
-"123554" 0 20 5 "Eastasia" 3 1 1 3 5 5 5
-"115639" 1 62 1 "Eurasia" 5 2 2 5 5 5 5
-"108965" 1 21 6 "Eurasia" 5 1 1 5 4 3 5
-"91858" 1 29 5 "Oceana" 5 2 1 5 5 3 5
-"59708" 1 51 1 "Oceana" 4 2 2 4 3 3 5
-"110095" 0 75 1 "Eurasia" 3 2 3 3 3 2 2
-"41344" 0 34 4 "Oceana" 3 2 2 3 5 4 5
-"111964" 1 71 2 "Eastasia" 4 2 2 4 4 3 5
-"23924" 0 60 5 "Oceana" 3 1 1 3 4 4 5
-"116672" 1 28 5 "Eurasia" 5 2 5 5 5 5 5
-"124356" 1 48 3 "Eastasia" 5 2 3 5 5 5 5
-"20075" 1 25 1 "Eastasia" 5 1 2 5 5 3 5
-"129355" 1 83 1 "Eastasia" 4 1 2 4 5 5 5
-"137663" 0 50 1 "Eastasia" 4 2 2 3 5 4 5
-"129119" 1 26 4 "Eastasia" 3 1 1 2 4 4 5
-"114086" 0 30 1 "Eastasia" 3 1 2 2 1 3 4
-"17881" 1 59 2 "Eastasia" 4 1 1 3 5 4 5
-"75870" 1 36 3 "Oceana" 4 2 2 3 4 4 5
-"43344" 0 26 4 "Oceana" 4 2 2 3 4 4 5
-"43369" 0 47 3 "Oceana" 4 3 1 3 5 4 5
-"93407" 1 64 1 "Eastasia" 3 1 3 2 2 2 5
-"58757" 1 43 1 "Oceana" 4 3 2 3 4 3 5
-"109911" 1 20 5 "Eurasia" 4 3 5 3 5 5 5
-"42296" 0 18 4 "Oceana" 4 2 2 3 4 3 5
-"1881" 0 20 2 "Oceana" 3 2 3 2 4 3 4
-"127830" 1 21 3 "Eastasia" 4 1 4 2 4 3 5
-"165923" 0 63 4 "Eastasia" 4 3 4 2 4 3 5
-"169505" 0 27 3 "Eastasia" 4 2 1 3 3 3 5
-"111809" 1 27 2 "Eastasia" 3 1 4 1 2 4 4
-"110608" 0 25 5 "Eurasia" 4 3 2 3 4 4 5
-"21883" 0 26 3 "Oceana" 4 1 1 3 4 4 5
-"119537" 1 30 1 "Eurasia" 3 1 1 2 4 1 5
-"120004" 0 20 3 "Eurasia" 3 1 1 2 4 1 5
-"92195" 1 31 5 "Oceana" 4 2 1 3 4 5 5
-"119535" 0 20 1 "Eurasia" 3 1 1 2 4 1 5
-"109387" 1 61 1 "Eurasia" 3 2 1 1 4 4 5
-"42168" 0 33 4 "Oceana" 4 3 2 3 4 4 5
-"88740" 1 23 1 "Oceana" 4 2 1 3 5 4 5
-"121943" 0 47 3 "Eastasia" 3 1 1 1 4 5 5
-"127747" 1 23 4 "Eastasia" 4 1 4 3 4 4 5
-"126257" 1 36 3 "Eastasia" 4 1 1 3 5 3 5
-"115888" 1 30 2 "Eurasia" 4 3 5 3 5 5 5
-"20393" 0 39 3 "Eastasia" 3 2 2 2 4 4 5
-"28638" 1 19 4 "Oceana" 4 3 3 3 4 4 5
-"134964" 1 20 5 "Eastasia" 4 3 3 3 4 3 5
-"116826" 1 25 4 "Eurasia" 4 3 4 3 4 4 5
-"50389" 1 49 6 "Eastasia" 4 1 2 3 4 4 5
-"128524" 0 45 5 "Eastasia" 4 2 3 3 4 4 5
-"118474" 0 25 2 "Eurasia" 3 2 4 2 3 5 4
-"1771" 0 46 2 "Oceana" 4 2 2 3 5 4 5
-"117246" 0 28 6 "Eurasia" 4 2 2 1 4 4 5
-"70366" 0 19 4 "Eastasia" 4 2 2 3 4 3 5
-"110185" 1 43 2 "Eurasia" 4 2 1 3 4 4 5
-"118861" 0 28 3 "Eurasia" 4 2 1 3 4 4 5
-"82600" 1 30 1 "Eastasia" 4 2 1 3 4 3 5
-"146857" 0 31 2 "Eastasia" 4 3 2 3 5 3 5
-"110568" 1 22 3 "Eurasia" 4 2 1 3 4 3 5
-"164144" 1 42 3 "Eastasia" 4 2 1 3 4 4 5
-"171811" 1 40 3 "Eastasia" 3 2 1 2 4 4 5
-"70818" 1 29 3 "Eastasia" 4 1 1 3 4 5 5
-"109912" 0 23 4 "Eurasia" 4 3 5 3 5 5 5
-"88267" 0 58 1 "Oceana" 4 2 2 3 5 5 5
-"123576" 0 24 3 "Eastasia" 4 2 2 3 1 5 5
-"124280" 1 19 3 "Eastasia" 4 1 2 3 5 5 5
-"78421" 1 48 1 "Oceana" 4 2 1 3 3 4 5
-"60617" NA 1 NA "Oceana" 4 3 4 3 4 4 4
-"116223" 0 84 2 "Eurasia" 5 2 2 4 4 5 5
-"117250" 0 63 1 "Eurasia" 4 2 1 3 5 5 4
-"60716" 1 17 1 "Oceana" 5 2 1 4 4 5 5
-"120123" 1 50 1 "Eurasia" 5 2 1 3 4 4 5
-"131444" 1 43 6 "Eastasia" 5 4 2 4 5 4 5
-"129590" 1 43 1 "Eastasia" 5 1 1 4 4 3 5
-"146355" 0 19 3 "Eastasia" 5 3 3 4 5 5 5
-"119313" 1 59 1 "Eurasia" 5 2 1 3 4 4 5
-"120182" 1 60 1 "Eurasia" 5 3 1 3 4 4 5
-"108806" 0 40 4 "Eurasia" 5 1 1 2 4 5 5
-"89062" 1 19 3 "Oceana" 5 2 1 4 5 4 5
-"80014" 0 21 1 "Eastasia" 5 4 3 4 5 4 5
-"119274" 0 70 1 "Eurasia" 5 1 4 4 4 2 5
-"164051" 1 23 4 "Eastasia" 5 2 1 3 5 4 5
-"120697" 0 55 2 "Eastasia" 5 3 3 3 4 4 5
-"88620" 1 32 3 "Oceana" 5 1 2 4 4 3 5
-"108783" 0 51 3 "Eurasia" 3 2 2 2 3 3 3
-"125138" 1 44 5 "Eastasia" 5 1 1 4 4 4 5
-"118810" 0 38 2 "Eurasia" 5 2 1 4 4 3 5
-"118357" 1 45 3 "Eurasia" 5 4 5 2 4 4 5
-"3073" 0 40 4 "Oceana" 4 3 4 3 4 4 4
-"119805" 0 28 4 "Eurasia" 5 4 5 4 5 5 5
-"78608" 0 48 1 "Oceana" 5 2 2 3 4 4 5
-"109023" 1 41 1 "Eurasia" 5 2 1 4 5 5 5
-"108928" 1 36 5 "Eurasia" 5 4 5 4 5 5 5
-"120563" 1 64 1 "Eurasia" 5 3 2 4 5 3 5
-"67154" 0 50 4 "Eastasia" 5 2 1 4 4 4 5
-"118114" 1 42 1 "Eurasia" 5 3 4 4 4 3 5
-"120147" 1 18 3 "Eurasia" 5 2 4 4 3 2 5
-"117780" 1 36 2 "Eurasia" 5 4 1 4 5 4 5
-"105904" 1 25 1 "Eastasia" 5 2 1 3 5 5 5
-"133299" 0 55 5 "Eastasia" 5 4 2 4 3 3 5
-"132348" 1 57 6 "Eastasia" 4 3 3 3 3 3 4
-"109354" 1 37 3 "Eurasia" 5 3 2 3 4 4 5
-"159708" 1 43 1 "Eastasia" 5 4 2 4 5 3 5
-"106245" 1 NA 1 "Eastasia" 5 2 2 4 5 5 5
-"120238" 1 34 3 "Eurasia" 4 1 3 3 3 4 4
-"89183" 1 40 2 "Oceana" 5 4 2 4 5 4 5
-"146303" 0 82 3 "Eastasia" 5 3 3 4 5 5 5
-"118887" 0 21 3 "Eurasia" 5 2 1 4 4 4 5
-"119142" 1 45 1 "Eurasia" 5 4 5 4 5 5 5
-"116020" 1 23 5 "Eurasia" 5 4 5 3 4 4 5
-"117930" 1 29 3 "Eurasia" 5 4 1 4 4 4 5
-"108370" 0 23 5 "Eurasia" 4 3 4 3 3 3 4
-"92330" 0 29 4 "Oceana" 5 3 3 4 4 4 5
-"118363" 1 40 4 "Eurasia" 5 4 4 4 4 4 5
-"138535" 1 45 1 "Eastasia" 5 2 3 4 5 4 5
-"119180" 1 31 3 "Eurasia" 5 2 1 4 4 4 5
-"75899" 0 75 3 "Oceana" 4 3 3 3 3 4 4
-"108662" 0 56 1 "Eurasia" 4 2 2 3 3 3 4
-"117796" 1 22 NA "Eurasia" 5 4 1 3 5 3 5
-"109234" 1 89 4 "Eurasia" 5 2 1 4 3 4 5
-"149954" 1 44 4 "Eastasia" 5 2 1 4 5 3 5
-"162577" 1 36 2 "Eastasia" 4 3 3 3 3 3 3
-"109394" 0 24 5 "Eurasia" 5 2 3 3 3 3 3
-"164915" 0 33 3 "Eastasia" 4 3 3 3 3 3 3
-"118593" 1 34 4 "Eurasia" 4 3 3 3 3 3 3
-"119527" 1 34 5 "Eurasia" 5 2 3 3 3 3 4
-"154731" 0 40 3 "Eastasia" 5 2 3 4 5 4 4
-"110214" 0 52 1 "Eurasia" 5 3 2 3 3 3 3
-"149219" 1 46 2 "Eastasia" 5 4 2 4 3 3 4
-"45504" 1 68 7 "Eastasia" 5 4 4 4 4 4 4
-"110948" 0 50 5 "Eurasia" 3 2 2 2 2 2 2
-"108779" 0 25 3 "Eurasia" 4 1 1 1 1 1 1
-"108556" 0 19 3 "Eurasia" 5 3 4 3 4 4 4
-"104234" 0 22 3 "Eastasia" 5 3 3 3 3 3 3
-"120201" 1 30 1 "Eurasia" 5 3 3 4 4 4 4
-"117801" 0 54 2 "Eurasia" 5 4 4 4 5 4 3
-"116254" 1 60 1 "Eurasia" 4 1 3 3 5 4 3
-"108589" 0 44 1 "Eurasia" 5 3 1 3 3 3 3
-"147776" 1 45 3 "Eastasia" 4 3 3 3 3 3 3
-"120368" 1 20 1 "Eurasia" 5 3 4 4 4 4 3
-"117175" 1 34 3 "Eurasia" 4 3 2 2 3 2 2
-"118122" 0 48 4 "Eurasia" 5 2 5 4 3 3 4
-"146621" 0 38 5 "Eastasia" 4 2 2 2 2 2 2
-"1864" 0 68 2 "Oceana" 5 3 3 3 3 3 3
-"119735" 1 26 1 "Eurasia" 5 4 2 4 5 2 2
-"154760" 1 19 1 "Eastasia" 5 4 4 3 3 3 4
-"108897" 1 45 2 "Eurasia" 3 1 2 1 3 2 2
-"117885" 0 54 1 "Eurasia" 5 4 1 4 5 5 3
-"117277" 1 23 2 "Eurasia" 5 1 2 4 3 3 3
-"109055" 0 28 5 "Eurasia" 5 2 2 3 3 3 3
-"104169" 1 35 3 "Eastasia" 5 3 3 3 3 3 3
-"120211" 1 55 1 "Eurasia" 5 3 2 4 4 3 4
-"92064" 1 29 3 "Oceana" 5 2 5 3 5 5 2
-"119167" 1 55 1 "Eurasia" 5 4 3 4 3 4 4
-"118089" 0 NA 1 "Eurasia" 5 2 5 4 3 3 4
-"26614" 0 33 7 "Oceana" 5 3 5 4 5 4 3
-"120431" 1 40 1 "Eurasia" 5 3 3 4 4 4 4
-"119108" 0 24 5 "Eurasia" 5 3 2 4 4 3 4
-"119293" 1 27 1 "Eurasia" 4 1 2 3 3 3 3
-"110306" 0 30 7 "Eurasia" 5 3 2 4 5 3 3
-"123808" 1 45 4 "Eastasia" 5 4 3 3 5 3 4
-"119329" 1 20 2 "Eurasia" 5 3 3 4 4 3 4
-"92438" 1 44 1 "Oceana" 4 3 5 2 4 4 1
-"120030" 1 55 1 "Eurasia" 5 2 2 3 3 3 4
-"168448" 1 65 3 "Eastasia" 5 4 4 4 3 4 4
-"147495" 1 33 2 "Eastasia" 4 3 3 3 3 3 3
-"118244" 0 46 2 "Eurasia" 5 4 2 4 4 4 4
-"118516" 0 50 1 "Eurasia" 5 3 3 4 4 5 3
-"117903" 1 26 2 "Eurasia" 5 4 4 4 4 4 4
-"142546" 0 70 1 "Eastasia" 5 2 3 3 4 4 3
-"119375" 1 22 1 "Eurasia" 5 4 3 3 4 3 3
-"120066" 1 41 1 "Eurasia" 4 2 4 3 3 4 2
-"108784" 1 35 3 "Eurasia" 4 2 2 3 2 2 3
-"109614" 1 39 5 "Eurasia" 4 3 3 2 3 3 2
-"116011" 1 36 1 "Eurasia" 5 3 4 3 2 4 2
-"107534" 1 41 2 "Eastasia" 4 2 5 3 2 4 3
-"111669" 1 25 4 "Eastasia" 5 3 4 4 3 4 4
-"118728" 0 44 2 "Eurasia" 5 4 5 4 5 4 4
-"121362" 1 43 4 "Eastasia" 5 1 3 1 3 3 3
-"108776" 0 49 3 "Eurasia" 5 3 4 3 2 3 3
-"109604" 1 31 5 "Eurasia" 4 3 2 2 3 1 3
-"120098" 0 53 1 "Eurasia" 4 2 3 3 3 3 3
-"116405" 1 40 1 "Eurasia" 4 2 3 3 3 3 2
-"141024" 0 42 5 "Eastasia" 5 2 4 3 3 5 3
-"108443" 0 40 2 "Eurasia" 5 4 1 1 5 4 2
-"110293" 0 23 3 "Eurasia" 5 3 3 3 4 4 2
-"117227" 0 40 3 "Eurasia" 5 3 3 3 3 3 3
-"117445" 1 44 4 "Eurasia" 5 2 4 2 4 4 4
-"110560" 0 38 4 "Eurasia" 4 1 2 2 1 2 1
-"20416" 0 48 3 "Eastasia" 5 2 3 3 2 3 3
-"60079" 0 57 1 "Oceana" 4 1 3 1 2 3 2
-"52621" 0 47 5 "Eastasia" 4 3 3 3 3 5 3
-"108533" 0 28 3 "Eurasia" 5 3 5 4 4 4 4
-"109068" 1 18 4 "Eurasia" 4 3 3 3 3 3 3
-"56676" 1 42 1 "Oceana" 5 4 1 3 3 2 4
-"58098" 0 41 1 "Oceana" 5 3 4 3 3 4 4
-"117252" 1 41 6 "Eurasia" 5 4 2 1 4 4 3
-"110212" 0 40 3 "Eurasia" 4 3 2 3 4 3 3
-"168326" 0 24 4 "Eastasia" 5 3 4 3 3 3 4
-"95744" 1 70 1 "Eastasia" 3 2 1 1 2 1 1
-"109491" 1 18 4 "Eurasia" 3 1 1 1 1 1 2
-"65788" 1 19 1 "Eastasia" 5 3 3 3 3 3 3
-"147766" 0 53 4 "Eastasia" 4 3 3 3 3 3 3
-"116952" 1 18 3 "Eurasia" 5 4 4 4 4 4 4
diff --git a/data/free2.tab.bz2 b/data/free2.tab.bz2
new file mode 100644
index 0000000..4c4be5e
Binary files /dev/null and b/data/free2.tab.bz2 differ
diff --git a/data/friendship.RData b/data/friendship.RData
index 054a142..983483d 100644
Binary files a/data/friendship.RData and b/data/friendship.RData differ
diff --git a/data/grunfeld.txt b/data/grunfeld.txt
deleted file mode 100644
index 8b8a16a..0000000
--- a/data/grunfeld.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-Year Ige Fge Cge Iw Fw Cw
-1935 33.1 1170.6 97.8 12.93 191.5 1.8
-1936 45.0 2015.8 104.4 25.90 516.0 .8
-1937 77.2 2803.3 118.0 35.05 729.0 7.4
-1938 44.6 2039.7 156.2 22.89 560.4 18.1
-1939 48.1 2256.2 172.6 18.84 519.9 23.5
-1940 74.4 2132.2 186.6 28.57 628.5 26.5
-1941 113.0 1834.1 220.9 48.51 537.1 36.2
-1942 91.9 1588.0 287.8 43.34 561.2 60.8
-1943 61.3 1749.4 319.9 37.02 617.2 84.4
-1944 56.8 1687.2 321.3 37.81 626.7 91.2
-1945 93.6 2007.7 319.6 39.27 737.2 92.4
-1946 159.9 2208.3 346.0 53.46 760.5 86.0
-1947 147.2 1656.7 456.4 55.56 581.4 111.1
-1947 146.3 1604.4 543.4 49.56 662.3 130.6
-1949 98.3 1431.8 618.3 32.04 583.8 141.8
-1950 93.5 1610.5 647.4 32.24 635.2 136.7
-1951 135.2 1819.4 671.3 54.38 723.8 129.7
-1952 157.3 2079.7 726.1 71.78 864.1 145.5
-1953 179.5 2371.6 800.3 90.08 1193.5 174.8
-1954 189.6 2759.9 888.9 68.60 1188.9 213.5
diff --git a/data/grunfeld.txt.gz b/data/grunfeld.txt.gz
new file mode 100644
index 0000000..cb2176c
Binary files /dev/null and b/data/grunfeld.txt.gz differ
diff --git a/data/hoff.tab b/data/hoff.tab
deleted file mode 100644
index 3c85344..0000000
--- a/data/hoff.tab
+++ /dev/null
@@ -1,37 +0,0 @@
-year L2SocSec Just503D Just503R RGovDumy
-1948 1.83 0 2.44 0
-1949 3.44 0 2.44 0
-1950 3.05 0 2.44 0
-1951 3.57 0 2.44 0
-1952 4.73 5.41 3.5 1
-1953 6.47 5.41 3.5 1
-1954 7.75 5.41 3.5 1
-1955 8.7 5.41 3.5 1
-1956 9.97 3.04 1.03 1
-1957 10.57 3.04 1.03 1
-1958 12.58 3.04 1.03 1
-1959 12.76 3.04 1.03 1
-1960 13.45 2.01 0.84 0
-1961 14.18 2.01 0.84 0
-1962 14.02 2.01 0.84 0
-1963 14.77 2.01 0.84 0
-1964 15.38 0.82 0.28 0
-1965 13.92 0.82 0.28 0
-1966 13.29 0.82 0.28 0
-1967 14.86 0.82 0.28 0
-1968 15.47 0.62 0.2 1
-1969 17.07 0.62 0.2 1
-1970 17.41 0.62 0.2 1
-1971 19.98 0.62 0.2 1
-1972 20.74 1.8 0.18 1
-1973 19.46 1.8 0.18 1
-1974 19.88 1.8 0.18 1
-1975 20.79 1.8 0.18 1
-1976 20.46 0.67 0.62 0
-1977 20.67 0.67 0.62 0
-1978 20.06 0.67 0.62 0
-1979 20.58 0.67 0.62 0
-1980 20.91 3.14 2.28 1
-1981 21.12 3.14 2.28 1
-1982 20.92 3.14 2.28 1
-1983 19.93 3.14 2.28 1
diff --git a/data/hoff.tab.gz b/data/hoff.tab.gz
new file mode 100644
index 0000000..36e71b9
Binary files /dev/null and b/data/hoff.tab.gz differ
diff --git a/data/homerun.txt b/data/homerun.txt
deleted file mode 100644
index 882876a..0000000
--- a/data/homerun.txt
+++ /dev/null
@@ -1,315 +0,0 @@
-"gameno" "month" "homeruns" "playerstatus" "player"
-"1" 1 "March" 1 0 "McGwire"
-"2" 2 "April" 1 0 "McGwire"
-"3" 3 "April" 1 0 "McGwire"
-"4" 4 "April" 1 0 "McGwire"
-"5" 5 "April" 0 0 "McGwire"
-"6" 6 "April" 0 0 "McGwire"
-"7" 7 "April" 0 0 "McGwire"
-"8" 8 "April" 0 0 "McGwire"
-"9" 9 "April" 0 0 "McGwire"
-"10" 10 "April" 0 0 "McGwire"
-"11" 11 "April" 0 0 "McGwire"
-"12" 12 "April" 0 0 "McGwire"
-"13" 13 "April" 3 0 "McGwire"
-"14" 14 "April" 0 0 "McGwire"
-"16" 16 "April" 1 0 "McGwire"
-"17" 17 "April" 0 0 "McGwire"
-"18" 18 "April" 0 0 "McGwire"
-"19" 19 "April" 1 0 "McGwire"
-"20" 20 "April" 0 0 "McGwire"
-"22" 22 "April" 0 0 "McGwire"
-"23" 23 "April" 1 0 "McGwire"
-"24" 24 "April" 0 0 "McGwire"
-"25" 25 "April" 0 0 "McGwire"
-"26" 26 "April" 0 0 "McGwire"
-"27" 27 "April" 1 0 "McGwire"
-"28" 28 "May" 1 0 "McGwire"
-"29" 29 "May" 0 0 "McGwire"
-"30" 30 "May" 0 0 "McGwire"
-"31" 31 "May" 0 0 "McGwire"
-"32" 32 "May" 0 0 "McGwire"
-"33" 33 "May" 0 0 "McGwire"
-"34" 34 "May" 1 0 "McGwire"
-"35" 35 "May" 0 0 "McGwire"
-"36" 36 "May" 1 0 "McGwire"
-"37" 37 "May" 0 0 "McGwire"
-"38" 38 "May" 1 0 "McGwire"
-"39" 39 "May" 0 0 "McGwire"
-"40" 40 "May" 1 0 "McGwire"
-"41" 41 "May" 0 0 "McGwire"
-"42" 42 "May" 1 0 "McGwire"
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diff --git a/data/homerun.txt.xz b/data/homerun.txt.xz
new file mode 100644
index 0000000..d7440b9
Binary files /dev/null and b/data/homerun.txt.xz differ
diff --git a/data/immi1.tab b/data/immi1.tab
deleted file mode 100644
index e2a0f1c..0000000
--- a/data/immi1.tab
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diff --git a/data/immi2.tab b/data/immi2.tab
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diff --git a/data/immi3.tab b/data/immi3.tab
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diff --git a/data/immi4.tab b/data/immi4.tab
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diff --git a/data/immi5.tab b/data/immi5.tab
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diff --git a/data/immigration.tab b/data/immigration.tab
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-"2442" 5 0.343410015106201 3 4 1
-"2443" 5 0.487359970808029 2 NA 1
-"2444" 5 0.65021002292633 7 6 1
-"2445" 4 0.364969998598099 1 6 1
-"2446" 4 0.785520017147064 2 NA 0
-"2447" 3 0.600469946861267 1 2 1
-"2448" 3 0.592509984970093 2 4 1
-"2449" 3 0.62978994846344 5 6 0
-"2450" NA 0.394959986209869 4 NA 1
-"2451" 5 0.532429993152618 4 5 0
-"2452" 5 0.581430017948151 7 5 0
-"2453" 5 0.592509984970093 2 3 1
-"2454" 3 0.408399999141693 7 4 0
-"2455" NA 0.394959986209869 6 4 0
-"2456" 4 0.511519968509674 5 5 0
-"2457" 4 0.614226460456848 6 5 0
-"2458" NA 0.592509984970093 4 7 0
-"2459" 2 0.401980012655258 1 NA 1
-"2460" 3 0.903599977493286 3 5 0
-"2461" 4 0.785520017147064 2 5 1
-"2462" 5 NA 1 NA 1
-"2463" 5 0.401980012655258 1 2 1
-"2464" 3 0.461620002985001 4 NA 0
-"2465" NA 0.267989993095398 2 4 1
-"2466" 1 0.461620002985001 1 NA 0
-"2467" 3 0.785520017147064 7 6 0
-"2468" 4 0.600469946861267 2 4 1
-"2469" 3 NA 3 4 1
-"2470" NA 0.785520017147064 5 NA 1
-"2471" 3 0.688880026340485 1 3 1
-"2472" 4 0.62978994846344 2 3 0
-"2473" 3 0.785520017147064 1 NA 1
-"2474" 5 0.387620002031326 1 1 1
-"2475" NA 0.312420010566711 4 NA 1
-"2476" 3 NA 1 4 1
-"2477" 3 NA 1 6 1
-"2478" 3 0.267989993095398 1 NA 0
-"2479" 3 0.592509984970093 2 NA 0
-"2480" 4 0.609759986400604 7 5 1
-"2481" NA 0.312420010566711 1 6 1
-"2482" 4 0.785520017147064 2 3 0
-"2483" 5 NA 4 4 0
-"2484" 3 NA 6 3 1
-"2485" NA 0.673849999904633 6 6 1
diff --git a/data/immigration.tab.bz2 b/data/immigration.tab.bz2
new file mode 100644
index 0000000..c8f62d4
Binary files /dev/null and b/data/immigration.tab.bz2 differ
diff --git a/data/klein.txt b/data/klein.txt
deleted file mode 100644
index dbab38b..0000000
--- a/data/klein.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-year C P P1 Wtot Wp Wg I K1 X G T X1 Tm
-1921 41.9 12.4 12.7 28.2 25.5 2.7 -0.2 182.8 45.6 3.9 7.7 44.9 -10
-1922 45 16.9 12.4 32.2 29.3 2.9 1.9 182.6 50.1 3.2 3.9 45.6 -9
-1923 49.2 18.4 16.9 37 34.1 2.9 5.2 184.5 57.2 2.8 4.7 50.1 -8
-1924 50.6 19.4 18.4 37 33.9 3.1 3 189.7 57.1 3.5 3.8 57.2 -7
-1925 52.6 20.1 19.4 38.6 35.4 3.2 5.1 192.7 61 3.3 5.5 57.1 -6
-1926 55.1 19.6 20.1 40.7 37.4 3.3 5.6 197.8 64 3.3 7 61 -5
-1927 56.2 19.8 19.6 41.5 37.9 3.6 4.2 203.4 64.4 4 6.7 64 -4
-1928 57.3 21.1 19.8 42.9 39.2 3.7 3 207.6 64.5 4.2 4.2 64.4 -3
-1929 57.8 21.7 21.1 45.3 41.3 4 5.1 210.6 67 4.1 4 64.5 -2
-1930 55 15.6 21.7 42.1 37.9 4.2 1 215.7 61.2 5.2 7.7 67 -1
-1931 50.9 11.4 15.6 39.3 34.5 4.8 -3.4 216.7 53.4 5.9 7.5 61.2 0
-1932 45.6 7 11.4 34.3 29 5.3 -6.2 213.3 44.3 4.9 8.3 53.4 1
-1933 46.5 11.2 7 34.1 28.5 5.6 -5.1 207.1 45.1 3.7 5.4 44.3 2
-1934 48.7 12.3 11.2 36.6 30.6 6 -3 202 49.7 4 6.8 45.1 3
-1935 51.3 14 12.3 39.3 33.2 6.1 -1.3 199 54.4 4.4 7.2 49.7 4
-1936 57.7 17.6 14 44.2 36.8 7.4 2.1 197.7 62.7 2.9 8.3 54.4 5
-1937 58.7 17.3 17.6 47.7 41 6.7 2 199.8 65 4.3 6.7 62.7 6
-1938 57.5 15.3 17.3 45.9 38.2 7.7 -1.9 201.8 60.9 5.3 7.4 65 7
-1939 61.6 19 15.3 49.4 41.6 7.8 1.3 199.9 69.5 6.6 8.9 60.9 8
-1940 65 21.1 19 53 45 8 3.3 201.2 75.7 7.4 9.6 69.5 9
-1941 69.7 23.5 21.1 61.8 53.3 8.5 4.9 204.5 88.4 13.8 11.6 75.7 10
diff --git a/data/klein.txt.gz b/data/klein.txt.gz
new file mode 100644
index 0000000..d1a80a4
Binary files /dev/null and b/data/klein.txt.gz differ
diff --git a/data/kmenta.txt b/data/kmenta.txt
deleted file mode 100644
index fcaba51..0000000
--- a/data/kmenta.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-q p d f a
- 98.485 100.323 87.4 98.0 1
- 99.187 104.264 97.6 99.1 2
-102.163 103.435 96.7 99.1 3
-101.504 104.506 98.2 98.1 4
-104.240 98.001 99.8 110.8 5
-103.243 99.456 100.5 108.2 6
-103.993 101.066 103.2 105.6 7
- 99.900 104.763 107.8 109.8 8
-100.350 96.446 96.6 108.7 9
-102.820 91.228 88.9 100.6 10
- 95.435 93.085 75.1 81.0 11
- 92.424 98.801 76.9 68.6 12
- 94.535 102.908 84.6 70.9 13
- 98.757 98.756 90.6 81.4 14
-105.797 95.119 103.1 102.3 15
-100.225 98.451 105.1 105.0 16
-103.522 86.498 96.4 110.5 17
- 99.929 104.016 104.4 92.5 18
-105.223 105.769 110.7 89.3 19
-106.232 113.490 127.1 93.0 20
diff --git a/data/kmenta.txt.gz b/data/kmenta.txt.gz
new file mode 100644
index 0000000..f38a759
Binary files /dev/null and b/data/kmenta.txt.gz differ
diff --git a/data/macro.tab b/data/macro.tab
deleted file mode 100644
index be8e6ce..0000000
--- a/data/macro.tab
+++ /dev/null
@@ -1,351 +0,0 @@
-"country" "year" "gdp" "unem" "capmob" "trade"
-"1" "United States" 1966 5.1111407 3.8000000 0 9.622906
-"2" "United States" 1967 2.2772829 3.8000000 0 9.983546
-"3" "United States" 1968 4.7000000 3.6000000 0 10.089120
-"4" "United States" 1969 2.8000000 3.5000000 0 10.435930
-"5" "United States" 1970 -0.2000000 4.9000000 0 10.495350
-"6" "United States" 1971 3.1000000 5.9000000 0 11.278270
-"7" "United States" 1972 5.4000000 5.6000000 0 11.217710
-"8" "United States" 1973 5.7000000 4.9000000 0 11.767050
-"9" "United States" 1974 -0.9000000 5.6000000 0 13.772550
-"10" "United States" 1975 -0.8000000 8.5000000 0 17.423260
-"11" "United States" 1976 4.7000000 7.7000000 0 16.522110
-"12" "United States" 1977 5.5000000 7.1000000 0 17.234920
-"13" "United States" 1978 4.7000000 6.1000000 0 17.540990
-"14" "United States" 1979 2.6000000 5.8000000 0 18.175910
-"15" "United States" 1980 -0.4000000 7.1000000 0 19.732850
-"16" "United States" 1981 3.4000000 7.5000000 0 21.510570
-"17" "United States" 1982 -3.0000000 9.5000000 0 20.538950
-"18" "United States" 1983 2.9000000 9.5000000 0 18.569720
-"19" "United States" 1984 7.2000000 7.5000000 0 17.815880
-"20" "United States" 1985 3.8000000 7.1000000 0 18.028990
-"21" "United States" 1986 2.8000000 7.0000000 0 17.203710
-"22" "United States" 1987 3.7000000 6.2000000 0 17.230950
-"23" "United States" 1988 4.6000000 5.5000000 0 18.294180
-"24" "United States" 1989 2.8000000 5.2700837 0 19.413526
-"25" "United States" 1990 0.9000000 5.4145596 0 20.638364
-"26" "Canada" 1966 6.8021676 3.6000000 0 38.454670
-"27" "Canada" 1967 2.9236458 4.1000000 0 40.161670
-"28" "Canada" 1968 5.6000000 4.8000000 0 41.065740
-"29" "Canada" 1969 5.2000000 4.7000000 0 42.768490
-"30" "Canada" 1970 2.6000000 5.9000000 0 44.165330
-"31" "Canada" 1971 7.0000000 6.4000000 0 43.849900
-"32" "Canada" 1972 5.8000000 6.3000000 0 42.678130
-"33" "Canada" 1973 7.5000000 5.6000000 0 43.884720
-"34" "Canada" 1974 3.5000000 5.4000000 0 46.415430
-"35" "Canada" 1975 1.1000000 6.9000000 0 50.493310
-"36" "Canada" 1976 6.1000000 7.1000000 0 48.161620
-"37" "Canada" 1977 2.2000000 8.1000000 0 46.121660
-"38" "Canada" 1978 3.9000000 8.4000000 0 48.003110
-"39" "Canada" 1979 3.4000000 7.5000000 0 51.226560
-"40" "Canada" 1980 1.0000000 7.5000000 0 54.932030
-"41" "Canada" 1981 4.0000000 7.6000000 0 55.771610
-"42" "Canada" 1982 -4.3000000 11.0000000 0 54.325000
-"43" "Canada" 1983 2.8000000 11.9000000 0 48.583550
-"44" "Canada" 1984 5.4000000 11.3000000 0 48.402080
-"45" "Canada" 1985 4.7000000 10.5000000 0 53.523450
-"46" "Canada" 1986 3.1000000 9.6000000 0 54.308200
-"47" "Canada" 1987 4.5000000 8.9000000 0 53.461100
-"48" "Canada" 1988 5.0000000 7.8000000 0 52.156830
-"49" "Canada" 1989 2.4000000 7.4952143 0 52.077395
-"50" "Canada" 1990 0.4000000 8.0595930 0 49.932476
-"51" "United Kingdom" 1966 1.8770983 1.5000000 -1 37.931900
-"52" "United Kingdom" 1967 2.2550651 2.3000000 -1 37.830320
-"53" "United Kingdom" 1968 4.1000000 2.5000000 -1 37.762960
-"54" "United Kingdom" 1969 1.5000000 2.4000000 -2 41.925460
-"55" "United Kingdom" 1970 2.2000000 2.6000000 -2 42.797890
-"56" "United Kingdom" 1971 2.7000000 3.5000000 -1 44.084340
-"57" "United Kingdom" 1972 2.3000000 3.8000000 -1 43.510690
-"58" "United Kingdom" 1973 7.6000000 2.7000000 -1 42.867210
-"59" "United Kingdom" 1974 -1.0000000 2.6000000 -1 48.822800
-"60" "United Kingdom" 1975 -0.7000000 4.0000000 -1 59.628370
-"61" "United Kingdom" 1976 3.8000000 5.5000000 -1 52.510370
-"62" "United Kingdom" 1977 1.0000000 5.8000000 -1 56.874880
-"63" "United Kingdom" 1978 3.6000000 5.7000000 -1 59.064990
-"64" "United Kingdom" 1979 2.2000000 5.3000000 -1 55.531340
-"65" "United Kingdom" 1980 -2.3000000 7.0000000 0 55.788930
-"66" "United Kingdom" 1981 -1.4000000 10.5000000 0 52.513500
-"67" "United Kingdom" 1982 1.5000000 11.2000000 0 50.545860
-"68" "United Kingdom" 1983 3.4000000 11.7000000 0 51.031590
-"69" "United Kingdom" 1984 1.8000000 11.7000000 0 52.773220
-"70" "United Kingdom" 1985 3.7000000 11.2000000 0 58.076640
-"71" "United Kingdom" 1986 3.5000000 11.2000000 0 57.582850
-"72" "United Kingdom" 1987 4.7000000 10.2000000 0 53.523350
-"73" "United Kingdom" 1988 4.1000000 8.3000000 0 52.650150
-"74" "United Kingdom" 1989 2.3000000 6.1140732 0 50.248182
-"75" "United Kingdom" 1990 0.8000000 5.4717446 0 52.355970
-"76" "Netherlands" 1966 1.8770983 1.0000000 -2 87.058950
-"77" "Netherlands" 1967 2.2550651 2.0000000 -2 85.449720
-"78" "Netherlands" 1968 2.3000000 1.9000000 -3 82.560510
-"79" "Netherlands" 1969 6.4000000 1.4000000 -2 82.631060
-"80" "Netherlands" 1970 6.7000000 1.1000000 -2 86.038790
-"81" "Netherlands" 1971 4.2000000 1.6000000 -2 91.876280
-"82" "Netherlands" 1972 3.3000000 2.8000000 -1 91.269110
-"83" "Netherlands" 1973 4.7000000 2.8000000 -1 87.190080
-"84" "Netherlands" 1974 4.0000000 3.5000000 -1 91.157950
-"85" "Netherlands" 1975 -0.1000000 5.3000000 -1 104.975700
-"86" "Netherlands" 1976 5.1000000 5.6000000 -1 96.280620
-"87" "Netherlands" 1977 2.3000000 5.5000000 -1 98.340400
-"88" "Netherlands" 1978 2.5000000 5.5000000 0 93.892990
-"89" "Netherlands" 1979 2.4000000 5.6000000 0 89.751190
-"90" "Netherlands" 1980 0.9000000 6.0000000 0 98.667540
-"91" "Netherlands" 1981 -0.7000000 8.9000000 0 105.550200
-"92" "Netherlands" 1982 -1.4000000 10.3000000 0 112.472700
-"93" "Netherlands" 1983 0.9000000 12.3000000 0 111.333700
-"94" "Netherlands" 1984 1.7000000 12.3000000 0 112.810500
-"95" "Netherlands" 1985 2.6000000 10.2000000 0 119.003100
-"96" "Netherlands" 1986 2.0000000 10.0000000 0 122.672100
-"97" "Netherlands" 1987 1.1000000 10.0000000 0 104.127300
-"98" "Netherlands" 1988 2.7000000 10.1000000 0 102.312000
-"99" "Netherlands" 1989 4.0000000 8.3122300 0 105.057288
-"100" "Netherlands" 1990 3.9000000 7.5087311 0 111.762226
-"101" "Belgium" 1966 3.1622589 2.7000000 -2 73.618440
-"102" "Belgium" 1967 3.8753976 3.7000000 -2 74.542040
-"103" "Belgium" 1968 4.2000000 4.5000000 -2 73.585590
-"104" "Belgium" 1969 6.6000000 3.6000000 -1 78.447790
-"105" "Belgium" 1970 6.4000000 2.9000000 -1 84.379250
-"106" "Belgium" 1971 3.7000000 2.9000000 -1 86.759470
-"107" "Belgium" 1972 5.3000000 3.4000000 0 86.106090
-"108" "Belgium" 1973 5.9000000 3.6000000 0 85.156660
-"109" "Belgium" 1974 4.1000000 4.0000000 0 94.414870
-"110" "Belgium" 1975 -1.5000000 6.7000000 0 108.177400
-"111" "Belgium" 1976 5.3000000 6.8000000 0 93.609210
-"112" "Belgium" 1977 0.4000000 7.8000000 0 98.267460
-"113" "Belgium" 1978 3.0000000 8.4000000 0 107.168100
-"114" "Belgium" 1979 2.1000000 8.7000000 0 104.390400
-"115" "Belgium" 1980 4.0000000 9.4000000 0 115.214600
-"116" "Belgium" 1981 -1.5000000 10.0000000 0 121.389600
-"117" "Belgium" 1982 1.5000000 11.7000000 0 130.571400
-"118" "Belgium" 1983 -0.1000000 12.9000000 0 138.485700
-"119" "Belgium" 1984 1.4000000 13.0000000 0 137.189600
-"120" "Belgium" 1985 0.9000000 12.0000000 0 146.020200
-"121" "Belgium" 1986 1.6000000 11.3000000 0 141.275500
-"122" "Belgium" 1987 1.9000000 11.1000000 0 127.210200
-"123" "Belgium" 1988 4.3000000 10.0000000 0 126.907600
-"124" "Belgium" 1989 3.6000000 9.2664093 0 133.281122
-"125" "Belgium" 1990 3.8000000 8.7341469 0 142.686084
-"126" "France" 1966 5.2141824 0.7000000 0 24.302560
-"127" "France" 1967 4.6882107 0.9000000 0 25.008780
-"128" "France" 1968 4.3000000 1.2000000 0 24.850140
-"129" "France" 1969 7.0000000 1.0000000 -1 25.402060
-"130" "France" 1970 5.7000000 1.2000000 -1 27.829750
-"131" "France" 1971 5.4000000 1.6000000 -1 31.511960
-"132" "France" 1972 5.9000000 2.0000000 -1 32.185850
-"133" "France" 1973 5.4000000 2.6000000 -1 32.615950
-"134" "France" 1974 3.2000000 2.8000000 -1 34.760360
-"135" "France" 1975 0.2000000 4.1000000 -1 43.205750
-"136" "France" 1976 5.2000000 4.4000000 -1 37.326510
-"137" "France" 1977 3.1000000 4.7000000 -1 40.429730
-"138" "France" 1978 3.8000000 5.2000000 -1 41.568830
-"139" "France" 1979 3.3000000 5.9000000 -1 40.255400
-"140" "France" 1980 1.1000000 6.3000000 -1 42.541650
-"141" "France" 1981 0.5000000 7.3000000 -1 44.891930
-"142" "France" 1982 1.8000000 8.1000000 -1 46.895390
-"143" "France" 1983 0.7000000 8.3000000 -1 46.224320
-"144" "France" 1984 1.3000000 9.9000000 -1 45.693210
-"145" "France" 1985 1.9000000 10.2000000 -1 47.646520
-"146" "France" 1986 2.3000000 10.4000000 -1 47.210230
-"147" "France" 1987 1.9000000 10.5000000 -1 41.627190
-"148" "France" 1988 3.5000000 9.9000000 -1 41.465140
-"149" "France" 1989 3.6000000 9.3791118 -1 42.861233
-"150" "France" 1990 2.6000000 8.9709547 -1 49.667118
-"151" "West Germany" 1966 2.9414163 0.7000000 0 37.894460
-"152" "West Germany" 1967 -0.1188883 2.1000000 0 38.813670
-"153" "West Germany" 1968 6.3000000 1.5000000 0 39.506420
-"154" "West Germany" 1969 7.8000000 0.9000000 0 41.404140
-"155" "West Germany" 1970 6.0000000 0.7000000 0 43.068930
-"156" "West Germany" 1971 2.9000000 0.8000000 0 43.210420
-"157" "West Germany" 1972 4.2000000 1.1000000 0 43.247700
-"158" "West Germany" 1973 4.7000000 1.2000000 0 42.768340
-"159" "West Germany" 1974 0.3000000 2.6000000 0 44.196360
-"160" "West Germany" 1975 -1.6000000 4.7000000 0 52.194840
-"161" "West Germany" 1976 5.4000000 4.6000000 0 49.916700
-"162" "West Germany" 1977 3.0000000 4.5000000 0 52.594910
-"163" "West Germany" 1978 2.9000000 4.3000000 0 52.035340
-"164" "West Germany" 1979 4.2000000 3.8000000 0 50.733770
-"165" "West Germany" 1980 1.4000000 3.8000000 0 53.401890
-"166" "West Germany" 1981 0.2000000 5.5000000 0 57.204180
-"167" "West Germany" 1982 -0.6000000 7.5000000 0 61.452470
-"168" "West Germany" 1983 1.2000000 9.1000000 0 62.337530
-"169" "West Germany" 1984 2.6000000 9.1000000 0 60.678390
-"170" "West Germany" 1985 2.0000000 9.3000000 0 64.168710
-"171" "West Germany" 1986 2.3000000 9.0000000 0 66.315470
-"172" "West Germany" 1987 1.8000000 8.9000000 0 59.980890
-"173" "West Germany" 1988 3.7000000 8.7000000 0 58.095540
-"174" "West Germany" 1989 3.2000000 6.8437490 0 59.254416
-"175" "West Germany" 1990 4.7000000 6.2089887 0 64.472380
-"176" "Austria" 1966 5.6429989 1.7000000 -2 50.828180
-"177" "Austria" 1967 3.0076867 1.8000000 -2 51.539860
-"178" "Austria" 1968 4.5000000 2.0000000 -2 50.877130
-"179" "Austria" 1969 6.3000000 1.8000000 -2 51.626360
-"180" "Austria" 1970 7.1000000 1.4000000 -2 55.524040
-"181" "Austria" 1971 5.1000000 1.2000000 -2 61.163130
-"182" "Austria" 1972 6.2000000 1.0000000 -2 60.626280
-"183" "Austria" 1973 4.9000000 1.0000000 -3 60.495470
-"184" "Austria" 1974 3.9000000 1.1000000 -1 60.685230
-"185" "Austria" 1975 -0.4000000 1.7000000 -1 66.363810
-"186" "Austria" 1976 4.6000000 1.7000000 -1 63.066760
-"187" "Austria" 1977 4.4000000 1.5000000 -1 66.726450
-"188" "Austria" 1978 0.5000000 1.8000000 -1 67.233540
-"189" "Austria" 1979 4.7000000 1.7000000 -1 66.624710
-"190" "Austria" 1980 3.0000000 1.6000000 -1 71.776940
-"191" "Austria" 1981 -0.1000000 2.1000000 -1 75.599920
-"192" "Austria" 1982 1.2000000 3.1000000 -1 77.917160
-"193" "Austria" 1983 2.1000000 3.8000000 -1 74.132980
-"194" "Austria" 1984 2.0000000 3.8000000 -1 73.286230
-"195" "Austria" 1985 2.5000000 3.6000000 -1 77.686450
-"196" "Austria" 1986 1.1000000 3.1000000 -1 80.934370
-"197" "Austria" 1987 1.9000000 3.8000000 -1 72.724470
-"198" "Austria" 1988 4.2000000 3.6000000 -1 70.362770
-"199" "Austria" 1989 3.7000000 3.1304348 -1 74.421892
-"200" "Austria" 1990 4.9000000 3.2331254 -1 79.472929
-"201" "Italy" 1966 5.9849301 5.9000000 -1 26.939980
-"202" "Italy" 1967 7.1776258 5.4000000 -1 28.237120
-"203" "Italy" 1968 6.5000000 5.7000000 -1 28.761100
-"204" "Italy" 1969 6.1000000 5.7000000 -1 29.418730
-"205" "Italy" 1970 5.3000000 5.4000000 -1 31.444620
-"206" "Italy" 1971 1.6000000 5.4000000 -1 32.473000
-"207" "Italy" 1972 3.2000000 6.4000000 -1 32.666760
-"208" "Italy" 1973 7.0000000 6.4000000 -1 34.196790
-"209" "Italy" 1974 4.1000000 5.4000000 -1 37.149280
-"210" "Italy" 1975 -3.6000000 5.9000000 -2 46.893480
-"211" "Italy" 1976 5.9000000 6.7000000 -1 43.181410
-"212" "Italy" 1977 1.9000000 7.2000000 -2 48.341280
-"213" "Italy" 1978 2.7000000 7.2000000 -1 48.478820
-"214" "Italy" 1979 4.9000000 7.7000000 -1 47.990130
-"215" "Italy" 1980 3.9000000 7.6000000 -1 51.303110
-"216" "Italy" 1981 0.2000000 8.4000000 -1 50.451220
-"217" "Italy" 1982 -0.5000000 9.1000000 -2 46.008000
-"218" "Italy" 1983 -0.2000000 9.9000000 -1 44.531230
-"219" "Italy" 1984 2.8000000 10.1000000 0 41.135250
-"220" "Italy" 1985 2.6000000 10.1000000 0 43.648930
-"221" "Italy" 1986 2.5000000 11.1000000 0 43.849610
-"222" "Italy" 1987 3.0000000 12.0000000 -1 37.016330
-"223" "Italy" 1988 3.9000000 12.0000000 -1 36.825300
-"224" "Italy" 1989 3.0000000 11.8115106 -1 38.838481
-"225" "Italy" 1990 2.0000000 10.7891162 -1 38.649988
-"226" "Finland" 1966 2.3721416 1.5000000 -3 42.164150
-"227" "Finland" 1967 2.1688613 2.9000000 -3 41.395250
-"228" "Finland" 1968 2.5000000 3.8000000 -2 40.356310
-"229" "Finland" 1969 9.6000000 2.8000000 -2 43.750690
-"230" "Finland" 1970 7.9000000 1.9000000 -2 47.382030
-"231" "Finland" 1971 2.1000000 2.2000000 -2 52.598210
-"232" "Finland" 1972 7.6000000 2.5000000 -2 50.480520
-"233" "Finland" 1973 6.7000000 2.3000000 -3 50.746260
-"234" "Finland" 1974 3.0000000 1.7000000 -3 51.496550
-"235" "Finland" 1975 1.2000000 2.2000000 -3 58.730770
-"236" "Finland" 1976 0.3000000 3.8000000 -4 53.379480
-"237" "Finland" 1977 0.2000000 5.8000000 -3 52.090850
-"238" "Finland" 1978 2.6000000 7.2000000 -3 55.153420
-"239" "Finland" 1979 7.4000000 5.9000000 -3 55.953200
-"240" "Finland" 1980 5.6000000 4.6000000 -3 61.350390
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diff --git a/data/macro.tab.xz b/data/macro.tab.xz
new file mode 100644
index 0000000..4ea5274
Binary files /dev/null and b/data/macro.tab.xz differ
diff --git a/data/mexico.tab b/data/mexico.tab
deleted file mode 100644
index 65a81cd..0000000
--- a/data/mexico.tab
+++ /dev/null
@@ -1,1360 +0,0 @@
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diff --git a/data/mid.tab b/data/mid.tab
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diff --git a/data/mid.tab.bz2 b/data/mid.tab.bz2
new file mode 100644
index 0000000..571dfa6
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diff --git a/data/newpainters.txt b/data/newpainters.txt
deleted file mode 100644
index 373f825..0000000
--- a/data/newpainters.txt
+++ /dev/null
@@ -1,55 +0,0 @@
-"Composition" "Drawing" "Colour" "Expression"
-"Da Udine" 200 100 400 100
-"Da Vinci" 400 400 100 400
-"Del Piombo" 100 200 400 300
-"Del Sarto" 200 400 200 300
-"Fr. Penni" 100 400 200 100
-"Guilio Romano" 400 400 100 400
-"Michelangelo" 100 400 100 300
-"Perino del Vaga" 400 400 100 300
-"Perugino" 100 200 300 200
-"Raphael" 400 400 300 400
-"F. Zucarro" 200 200 200 300
-"Fr. Salviata" 300 400 200 300
-"Parmigiano" 200 400 100 300
-"Primaticcio" 400 300 100 300
-"T. Zucarro" 300 300 300 300
-"Volterra" 200 400 100 300
-"Barocci" 300 400 100 300
-"Cortona" 400 300 300 300
-"Josepin" 200 200 100 100
-"L. Jordaens" 300 200 200 300
-"Testa" 200 400 100 300
-"Vanius" 400 400 300 400
-"Bassano" 100 100 400 100
-"Bellini" 100 100 300 100
-"Giorgione" 100 100 400 200
-"Murillo" 100 100 300 200
-"Palma Giovane" 200 100 300 300
-"Palma Vecchio" 100 100 400 100
-"Pordenone" 100 300 400 200
-"Tintoretto" 400 300 400 200
-"Titian" 200 400 400 300
-"Veronese" 400 200 400 100
-"Albani" 300 300 300 300
-"Caravaggio" 100 100 400 100
-"Corregio" 300 200 300 400
-"Domenichino" 400 400 200 400
-"Guercino" 400 200 300 200
-"Lanfranco" 300 200 300 200
-"The Carraci" 400 400 300 400
-"Durer" 100 200 300 300
-"Holbein" 200 200 400 400
-"Pourbus" 100 400 100 300
-"Van Leyden" 100 100 100 200
-"Diepenbeck" 200 200 300 300
-"J. Jordaens" 200 100 400 300
-"Otho Venius" 300 300 300 300
-"Rembrandt" 400 100 400 400
-"Rubens" 400 200 400 400
-"Teniers" 400 200 300 300
-"Van Dyck" 400 200 400 400
-"Bourdon" 200 100 200 200
-"Le Brun" 400 400 200 400
-"Le Suer" 400 400 100 400
-"Poussin" 400 400 100 400
diff --git a/data/newpainters.txt.bz2 b/data/newpainters.txt.bz2
new file mode 100644
index 0000000..5f00239
Binary files /dev/null and b/data/newpainters.txt.bz2 differ
diff --git a/data/sanction.tab b/data/sanction.tab
deleted file mode 100644
index 5157911..0000000
--- a/data/sanction.tab
+++ /dev/null
@@ -1,79 +0,0 @@
-"mil" "coop" "target" "import" "export" "cost" "num" "ncost"
-"1" 1 4 3 1 1 4 15 "major loss"
-"2" 0 2 3 0 1 3 4 "modest loss"
-"3" 0 1 3 1 0 2 1 "little effect"
-"4" 1 1 3 1 1 2 1 "little effect"
-"5" 0 1 3 1 1 2 1 "little effect"
-"6" 0 1 3 0 1 2 1 "little effect"
-"7" 1 2 2 0 1 2 3 "little effect"
-"8" 0 1 3 0 0 2 3 "little effect"
-"9" 0 2 1 0 0 1 2 "net gain"
-"10" 1 2 3 1 1 2 1 "little effect"
-"11" 1 1 2 0 0 1 1 "net gain"
-"12" 0 1 2 1 1 2 1 "little effect"
-"13" 0 3 1 1 1 2 8 "little effect"
-"14" 0 3 3 1 1 4 7 "major loss"
-"15" 0 3 2 1 1 3 21 "modest loss"
-"16" 0 1 2 0 0 1 1 "net gain"
-"17" 0 4 2 1 1 2 7 "little effect"
-"18" 0 3 3 0 0 2 4 "little effect"
-"19" 0 1 1 0 0 1 1 "net gain"
-"20" 0 3 3 1 0 3 120 "modest loss"
-"21" 0 4 3 0 0 2 7 "little effect"
-"22" 0 1 2 0 0 1 1 "net gain"
-"23" 0 1 2 1 1 4 1 "major loss"
-"24" 0 1 2 0 0 1 1 "net gain"
-"25" 0 1 1 0 0 1 1 "net gain"
-"26" 0 3 2 1 1 2 32 "little effect"
-"27" 0 1 2 1 0 2 1 "little effect"
-"28" 0 1 2 1 0 2 1 "little effect"
-"29" 0 1 2 0 0 1 1 "net gain"
-"30" 0 4 2 1 1 3 150 "modest loss"
-"31" 0 1 2 0 0 1 1 "net gain"
-"32" 0 1 2 0 0 1 1 "net gain"
-"33" 0 1 1 0 0 1 1 "net gain"
-"34" 0 1 2 0 1 1 5 "net gain"
-"35" 0 2 1 1 1 2 2 "little effect"
-"36" 0 3 3 0 1 1 10 "net gain"
-"37" 0 1 2 0 0 1 1 "net gain"
-"38" 0 1 1 0 0 1 1 "net gain"
-"39" 0 1 2 0 0 1 1 "net gain"
-"40" 0 2 3 0 1 2 2 "little effect"
-"41" 0 2 2 0 1 2 1 "little effect"
-"42" 0 2 3 0 0 2 2 "little effect"
-"43" 0 1 3 1 0 2 1 "little effect"
-"44" 0 2 3 0 1 2 1 "little effect"
-"45" 0 1 1 1 1 1 1 "net gain"
-"46" 0 1 2 0 1 1 1 "net gain"
-"47" 0 1 3 0 1 2 1 "little effect"
-"48" 0 2 1 1 0 1 1 "net gain"
-"49" 0 1 3 0 0 1 1 "net gain"
-"50" 0 1 2 0 0 1 1 "net gain"
-"51" 0 1 2 0 1 2 1 "little effect"
-"52" 0 1 3 0 1 2 1 "little effect"
-"53" 0 1 1 0 1 1 1 "net gain"
-"54" 0 1 1 0 0 1 2 "net gain"
-"55" 0 1 2 0 0 1 1 "net gain"
-"56" 0 1 2 0 1 2 1 "little effect"
-"57" 0 2 2 0 1 2 3 "little effect"
-"58" 0 2 3 0 1 2 2 "little effect"
-"59" 0 2 3 0 1 2 2 "little effect"
-"60" 0 3 2 1 1 3 9 "modest loss"
-"61" 1 3 2 0 0 1 7 "net gain"
-"62" 0 1 3 1 1 3 1 "modest loss"
-"63" 0 3 1 1 1 3 10 "modest loss"
-"64" 0 2 2 0 0 1 2 "net gain"
-"65" 0 3 3 1 1 2 8 "little effect"
-"66" 0 2 1 0 0 1 2 "net gain"
-"67" 0 3 3 0 1 3 13 "modest loss"
-"68" 0 1 2 0 1 2 1 "little effect"
-"69" 0 1 2 1 0 2 1 "little effect"
-"70" 0 3 1 1 1 2 4 "little effect"
-"71" 0 2 3 0 1 3 1 "modest loss"
-"72" 0 2 2 0 0 1 8 "net gain"
-"73" 1 3 1 1 1 2 14 "little effect"
-"74" 0 2 1 0 0 1 2 "net gain"
-"75" 0 1 3 0 1 2 1 "little effect"
-"76" 0 4 3 1 0 2 13 "little effect"
-"77" 0 1 2 0 0 1 1 "net gain"
-"78" 1 3 1 1 1 2 10 "little effect"
diff --git a/data/sanction.tab.bz2 b/data/sanction.tab.bz2
new file mode 100644
index 0000000..a7bfbec
Binary files /dev/null and b/data/sanction.tab.bz2 differ
diff --git a/data/sna.ex.RData b/data/sna.ex.RData
index b80635c..8dbef78 100644
Binary files a/data/sna.ex.RData and b/data/sna.ex.RData differ
diff --git a/data/swiss.txt b/data/swiss.txt
deleted file mode 100644
index 3e8c981..0000000
--- a/data/swiss.txt
+++ /dev/null
@@ -1,48 +0,0 @@
-"Fertility" "Agriculture" "Examination" "Education" "Catholic" "Infant.Mortality"
-"Courtelary" 80.2 17 15 12 9.96 22.2
-"Delemont" 83.1 45.1 6 9 84.84 22.2
-"Franches-Mnt" 92.5 39.7 5 5 93.4 20.2
-"Moutier" 85.8 36.5 12 7 33.77 20.3
-"Neuveville" 76.9 43.5 17 15 5.16 20.6
-"Porrentruy" 76.1 35.3 9 7 90.57 26.6
-"Broye" 83.8 70.2 16 7 92.85 23.6
-"Glane" 92.4 67.8 14 8 97.16 24.9
-"Gruyere" 82.4 53.3 12 7 97.67 21
-"Sarine" 82.9 45.2 16 13 91.38 24.4
-"Veveyse" 87.1 64.5 14 6 98.61 24.5
-"Aigle" 64.1 62 21 12 8.52 16.5
-"Aubonne" 66.9 67.5 14 7 2.27 19.1
-"Avenches" 68.9 60.7 19 12 4.43 22.7
-"Cossonay" 61.7 69.3 22 5 2.82 18.7
-"Echallens" 68.3 72.6 18 2 24.2 21.2
-"Grandson" 71.7 34 17 8 3.3 20
-"Lausanne" 55.7 19.4 26 28 12.11 20.2
-"La Vallee" 54.3 15.2 31 20 2.15 10.8
-"Lavaux" 65.1 73 19 9 2.84 20
-"Morges" 65.5 59.8 22 10 5.23 18
-"Moudon" 65 55.1 14 3 4.52 22.4
-"Nyone" 56.6 50.9 22 12 15.14 16.7
-"Orbe" 57.4 54.1 20 6 4.2 15.3
-"Oron" 72.5 71.2 12 1 2.4 21
-"Payerne" 74.2 58.1 14 8 5.23 23.8
-"Paysd'enhaut" 72 63.5 6 3 2.56 18
-"Rolle" 60.5 60.8 16 10 7.72 16.3
-"Vevey" 58.3 26.8 25 19 18.46 20.9
-"Yverdon" 65.4 49.5 15 8 6.1 22.5
-"Conthey" 75.5 85.9 3 2 99.71 15.1
-"Entremont" 69.3 84.9 7 6 99.68 19.8
-"Herens" 77.3 89.7 5 2 100 18.3
-"Martigwy" 70.5 78.2 12 6 98.96 19.4
-"Monthey" 79.4 64.9 7 3 98.22 20.2
-"St Maurice" 65 75.9 9 9 99.06 17.8
-"Sierre" 92.2 84.6 3 3 99.46 16.3
-"Sion" 79.3 63.1 13 13 96.83 18.1
-"Boudry" 70.4 38.4 26 12 5.62 20.3
-"La Chauxdfnd" 65.7 7.7 29 11 13.79 20.5
-"Le Locle" 72.7 16.7 22 13 11.22 18.9
-"Neuchatel" 64.4 17.6 35 32 16.92 23
-"Val de Ruz" 77.6 37.6 15 7 4.97 20
-"ValdeTravers" 67.6 18.7 25 7 8.65 19.5
-"V. De Geneve" 35 1.2 37 53 42.34 18
-"Rive Droite" 44.7 46.6 16 29 50.43 18.2
-"Rive Gauche" 42.8 27.7 22 29 58.33 19.3
diff --git a/data/swiss.txt.bz2 b/data/swiss.txt.bz2
new file mode 100644
index 0000000..27de507
Binary files /dev/null and b/data/swiss.txt.bz2 differ
diff --git a/data/tobin.txt b/data/tobin.txt
deleted file mode 100644
index f7ce8db..0000000
--- a/data/tobin.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-"durable" "age" "quant"
-"1" 0 57.7 236
-"2" 0.7 50.9 283
-"3" 0 48.5 207
-"4" 0 41.7 220
-"5" 0 47.7 238
-"6" 0 59.8 216
-"7" 0 44.3 284
-"8" 3.7 45.1 221
-"9" 0 51.7 275
-"10" 3 50 269
-"11" 10.4 46.8 207
-"12" 0 58 249
-"13" 0 58.9 246
-"14" 0 40 277
-"15" 1.5 34.1 231
-"16" 0 39.9 219
-"17" 0 33.4 240
-"18" 3.5 48.1 266
-"19" 6.1 46.6 214
-"20" 0 53.1 251
diff --git a/data/tobin.txt.gz b/data/tobin.txt.gz
new file mode 100644
index 0000000..cc932bc
Binary files /dev/null and b/data/tobin.txt.gz differ
diff --git a/data/turnout.tab b/data/turnout.tab
deleted file mode 100644
index e87b572..0000000
--- a/data/turnout.tab
+++ /dev/null
@@ -1,2001 +0,0 @@
-"race" "age" "educate" "income" "vote"
-"1" "white" 60 14.0 3.3458 1
-"2" "white" 51 10.0 1.8561 0
-"3" "white" 24 12.0 0.6304 0
-"4" "white" 38 8.0 3.4183 1
-"5" "white" 25 12.0 2.7852 1
-"6" "white" 67 12.0 2.3866 1
-"7" "white" 40 12.0 4.2857 0
-"8" "white" 56 10.0 9.3205 1
-"9" "white" 32 12.0 3.8797 1
-"10" "white" 75 16.0 2.7031 1
-"11" "white" 46 15.0 11.2307 1
-"12" "white" 52 12.0 8.6696 1
-"13" "white" 22 12.0 1.7443 0
-"14" "white" 60 12.0 0.2253 0
-"15" "white" 24 14.0 4.3348 1
-"16" "white" 30 10.0 0.7953 1
-"17" "white" 46 12.0 2.9072 1
-"18" "white" 69 9.0 1.1893 1
-"19" "others" 60 12.0 1.1277 1
-"20" "white" 34 12.0 0.5523 1
-"21" "white" 76 14.0 0.7294 1
-"22" "white" 29 12.0 4.3348 1
-"23" "white" 49 13.0 7.7751 1
-"24" "white" 29 14.0 3.1356 0
-"25" "white" 47 16.0 5.2331 1
-"26" "white" 39 16.0 10.0376 1
-"27" "white" 58 8.0 4.6408 1
-"28" "white" 44 12.0 3.7972 1
-"29" "white" 22 8.0 0.2071 0
-"30" "white" 31 12.0 11.2307 1
-"31" "white" 74 12.0 0.8236 1
-"32" "white" 40 12.0 4.4578 1
-"33" "others" 27 12.0 3.5800 1
-"34" "white" 65 8.0 2.9170 1
-"35" "others" 70 5.0 4.0702 1
-"36" "white" 34 12.0 3.3834 1
-"37" "white" 54 12.0 4.9624 1
-"38" "white" 23 7.0 3.2949 0
-"39" "white" 84 8.0 0.7294 0
-"40" "white" 37 16.0 5.9111 1
-"41" "white" 67 14.0 1.8380 1
-"42" "others" 33 12.0 1.7443 1
-"43" "white" 22 12.0 3.7972 0
-"44" "white" 57 12.0 4.9624 1
-"45" "white" 26 11.0 4.6690 1
-"46" "white" 23 12.0 3.3834 1
-"47" "white" 79 10.0 1.8640 1
-"48" "white" 53 8.0 0.7953 1
-"49" "white" 25 12.0 5.8684 0
-"50" "others" 38 9.0 0.6765 0
-"51" "others" 68 8.0 2.7068 1
-"52" "white" 28 16.0 3.4183 1
-"53" "white" 43 15.0 2.8311 1
-"54" "white" 80 12.0 0.1727 0
-"55" "white" 31 12.0 2.3866 0
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-"57" "white" 37 13.0 4.3754 1
-"58" "white" 68 14.0 1.8429 0
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-"60" "white" 50 14.0 7.8949 0
-"61" "white" 37 13.0 0.6304 0
-"62" "white" 59 12.0 3.4183 1
-"63" "white" 35 16.0 3.5800 1
-"64" "white" 35 15.0 7.0036 0
-"65" "white" 49 8.0 2.4811 1
-"66" "others" 44 16.0 9.3205 1
-"67" "white" 77 12.0 3.7435 1
-"68" "white" 22 10.0 1.4498 0
-"69" "white" 80 14.0 0.5813 1
-"70" "white" 48 11.0 7.0036 1
-"71" "white" 24 15.0 2.7722 1
-"72" "white" 42 16.0 7.5945 1
-"73" "white" 67 19.0 6.0903 1
-"74" "others" 42 10.0 1.2659 0
-"75" "white" 54 10.0 4.6408 1
-"76" "white" 45 12.0 5.2331 1
-"77" "white" 57 9.0 1.1277 1
-"78" "others" 46 12.0 0.4607 0
-"79" "white" 58 12.0 3.7972 1
-"80" "white" 76 4.0 2.5196 0
-"81" "white" 28 16.0 0.1936 1
-"82" "white" 41 10.0 5.2331 1
-"83" "white" 83 8.0 1.3131 0
-"84" "others" 47 12.0 5.2331 1
-"85" "white" 81 12.0 1.4498 1
-"86" "white" 59 8.0 3.3102 1
-"87" "others" 20 16.0 1.2094 1
-"88" "white" 45 8.0 1.8561 1
-"89" "white" 76 6.0 0.5813 0
-"90" "white" 53 9.0 4.9624 1
-"91" "others" 40 16.0 5.2331 1
-"92" "white" 38 12.0 4.6408 1
-"93" "white" 30 15.0 7.7751 1
-"94" "white" 29 19.0 2.5220 1
-"95" "white" 72 12.0 0.4607 1
-"96" "white" 78 6.0 0.7568 1
-"97" "white" 65 13.0 3.7042 1
-"98" "white" 31 12.0 3.3558 1
-"99" "white" 21 12.0 4.8901 1
-"100" "white" 50 12.0 4.6408 1
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-"102" "others" 71 11.0 0.7294 1
-"103" "white" 49 14.0 7.7751 1
-"104" "white" 47 9.0 3.3458 1
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-"106" "others" 31 16.0 6.2740 1
-"107" "white" 54 12.0 4.0702 1
-"108" "white" 40 16.0 6.7838 1
-"109" "white" 29 12.0 3.4183 1
-"110" "white" 42 12.0 7.0036 1
-"111" "white" 67 16.0 5.9469 1
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-"113" "white" 54 12.0 2.9170 1
-"114" "white" 63 8.0 1.3566 1
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-"1980" "white" 51 14.0 4.0702 0
-"1981" "white" 60 11.0 1.3566 1
-"1982" "others" 43 11.0 1.5788 1
-"1983" "white" 26 13.0 4.8954 1
-"1984" "white" 50 8.0 2.2788 1
-"1985" "white" 39 12.0 3.4183 1
-"1986" "white" 78 8.0 1.3566 1
-"1987" "white" 24 11.0 3.4183 1
-"1988" "others" 62 9.0 0.7294 0
-"1989" "white" 74 12.0 2.8311 1
-"1990" "white" 48 9.0 2.2788 1
-"1991" "white" 76 7.0 1.3566 1
-"1992" "white" 30 19.0 3.5800 1
-"1993" "white" 28 12.0 6.3304 1
-"1994" "white" 58 12.0 0.1936 0
-"1995" "white" 22 7.0 0.2364 0
-"1996" "white" 26 16.0 3.3834 0
-"1997" "white" 34 12.0 2.9170 1
-"1998" "white" 51 16.0 7.8949 1
-"1999" "white" 22 10.0 2.4811 0
-"2000" "white" 59 10.0 0.5523 0
diff --git a/data/turnout.tab.bz2 b/data/turnout.tab.bz2
new file mode 100644
index 0000000..14e86f6
Binary files /dev/null and b/data/turnout.tab.bz2 differ
diff --git a/data/voteincome.txt b/data/voteincome.txt
deleted file mode 100644
index b247d5b..0000000
--- a/data/voteincome.txt
+++ /dev/null
@@ -1,1501 +0,0 @@
-"state" "year" "vote" "income" "education" "age" "female"
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-ET
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-0.75 w
-[] 0 d
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-ET
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 135.01 150.71 Tm (10) Tj
-ET
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-ET
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 312.97 150.71 Tm (30) Tj
-ET
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 401.96 150.71 Tm (40) Tj
-ET
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-BT
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-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 179.30 Tm (1.0) Tj
-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 198.50 Tm (2.0) Tj
-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 217.71 Tm (3.0) Tj
-ET
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-BT
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-ET
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-ET
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-1.000 0.000 0.000 RG
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-[ 3.00 5.00] 0 d
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-0.000 0.000 0.000 RG
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-ET
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-ET
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-ET
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-ET
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-ET
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-ET
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-ET
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-ET
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-/F3 1 Tf 10.00 0.00 -0.00 10.00 204.72 322.91 Tm [(Expected V) 60 (alues: P\(Y=j|X\))] TJ
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-ET
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-ET
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-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 260.49 Tm (3) Tj
-ET
-BT
-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 278.02 Tm (4) Tj
-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 295.55 Tm (5) Tj
-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 313.08 Tm (6) Tj
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-ET
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-ET
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-ET
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-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 228.52 Tm (0.05) Tj
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-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 307.05 Tm (0.20) Tj
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-ET
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-ET
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diff --git a/inst/doc/Zcommands.tex b/inst/doc/Zcommands.tex
deleted file mode 100644
index ff2e5c2..0000000
--- a/inst/doc/Zcommands.tex
+++ /dev/null
@@ -1,801 +0,0 @@
-\chapter{Statistical Commands}
-
-\section{Zelig Commands}\label{s:commands}
-
-\subsection{Quick Overview}\label{overview}
-
-For any statistical model, Zelig does its work with a combination of
-three commands.
-\begin{figure}[h!]
-\caption{Main Zelig commands (solid arrows) and some options (dashed arrows)}
-\label{3steps}
-\begin{center}
-\begin{displaymath}
-\SelectTips{eu}{12} \UseTips
-\xymatrix@!C{\quad {\rm Imputation}
-\ar@{-->}[dr] & & {\rm Matching} \ar@{-->}[dl] \\
-{\rm Validation } \ar@{<--}[r] & *+[F-:<10pt>]{{\tt zelig()}} \ar@{->}[d] \ar@{-->}[r] &
-{\tt summary()}\\
- & *+[F-:<10pt>]{{\tt setx()}} \ar@{-->}[r] \ar@{->}[d] & {\tt whatif()}\\
-& *+[F-:<10pt>]{{\tt sim()}} \ar@{-->}[dr] \ar@{-->}[dl] & \\
-{\tt summary()} & &{\tt plot()} }
-\end{displaymath}
-\end{center}
-\end{figure}
-
-\begin{enumerate}
-\item Use \texttt{zelig()} to run the chosen statistical model on a
-given data set, with a specific set of variables. For standard
-likelihood models, for example, this step estimates the coefficients,
-other model parameters, and a variance-covariance matrix. In
-addition, you may choose from a variety of options:
-\begin{itemize}
-\item Pre-process data: Prior to calling {\tt zelig()}, you may
- choose from a variety of data pre-processing commands (matching or
- multiple imputation, for example) to make your statistical
- inferences more accurate.
-\item Summarize model: After calling {\tt zelig()}, you may summarize
- the fitted model output using {\tt summary()}.
-\item Validate model: After calling {\tt zelig()}, you may choose to
- validate the fitted model. This can be done, for example, by using
- cross-validation procedures and diagnostics tools.
-\end{itemize}
-\item Use \texttt{setx()} to set each of the explanatory variables to
- chosen (actual or counterfactual) values in preparation for
- calculating quantities of interest. After calling {\tt setx()}, you
- may use \hlink{WhatIf}{/whatif/} to evaluate these choices by
- determining whether they involve interpolation (i.e., are inside the
- convex hull of the observed data) or extrapolation, as well as how
- far these counterfactuals are from the data. Counterfactuals chosen
- in \texttt{setx()} that involve extrapolation far from the data can
- generate considerably more model dependence (see \cite{KinZen06},
- \cite{KinZen07}, \cite{StoKinZen05}).
-\item Use \texttt{sim()} to draw simulations of your quantity of
- interest (such as a predicted value, predicted probability, risk
- ratio, or first difference) from the model. (These simulations may
- be drawn using an asymptotic normal approximation (the default),
- bootstrapping, or other methods when available, such as directly
- from a Bayesian posterior.) After calling {\tt sim()}, use any of
-the following to summarize the simulations:
-\begin{itemize}
-\item The {\tt summary()} function gives a numerical display. For
-multiple {\tt setx()} values, {\tt summary()} lets you summarize
-simulations by choosing one or a subset of observations.
-\item If the {\tt setx()} values consist of only one observation, {\tt
-plot()} produces density plots for each quantity of interest.
-\end{itemize}
-\end{enumerate}
-Whenever possible, we use {\tt z.out} as the {\tt zelig()} output
-object, \texttt{x.out} as the \texttt{setx()} output object, and {\tt
-s.out} as the {\tt sim()} output object, but you may choose other
-names.
-
-\subsection{Examples}
-\label{mi.ex}
-\begin{itemize}
-\item Use the \texttt{turnout} data set included with Zelig to
- estimate a logit model of an individual's probability of voting as
- function of race and age. Simulate the predicted probability of
- voting for a white individual, with age held at its mean:
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ race + age, model = "logit", data = turnout)
-> x.out <- setx(z.out, race = "white")
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-\end{verbatim}
-\item Compute a first difference and risk ratio, changing education
- from 12 to 16 years, with other variables held at their means in the
- data:
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-> x.low <- setx(z.out, educate = 12)
-> x.high <- setx(z.out, educate = 16)
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-> summary(s.out) # Numerical summary.
-> plot(s.out) # Graphical summary.
-\end{verbatim}
-\item Calculate expected values for every observation in your
- data set:
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-> x.out <- setx(z.out, fn = NULL)
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-\end{verbatim}
-
-\item Use five multiply imputed data sets from \cite{SchSla01} in an
- ordered logit model:
-\begin{verbatim}
-> data(immi1, immi2, immi3, immi4, immi5)
-> z.out <- zelig(as.factor(ipip) ~ wage1992 + prtyid + ideol,
- model = "ologit",
- data = mi(immi1, immi2, immi3, immi4, immi5))
-\end{verbatim}
-
-\item Use the nearest propensity score matching via {\it MatchIt}
- package, and then calculate the conditional average treatment effect
- of the job training program based on the linear regression model:
-\begin{verbatim}
-> library(MatchIt)
-> data(lalonde)
-> m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age,
- data = lalonde, method = "nearest")
-> m.data <- match.data(m.out)
-> z.out <- zelig(re78 ~ treat + distance + re74 + re75 + educ + black +
- hispan + age, data = m.data, model = "ls")
-> x.out0 <- setx(z.out, fn = NULL, treat = 0)
-> x.out1 <- setx(z.out, fn = NULL, treat = 1)
-> s.out <- sim(z.out, x=x.out0, x1=x.out1)
-> summary(s.out)
-\end{verbatim}
-
-\item Validate the fitted model using the leave-one-out cross
- validation procedure and calculating the average squared prediction
- error via {\it boot} package. For example:
-\begin{verbatim}
-> library(boot)
-> data(turnout)
-> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-> cv.out <- cv.glm(z.out, data = turnout)
-> print(cv.out$delta)
-\end{verbatim}
-
-\end{itemize}
-
-\subsection{Details}
-
-\begin{enumerate}
-\item {\tt z.out <- zelig(formula, model, data, by = NULL, \dots)}
-
- The {\tt zelig()} command estimates a selected statistical model
- given the specified data. You may name the output object
- (\texttt{z.out} above) anything you desire. You must include three
- required arguments, in the following order:
-
- \begin{enumerate}
-
- \item \texttt{formula} takes the form {\tt y \~\, x1 + x2}, where
- {\tt y} is the dependent variable and {\tt x1} and {\tt x2} are
- the explanatory variables, and {\tt y}, {\tt x1}, and {\tt x2} are
- contained in the same dataset. The {\tt +} symbol means
- ``inclusion'' not ``addition.'' You may include interaction terms
- in the form of {\tt x1*x2} without having to compute them in prior
- steps or include the main effects separately. For example, R
- treats the formula {\tt y \~\, x1*x2} as {\tt y \~\, x1 + x2 +
- x1*x2}. To prevent R from automatically including the separate
- main effect terms, use the {\tt I()} function, thus: {\tt y \~\,
- I(x1 * x2)}.
-
- \item \texttt{model} lets you choose which statistical model to run.
- You must put the name of the model in quotation marks, in the form
- {\tt model = "ls"}, for example. See \Sref{s:models} for a list
- of currently supported models.
-
- \item \texttt{data} specifies the data frame containing the
- variables called in the formula, in the form {\tt data = mydata}.
- Alternatively, you may input multiply imputed datasets in the form
- {\tt data = mi(data1, data2, \dots)}.\footnote{Multiple
- imputation is a method of dealing with missing values in your
- data which is more powerful than the usual list-wise deletion
- approach. You can create multiply imputed datasets with a
- program such as
- \hlink{Amelia}{http://gking.harvard.edu/stats.shtml\#amelia};
- see \hlink{King, Honaker, Joseph, Scheve
- (2000)}{http://gking.harvard.edu/files/abs/evil-abs.shtml}.
- \label{mi}} If you are working with matched data created using
- \hlink{MatchIt}{http://gking.harvard.edu/matchit/}, you may create a
- data frame within the {\tt zelig()} statement by using {\tt data =
- match.data(\dots)}. In all cases, the data frame or MatchIt
- object must have been previously loaded into the working memory.
-
-\item {\tt by} (an optional argument which is by default {\tt NULL})
- allows you to choose a factor variable (see \Sref{factors}) in the
- data frame as a subsetting variable. For each of the unique
- strata defined in the {\tt by} variable, {\tt zelig()} does a
- separate run of the specified model. The variable chosen should
- \emph{not} be in the formula, because there will be no variance in
- the {\tt by} variable in the subsets. If you have one data set for
- all 191 countries in the UN, for example, you may use the {\tt by}
- option to run the same model 191 times, once on each country, all
- with a single {\tt zelig()} statement. You may also use the {\tt
- by} option to run models on
- \hlink{MatchIt}{http://gking.harvard.edu/matchit/} subclasses.
-
-\item The output object, {\tt z.out}, contains all of the options
- chosen, including the name of the data set. Because data sets may
- be large, Zelig does not store the full data set, but only the name
- of the dataset. Every time you use a Zelig function, it looks for
- the dataset with the appropriate name in working memory. (Thus, it
- is critical that you do \emph{not} change the name of your data set,
- or perform any additional operations on your selected variables
- between calling {\tt zelig()} and {\tt setx()}, or between {\tt
- setx()} and {\tt sim()}.)
-
-\item If you would like to view the regression output at this
- intermediate step, type {\tt summary(z.out)} to return the
- coefficients, standard errors, $t$-statistics and $p$-values. We
- recommend instead that you calculate quantities of interest;
- creating {\tt z.out} is only the first of three steps in this task.
- \end{enumerate}
-
-\item {\tt x.out <- setx(z.out, fn = list(numeric = mean, ordered =
- median, others = mode), data = NULL, cond = FALSE, \dots)}
-
- The {\tt setx()} command lets you choose values for the explanatory
- variables, with which {\tt sim()} will simulate quantities of
- interest. There are two types of {\tt setx()} procedures:
-\begin{itemize}
-\item You may perform the usual \emph{unconditional} prediction (by
- default, {\tt cond = FALSE}), by explicitly choosing the values of
- each explanatory variable yourself or letting {\tt setx()} compute
- them, either from the data used to create {\tt z.out} or from a new
- data set specified in the optional {\tt data} argument. You may
- also compute predictions for all observed values of your explanatory
- variables using {\tt fn = NULL}.
-
-\item Alternatively, for advanced uses, you may perform
- \emph{conditional} prediction ({\tt cond = TRUE}), which predicts
- certain quantities of interest by conditioning on the observed value
- of the dependent variable. In a simple linear regression model,
- this procedure is not particularly interesting, since the
- conditional prediction is merely the observed value of the dependent
- variable for that observation. However, conditional prediction is
- extremely useful for other models and methods, including the
- following:
- \begin{itemize}
- \item In a matched sampling design, the sample average treatment
- effect for the treated can be estimated by computing the
- difference between the observed dependent variable for the treated
- group and their expected or predicted values of the dependent
- variable under no treatment \citep{HoImaKin07}.
- \item With censored data, conditional prediction will ensure that
- all predicted values are greater than the censored observed
- values \citep{KinAltBur90}.
- \item In ecological inference models, conditional prediction
- guarantees that the predicted values are on the tomography line
- and thus restricted to the known bounds
- \citep{King97,AdoKinHer03}.
- \item The conditional prediction in many linear random effects (or
- Bayesian hierarchical) models is a weighted average of the
- unconditional prediction and the value of the dependent variable for
- that observation, with the weight being an estimable function of the
- accuracy of the unconditional prediction \citep[see][]{GelKin94a}.
- When the unconditional prediction is highly certain, the weight on
- the value of the dependent variable for this observation is very
- small, hence reducing inefficiency; when the unconditional
- prediction is highly uncertain, the relative weight on the
- unconditional prediction is very small, hence reducing bias.
- Although the simple weighted average expression no longer holds in
- nonlinear models, the general logic still holds and the mean square
- error of the measurement is typically reduced
- \citep[see][]{KinMurSal04}.
- \end{itemize}
- In these and other models, conditioning on the observed value of the
- dependent variable can vastly increase the accuracy of prediction
- and measurement.
-\end{itemize}
-
- The {\tt setx()} arguments for \textbf{unconditional} prediction are
- as follows:
-
- \begin{enumerate}
-
- \item {\tt z.out}, the {\tt zelig()} output object, must be
- included first.
-
- \item You can set particular explanatory variables to specified
- values. For example:
-\begin{verbatim}
-> z.out <- zelig(vote ~ age + race, model = "logit", data = turnout)
-> x.out <- setx(z.out, age = 30)
-\end{verbatim}
- \texttt{setx()} sets the variables \emph{not} explicitly listed to
- their mean if numeric, and their median if ordered factors, and
- their mode if unordered factors, logical values, or character
- strings. Alternatively, you may specify one explanatory variable
- as a range of values, creating one observation for every unique
- value in the range of values:\footnote{If you allow more than one variable to
-vary at a time, you risk confounding the predictive effect of the variables in
-question.} \begin{verbatim}
-> x.out <- setx(z.out, age = 18:95)
-\end{verbatim}
- This creates 78 observations with with age set to 18 in the first
- observation, 19 in the second observation, up to 95 in the 78th
- observation. The other variables are set to their default values,
- but this may be changed by setting {\tt fn}, as described next.
-
- \item Optionally, \texttt{fn} is a list which lets you to choose a different
- function to apply to explanatory variables of class
- \begin{itemize}
- \item {\tt numeric}, which is {\tt mean} by default,
- \item {\tt ordered} factor, which is {\tt median} by default, and
- \item {\tt other} variables, which consist of logical variables,
- character string, and unordered factors, and are set to their
- {\tt mode} by default.
- \end{itemize}
- While any function may be applied to numeric variables, {\tt mean}
- will default to median for ordered factors, and mode is the only
- available option for other types of variables. In the special case,
- {\tt fn = NULL}, {\tt setx()} returns all of the observations.
-
- \item You cannot perform other math operations within the {\tt fn}
- argument, but can use the output from one call of {\tt setx} to
- create new values for the explanatory variables. For example, to
- set the explanatory variables to one standard deviation below their
- mean:
-\begin{verbatim}
-> X.sd <- setx(z.out, fn = list(numeric = sd))
-> X.mean <- setx(z.out, fn = list(numeric = mean))
-> x.out <- X.mean - X.sd
-\end{verbatim}
-
- \item Optionally, \texttt{data} identifies a new data frame (rather
- than the one used to create {\tt z.out}) from which the {\tt
- setx()} values are calculated. You can use this argument to set
- values of the explanatory variables for hold-out or out-of-sample
- fit tests.
- \item The {\tt cond} is always {\tt FALSE} for unconditional
- prediction.
- \end{enumerate}
-
- If you wish to calculate risk ratios or first differences, call {\tt
- setx()} a second time to create an additional set of the values for
- the explanatory variables. For example, continuing from the example
- above, you may create an alternative set of explanatory variables
- values one standard deviation above their mean:
-\begin{verbatim}
-> x.alt <- X.mean + X.sd
-\end{verbatim}
-
- The required arguments for \textbf{conditional} prediction are as
- follows:
- \begin{enumerate}
- \item {\tt z.out}, the {\tt zelig()} output object, must be included
- first.
-
- \item {\tt fn}, which equals {\tt NULL} to indicate that all of the
- observations are selected. You may only perform conditional
- inference on actual observations, not the mean of observations or
- any other function applied to the observations. Thus, if {\tt fn}
- is missing, but {\tt cond = TRUE}, {\tt setx()} coerces {\tt fn =
- NULL}.
-
- \item {\tt data}, the data for conditional prediction.
-
- \item {\tt cond}, which equals {\tt TRUE} for conditional prediction.
- \end{enumerate}
- Additional arguments, such as any of the variable names, are ignored
- in conditional prediction since the actual values of that observation
- are used.
-
-\item {\tt s.out <- sim(z.out, x = x.out, x1 = NULL, num = c(1000,
- 100), bootstrap = FALSE, bootfn = NULL, \dots)}
-
- The {\tt sim()} command simulates quantities of interest given the
- output objects from \texttt{zelig()} and \texttt{setx()}. This
- procedure uses only the assumptions of the statistical model. The
- {\tt sim()} command performs either unconditional or conditional
- prediction depending on the options chosen in {\tt setx()}.
-
- The arguments are as follows for \textbf{unconditional} prediction:
- \begin{enumerate}
- \item {\tt z.out}, the model output from {\tt zelig()}.
- \item {\tt x}, the output from the {\tt setx()} procedure performed
- on the model output.
- \item Optionally, you may calculate first differences by specifying
- {\tt x1}, an additional {\tt setx()} object. For example, using
- the {\tt x.out} and {\tt x.alt}, you may generate first
- differences using:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out, x1 = x.alt)
-\end{verbatim}
-
- \item By default, the number of simulations, {\tt num}, equals 1000
- (or 100 simulations if bootstrap is selected), but this may be
- decreased to increase computational speed, or increased for
- additional precision.
-
- \item Zelig simulates parameters from classical \emph{maximum
- likelihood} models using asymptotic normal approximation to the
- log-likelihood. This is the same assumption as used for
- frequentist hypothesis testing (which is of course equivalent to
- the asymptotic approximation of a Bayesian posterior with improper
- uniform priors). See \hlink{King, Tomz, and Wittenberg
- (2000)}{http://gking.harvard.edu/files/abs/making-abs.shtml}.
- For \emph{Bayesian models}, Zelig simulates quantities of interest
- from the posterior density, whenever possible. For \emph{robust
- Bayesian models}, simulations are drawn from the identified
- class of Bayesian posteriors.
-
- \item Alternatively, you may set {\tt bootstrap = TRUE} to simulate
- parameters using bootstrapped data sets. If your dataset is
- large, bootstrap procedures will usually be more memory intensive
- and time-consuming than simulation using asymptotic normal
- approximation. The type of bootstrapping (including the sampling
- method) is determined by the optional argument {\tt bootfn},
- described below.
-
- \item If {\tt bootstrap = TRUE} is selected, {\tt sim()} will
- bootstrap parameters using the default {\tt bootfn}, which
- re-samples from the data frame with replacement to create a
- sampled data frame of the same number of observations, and then
- re-runs {\tt zelig()} (inside {\tt sim()}) to create one set of
- bootstrapped parameters. Alternatively, you may create a function
- outside the {\tt sim()} procedure to handle different bootstrap
- procedures. Please consult {\tt help(boot)} for more
- details.\footnote{If you choose to create your own {\tt bootfn},
- it must include the the following three arguments: {\tt data},
- the original data frame; one of the sampling methods described
- in {\tt help(boot)}; and {\tt object}, the original {\tt
- zelig()} output object. The alternative bootstrapping
- function must sample the data, fit the model, and extract the
- model-specific parameters.}
- \end{enumerate}
-
- For \textbf{conditional} prediction, {\tt sim()} takes only two
- required arguments:
- \begin{enumerate}
- \item {\tt z.out}, the model output from {\tt zelig()}.
- \item {\tt x}, the conditional output from {\tt setx()}.
- \item Optionally, for duration models, {\tt cond.data}, which is the
- {\tt data} argument from {\tt setx()}. For models for duration
- dependent variables (see \Sref{duration}), {\tt sim()} must impute
- the uncensored dependent variables before calculating the average
- treatment effect. Inputting the {\tt cond.data} allows {\tt
- sim()} to generate appropriate values.
- \end{enumerate}
- Additional arguments are ignored or generate error messages.
-
-\end{enumerate}
-
-\subsubsection*{Presenting Results}
-\begin{enumerate}
-\item Use {\tt summary(s.out)} to print a summary of your simulated
- quantities. You may specify the number of significant digits as:
-\begin{verbatim}
-> print(summary(s.out), digits = 2)
-\end{verbatim}
-\item Alternatively, you can plot your results using
- \texttt{plot(s.out)}.
-\item You can also use \texttt{names(s.out)} to see the names and a
- description of the elements in this object and the {\tt \$} operator
- to extract particular results. For most models, these are: {\tt
- s.out\$qi\$pr} (for predicted values), {\tt s.out\$qi\$ev} (for
- expected values), and {\tt s.out\$qi\$fd} (for first differences in
- expected values). For the logit, probit, multinomial logit, ordinal
- logit, and ordinal probit models, quantities of interest also
- include {\tt s.out\$qi\$rr} (the risk ratio).
-\end{enumerate}
-
-\section{Supported Models}\label{s:models}
-
-We list here all models implemented in Zelig, organized by the nature
-of the dependent variable(s) to be predicted, explained, or described.
-
-\begin{enumerate}
-\item {\bf Continuous Unbounded} dependent variables can take any
-real value in the range $(-\infty, \infty)$. While most of these
-models take a continuous dependent variable, Bayesian factor analysis takes multiple
-continuous dependent variables.
-
- \begin{enumerate}
- \item {\tt "ls"}: The {\it linear least-squares} (see \Sref{ls})
- calculates the coefficients that minimize the sum of squared
- residuals. This is the usual method of computing linear
- regression coefficients, and returns unbiased estimates of
- $\beta$ and $\sigma^2$ (conditional on the specified model).
-
- \item {\tt "normal"}: The {\it Normal} (see \Sref{normal}) model
- computes the maximum-likelihood estimator for a Normal
- stochastic component and linear systematic component. The
- coefficients are identical to \texttt{ls}, but the
- maximum likelihood estimator for $\sigma^2$ is consistent
- but biased.
- \item {\tt "normal.bayes"}: The {\it Bayesian Normal} regression
-model (\Sref{normal.bayes}) is similar to maximum likelihood Gaussian
-regression, but makes valid small sample inferences via draws from the
-exact posterior and also allows for priors.
-
- \item {\tt "netls"}: The {\it network least squares}
- regression (\Sref{netls}) is similar to least squares regression
- for continuous-valued proximity matrix dependent variables. Proximity
-matrices are also known as sociomatrices, adjacency matrices, and
-matrix representations of directed graphs.
-
- \item {\tt "tobit"}: The {\it tobit} regression model (see
- \Sref{tobit}) is a Normal distribution with left-censored observations.
-
- \item {\tt "tobit.bayes"}: The {\it Bayesian tobit} distribution
- (see \Sref{tobit.bayes}) is a Normal distribution that has either
- left and/or right censored observations.
-
- \item {\tt "arima"}: Use \emph{auto-regressive, integrated, moving-average}
- (ARIMA) models for time series data (see \Sref{arima}.
-
-\item {\tt "factor.bayes"}: The {\it Bayesian factor analysis} model (see
-\Sref{factor.bayes}) estimates multiple observed continuous dependent variables
-as a function of latent explanatory variables.
-
- \end{enumerate}
- \item {\bf Dichotomous} dependent variables consist of two discrete
-values, usually $(0,1)$.
- \begin{enumerate}
- \item {\tt "logit"}: {\it Logistic regression} (see \Sref{logit})
- specifies $\Pr(Y=1)$ to be a(n inverse) logistic transformation
- of a linear function of a set of explanatory variables.
- \item {\tt "relogit"}: The {\it rare events logistic} regression
- option (see \Sref{relogit}) estimates the same model as the
- logit, but corrects for bias due to rare events (when one of the
- outcomes is much more prevalent than the other). It also
- optionally uses prior correction to correct for choice-based
- (case-control) sampling designs.
- \item {\tt "logit.bayes"}: {\it Bayesian logistic regression} (see
-\Sref{logit.bayes}) is similar to maximum likelihood logistic
-regression, but makes valid small sample inferences via draws from the
-exact posterior and also allows for priors.
- \item {\tt "probit"}: {\it Probit regression} (see \Sref{probit})
- Specifies $\Pr(Y=1)$ to be a(n inverse) CDF normal transformation
- as a linear function of a set of explanatory variables.
- \item {\tt "probit.bayes"}: {\it Bayesian probit} regression (see
-\Sref{probit.bayes}) is similar to maximum likelihood probit
-regression, but makes valid small sample inferences via draws from the
-exact posterior and also allows for priors.
-
- \item {\tt "netlogit"}: The {\it network logistic}
- regression (\Sref{netlogit}) is similar to logistic regression
- for binary-valued proximity matrix dependent variables. Proximity
-matrices are also known as sociomatrices, adjacency matrices, and
-matrix representations of directed graphs.
-
- \item {\tt "blogit"}: The {\it bivariate logistic} model (see
- \Sref{blogit}) models $\Pr(Y_{i1} = y_1, Y_{i2} = y_2)$ for
-$(y_1, y_2) = {(0,0), (0,1), (1,0), (1,1)}$ according
-to a bivariate logistic density.
- \item {\tt "bprobit"}: The {\it bivariate probit} model (see
- \Sref{bprobit}) models $\Pr(Y_{i1} = y_1, Y_{i2} = y_2)$ for
-$(y_1, y_2) = {(0,0), (0,1), (1,0), (1,1)}$ according to a bivariate normal density.
- \item {\tt "irt1d"}: The {\it one-dimensional item response} model
-(see \Sref{irt1d}) takes multiple dichotomous dependent variables and models
-them as a function of \emph{one} latent (unobserved) explanatory variable.
- \item {\tt "irtkd"}: The {\it k-dimensional item response} model
-(see \Sref{irtkd}) takes multiple dichotomous dependent variables and models
-them as a function of $k$ latent (unobserved) explanatory variables.
-\end{enumerate}
- \item {\bf Ordinal} are used to model
- ordered, discrete dependent variables. The values of the outcome
- variables (such as kill, punch, tap, bump) are ordered, but the
- distance between any two successive categories is not known
- exactly. Each dependent variable may be thought of as linear, with
-one continuous, unobserved dependent variable observed through a mechanism
- that only returns the ordinal choice.
- \begin{enumerate}
- \item {\tt "ologit"}: The {\it ordinal logistic} model (see
- \Sref{ologit}) specifies the stochastic component of the
- unobserved variable to be a standard logistic distribution.
- \item {\tt "oprobit"}: The {\it ordinal probit} distribution (see
- \Sref{oprobit}) specifies the stochastic component of the
- unobserved variable to be standardized normal.
- \item {\tt "oprobit.bayes"}: {\it Bayesian ordinal probit} model
-(see \Sref{oprobit.bayes}) is similar to ordinal probit
-regression, but makes valid small sample inferences via draws from the
-exact posterior and also allows for priors.
- \item {\tt "factor.ord"}: {\it Bayesian ordered factor analysis}
-(see \Sref{factor.ord}) models observed, ordinal dependent variables
-as a function of latent explanatory variables.
- \end{enumerate}
- \item {\bf Multinomial} dependent variables are unordered, discrete
- categorical responses. For example, you could model an
- individual's choice among brands of orange juice or among
- candidates in an election.
- \begin{enumerate}
- \item {\tt "mlogit"}: The {\it multinomial logistic} model (see
- \Sref{mlogit}) specifies categorical responses distributed
- according to the multinomial stochastic component and logistic
- systematic component.
- \item {\tt "mlogit.bayes"}: {\it Bayesian multinomial logistic} regression (see
-\Sref{mlogit.bayes}) is similar to maximum likelihood multinomial logistic
-regression, but makes valid small sample inferences via draws from the
-exact posterior and also allows for priors.
-% \item {\tt "mprobit"}: The {\it multinomial probit} model (see
-% \Sref{mprobit}) specifies categorical responses distributed
-% according to the multinomial stochastic component and Normal
-% systematic component.
- \end{enumerate}
- \item {\bf Count} dependent variables are non-negative integer
-values, such as the number of presidential vetoes or the number of
-photons that hit a detector.
- \begin{enumerate}
- \item {\tt "poisson"}: The {\it Poisson} model (see
- \Sref{poisson}) specifies the expected number of events that
- occur in a given observation period to be an exponential
- function of the explanatory variables. The Poisson stochastic
- component has the property that, $\lambda = \textrm{E}(Y_i|X_i)
- = \textrm{V}(Y_i|X_i)$.
- \item {\tt "poisson.bayes"}: {\it Bayesian Poisson} regression (see
-\Sref{poisson.bayes}) is similar to maximum likelihood Poisson
-regression, but makes valid small sample inferences via draws from the
-exact posterior and also allows for priors.
- \item {\tt "negbin"}: The {\it negative binomial} model (see
- \Sref{negbin}) has the same systematic component as the Poisson,
- but allows event counts to be over-dispersed, such that
- $\textrm{V}(Y_i|X_i) > \textrm{E}(Y_i|X_i)$.
- \end{enumerate}
-
- \item {\bf Continuous Bounded}\label{duration} dependent variables
-that are continuous only over a certain range, usually $(0, \infty)$.
-In addition, some models (exponential, lognormal, and
-Weibull) are also censored for values greater than some censoring
-point, such that the dependent variable has some units fully observed
-and others that are only partially observed (censored).
-
- \begin{enumerate}
-
- \item {\tt "gamma"}: The {\it Gamma} model (see \Sref{gamma}) for
- positively-valued, continuous dependent variables that are fully
-observed (no censoring).
-
- \item {\tt "exp"}: The {\it exponential} model (see \Sref{exp}) for
- right-censored dependent variables assumes that the hazard function
- is constant over time. For some variables, this may be an unrealistic
- assumption as subjects are more or less likely to fail the longer
- they have been exposed to the explanatory variables.
-
- \item {\tt "weibull"}: The {\it Weibull} model (see \Sref{weibull})
- for right-censored dependent variables relaxes the
- assumption of constant hazard by including an additional scale
- parameter $\alpha$: If $\alpha > 1$, the risk of failure increases
- the longer the subject has survived; if $\alpha < 1$, the risk of
- failure decreases the longer the subject has survived. While {\tt
- zelig()} estimates $\alpha$ by default, you may optionally fix
- $\alpha$ at any value greater than 0. Fixing $\alpha = 1$ results
- in an exponential model.
-
- \item {\tt "lognorm"}: The {\it log-normal} model (see
- \Sref{lognorm}) for right-censored duration dependent variables
- specifies the hazard function non-monotonically, with increasing
- hazard over part of the observation period and decreasing hazard
- over another.
-
-\end{enumerate}
-
-\item {\bf Mixed} dependent variables include models that take more
-than one dependent variable, where the dependent variables come from
-two or more of categories above. (They do not need
-to be of a homogeneous type.)
-\begin{enumerate}
-\item The {\it Bayesian mixed factor analysis} model, in contrast to the Bayesian factor
-analysis model and ordinal factor analysis model, can model both types
-of dependent variables as a function of latent explanatory variables.
-\end{enumerate}
-
-\item {\bf Ecological inference} models estimate unobserved internal
-cell values given contingency tables with observed row and column
-marginals.
-\begin{enumerate}
-\item {\tt ei.hier}: The {\it hierarchical {\sc ei}} model
-(see \Sref{ei.hier}) produces estimates for a cross-section of $2 \times 2$
-tables.
-\item {\tt ei.dynamic}: {\it Quinn's dynamic Bayesian {\sc ei}} model (see
-\Sref{ei.dynamic}) estimates a dynamic Bayesian model for $2 \times 2$
-tables with temporal dependence across tables.
-\item {\tt ei.RxC}: The $R \times C$ {\sc ei} model (see \Sref{eiRxC})
-estimates a hierarchical Multinomial-Dirichlet {\sc ei} model for
-contingency tables with more than 2 rows or columns.
-\end{enumerate}
-
-
-% \item {\bf Models for Contingency Tables}
-% \begin{enumerate}
-% \item {\tt "mloglm"}: Use a {\it multinomial log-linear} model (see
-% \Sref{mloglm}) to estimate log-linear models for contingency
-% tables. These models use raw counts in tables as the dependent
-% variable and apply a Poisson regression model. Many of the rich
-% set of models that result are asymptotically equavlent to
-% multinomial logit models.
-% \end{enumerate}
-\end{enumerate}
-
-\section{Replication Procedures}
-
-A large part of any statistical analysis is documenting your work such
-that given the same data, anyone may replicate your results. In
-addition, many journals require the creation and dissemination of
-``replication data sets'' in order that others may replicate your
-results (see \hlink{King,
-1995}{http://gking.harvard.edu/data.shtml\#repl}\nocite{King95}).
-Whether you wish to create replication materials for your own records,
-or contribute data to others as a companion to your published work,
-Zelig makes this process easy.
-
-\subsection{Saving Replication Materials}
-
-Let {\tt mydata} be your final data set, {\tt z.out} be your {\tt
- zelig()} output, and {\tt s.out} your {\tt sim()} output. To save
-all of this in one file, type:
-\begin{verbatim}
-> save(mydata, z.out, s.out, file = "replication.RData")
-\end{verbatim}
-This creates the file replication.RData in your working directory.
-You may compress this file using zip or gzip tools.
-
-If you have run several specifications, all of these estimates may be
-saved in one .RData file. Even if you only created quantities of
-interest from one of these models, you may still save all the
-specifications in one file. For example:
-\begin{verbatim}
-> save(mydata, z.out1, z.out2, s.out, file = "replication.RData")
-\end{verbatim}
-
-Although the .RData format can contain data sets as well as output
-objects, it is not the most space-efficient way of saving large data
-sets. In an uncompressed format, ASCII text files take up less space
-than data in .RData format. (When compressed, text-formatted data is
-still smaller than .RData-formatted data.) Thus, if you have more
-than 100,000 observations, you may wish to save the data set
-separately from the Zelig output objects. To do this, use the {\tt
- write.table()} command. For example, if {\tt mydata} is a data
-frame in your workspace, use {\tt write.table(mydata, file =
- "mydata.tab")} to save this as a tab-delimited ASCII text file. You
-may specify other delimiters as well; see {\tt
- help.zelig("write.table")} for options.
-
-\subsection{Replicating Analyses}
-
-If the data set and analyses are all saved in one .RData file, located
-in your working directory, you may simply type:
-\begin{verbatim}
-> load("replication.RData") # Loads the replication file.
-> z.rep <- repl(z.out) # To replicate the model only.
-> s.rep <- repl(s.out) # To replicate the model and
- # quantities of interest.
-\end{verbatim}
-By default, {\tt repl()} uses the same options used to create the
-original output object. Thus, if the original {\tt s.out} object used
-bootstrapping with 245 simulations, the {\tt s.rep} object will
-similarly have 245 bootstrapped simulations. In addition, you may use
-the {\tt prev} option when replicating quantities of interest to reuse
-rather than recreate simulated parameters. Type {\tt
- help.zelig("repl")} to view the complete list of options for
-{\tt repl()}.
-
-If the data were saved in a text file, use {\tt read.table()} to load
-the data, and then replicate the analysis:
-\begin{verbatim}
-> dat <- read.table("mydata.tab", header = TRUE) # Where `dat' is the same
-> load("replication.RData") # as the name used in
-> z.rep <- repl(z.out) # `z.out'.
-> s.rep <- repl(s.out)
-\end{verbatim}
-If you have problems loading the data, please refer to \Sref{load.data}.
-
-Finally, you may use the {\tt identical()} command to ensure that the
-replicated regression output is in every way identical to the original
-{\tt zelig()} output.\footnote{The {\tt identical()} command checks
- that numeric values are identical to the maximum number of decimal
- places (usually 16), and also checks that the the two objects have
- the same class (numeric, character, integer, logical, or factor).
- Refer to {\tt help(identical)} for more information.} For example:
-\begin{verbatim}
-> identical(z.out$coef, z.rep$coef) # Checks the coefficients.
-\end{verbatim}
-Simulated quantities of interest will vary from the original
-quantities if parameters are re-simulated or re-sampled. If you wish to
-use {\tt identical()} to verify that the quantities of interest are
-identical, you may use
-\begin{verbatim}
-# Re-use the parameters simulated (and stored) in the original sim() output.
-> s.rep <- repl(s.out, prev = s.out$par)
-
-# Check that the expected values are identical. You may do this for each qi.
-> identical(s.out$qi$ev, s.rep$qi$ev)
-\end{verbatim} %$
-
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "zelig"
-%%% End:
diff --git a/inst/doc/acknowledgments.tex b/inst/doc/acknowledgments.tex
deleted file mode 100644
index 5bea3b9..0000000
--- a/inst/doc/acknowledgments.tex
+++ /dev/null
@@ -1,35 +0,0 @@
-\section*{Acknowledgments}
-
-The Zelig project would not have been possible without considerable
-help from many sources. Our special thanks go to the \hlink{R core
- team}{http://www.r-project.org/contributors.html} for providing an
-excellent modular, open-source platform for the entire statistics and
-methodological community.
-
-The authors of the following R packages have provided some of the
-models available through Zelig: {\tt MASS} by William N. Venables and
-Brian D. Ripley; {\tt MCMCpack} by Andrew D. Martin and Kevin M.
-Quinn; {\tt survival} by Terry Therneau and Brian D. Ripley; and {\tt
- VGAM} by Thomas Yee.
-
-The authors of the following R packages have provided some of the
-auxiliary statistical procedures and methods available through Zelig:
-{\tt boot} by Angelo Canty and Brian Ripley; {\tt coda} by Martyn
-Plummer, Nicky Best, Kate Cowles, and Karen Vines; {\tt sandwich} by
-Achim Zeileis; and {\tt zoo} by Achim Zeileis and Gabor Grothendieck.
-
-Our appreciation also goes to Ferdinand Alimadhi, Skyler J.\ Cranmer,
-Ben Goodrich, Justin Grimmer, Yang Lin, and Ying Lu, who have
-contributed either code or documentation; Dan Hopkins, Ian Yohai, and
-others who have given valuable feedback from the courses in which they
-have used Zelig as a teaching tool.
-
-For research support, we thank the National Institutes of Aging (P01
-AG17625-01), the National Science Foundation (SES-0318275,
-SES-0550873, IIS-9874747, SES-0112072), the Mexican Ministry of
-Health, the U.S.\ Library of Congress (PA\# NDP03-1), and the
-Princeton University Committee on Research in the Humanities and
-Social Sciences.
-
-As usual, all errors are our responsibility.
-
diff --git a/inst/doc/addingModels.tex b/inst/doc/addingModels.tex
deleted file mode 100644
index d2a7cfd..0000000
--- a/inst/doc/addingModels.tex
+++ /dev/null
@@ -1,510 +0,0 @@
-\chapter{Adding Models and Methods to Zelig}
-\label{c:addingmodels}
-
-Zelig is highly modular. You can add methods to Zelig \emph{and}, if
-you wish, release your programs as a stand-alone package. By making
-your package compatible with Zelig, you will advertise your package
-and help it achieve a widespread distribution.
-
-This chapter assumes that your model is written as a function that
-takes a user-defined formula and data set (see Chapter \ref{s:new}),
-and returns a list of output that includes (at the very least) the
-estimated parameters and terms that describe the data used to fit the
-model. You should choose a class (either S3 or S4 class) for this
-list of output, and provide appropriate methods for generic functions
-such as {\tt summary()}, {\tt print()}, {\tt coef()} and {\tt vcov()}.
-
-To add new models to Zelig, you need to provide six R functions,
-illustrated in Figure \ref{add}. Let {\tt mymodel} be a new model
-with class {\tt "myclass"}.
-
-\begin{figure*}[h!]
-\caption{Six functions (solid boxes) to implement a new Zelig model}
-\label{add}
-\begin{center}
-\setlength{\unitlength}{0.5mm}
-\begin{picture}(160,170)(0,0)
-\linethickness{0.75pt}
-
-\put(0,166){Estimate}
-
-\put(70,162){\line(0,-1){42}}
-\put(50,162){\dashbox{2}(40,12){{\tt zelig()}}}
-\multiput(70,144)(0,-24){2}{\line(1,0){9}}
-\put(80,138){\framebox(83,12){(1) {\tt zelig2mymodel()}}}
-\put(80,114){\framebox(57,12){(2) {\tt mymodel()}}}
-
-\put(0,96){Interpret}
-
-\put(70,92){\line(0,-1){42}}
-\put(50,92){\dashbox{2}(40,12){{\tt sim()}}}
-\multiput(70,74)(0,-24){2}{\line(1,0){9}}
-\put(80,68){\framebox(83,12){(3) {\tt param.myclass()}}}
-\put(80,44){\framebox(69,12){(4) {\tt qi.myclass()}}}
-
-\put(0,26){Plot}
-
-\put(50,0){\framebox(105,12){(6) {\tt plot.zelig.mymodel()}}}
-
-\end{picture}
-\end{center}
-\end{figure*}
-
-These functions are as follows:
-\begin{enumerate}
-\item {\tt zelig2mymodel()} translates {\tt zelig()} arguments into
-the arguments for {\tt mymodel()}.
-\item {\tt mymodel()} estimates your statistical procedure.
-\item {\tt param.myclass()} simulates parameters for your model.
-Alternatively, if your model's parameters consist of one vector with a
-correspondingly observed variance-covariance matrix, you may write
-\emph{two} simple functions to substitute for {\tt param.myclass()}:
-\begin{enumerate}
-\item {\tt coef.myclass()} to extract the coefficients from your model
-output, and
-\item {\tt vcov.myclass()} to extract the variance-covariance matrix
-from your model.
-\end{enumerate}
-\item {\tt qi.myclass()} calculates expected values, simulates
-predicted values, and generates other quantities of interest for your
-model (applicable only to models that take explanatory variables).
-\item {\tt plot.zelig.mymodel()} to plot the simulated quantities of
-interest from your model.
-\item A {\bf reference manual page} to document the model.
- (See~\Sref{s:format})
-\item A function ({\tt describe.mymodel()}) describing the inputs to
-your model, for use with a graphical user interface. (See \Sref{describe.mymodel}).
-\item An optional {\bf demo script} {\tt mymodel.R} which contains commented code for
- the models contained in the example section of your reference manual
- page.
-\end{enumerate}
-
-\section{Making the Model Compatible with Zelig}\label{compatible}
-
-You can develop a model, write the model-fitting function, and test it
-within the Zelig framework without explicit intervention from the
-Zelig team. (We are, of course, happy to respond to any questions or
-suggestions for improvement.)
-
-Zelig's modularity relies on two R programming conventions:
-\begin{enumerate}
-\item \emph{\bf wrappers}, which pass arguments from R functions to
- other R functions or to foreign function calls (such as C, C++, or
- Fortran functions); and
-\item \emph{\bf classes}, which tell generic functions how to handle
- objects of a given class.
-\end{enumerate}
-Specific methods for R generic functions take the general form: {\tt
- method.class()}, where {\tt method} is the name of the generic
-procedure to be performed and {\tt class} is the class of the object.
-You may define, for example, {\tt summary.contrib()} to summarize the
-output of your model. Note that for S4 classes, the name of generic
-functions does not have to be {\tt method.class()} so long as users
-can call them via {\tt method()}.
-
-\subsubsection{To Work with {\tt zelig()}}
-
-Zelig has implemented a unique method for incorporating new models
-which lets contributors test their models \emph{within} the Zelig
-framework \emph{without} any modification of the {\tt zelig()}
-function itself.
-
-Using a wrapper function {\tt zelig2contrib()} (where {\tt contrib} is
-the name of your new model), {\tt zelig2contrib()} redefines the inputs to
-{\tt zelig()} to work with the inputs you need for your function {\tt
-contrib()}. For example, if you type
-\begin{verbatim}
-zelig(..., model = "normal.regression")
-\end{verbatim}
-{\tt zelig()} looks for a {\tt zelig2normal.regression()} wrapper in any
-environment (either attached libraries or your workspace). If the
-wrapper exists, then {\tt zelig()} runs the model.
-
-If you have a pre-existing model, writing a {\tt zelig2contrib()}
-function is quite easy. Let's say that your model is {\tt contrib()},
-and takes the following arguments: {\tt formula}, {\tt data}, {\tt
- weights}, and {\tt start}. The {\tt zelig()} function, in contrast,
-only takes the {\tt formula}, {\tt data}, {\tt model}, and {\tt by}
-arguments. You may use the {\tt \dots} to pass additional arguments
-from {\tt zelig()} to {\tt zelig2contrib()}, and {\tt <- NULL} to omit
-the elements you do not need. Continuing the Normal regression example
-from~\Sref{normal.regression}, let {\tt formula}, {\tt model}, and {\tt data} be
-the inputs to {\tt zelig()}, {\tt M} is the number of subsets, and
-{\tt \dots} are the additional arguments not defined in the {\tt
- zelig()} call, but passed to {\tt normal.regression()}.
-\begin{verbatim}
-zelig2normal.regression <- function(formula, model, data, M, ...) {
- mf <- match.call(expand.dots = TRUE) # [1]
- mf$model <- mf$M <- NULL # [2]
- mf[[1]] <- as.name("normal.regression") # [3]
- as.call(mf) # [4]
-}
-\end{verbatim}
-The bracketed numbers above correspond to the comments below:
-\begin{enumerate}
-\item Create a call (an expression to be evaluated) by
- creating a list of the arguments in {\tt zelig2normal.regression()}, including
- the extra arguments taken by {\tt normal.regression()}, but not by {\tt zelig()}.
- All wrappers must take the same standardized arguments ({\tt
- formula}, {\tt model}, {\tt data}, and {\tt M}), which may be used
- in the wrapper function to manipulate the {\tt zelig()} call into
- the {\tt normal.regression()} call. Additional arguments to {\tt normal.regression()}, such
- as {\tt start.val} are passed implicitly from {\tt zelig()} using the
- {\tt ...} operator.
-
- \item Erase extraneous information from the call
- object {\tt mf}. In this wrapper, {\tt model} and {\tt M} are not
- used. In other models, these are used to further manipulate the
- call, and so are included in the standard inputs to all wrappers.
-
-\item Reassign the first element of the call
- (currently {\tt zelig2normal.regression}) with the name of the function to be
- evaluated, {\tt normal.regression()}.
-
-\item Return the call to {\tt zelig()}, which will
- evaluate the call for each multiply-imputed data set, each subset
- defined in {\tt by}, or simply {\tt data}.
-\end{enumerate}
-
-If you use an S4 class to represent your model, say {\tt mymodel},
-within {\tt zelig.default()}, Zelig's internal function, {\tt
- create.ZeligS4()}, automatically creates a new S4 class called {\tt
- ZeligS4mymodel} in the global environment with two additional slots.
-These include {\tt zelig}, which stores the name of the model, and
-{\tt zelig.data}, which stores the data frame if {\tt save.data=TRUE}
-and is empty otherwise. These names are taken from the original call.
-This new output inherits the original class {\tt mymodel} so all the
-generic functions associated with {\tt mymodel} should still work. If
-you would like to see an example, see the models implemented using the
-VGAM package, such as multinomial probit.
-
-\subsubsection{To Work with {\tt setx()}}
-
-In the case of {\tt setx()}, most models will use {\tt
- setx.default()}, which in turn relies on the generic R function {\tt
- model.matrix()}. For this procedure to work, your list of output
-must include:
-\begin{itemize}
-\item {\tt terms}, created by {\tt model.frame()}, or manually;
-\item {\tt formula}, the formula object input by the user;
-\item {\tt xlevels}, which define the strata in the explanatory
- variables; and
-\item {\tt contrasts}, an optional element which defines the type of
- factor variables used in the explanatory variables. See {\tt
- help(contrasts)} for more information.
-\end{itemize}
-
-If your model output does not work with {\tt setx.default()}, you must
-write your own {\tt setx.contrib()} function. For example, models fit
-to multiply-imputed data sets have output from {\tt zelig()} of class
-{\tt "MI"}. The special {\tt setx.MI()} wrapper pre-processes the
-{\tt zelig()} output object and passes the appropriate arguments to
-{\tt setx.default()}.
-
-\subsubsection{Compatibility with {\tt sim()}}
-
-Simulating quantities of interest is an integral part of interpreting
-model results. To use the functionality built into the Zelig {\tt
-sim()} procedure, you need to provide a way to simulate parameters
-(called a {\tt param()} function), and a method for calculating or
-drawing quantities of interest from the simulated parameters (called a
-{\tt qi()} function).
-
-\paragraph{Simulating Parameters}
-
-Whether you choose to use the default method, or write a
-model-specific method for simulating parameters, these functions
-require the same three inputs:
-\begin{itemize}
-\item {\tt object}: the estimated model or {\tt zelig()} output.
-\item {\tt num}: the number of simulations.
-\item {\tt bootstrap}: either {\tt TRUE} or {\tt FALSE}.
-\end{itemize}
-The output from {\tt param()} should be either
-\begin{itemize}
-\item If {\tt bootstrap = FALSE} (default), an matrix with rows
-corresponding to simulations and columns corresponding to model
-parameters. Any ancillary parameters should be included in the output
-matrix.
-\item If {\tt bootstrap = TRUE}, a vector containing all model
-parameters, including ancillary parameters.
-\end{itemize}
-
-There are two ways to simulate parameters:
-\begin{enumerate}
-
-\item Use the {\tt param.default()} function to extract parameters
- from the model and, if bootstrapping is not selected, simulate
- coefficients using asymptotic normal approximation. The {\tt
- param.default()} function relies on two R functions:
-
- \begin{enumerate}
- \item {\tt coef()}: extracts the coefficients. Continuing the
- Normal regression example from above, the appropriate {\tt coef.normal()}
- function is simply:
-\begin{verbatim}
-coef.normal <- function(object)
- object$coefficients
-\end{verbatim} %$
- \item {\tt vcov()}: extracts the variance-covariance matrix. Again
- continuing the Poisson example from above:
-\begin{verbatim}
-vcov.normal <- function(object)
- object$variance
-\end{verbatim} %$
-\end{enumerate}
-
-\item Alternatively, you can write your own {\tt param.contrib()}
- function. This is appropriate when:
- \begin{enumerate}
- \item Your model has auxiliary parameters, such as $\sigma$ in the
- case of the Normal distribution.
- \item Your model performs some sort of correction to the coefficients
- or the variance-covariance matrix, which cannot be performed in
- either the {\tt coef.contrib()} or the {\tt vcov.contrib()}
- functions.
- \item Your model does not rely on asymptotic approximation to the
- log-likelihood. For Bayesian Markov-chain monte carlo models, for
- example, the {\tt param.contrib()} function ({\tt
- param.MCMCzelig()} in this case) simply extracts the model
- parameters simulated in the model-fitting function.
- \end{enumerate}
- Continuing the Normal example,
-\begin{verbatim}
-param.normal <- function(object, num = NULL, bootstrap = FALSE,
- terms = NULL) {
- if (!bootstrap) {
- par <- mvrnorm(num, mu = coef(object), Sigma = vcov(object))
- Beta <- parse.par(par, terms = terms, eqn = "mu")
- sigma2 <- exp(parse.par(par, terms = terms, eqn = "sigma2"))
- res <- cbind(Beta, sigma2)
- }
- else {
- par <- coef(object)
- Beta <- parse.par(par, terms = terms, eqn = "mu")
- sigma2 <- exp(parse.par(par, terms = terms, eqn = "sigma2"))
- res <- c(coef, sigma2)
- }
- res
-}
-\end{verbatim}
-\end{enumerate}
-
-\paragraph{Calculating Quantities of Interest}
-
-All models require a model-specific method for calculating quantities
-of interest from the simulated parameters. For a model of class {\tt
- contrib}, the appropriate {\tt qi()} function is {\tt
- qi.contrib()}. This function should calculate, at the bare minimum,
-the following quantities of interest:
-
-\begin{itemize}
-\item {\tt ev}: the expected values, calculated from the analytic
- solution for the expected value as a function of the systematic
- component and ancillary parameters.
-\item {\tt pr}: the predicted values, drawn from a distribution
- defined by the predicted values. If R does not have a built-in
- random generator for your function, you may take a random draw from
- the uniform distribution and use the inverse CDF method to calculate
- predicted values.
-\item {\tt fd}: first differences in the expected value, calculated
- by subtracting the expected values given the specified {\tt x} from
- the expected values given {\tt x1}.
-\item {\tt ate.ev}: the average treatment effect calculated using the
- expected values {\tt ev}. This is simply {\tt y - ev}, averaged across
- simulations for each observation.
-\item {\tt ate.pr}: the average treatment effect calculated using
- the predicted values {\tt pr}. This is simply {\tt y - pr},
- averaged across simulations for each observation.
-\end{itemize}
-The required arguments for the {\tt qi()} function are:
-\begin{itemize}
-\item {\tt object}: the zelig output object.
-\item {\tt par}: the simulated parameters.
-\item {\tt x}: the matrix of explanatory variables (created using {\tt
- setx()}).
-\item {\tt x1}: the optional matrix of alternative values for first
- differences (also created using {\tt setx()}). If first differences
- are inappropriate for your model, you should put in a {\tt
- warning()} or {\tt stop()} if {\tt x1} is not {\tt NULL}.
-\item {\tt y}: the optional vector or matrix of dependent variables
- (for calculating average treatment effects). If average treatment
- effects are inappropriate for your model, you should put in a {\tt
- warning()} or {\tt stop()} if conditional prediction has been
- selected in the {\tt setx()} step.
-\end{itemize}
-
-Continuing the Normal regression example from above, the appropriate {\tt
- qi.normal()} function is as follows:
-\begin{verbatim}
-qi.normal <- function(object, par, x, x1 = NULL, y = NULL) {
- Beta <- parse.par(par, eqn = "mu") # [1]
- sigma2 <- parse.par(par, eqn = "sigma2") # [2]
- ev <- Beta %*% t(x) # [3a]
- pr <- matrix(NA, ncol = ncol(ev), nrow = nrow(ev))
- for (i in 1:ncol(ev))
- pr[,i] <- rnorm(length(ev[,i]), mean = ev[,i], # [4]
- sigma = sd(sigma2[i]))
- qi <- list(ev = ev, pr = pr)
- qi.name <- list(ev = "Expected Values: E(Y|X)",
- pr = "Predicted Values: Y|X")
- if (!is.null(x1)){
- ev1 <- par %*% t(x1) # [3b]
- qi$fd <- ev1 - ev
- qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
- }
- if (!is.null(y)) {
- yvar <- matrix(rep(y, nrow(par)), nrow = nrow(par), byrow = TRUE)
- tmp.ev <- yvar - qi$ev
- tmp.pr <- yvar - qi$pr
- qi$ate.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(par))
- qi$ate.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(par))
- qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
- qi.name$ate.pr <- "Average Treatment Effect: Y - PR"
- }
- list(qi=qi, qi.name=qi.name)
-}
-\end{verbatim}
-There are five lines of code commented above. By changing these five
-lines in the following \emph{four} ways, you can write {\tt qi()}
-function appropriate to almost any model:
-\begin{enumerate}
-\item Extract any systematic parameters by substituting the name of
-your systematic parameter (defined in {\tt describe.mymodel()}).
-\item Extract any ancillary parameters (defined in {\tt
-describe.mymodel()}) by substituting their names
-here.
-\item Calculate the expected value using the inverse link function and
-$\eta = X \beta$. (For the normal model, this is linear.) You will
-need to make this change in two places, at Comment [3a] and [3b].
-\item Replace {\tt rnorm()} with a function that takes random draws
-from the stochastic component of your model.
-\end{enumerate}
-
-\section{Getting Ready for the GUI}
-
-Zelig can work with a variety of graphical user interfaces (GUIs). GUIs
-work by knowing {\it a priori} what a particular model
-accepts, and presenting only those options to the user in some sort of
-graphical interface. Thus, in order for your model to work with a
-GUI, you must describe your model in terms that the GUI can
-understand. For models written using the guidelines in Chapter
-\ref{s:new}, your model will be compatible with (at least)
-the \hlink{Virtual Data Center}{http://thedata.org} GUI. For
-pre-existing models, you will need to create a {\tt describe.*()}
-function for your model following the examples in
-\Sref{describe.mymodel}.
-
-\section{Formatting Reference Manual Pages} \label{s:format}
-
-One of the primary advantages of Zelig is that it fully documents the
-included models, in contrast to the programming-orientation of R
-documentation which is organized by function. Thus, we ask that Zelig
-contributors provide similar documentation, including the syntax and
-arguments passed to {\tt zelig()}, the systematic and stochastic
-components to the model, the quantities of interest, the output
-values, and further information (including references). There are
-several ways to provide this information:
-\begin{itemize}
-\item If you have an existing package documented using the .Rd help
- format, {\tt help.zelig()} will automatically search R-help in
- addition to Zelig help.
-\item If you have an existing package documented using on-line HTML
- files with static URLs (like Zelig or MatchIt), you need to provide
- a {\tt PACKAGE.url.tab} file which is a two-column table containing
- the name of the function in the first column and the url in the
- second. (Even though the file extension is {\tt .url.tab}, the file
- should be a tab- or space-delimited text file.) For example:
-\begin{verbatim}
-command http://gking.harvard.edu/zelig/docs/Main_Commands.html
-model http://gking.harvard.edu/zelig/docs/Specific_Models.html
-\end{verbatim}
-If you wish to test to see if your {\tt .url.tab} files works, simply
-place it in your R library/Zelig/data/ directory. (You do not need to
-reinstall Zelig to test your {\tt .url.tab} file.)
-\item Preferred method: You may provide a \LaTeXe\ {\tt .tex} file. This document uses
- the book style and supports commands from the following packages:
- {\tt graphicx}, {\tt natbib}, {\tt amsmath}, {\tt amssymb}, {\tt
- verbatim}, {\tt epsf}, and {\tt html}. Because model pages are
- incorporated into this document using {\tt $\backslash$include\{\}},
- you should make sure that your document compiles before submitting
- it. Please adhere to the following conventions for your model page:
- \begin{enumerate}
- \item All mathematical formula should be typeset using the {\tt
- equation*} and {\tt array}, {\tt eqnarray*}, or {\tt align}
- environments. Please avoid {\tt displaymath}. (It looks funny in
- html.)
- \item All commands or R objects should use the {\tt texttt}
- environment.
- \item The model begins as a subsection of a larger document, and
- sections within the model page are of sub-subsection level.
-\item For stylistic consistency, please avoid using the {\tt
- description} environment.
-\end{enumerate}
-
-Each \LaTeX\ model page should include the following elements. Let
-{\tt contrib} specify the new model.
-
-\subsubsection*{Help File Template}
-\begin{verbatim}
-\subsection{{\tt contrib}: Full Name for [type] Dependent Variables}
-\label{contrib}
-
-\subsubsection{Syntax}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item First Example
-\item Second Example
-\end{enumerate}
-
-\subsubsection{Model}
-\begin{itemize}
-\item The observation mechanism, if applicable.
-\item The stochastic component.
-\item The systematic component.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected value of your distribution, including the formula
- for the expected value as a function of the systemic component and
- ancillary paramters.
-\item The predicted value drawn from the distribution defined by the
- corresponding expected value.
-\item The first difference in expected values, given when x1 is specified.
-\item Other quantities of interest.
-\end{itemize}
-
-\subsubsection{Output Values}
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item
- \item
- \end{itemize}
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item
- \item
- \end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}:
- \begin{itemize}
- \item
- \item
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Further Information}
-
-\subsubsection{Contributors}
-\end{verbatim}
-\end{itemize}
-
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/commands/zelig"
-%%% End:
diff --git a/inst/doc/aov.Rnw b/inst/doc/aov.Rnw
deleted file mode 100644
index 0583cce..0000000
--- a/inst/doc/aov.Rnw
+++ /dev/null
@@ -1,252 +0,0 @@
-\SweaveOpts{results=hide,prefix.string=vigpics/aov}
-\include{zinput}
-%\VignetteIndexEntry{Fit an Analysis of Variance Model}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,continuous,analysis variance, error, multiple responses,sum squares, residual}
-%\VignettePackage{Zelig}
-\begin{document}
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-pkg <- search()
-if(!length(grep("package:Zelig",pkg)))
-library(Zelig)
-@
-
-\section{{\tt aov}: Analysis of Variance for Continuous Dependent Variables}
-\label{aov}
-
-Model ``aov'' uses least squares regression to estimate the residual
-sum of squares and degrees of freedom for each explanatory variable
-around the best linear predictor for the specified dependent
-variables. Model ``aov'' is particularly useful for the analysis of
-randomized experiments with more than one strata or group (e.g.,
-balanced incomplete block design). For the model with only one
-strata, ``aov'' reduces to ``ls''.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2 + Error(Z), model = "aov", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-where the {\tt Error()} term is optional and takes strata formula.
-
-\subsubsection{Examples}\begin{enumerate}
-
-\item Basic Example of aov.
-
-Attach sample data and set orthogonal contrasts:
-<<Example1.data>>=
- data(npk, package="MASS")
- op <- options(contrasts=c("contr.helmert", "contr.poly"))
-@
-Estimate the model \citep[p.165]{VenRip02}:
-<<Example1.zelig>>=
- z.out1 <- zelig(yield ~ block + N * P + K, model="aov", data=npk)
-@
-Summarize the fitted model:
-<<Example1.summary>>=
- summary(z.out1)
-@
-Set explanatory variables to their default (mean/mode) values
-<<Example1.setx>>=
- x <- setx(z.out1)
-@
-Simulate model at values explanatory variables as in x
-<<Example1.sim>>=
- s.out1 <- sim(z.out1, x = x)
-@
-<<Example1.summary.sim>>=
-summary(s.out1)
-@
-\begin{center}
-<<label=ExamplesPlot1,fig=true,echo=true, width=5,height=2.5>>=
- plot(s.out1)
-@
-\end{center}
-
-\item Example with {\tt Error()} term allowing for more than one
- source of random variation in an experiment (multistratum model).
-
-Estimate the model:
-<<Example2.zelig>>=
- z.out2 <- zelig(yield ~ N*P*K + Error(block), model="aov",data=npk)
-@
-Summarize regression coefficients:
-<<Example2.summary>>=
- summary(z.out2)
-@
-Set explanatory variables to their default (mean/mode) values
-<<Example2.setx>>=
- x <- setx(z.out2)
-@
-Simulate model at values explanatory variables as in x
-<<Example2.sim>>=
- s.out2 <- sim(z.out2, x = x)
-@
-<<Example2.summary.sim>>=
- summary(s.out2)
-@
-\begin{center}
-<<label=ExamplesPlot2,fig=true,echo=true, width=5, height=4>>=
- plot(s.out2)
-@
-\end{center}
-
-\item Example with {\it Error()} term (multistratum model) and first
- differences.
-
-Reset to previous contrasts
-<<Options>>=
-options(op)
-@
-Estimate model \citep[p.283]{VenRip02}:
-<<Example3.zelig>>=
- z.out3 <- zelig(Y ~ N*V + Error(B/V), model="aov", data=oats)
-@
-Summarize regression coefficients:
-<<Example3.summary>>=
- summary(z.out3)
-@
-Set explanatory variables using mode
-<<Example3.setx>>=
- x.out <- setx(z.out3, N="0.0cwt", V="Golden.rain")
- x.out1 <- setx(z.out3, N="0.0cwt", V="Victory")
-@
-Simulate model at values explanatory variables as in x
-<<Example3.sim>>=
- s.out3 <- sim(z.out3, x = x.out,x1=x.out1)
-@
-<<Example3.summary.sim>>=
-summary(s.out3)
-@
-\begin{center}
-<<label=ExamplesPlot3,fig=true,echo=true, width=8, height=6>>=
- plot(s.out3)
-@
-\end{center}
-
-\end{enumerate}
-
-\subsubsection{Model}
-\begin{itemize}
-\item The \emph{stochastic component} is described by a normal density
- with mean $\mu_i$ and the common variance $\sigma^2$
- \begin{equation*}
- Y_i \; \sim \; f(y_i \mid \mu_i, \sigma^2).
- \end{equation*}
-\item The \emph{systematic component} models the conditional mean as
- \begin{equation*}
- \mu_i = x_i \beta
- \end{equation*}
- where $x_i$ is the vector of covariates, and $\beta$ is the vector
- of coefficients.
-
- The least squares estimator is the best linear predictor of a
- dependent variable given $x_i$, and minimizes the sum of squared
- residuals, $\sum_{i=1}^n (Y_i-x_i \beta)^2$. The output of {\tt aov}
- model is the sum of squared residuals. Note that {\tt aov} is the
- same model as {\tt ls} but the output values of function call
- {\tt zelig} are different. You may check that {\tt name(z.out)}
- returns the same arguments for the two models.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations
- from the stochastic component,
-\begin{equation*}
-E(Y) = x_i \beta,\end{equation*}
-given a draw of $\beta$ from its sampling distribution.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "aov", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
- \item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: fitted values.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the residuals sum of squares estimated
- with their associated degree of freedom, their mean squares,
- $F$-values, and $F$-statistics for all explanatory variables.
-
- \item {\tt residuals}: the sum of square, mean, degree of freedom,
- $F$-values, and $F$-statistics for the vector of residuals or
- standard errors that check the adequecy of the fit for the
- dependent variable versus the true values or data points.
-
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first differences (or
- differences in expected values) for the specified values of {\tt
- x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-
-\subsection* {How to Cite}
-\input{cites/aov}
-\input{citeZelig}
-
-\subsection* {See also}
-The analysis of variance model is part of the stats package by William
-N. Venables and Brian D. Ripley \citep{VenRip02}. In addition,
-advanced users may wish to refer to \texttt{help(aov)} and
-\texttt{help(lm)}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
diff --git a/inst/doc/aov.log b/inst/doc/aov.log
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-h, portuges, romanian, russian, serbian, slovak, slovene, spanish, swedish, tur
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-\openout2 = `zinput.aux'.
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-uesky/cm/cmbx12.pfb>
-Output written on arima.pdf (10 pages, 120718 bytes).
diff --git a/inst/doc/arima.pdf b/inst/doc/arima.pdf
deleted file mode 100644
index 1b9d3bf..0000000
Binary files a/inst/doc/arima.pdf and /dev/null differ
diff --git a/inst/doc/arima.Rnw b/inst/doc/arima.tex
similarity index 86%
rename from inst/doc/arima.Rnw
rename to inst/doc/arima.tex
index 7c388ef..716e585 100644
--- a/inst/doc/arima.Rnw
+++ b/inst/doc/arima.tex
@@ -1,21 +1,14 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/arima}
+
\include{zinput}
%\VignetteIndexEntry{ARIMA Models for Time Series Data}
%\VignetteDepends{Zelig}
%\VignetteKeyWords{model,time series}
%\VignettePackage{Zelig}
+\usepackage{Sweave}
\begin{document}
\nobibliography*
\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-pkg <- search()
-if(!length(grep("package:Zelig",pkg)))
-library(Zelig)
-@
\section{{\tt ARIMA}: ARIMA Models for Time Series Data}
\label{ARIMA}
@@ -102,31 +95,44 @@ PACF plots to determine the order of an ARIMA model.
\item No covariates\newline
Estimate the ARIMA model, and summarize the results.
-<<Examples.data>>=
- data(approval)
-@
-<<Example1.zelig>>=
-z.out1 <- zelig(Diff(approve, 1) ~ lag.eps(2) + lag.y(2),
- data = approval, model = "arima")
-summary(z.out1)
-@
+\begin{Schunk}
+\begin{Sinput}
+> data(approval)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> z.out1 <- zelig(Diff(approve, 1) ~ lag.eps(2) + lag.y(2), data = approval,
++ model = "arima")
+> summary(z.out1)
+\end{Sinput}
+\end{Schunk}
Set the number of time periods (ahead) for the prediction to run.
for which you would like the prediction to run:
-<<Example1.setx>>=
-x.out1 <- setx(z.out1, pred.ahead = 10)
-@
+\begin{Schunk}
+\begin{Sinput}
+> x.out1 <- setx(z.out1, pred.ahead = 10)
+\end{Sinput}
+\end{Schunk}
Simulate the predicted quantities of interest:
-<<Example1.sim>>=
-s.out1 <- sim(z.out1, x = x.out1)
-@
+\begin{Schunk}
+\begin{Sinput}
+> s.out1 <- sim(z.out1, x = x.out1)
+\end{Sinput}
+\end{Schunk}
Summarize and plot the results:
-<<Example1.summary>>=
-summary(s.out1)
-@
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out1)
+\end{Sinput}
+\end{Schunk}
\begin{center}
-<<label=Example1Plot,fig=true>>=
- plot(s.out1,lty.set=2)
-@
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out1, lty.set = 2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/arima-Example1Plot}
\end{center}
\item Calculating a treatment effect\newline
@@ -134,30 +140,41 @@ summary(s.out1)
Estimate an ARIMA model with exogenous regressors, in addition to
lagged errors and lagged values of the dependent variable.
-<<Example2.zelig>>=
-z.out2<- zelig(Diff(approve, 1)~ iraq.war + sept.oct.2001 + avg.price + lag.eps(1) + lag.y(2),
- data=approval, model="arima")
-@
+\begin{Schunk}
+\begin{Sinput}
+> z.out2 <- zelig(Diff(approve, 1) ~ iraq.war + sept.oct.2001 +
++ avg.price + lag.eps(1) + lag.y(2), data = approval, model = "arima")
+\end{Sinput}
+\end{Schunk}
To calculate a treatment effect, provide one counterfactual value for
one time period for one of the exogenous regressors (this is the
counterfactual treatment).
-<<Example2.setx>>=
-x.out2<- setx(z.out2, sept.oct.2001=list(time=45, value=0), cond=T)
-
-@
+\begin{Schunk}
+\begin{Sinput}
+> x.out2 <- setx(z.out2, sept.oct.2001 = list(time = 45, value = 0),
++ cond = T)
+\end{Sinput}
+\end{Schunk}
Simulate the quantities of interes
-<<Example2.sim>>=
-s.out2 <- sim(z.out2, x = x.out2)
-@
+\begin{Schunk}
+\begin{Sinput}
+> s.out2 <- sim(z.out2, x = x.out2)
+\end{Sinput}
+\end{Schunk}
Summarizing and plotting the quantities of interest.
-<<Example2.summary>>=
-summary(s.out2)
-@
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out2)
+\end{Sinput}
+\end{Schunk}
%the plot does not work
\begin{center}
-<<label=Example2Plot,fig=true>>=
- plot(s.out2)
-@
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out2)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/arima-Example2Plot}
\end{center}
\item Calculating first differences\newline
@@ -165,25 +182,34 @@ summary(s.out2)
Continuing the example from above, calculate first differences by
selecting several counterfactual values for one of the exogenous
regressors.
-<<Example3.setx>>=
-x.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value=0))
-x1.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value=1))
-@
+\begin{Schunk}
+\begin{Sinput}
+> x.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value = 0))
+> x1.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value = 1))
+\end{Sinput}
+\end{Schunk}
Simulating the quantities of interest
-<<Example3.sim>>=
-s.out3 <- sim(z.out2, x = x.out3, x1 = x1.out3)
-@
+\begin{Schunk}
+\begin{Sinput}
+> s.out3 <- sim(z.out2, x = x.out3, x1 = x1.out3)
+\end{Sinput}
+\end{Schunk}
Summarizing and plotting the quantities of interest. Choosing {\tt
pred.se = TRUE} only displays the uncertainty resulting from parameter
estimation.
-<<Example3.summary>>=
-summary(s.out3)
-@
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out3)
+\end{Sinput}
+\end{Schunk}
%the plot does not work
\begin{center}
-<<label=Example3Plot,fig=true>>=
-plot(s.out3, pred.se = TRUE)
-@
+\begin{Schunk}
+\begin{Sinput}
+> plot(s.out3, pred.se = TRUE)
+\end{Sinput}
+\end{Schunk}
+\includegraphics{vigpics/arima-Example3Plot}
\end{center}
\end{enumerate}
@@ -321,10 +347,4 @@ help(arima)}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after <- search()
- torm <- setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
\end{document}
diff --git a/inst/doc/asa.bst b/inst/doc/asa.bst
new file mode 100644
index 0000000..6e7016d
--- /dev/null
+++ b/inst/doc/asa.bst
@@ -0,0 +1,1505 @@
+%%
+%% This is file `asa.bst',
+%% generated with the docstrip utility.
+%%
+%% The original source files were:
+%%
+%% merlin.mbs (with options: `,ay,nat,nm-rev,ed-rev,nmdash,dt-beg,yr-par,note-yr,tit-qq,atit-u,thtit-a,vnum-x,volp-com,pp-last,add-pub,pre-pub,edpar,edby,edbyx,blk-com,pp,ed,abr,ednx,ord,em-it,nfss')
+%% ----------------------------------------
+%% *** BibTeX Style for ASA Journals ***
+%% (Brett Presnell, 24 August 1998)
+%%
+ %-------------------------------------------------------------------
+ % The original source file contains the following version information:
+ % \ProvidesFile{merlin.mbs}[1998/02/25 3.85a (PWD)]
+ %
+ % NOTICE:
+ % This file may be used for non-profit purposes.
+ % It may not be distributed in exchange for money,
+ % other than distribution costs.
+ %
+ % The author provides it `as is' and does not guarantee it in any way.
+ %
+ % Copyright (C) 1994-98 Patrick W. Daly
+ %-------------------------------------------------------------------
+ % For use with BibTeX version 0.99a or later
+ %-------------------------------------------------------------------
+ % This bibliography style file is intended for texts in ENGLISH
+ % This is an author-year citation style bibliography. As such, it is
+ % non-standard LaTeX, and requires a special package file to function properly.
+ % Such a package is natbib.sty by Patrick W. Daly
+ % The form of the \bibitem entries is
+ % \bibitem[Jones et al.(1990)]{key}...
+ % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}...
+ % The essential feature is that the label (the part in brackets) consists
+ % of the author names, as they should appear in the citation, with the year
+ % in parentheses following. There must be no space before the opening
+ % parenthesis!
+ % With natbib v5.3, a full list of authors may also follow the year.
+ % In natbib.sty, it is possible to define the type of enclosures that is
+ % really wanted (brackets or parentheses), but in either case, there must
+ % be parentheses in the label.
+ % The \cite command functions as follows:
+ % \citet{key} ==>> Jones et al. (1990)
+ % \citet*{key} ==>> Jones, Baker, and Smith (1990)
+ % \citep{key} ==>> (Jones et al., 1990)
+ % \citep*{key} ==>> (Jones, Baker, and Smith, 1990)
+ % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2)
+ % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990)
+ % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32)
+ % \citeauthor{key} ==>> Jones et al.
+ % \citeauthor*{key} ==>> Jones, Baker, and Smith
+ % \citeyear{key} ==>> 1990
+ %---------------------------------------------------------------------
+
+ENTRY
+ { address
+ author
+ booktitle
+ chapter
+ edition
+ editor
+ howpublished
+ institution
+ journal
+ key
+ month
+ note
+ number
+ organization
+ pages
+ publisher
+ school
+ series
+ title
+ type
+ volume
+ year
+ }
+ {}
+ { label extra.label sort.label short.list }
+
+INTEGERS { output.state before.all mid.sentence after.sentence after.block }
+
+FUNCTION {init.state.consts}
+{ #0 'before.all :=
+ #1 'mid.sentence :=
+ #2 'after.sentence :=
+ #3 'after.block :=
+}
+
+STRINGS { s t }
+
+FUNCTION {output.nonnull}
+{ 's :=
+ output.state mid.sentence =
+ { ", " * write$ }
+ { output.state after.block =
+ { add.period$ write$
+ newline$
+ "\newblock " write$
+ }
+ { output.state before.all =
+ 'write$
+ { add.period$ " " * write$ }
+ if$
+ }
+ if$
+ mid.sentence 'output.state :=
+ }
+ if$
+ s
+}
+
+FUNCTION {output}
+{ duplicate$ empty$
+ 'pop$
+ 'output.nonnull
+ if$
+}
+
+FUNCTION {output.check}
+{ 't :=
+ duplicate$ empty$
+ { pop$ "empty " t * " in " * cite$ * warning$ }
+ 'output.nonnull
+ if$
+}
+
+FUNCTION {fin.entry}
+{ add.period$
+ write$
+ newline$
+}
+
+FUNCTION {new.block}
+{ output.state before.all =
+ 'skip$
+ { after.block 'output.state := }
+ if$
+}
+
+FUNCTION {new.sentence}
+{ output.state after.block =
+ 'skip$
+ { output.state before.all =
+ 'skip$
+ { after.sentence 'output.state := }
+ if$
+ }
+ if$
+}
+
+FUNCTION {add.blank}
+{ " " * before.all 'output.state :=
+}
+
+FUNCTION {date.block}
+{
+ skip$
+}
+
+FUNCTION {not}
+{ { #0 }
+ { #1 }
+ if$
+}
+
+FUNCTION {and}
+{ 'skip$
+ { pop$ #0 }
+ if$
+}
+
+FUNCTION {or}
+{ { pop$ #1 }
+ 'skip$
+ if$
+}
+
+FUNCTION {non.stop}
+{ duplicate$
+ "}" * add.period$
+ #-1 #1 substring$ "." =
+}
+
+FUNCTION {new.block.checkb}
+{ empty$
+ swap$ empty$
+ and
+ 'skip$
+ 'new.block
+ if$
+}
+
+FUNCTION {field.or.null}
+{ duplicate$ empty$
+ { pop$ "" }
+ 'skip$
+ if$
+}
+
+FUNCTION {emphasize}
+{ duplicate$ empty$
+ { pop$ "" }
+ { "\textit{" swap$ * "}" * }
+ if$
+}
+
+FUNCTION {capitalize}
+{ "u" change.case$ "t" change.case$ }
+
+FUNCTION {space.word}
+{ " " swap$ * " " * }
+
+ % Here are the language-specific definitions for explicit words.
+ % Each function has a name bbl.xxx where xxx is the English word.
+ % The language selected here is ENGLISH
+FUNCTION {bbl.and}
+{ "and"}
+
+FUNCTION {bbl.editors}
+{ "eds." }
+
+FUNCTION {bbl.editor}
+{ "ed." }
+
+FUNCTION {bbl.edby}
+{ "edited by" }
+
+FUNCTION {bbl.edition}
+{ "ed." }
+
+FUNCTION {bbl.volume}
+{ "vol." }
+
+FUNCTION {bbl.of}
+{ "of" }
+
+FUNCTION {bbl.number}
+{ "no." }
+
+FUNCTION {bbl.nr}
+{ "no." }
+
+FUNCTION {bbl.in}
+{ "in" }
+
+FUNCTION {bbl.pages}
+{ "pp." }
+
+FUNCTION {bbl.page}
+{ "p." }
+
+FUNCTION {bbl.chapter}
+{ "chap." }
+
+FUNCTION {bbl.techrep}
+{ "Tech. Rep." }
+
+FUNCTION {bbl.mthesis}
+{ "Master's thesis" }
+
+FUNCTION {bbl.phdthesis}
+{ "Ph.D. thesis" }
+
+FUNCTION {bbl.first}
+{ "1st" }
+
+FUNCTION {bbl.second}
+{ "2nd" }
+
+FUNCTION {bbl.third}
+{ "3rd" }
+
+FUNCTION {bbl.fourth}
+{ "4th" }
+
+FUNCTION {bbl.fifth}
+{ "5th" }
+
+FUNCTION {bbl.st}
+{ "st" }
+
+FUNCTION {bbl.nd}
+{ "nd" }
+
+FUNCTION {bbl.rd}
+{ "rd" }
+
+FUNCTION {bbl.th}
+{ "th" }
+
+MACRO {jan} {"Jan."}
+
+MACRO {feb} {"Feb."}
+
+MACRO {mar} {"Mar."}
+
+MACRO {apr} {"Apr."}
+
+MACRO {may} {"May"}
+
+MACRO {jun} {"Jun."}
+
+MACRO {jul} {"Jul."}
+
+MACRO {aug} {"Aug."}
+
+MACRO {sep} {"Sep."}
+
+MACRO {oct} {"Oct."}
+
+MACRO {nov} {"Nov."}
+
+MACRO {dec} {"Dec."}
+
+FUNCTION {eng.ord}
+{ duplicate$ "1" swap$ *
+ #-2 #1 substring$ "1" =
+ { bbl.th * }
+ { duplicate$ #-1 #1 substring$
+ duplicate$ "1" =
+ { pop$ bbl.st * }
+ { duplicate$ "2" =
+ { pop$ bbl.nd * }
+ { "3" =
+ { bbl.rd * }
+ { bbl.th * }
+ if$
+ }
+ if$
+ }
+ if$
+ }
+ if$
+}
+
+MACRO {acmcs} {"ACM Computing Surveys"}
+
+MACRO {acta} {"Acta Informatica"}
+
+MACRO {cacm} {"Communications of the ACM"}
+
+MACRO {ibmjrd} {"IBM Journal of Research and Development"}
+
+MACRO {ibmsj} {"IBM Systems Journal"}
+
+MACRO {ieeese} {"IEEE Transactions on Software Engineering"}
+
+MACRO {ieeetc} {"IEEE Transactions on Computers"}
+
+MACRO {ieeetcad}
+ {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"}
+
+MACRO {ipl} {"Information Processing Letters"}
+
+MACRO {jacm} {"Journal of the ACM"}
+
+MACRO {jcss} {"Journal of Computer and System Sciences"}
+
+MACRO {scp} {"Science of Computer Programming"}
+
+MACRO {sicomp} {"SIAM Journal on Computing"}
+
+MACRO {tocs} {"ACM Transactions on Computer Systems"}
+
+MACRO {tods} {"ACM Transactions on Database Systems"}
+
+MACRO {tog} {"ACM Transactions on Graphics"}
+
+MACRO {toms} {"ACM Transactions on Mathematical Software"}
+
+MACRO {toois} {"ACM Transactions on Office Information Systems"}
+
+MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"}
+
+MACRO {tcs} {"Theoretical Computer Science"}
+
+INTEGERS { nameptr namesleft numnames }
+
+FUNCTION {format.names}
+{ 's :=
+ #1 'nameptr :=
+ s num.names$ 'numnames :=
+ numnames 'namesleft :=
+ { namesleft #0 > }
+ { s nameptr
+ "{vv~}{ll}{, jj}{, f.}" format.name$
+ 't :=
+ nameptr #1 >
+ {
+ namesleft #1 >
+ { ", " * t * }
+ {
+ numnames #2 >
+ { "," * }
+ 'skip$
+ if$
+ s nameptr "{ll}" format.name$ duplicate$ "others" =
+ { 't := }
+ { pop$ }
+ if$
+ t "others" =
+ {
+ " et~al." *
+ }
+ { bbl.and space.word * t * }
+ if$
+ }
+ if$
+ }
+ 't
+ if$
+ nameptr #1 + 'nameptr :=
+ namesleft #1 - 'namesleft :=
+ }
+ while$
+}
+FUNCTION {format.names.ed}
+{ format.names }
+FUNCTION {format.key}
+{ empty$
+ { key field.or.null }
+ { "" }
+ if$
+}
+
+FUNCTION {format.authors}
+{ author empty$
+ { "" }
+ { author format.names }
+ if$
+}
+
+FUNCTION {format.editors}
+{ editor empty$
+ { "" }
+ { editor format.names
+ editor num.names$ #1 >
+ { " (" * bbl.editors * ")" * }
+ { " (" * bbl.editor * ")" * }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.in.editors}
+{ editor empty$
+ { "" }
+ { editor format.names.ed
+ }
+ if$
+}
+
+FUNCTION {format.note}
+{ note empty$
+ { "" }
+ { note #1 #1 substring$
+ duplicate$ "{" =
+ 'skip$
+ { output.state mid.sentence =
+ { "l" }
+ { "u" }
+ if$
+ change.case$
+ }
+ if$
+ note #2 global.max$ substring$ *
+ }
+ if$
+}
+
+FUNCTION {format.title}
+{ title empty$
+ { "" }
+ { title
+ "\enquote{" swap$ *
+ non.stop
+ { ",} " * }
+ { "} " * }
+ if$
+ }
+ if$
+}
+
+FUNCTION {end.quote.title}
+{ title empty$
+ 'skip$
+ { before.all 'output.state := }
+ if$
+}
+
+FUNCTION {format.full.names}
+{'s :=
+ #1 'nameptr :=
+ s num.names$ 'numnames :=
+ numnames 'namesleft :=
+ { namesleft #0 > }
+ { s nameptr
+ "{vv~}{ll}" format.name$
+ 't :=
+ nameptr #1 >
+ {
+ namesleft #1 >
+ { ", " * t * }
+ {
+ numnames #2 >
+ { "," * }
+ 'skip$
+ if$
+ s nameptr "{ll}" format.name$ duplicate$ "others" =
+ { 't := }
+ { pop$ }
+ if$
+ t "others" =
+ {
+ " et~al." *
+ }
+ { bbl.and space.word * t * }
+ if$
+ }
+ if$
+ }
+ 't
+ if$
+ nameptr #1 + 'nameptr :=
+ namesleft #1 - 'namesleft :=
+ }
+ while$
+}
+
+FUNCTION {author.editor.key.full}
+{ author empty$
+ { editor empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { editor format.full.names }
+ if$
+ }
+ { author format.full.names }
+ if$
+}
+
+FUNCTION {author.key.full}
+{ author empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { author format.full.names }
+ if$
+}
+
+FUNCTION {editor.key.full}
+{ editor empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { editor format.full.names }
+ if$
+}
+
+FUNCTION {make.full.names}
+{ type$ "book" =
+ type$ "inbook" =
+ or
+ 'author.editor.key.full
+ { type$ "proceedings" =
+ 'editor.key.full
+ 'author.key.full
+ if$
+ }
+ if$
+}
+
+FUNCTION {output.bibitem}
+{ newline$
+ "\bibitem[{" write$
+ label write$
+ ")" make.full.names duplicate$ short.list =
+ { pop$ }
+ { * }
+ if$
+ "}]{" * write$
+ cite$ write$
+ "}" write$
+ newline$
+ ""
+ before.all 'output.state :=
+}
+
+FUNCTION {n.dashify}
+{
+ 't :=
+ ""
+ { t empty$ not }
+ { t #1 #1 substring$ "-" =
+ { t #1 #2 substring$ "--" = not
+ { "--" *
+ t #2 global.max$ substring$ 't :=
+ }
+ { { t #1 #1 substring$ "-" = }
+ { "-" *
+ t #2 global.max$ substring$ 't :=
+ }
+ while$
+ }
+ if$
+ }
+ { t #1 #1 substring$ *
+ t #2 global.max$ substring$ 't :=
+ }
+ if$
+ }
+ while$
+}
+
+FUNCTION {word.in}
+{ bbl.in
+ " " * }
+
+FUNCTION {format.date}
+{ year duplicate$ empty$
+ { "empty year in " cite$ * "; set to ????" * warning$
+ pop$ "????" }
+ 'skip$
+ if$
+ extra.label *
+ before.all 'output.state :=
+ " (" swap$ * ")" *
+}
+
+FUNCTION {format.btitle}
+{ title emphasize
+}
+
+FUNCTION {tie.or.space.connect}
+{ duplicate$ text.length$ #3 <
+ { "~" }
+ { " " }
+ if$
+ swap$ * *
+}
+
+FUNCTION {either.or.check}
+{ empty$
+ 'pop$
+ { "can't use both " swap$ * " fields in " * cite$ * warning$ }
+ if$
+}
+
+FUNCTION {format.bvolume}
+{ volume empty$
+ { "" }
+ { bbl.volume volume tie.or.space.connect
+ series empty$
+ 'skip$
+ { bbl.of space.word * series emphasize * }
+ if$
+ "volume and number" number either.or.check
+ }
+ if$
+}
+
+FUNCTION {format.number.series}
+{ volume empty$
+ { number empty$
+ { series field.or.null }
+ { output.state mid.sentence =
+ { bbl.number }
+ { bbl.number capitalize }
+ if$
+ number tie.or.space.connect
+ series empty$
+ { "there's a number but no series in " cite$ * warning$ }
+ { bbl.in space.word * series * }
+ if$
+ }
+ if$
+ }
+ { "" }
+ if$
+}
+
+FUNCTION {is.num}
+{ chr.to.int$
+ duplicate$ "0" chr.to.int$ < not
+ swap$ "9" chr.to.int$ > not and
+}
+
+FUNCTION {extract.num}
+{ duplicate$ 't :=
+ "" 's :=
+ { t empty$ not }
+ { t #1 #1 substring$
+ t #2 global.max$ substring$ 't :=
+ duplicate$ is.num
+ { s swap$ * 's := }
+ { pop$ "" 't := }
+ if$
+ }
+ while$
+ s empty$
+ 'skip$
+ { pop$ s }
+ if$
+}
+
+FUNCTION {convert.edition}
+{ edition extract.num "l" change.case$ 's :=
+ s "first" = s "1" = or
+ { bbl.first 't := }
+ { s "second" = s "2" = or
+ { bbl.second 't := }
+ { s "third" = s "3" = or
+ { bbl.third 't := }
+ { s "fourth" = s "4" = or
+ { bbl.fourth 't := }
+ { s "fifth" = s "5" = or
+ { bbl.fifth 't := }
+ { s #1 #1 substring$ is.num
+ { s eng.ord 't := }
+ { edition 't := }
+ if$
+ }
+ if$
+ }
+ if$
+ }
+ if$
+ }
+ if$
+ }
+ if$
+ t
+}
+
+FUNCTION {format.edition}
+{ edition empty$
+ { "" }
+ { output.state mid.sentence =
+ { convert.edition "l" change.case$ " " * bbl.edition * }
+ { convert.edition "t" change.case$ " " * bbl.edition * }
+ if$
+ }
+ if$
+}
+
+INTEGERS { multiresult }
+
+FUNCTION {multi.page.check}
+{ 't :=
+ #0 'multiresult :=
+ { multiresult not
+ t empty$ not
+ and
+ }
+ { t #1 #1 substring$
+ duplicate$ "-" =
+ swap$ duplicate$ "," =
+ swap$ "+" =
+ or or
+ { #1 'multiresult := }
+ { t #2 global.max$ substring$ 't := }
+ if$
+ }
+ while$
+ multiresult
+}
+
+FUNCTION {format.pages}
+{ pages empty$
+ { "" }
+ { pages multi.page.check
+ { bbl.pages pages n.dashify tie.or.space.connect }
+ { bbl.page pages tie.or.space.connect }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.journal.pages}
+{ pages empty$
+ 'skip$
+ { duplicate$ empty$
+ { pop$ format.pages }
+ {
+ ", " *
+ pages n.dashify *
+ }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.vol.num.pages}
+{ volume field.or.null
+}
+
+FUNCTION {format.chapter.pages}
+{ chapter empty$
+ { "" }
+ { type empty$
+ { bbl.chapter }
+ { type "l" change.case$ }
+ if$
+ chapter tie.or.space.connect
+ }
+ if$
+}
+
+FUNCTION {format.in.ed.booktitle}
+{ booktitle empty$
+ { "" }
+ { editor empty$
+ { word.in booktitle emphasize * }
+ { word.in booktitle emphasize *
+ ", " *
+ editor num.names$ #1 >
+ { bbl.editors }
+ { bbl.editor }
+ if$
+ * " " *
+ format.in.editors *
+ }
+ if$
+ }
+ if$
+}
+
+FUNCTION {format.thesis.type}
+{ type empty$
+ 'skip$
+ { pop$
+ type "t" change.case$
+ }
+ if$
+}
+
+FUNCTION {format.tr.number}
+{ type empty$
+ { bbl.techrep }
+ 'type
+ if$
+ number empty$
+ { "t" change.case$ }
+ { number tie.or.space.connect }
+ if$
+}
+
+FUNCTION {format.article.crossref}
+{
+ word.in
+ " \cite{" * crossref * "}" *
+}
+
+FUNCTION {format.book.crossref}
+{ volume empty$
+ { "empty volume in " cite$ * "'s crossref of " * crossref * warning$
+ word.in
+ }
+ { bbl.volume volume tie.or.space.connect
+ bbl.of space.word *
+ }
+ if$
+ " \cite{" * crossref * "}" *
+}
+
+FUNCTION {format.incoll.inproc.crossref}
+{
+ word.in
+ " \cite{" * crossref * "}" *
+}
+
+FUNCTION {format.publisher}
+{ publisher empty$
+ { "empty publisher in " cite$ * warning$ }
+ 'skip$
+ if$
+ ""
+ address empty$ publisher empty$ and
+ 'skip$
+ {
+ address empty$
+ 'skip$
+ { address * }
+ if$
+ publisher empty$
+ 'skip$
+ { address empty$
+ 'skip$
+ { ": " * }
+ if$
+ publisher *
+ }
+ if$
+ }
+ if$
+ output
+}
+
+STRINGS {oldname}
+
+FUNCTION {name.or.dash}
+{ 's :=
+ oldname empty$
+ { s 'oldname := s }
+ { s oldname =
+ { "---" }
+ { s 'oldname := s }
+ if$
+ }
+ if$
+}
+
+FUNCTION {article}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title "title" output.check
+ end.quote.title
+ crossref missing$
+ { journal
+ emphasize
+ "journal" output.check
+ format.vol.num.pages output
+ }
+ { format.article.crossref output.nonnull
+ format.pages output
+ }
+ if$
+ format.journal.pages
+ format.note output
+ fin.entry
+}
+
+FUNCTION {book}
+{ output.bibitem
+ author empty$
+ { format.editors "author and editor" output.check
+ editor format.key output
+ name.or.dash
+ }
+ { format.authors output.nonnull
+ name.or.dash
+ crossref missing$
+ { "author and editor" editor either.or.check }
+ 'skip$
+ if$
+ }
+ if$
+ format.date "year" output.check
+ date.block
+ format.btitle "title" output.check
+ crossref missing$
+ { format.bvolume output
+ format.number.series output
+ format.publisher
+ }
+ {
+ format.book.crossref output.nonnull
+ }
+ if$
+ format.edition output
+ format.note output
+ fin.entry
+}
+
+FUNCTION {booklet}
+{ output.bibitem
+ format.authors output
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title "title" output.check
+ end.quote.title
+ howpublished output
+ address output
+ format.note output
+ fin.entry
+}
+
+FUNCTION {inbook}
+{ output.bibitem
+ author empty$
+ { format.editors "author and editor" output.check
+ editor format.key output
+ name.or.dash
+ }
+ { format.authors output.nonnull
+ name.or.dash
+ crossref missing$
+ { "author and editor" editor either.or.check }
+ 'skip$
+ if$
+ }
+ if$
+ format.date "year" output.check
+ date.block
+ format.btitle "title" output.check
+ crossref missing$
+ {
+ format.publisher
+ format.bvolume output
+ format.chapter.pages "chapter and pages" output.check
+ format.number.series output
+ }
+ {
+ format.chapter.pages "chapter and pages" output.check
+ format.book.crossref output.nonnull
+ }
+ if$
+ format.edition output
+ format.pages "pages" output.check
+ format.note output
+ fin.entry
+}
+
+FUNCTION {incollection}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title "title" output.check
+ end.quote.title
+ crossref missing$
+ { format.in.ed.booktitle "booktitle" output.check
+ format.publisher
+ format.bvolume output
+ format.number.series output
+ format.chapter.pages output
+ format.edition output
+ }
+ { format.incoll.inproc.crossref output.nonnull
+ format.chapter.pages output
+ }
+ if$
+ format.pages "pages" output.check
+ format.note output
+ fin.entry
+}
+
+FUNCTION {inproceedings}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title "title" output.check
+ end.quote.title
+ crossref missing$
+ { format.in.ed.booktitle "booktitle" output.check
+ publisher empty$
+ { organization output
+ address output
+ }
+ { organization output
+ format.publisher
+ }
+ if$
+ format.bvolume output
+ format.number.series output
+ format.pages output
+ }
+ { format.incoll.inproc.crossref output.nonnull
+ format.pages output
+ }
+ if$
+ format.note output
+ fin.entry
+}
+
+FUNCTION {conference} { inproceedings }
+
+FUNCTION {manual}
+{ output.bibitem
+ format.authors output
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.btitle "title" output.check
+ organization output
+ address output
+ format.edition output
+ format.note output
+ fin.entry
+}
+
+FUNCTION {mastersthesis}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title "title" output.check
+ end.quote.title
+ bbl.mthesis format.thesis.type output.nonnull
+ school "school" output.check
+ address output
+ format.note output
+ fin.entry
+}
+
+FUNCTION {misc}
+{ output.bibitem
+ format.authors output
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title output
+ end.quote.title
+ howpublished output
+ format.note output
+ fin.entry
+}
+
+FUNCTION {phdthesis}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title "title" output.check
+ end.quote.title
+ bbl.phdthesis format.thesis.type output.nonnull
+ school "school" output.check
+ address output
+ format.note output
+ fin.entry
+}
+
+FUNCTION {proceedings}
+{ output.bibitem
+ format.editors output
+ editor format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.btitle "title" output.check
+ format.bvolume output
+ format.number.series output
+ address output
+ organization output
+ publisher output
+ format.note output
+ fin.entry
+}
+
+FUNCTION {techreport}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title "title" output.check
+ end.quote.title
+ format.tr.number output.nonnull
+ institution "institution" output.check
+ address output
+ format.note output
+ fin.entry
+}
+
+FUNCTION {unpublished}
+{ output.bibitem
+ format.authors "author" output.check
+ author format.key output
+ name.or.dash
+ format.date "year" output.check
+ date.block
+ format.title "title" output.check
+ end.quote.title
+ format.note "note" output.check
+ fin.entry
+}
+
+FUNCTION {default.type} { misc }
+
+READ
+
+FUNCTION {sortify}
+{ purify$
+ "l" change.case$
+}
+
+INTEGERS { len }
+
+FUNCTION {chop.word}
+{ 's :=
+ 'len :=
+ s #1 len substring$ =
+ { s len #1 + global.max$ substring$ }
+ 's
+ if$
+}
+
+FUNCTION {format.lab.names}
+{ 's :=
+ s #1 "{vv~}{ll}" format.name$
+ s num.names$ duplicate$
+ #2 >
+ { pop$
+ " et~al." *
+ }
+ { #2 <
+ 'skip$
+ { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
+ {
+ " et~al." *
+ }
+ { bbl.and space.word * s #2 "{vv~}{ll}" format.name$
+ * }
+ if$
+ }
+ if$
+ }
+ if$
+}
+
+FUNCTION {author.key.label}
+{ author empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { author format.lab.names }
+ if$
+}
+
+FUNCTION {author.editor.key.label}
+{ author empty$
+ { editor empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { editor format.lab.names }
+ if$
+ }
+ { author format.lab.names }
+ if$
+}
+
+FUNCTION {editor.key.label}
+{ editor empty$
+ { key empty$
+ { cite$ #1 #3 substring$ }
+ 'key
+ if$
+ }
+ { editor format.lab.names }
+ if$
+}
+
+FUNCTION {calc.short.authors}
+{ type$ "book" =
+ type$ "inbook" =
+ or
+ 'author.editor.key.label
+ { type$ "proceedings" =
+ 'editor.key.label
+ 'author.key.label
+ if$
+ }
+ if$
+ 'short.list :=
+}
+
+FUNCTION {calc.label}
+{ calc.short.authors
+ short.list
+ "("
+ *
+ year duplicate$ empty$
+ { pop$ "????" }
+ 'skip$
+ if$
+ *
+ 'label :=
+}
+
+FUNCTION {sort.format.names}
+{ 's :=
+ #1 'nameptr :=
+ ""
+ s num.names$ 'numnames :=
+ numnames 'namesleft :=
+ { namesleft #0 > }
+ { s nameptr
+ "{vv{ } }{ll{ }}{ f{ }}{ jj{ }}"
+ format.name$ 't :=
+ nameptr #1 >
+ {
+ " " *
+ namesleft #1 = t "others" = and
+ { "zzzzz" * }
+ { t sortify * }
+ if$
+ }
+ { t sortify * }
+ if$
+ nameptr #1 + 'nameptr :=
+ namesleft #1 - 'namesleft :=
+ }
+ while$
+}
+
+FUNCTION {sort.format.title}
+{ 't :=
+ "A " #2
+ "An " #3
+ "The " #4 t chop.word
+ chop.word
+ chop.word
+ sortify
+ #1 global.max$ substring$
+}
+
+FUNCTION {author.sort}
+{ author empty$
+ { key empty$
+ { "to sort, need author or key in " cite$ * warning$
+ ""
+ }
+ { key sortify }
+ if$
+ }
+ { author sort.format.names }
+ if$
+}
+
+FUNCTION {author.editor.sort}
+{ author empty$
+ { editor empty$
+ { key empty$
+ { "to sort, need author, editor, or key in " cite$ * warning$
+ ""
+ }
+ { key sortify }
+ if$
+ }
+ { editor sort.format.names }
+ if$
+ }
+ { author sort.format.names }
+ if$
+}
+
+FUNCTION {editor.sort}
+{ editor empty$
+ { key empty$
+ { "to sort, need editor or key in " cite$ * warning$
+ ""
+ }
+ { key sortify }
+ if$
+ }
+ { editor sort.format.names }
+ if$
+}
+
+FUNCTION {presort}
+{ calc.label
+ label sortify
+ " "
+ *
+ type$ "book" =
+ type$ "inbook" =
+ or
+ 'author.editor.sort
+ { type$ "proceedings" =
+ 'editor.sort
+ 'author.sort
+ if$
+ }
+ if$
+ #1 entry.max$ substring$
+ 'sort.label :=
+ sort.label
+ *
+ " "
+ *
+ title field.or.null
+ sort.format.title
+ *
+ #1 entry.max$ substring$
+ 'sort.key$ :=
+}
+
+ITERATE {presort}
+
+SORT
+
+STRINGS { last.label next.extra }
+
+INTEGERS { last.extra.num number.label }
+
+FUNCTION {initialize.extra.label.stuff}
+{ #0 int.to.chr$ 'last.label :=
+ "" 'next.extra :=
+ #0 'last.extra.num :=
+ #0 'number.label :=
+}
+
+FUNCTION {forward.pass}
+{ last.label label =
+ { last.extra.num #1 + 'last.extra.num :=
+ last.extra.num int.to.chr$ 'extra.label :=
+ }
+ { "a" chr.to.int$ 'last.extra.num :=
+ "" 'extra.label :=
+ label 'last.label :=
+ }
+ if$
+ number.label #1 + 'number.label :=
+}
+
+FUNCTION {reverse.pass}
+{ next.extra "b" =
+ { "a" 'extra.label := }
+ 'skip$
+ if$
+ extra.label 'next.extra :=
+ extra.label
+ duplicate$ empty$
+ 'skip$
+ { "{\natexlab{" swap$ * "}}" * }
+ if$
+ 'extra.label :=
+ label extra.label * 'label :=
+}
+
+EXECUTE {initialize.extra.label.stuff}
+
+ITERATE {forward.pass}
+
+REVERSE {reverse.pass}
+
+FUNCTION {bib.sort.order}
+{ sort.label
+ " "
+ *
+ year field.or.null sortify
+ *
+ " "
+ *
+ title field.or.null
+ sort.format.title
+ *
+ #1 entry.max$ substring$
+ 'sort.key$ :=
+}
+
+ITERATE {bib.sort.order}
+
+SORT
+
+FUNCTION {begin.bib}
+{ preamble$ empty$
+ 'skip$
+ { preamble$ write$ newline$ }
+ if$
+ "\begin{thebibliography}{" number.label int.to.str$ * "}" *
+ write$ newline$
+ "\renewcommand{\enquote}[1]{``#1''}"
+ write$ newline$
+ "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi"
+ write$ newline$
+}
+
+EXECUTE {begin.bib}
+
+EXECUTE {init.state.consts}
+
+ITERATE {call.type$}
+
+FUNCTION {end.bib}
+{ newline$
+ "\end{thebibliography}" write$ newline$
+}
+
+EXECUTE {end.bib}
+%% End of customized bst file
+%%
+%% End of file `asa.bst'.
diff --git a/inst/doc/blogit.Rnw b/inst/doc/blogit.Rnw
index 100321e..7fad30b 100644
--- a/inst/doc/blogit.Rnw
+++ b/inst/doc/blogit.Rnw
@@ -326,7 +326,7 @@ below.
\subsection*{See also}
The bivariate logit function is part of the VGAM package by Thomas Yee \citep{YeeHas03}. In addition, advanced users may wish to refer to \texttt{help(vglm)}
in the VGAM library. Additional documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.Sample data are from \cite{Martin92}
+\url{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.Sample data are from \cite{Martin92}
\bibliographystyle{asa}
diff --git a/inst/doc/blogit.log b/inst/doc/blogit.log
deleted file mode 100644
index 0f0e91c..0000000
--- a/inst/doc/blogit.log
+++ /dev/null
@@ -1,690 +0,0 @@
-This is pdfeTeX, Version 3.141592-1.21a-2.2 (Web2C 7.5.4) (format=pdflatex 2009.9.1) 20 JAN 2010 20:46
-entering extended mode
-**blogit
-(./blogit.tex
-LaTeX2e <2003/12/01>
-Babel <v3.8d> and hyphenation patterns for american, french, german, ngerman, b
-ahasa, basque, bulgarian, catalan, croatian, czech, danish, dutch, esperanto, e
-stonian, finnish, greek, icelandic, irish, italian, latin, magyar, norsk, polis
-h, portuges, romanian, russian, serbian, slovak, slovene, spanish, swedish, tur
-kish, ukrainian, nohyphenation, loaded.
-\@input{zinput.aux}
-\openout2 = `zinput.aux'.
-
-(./zinput.tex (/usr/share/texmf/tex/latex/base/book.cls
-Document Class: book 2004/02/16 v1.4f Standard LaTeX document class
-(/usr/share/texmf/tex/latex/base/bk12.clo
-File: bk12.clo 2004/02/16 v1.4f Standard LaTeX file (size option)
-)
-\c at part=\count79
-\c at chapter=\count80
-\c at section=\count81
-\c at subsection=\count82
-\c at subsubsection=\count83
-\c at paragraph=\count84
-\c at subparagraph=\count85
-\c at figure=\count86
-\c at table=\count87
-\abovecaptionskip=\skip41
-\belowcaptionskip=\skip42
-\bibindent=\dimen102
-) (./Rd.sty
-Package: Rd
-
-(/usr/share/texmf/tex/latex/base/ifthen.sty
-Package: ifthen 2001/05/26 v1.1c Standard LaTeX ifthen package (DPC)
-)
-(/usr/share/texmf/tex/latex/tools/longtable.sty
-Package: longtable 2004/02/01 v4.11 Multi-page Table package (DPC)
-\LTleft=\skip43
-\LTright=\skip44
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-\LT at firsthead=\box27
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-\LT at lastfoot=\box29
-\LT at cols=\count89
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-\c at LT@tables=\count91
-\c at LT@chunks=\count92
-\LT at p@ftn=\toks14
-)
-(/usr/share/texmf/tex/latex/tools/bm.sty
-Package: bm 2004/02/26 v1.1c Bold Symbol Support (DPC/FMi)
-\symboldoperators=\mathgroup4
-\symboldletters=\mathgroup5
-\symboldsymbols=\mathgroup6
-LaTeX Font Info: Redeclaring math alphabet \mathbf on input line 137.
-LaTeX Info: Redefining \bm on input line 203.
-)
-(/usr/share/texmf/tex/latex/base/alltt.sty
-Package: alltt 1997/06/16 v2.0g defines alltt environment
-)
-(/usr/share/texmf/tex/latex/tools/verbatim.sty
-Package: verbatim 2003/08/22 v1.5q LaTeX2e package for verbatim enhancements
-\every at verbatim=\toks15
-\verbatim at line=\toks16
-\verbatim at in@stream=\read1
-)
-(/usr/share/latex2html/texinputs/url.sty
-Package: url 1999/03/02 ver 1.4 Verb mode for urls, email addresses, and file
- names
-) (./upquote.sty
-Package: upquote 2003/08/11 v1.1 Covington's upright-quote modification to verb
-atim and verb
-
-(/usr/share/texmf/tex/latex/base/textcomp.sty
-Package: textcomp 2004/02/22 v1.99f Standard LaTeX package
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-(textcomp) 5 = only ISO-Adobe without \textcurrency
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diff --git a/inst/doc/bprobit.Rnw b/inst/doc/bprobit.Rnw
index c36f642..5a51712 100644
--- a/inst/doc/bprobit.Rnw
+++ b/inst/doc/bprobit.Rnw
@@ -362,7 +362,7 @@ obtain a default summary of information through
\subsection*{See also}
The bivariate probit function is part of the VGAM package by Thomas Yee \citep{YeeHas03}. In addition, advanced users may wish to refer to \texttt{help(vglm)}
in the VGAM library. Additional documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.Sample data are from \cite{Martin92}
+\url{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.Sample data are from \cite{Martin92}
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deleted file mode 100644
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--- a/inst/doc/chopit.Rnw
+++ /dev/null
@@ -1,386 +0,0 @@
-\SweaveOpts{eval=false, results=hide, prefix.string=vigpics/chopit}
-\include{zinput}
-%\VignetteIndexEntry{Compound Hierarchical Ordered Probit for Survey Vignettes}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,chopit,anchors,regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt chopit}: Compound Hierarchical Ordered Probit for Survey
-Vignettes}
-\label{chopit}
-
-The Compound Hierarchical Ordered Probit ({\sc chopit}) model corrects
-for ``differential item functioning'' or ``interpersonal
-comparability'' in ordinal survey responses. Given a self-assessment
-question (such as, ``How healthy are you? Excellent, good, fair, or
-poor.''), different respondents may interpret the response categories
-in different ways, such that excellent health to one individual may be
-fair health to a hypochondriac. For each ordinal self-assessment to be
-corrected, the {\sc chopit} model requires one or more vignette
-question (such as a description of a hypothetical person's health,
-followed by the same response categories as the self-assessment), and
-a set of associated explanatory variables for the respondent. The key
-assumption of the approach is that the thresholds (which determine how
-respondents translate their views into the response categories) have
-the same effect for different questions asked of the same respondent,
-but may differ across respondents; the model uses
-a parametric specification to predict the thresholds associated with an
-individual. The self-assessment and vignette questions may be taken
-from different surveys, so long as both surveys include the same
-explanatory variable questions to predict the thresholds. For ordinal
-data (without vignettes), see \Sref{oprobit}, \Sref{ologit}, and
-\Sref{oprobit.bayes}.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> fml <- list(self = Y ~ X1 + X2,
- vign = cbind(Z1, Z2, Z3) ~ 1,
- tau = ~ X1 + X2)
-> z.out <- zelig(fml, data = list(self = data1, vign = data2),
- model = "chopit")
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out, x1 = NULL)
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-In this hierarchical model, the {\tt formula} and {\tt data} inputs to
-{\tt zelig()} are lists with the following structure:
-\begin{itemize}
-\item The {\tt formula} is a list with three {\tt formula} objects
-corresponding to:
-\begin{itemize}
-\item {\tt self}: Specifies the self-response question ({\tt Y}) as a function
-of a set of explanatory variables.
-\item {\tt vign}: Specifies the vignette questions on the left-hand
-side as a matrix in the form {\tt cbind(Z1, Z2, Z3)}.
-\item {\tt tau}: Specifies explanatory variables that constrain the cut points
-across both the vignette and self-response questions. These
-explanatory variables do not necessarily need to overlap with the set
-of explanatory variables specified in the {\tt self} formula, but must
-be observed in both the {\tt vign} and {\tt self} data frames,
-described below.
-\end{itemize}
-\item The {\tt data} argument is a list of two data frames with
-\begin{itemize}
-\item {\tt self}: A data frame containing the self-response
-question(s) specified in the {\tt self} formula and associated
-explanatory variables listed in the {\tt self} and {\tt tau} formulas.
-\item {\tt vign}: A data frame containing the vignette questions
-specified in the {\tt vign} formula and associated explanatory
-variables listed in the {\tt tau} formula.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes many
-additional options for compound hierarchical ordered probit
-regression, see {\tt help(chopit)} and \cite{WanKinLau07} for details.
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Basic Example}
-
-Setting up the formula as a list for the self-response, vignettes, and
-the cut points (drawn from both the self-response and vignette data sets).
-<<<Example.formula>>=
-formula <- list(self = y ~ sex + age + educ + factor(country),
- vign = cbind(v1, v2, v3, v4, v5) ~ 1,
- tau = ~ sex + age + educ + factor(country))
-@
-Attaching the sample data sets. The {\tt free1} data correspond to
-the self-response data, and the {\tt free2} data correspond to the
-vignette subset. Note that the variables specified in the {\tt tau}
-formula must be in both data sets.
-<<<Examples.library, echo=false>>=
- if(is.na(match("anchors",.packages()))){
- message("Loading package anchors...")
- library(anchors)
- }
-@
-<<Example.sample>>=
-data(free1, free2)
-@
-<<Example.data>>=
-data <- list(self = free1, vign = free2)
-@
-Estimating parameter values for the {\sc chopit} regression:
-<<Example.zelig>>=
- z.out <- zelig(formula, data = data, model = "chopit")
-@
-Setting values for the explanatory variables to their default values:
-<<Example.setx>>=
- x.out1 <- setx(z.out)
-@
-Simulating quantities of interest from the sampling distribution.
-<<Example.sim>>=
- s.out1 <- sim(z.out, x = x.out1)
-@
-<<Example.summary>>=
- summary(s.out1)
-@
-
-\item {Simulating First Differences}
-
-Estimate the first difference in expected values between the average
-age (about 40 years old) and a 25 year old individual, with the other
-explanatory variables held at their default values:
-<<FirstDifferences.setx>>=
- x.out2 <- setx(z.out, age = 25)
-@
-<<FirstDifferences.sim>>=
- s.out2 <- sim(z.out, x = x.out1, x1 = x.out2)
-@
-<<FirstDifferences.summary>>=
- summary(s.out2)
-@
-
-\item {Conditional prediction}
-
-Conditional prediction generates expected values that are conditional
-on the observed self-response.
-<<Conditional.setx>>=
-x.out3 <- setx(z.out, cond = TRUE)
-@
-Since conditional prediction involves numeric integration, the
-procedure takes approximately one second per observation in {\tt
-x.out3} on 64-bit R.
-<<Conditional.sim>>=
-s.out3 <- sim(z.out, x = x.out3)
-@
-<<Conditional.summary>>=
-summary(s.out3)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-This model has two sets of response variables, one for the
-self-assessment and one for the vignettes. Let $Y_i$ be the observed
-ordinal self-assessment for respondents $i = 1, \dots, n$, and
-$Z_{lj}$ be the ordinal vignette responses for individuals $l = 1,
-\dots, L$ in the vignette subset for $j = 1, \dots, J$ vignette
-questions, such that both $\{Y_i, Z_{lj} \}$ take integer values $k
-= 1, \dots, K$ corresponding to the same ordinal assessment response categories.
-
-\begin{itemize}
-\item The \emph{stochastic components} are described by unobserved continuous
- variables, $Y_i^*$ and $Z_{lj}^*$, which follows normal
- distributions with mean $\mu_i$ and variance $\sigma^2$ in
- the case of $Y_i^*$, and mean $\theta_j$ and variance
- $\sigma_j^2$ in the case of each $Z_{lj}^*$. Using the default
-identification mechanism, the variance $\sigma^2$ for the
-self-assessment is fixed to 1. Thus,
-\begin{eqnarray*}
- Y_i^* & \sim & N(\mu_i, 1)\\
- Z_{lj}^* & \sim& N(\theta_j, \sigma_j^2)
-\end{eqnarray*}
-such that each vignette response $j$ has a scalar mean $\theta_j$ and
-variance $\sigma_j^2$ that does not vary over observations
-$l$. In cases where more than one self-response was administered to the same subject, an additional random effect may be included in
-the distribution of the latent $Y_i^*$ in the form
-\begin{eqnarray*}
- Y_i^* & \sim & N(\mu_i, 1 + \omega^2)\\
-\end{eqnarray*}
-where the variance term is obtained via the proof described in
-Appendix A of \cite{KinMurSal04}.
-
-The observation mechanisms that divide the continuous $\{ Y_i^*,
-Z_{lj}^* \}$ into the discrete $\{ Y_i, Z_{lj} \}$ are
-\begin{eqnarray*}
- Y_i & = & k \quad {\rm if} \quad \tau_{i}^{k-1} \le Y_i^* \le \tau_{i}^{k}
- \; {\rm for} \; k = 1,\dots, K \\
- Z_{lj} & = & k \quad {\rm if} \quad \tau_{l}^{k-1} \le Z_{lj}^* \le \tau_{l}^{k}
- \; {\rm for} \; k = 1,\dots, K
-\end{eqnarray*}
-where the threshold parameters $\tau$ vary over individuals $\{i, l
-\}$, but are subject to the following constraints within each
-individual: $\tau^p < \tau^q$ for all $p < q$ and $\tau_0=-\infty$ and
-$\tau_K=\infty$.
-
-\item There are three \emph{systematic components} in the model.
-\begin{itemize}
-
-\item For the self-assessment component, let
-\begin{eqnarray*}
-\mu_i &=& x_i \beta
-\end{eqnarray*}
-where $x_i$ is the vector of $q$ explanatory variables for observation $i$,
-and $\beta$ is the associated vector of coefficients.
-
-\item In addition, the threshold parameters also vary over individuals
-in the self-assessment component as follows
-\begin{eqnarray*}
-\tau_{i}^{1} &=& v_i \gamma^1 \\
-\tau_{i}^{k} &=& \tau_{i}^{k-1} + \exp(v_i \gamma^k) \; {\rm for} \;
-k = 2, \dots, K
-\end{eqnarray*}
-where $v_i$ is the vector of $p$ explanatory variables for observation
-$i$, and $\gamma^k$ for $k = 1, \dots, K$ are the vectors of
-coefficients associated with each categorical response. Thus, the
-threshold parameters vary over individuals since $v_i$ vary, and over
-response categories since the $\gamma^k$ vary over the threshold
-parameters.
-
-\item Similarly, the threshold parameters vary over individuals in the
-vignette component as follows
-\begin{eqnarray*}
-\tau_{l}^{1} &=& v_l \gamma^1 \\
-\tau_{l}^{k} &=& \tau_{l}^{k-1} + \exp(v_l \gamma^k) \; {\rm for} \;
-k = 2, \dots, K
-\end{eqnarray*}
-where $v_l$ is a vector of $p$ explanatory variables for observation
-$l$ in the vignette subset, and $\gamma^k$ are restricted to be the
-same $\gamma^k$ used to parameterize the threshold parameters for the
-self-assessment component.
-\end{itemize}
-As \cite{KinMurSal04} note, the interpersonal comparability of
-responses (or response consistency) is achieved by constraining
-$\gamma^k$ to be the same in both the self-assessment and vignette
-components of the model. Note that the variables included in $v_i$ and
-$v_l$ are the same, but the observed values of those variables differ across
-the vignette and self-response samples.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\sc chopit} model
-is the expected value of the posterior density for the systematic
-component $\mu_i$,
-\begin{equation*}
-\textrm{EV} \; = \; E(\mu_i \mid x_i) \; = \; x_i \beta
-\end{equation*}
-given draws of $\beta$ from its sampling distribution.
-
-\item The first difference is the difference in the expected value of
-the posterior density for the systematic component $\mu_i$ given $x_1$
-and $x_0$:
-\begin{equation*}
-\textrm{FD} = E(\mu_i \mid x_1) - E(\mu_i \mid x_0).
-\end{equation*}
-
-\item In conditional prediction models, the conditional expected
-values ({\tt qi\$cev}) are the expected value of the distribution of
-$\mu_i$ conditional on the observed self-assessment response $Y_i$,
-%%This quantity is calculated in two steps. First, using draws of
-%%$\gamma^k$ for $k = 1, \dots, K$ from their posterior distributions,
-%%we calculate simulations of the threshold parameters
-%%\begin{eqnarray*}
-%%\tau_i^1 &=& v_i \gamma^1 \\
-%%\tau_i^k &=& \tau_i^{k-1} + \exp(v_i \gamma^k) \; {\rm for} \; k = 2, \dots, K.
-%%\end{eqnarray*}
-%%Next, we use adaptive integration to calculate the expected value of
-%%the posterior distribution conditional on $Y_i$:
-%%\begin{eqnarray*}
-%%E(\mu_i \mid x_i, Y_i) &=& \int \mu_i P(\mu_i \mid \tau_i, \beta, x_i,
-%%Y_i) d \mu_i
-%%\end{eqnarray*}
-where
-\begin{eqnarray*}
-P(\mu_i \mid \tau_i, \beta, x_i, Y_i) &=& \prod_{k=1}^K [ \Phi(\tau_i^k -
-\mu_i) - \Phi(\tau_i^{k-1} - \mu_i) ] \times N(x_i \beta, x_i
-\widehat{V}(\widehat{\beta}) x_i^{\prime} + \widehat{\omega}^2)
-\end{eqnarray*}
-given the simulations of the threshold parameters calculated above,
-draws of $\beta$ from its sampling distribution, and the
-estimated variance-covariance matrix for $\widehat{\beta}$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(\dots,
- model = "chopit")}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the estimated parameters by using {\tt z.out\$par}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt par}: the maximum likelihood parameter estimates for
-$\widehat{\gamma}^k$ for $k = 1, \dots, K$ response categories,
-log($\widehat{\omega}$) (if estimated), log($\widehat{\sigma}$) (if
-estimated), log($\widehat{\sigma_j}$) for $j = 1, \dots, J$ vignette
-questions, $\widehat{\theta_j}$, and $\widehat{\beta}$.
-\item {\tt chopit.hessian}: the estimated Hessian matrix, with rows
-and columns corresponding to the elements in {\tt par}.
-\item {\tt value}: the value of the log-likelihood at its maximum
-\item {\tt counts}: the number of function and gradient calls to
-reach the maximum.
-\item {\tt formula}: the formula for {\tt self}, {\tt vign}, and {\tt
-tau} selected by the user.
-\item {\tt call}: the call to {\tt zelig()}.
-\item {\tt \dots}: additional outputs described in {\tt
-help(chopit)}.
- \end{itemize}
-
-\item Typing {\tt summary(z.out)} provides a useful summary of the
-output from {\tt zelig()}, but no items can be extracted.
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the
- specified values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- values for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$cev}: the simulated conditional expected value given
-{\tt x}.
- \end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/chopit}
-\input{citeZelig}
-
-\subsection* {See also}
-The {\sc chopit} model is part of the anchors package by Jonathan
-Wand, Gary King, and Olivia Lau \citep{WanKinLau07}. Advanced users
-may wish to refer to \texttt{help(chopit)}, as well as
-\cite{KinMurSal04} and \cite{KinWan07}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
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diff --git a/inst/doc/chopit.pdf b/inst/doc/chopit.pdf
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diff --git a/inst/doc/cites/aov.tex b/inst/doc/cites/aov.tex
new file mode 100644
index 0000000..89bd3d1
--- /dev/null
+++ b/inst/doc/cites/aov.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ aov } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "aov: Fit an Analysis of Variance Model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/arima.tex b/inst/doc/cites/arima.tex
new file mode 100644
index 0000000..fd0aaf1
--- /dev/null
+++ b/inst/doc/cites/arima.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ arima } Zelig model:
+ \begin{verse}
+ Justin Grimmer. 2007. "arima: Arima models for Time Series Data" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/blogit.tex b/inst/doc/cites/blogit.tex
new file mode 100644
index 0000000..1f2be47
--- /dev/null
+++ b/inst/doc/cites/blogit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ blogit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "blogit: Bivariate Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/bprobit.tex b/inst/doc/cites/bprobit.tex
new file mode 100644
index 0000000..80acef3
--- /dev/null
+++ b/inst/doc/cites/bprobit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ bprobit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "bprobit: Bivariate Probit Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/chopit.tex b/inst/doc/cites/chopit.tex
new file mode 100644
index 0000000..32113a5
--- /dev/null
+++ b/inst/doc/cites/chopit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ chopit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "chopit: Compound Hierarchical Ordinal Probit Regression for Survey Vignettes" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/cloglog.net.tex b/inst/doc/cites/cloglog.net.tex
new file mode 100644
index 0000000..f28993b
--- /dev/null
+++ b/inst/doc/cites/cloglog.net.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ cloglog.net } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "cloglog.net: Social Network Complementary Log Log Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/coxph.tex b/inst/doc/cites/coxph.tex
new file mode 100644
index 0000000..7ef85d6
--- /dev/null
+++ b/inst/doc/cites/coxph.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ coxph } Zelig model:
+ \begin{verse}
+ Patrick Lam. 2007. "coxph: Cox Proportional Hazard Regression for Duration Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/ei.RxC.tex b/inst/doc/cites/ei.RxC.tex
new file mode 100644
index 0000000..81ae8f6
--- /dev/null
+++ b/inst/doc/cites/ei.RxC.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ ei.RxC } Zelig model:
+ \begin{verse}
+ Jason Wittenberg, Ferdinand Alimadhi, Badri Narayan Bhaskar, and Olivia Lau. 2007. "ei.RxC: Hierarchical Multinomial-Dirichlet Ecological Inference Model for R x C Tables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/ei.dynamic.tex b/inst/doc/cites/ei.dynamic.tex
new file mode 100644
index 0000000..8c714ba
--- /dev/null
+++ b/inst/doc/cites/ei.dynamic.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ ei.dynamic } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "ei.dynamic: Quinn's Dynamic Ecological Inference Model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/ei.hier.tex b/inst/doc/cites/ei.hier.tex
new file mode 100644
index 0000000..6636fca
--- /dev/null
+++ b/inst/doc/cites/ei.hier.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ ei.hier } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "ei.hier: Hierarchical Ecological Inference Model for 2 x 2 Tables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/exp.tex b/inst/doc/cites/exp.tex
new file mode 100644
index 0000000..5176c81
--- /dev/null
+++ b/inst/doc/cites/exp.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ exp } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "exp: Exponential Regression for Duration Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/factor.bayes.tex b/inst/doc/cites/factor.bayes.tex
new file mode 100644
index 0000000..5a9c53a
--- /dev/null
+++ b/inst/doc/cites/factor.bayes.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ factor.bayes } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "factor.bayes: Bayesian Factor Analysis" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/factor.mix.tex b/inst/doc/cites/factor.mix.tex
new file mode 100644
index 0000000..d94e52b
--- /dev/null
+++ b/inst/doc/cites/factor.mix.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ factor.mix } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "factor.mix: Mixed Data Factor Analysis" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/factor.ord.tex b/inst/doc/cites/factor.ord.tex
new file mode 100644
index 0000000..a532823
--- /dev/null
+++ b/inst/doc/cites/factor.ord.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ factor.ord } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "factor.ord: Ordinal Data Factor Analysis" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/gamma.gee.tex b/inst/doc/cites/gamma.gee.tex
new file mode 100644
index 0000000..9a55044
--- /dev/null
+++ b/inst/doc/cites/gamma.gee.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ gamma.gee } Zelig model:
+ \begin{verse}
+ Patrick Lam. 2007. "gamma.gee: General Estimating Equation for Gamma Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/gamma.mixed.tex b/inst/doc/cites/gamma.mixed.tex
new file mode 100644
index 0000000..5165130
--- /dev/null
+++ b/inst/doc/cites/gamma.mixed.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ gamma.mixed } Zelig model:
+ \begin{verse}
+ Delia Bailey and Ferdinand Alimadhi. 2007. "gamma.mixed: Mixed effects gamma model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/gamma.net.tex b/inst/doc/cites/gamma.net.tex
new file mode 100644
index 0000000..bff4d89
--- /dev/null
+++ b/inst/doc/cites/gamma.net.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ gamma.net } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "gamma.net: Social Network Gamma Regression for Continuous, Positive Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/gamma.survey.tex b/inst/doc/cites/gamma.survey.tex
new file mode 100644
index 0000000..bc36d16
--- /dev/null
+++ b/inst/doc/cites/gamma.survey.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ gamma.survey } Zelig model:
+ \begin{verse}
+ Nicholas Carnes. 2008. "gamma.survey: Survey-Weighted Gamma Regression for Continuous, Positive Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/gamma.tex b/inst/doc/cites/gamma.tex
new file mode 100644
index 0000000..df4aee2
--- /dev/null
+++ b/inst/doc/cites/gamma.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ gamma } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "gamma: Gamma Regression for Continuous, Positive Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/irt1d.tex b/inst/doc/cites/irt1d.tex
new file mode 100644
index 0000000..6c5fcf7
--- /dev/null
+++ b/inst/doc/cites/irt1d.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ irt1d } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "irt1d: One Dimensional Item Response Model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/irtkd.tex b/inst/doc/cites/irtkd.tex
new file mode 100644
index 0000000..b41c2dd
--- /dev/null
+++ b/inst/doc/cites/irtkd.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ irtkd } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "irtkd: K-Dimensional Item Response Model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/logit.bayes.tex b/inst/doc/cites/logit.bayes.tex
new file mode 100644
index 0000000..473fb1c
--- /dev/null
+++ b/inst/doc/cites/logit.bayes.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ logit.bayes } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "logit.bayes: Bayesian Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/logit.gam.tex b/inst/doc/cites/logit.gam.tex
new file mode 100644
index 0000000..499b0a1
--- /dev/null
+++ b/inst/doc/cites/logit.gam.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ logit.gam } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "logit.gam: Generalized Additive Model for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/logit.gee.tex b/inst/doc/cites/logit.gee.tex
new file mode 100644
index 0000000..8a8f782
--- /dev/null
+++ b/inst/doc/cites/logit.gee.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ logit.gee } Zelig model:
+ \begin{verse}
+ Patrick Lam. 2007. "logit.gee: General Estimating Equation for Logistic Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/logit.mixed.tex b/inst/doc/cites/logit.mixed.tex
new file mode 100644
index 0000000..67c5863
--- /dev/null
+++ b/inst/doc/cites/logit.mixed.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ logit.mixed } Zelig model:
+ \begin{verse}
+ Delia Bailey and Ferdinand Alimadhi. 2007. "logit.mixed: Mixed effects logistic model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/logit.net.tex b/inst/doc/cites/logit.net.tex
new file mode 100644
index 0000000..20fad34
--- /dev/null
+++ b/inst/doc/cites/logit.net.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ logit.net } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "logit.net: Social Network Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/logit.survey.tex b/inst/doc/cites/logit.survey.tex
new file mode 100644
index 0000000..da8a5b6
--- /dev/null
+++ b/inst/doc/cites/logit.survey.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ logit.survey } Zelig model:
+ \begin{verse}
+ Nicholas Carnes. 2007. "logit.survey: Survey-Weighted Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/logit.tex b/inst/doc/cites/logit.tex
new file mode 100644
index 0000000..26c3ca4
--- /dev/null
+++ b/inst/doc/cites/logit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ logit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2008. "logit: Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/lognorm.tex b/inst/doc/cites/lognorm.tex
new file mode 100644
index 0000000..351293b
--- /dev/null
+++ b/inst/doc/cites/lognorm.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ lognorm } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "lognorm: Log-Normal Regression for Duration Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/ls.mixed.tex b/inst/doc/cites/ls.mixed.tex
new file mode 100644
index 0000000..46ee739
--- /dev/null
+++ b/inst/doc/cites/ls.mixed.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ ls.mixed } Zelig model:
+ \begin{verse}
+ Delia Bailey and Ferdinand Alimadhi. 2007. "ls.mixed: Mixed effects linear model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/ls.net.tex b/inst/doc/cites/ls.net.tex
new file mode 100644
index 0000000..134e16f
--- /dev/null
+++ b/inst/doc/cites/ls.net.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ ls.net } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "ls.net: Social Network Least Squares Regression for Continuous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/ls.tex b/inst/doc/cites/ls.tex
new file mode 100644
index 0000000..78c5617
--- /dev/null
+++ b/inst/doc/cites/ls.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ ls } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "ls: Least Squares Regression for Continuous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/mlogit.bayes.tex b/inst/doc/cites/mlogit.bayes.tex
new file mode 100644
index 0000000..5cdba0b
--- /dev/null
+++ b/inst/doc/cites/mlogit.bayes.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ mlogit.bayes } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "mlogit.bayes: Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/mlogit.tex b/inst/doc/cites/mlogit.tex
new file mode 100644
index 0000000..4c7b26c
--- /dev/null
+++ b/inst/doc/cites/mlogit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ mlogit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "mlogit: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/mprobit.tex b/inst/doc/cites/mprobit.tex
new file mode 100644
index 0000000..db34c34
--- /dev/null
+++ b/inst/doc/cites/mprobit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ mprobit } Zelig model:
+ \begin{verse}
+ Kosuke Imai. 2009. "mprobit: Multinomial Probit Model via Markov Chain Monte Carlo Method" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/negbin.tex b/inst/doc/cites/negbin.tex
new file mode 100644
index 0000000..5715f18
--- /dev/null
+++ b/inst/doc/cites/negbin.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ negbin } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "negbin: Negative Binomial Regression for Event Count Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/normal.bayes.tex b/inst/doc/cites/normal.bayes.tex
new file mode 100644
index 0000000..9f832a4
--- /dev/null
+++ b/inst/doc/cites/normal.bayes.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ normal.bayes } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "normal.bayes: Bayesian Normal Linear Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/normal.gam.tex b/inst/doc/cites/normal.gam.tex
new file mode 100644
index 0000000..08cfcb5
--- /dev/null
+++ b/inst/doc/cites/normal.gam.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ normal.gam } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "normal.gam: Generalized Additive Model for Continuous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/normal.gee.tex b/inst/doc/cites/normal.gee.tex
new file mode 100644
index 0000000..055c3b4
--- /dev/null
+++ b/inst/doc/cites/normal.gee.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ normal.gee } Zelig model:
+ \begin{verse}
+ Patrick Lam. 2007. "normal.gee: General Estimating Equation for Normal Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/normal.net.tex b/inst/doc/cites/normal.net.tex
new file mode 100644
index 0000000..63f2255
--- /dev/null
+++ b/inst/doc/cites/normal.net.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ normal.net } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "normal.net: Social Network Normal Regression for Continuous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/normal.survey.tex b/inst/doc/cites/normal.survey.tex
new file mode 100644
index 0000000..aa801c9
--- /dev/null
+++ b/inst/doc/cites/normal.survey.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ normal.survey } Zelig model:
+ \begin{verse}
+ Nicholas Carnes. 2008. "normal.survey: Survey-Weighted Normal Regression for Continuous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/normal.tex b/inst/doc/cites/normal.tex
new file mode 100644
index 0000000..d9f5b5b
--- /dev/null
+++ b/inst/doc/cites/normal.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ normal } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "normal: Normal Regression for Continuous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/ologit.tex b/inst/doc/cites/ologit.tex
new file mode 100644
index 0000000..8ccc337
--- /dev/null
+++ b/inst/doc/cites/ologit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ ologit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "ologit: Ordinal Logistic Regression for Ordered Categorical Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/oprobit.bayes.tex b/inst/doc/cites/oprobit.bayes.tex
new file mode 100644
index 0000000..92f9694
--- /dev/null
+++ b/inst/doc/cites/oprobit.bayes.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ oprobit.bayes } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "oprobit.bayes: Bayesian Ordered Probit Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/oprobit.tex b/inst/doc/cites/oprobit.tex
new file mode 100644
index 0000000..904b29c
--- /dev/null
+++ b/inst/doc/cites/oprobit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ oprobit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "oprobit: Ordinal Probit Regression for Ordered Categorical Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/poisson.bayes.tex b/inst/doc/cites/poisson.bayes.tex
new file mode 100644
index 0000000..ff30ae1
--- /dev/null
+++ b/inst/doc/cites/poisson.bayes.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ poisson.bayes } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "poisson.bayes: Bayesian Poisson Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/poisson.gam.tex b/inst/doc/cites/poisson.gam.tex
new file mode 100644
index 0000000..6580d35
--- /dev/null
+++ b/inst/doc/cites/poisson.gam.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ poisson.gam } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "poisson.gam: Generalized Additive Model for Event Count Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/poisson.gee.tex b/inst/doc/cites/poisson.gee.tex
new file mode 100644
index 0000000..92dcba6
--- /dev/null
+++ b/inst/doc/cites/poisson.gee.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ poisson.gee } Zelig model:
+ \begin{verse}
+ Patrick Lam. 2007. "poisson.gee: General Estimating Equation for Poisson Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/poisson.mixed.tex b/inst/doc/cites/poisson.mixed.tex
new file mode 100644
index 0000000..8fc4c45
--- /dev/null
+++ b/inst/doc/cites/poisson.mixed.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ poisson.mixed } Zelig model:
+ \begin{verse}
+ Delia Bailey and Ferdinand Alimadhi. 2007. "poisson.mixed: Mixed effects poisson model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/poisson.net.tex b/inst/doc/cites/poisson.net.tex
new file mode 100644
index 0000000..cc49d4f
--- /dev/null
+++ b/inst/doc/cites/poisson.net.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ poisson.net } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "poisson.net: Social Network Poisson Regression for Event Count Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/poisson.survey.tex b/inst/doc/cites/poisson.survey.tex
new file mode 100644
index 0000000..4738727
--- /dev/null
+++ b/inst/doc/cites/poisson.survey.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ poisson.survey } Zelig model:
+ \begin{verse}
+ Nicholas Carnes. 2008. "poisson.survey: Survey-Weighted Poisson Regression for Event Count Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/poisson.tex b/inst/doc/cites/poisson.tex
new file mode 100644
index 0000000..a2e1963
--- /dev/null
+++ b/inst/doc/cites/poisson.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ poisson } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "poisson: Poisson Regression for Event Count Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/probit.bayes.tex b/inst/doc/cites/probit.bayes.tex
new file mode 100644
index 0000000..e489b5e
--- /dev/null
+++ b/inst/doc/cites/probit.bayes.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ probit.bayes } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "probit.bayes: Bayesian Probit Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/probit.gam.tex b/inst/doc/cites/probit.gam.tex
new file mode 100644
index 0000000..94c7da4
--- /dev/null
+++ b/inst/doc/cites/probit.gam.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ probit.gam } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "probit.gam: Generalized Additive Model for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/probit.gee.tex b/inst/doc/cites/probit.gee.tex
new file mode 100644
index 0000000..eb13c90
--- /dev/null
+++ b/inst/doc/cites/probit.gee.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ probit.gee } Zelig model:
+ \begin{verse}
+ Patrick Lam. 2007. "probit.gee: General Estimating Equation for Probit Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/probit.mixed.tex b/inst/doc/cites/probit.mixed.tex
new file mode 100644
index 0000000..acdc04e
--- /dev/null
+++ b/inst/doc/cites/probit.mixed.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ probit.mixed } Zelig model:
+ \begin{verse}
+ Delia Bailey and Ferdinand Alimadhi. 2007. "probit.mixed: Mixed effects probit model" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/probit.net.tex b/inst/doc/cites/probit.net.tex
new file mode 100644
index 0000000..6bf8d72
--- /dev/null
+++ b/inst/doc/cites/probit.net.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ probit.net } Zelig model:
+ \begin{verse}
+ Skyler J. Cranmer. 2007. "probit.net: Social Network Probit Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/probit.survey.tex b/inst/doc/cites/probit.survey.tex
new file mode 100644
index 0000000..d1def36
--- /dev/null
+++ b/inst/doc/cites/probit.survey.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ probit.survey } Zelig model:
+ \begin{verse}
+ Nicholas Carnes. 2008. "probit.survey: Survey-Weighted Probit Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/probit.tex b/inst/doc/cites/probit.tex
new file mode 100644
index 0000000..704caee
--- /dev/null
+++ b/inst/doc/cites/probit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ probit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "probit: Probit Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/quantile.tex b/inst/doc/cites/quantile.tex
new file mode 100644
index 0000000..2b7b4f0
--- /dev/null
+++ b/inst/doc/cites/quantile.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ quantile } Zelig model:
+ \begin{verse}
+ Alexander D'Amour. 2008. "quantile: Quantile Regression for Continuous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/relogit.tex b/inst/doc/cites/relogit.tex
new file mode 100644
index 0000000..68db00a
--- /dev/null
+++ b/inst/doc/cites/relogit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ relogit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/rq.tex b/inst/doc/cites/rq.tex
new file mode 100644
index 0000000..29ae716
--- /dev/null
+++ b/inst/doc/cites/rq.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ rq } Zelig model:
+ \begin{verse}
+ Alexander D'Amour. 2008. "rq: Quantile Regression for Continuous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/sur.tex b/inst/doc/cites/sur.tex
new file mode 100644
index 0000000..55a6694
--- /dev/null
+++ b/inst/doc/cites/sur.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ sur } Zelig model:
+ \begin{verse}
+ Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. "sur: Seemingly Unrelated Regression" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/threesls.tex b/inst/doc/cites/threesls.tex
new file mode 100644
index 0000000..4278b0d
--- /dev/null
+++ b/inst/doc/cites/threesls.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ threesls } Zelig model:
+ \begin{verse}
+ Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. "threesls: Three Stage Least Squares" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/tobit.bayes.tex b/inst/doc/cites/tobit.bayes.tex
new file mode 100644
index 0000000..acb5bd3
--- /dev/null
+++ b/inst/doc/cites/tobit.bayes.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ tobit.bayes } Zelig model:
+ \begin{verse}
+ Ben Goodrich and Ying Lu. 2007. "tobit.bayes: Bayesian Linear Regression for a Censored Dependent Variable" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/tobit.tex b/inst/doc/cites/tobit.tex
new file mode 100644
index 0000000..71448ea
--- /dev/null
+++ b/inst/doc/cites/tobit.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ tobit } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "tobit: Linear regression for Left-Censored Dependent Variable" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/twosls.tex b/inst/doc/cites/twosls.tex
new file mode 100644
index 0000000..923c40b
--- /dev/null
+++ b/inst/doc/cites/twosls.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ twosls } Zelig model:
+ \begin{verse}
+ Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. "twosls: Two Stage Least Squares" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cites/weibull.tex b/inst/doc/cites/weibull.tex
new file mode 100644
index 0000000..39ae932
--- /dev/null
+++ b/inst/doc/cites/weibull.tex
@@ -0,0 +1,4 @@
+To cite the \emph{ weibull } Zelig model:
+ \begin{verse}
+ Kosuke Imai, Gary King, and Oliva Lau. 2007. "weibull: Weibull Regression for Duration Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"\url{http://gking.harvard.edu/zelig}
+\end{verse}
\ No newline at end of file
diff --git a/inst/doc/cloglog.net.Rnw b/inst/doc/cloglog.net.Rnw
deleted file mode 100644
index 20d77b1..0000000
--- a/inst/doc/cloglog.net.Rnw
+++ /dev/null
@@ -1,178 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/cloglognet}
-\include{zinput}
-%\VignetteIndexEntry{Social Network Complementary Log Log Regression for Dichotomous Dependent Variables }
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,cloglog.net}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt cloglog.net}: Network Complementary Log Log Regression for Dichotomous Proximity Matrix Dependent Variables}
-
-Use network complementary log log regression analysis for a dependent variable that is a binary valued proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "cloglog.net", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-data(friendship)
-
-
-@
-Estimate model:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(friends ~ advice + prestige + perpower, model = "cloglog.net", data = friendship)
-summary(z.out)
-
-@
-Setting values for the explanatory variables to their default values:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
- x.out <- setx(z.out)
-
-@
-Simulating quantities of interest from the posterior distribution.
-<<echo=TRUE, results=hide, fig=FALSE>>=
-s.out <- sim(z.out, x = x.out)
-summary(s.out)
-plot(s.out)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE, echo=false,width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low personal power (25th percentile) and high personal power (75th percentile) while all the other variables are held at their default values.
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
-x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.25))
-
-s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-summary(s.out2)
-plot(s.out2)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12,echo=false, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-The {\tt cloglog.net} model performs a complementary log log regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard complementary log log regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which pr [...]
-
-Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{equation*}
-Y_{i} \sim \text{Bernoulli} ( \pi_{i})\\
-\end{equation*}
-where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_{i} = 1 - \exp[\exp (-x_i \beta)]
-\end{equation*}
-where $x_i$ the vector of $k$ explanatory variables for observation $i$ and $\beta$ is the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network complementary log log regression are the same as those for the standard complementary log log regression.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the {\tt cloglog.nett} model are simulations of the predicted probability of a success:
-\begin{equation*}
-E(Y) = \pi_{i} = 1 - \exp[\exp (-x_i \beta)],
-\end{equation*}
-given draws of $\beta$ from its sampling distribution.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference ({\tt qi\$fd}) for the network complementary log log model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
-\end{equation*}
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "cloglog.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike\'s Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-
-\subsection* {How to Cite}
-
-\input{cites/cloglog.net}
-\input{citeZelig}
-\subsection* {See also}
-The network complementary log log regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netbinom)}. Sample data are fictional.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
diff --git a/inst/doc/cloglog.net.log b/inst/doc/cloglog.net.log
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-mi-nus twice the max-i-mized log-likelihood
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diff --git a/inst/doc/cloglog.net.pdf b/inst/doc/cloglog.net.pdf
deleted file mode 100644
index 2ac3334..0000000
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diff --git a/inst/doc/coda_diag.tex b/inst/doc/coda_diag.tex
deleted file mode 100644
index c915940..0000000
--- a/inst/doc/coda_diag.tex
+++ /dev/null
@@ -1,32 +0,0 @@
-\subsubsection{Convergence}
-
-Users should verify that the Markov Chain converges to its stationary
-distribution. After running the \texttt{zelig()} function but before
-performing \texttt{setx()}, users may conduct the following
-convergence diagnostics tests:
-
-\begin{itemize}
-\item \texttt{geweke.diag(z.out\$coefficients)}: The Geweke diagnostic tests
-the null hypothesis that the Markov chain is in the stationary distribution
-and produces z-statistics for each estimated parameter.
-
-\item \texttt{heidel.diag(z.out\$coefficients)}: The Heidelberger-Welch
-diagnostic first tests the null hypothesis that the Markov Chain is in the
-stationary distribution and produces p-values for each estimated parameter.
- Calling \texttt{heidel.diag()} also produces
-output that indicates whether the mean of a marginal posterior distribution
-can be estimated with sufficient precision, assuming that the Markov Chain is
-in the stationary distribution.
-
-\item \texttt{raftery.diag(z.out\$coefficients)}: The Raftery diagnostic
-indicates how long the Markov Chain should run before considering draws from
-the marginal posterior distributions sufficiently representative of the
-stationary distribution.
-\end{itemize}
-
-\noindent If there is evidence of non-convergence, adjust the values
-for \texttt{burnin} and \texttt{mcmc} and rerun \texttt{zelig()}.
-
-Advanced users may wish to refer to \texttt{help(geweke.diag)},
-\texttt{help(heidel.diag)}, and \texttt{help(raftery.diag)} for more
-information about these diagnostics.
diff --git a/inst/doc/contributors.tex b/inst/doc/contributors.tex
deleted file mode 100644
index c2830e4..0000000
--- a/inst/doc/contributors.tex
+++ /dev/null
@@ -1,15 +0,0 @@
-function is part of the MCMCpack library by Andrew D.
-Martin and Kevin M. Quinn. If you use this model, please cite:
-\begin{verse}
-\bibentry{MarQui05}.
-\end{verse}
-The convergence diagnostics are part of the CODA library
-by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. These diagnostics
-should be cited as:
-\begin{verse}
-\bibentry{PluBesCowVin05}.
-\end{verse}
-\noindent Sample data are adapted from
-\begin{verse}
-\bibentry{MarQui05}.
-\end{verse}
diff --git a/inst/doc/contributors2.tex b/inst/doc/contributors2.tex
deleted file mode 100644
index 0958836..0000000
--- a/inst/doc/contributors2.tex
+++ /dev/null
@@ -1,11 +0,0 @@
-function is part of the MCMCpack library by Andrew D.
-Martin and Kevin M. Quinn. If you use this model, please cite:
-\begin{verse}
-\bibentry{MarQui05}
-\end{verse}
-The convergence diagnostics are part of the CODA library
-by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. These diagnostics
-should be cited as:
-\begin{verse}
-\bibentry{PluBesCowVin05}
-\end{verse}
diff --git a/inst/doc/contributors_systemfit.tex b/inst/doc/contributors_systemfit.tex
deleted file mode 100644
index 90a143a..0000000
--- a/inst/doc/contributors_systemfit.tex
+++ /dev/null
@@ -1,4 +0,0 @@
-The \texttt{systemfit} library by Jeff Hamann and Arne Henningsen. I fyou use this model, please cite:
-\begin{verse}
-\bibentry{HamaHenn05}
-\end{verse}
diff --git a/inst/doc/coxph.Rnw b/inst/doc/coxph.Rnw
deleted file mode 100644
index 7c601fd..0000000
--- a/inst/doc/coxph.Rnw
+++ /dev/null
@@ -1,415 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/coxph}
-\include{zinput}
-%\VignetteIndexEntry{Cox Proportional Hazards Regression for Duration Dependent Variables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,cox proportional hazards}
-%\VignettePackage{Zelig}
-\begin{document}
-
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-
-\section{\texttt{coxph}: Cox Proportional Hazards Regression for Duration Dependent Variables}
-\label{coxph}
-
-Choose the Cox proportional hazards regression model if the values in your dependent
-variable are duration observations. The advantage of the semi-parametric Cox proportional hazards model over fully parametric models such as the exponential or Weibull models is that it makes no assumptions about the shape of the baseline hazard. The model only requires the proportional hazards assumption that the baseline hazard does not vary across observations. The baseline hazard can be estimated from the model via post-hoc analysis.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X1 + X2, model = "coxph", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Cox proportional hazards models require that the dependent variable be in the form {\tt
- Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
-For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
-duration (lifetime, for example), and the associated $c_i$ is a binary
-variable such that $c_i = 1$ if the duration is not censored ({\it
- e.g.}, the subject dies during the study) or $c_i = 0$ if the
-duration is censored ({\it e.g.}, the subject is still alive at the
-end of the study). If $c_i$ is omitted, all Y are assumed to be
-completed; that is, $c_i$ defaults to 1 for all observations.
-
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following additional options for Cox proportional hazards regression:
-
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE, zelig()} computes robust standard errors based on sandwich estimators (see \citet{Huber81} and \citet{White80}) based on the options in {\tt cluster}.
-
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a variable to define groups of correlated observations. Let {\tt X3} be a variable that consists of either discrete numeric values, character strings, or factors that define the clusters. Then
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y,C) ~ X1 + X2, robust = TRUE, cluster = "X3",
- model = "coxph", data = mydata)
-\end{verbatim}
-
-means that the observations can be correlated within the clusters defined by the variable {\tt X3}, and that robust standard errors should be calculated according to those clusters. If {\tt robust = TRUE} but {\tt cluster} is not specified, {\tt zelig()} assumes that each observation falls into its own cluster.
-
-\item {\tt method}: defaults to {\tt "efron"}. Use this argument to specify how to handle ties within event times. The model assumes that no two event times should theoretically ever be the same, and any ties that occur are simply because the observation mechanism is not precise enough. In practice, ties often exist in the data so the model commonly uses one of three methods to deal with ties.
-
-\begin{itemize}
-
-\item \textbf{Breslow method} ({\tt method = "breslow"}): This method is the simplest computationally but also the least precise, especially as the number of tied events increases.
-
-%Because it is impossible to tell which of the tied events occurred first, the Breslow method simply treats the risk set as the same for all tied events in the risk set. Suppose observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. Theoretically, if the event occurred in 1 before in 3, then the risk set for observation 3 would have dropped observation 1. However, since we cannot tell which event occurred first, in the partial likelihood, the risk set for observat [...]
-%\begin{equation*}
-%L(\beta | y) = \prod_{j=1}^J \frac{\prod_{i \in D_j}\exp(x_i \beta)}{\left[ \sum_{k \in R_j} \exp(x_k \beta)\right] ^{d_j}}
-%\end{equation*}
-%where $j$ indexes the $J$ number of risk sets, $k$ denotes the observations in the risk sets, and $i$ denotes the observations for which the events occur .
-
-\item \textbf{Efron method} ({\tt method = "efron"}): This is the default method and is more intensive computationally but also more precise than the Breslow method.
-
-%The Efron method is more precise because it tries to account for how the risk set changes depending on the sequence of tied events. For an intuition behind the Efron approximation, suppose as in the previous example that observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. If the event occurred in 1 before 3, then the risk set for the second event would consist of observations $\{2, 3, 4\}$. On the other hand, if the event occurred in 3 before 1, then the risk [...]
-%\begin{equation*}
-%L(\beta | y) = \prod_{j=1}^J \left(\frac{\prod_{i \in D_j}\exp(x_i \beta)}{\prod_{r=1}^{d_j} \left[\sum_{k \in R_j} \exp(x_k \beta) - \frac{r-1}{d_j} \sum_{k \in D_j} \exp(x_k \beta)\right] } \right)
-%\end{equation*}
-%where $r$ indexes $D_j$, which is the set of $d_j$ tied events for the $j$th risk set, and all other subscripts are the same as above. In the example above, $D_j = \{1, 3\}$ with size $d_j = 2$ and observation 3 is the $r=2$nd observation in $D_j$.
-
-\item \textbf{Exact discrete method} ({\tt method = "exact"}): This is the preferred method if the number of distinct events is rather small due to a large number of ties. Although it can be very computationally intensive, the exact discrete method, which computes the exact partial likelihood, is the most precise method when there are many ties.
-
-%Unlike the Breslow and Efron methods, which assume a continuous time process, the exact discrete method assumes a discrete time process where the tied events actually do occur at exactly the same time. The method begins by assuming that the data are grouped into risk sets $R_j$. In each risk set, denote a binary dependent variable for each observation that takes on the value of 1 for each observation that experiences the event and 0 for each observation that does not experience the ev [...]
-%\begin{eqnarray*}
-%\mathrm{Pr}(D_j | d_j) = \frac{\prod_{i \in D_j} \exp(x_i \beta)}{\sum_{m=1}^M \left[\prod_{k \in A_{jm}}\exp(x_k \beta)\right] }
-%\end{eqnarray*}
-%where $A_{jm}$ is a set of observations that represents one combination of $d_j$ number of 1s in $R_j$. There are $M$ possible combinations for each risk set. For example, in the case where there are two events among a risk set of four observations $\{1,2,3,4\}$, there are 6 combinations of choosing 2 events out of 4 observations. Therefore, $M = 6$ and $A_{j1} = \{1,2\}, A_{j2} = \{1,3\}, A_{j3} = \{1,4\} ,A_{j4} = \{2,3\}, A_{j5} = \{2,4\}, A_{j6} = \{3,4\}$. The partial likelihoo [...]
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Stratified Cox Model}
-
-In addition, {\tt zelig()} also supports the stratified Cox model, where the baseline hazards are assumed to be different across different strata but the coefficients are restricted to be the same across strata. Let {\tt id} be a variable that consists of either discrete numeric values, character strings, or factors that define the strata. Then the stratified Cox model can be estimated using {\tt strata()} in the formula. The user can then find quantities of interest for a specific st [...]
-
-\begin{itemize}
-\item If strata were defined by a variable ({\tt strata(id)}), then strata should be defined as {\tt strata = "id=5"}.
-\item If strata were defined by a mathematical expression ({\tt strata(id>10)}), then strata should be defined as {\tt strata = "id>10=TRUE"} or {\tt strata = "id>10=FALSE"}.
-\end{itemize}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y,C) ~ X1 + X2 + strata(id), model = "coxph",
- data = mydata)
-> x.out <- setx(z.out, strata = "id=5")
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-
-\subsubsection{Time-Varying Covariates}
-
-{\tt zelig()} also supports the use of time-varying covariates for the Cox model, where some or all of the covariates change over time for each case. Let ``case'' refer to each unit in the data. Then each case can have one or more ``observations'', where each observation has a different value for one or more covariates for a specific case. \\
-
-\noindent Estimating a time-varying covariate model with {\tt zelig()} involves setting up the data differently to reflect a counting process. In the typical non-time-varying covariate model, the cases include a duration time ($Y$), a censoring mechanism ($C$), and covariates ($X$). A typical dataset would look like this:
-
-\begin{table}[!htp]
- \begin{center}
-\begin{tabular}{lllll}
-\hline
-Case & Y & C & X1 & X2 \\
-\hline
-1 & 35 & 0 & 4 & 7 \\
-2 & 56 & 1 & 6 & 11\\
-\hline
-\end{tabular}
- \end{center}
-\end{table}
-
-\noindent The user would then estimate the model and find quantities of interest using the following syntax:
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y,C) ~ X1 + X2, model = "coxph", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\noindent With time-varying covariates, each case is composed of multiple observations with start times, stop times, censoring (event) mechanisms, and covariates. The covariates are assumed to be constant within the intervals defined by the start and stop times. The covariates change only between intervals. Thus, the covariates are constant at each observation. The censoring mechanism equals 1 when an event occurs at the stop time and equals 0 if the observation is censored or if no [...]
-
-\begin{table}[!htp]
- \begin{center}
-\begin{tabular}{llllll}
-\hline
-Case & Start & Stop & C & X1 & X2 \\
-\hline
-1 & 0 & 26 & 0 & 4 & 7 \\
-1 & 26 & 35 & 0 & 4 & 10 \\
-2 & 0 & 39 & 0 & 6 & 11 \\
-2 & 39 & 56 & 1 & 9 & 5 \\
-\hline
-\end{tabular}
- \end{center}
-\end{table}
-
-\noindent The user would then estimate the model and find quantities of interest using the following syntax:
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Start,Stop,C) ~ X1 + X2, model = "coxph",
- data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Example 1: Basic Example}
-
-Attaching the sample dataset:
-<<Example.data>>=
-data(coalition)
-@
-Estimating parameter values for the coxph regression:
-<<Example.zelig>>=
-z.out1 <- zelig(Surv(duration, ciep12) ~ invest + numst2 + crisis, robust = TRUE, cluster = "polar", model = "coxph", data = coalition)
-@
-Setting values for the explanatory variables:
-<<Example.setx>>=
-x.low1<- setx(z.out1, numst2 = 0)
-x.high1 <- setx(z.out1, numst2 = 1)
-@
-Simulating quantities of interest:
-<<Example.sim>>=
-s.out1 <- sim(z.out1, x = x.low1, x1 = x.high1)
-@
-<<Example.summary.sim>>=
-summary(s.out1)
-@
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out1)
-@
-\end{center}
-
-\item {Example 2: Example with Stratified Cox Model}
-
-Estimating parameter values for the stratified coxph regression:
-<<Example2.zelig>>=
-z.out2 <- zelig(Surv(duration, ciep12) ~ invest + strata(polar) + numst2 + crisis, model = "coxph", data = coalition)
-@
-Setting values for the explanatory variables:
-<<Example2.setx>>=
-x.low2 <- setx(z.out2, numst2 = 0, strata = "polar=3")
-x.high2 <- setx(z.out2, numst2 = 1, strata = "polar=3")
-@
-Simulating quantities of interest:
-<<Example2.sim>>=
-s.out2 <- sim(z.out2, x = x.low2, x1 = x.high2)
-@
-<<Example2.summary.sim>>=
-summary(s.out2)
-@
-\begin{center}
-<<label=ExamplePlot2,fig=true,echo=true>>=
-plot(s.out2)
-@
-\end{center}
-
-\item {Example 3: Example with Time-Varying Covariates}
-
-Create sample toy dataset (from {\tt survival} package):
-<<Example3.data>>=
-toy <- as.data.frame(list(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8),
- stop=c(2, 3, 6, 7, 8, 9, 9, 9,14,17),
- event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0),
- x=c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0),
- x1=c(5, 5, 7, 4, 5, 6, 3, 2, 7, 4) ))
-@
-Estimating parameter values for the coxph regression:
-<<Example3.zelig>>=
-z.out3 <- zelig(Surv(start, stop, event) ~ x + x1, model = "coxph", data = toy)
-@
-Setting values for the explanatory variables:
-<<Example3.setx>>=
-x.low3 <- setx(z.out3, x = 0)
-x.high3 <- setx(z.out3, x = 1)
-@
-Simulating quantities of interest:
-<<Example3.sim>>=
-s.out3 <- sim(z.out3, x = x.low3, x1 = x.high3)
-@
-<<Example3.summary.sim>>=
-summary(s.out3)
-@
-\begin{center}
-<<label=ExamplePlot3,fig=true,echo=true>>=
-plot(s.out3)
-@
-\end{center}
-
-\end{enumerate}
-
-\subsubsection{The Model}
-Let $Y_i^*$ be the survival time for observation $i$. This variable
-might be censored for some observations at a fixed time $y_c$ such
-that the fully observed dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \textit{stochastic component} is described by the distribution of the partially observed variable $Y^*$:
-\begin{equation*}
-Y_i^* \sim f(y_i^* | \mu_i, \alpha)
-\end{equation*}
-where $f$ is an unspecified distribution with some mean $\mu_i$ and shape $\alpha$. In the Cox proportional hazards model, the distributional form of the duration times is unknown and left unparameterized. Instead it uses the proportional hazards assumption to model the set of (ordered) event times on particular covariates.
-
-An important component of all survival models is the hazard function $h(t)$, which measures the probability of an observation not surviving past time $t$ given survival up to $t$. The hazard function is given by
-\begin{equation*}
-h_i(t) = \lambda(t) \times \lambda_i
-\end{equation*}
-where $\lambda(t)$ is the baseline hazard (when all covariates equal 0), which varies over $t$ but not over $i$, and $\lambda_i$ is the parameterized part of the hazard function, which varies over $i$ but not over $t$ (the proportional hazards assumption).
-
-The model estimates the parameters without a distributional assumption on the duration times by focusing on the occurrence of events and ignoring the time between events. The data are reconceptualized from duration times to $K$ discrete event times such that each $y_i$ corresponds to exactly one event time $t_i$. The model assumes that no two $y_i$ have the same event times.
-
-For each event time, denote $R(t_i)$ as the set of all observations $j$ that are at risk at $t_i$. Given that an event occurred at $t_i$, we are interested in the conditional probability that the event occurred in observation $i$. The conditional probability is given by
-\begin{eqnarray*}
-\mathrm{Pr}(y_i = t_i \mid \mathrm{an \: event \: at \:}t_i) &=& \frac{h_i(t_i)}{\sum_{j \in R(t_i)} h_j(t_i)}\\
-&=& \frac{\lambda(t_i) \, \lambda_i}{\sum_{j \in R(t_i)} \lambda(t_i) \, \lambda_j}\\
-&=& \frac{\lambda_i}{\sum_{j \in R(t_i)} \lambda_j}
-\end{eqnarray*}
-where the numerator denotes the probability of observation $i$ experiencing the event at $t_i$ and the denominator denotes the probability that an event occurred at $t_i$.
-%
-%The model estimates the parameters without a distributional assumption on the duration times by focusing on the occurrence of events and ignoring the time between events. The data are first sorted by $n$ discrete event times (indexed by $j$) such that $t_1 < t_2 < ... < t_n$, where $n$ corresponds to both the number of events and the number of uncensored observations. The model assumes that there are no ties among the $n$ discrete event times. See Additional Inputs for methods to dea [...]
-%
-%Each $y_i^*$ corresponds to one event time $t_j$ at which the event occurs for $i$ (for example, if the 5th observation experienced the shortest duration of 2, then $y_5^* = t_1 = 2$). For each $j$ event time, denote $R_j$ as the set of all observations $k$ that are at risk at time $t_j$. Then for each $t_j$, we are interested in the conditional probability of the event occurring in the \textbf{corresponding} observation $i$ given that an event occurred at $t_j$. Note that for each e [...]
-%\begin{eqnarray*}
-%\mathrm{Pr}(y_i^* = t_j \mid \mathrm{1 \: event \: at \:}t_j) &=& \frac{h_i(t_j)}{\sum_{k \in R_j} h_k(t_j)}\\
-%&=& \frac{\lambda(t_j) \, \lambda_i}{\sum_{k \in R_j} \lambda(t_j) \, \lambda_k}\\
-%&=& \frac{\lambda_i}{\sum_{k \in R_j} \lambda_k}
-%\end{eqnarray*}
-%where the numerator denotes the probability of observation $i$ experiencing the event at $t_j$ and the denominator denotes the probability that an event occurred at $t_j$. Observations that are censored contribute to the denominator for event times prior to its censoring (as part of the risk set) but not the numerator since censored observations do not have corresponding event times. Each $j$ event time contributes one conditional probability to the partial likelihood function, which [...]
-
-\item The \textit{systematic component} $\lambda_i$ is modeled as
-\begin{equation*}
-\lambda_i = \exp(x_i \beta)
-\end{equation*}
-where $x_i$ is the vector of explanatory variables, and $\beta$ is the vector of coefficients.
-
-\item Each risk set (and thus each event time) contributes one conditional probability to the partial likelihood function, given by
-\begin{equation*}
-L(\beta | y) = \prod_{i=1}^K \left[ \frac{\exp(x_i \beta)}{\sum_{j \in R(t_i)} \exp(x_j \beta)} \right] ^{c_i}
-\end{equation*}
-where $c_i$ is the binary censoring variable. Note that event times corresponding to censored observations are not counted since their corresponding terms for the partial likelihood are exponentiated to 0. However, all censored observations are considered part of the risk sets $R(t_i)$ for all event times prior to their censoring, but otherwise do not contribute to the partial likelihood. For an example, see \citet[53]{BoxJon04}.
-
-\item In the case of the Cox model with time-varying covariates, the partial likelihood function is similarly given by
-\begin{equation*}
-L(\beta | y) = \prod_{i=1}^K \left[ \frac{\exp(x_i(t_i) \; \beta)}{\sum_{j \in R(t_i)} \exp(x_j(t_i) \; \beta)} \right] ^{c_i}
-\end{equation*}
-where $x_i(t_i)$ is the value of the covariates at time $t_i$. Denote ``cases'' as the units in our data. Each case is composed of one or more observations corresponding to different values in one or more covariates. At each event time $t_i$, the partial likelihood evaluates the hazard of the case in which the event occurred in with its covariate values at $t_i$ (the numerator) and the hazard of all the other cases at risk at $t_i$ (risk set $R(t_i)$) with their covariate values at $t [...]
-
-
-\item Although the model assumes that there are no tied event times, in practice, data often have tied event times due to imprecise measurement. There are three commonly used methods to deal with tied event times.
-\begin{itemize}
-\item \textbf{Breslow method}: The Breslow method simply treats the risk set as the same for all tied events in the risk set. Suppose observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. Theoretically, if the event occurred in 1 before in 3, then the risk set for observation 3 would have dropped observation 1. However, since we cannot tell which event occurred first, in the partial likelihood, the risk set for observation 1 and observation 3 are the same, consis [...]
-\begin{equation*}
-L(\beta | y) = \prod_{i=1}^K \left[ \frac{\prod_{i \in D_i}\exp(x_i \beta)}{\left[ \sum_{j \in R(t_i)} \exp(x_j \beta)\right] ^{d_i}} \right] ^ {c_i}
-\end{equation*}
-
-\item \textbf{Efron method}: The Efron method is more precise because it tries to account for how the risk set changes depending on the sequence of tied events. For an intuition behind the Efron approximation, suppose as in the previous example that observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. If the event occurred in 1 before 3, then the risk set for the second event would consist of observations $\{2, 3, 4\}$. On the other hand, if the event occurred i [...]
-\begin{equation*}
-L(\beta | y) = \prod_{i=1}^K \left[\frac{\prod_{i \in D_i}\exp(x_i \beta)}{\prod_{r=1}^{d_i} \left[\sum_{j \in R(t_i)} \exp(x_j \beta) - \frac{r-1}{d_i} \sum_{j \in D_i} \exp(x_j \beta)\right] } \right] ^ {c_i}
-\end{equation*}
-where $r$ indexes $D_i$, which is the set of $d_i$ tied events for the $i$th risk set.
-
-\item \textbf{Exact discrete method}: Unlike the Breslow and Efron methods, which assume a continuous time process, the exact discrete method assumes a discrete time process where the tied events actually do occur at exactly the same time. The method begins by assuming that the data are grouped into risk sets $R(t_i)$. In each risk set and for each observation, denote a binary dependent variable which takes on the value of 1 for each observation that experiences the event and 0 for eac [...]
-\begin{eqnarray*}
-\mathrm{Pr}(D_i | d_i) = \frac{\prod_{i \in D_i} \exp(x_i \beta)}{\sum_{m=1}^M \left[\prod_{j \in A_{im}}\exp(x_j \beta)\right] }
-\end{eqnarray*}
-where $A_{im}$ is a set of observations that represents one combination of $d_i$ number of 1s in $R(t_i)$. There are $M$ possible combinations for each risk set. The partial likelihood then takes the conditional probability over each $i$ risk set. Note that the exact discrete approximation method is equivalent to a conditional logit model.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-
-\item The hazard ratio ({\tt qi\$hr}) is defined as
-\begin{equation*}
-\textrm{HR} = \frac{h(t \mid x_1)}{h(t \mid x)} = \frac{\lambda(t) \exp(x_1 \beta)}{\lambda(t) \exp(x \beta)} = \frac{\exp(x_1 \beta)}{\exp(x \beta)}
-\end{equation*}
-given draws of $\beta$ from its sampling distribution, where $x$ and $x_1$ are values of the independent variables chosen by the user. Typically, $x$ and $x_1$ should only differ over one independent variable to interpret the effect of that variable on the hazard rate. In a stratified Cox model, the strata should be the same in both $x$ and $x_1$.
-
-\item The survival function ({\tt qi\$survival}) is defined as the fraction of observations surviving past time $t$. It is derived from the cumulative hazard function ({\tt exp(-cumhaz)}). The confidence interval of the survival function is drawn on the {\tt log(survival)} scale.
-
-\item The cumulative hazard function ({\tt qi\$cumhaz}) is defined as {\tt -log(survival)}. Although there is no direct interpretation, the cumulative hazard function is estimated from the data and then other quantities of interest are derived from the cumulative hazard function.
-
-\item The hazard function ({\tt qi\$hazard}) is defined as the probability of an observation not surviving past time $t$ given survival up to $t$. It is derived directly from the cumulative hazard function.
-
-\item For MI data, if survival times are multiply imputed, we suggest having a larger number of imputed datasets. Because the quantities of interest are derived semi-parametrically, there may be instances in which survival times appear only in one or a small fraction of the multiply imputed datasets, which may bias the results.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(Surv(y,c) \~\, x,
- model = "coxph", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt var}: the variance-covariance matrix.
- \item {\tt residuals}: the working residuals of the fit.
- \item {\tt loglik}: the log-likelihood for the baseline and full models
- \item {\tt linear.predictors}: a mean-adjusted linear predictor $x_i \beta$, where $x_i = x_i - \mathrm{mean}(x)$.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coef}: the parameter estimates with their
- associated standard errors, $p$-values, and $z$-statistics.
- \item {\tt conf.int}: $\exp(\beta)$ and their associated confidence intervals.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$hr}: the simulated hazard ratios for the
- specified values of {\tt x} and {\tt x1}.
- \item {\tt qi\$survival}: the estimated survival function for the values specified in {\tt x}.
- \item {\tt qi\$cumhaz}: the estimated cumulative hazard function for the values specified in {\tt x}.
- \item {\tt qi\$hazard}: the estimated hazard function for the values specified in {\tt x}.
- \end{itemize}
-\end{itemize}
-
-\subsection*{How To Cite}
-
-%\input{cites/coxph}
-\input{citeZelig}
-
-\subsection*{See also}
-
-The Cox proportional hazards model is part of the survival library by Terry Therneau \citep{TheGra00}, ported to R by Thomas Lumley. Advanced users may wish to refer to {\tt help(coxph)} and {\tt help(survfit)} in the survival library. Sample data are from \citet{KinAltBurLav90}
-
-\bibliographystyle{asa}
-\bibliography{gk}
-
-<<afterpkgs, echo=FALSE>>=
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- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
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-
-\end{document}
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-\section{{\tt describe}: Describe a model's systematic and stochastic
-parameters}
-\label{describe.mymodel}
-
-\subsubsection{Description}
-
-In order to use {\tt parse.formula()}, {\tt parse.par()}, and the {\tt
-model.*.multiple()} commands, you must write a {\tt describe.mymodel()}
-function where {\tt mymodel} is the name of your modeling function.
-(Hence, if your function is called {\tt normal.regression()}, you need
-to write a {\tt describe.normal.regression()} function.) Note that
-{\tt describe()} is \emph{not} a generic function, but is called by
-{\tt parse.formula(\dots, model = "mymodel")} using a combination of
-{\tt paste()} and {\tt exists()}. You will never need to call {\tt
-describe.mymodel()} directly, since it will be called from {\tt
-parse.formula()} as that function checks the user-input formula or
-list of formulas.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-describe.mymodel()
-\end{verbatim}
-
-\subsubsection{Arguments}\label{categories}
-The {\tt describe.mymodel()} function takes no arguments.
-
-\subsubsection{Output Values}
-The {\tt describe.mymodel()} function returns a list with the
-following information:
-\begin{itemize}
-\item {\tt category}: a character string, consisting of one of the
-following:
-\begin{itemize}
-\item {\tt "continuous"}: the dependent variable is continuous, numeric, and
-unbounded (e.g., normal regression), but may be censored with an associated censoring
-indicator (e.g., tobit regression).
-\item {\tt "dichotomous"}: the dependent variable takes two discrete integer
-values, usually 0 and 1 (e.g., logistic regression).
-\item {\tt "ordinal"}: the dependent variable is an ordered factor
-response, taking 3 or more discrete values which are arranged in an
-ascending or descending manner (e.g., ordered logistic regression).
-\item {\tt "multinomial"}: the dependent variable is an unordered
-factor response, taking 3 or more discrete values which are arranged
-in no particular order (e.g., multinomial logistic regression).
-\item {\tt "count"}: the dependent variable takes integer values
-greater than or equal to 0 (e.g., Poisson regression).
-\item {\tt "bounded"}: the dependent variable is a continuous numeric variable, that
-is restricted (bounded within) some range (e.g., $(0, \infty)$). The variable may
-also be censored either on the left and/or right side, with an associated censoring
-indicator (e.g., Weibull regression).
-\item {\tt "mixed"}: the dependent variables are a mix of the above
-categories (usually applies to multiple equation models).
-\end{itemize}
-Selecting the category is particularly important since it sets certain
-interface parameters for the entire GUI.
-
-\item {\tt package}: (optional) a list with the following elements
-
- \begin{itemize}
-
- \item {\tt name}: a characters string with the name of the package
- containing the {\tt mymodel()} function.
-
- \item {\tt version}: the minimum version number that works with
- Zelig.
-
- \item {\tt CRAN}: if the package is not hosted on CRAN mirrors,
- provide the URL here as a character string. You should be able
- to install your package from this URL using {\tt name}, {\tt
-version}, and {\tt CRAN}:
-\begin{verbatim}
-install.packages(name, repos = CRAN, installWithVers = TRUE)
-\end{verbatim}
-By default, {\tt CRAN = "http://cran.us.r-project.org/"}.
-\end{itemize}
-
-\item {\tt parameters}: For each systematic and stochastic parameter
-(or set of parameters) in your model, you should create a list (named
-after the parameters as given in your model's notation, e.g., {\tt
-mu}, {\tt sigma}, {\tt theta}, etc.; not literally {\tt myparameter})
-with the following information:
-\begin{itemize}
-
-\item {\tt equations}: an integer number of equations for the
-parameter. For parameters that can take an undefined number of
-equations (for example in seemingly unrelated regression), use {\tt
-c(2, Inf)} or {\tt c(2, 999)} to indicate that the parameter can take
-a minimum of two equations up to a theoretically infinite number of
-equations.
-
-\item {\tt tagsAllowed}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a given parameter allows constraints. If there is
-only one equation for a parameter (for example, {\tt mu} for the
-normal regression model has {\tt equations = 1}), then {\tt
-tagsAllowed = FALSE} by default. If there are two or more equations
-for the parameter (for example, {\tt mu} for the bivariate probit
-model has {\tt equations = 2}), then {\tt tagsAllowed = TRUE} by
-default.
-
-\item {\tt depVar}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a parameter requires a corresponding dependent
-variable.
-
-\item {\tt expVar}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a parameter allows explanatory variables. If {\tt
-depVar = TRUE} and {\tt expVar = TRUE}, we call the parameter a
-``systematic component'' and {\tt parse.formula()} will fail if
-formula(s) are not specified for this parameter. If {\tt
-depVar = FALSE} and {\tt expVar = TRUE}, the parameter is estimated as
-a scalar ancillary parameter, with default formula \verb|~ 1|, if the
-user does not specify a formula explicitly. If {\tt depVar = FALSE}
-and {\tt expVar = FALSE}, the parameter can only be estimated as a
-scalar ancillary parameter.
-
-\item {\tt specialFunction}: (optional) a character string giving the
-name of a function that appears on the left-hand side of the formula.
-Options include {\tt "Surv"}, {\tt "cbind"}, and {\tt "as.factor"}.
-
-\item {\tt varInSpecial}: (optional) a scalar or vector giving the
-number of variables taken by the {\tt specialFunction}. For example,
-{\tt Surv()} takes a minimum of 2 arguments, and a maximum of 4
-arguments, which is represented as {\tt c(2, 4)}.
-
-\end{itemize}
-If you have more than one parameter (or set of parameters) in
-your model, you will need to produce a {\tt myparameter} list for each
-one. See examples below for details.
-\end{itemize}
-
-\subsubsection{Examples}
-For a Normal regression model with mean {\tt mu} and scalar variance
-parameter {\tt sigma2}, the minimal {\tt describe.*()} function is as
-follows:
-\begin{verbatim}
-describe.normal.regression <- function() {
- category <- "continuous"
- mu <- list(equations = 1, # Systematic component
- tagsAllowed = FALSE,
- depVar = TRUE,
- expVar = TRUE)
- sigma2 <- list(equations = 1, # Scalar ancillary parameter
- tagsAllowed = FALSE,
- depVar = FALSE,
- expVar = FALSE)
- pars <- list(mu = mu, sigma2 = sigma2)
- model <- list(category = category, parameters = pars)
-}
-\end{verbatim}
-See \Sref{normal.regression} for full code to execute this model from
-scratch in R with Zelig.
-
-Now consider a bivariate probit model with parameter vector {\tt mu} and
-correlation parameter {\tt rho} (which may or may not take explanatory
-variables). Since the bivariate probit function uses the {\tt pmvnorm()}
-function from the mvtnorm library, we list this under {\tt package}.
-\begin{verbatim}
-describe.bivariate.probit <- function() {
- category <- "dichotomous"
- package <- list(name = "mvtnorm",
- version = "0.7")
- mu <- list(equations = 2, # Systematic component
- tagsAllowed = TRUE,
- depVar = TRUE,
- expVar = TRUE)
- rho <- list(equations = 1, # Optional systematic component
- tagsAllowed = FALSE, # Estimated as an ancillary
- depVar = FALSE, # parameter by default
- expVar = TRUE)
- pars <- list(mu = mu, rho = rho)
- list(category = category, package = package, parameters = pars)
-}
-\end{verbatim}
-See \Sref{bivariate.probit} for the full code to write this model from
-scratch in R with Zelig.
-
-For a multinomial logit model, which takes an undefined number of
-equations (corresponding to each level in the response variable):
-\begin{verbatim}
-describe.multinomial.logit <- function() {
- category <- "multinomial"
- mu <- list(equations = c(1, Inf),
- tagsAllowed = TRUE,
- depVAR = TRUE,
- expVar = TRUE,
- specialFunction <- "as.factor",
- varInSpecial <- c(1, 1))
- list(category = category, parameters = list(mu = mu))
-}
-\end{verbatim}
-(This example does not have corresponding executable sample code.)
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{s:new} for an overview of how the {\tt describe.*()}
-function works with {\tt parse.formula()}.
-\item \Sref{parse.formula} for information on {\tt parse.formula()}.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/ei.RxC.Rnw b/inst/doc/ei.RxC.Rnw
deleted file mode 100644
index 0fcef11..0000000
--- a/inst/doc/ei.RxC.Rnw
+++ /dev/null
@@ -1,228 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/eiRxC}
-\include{zinput}
-%\VignetteIndexEntry{Hierarchical Multinomial-Dirichlet Ecological Inference Model}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,ecological, many contingency tables, Gibbs sampling}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological
- Inference Model for $R \times C$ Tables}\label{ei.RxC}
-
-Given $n$ contingency tables, each with observed marginals (column and
-row totals), ecological inference ({\sc ei}) estimates the internal
-cell values in each table. The hierarchical Multinomial-Dirichlet
-model estimates cell counts in $R \times C$ tables. The model is
-implemented using a nonlinear least squares approximation and, with
-bootstrapping for standard errors, had good frequentist properties.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(T0, T1, T2, T3) ~ X0 + X1,
- covar = NULL,
- model = "ei.RxC", data = mydata)
-> x.out <- setx(z.out, fn = NULL)
-> s.out <- sim(z.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\begin{itemize}
-\item \texttt{T0}, \texttt{T1}, \texttt{T2},\ldots, \texttt{TC}:
- numeric vectors (either counts, or proportions that sum to one for
- each row) containing the column margins of the units to be analyzed.
-
-\item \texttt{X0}, {\tt X1}, {\tt X2},\ldots,{\tt XR}: numeric vectors
- (either counts, or proportions that sum to one for each row)
- containing the row margins of the units to be analyzed.
-
-\item {\tt covar}: (optional) a covariate that varies across tables,
-specified as \verb|covar = ~ Z1|, for example. (The model only
-accepts one covariate.)
-\end{itemize}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples: No covariate \\
- Attaching the example dataset:
-<<ExampleNoCov.data>>=
- data(Weimar)
-@
-Estimating the model:
-<<ExampleNoCov.zelig>>=
- z.out <- zelig(cbind(Nazi, Government, Communists, FarRight, Other) ~
- shareunemployed + shareblue + sharewhite + shareself +
- sharedomestic, model = "ei.RxC", data = Weimar)
- summary(z.out)
-@
-
-Estimate fractions of different social groups that support political parties:
-<<ExampleNoCov.sim>>=
- s.out <- sim(z.out, num = 10)
-@
-
-Summarizing fractions of different social groups that support political parties:
-<<ExampleNoCov.summary>>=
- summary(s.out)
-@
-
- \item Example of covariates being present in the model \\
-
- Using the example dataset Weimar and estimating the model
-<<ExampleCov.zelig>>=
- z.out <- zelig(cbind(Nazi, Government, Communists, FarRight, Other) ~
- shareunemployed + shareblue + sharewhite + shareself +
- sharedomestic,
- covar = ~ shareprotestants,
- model = "ei.RxC", data = Weimar)
- summary(z.out)
-@
-
-Set the covariate to its default (mean/median) value
-
-<<ExampleCov.setx>>=
- x.out <- setx(z.out)
-@
-
-Estimate fractions of different social groups that support political parties:
-
-\begin{verbatim}
-> s.out <- sim(z.out, num = 100)
-\end{verbatim}
-
-Summarizing fractions of different social groups that support political parties:
-\begin{verbatim}
-> s.out <- summary(s.out)
-\end{verbatim}
-
-\end{enumerate}
-
-\clearpage
-
-\subsubsection{Model}
-Consider the following $5 \times 5$ contingency table for the voting
-patterns in Weimar Germany. For each geographical unit $i$ ($i = 1,
-\dots, p$), the marginals $T_{1i}$,\dots, $T_{Ci}$, $X_{1i}$,\dots,
-$X_{Ri}$ are known for each of the $p$ electoral precincts, and we
-would like to estimate ($\beta_i^{rc}, r=1,\dots,R, c=1,\dots,C-1$)
-which are the fractions of people in social class $r$ who vote for
-party $c$, for all $r$ and $c$.
-\begin{table}[!h]
- \begin{center}
- \begin{tabular}{l|ccccc|c}
- & Nazi & Government & Communists & Far Right & Other \\
- \hline
- Unemployed & $\beta_{11}^{i}$ & $\beta_{12}^{i}$ & $\beta_{13}^{i}$ & $\beta_{14}^{i}$ & $1-\sum_{c=1}^4 \beta_{1c}^i$ & $X_1^i$ \\
- Blue & $\beta_{21}^{i}$ & $\beta_{22}^{i}$ & $\beta_{23}^{i}$ & $\beta_{24}^{i}$ & $1-\sum_{c=1}^4 \beta_{2c}^i$ & $X_2^i$ \\
- White & $\beta_{31}^{i}$ & $\beta_{32}^{i}$ & $\beta_{33}^{i}$ & $\beta_{34}^{i}$ & $1-\sum_{c=1}^4 \beta_{3c}^i$ & $X_3^i$ \\
- Self & $\beta_{41}^{i}$ & $\beta_{42}^{i}$ & $\beta_{43}^{i}$ & $\beta_{44}^{i}$ & $1-\sum_{c=1}^4 \beta_{4c}^i$ & $X_4^i$ \\
- Domestic & $\beta_{51}^{i}$ & $\beta_{52}^{i}$ & $\beta_{53}^{i}$ & $\beta_{54}^{i}$ & $1-\sum_{c=1}^4 \beta_{5c}^i$ & $X_5^i$ \\
- \hline
- & $T_{1i}$ & $T_{2i}$ & $T_{3i}$ & $T_{4i}$ & $1-\sum_{c=1}^4 \beta_{ci}$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent
-The marginal values $X_{1i},\dots,X_{Ri}$, $T_{1i},\dots,T_{Ci}$ may be
-observed as counts or fractions.
-
-Let $T_i^{'}=(T_{1i}^{'},T_{2i}^{'},\dots,T_{Ci}^{'})$ be the number
-of voting age persons who turn out to vote for different parties.
-There are three levels of hierarchy in the
-Multinomial-Dirichlet {\sc ei} model. At the first stage, we model
-the data as:
-\begin{itemize}
-
-\item The \emph{stochastic component} is described $T_i^{'}$ which
-follows a multinomial distribution:
-\begin{eqnarray*}
-T_i^{'} &\sim & \textrm{Multinomial}(\Theta_{1i},\dots,\Theta_{Ci})
-\end{eqnarray*}
-
-\item The \emph{systematic components} are
-\begin{eqnarray*}
-\Theta_{ci}=\sum_{r=1}^{R}\beta_{rc}^{i}X_{ri} & \textrm{for} & c=1,\dots,C
-\end{eqnarray*}
-
-\end{itemize}
-
-At the second stage, we use an optional covariate to model
-$\Theta_{ci}$'s and $\beta_{qrc}^{i}$:
-\begin{itemize}
-\item The \emph{stochastic component} is described by
-$\beta_{r}^{i}=(\beta_{r1},\beta_{r2},\dots,\beta_{r,C-1})$ for
- $i=1,\dots,,p$ and $r=1,\dots,R$, which follows a Dirichlet distribution:
-
- \begin{eqnarray*}
- \beta_r^{i} &\sim & \textrm{Dirichlet}(\alpha_{r1}^{i},\dots,\alpha_{rc}^{i})
- \end{eqnarray*}
-
-\item The \emph{systematic components} are
-\begin{eqnarray*}
- \alpha_{rc}^{i}=\frac{d_{r}exp(\gamma_{rc}+\delta_{rc}Z_{i})}{d_r(1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i))} = \frac{exp(\gamma_{rc}+\delta_{rc}Z_i)}{1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i)}
-\end{eqnarray*}
-for $i=1, \dots,p$, $r=1,\dots,R$, and $c=1, \dots, C-1$.
-
-In the third stage, we assume that the regression parameters (the
-$\gamma_{rc}$'s and $\delta_{rc}$'s) are \emph{a priori} independent,
-and put a flat prior on these regression parameters. The parameters
-$d_{r}$ for $r=1,\dots,R$ are assumed to follow exponential distributions with
-mean $\frac{1}{\lambda}$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-> z.out <- zelig(cbind(T0, T1, T2) ~ X0 + X1 + X2,
- model = "ei.RxC", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)}. For example,
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object
- \texttt{z.out\$coefficients} are the estimates of $\gamma_{ij}$ (and
- also $\delta_{ij}$, if covariates are present). The parameters are
- returned as a single vector of length $R\times (C-1)$. If there is a
- covariate, $\delta$ is concatenated to it.
-
-\item From the \texttt{sim()} output object, you may extract the
- parameters $\beta_{ij}$ corresponding to the estimated fractions of
- different social groups that support different political parties, by
- using \texttt{s.out\$qi\$ev}. For each precinct, that will be a
- matrix with dimensions: simulations $\times R \times C$. \\
- \texttt{summary(s.out)} will give you the nationwide aggregate parameters.
-\end{itemize}
-
-\subsection*{How to Cite}
-\input{cites/ei.RxC}
-\input{citeZelig}
-\subsection*{See also}
-For more information please see \cite{RosJiaKin01}
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/ei.RxC.log b/inst/doc/ei.RxC.log
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@@ -1,306 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/eiDynamic}
-\include{zinput}
-%\VignetteIndexEntry{Quinn's Dynamic Ecological Inference}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,ecological, contingency tables, Gibbs sampling}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-%%% the following inputs do not work
-%\SweaveInput{ei.hier}
-%\SweaveInput{eiRxC}
-\section{{\tt ei.dynamic}: Quinn's Dynamic Ecological Inference
-Model}\label{ei.dynamic}
-
-Given contingency tables with observed marginals, ecological inference
-({\sc ei}) models estimate each internal cell value for each table.
-Quinn's dynamic {\sc ei} model estimates a dynamic Bayesian model for
-$2 \times 2$ tables with temporal dependence across tables (units).
-The model is implemented using a Markov Chain Monte Carlo algorithm
-(via a combination of slice and Gibbs sampling).
-For a hierarchical Bayesian implementation of {\sc ei} see Quinn's
-dynamic {\sc ei} model (\Sref{ei.hier}). For contingency tables larger than
-2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "MCMCei.dynamic", data = mydata)
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\begin{itemize}
-\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
-proportions) containing the column margins of the units to be
-analyzed.
-
-\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
-proportions) containing the row margins of the units to be
-analyzed.
-
-\item \texttt{N}: total counts in each contingency table (unit). If
-\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
-you must specify \texttt{N}.
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional inputs for
-\texttt{ei.dynamic} to monitor the convergence of the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 5,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 50,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed of 12345.
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to
-specify priors and other parameters:
-
-\begin{itemize}
-\item \texttt{W}: a $p \times p$ numeric matrix describing the
-structure of the temporal dependence among elements of $\theta_0$ and
-$\theta_1$. The default value is 0, which constructs a weight matrix
-corresponding to random walk priors for $\theta_0$ and $\theta_1$
-(assuming that the tables are equally spaced throughout time, and that
-the elements of \texttt{t0},
-\texttt{t1},\texttt{x0},\texttt{x1} are temporally ordered).
-
-\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$. The default is 0.825.
-
-\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$. The default is 0.0105.
-
-\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$. The default is 0.825.
-
-\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$. The default is 0.0105.
-\end{itemize}
-
-\noindent Users may wish to refer to \texttt{help(MCMCdynamicEI)} for more options.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples \\
- Attaching the example dataset:
-<<Examples.data>>=
- data(eidat)
-
-@
-Estimating the model using \texttt{ei.dynamic}:
-<<Examples.zelig>>=
- z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.dynamic",
- data = eidat, mcmc = 40000, thin = 10, burnin = 10000,
- verbose = TRUE)
- summary(z.out)
-@
-Setting values for in-sample simulations given the marginal values
-of {\tt t0}, {\tt t1}, {\tt x0}, and {\tt x1}:
-<<Examples.setx>>=
- x.out <- setx(z.out, fn = NULL, cond = TRUE)
-@
-In-sample simulations from the posterior distribution:
-<<Examples.sim>>=
-s.out <- sim(z.out, x = x.out)
-@
-Summarizing in-sample simulations at aggregate level
-weighted by the count in each unit:
-<<Examples.summary>>=
- summary(s.out)
-@
-Summarizing in-sample simulations at unit level for the first 5 units:
-<<Examples.summary.subset>>=
- summary(s.out, subset = 1:5)
-@
-\end{enumerate}
-\clearpage
-\subsubsection{Model}
-Consider the following $2 \times 2$ contingency table for the racial
-voting example. For each geographical unit $i = 1, \dots, p$, the
-marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
-would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
-\begin{table}[h!]
- \begin{center}
- \begin{tabular}{l|cc|c}
- & No Vote & Vote & \\
- \hline
- Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
- White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
- \hline
- & $t_i^0$ & $t_i^1$ & $N_i$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$, $t_i^1$ are
-observed as either counts or fractions. If fractions, the counts can
-be obtained by multiplying by the total counts per table $N_i =
-n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$, and rounding to the
-nearest integer. Although there are four internal cells, only two
-unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
-$n_{i}^{11} = s_i^1 - n_{i}^{10}$. \\
-
-The hierarchical Bayesian model for ecological inference in
-$2 \times 2$ is illustrated as following:
-\begin{itemize}
-\item The \emph{stochastic component} of the model assumes that
-\begin{eqnarray*}
-n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
-x_i^0, \beta_i^b\right) ,\\
-n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
-x_i^1, \beta_i^w\right) \\
-\end{eqnarray*}
-where $\beta_{i}^{b}$ is the fraction of the black voters who vote
-and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
-and $\beta_i^w$ as well as their aggregate summaries are the focus
-of inference.
-
-\item The \emph{systematic component} of the model is
-\begin{eqnarray*}
-\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
-\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
-\end{eqnarray*}
-The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
-and $\theta_i^1$ now take value on the real line. (Future versions may allow
-$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
-
-\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
- are given by
-\begin{eqnarray*}
-\theta_{i}^{0} \mid\sigma_{0}^{2}&\propto & \frac{1}{\sigma_0^{p}} \exp
-\left( -\frac{1}{2\sigma_{0}^2}\theta_0' P \theta_0 \right) \\
-\theta_{i}^{1} \mid\sigma_{1}^{2}&\propto & \frac{1}{\sigma_1^{p}} \exp
-\left( -\frac{1}{2\sigma_{1}^2}\theta_1' P \theta_1 \right)
-\end{eqnarray*}
-where $P$ is a $p \times p$ matrix whose off diagonal elements
-$P_{ts}$ ($t \ne s$) equal $-W_{ts}$ (the negative values of the corresponding
-elements of the weight matrix $W$), and diagonal elements
-$P_{tt}=\sum_{s\ne t} W_{ts}$. Scale parameters $\sigma_{0}^2$ and
-$\sigma_{1}^2$ have hyperprior distributions as given below.
-
-\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
-given by
-\begin{eqnarray*}
-\sigma_{0}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right) ,\\
-\sigma_{1}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right) ,
-\end{eqnarray*}
-where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
-(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$
- are the scale parameters. \\
-
-The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
-and $\sigma_{1}^{2}$ are chosen such that the prior distributions for
-$\beta^b$ and $\beta^w$ are flat.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-
-\begin{verbatim}
-> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "ei.dynamic", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)}, see the draws from the
-posterior distribution of the quantities of interest by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out},
-you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the parameters.
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{N}: the total counts when the inputs are fractions.
-\item \texttt{seed}: the random seed used in the model.
-\end{itemize}
-
-\item From \texttt{summary(z.out)}, you may extract:
-\begin{itemize}
-\item \texttt{summary}: a matrix containing the summary information of the
-posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
-the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
-the posterior distribution. The first $p$ rows correspond to
-$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
-\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
-$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
-\texttt{p1table}$i$. The last four rows contain information about
-$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
-variances of $\theta_0$ and $\theta_1$.
-\end{itemize}
-\item From the \texttt{sim()} output object \texttt{s.out}, you may
-extract quantities of interest arranged as arrays indexed by
-simulation $\times$ column $\times$ row $\times$ observation, where
-column and row refer to the column dimension and the row dimension of
-the ecological table, respectively. In this model, only $2 \times 2$
-contingency tables are analyzed, hence column$=2$ and row$=2$ in all
-cases. Available quantities are:
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values of each internal
-cell given the observed marginals.
-\item \texttt{qi\$pr}: the simulated expected values of each internal
-cell given the observed marginals.
-\end{itemize}
-
-\end{itemize}
-
-\subsection*{How to Cite}
-\input{cites/ei.dynamic}
-\input{citeZelig}
-\subsection*{See also}
-\emph{ei.dynamic} function is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}.
-The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}. Sample date are adapted from \cite{MarQui05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
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@@ -1,311 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/eiHier}
-\include{zinput}
-%\VignetteIndexEntry{Hierarchical Ecological Inference Model for 2x2 tables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,ecological, two contingency tables, Gibbs sampling}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-%\SweaveInput{ei.dynamic}
-\section{{\tt ei.hier}: Hierarchical Ecological Inference Model for
-$2 \times 2$ Tables} \label{ei.hier}
-
-Given contingency tables with observed marginals, ecological inference
-({\sc ei}) models estimate each internal cell value for each table.
-The hierarchical {\sc ei} model estimates a Bayesian model for $2
-\times 2$ tables. The model is implemented using a Markov Chain Monte
-Carlo algorithm (via a combination of slice and Gibbs sampling).
-For a Bayesian implementation of {\sc ei} that accounts for temporal
-dependence, see Quinn's dynamic {\sc ei} model (\Sref{ei.dynamic}).
-For contingency tables larger than
-2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "MCMCei.hier", data = mydata)
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\begin{itemize}
-\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
-proportions) containing the column margins of the units to be
-analyzed.
-
-\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
-proportions) containing the row margins of the units to be
-analyzed.
-
-\item \texttt{N}: total counts per contingency table (unit). If
-\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
-you must specify \texttt{N}.
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional inputs for
-\texttt{ei.hier} to monitor the convergence of the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 5,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 50,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is \texttt{NA} which
-corresponds to a random seed of 12345.
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to specify
- prior parameters used in the model:
-
-\begin{itemize}
-\item \texttt{m0}: prior mean of $\mu_{0}$ (defaults to 0).
-
-\item \texttt{M0}: prior variance of $\mu_{0}$ (defaults to 2.287656).
-
-\item \texttt{m1}: prior mean of $\mu_{1}$ (defaults to 0).
-
-\item \texttt{M1}: prior variance of $\mu_{1}$ (defaults to 2.287656).
-
-\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$ (defaults to 0.825).
-
-\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$ (defaults to 0.0105).
-
-\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$ (defaults to 0.825).
-
-\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$ (defaults to 0.0105).
-\end{itemize}
-
-\noindent Users may wish to refer to \texttt{help(MCMChierEI)} for more information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples \\
- Attaching the example dataset:
-<<Examples.data>>=
- data(eidat)
- eidat
-@
-Estimating the model using \texttt{ei.hier}:
-<<Examples.zelig>>=
-z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.hier",
- data = eidat, mcmc = 40000, thin = 10, burnin = 10000,
- verbose = TRUE)
- summary(z.out)
-@
-
-Setting values for in-sample simulations given marginal values
-of {\tt x0}, {\tt x1}, {\tt t0}, and {\tt t1}:
-<<Examples.setx>>=
- x.out <- setx(z.out, fn = NULL, cond = TRUE)
-@
-
-In-sample simulations from the posterior distribution:
-<<Examples.sim>>=
- s.out <- sim(z.out, x = x.out)
-@
-Summarizing in-sample simulations at aggregate level
-weighted by the count in each unit:
-<<Examples.summary>>=
-summary(s.out)
-@
-Summarizing in-sample simulations at unit level for the first 5 units:
-<<Examples.summary.subset>>=
-summary(s.out, subset = 1:5)
-@
-
-\end{enumerate}
-\clearpage
-\subsubsection{Model}
-Consider the following $2 \times 2$ contingency table for the racial
-voting example. For each geographical unit $i = 1, \dots, p$, the
-marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
-would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
-
-\begin{table}[!h]
- \begin{center}
- \begin{tabular}{l|cc|c}
- & No Vote & Vote & \\
- \hline
- Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
- White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
- \hline
- & $t_i^0$ & $t_i^1$ & $N_i$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$,
-$t_i^1$ are observed as either counts or fractions. If fractions, the
-counts can be obtained by multiplying by the total counts per table
-$N_i = n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$ and rounding to the
-nearest integer. Although there are four internal cells, only two
-unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
-$n_{i}^{11} = s_i^1 - n_{i}^{10}$.
-
-The hierarchical Bayesian model for ecological inference in $2 \times
-2$ is illustrated as following:
-\begin{itemize}
-\item The \emph{stochastic component} of the model assumes that
-\begin{eqnarray*}
-n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
-x_i^0, \beta_i^b\right) ,\\
-n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
-x_i^1, \beta_i^w\right) \\
-\end{eqnarray*}
-where $\beta_{i}^{b}$ is the fraction of the black voters who vote
-and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
-and $\beta_i^w$ as well as their aggregate level summaries are the focus
-of inference.
-
-\item The \emph{systematic component} is
-\begin{eqnarray*}
-\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
-\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
-\end{eqnarray*}
-The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
-and $\theta_i^1$ now take value on the real line. (Future versions may allow
-$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
-
-\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
- are given by
-\begin{eqnarray*}
-\theta_{i}^{0} \mid\mu_{0},\sigma_{0}^{2}&\sim&\textrm{Normal}\left( \mu
-_{0},\sigma_{0}^{2}\right),\\
-\theta_{i}^{1} \mid\mu_{1},\sigma_{1}^{2}&\sim&\textrm{Normal}\left( \mu
-_{1},\sigma_{1}^{2}\right)
-\end{eqnarray*}
-where $\mu_{0}$ and $\mu_{1}$ are the means, and $\sigma_{0}^{2}$
-and $\sigma_{1}^{2}$ are the variances of the two corresponding
-(independent) normal distributions.
-
-\item The \emph{hyperpriors} for $\mu_{0}$ and $\mu_{1}$ are given by
-\begin{eqnarray*}
-\mu_{0} &\sim& \textrm{Normal} \left( m_{0}, M_{0} \right) ,\\
-\mu_{1} &\sim& \textrm{Normal} \left( m_{1}, M_{1} \right) ,
-\end{eqnarray*}
-where $m_{0}$ and $m_{1}$ are the means of the (independent) normal
-distributions while $M_{0}$ and $M_{1}$ are the variances.
-
-\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
-given by
-\begin{eqnarray*}
-\sigma_{0}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right),\\
-\sigma_{1}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right),
-\end{eqnarray*}
-where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
-(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$ are
-the scale parameters.
-
-The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
-and $\sigma_{1}^{2}$ are chosen such that the prior distributions of
-$\beta^b$ and $\beta^w$ are flat.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "ei.hier", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)},
-see the draws from the posterior distribution of
-the quantities of interest by using \texttt{z.out\$coefficients},
-and a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out},
-you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the parameters.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{N}: the total counts when the inputs are fractions.
-\item \texttt{seed}: the random seed used in the model.
-\end{itemize}
-
-\item From \texttt{summary(z.out)}, you may extract:
-\begin{itemize}
-\item \texttt{summary}: a matrix containing the summary information of the
-posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
-the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
-the posterior distribution. The first $p$ rows correspond to
-$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
-\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
-$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
-\texttt{p1table}$i$. The last four rows contain information about
-$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
-variances of $\theta_0$ and $\theta_1$.
-\end{itemize}
-\item From the \texttt{sim()} output object \texttt{s.out}, you may
-extract quantities of interest arranged as arrays indexed by
-simulation $\times$ column $\times$ row $\times$ observation, where
-column and row refer to the column dimension and the row dimension of
-the contingency table, respectively. In this model, only $2 \times 2$
-contingency tables are analyzed, hence column$=2$ and row$=2$ in all
-cases. Available quantities are:
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values of each internal cell
-given the observed marginals.
-\item \texttt{qi\$pr}: the simulated expected values of each internal cell
-given the observed marginals.
-\end{itemize}
-
-\end{itemize}
-
-\subsection*{How to Cite}
-\input{cites/ei.hier}
-\input{citeZelig}
-\subsection*{See also}
-\emph{ei.hier} function is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}.
-The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}. Sample date are adapted from \cite{MarQui05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
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- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
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-\SweaveOpts{results=hide, prefix.string=vigpics/exp}
-\include{zinput}
-%\VignetteIndexEntry{Exponential Regression for Duration Dependent Variables}
-%\VignetteDepends{Zelig, survival}
-%\VignetteKeyWords{model,exponential,regression, time}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-pkg <- search()
-if(!length(grep("package:Zelig",pkg)))
-library(Zelig)
-@
-
-\section{{\tt exp}: Exponential Regression for Duration Dependent Variables}\label{exp}
-
-Use the exponential duration regression model if you have a dependent
-variable representing a duration (time until an event). The model
-assumes a constant hazard rate for all events. The dependent variable
-may be censored (for observations have not yet been completed when
-data were collected).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X, model = "exp", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Exponential models require that the dependent variable be in the form
-{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
-For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
-duration (lifetime, for example), and the associated $c_i$ is a binary
-variable such that $c_i = 1$ if the duration is not censored ({\it
- e.g.}, the subject dies during the study) or $c_i = 0$ if the
-duration is censored ({\it e.g.}, the subject is still alive at the
-end of the study and is know to live at least as long as $y_i$). If
-$c_i$ is omitted, all Y are assumed to be completed; that is, time
-defaults to 1 for all observations.
-
-\subsubsection{Input Values}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for exponential regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) and the options selected in
-{\tt cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "exp", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-<<Example.data>>=
- data(coalition)
-@
-
-Estimate the model:
-<<Example.zelig>>=
- z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp",
- data = coalition)
-@
-View the regression output:
-<<Example.summary>>=
- summary(z.out)
-@
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-<<Example.setx>>=
- x.low <- setx(z.out, numst2 = 0)
- x.high <- setx(z.out, numst2 = 1)
-@
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
-<<Example.sim>>=
- s.out <- sim(z.out, x = x.low, x1 = x.high)
-@
-Summarize quantities of interest and produce some plots:
-<<Example.summary>>=
- summary(s.out)
-@
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=true>>=
- plot(s.out)
-@
-\end{center}
-
-\subsubsection{Model}
-
-Let $Y_i^*$ be the survival time for observation $i$. This variable
-might be censored for some observations at a fixed time $y_c$ such
-that the fully observed dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c \\
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by the distribution
- of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
- the exponential distribution whose density function is given by
- \begin{equation*}
- f(y_i^*\mid \lambda_i) = \frac{1}{\lambda_i} \exp\left(-\frac{y_i^*}{\lambda_i}\right)
- \end{equation*}
- for $y_i^*\ge 0$ and $\lambda_i>0$. The mean of this distribution is
- $\lambda_i$.
-
- In addition, survival models like the exponential have three
- additional properties. The hazard function $h(t)$ measures the
- probability of not surviving past time $t$ given survival up to
- $t$. In general, the hazard function is equal to $f(t)/S(t)$ where
- the survival function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the
- fraction still surviving at time $t$. The cumulative hazard
- function $H(t)$ describes the probability of dying before time $t$.
- In general, $H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case
- of the exponential model,
-\begin{eqnarray*}
-h(t) &=& \frac{1}{\lambda_i} \\
-S(t) &=& \exp\left( -\frac{t}{\lambda_i} \right) \\
-H(t) &=& \frac{t}{\lambda_i}
-\end{eqnarray*}
-For the exponential model, the hazard function $h(t)$ is constant over
-time. The Weibull model and lognormal models allow the hazard
-function to vary as a function of elapsed time (see \Sref{weibull} and
-\Sref{lognorm} respectively).
-
-\item The \emph{systematic component} $\lambda_i$ is modeled as
- \begin{equation*}
- \lambda_i = \exp(x_i \beta),
- \end{equation*}
- where $x_i$ is the vector of explanatory variables, and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the exponential model are
- simulations of the expected duration given $x_i$ and draws of
- $\beta$ from its posterior, $$E(Y) = \lambda_i = \exp(x_i \beta).$$
-
-\item The predicted values ({\tt qi\$pr}) are draws from the
- exponential distribution with rate equal to the expected value.
-
-\item The first difference (or difference in expected values, {\tt
- qi\$ev.diff}), is
-\begin{equation}
-\textrm{FD} \; = \; E(Y \mid x_1) - E(Y \mid x),
-\end{equation}
-where $x$ and $x_1$ are different vectors of values for the
-explanatory variables.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is \begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) - E[Y_i(t_i=0)]
- \right\}, \end{equation*} where $t_i$ is a binary explanatory
- variable defining the treatment ($t_i=1$) and control ($t_i=0$)
- groups. When $Y_i(t_i=1)$ is censored rather than observed, we
- replace it with a simulation from the model given available
- knowledge of the censoring process. Variation in the simulations
- is due to two factors: uncertainty in the imputation process for
- censored $y_i^*$ and uncertainty in simulating $E[Y_i(t_i=0)]$, the
- counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the same
- except that the treatment indicator is switched to $t_i=0$.
-
- \item In conditional prediction models, the average predicted
- treatment effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\}, \end{equation*} where $t_i$ is a
- binary explanatory variable defining the treatment ($t_i=1$) and
- control ($t_i=0$) groups. When $Y_i(t_i=1)$ is censored rather than
- observed, we replace it with a simulation from the model given
- available knowledge of the censoring process. Variation in the
- simulations is due to two factors: uncertainty in the imputation
- process for censored $y_i^*$ and uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
- for observations in the treatment group, under the assumption that
- everything stays the same except that the treatment indicator is
- switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
- C) \~\, X, model = "exp", data)}, then you may examine the
-available information in \texttt{z.out} by using
-\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
- z.out\$coefficients}, and a default summary of information
-through \texttt{summary(z.out)}. Other elements available through
-the {\tt \$} operator are listed below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt icoef}: parameter estimates for the intercept and scale
- parameter. While the scale parameter varies for the Weibull
- distribution, it is fixed to 1 for the exponential distribution
- (which is modeled as a special case of the Weibull).
- \item {\tt var}: the variance-covariance matrix for the estimates
- of $\beta$.
- \item {\tt loglik}: a vector containing the log-likelihood for the
- model and intercept only (respectively).
- \item {\tt linear.predictors}: the vector of
- $x_{i}\beta$.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item Most of this may be conveniently summarized using {\tt
- summary(z.out)}. From {\tt summary(z.out)}, you may
- additionally extract:
- \begin{itemize}
- \item {\tt table}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics. For
- example, {\tt summary(z.out)\$table}
- \end{itemize}
-
-\item From the {\tt sim()} output stored in {\tt s.out}:
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from a
- distribution defined by the expected values.
- \item {\tt qi\$fd}: the simulated first differences between the
- simulated expected values for {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-
-
-\subsection* {How to Cite}
-\input{cites/exp}
-\input{citeZelig}
-
-\subsection* {See also}
-The exponential function is part of the survival library by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library and \cite{VenRip02}.Sample
-data are from \cite{KinAltBur90}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
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-(./exp.tex
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-h, portuges, romanian, russian, serbian, slovak, slovene, spanish, swedish, tur
-kish, ukrainian, nohyphenation, loaded.
-\@input{zinput.aux}
-\openout2 = `zinput.aux'.
-
-(./zinput.tex (/usr/share/texmf/tex/latex/base/book.cls
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-\c at chapter=\count80
-\c at section=\count81
-\c at subsection=\count82
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-\c at subparagraph=\count85
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deleted file mode 100644
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--- a/inst/doc/factor.bayes.Rnw
+++ /dev/null
@@ -1,293 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/factorBayes}
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Factor Analysis}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,latent factors,continuous,explanatory variables, normal, Gibbs}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-pkg <- search()
-if(!length(grep("package:Zelig",pkg)))
-library(Zelig)
-@
-
-\section{\texttt{factor.bayes}: Bayesian Factor Analysis}
-\label{factor.bayes}
-
-Given some unobserved explanatory variables and observed dependent
-variables, the Normal theory factor analysis model estimates the
-latent factors. The model is implemented using a Markov Chain Monte
-Carlo algorithm (Gibbs sampling with data augmentation). For factor
-analysis with ordinal dependent variables, see ordered factor analysis
-(\Sref{factor.ord}), and for a mix of types of dependent variables,
-see the mixed factor analysis model (\Sref{factor.mix}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 2,
- model = "factor.bayes", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} takes the following functions for {\tt factor.bayes}:
-\begin{itemize}
-\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: variables of interest in
-factor analysis (manifest variables), assumed to be normally
-distributed. The model requires a minimum of three manifest variables.
-
-\item \texttt{factors}: number of the factors to be fitted (defaults to 2).
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional arguments for
-model specification:
-\begin{itemize}
-\item \texttt{lambda.constraints}: list containing the equality or
-inequality constraints on the factor loadings. Choose from one of the
-following forms:
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-
-\item \texttt{std.var}: defaults to {\tt FALSE} (manifest variables
-are rescaled to zero mean, but retain observed variance). If
-\texttt{TRUE}, the manifest variables are rescaled to be mean zero and
-unit variance.
-
-\end{itemize}
- In addition, {\tt zelig()} accepts the following additional inputs
-for {\tt bayes.factor}:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{Lambda.start}: starting values of the factor loading
-matrix $\Lambda$, either a scalar (all unconstrained loadings are set
-to that value), or a matrix with compatible dimensions. The default
-is \texttt{NA}, where the start value are set to be 0 for
-unconstrained factor loadings, and 0.5 or $-$0.5 for constrained
-factor loadings (depending on the nature of the constraints).
-
-\item \texttt{Psi.start}: starting values for the uniquenesses, either
-a scalar (the starting values for all diagonal elements of $\Psi$ are
-set to be this value), or a vector with length equal to the number of
-manifest variables. In the latter case, the starting values of the
-diagonal elements of $\Psi$ take the values of \texttt{Psi.start}. The
-default value is \texttt{NA} where the starting values of the all the
-uniquenesses are set to be 0.5.
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-stores the posterior draws of the factor scores. (Storing factor
-scores may take large amount of memory for a large number of draws
-or observations.)
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to
-specify prior parameters:
-
-\begin{itemize}
-
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, that value will be the prior mean for
-all the factor loadings. Defaults to 0.
-
-\item \texttt{L0}: precision parameter of the Normal prior
-for the factor loadings, either a scalar or a matrix with the same
-dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
-the precision matrix will be a diagonal matrix with the diagonal
-elements set to that value. The default value is 0, which leads to an
-improper prior.
-
-\item \texttt{a0}: the shape parameter of the Inverse Gamma prior for
-the uniquenesses is \texttt{a0/2}. It can take a scalar value or a
-vector. The default value is 0.001.
-
-\item \texttt{b0}: the shape parameter of the Inverse Gamma prior for
-the uniquenesses is \texttt{b0/2}. It can take a scalar value or a
-vector. The default value is 0.001.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCfactanal)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<BasicExample.data>>=
- data(swiss)
- names(swiss) <- c("Fert","Agr","Exam","Educ","Cath","InfMort")
-@
-Factor analysis:
-<<BasicExample.zelig>>=
- z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
- model = "factor.bayes", data = swiss, factors = 2,
- verbose = TRUE, a0 = 1, b0 = 0.15,
- burnin = 5000, mcmc = 50000)
-@
-%it is not in the demo and it fails
-Checking for convergence before summarizing the estimates:
-<<BasicExample.coeff>>=
- algor <- try(geweke.diag(z.out$coefficients), silent=T)
-if(class(algor)=="try-error")
-print(algor)
-@
-
-Since the algorithm did not converge, we now add some constraints on
-$\Lambda$.
-
-\item {Putting Constraints on $\Lambda$} \\
-Put constraints on Lambda to optimize the algorithm:
-<<Constraints.zelig>>=
-z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
- model = "factor.bayes", data = swiss, factors = 2,
- lambda.constraints = list(Exam=list(1,"+"),
- Exam=list(2,"-"), Educ=c(2,0),
- InfMort=c(1,0)),
- verbose = TRUE, a0 = 1, b0 = 0.15,
- burnin = 5000, mcmc = 50000)
-
- geweke.diag(z.out$coefficients)
- heidel.diag(z.out$coefficients)
- raftery.diag(z.out$coefficients)
- summary(z.out)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-Suppose for observation $i$ we observe $K$ variables and hypothesize
-that there are $d$ underlying factors such that:
-\begin{eqnarray*}
-Y_i = \Lambda \phi_i+\epsilon_i
-\end{eqnarray*}
-where $Y_{i}$ is the vector of $K$ manifest variables for observation
-$i$. $\Lambda$ is the $K \times d$ factor loading matrix and $\phi_i$
-is the $d$-vector of latent factor scores. Both $\Lambda$ and $\phi$
-need to be estimated.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by:
-\begin{eqnarray*}
-\epsilon_{i} \sim \textrm{Normal}(0, \Psi).
-\end{eqnarray*}
-where $\Psi$ is a diagonal, positive definite matrix. The diagonal elements
-of $\Psi$ are referred to as uniquenesses.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_i = E(Y_i) = \Lambda\phi_i
-\end{eqnarray*}
-
-\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The independent conjugate \emph{prior} for each $\Psi_{ii}$ is given by
-\begin{eqnarray*}
-\Psi_{ii} \sim \textrm{InverseGamma}(\frac{a_0}{2}, \frac{b_0}{2}), \textrm{ for }
-i = 1, \ldots, k.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is
-\begin{eqnarray*}
-\phi_i &\sim& \textrm{Normal}(0, I_d), \textrm{ for } i = 1, \ldots, n.
-\end{eqnarray*}
-where $I_d$ is a $ d\times d $ identity matrix.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings and the uniquenesses. If
-\texttt{store.scores = TRUE}, the estimated factors scores are also
-contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for factor analysis models.
-
-\end{itemize}
-
-
-\subsection* {How to Cite}
-
-\input{cites/factor.bayes}
-\input{citeZelig}
-
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
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diff --git a/inst/doc/factor.bayes.pdf b/inst/doc/factor.bayes.pdf
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diff --git a/inst/doc/factor.mix.Rnw b/inst/doc/factor.mix.Rnw
deleted file mode 100644
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+++ /dev/null
@@ -1,299 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/factorMix}
-\include{zinput}
-%\VignetteIndexEntry{Mixed Data Factor Analysis}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,mixed, factors latent, continuous, ordinal,Gibbs}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-\
-\section{\texttt{factor.mix}: Mixed Data Factor Analysis}\label{factor.mix}
-
-Mixed data factor analysis takes both continuous and ordinal dependent
-variables and estimates a model for a given number of latent factors.
-The model is estimated using a Markov Chain Monte Carlo algorithm
-(Gibbs sampler with data augmentation). Alternative models include
-Bayesian factor analysis for continuous variables
-(\Sref{factor.bayes}) and Bayesian factor analysis for ordinal
-variables (\Sref{factor.ord}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
- model = "factor.mix", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments for {\tt factor.mix}:
-\begin{itemize}
-
-\item \texttt{Y1}, {\tt Y2}, \texttt{Y3}, {\tt \dots}: The dependent variables of
-interest, which can be a mix of ordinal and continuous variables. You
-must have more dependent variables than factors.
-
-\item \texttt{factors}: The number of the factors to be fitted.
-
-\end{itemize}
-
-
-\subsubsection{Additional Inputs}
-
-The model accepts the following additional arguments to monitor
-convergence:
-\begin{itemize}
-\item \texttt{lambda.constraints}: A list that contains the equality or
-inequality constraints on the factor loadings.
-
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-Unlike Bayesian factor analysis for continuous variables
-(\Sref{factor.bayes}), the first column of $\Lambda$ corresponds to
-negative item difficulty parameters and should not be constrained in
-general.
-
-\item \texttt{std.mean}: defaults to {\tt TRUE}, which rescales the
-continuous manifest variables to have mean 0.
-
-\item \texttt{std.var}: defaults to {\tt TRUE}. which rescales the
-continuous manifest variables to have unit variance.
-\end{itemize}
-
-
-\noindent \texttt{factor.mix} accepts the following additional arguments
-to monitor the sampling scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded. The default value is 1,000.
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin.
- The default value is 20,000.
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: tuning parameter, which can be either a
-scalar or a vector of length $K$. The value of the tuning parameter
-must be positive. The default value is \texttt{1.2}.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen. The default
-is \texttt{FALSE}.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{lambda.start}: starting values of the factor loading
-matrix $\Lambda$ for the Markov chain, either a scalar (starting
-values of the unconstrained loadings will be set to that value), or a
-matrix with compatible dimensions. The default is \texttt{NA}, where
-the start values for the first column of $\Lambda$ are set based on
-the observed pattern, while for the rest of the columns of $\Lambda$,
-the start values are set to be 0 for unconstrained factor loadings,
-and 1 or $-$1 for constrained factor loadings (depending on the nature
-of the constraints).
-
-\item \texttt{psi.start}: starting values for the diagonals of the error variance
-(uniquenesses) matrix. Since the starting values for the ordinal
-variables are constrained to 1 (to identify the model), you may only
-specify the starting values for the continuous variables. For the
-continuous variables, you may specify {\tt psi.start} as a scalar or a
-vector with length equal to the number of continuous variables. If a
-scalar, that starting value is recycled for all continuous variables.
-If a vector, the starting values should correspond to each of the
-continuous variables. The default value is \texttt{NA}, which means
-the starting values of all the continuous variable uniqueness are set
-to 0.5.
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, storing the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-the posterior draws of the factor scores are stored. (Storing factor
-scores may take large amount of memory for a a large number of draws
-or observations.)
-
-\end{itemize}
-Use the following additional arguments to specify prior parameters used in the model:
-\begin{itemize}
-
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, then that value will be the prior mean
-for all the factor loadings. The default value is 0.
-
-\item \texttt{L0}: precision parameter of Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as $\Lambda$.
-If a scalar value, then the precision matrix will be
-a diagonal matrix with the diagonal elements set to that value.
-The default value is 0 which leads to an improper prior.
-
-\item \texttt{a0}: {\tt a0/2} is the shape parameter of the Inverse Gamma priors for
-the uniquenesses. It can take a scalar value or a vector. The default
-value is 0.001.
-
-\item \texttt{b0}: {\tt b0/2} is the shape parameter of the Inverse Gamma priors for
-the uniquenesses. It can take a scalar value or a vector. The default
-value is 0.001.
-
-\end{itemize}
-Zelig users may wish to refer to \texttt{help(MCMCmixfactanal)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<Examples.data>>=
- data(PErisk)
-@
-Factor analysis for mixed data using \texttt{factor.mix}:
-<<Examples.zelig>>=
- z.out<-zelig(cbind(courts, barb2, prsexp2, prscorr2, gdpw2) ~ NULL,
- data = PErisk, model = "factor.mix", factors = 1,
- burnin = 5000, mcmc = 100000, thin = 50, verbose = TRUE,
- L0 = 0.25, tune=1.2)
-@
-
-Checking for convergence before summarizing the estimates:
-<<Examples.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<Examples.heidel>>=
- heidel.diag(z.out$coefficients)
-@
-<<Examples.summary>>=
- summary(z.out)
-@
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a $K$-vector of observed variables for observation $i$,
-The $k$th variable can be either continuous or ordinal. When $Y_{ik}$ is an
-ordinal variable, it takes value from 1 to $J_k$ for $k=1,\ldots, K$ and for
-$i =1, \ldots, n$. The distribution of $Y_{ik}$ is assumed to be
-governed by another $K$-vector of unobserved continuous variable $Y_{ik}^*$.
-There are $d$ underlying factors. When $Y_{ik}$ is continuous, we let
-$Y_{ik}^*=Y_{ik}$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described in terms of $Y_i^*$:
-\begin{eqnarray*}
-Y_{i}^* &\sim& \textrm{Normal}_K (\mu_i, I_K),
-\end{eqnarray*}
-where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
-
-For ordinal $Y_{ik}$,
-\begin{eqnarray*}
-Y_{ik} = j \quad {\rm if} \quad \gamma_{(j-1),k} \le Y_{ik}^* \le
-\gamma_{jk} \quad \textrm{ for } \quad j=1,\ldots, J_k; k=1,\ldots, K.
-\end{eqnarray*}
-where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for the $k$th
-variable with the following constraints, $\gamma_{lk} < \gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k k}=\infty$ for any $k=1, \ldots, K$.
-It follows that the probability of observing $Y_{ik}$ belonging to category
-$j$ is,
-\begin{eqnarray*}
-\Pr(Y_{ik}=j) &=&\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \quad
-\textrm{ for } j=1,\ldots,J_k
-\end{eqnarray*}
-where $\Phi(\cdot\mid\mu_{ik})$ is the cumulative distribution function of the
-Normal distribution with mean $\mu_{ik}$ and variance 1.
-
-\item The \emph{systematic component} is given by,
-\begin{eqnarray*}
-\mu_i &=& \Lambda\phi_i,
-\end{eqnarray*}
-where $\Lambda$ is a $K \times d$ matrix of factor loadings for each variable,
-$\phi_i$ is a $d$-vector of factor scores for observation $i$. Note both
-$\Lambda$ and $\phi$ are estimated..
-
-\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} &\sim& \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is,
-\begin{eqnarray*}
-\phi_{i} \sim \textrm{Normal}(0, I_{d-1}), \quad {\rm for} \quad i=2, \ldots, n.
-\end{eqnarray*}
-where $I_{d-1}$ is a $ (d-1) \times (d-1) $ identity matrix. Note the
-first element of $\phi_i$ is 1.
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.mix", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings, the estimated cut points $\gamma$ for each
-variable. Note the first element of $\gamma$ is normalized to be 0. If
-\texttt{store.scores = TRUE}, the estimated factors scores are also contained in
-\texttt{coefficients}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()} procedure is
-not applicable for factor analysis models.
-
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/factor.mix}
-\input{citeZelig}
-
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/factor.mix.log b/inst/doc/factor.mix.log
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@@ -1,276 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/factorOrd}
-\include{zinput}
-%\VignetteIndexEntry{Ordinal Data Factor Analysis}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,factors latent, ordinal,Gibbs}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-\section{\texttt{factor.ord}: Ordinal Data Factor Analysis}
-\label{factor.ord}
-
-Given some unobserved explanatory variables and observed ordinal
-dependent variables, this model estimates latent factors using a Gibbs
-sampler with data augmentation. For factor analysis for continuous
-data, see \Sref{factor.bayes}. For factor analysis for mixed data
-(including both continuous and ordinal variables), see
-\Sref{factor.mix}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
- model = "factor.ord", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments for {\tt factor.ord}: :
-\begin{itemize}
-\item \texttt{Y1, Y2}, and \texttt{Y3}: variables of interest in
-factor analysis (manifest variables), assumed to be ordinal
-variables. The number of manifest variables must be greater than the
-number of the factors.
-
-\item \texttt{factors}: number of the factors to be fitted (defaults
-to 1).
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following arguments for model
-specification:
-\begin{itemize}
-\item \texttt{lambda.constraints}: list that contains the equality or
-inequality constraints on the factor loadings. A typical entry in the list
-has one of the following forms:
-\begin{itemize}
-\item {\tt varname = list()}: by default, no constraints are imposed.
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c};
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-%Unlike \texttt{factanal} the $\Lambda$ matrix
-%has \texttt{factors+1} columns.
-The first column of $\Lambda$ should not be constrained in general.
-
-\item \texttt{drop.constantvars}: defaults to {\tt TRUE}, dropping the
-manifest variables that have no variation before fitting the model.
-
-\end{itemize}
-
-The model accepts the following arguments to monitor the convergence
-of the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: tuning parameter for Metropolis-Hasting sampling,
-either a scalar or a vector of length $K$. The value of the tuning
-parameter must be positive. The default value is 1.2.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
-progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The
-default is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{Lambda.start}: starting values of the factor loading
-matrix $\Lambda$ for the Markov chain, either a scalar (all
-unconstrained loadings are set to that value), or a matrix with
-compatible dimensions. The default is {\tt NA}, such that the start
-values for the first column are set based on the observed pattern,
-while the remaining columns have start values set to 0 for
-unconstrained factor loadings, and -1 or 1 for constrained loadings
-(depending on the nature of the constraints).
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-stores the posterior draws of the factor scores. (Storing factor
-scores may take large amount of memory for a a large number of draws
-or observations.)
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, that value will be the prior mean for
-all the factor loadings. Defaults to 0.
-
-\item \texttt{L0}: precision parameter of the Normal prior
-for the factor loadings, either a scalar or a matrix with the same
-dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
-the precision matrix will be a diagonal matrix with the diagonal
-elements set to that value. The default value is 0, which leads to an
-improper prior.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCordfactanal)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<Examples.data>>=
- data(newpainters)
-@
-
-Factor analysis for ordinal data using \texttt{factor.ord}:
-<<Examples.zelig>>=
- z.out <- zelig(cbind(Composition,Drawing,Colour,Expression)~NULL,
- data=newpainters, model="factor.ord",
- factors=1, L0=0.5,
- burin=5000,mcmc=30000, thin=5, tune=1.2,verbose=TRUE)
-
-@
-Checking for convergence before summarizing the estimates:
-<<Examples.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<Examples.heidel>>=
- heidel.diag(z.out$coefficients)
-@
-<<Examples.raftery>>=
- raftery.diag(z.out$coefficients)
-@
-<<Examples.summary>>=
- summary(z.out)
-@
-
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of $K$ observed ordinal variables for
-observation $i$, each ordinal variable $k$ for $k=1,\ldots, K$ takes
-integer value $j=1, \ldots, J_k$. The distribution of $Y_i$ is assumed
-to be governed by another $k$-vector of unobserved continuous variable
-$Y_i^*$. There are $d$ underlying factors.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described in terms of the latent
-variable $Y_i^*$:
-\begin{eqnarray*}
-Y_{i}^* \sim \textrm{Normal}_K(\mu_i, I_K),
-\end{eqnarray*}
-where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i$ is the mean vector
-for $Y_i^*$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
-
-Instead of $Y_{ik}^*$, we observe ordinal variable $Y_{ik}$,
-\begin{eqnarray*}
-Y_{ik} = j \textrm{ if } \gamma_{(j-1),k} \le Y_{ik}^* \le \gamma_{jk}
-\textrm{ for } \quad j=1,\ldots, J_k, k=1,\ldots, K.
-\end{eqnarray*}
-where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for
-the $k$th variable with the following constraints, $\gamma_{lk} <
-\gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k
-k}=\infty$ for any $k=1, \ldots, K$. It follows that the probability
-of observing $Y_{ik}$ belonging to category $j$ is,
-\begin{eqnarray*}
-\Pr(Y_{ik}=j) =\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \textrm{ for } j=1,\ldots,J_k
-\end{eqnarray*}
-where $\Phi(\cdot \mid\mu_{ik})$ is the cumulative distribution
-function of the Normal distribution with mean $\mu_{ik}$ and variance
-1.
-
-\item The \emph{systematic component} is given by,
-\begin{eqnarray*}
-\mu_i = \Lambda\phi_i,
-\end{eqnarray*}
-where $\Lambda$ is a $K \times d$ matrix of factor loadings for each
-variable, $\phi_i$ is a $d$-vector of factor scores for observation
-$i$. Note both $\Lambda$ and $\phi$ need to be estimated.
-
-\item The independent conjugate \emph{prior} for each element of $\Lambda$,
-$\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is,
-\begin{eqnarray*}
-\phi_{i(2:d)} \sim \textrm{Normal}(0, I_{d-1}), \textrm{ for } \quad i=2, \ldots, n.
-\end{eqnarray*}
-where $I_{d-1}$ is a $ (d-1)\times (d-1) $ identity matrix. Note the
-first element of $\phi_i$ is 1.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.ord", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior
-distribution of the \texttt{coefficients} by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings, the estimated cut points $\gamma$ for each
-variable. Note the first element of $\gamma$ is normalized to be 0. If
-\texttt{store.scores=TRUE}, the estimated factors scores are also contained in
-\texttt{coefficients}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()} procedure is
-not applicable for factor analysis models.
-
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/factor.ord}
-\input{citeZelig}
-
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
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-\chapter{Frequently Asked Questions}
-
-\section{For All Zelig Users}
-
-\subsection*{How do I cite Zelig?}
-We would appreciate if you would cite Zelig as:
-\begin{verse}
- Imai, Kosuke, Gary King and Olivia Lau. 2006. ``Zelig:
- Everyone's Statistical Software,'' \url{http://GKing.Harvard.Edu/zelig}.
-\end{verse}
-Please also cite the contributors for the models or methods you are
-using. These citations can be found in the contributors section of
-each model or command page.
-
-\subsection*{Why can't I install Zelig?}
-
-You must be connected to the internet to install packages from web
-depositories. In addition, there are a few platform-specific reasons
-why you may have installation problems:
-
-\begin{itemize}
-\item \textbf{On Windows}: If you are using the very latest version of
- R, you may not be able to install Zelig until we update Zelig to
- work on the latest release of R. If you wish to install Zelig in the
- interim, check the Zelig release notes (\Sref{release.notes}) and
- download the appropriate version of R to work with the last release
- of Zelig. You may have to manually download and install Zelig.
-
-\item \textbf{On Mac}: If the latest version of Zelig is not yet
- available at CRAN but you would like to install it on your Mac, try
- typing the following at your R prompt:
-\begin{verbatim}
- install.packages("Zelig", repos = "http://gking.harvard.edu", type = "source")
-\end{verbatim}
-
-\item \textbf{On Mac or Linux systems}:
-If you get the following
- warning message at the end of your installation:
-\begin{verbatim}
-Installation of package VGAM had non-zero exit status in ...
-\end{verbatim}
-this means that you were not able to install VGAM properly. Make sure
-that you have the g77 Fortran compiler. For PowerPC Macs, download
-g77 from
-\hlink{http://hpc.sourceforge.net}{http://hpc.sourceforge.net}). For
-Intel Macs, download the
-\hlink{xcode}{http://developer.apple.com/tools/xcode/} Apple developer
-tools. After installation, try to install Zelig again.
-\end{itemize}
-
-\subsection*{Why can't I install R?}
-
-If you have problems installing R (rather than Zelig), you should
-check the \hlink{R FAQs}{http://cran.r-project.org/faqs.html} for your
-platform. If you still have problems, you can search the
-\hlink{archives}{https://www.stat.math.ethz.ch/mailman/listinfo/r-help}
-for the R help mailing list, or email the list directly at
-\hlink{r-help at stat.math.ethz.ch}{mailto:r-help at stat.math.ethz.ch}.
-
-\subsection*{Why can't I load data?}
-
-When you start R, you need to specify your working directory. In
-linux R, this is done pretty much automatically when you start R,
-whether within ESS or in a terminal window. In Windows R, you may
-wish to specify a working directory so that you may load data without
-typing in long directory paths to your data files, and it is important
-to remember that \emph{Windows} R uses the \emph{Linux} directory
-delimiter. That is, if you right click and select the ``Properties''
-link on a Windows file, the slashes are backslashes ($\backslash$), but Windows R
-uses forward slashes ({\tt /}) in directory paths. Thus, the Windows
-link may be {\tt C:$\backslash$Program Files$\backslash$R$\backslash$\rwvers$\backslash$}, but you would type
- {\tt C:/Program Files/R/\rwvers/} in Windows R.
-
-When you start R in Windows, the working directory is by default the
-directory in which the R executible is located.
-\begin{verbatim}
-# Print your current working directory.
-> getwd()
-
-# To read data not located in your working directory.
-> data <- read.table("C:/Program Files/R/newwork/mydata.tab")
-
-# To change your working directory.
-> setwd("C:/Program Files/R/newwork")
-
-# Reading data in your working directory.
-> data <- read.data("mydata.tab")
-\end{verbatim}
-Once you have set the working directory, you no longer need to type
-the entire directory path.
-
-\subsection*{Where can I find old versions of Zelig?}
-
-For some replications, you may require older versions of Zelig.
-\begin{itemize}
-\item {\bf Windows} users may find old binaries at
-\url{http://gking.harvard.edu/bin/windows/contrib/}
-and selecting the appropriate version of R.
-
-\item {\bf Linux} and {\bf MacOSX} users may find source files at
-\url{http://gking.harvard.edu/src/contrib/}
-\end{itemize}
-If you want an older version of Zelig because you are using an older
-version of R, we strongly suggest that you update R and install the
-latest version of Zelig.
-
-\subsection*{Some Zelig functions don't work for me!}
-
-If this is a new phenomenon, there may be functions in your
-namespace that are overwriting Zelig functions. In particular, if you
-have a function called {\tt zelig}, {\tt setx}, or {\tt
-sim} in your workspace, the corresponding functions in Zelig will not
-work. Rather than deleting things that you need, R will tell you the
-following when you load the Zelig library:
-\begin{verbatim}
-Attaching package: 'Zelig'
- The following object(s) are masked _by_ .GlobalEnv :
- sim
-\end{verbatim}
-In this case, simply rename your {\tt sim} function to something else
-and load Zelig again:
-\begin{verbatim}
-> mysim <- sim
-> detach(package:Zelig)
-> library(Zelig)
-\end{verbatim}
-
-\subsection*{Who can I ask for help? How do I report bugs?}
-
-If you find a bug, or cannot figure something out, please follow these
-steps: (1) Reread the relevant section of \hlink{the
- documentation}{http://gking.harvard.edu/zelig/docs/}. (2)
-\hlink{Update
- Zelig}{http://gking.harvard.edu/zelig/docs/Installation.html} if you
-don't have the current version. (3) Rerun the same code and see if
-the bug has been fixed. (4) Check our list
-of \hlink{frequently asked
- questions}{http://gking.harvard.edu/zelig/docs/Frequently_Asked_Quest.html}.
-(5) \hlink{Search or browse
- messages}{http://lists.hmdc.harvard.edu/lists/zelig/} to find a
-discussion of your issue on the zelig listserv.
-
-If none of these work, then if you haven't already, please (6)
-\hlink{subscribe to the Zelig
- listserv}{http://lists.hmdc.harvard.edu/index.cgi?info=zelig} and
-(7) send your question to the listserv at
-\texttt{zelig at lists.gking.harvard.edu}. Please explain exactly what you did
-and include the full error message, including the {\tt traceback()}.
-You should get an answer from the developers or another user in short
-order.
-
-\subsection*{How do I increase the memory for R?}
-
-Windows users may get the error that R has run out of memory.
-
-If you have R already installed and subsequently install more RAM, you
-may have to reinstall R in order to take advantage of the additional
-capacity.
-
-You may also set the amount of available memory manually. Close R,
-then right-click on your R program icon (the icon on your desktop or
-in your programs directory). Select ``Properties'', and then select
-the ``Shortcut'' tab. Look for the ``Target'' field and after the
-closing quotes around the location of the R executible, add
-\begin{verbatim}
---max-mem-size=500M
-\end{verbatim}
-as shown in the figure below. You may increase this value up to 2GB
-or the maximum amount of physical RAM you have installed.
-
-\begin{figure}[h!]
-\begin{center}
-\includegraphics{figs/increase}
-\end{center}
-\end{figure}
-
-If you get the error that R cannot allocate a vector of length x,
-close out of R and add the following line to the ``Target'' field:
-\begin{verbatim}
---max-vsize=500M
-\end{verbatim}
-or as appropriate.
-
-You can always check to see how much memory R has available by typing
-at the R prompt
-\begin{verbatim}
-> round(memory.limit()/2^20, 2)
-\end{verbatim}
-which gives you the amount of available memory in MB.
-
-%\subsection*{I still run out of memory! Now what?}
-
-%Most users who run out of memory do so because they choose in-sample
-%prediction ({\tt setx(\dots, fn = NULL, \dots)}). To get around this, you
-%can write a loop for {\tt sim()}:
-
-%\begin{verbatim}
-%z.out <- zelig(...)
-%x.out <- setx(z.out, fn = NULL, ...)
-%s.out <- sim(z.out, x = x.out[1,])
-%for (i in 2:nrow(x.out)) {
-% tmp <- summary(sim(z.out, x = xout[i,]))
-% results <- prior.average(results, tmp, i)
-%}
-%results
-%\end{verbatim}
-%where {\tt prior.average()} is a special function we have written to
-%calculate running summary statistics.
-
-\subsection*{Why doesn't the pdf print properly?}
-
-Zelig uses several special \LaTeX\ environments. If the pdf looks right
-on the screen, there are two possible reasons why it's not printing
-properly:
-\begin{itemize}
-\item Adobe Acrobat isn't cleaning up the document. Updating to
- Acrobat Reader 6.0.1 or higher should solve this problem.
-\item Your printer doesn't support PostScript Type 3 fonts. Updating
- your print driver should take care of this problem.
-\end{itemize}
-
-\subsection*{R is neat. How can I find out more?}
-
-R is a collective project with contributors from all over the world.
-Their website
-(\hlink{{\tt http://www.r-project.org}}{http://www.r-project.org}) has more
-information on the R project, R packages, conferences, and other
-learning material.
-
-In addition, there are several canonical references which you may
-wish to peruse:
-\begin{verse}
-Venables, W.N. and B.D. Ripley. 2002. \emph{Modern Applied Statistics with S.} 4th Ed. Springer-Verlag. \\
-Venables, W.N. and B.D. Ripley. 2000. \emph{S Programming.} Springer-Verlag. \\
-\end{verse}
-
-\section{For Zelig Contributors}
-
-\subsection*{Where can I find the source code for Zelig?}
-
-Zelig is distributed under the \hlink{GNU General Public License,
-Version 2}{http://www.gnu.org/licenses/gpl.txt}. After installation,
-the source code is located in your R library directory. For Linux
-users who have followed our installation example, this is {\tt
-\~{}/.R/library/Zelig/}. For Windows users under R \fullrvers, this is by
-default {\tt C:$\backslash$Program Files$\backslash$R$\backslash$\rwvers$\backslash$library$\backslash$Zelig$\backslash$}.
-For Macintosh users, this is {\tt \~{}/Library/R/library/Zelig/}.
-
-In addition, you may download the latest Zelig
-source code as a tarball'ed directory from
-\hlink{{\tt http://gking.harvard.edu/src/contrib/}}{http://gking.harvard.edu/src/contrib/}.
-(This makes it easier to distinguish functions which are run together during installation.)
-
-\subsection*{How can I make my R programs run faster?}
-
-Unlike most commercial statistics programs which rely on precompiled
-and pre-packaged routines, R allows users to program functions and run
-them in the same environment. If you notice a perceptible lag when
-running your R code, you may improve the performance of your programs
-by taking the following steps:
-
-\begin{itemize}
-
-\item Reduce the number of loops. If it is absolutely necessary to run
-loops in loops, the inside loop should have the most number of cycles
-because it runs faster than the outside loop. Frequently, you can
-eliminate loops by using vectors rather than scalars. Most R functions
-deal with vectors in an efficient and mathematically intuitive manner.
-
-\item Do away with loops altogether. You can vectorize functions
-using the {\tt apply}, {\tt mapply()}, {\tt sapply()}, {\tt lapply()},
-and {\tt replicate()} functions. If you specify the function passed
-to the above {\tt *apply()} functions properly, the R consensus is that
-they should run significantly faster than loops in general.
-
-\item You can compile your code using C or Fortran. R is not compiled,
-but can use bits of precompiled code in C or Fortran, and
-calls that code seamlessly from within R wrapper functions (which pass
-input from the R function to the C code and back to R). Thus, almost
-every regression package includes C or Fortran algorithms, which are
-locally compiled in the case of Linux systems or precompiled in the
-case of Windows distributions. The recommended Linux compilers are
-gcc for C and g77 for Fortran, so you should make sure that your code
-is compatible with those standards to achieve the widest possible
-distribution.
-
-\end{itemize}
-
-\subsection*{Which compilers can I use with R and Zelig?}
-
-In general, the C or Fortran algorithms in your package should compile
-for any platform. While Windows R packages are distributed as
-compiled binaries, Linux R compiles packages locally during
-installation. Thus, to ensure the widest possible audience for your
-package, you should make sure that your code will compile on gcc (for
-C and C++), or on g77 (for Fortran).
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/gamma.gee.Rnw b/inst/doc/gamma.gee.Rnw
deleted file mode 100644
index 494adaa..0000000
--- a/inst/doc/gamma.gee.Rnw
+++ /dev/null
@@ -1,301 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/gamma_gee}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Estimating Equation for Gamma Regression}
-%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, gamma regression}
-%\VignettePackage{Zelig}
-\begin{document}
-
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{gamma.gee}: Generalized Estimating Equation for Gamma Regression}
-\label{gamma.gee}
-
-The GEE gamma is similar to standard gamma
-regression (appropriate when you have an uncensored, positive-valued, continuous dependent variable such as the time until a parliamentary cabinet falls). Unlike in gamma
-regression, GEE gamma allows for dependence within clusters, such as
-in longitudinal data, although its use is not limited to just
-panel data. GEE models make no distributional assumptions but require three specifications: a mean function, a variance function, and a ``working''
-correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
-the standard errors can be obtained using a robust ``sandwich''
-estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "gamma.gee",
- id = "X3", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\noindent where \texttt{id} is a variable which identifies the clusters. The data should be sorted by \texttt{id} and should be ordered within each cluster when appropriate.
-
-\subsubsection{Additional Inputs}
-
-\begin{itemize}
-\item \texttt{robust}: defaults to \texttt{TRUE}. If \texttt{TRUE}, consistent standard errors are estimated using a ``sandwich'' estimator.
-\end{itemize}
-Use the following arguments to specify the structure of the ``working'' correlations within clusters:
-
-\begin{itemize}
-\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
-\begin{itemize}
-\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard gamma regression. The ``working'' correlation matrix is the identity matrix.
-\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
-\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
- $${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{|t-t'|} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
- If (\texttt{corstr = "stat\_M\_dep"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. Choose this option when the correlations are assumed to be the same for observations of the same $|t-t'|$ periods apart for $|t-t'| \leq m$.
-\begin{center}
-Sample ``working'' correlation for Stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_1 & \alpha_2 & 0 & 0 \\
-\alpha_1 & 1 & \alpha_1 & \alpha_2 & 0 \\
-\alpha_2 & \alpha_1 & 1 & \alpha_1 & \alpha_2 \\
-0 & \alpha_2 & \alpha_1 & 1 & \alpha_1 \\
-0 & 0 & \alpha_2 & \alpha_1 & 1
-\end{array} \right) $
-\end{center}
-
-\item Non-stationary $m$ dependent (\texttt{corstr =
- "non\_stat\_M\_dep"}):
-$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{tt'} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
-If (\texttt{corstr = "non\_stat\_M\_dep"}), you must also specify
-\texttt{Mv} = $m$, where $m$ is the number of periods $t$ of
-dependence. This option relaxes the assumption that the
-correlations are the same for all observations of the same $|t-t'|$
-periods apart.
-\begin{center}
-Sample ``working'' correlation for Non-stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_{12} & \alpha_{13} & 0 & 0 \\
-\alpha_{12} & 1 & \alpha_{23} & \alpha_{24} & 0 \\
-\alpha_{13} & \alpha_{23} & 1 & \alpha_{34} & \alpha_{35} \\
-0 & \alpha_{24} & \alpha_{34} & 1 & \alpha_{45} \\
-0 & 0 & \alpha_{35} & \alpha_{45} & 1
-\end{array} \right) $
-\end{center}
-\item Exchangeable (\texttt{corstr = "exchangeable"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha$, $\forall t, t'$ with $t\ne t'$. Choose this option if the correlations are assumed to be the same for all observations within the cluster.
-\begin{center}
-Sample ``working'' correlation for Exchangeable\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha & \alpha & \alpha \\
-\alpha & 1 & \alpha & \alpha & \alpha \\
-\alpha & \alpha & 1 & \alpha & \alpha \\
-\alpha & \alpha & \alpha & 1 & \alpha \\
-\alpha & \alpha & \alpha & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Stationary $m$th order autoregressive (\texttt{corstr = "AR-M"}): If (\texttt{corstr = "AR-M"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. For example, the first order autoregressive model (AR-1) implies ${\rm cor}(y_{it},
- y_{it'})=\alpha^{|t-t'|}, \forall t, t'$ with $t\ne t'$. In AR-1, observation 1 and observation 2 have a correlation of $\alpha$. Observation 2 and observation 3 also have a correlation of $\alpha$. Observation 1 and observation 3 have a correlation of $\alpha^2$, which is a function of how 1 and 2 are correlated ($\alpha$) multiplied by how 2 and 3 are correlated ($\alpha$). Observation 1 and 4 have a correlation that is a function of the correlation between 1 and 2, 2 and 3, and [...]
-\begin{center}
-Sample ``working'' correlation for Stationary AR-1 ({\tt Mv}=1)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha^2 & \alpha^3 & \alpha^4 \\
-\alpha & 1 & \alpha & \alpha^2 & \alpha^3 \\
-\alpha^2 & \alpha & 1 & \alpha & \alpha^2 \\
-\alpha^3 & \alpha^2 & \alpha & 1 & \alpha \\
-\alpha^4 & \alpha^3 & \alpha^2 & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Unstructured (\texttt{corstr = "unstructured"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha_{tt'}$, $\forall t, t'$ with
- $t\ne t'$. No constraints are placed on the correlations, which are then estimated from the data.
-\end{itemize}
-\item \texttt{Mv}: defaults to 1. It specifies the number of periods of correlation and only needs to be specified when \texttt{corstr} is {\tt "stat\_M\_dep"}, {\tt "non\_stat\_M\_dep"}, or {\tt "AR-M"}.
-\item \texttt{R}: defaults to \texttt{NULL}. It specifies a user-defined correlation matrix rather than estimating it from the data. The argument is used only when \texttt{corstr} is {\tt "fixed"}. The input is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Example with Exchangeable Dependence}
-
-Attaching the sample turnout dataset:
-<<Example.data>>=
-data(coalition)
-@
-Sorted variable identifying clusters
-<<Example.cluster>>=
-coalition$cluster <- c(rep(c(1:62),5),rep(c(63),4))
-sorted.coalition <- coalition[order(coalition$cluster),]
-@
-Estimating model and presenting summary:
-<<Example.zelig>>=
-z.out <- zelig(duration ~ fract + numst2, model = "gamma.gee", id = "cluster", data = sorted.coalition, robust=TRUE, corstr="exchangeable")
-summary(z.out)
-@
-Setting the explanatory variables at their default values
-(mode for factor variables and mean for non-factor variables),
-with numst2 set to the vector 0 = no crisis, 1 = crisis.
-<<Example.setx>>=
-x.low <- setx(z.out, numst2 = 0)
-x.high <- setx(z.out, numst2 = 1)
-@
-Simulate quantities of interest
-<<Example.sim>>=
-s.out <- sim(z.out, x = x.low, x1 = x.high)
-summary(s.out)
-@
-Generate a plot of quantities of interest:
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out)
-@
-\end{center}
-\end{enumerate}
-
-\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the positive-valued, continuous dependent variable for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
-where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
-
-\begin{itemize}
-
-\item The \emph{stochastic component} is given by the joint and marginal distributions
-\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \lambda_{i})\\
-Y_{it} &\sim& g(y_{it} \mid \lambda_{it})
-\end{eqnarray*}
-where $f$ and $g$ are unspecified distributions with means $\lambda_{i}$ and $\lambda_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-
-\item The \emph{systematic component} is the \textit{mean function}, given by:
-\begin{equation*}
-\lambda_{it} = \frac{1}{x_{it} \beta}
-\end{equation*}
-where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
-and $\beta$ is the vector of coefficients.
-
-\item The \textit{variance function} is given by:
-\begin{equation*}
-V_{it} = \lambda_{it}^2 = \frac{1}{(x_{it} \beta)^2}
-\end{equation*}
-
-\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = \lambda_{it}^2$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-
-\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
-
-\end{itemize}
-
-
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item All quantities of interest are for marginal means rather than joint means.
-\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
-\item The expected values ({\tt qi\$ev}) for the GEE gamma model is the mean: $$E(Y) =
- \lambda_{c} = \frac{1}{x_c \beta},$$ given draws of $\beta$ from
- its sampling distribution, where $x_{c}$ is a vector of values, one for
-each independent variable, chosen by the user.
-
-\item The first difference ({\tt qi\$fd}) for the GEE gamma model is defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- E[Y_{it}(tr_{it}=0)] \right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the treatment
- ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_{it}(tr_{it}=0)]$,
- the counterfactual expected value of $Y_{it}$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $tr_{it}=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "gamma.gee", id, data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component.
- \item {\tt linear.predictors}: the vector of $x_{it}\beta$
- \item {\tt max.id}: the size of the largest cluster.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $z$-statistics.
- \item {\tt working.correlation}: the ``working'' correlation matrix
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the
- specified values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection*{How To Cite}
-
-\input{cites/gamma.gee}
-\input{citeZelig}
-
-\subsection*{See also}
-The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinAltBur90}.
-
-\bibliographystyle{asa}
-\bibliography{gk}
-
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-
-\end{document}
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deleted file mode 100644
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+++ /dev/null
@@ -1,182 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/gammanet}
-\include{zinput}
-%\VignetteIndexEntry{Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,least squares,continuous, regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt gamma.net}: Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables}
-
-Use the network gamma regression model if you have a positive-valued dependent variable that is a binary valued proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs). The gamma distribution assumes that all waiting times are complete by the end of the study (censoring is not allowed).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "gamma.net", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following additional options for network gamma regression:
-
-\begin{itemize}
-\item {\tt LF}: specifies the link function to be used for the network gamma regression. Default is {\tt LF="inverse"}, but {\tt LF} can also be set to {\tt "identity"} or {\tt "log"} by the user.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-data(friendship)
-
-
-
-@
-Estimate model:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(per ~ perpower, LF="inverse", model="gamma.net", data=friendship)
-summary(z.out)
-
-@
-Setting values for the explanatory variables to their default values:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.out <- setx(z.out)
-
-@
-Simulating quantities of interest from the posterior distribution.
-<<echo=TRUE, results=hide, fig=FALSE>>=
-s.out <- sim(z.out, x = x.out)
-summary(s.out)
-plot(s.out)
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, echo=false, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-
-\item Simulating First Differences
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.low <- setx(z.out, numst2 = 0)
-x.high <- setx(z.out, numst2 = 1)
-
-s.out2 <- sim(z.out, x = x.low, x1 = x.high)
-summary(s.out2)
-plot(s.out2)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE, echo=false, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-The {\tt gamma.net} model performs a gamma regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard gamma regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the $\m [...]
-
-Let $Y_{i}$ be the dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$.
-\begin{itemize}
-\item The Gamma distribution with scale parameter $\alpha$ has a \emph{stochastic component} given by
-\begin{eqnarray*}
-Y &\sim \text{Gamma} ( y_{i} | \lambda_i, \alpha)\\
-f(y) & = \frac{1}{\alpha^{\lambda_i}\Gamma \lambda_i} y_i^{\lambda_i - 1} \exp - \left[ \frac{y_i}{\alpha} \right]
-\end{eqnarray*}
-for $\alpha, \lambda_i, y_i > 0$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\lambda_{i} = \frac{1}{x_{i}\beta}.
-\end{equation*}
-
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network gamma regression are the same as those for the standard gamma regression.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) are simulations of the mean of the stochastic component given draws of $\alpha$ and $\beta$ from their posteriors:
-\begin{equation*}
-E(Y) = \alpha_i \lambda.
-\end{equation*}
-
-\item The predicted values ({\tt qi\$pr}) are draws from the gamma distribution for each set of parameters $(\alpha, \lambda_i)$.
-
-\item The first difference ({\tt qi\$fd}) for the network gamma model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y | x_{1}) - \text{Pr}(Y | x)
-\end{equation*}
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "gamma.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike\'s Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values drawn from a distribution defined by $(\alpha_i, \lambda)$.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
- \subsection* {How to Cite}
-
-\input{cites/gamma.net}
-\input{citeZelig}
-\subsection* {See also}
-The network gamma regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(gamma.net)}. Sample data are fictional.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
diff --git a/inst/doc/gamma.net.log b/inst/doc/gamma.net.log
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diff --git a/inst/doc/gk.bib b/inst/doc/gk.bib
new file mode 100644
index 0000000..49199f5
--- /dev/null
+++ b/inst/doc/gk.bib
@@ -0,0 +1,20132 @@
+% A bibtex file for papers by or coauthored with Gary King
+%
+% To add references, first please CHECK that your doesn't already
+% exist in this file and
+% then add entries only at the end.
+%
+% Use these rules for the reference label:
+%
+% -if one author: use last name and last 2 digits of the year: Tobler79.
+% -if multiple authors, use 1st 3 letters of each of UP TO the first three
+% authors and the last 2 digits of the year: KinTomWit00.
+% -if necessary add lower-case letters for multiple entries in a year: King02, King02b
+% (the first one should NOT have an 'a' afterwards)
+%
+% -feel free to use the abbreviations at the start, or add to them.
+% -Use authors full names when known.
+%
+% please be sure to commit changes to CVS regularly as a number of
+% people are all using this at the same time.
+
+ at STRING{ prq = "Political Research Quarterly"}
+ at STRING{ apsr = "American Political Science Review"}
+ at STRING{ ajps = "American Journal of Political Science"}
+ at STRING{ jop = "Journal of Politics"}
+ at STRING{ bjps = "British Journal of Political Science"}
+ at STRING{ jleo = "Journal of Law, Economics, and Organization"}
+ at STRING{ isa = "Paper presented at the annual meetings of the International Studies Association"}
+ at STRING{ apsa = "Paper presented at the annual meetings of the American Political Science Association"}
+ at STRING{ cp = "Comparative Politics"}
+ at STRING{ io = "International Organization"}
+ at STRING{ midwest = "Paper presented at the Annual Meeting of the Midwest Political Science Association"}
+ at STRING{ mpsa = "midwest"}
+ at STRING{ southern = "Paper presented at the Annual Meeting of the Southern Political Science Association"}
+ at STRING{ icpsr = "Inter-University Consortium for Political and Social Research"}
+ at STRING{ jasa = "Journal of the American Statistical Association"}
+ at STRING{ lsq = "Legislative Studies Quarterly"}
+ at STRING{ isq = "International Studies Quarterly"}
+ at STRING{ tas = "The American Statistician"}
+ at STRING{ jbes = "Journal of Business \& Economic Statistics"}
+ at STRING{ joe = "Journal of Econometrics"}
+ at STRING{ wp = "World Politics"}
+ at STRING{ cup = "Cambridge University Press"}
+ at STRING{ hup = "Harvard University Press"}
+ at STRING{ ny = "New York"}
+ at STRING{ sv = "Springer Verlag"}
+ at STRING{ pup = "Princeton University Press"}
+ at STRING{ ucp = "University of California Press"}
+ at STRING{ ap = "Academic Press"}
+ at STRING{ wb = "The World Bank"}
+ at STRING{ eas = "Europe-Asia Studies"}
+ at STRING{ jet = "Journal of Economic Theory"}
+ at STRING{ jrssA = "Journal of the Royal Statistical Society, A"}
+ at STRING{ jrssb = "Journal of the Royal Statistical Society, B"}
+ at STRING{ poq = "Public Opinion Quarterly"}
+ at STRING{ pnas = "Proceedings of the National Academy of Sciences"}
+ at STRING{ ai = "Artificial Intelligence"}
+ at STRING{ pa = "Political Analysis"}
+ at STRING{ ps = "PS: Political Science and Politics"}
+ at STRING{ smr = "Sociological Methods and Research"}
+ at STRING{ sim = "Statistics in Medicine"}
+ at STRING{ asr = "American Sociological Review"}
+ at STRING{ bmj = "British Medical Journal"}
+ at STRING{ lan = "Lancet"}
+ at STRING{ dem = "Demography"}
+ at STRING{ bull = "Bulletin of WHO"}
+ at STRING{ ssm = "Social Science and Medicine"}
+ at STRING{ mitai = "Artificial Intelligence Laboratory, Massachusetts Institute of Technology"}
+ at STRING{ nc = "Neural Computation"}
+
+ at article{AbaDruLeb02,
+ author = {Alberto Abadie and David Druckker and Jane Leber
+ Herr and Guido W. Imbens},
+ title = {Implementing Matching Estimators for Average
+ Treatment Effects in Stata},
+ journal = {The Stata Journal},
+ volume = 1,
+ year = 2002,
+ pages = {1--18},
+ number = 1
+}
+
+ at article{Bin83,
+ author = {David A. Binder},
+ title = {On the Variance of Asymptoticaly Normal Estimators from Complex Surveys},
+ year = {1983},
+ journal = {International Statistical Review},
+ volume = {51},
+ number = {3},
+ pages = {279--292}
+}
+
+ at article{HorTho52,
+ author = {D. G. Horvitz and D.J. Thompson},
+ title = {A Generalization of Sampling without Replacement
+ from a Finite Universe},
+ year = {1952},
+ journal = {Journal of the American Statistical Association},
+ volume = {47},
+ pages = {663--685}
+}
+
+ at article{AbaGelLev08,
+ author = {Kobi Abayomi and Andrew Gelman and Marc Levy},
+ title = {Diagnostics for Multivariate Imputations},
+ journal = {Applied Statistics},
+ volume = {57},
+ number = {3},
+ pages = {273--291},
+ year = {2008}
+}
+
+ at misc{AbaImb05,
+ author = {Alberto Abadie and Guido Imbens},
+ title = {Estimation of the Conditional Variance in Paired
+ Experiments},
+ year = 2006,
+ howpublished = {KSG Working Paper},
+ note =
+ {{http://ksghome.harvard.edu/\~{}.aabadie.academic.ksg/cve.pdf}}
+}
+
+
+ at misc{AbaImb09,
+ author = {Alberto Abadie and Guido Imbens},
+ title = {A Martingale Representation for Matching Estimators},
+ year = 2009,
+ howpublished = {IZA Discussion Papers number 4073},
+ note = {{http://ftp.iza.org/dp4073.pdf}}
+}
+
+
+
+ at article{Imb00,
+ author = {Guido Imbens},
+ title = {The role of the propensity score in estimating the dose-response functions},
+ year = {2000},
+ journal = {Biometrika},
+ pages = {706--710},
+ volume = {87},
+ issue = {3}
+}
+
+ at article{AbaImb07b,
+ author = {Alberto Abadie and Guido Imbens},
+ title = {On the Failure of the Bootstrap for Matching Estimators},
+ year = {2007},
+ journal = {Econometrica},
+ pages = {1537--1557},
+ volume = {76},
+ issue = {6}
+}
+
+ at article{AbaImb06,
+ author = {Abadie, Alberto and Imbens, Guido W.},
+ title = {Large Sample Properties of Matching Estimators for
+ Average Treatment Effects},
+ journal = {Econometrica},
+ volume = {74},
+ year = {2006},
+ pages = {235--267},
+ number = {1}
+}
+
+ at inproceedings{Abu-Mostafa92,
+ author={Y. Abu-Mostafa},
+ title={A Method for Learning from Hints},
+ booktitle={Advances in Neural information processings systems 5},
+ year={1992},
+ publisher={Morgan Kaufmann Publishers},
+ address={San Mateo, CA},
+ editor={S. J. Hanson and Jack D. Cowan and C. Lee Giles}
+}
+
+ at article{Abu-Mostafa92,
+ author={Y. Abu-Mostafa},
+ title={A Method for Learning from Hints},
+ journal={Advances in Neural information processings systems 5},
+ volume={1992},
+ pages={Morgan Kaufmann Publishers},
+ month={San Mateo CA},
+ number={S. J. Hanson and Jack D. Cowan and C. Lee Giles}
+}
+
+ at book{Achen86,
+ author={Christopher Achen},
+ title={Statistical Analysis of Quasi-experiments},
+ publisher={University of California Press},
+ year={1986},
+ address={Berkeley}
+}
+
+ at techreport{AdaCoaRue00,
+ author={Michelle Adato and David Coady and Marie Ruel},
+ title={An Operations Evaluation of Progresa from the Perspective of Beneficiaries,
+ Promotoras, School Directors and Health Staff},
+ institution={International Food Policy Research Institute},
+ year={2000},
+ month={August},
+ type={Final Report},
+ address={2033 K Street, NW Washington, DC 20006}
+}
+
+ at article{AdaGla05,
+ author={Adamic, L.A. and Glance, N.},
+ title={{The political blogosphere and the 2004 US election: divided they blog}},
+ journal={Proceedings of the 3rd international workshop on Link discovery},
+ year={2005},
+ pages={36--43},
+ publisher={ACM Press New York, NY, USA}
+}
+
+ at article{AgoDyn04,
+ author={Roberto Agodini and Mark Dynarski},
+ title={Are experiments the only option? {A} look at dropout prevention programs},
+ journal={Review of Economics and Statistics},
+ volume= 86,
+ year= 2004,
+ pages={180-194},
+ month={February},
+ number= 1
+}
+
+ at unpublished{AgrRajSri03,
+ author={Rakesh Agrawal and Sridhar Rajagopolan and Ramakrishnan Srikant and Yirong
+ Xu},
+ title={Mining Newsgroups Using Networks Arising from Social Behavior},
+ note={IBM ALmaden Research Center 650 Harry Rd., San Jose, CA 95120},
+ year={2003},
+ month={May}
+}
+
+ at book{Aitchison86,
+ author={J. Aitchison},
+ title={The Statistical Analysis of Compositional Data},
+ publisher={Chapman and Hall},
+ year= 1986,
+ address={London}
+}
+
+ at article{Albert88,
+ author={James H. Albert},
+ title={Computational Methods Using a Bayesian Hierarchical Generalized Linear Model},
+ journal={Journal of the American Statistical Association},
+ volume={83},
+ year={1988},
+ pages={1037-1004},
+ month={December},
+ number={404}
+}
+
+ at article{AldMcK77,
+ author={John H. Aldrich and Richard D. McKelvey},
+ title={A Method of Scaling With Applications to the 1968 and 1972 Presidential
+ Elections},
+ journal= apsr,
+ volume= 71,
+ year= 1977,
+ pages={111-130},
+ month={March}
+}
+
+ at article{AleTab90,
+ author={Alberto Alesina and Guido Tabellini},
+ title={A Positive Theory of Fiscal Deficits and Government Debt},
+ journal={The Review of Economic Studies},
+ volume={57},
+ year={1990},
+ pages={403-414},
+ month={July},
+ number={3}
+}
+
+ at article{Alho00,
+ author={J. M. Alho},
+ title={Discussion},
+ journal={North American Actuarial Journal},
+ volume= 4,
+ year= 2000,
+ pages={91--93},
+ number= 1
+}
+
+ at article{Alho92,
+ author={J. M. Alho},
+ title={{Comment on ``Modeling and Forecasting U.S. Mortality'' by R. Lee and L.
+ Carter}},
+ journal= jasa,
+ volume= 87,
+ year= 1992,
+ pages={673--674},
+ month={September},
+ number= 419
+}
+
+ at article{AlSaCr76,
+ author={James Alt and Bo Sarlvik and Ivor Crewe},
+ title={Individual Differences Scaling and Group Attitude Structures: British Party
+ Imagery in 1974},
+ journal={Quality and Quantity},
+ volume= 10,
+ year= 1976,
+ pages={297--320},
+ month={October}
+}
+
+ at book{AltGilMcD03,
+ author={Micah Altman and Jeff Gill and Michael P. McDonald},
+ title={Numerical Issues in Statistical Computing for the Social Scientist},
+ publisher={John Wiley and Sons},
+ year= 2003,
+ address={New York}
+}
+
+ at article{Altman85,
+ author={Douglas G. Altman},
+ title={Comparability of Randomised Groups},
+ journal={The Statistician},
+ volume={34},
+ year={1985},
+ pages={125-136},
+ number={1}
+}
+
+ at article{Altman98,
+ author={Douglas G. Altman and Jonathan J. Deeks and David L. Sackett},
+ title={Odds Ratios Should be Avoided When Events are Common},
+ journal={British Medical Journal},
+ volume= 317,
+ year= 1998,
+ pages= 1318,
+ month={Nov. 7}
+}
+
+ at article{AltMcD03,
+ author={Micah Altman and Michael P. McDonald},
+ title={Replication with Attention to Numerical Accuracy},
+ journal={Political Analysis},
+ volume={11},
+ year={2003},
+ pages={302-307},
+ number={3}
+}
+
+ at article{AltRub70,
+ author={Robert P. Althauser and Donald B. Rubin},
+ title={The computerized construction of a matched sample},
+ journal={American Journal of Sociology},
+ volume= 76,
+ year= 1970,
+ pages={325-346},
+ month={September}
+}
+
+ at article{AlvBre95,
+ author={Michael R. Alvarez and John Brehm},
+ title={American Ambivalence Toward Abortion Policy: A Heteroskedastic probit Method
+ for Assessing Conflicting Values},
+ journal={American Journal of Political Science},
+ volume={39},
+ year={1995},
+ pages={1055-82},
+ month={November}
+}
+
+ at article{AlvBre97,
+ author={Michael R. Alvarez and John Brehm},
+ title={Are Americans Ambivalent Towards Racial Policies},
+ journal={American Journal of Political Science},
+ volume={41},
+ year={1997},
+ pages={345-374},
+ month={April},
+ number={2}
+}
+
+ at article{AlvGarLan91,
+ author={Michael R.\ Alvarez and Geoffrey Garrett and Peter Lange},
+ title={Government Partisanship, Labor Organization, and Macroeconomic Performance},
+ journal= apsr,
+ volume= 85,
+ year= 1991,
+ pages={539--556}
+}
+
+ at article{AmoMccZim97,
+ author={A.F. Amos and D.J. McCarty and P. Zimmet},
+ title={The Rising Global Burden of Diabetes and its Complications: Estimates and
+ Projections to the Year 2010},
+ journal={Diabetic Medicine},
+ volume= 14,
+ year= 1997,
+ tpages={S7--S85}
+}
+
+ at book{AndBasHum83,
+ author={Andy B. Anderson and Alexander Basilevsky and Derek P.J. Hum},
+ title={Missing Data: A Review of the Literature},
+ publisher={Academic Press, Inc},
+ year={1983},
+ editor={Peter H. Rossi and James D. Writght and Andy B. Anderson}
+}
+
+ at article{AndGib06,
+ author={Krister Andersson and Clark C. Gibson},
+ title={Decentralized Governance and Environmental Change: Local Institutional Moderation
+ of Defroestation in Bolivia},
+ journal={Journal of Policy Analysis and Management},
+ volume={26},
+ year={2006},
+ pages={99-123},
+ number={1}
+}
+
+ at article{AndGreMcc05,
+ author={Richard G. Anderson and William H. Greene and B.D. McCullough and H.D. Vinod},
+ title={The Role of Data \& Program Code Archives in the Future of Economic Research},
+ year= 2005,
+ month={July},
+ note={Federal Reserve Bank of St. Louis Research Division}
+}
+
+ at article{Andrews91,
+ author={Donald W.K. Andrews},
+ title={Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation},
+ journal={Econometrica},
+ volume={59},
+ year={1991},
+ pages={817--858},
+ month={May},
+ number={3}
+}
+
+ at article{AndZom01,
+ author={A.S. Andreou and G.A. Zombanakis},
+ title={A Neural Network Measurement of Relative Military Security--The Case of
+ Greece and Cyprus},
+ journal={Defence and Peace Economics},
+ volume= 12,
+ year= 2001,
+ pages={303--324},
+ number= 4,
+ annote={have not read primary source. looks promising, given secondary source comments:
+ all input variables are financial and the output variable--relative security--is
+ population/demographics based. Arms race scenarios are simulated by increasing
+ and decreasing financial covariates.}
+}
+
+ at article{AngAngFro94,
+ author={G. De Angelis and R. De Angelis and L. Frova and A. Verdecchia},
+ title={MIAMOD: A Computer Package to Estimate Chronic Disease Morbidity Using Mortality
+ and Survival Data},
+ journal={Computer Methods and Programs in Biomedicine},
+ volume= 44,
+ year= 1994,
+ pages={99--107}
+}
+
+ at article{AngImb95,
+ author={Joshua D. Angrist and Guido W. Imbens},
+ title={Two-Stage Least Squares Estimation of Average Causal Effects in Models withVariable
+ Treatment Intensity},
+ journal={Journal of the American Statistical Association},
+ volume={90},
+ year={1995},
+ pages={431-442},
+ month={June},
+ number={430}
+}
+
+ at article{AngImbRub96,
+ author={Angrist, Joshua D. and Imbens, Guido W. and Rubin, Donald B.},
+ title={Identification of Causal Effects Using Instrumental Variables (with discussion)},
+ journal={Journal of the American Statistical Association},
+ volume={91},
+ year={1996},
+ pages={444--455},
+ optnumber={434}
+}
+
+ at article{Angress59,
+ author={Werner T. Angress},
+ title={The Political Role of the Peasantry in the Weimar Republic},
+ journal={The Review of Politics},
+ volume= 21,
+ year= 1959,
+ pages={530--549},
+ number= 3
+}
+
+ at unpublished{AnkBlaCol99,
+ author = {Martha Anker and Robert E. Black and Christopher
+ Coldham and Henry D. Kalter and Maria A. Quigley and
+ David Ross and Robert W. Snow},
+ title = {A Standard Verbal Autopsy Method for Investigating
+ Causes of Death in Infants and Children},
+ note = {World Health Organization, Department of
+ communicable Disease Surveillance and Response},
+ year = {1999},
+ journal = {World Health Organization}
+}
+
+ at book{Anker03,
+ author = {Martha Anker},
+ title = {Investigating Cause of Death During an Outbreak of
+ Ebola Virus Haemorrhagic Fever: Draft Verbal Autopsy
+ Instrument},
+ publisher = {World Health Organization},
+ year = 2003,
+ address = {Geneva}
+}
+
+ at article{Anker97,
+ author = {Martha Anker},
+ title = {The Effect of Misclassification Error on Reported
+ Cause-Specific Mortality Fractions from Verbal
+ Autopsy},
+ journal = {International Journal of Epidemiology},
+ volume = {26},
+ year = {1997},
+ pages = {1090-1096}
+}
+
+ at article{AppBosGra96,
+ author = {A. Appels, et al},
+ title = {Self-Rated Health and Mortality in a Lithuanian and
+ Dutch Population},
+ journal = {Social Science and Medicine},
+ volume = 42,
+ year = 1996,
+ pages = {{681-89}},
+ number = 5
+}
+
+ at techreport{Arendt03,
+ author = {Jacob N. Arendt},
+ title = {Social gradients in self-rated health in Denmark -
+ gender differences and health risk factors in
+ dynamic context},
+ institution = {AKF, Institute of Local Government Studies},
+ year = 2003,
+ month = {May},
+ address = {Nyropsgade 37, 1602 Copenhagen V, Denmark}
+}
+
+ at book{Arendt73,
+ author = {Arendt, Hannah},
+ title = {The Origins of Totalitarianism},
+ publisher = {Harcourt Brace Jovanovich},
+ year = 1973,
+ address = {New York}
+}
+
+ at incollection{Armstrong01,
+ author = {J. Scott Armstrong},
+ title = {Extrapolation of Time Series and Cross-Sectional
+ Data},
+ booktitle = {Principles of Forecasting: A Handbook for
+ Researchers and Practitioners},
+ publisher = {Kluwer},
+ year = 2001,
+ editor = {J. Scott Armstrong},
+ pages = {217--243}
+}
+
+ at unpublished{Ashworth01,
+ author = {Scott Ashworth},
+ title = {Reputational Dynamics and Congressional Careers},
+ note = {Harvard University},
+ year = 2001,
+ annote = {introduce the single crossing property in political
+ science}
+}
+
+ at article{AssPocEno00,
+ author = {Susan F. Assmann and Stuart J. Pocock, Laura E. Enos
+ and Linda E. Kasten},
+ title = {Subgroup analysis and other (mis)uses of baseline
+ data in clinical trials},
+ journal = {The Lancet},
+ volume = {355},
+ year = {2000},
+ pages = {1064-1069},
+ month = {March}
+}
+
+ at article{AusMam06,
+ author={Peter C. Austin and Muhammad M. Mamdani},
+ title={A comparison of propensity score methods: A case-study estimating the effectiveness
+ of post-AMI statin use},
+ journal={Statistics in Medicine},
+ volume={25},
+ year={2006},
+ pages={2084-2106}
+}
+
+ at article{AusMamStu05,
+ author={Peter C. Austin and Muhammad M. Mamdani and Therese A. Stukel and Geoffrey
+ M. Anderson and Jack V. Tu},
+ title={The use of the propensity score for estimating treatment effects: {A}dministrative
+ versus clinical data},
+ journal={Statistics in Medicine},
+ volume={24},
+ year={2005},
+ pages={1563-1578}
+}
+
+ at article{AvlSchDav98,
+ author={Kirsten Avlund, Kirsten Schultz-Larsen, and Michael Davidson},
+ title={Tiredness in Daily Activities at Age 70 as a Predictor of Mortality During
+ the Next 10 Years},
+ journal={Journal of Clinical Epidemiology},
+ volume= 51,
+ year= 1998,
+ pages={{323-33}}
+}
+
+ at article{BacKin04,
+ author={Bachrach, Christine A. and King, Roslind B.},
+ title={{Data Sharing and Duplication: Is There a Problem?}},
+ journal={Archives of Pediatric and Adolescent Medicine},
+ volume= 158,
+ year={2004},
+ month={September},
+ number= 9
+}
+
+ at article{BagHopMas02,
+ author={A. Bagust and P.K. Hopkinson and L. Maslove and C.J. Currie},
+ title={The Projected Health Care Burden of Type 2 Diabetes in the UK from 2000
+ to 2060},
+ journal={Diabetic Medicine},
+ volume= 19,
+ year= 2002,
+ pages={1--5},
+ number= 4
+}
+
+ at book{Balderston02,
+ author={Theo Balderston},
+ title={Economics and Politics in the Weimar Republic},
+ publisher={Cambridge University Press},
+ year= 2002,
+ address={Cambridge}
+}
+
+ at article{BanBan92,
+ author={A.T. Bang and R.A. Bang and the SEARCH team},
+ title={Diagnosis of causes of childhood deaths in developing countries by verbal
+ autosy: suggested criteria},
+ journal={Bulletin of the World Health Organization},
+ volume={70},
+ year={1992},
+ pages={499-507},
+ number={4}
+}
+
+ at article{BaqBlaAri98,
+ author={A.H. Baqui and R.E. Black and S.E. Arifeen and K. Hill and S.N. Mitra and
+ A.Al Sabir},
+ title={Causes of childhood deaths in Bangladesh: results of a nationwide verbal
+ autopsy study},
+ journal={Bulletin of the World Health Organization},
+ volume={76},
+ year={1998},
+ pages={161},
+ number={2}
+}
+
+ at article{BarFraHil03,
+ author={John Barnard and Constantine E. Frangakis and Jennifer L. Hill and Donald
+ B. Rubin},
+ title={{Principal Stratification Approach to Broken Randomized Experiments: A Case
+ Study of School Choice Vouchers in New York City.}},
+ journal={Journal of the American Statistical Association},
+ volume={98},
+ year={2003},
+ pages={299-324},
+ number={462}
+}
+
+ at book{Barkai90,
+ author={Barkai, Avram},
+ title={Nazi Economics: Ideology, Theory, and Policy},
+ address={Oxford},
+ publisher={Berg Press},
+ year={1990}
+}
+
+ at article{BarPonCor00,
+ author={Ivana C. H. C. Barr{\^e}to and L{\'i}gia Kerr Pontes and e Luciano Corr{\^e}a},
+ title={Vigil{\^a}ncia de {\'o}bitos infantis em sistemas locais de sa{\'u}de: avalia{\c{c}}{\~a}o da
+ aut{\'o}psia verbal e das informa{\c{c}}{\~o}es de agentes de sa{\'u}de},
+ journal={Rev Panam Salud Publica / Pan Am Journal of Public Health},
+ volume={7},
+ year={2000},
+ pages={303-312},
+ number={5}
+}
+
+ at article{Bartels96,
+ author={Bartels, Larry M.},
+ title={Uninformed Votes: Information Effects in Presidential Elections},
+ journal={American Journal of Political Science},
+ volume= 40,
+ year= 1996,
+ pages={194--230}
+}
+
+ at unpublished{Bartels98,
+ author={Larry Bartels},
+ title={Panel Attrition and Panel Conditioning in American National Election Sudies},
+ note={Paper prepared for the 1998 meetings of the Society for Political Methodology,
+ San Deigo},
+ year={1998}
+}
+
+ at article{BasEst01,
+ author={S.A. Bashir and J. Esteve},
+ title={Projecting Cancer Incidence and Mortality Using Bayesian Age-Period-Cohort
+ Models},
+ journal={Journal of Epidemiology and Biostatistics},
+ volume= 6,
+ year= 2001,
+ pages={287--296},
+ number= 3
+}
+
+ at unpublished{BatFerHab06,
+ author={Robert Bates and Karen Feree and James Habyarimana and Macartan Humphreys
+ and Smita Singh},
+ title={The Africa Research Program},
+ note={{http://africa.gov.harvard.edu}},
+ year= 2006
+}
+
+ at article{Bath03,
+ author={Peter A. Bath, PhD},
+ title={Differences Between Older Men and Women in the Self-Rated Health-Mortality
+ Relationship},
+ journal={The Gerontologist},
+ volume= 43,
+ year= 2003,
+ pages={{387-95}}
+}
+
+ at article{Baum88,
+ author={Lawrence Baum},
+ title={Measuring Policy Change in the U.S. Supreme Court},
+ journal= apsr,
+ volume= 82,
+ year= 1988,
+ pages={905--912},
+ month={September},
+ number= 3
+}
+
+ at article{BeaMei89,
+ author={Michael L. Beach and Paul Meier},
+ title={Choosing Covariates in the Analysis of Clinical Trials},
+ journal={Controlled Clinical Trials},
+ volume={10},
+ year={1989},
+ pages={161S-175S}
+}
+
+ at incollection{Bearce00,
+ author={David Bearce},
+ title={Economic Sanctions and Neural Networks: Forecasting Effectiveness and Reconsidering
+ Cooperation},
+ booktitle={Political Complexity: Non Linear Models of Politics},
+ publisher={University of Michigan Press},
+ year= 2000,
+ address={Ann Arbor, MI},
+ editor={Diana Richards},
+ pages={269--295},
+ annote={asks whether real-world forecasting needs make NN preferable to traditional
+ (and linear) analysis. Looks at effectiveness of sanctions, using about
+ 100 quantitative cases first examined in 1980s. NNs are shown to forecast
+ twice as well as traditional methods.}
+}
+
+ at book{BecChaWil88,
+ author={Richard A. Becker and John M. Chambers and Allan R. Wilks},
+ title={The New S. language},
+ publisher={Wadsworth},
+ year={1988},
+ address={New York}
+}
+
+ at article{BecIch02,
+ author={Sascha O. Becker and Andrea Ichino},
+ title={Estimation of average treatment effects based on propensity scores},
+ journal={The Stata Journal},
+ volume= 2,
+ year= 2002,
+ pages={358-377},
+ number= 4
+}
+
+ at article{BecIch02,
+ author={Sascha O. Becker and Andrea Ichino},
+ title={Stata programs for ATT estimation based on propensity score matching},
+ journal={The Stata Journal},
+ volume= 2,
+ year= 2002,
+ pages={358--377},
+ number= 4
+}
+
+ at article{BecJac98,
+ author={Nathaniel Beck and Simon Jackman},
+ title={Beyond Linearity by Default: Generalized Additive Model},
+ journal= ajps,
+ volume= 42,
+ year= 1998,
+ pages={596--627},
+ month={April},
+ number= 2
+}
+
+ at article{BecKat95,
+ author={Nathaniel Beck and Jonathan Katz},
+ title={``What to Do (and Not to Do) with Time-Series-Cross-Section Data''},
+ journal= apsr,
+ volume= 89,
+ year= 1995,
+ pages={634--647}
+}
+
+ at article{BecKat96,
+ author={Nathaniel Beck and Jonathan Katz},
+ title={Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section
+ Model},
+ journal= pa,
+ volume={VI},
+ year= 1996,
+ pages={1--36}
+}
+
+ at article{BecKatTuc98,
+ author={Nathaniel Beck and Jonathan Katz and Richard Tucker},
+ title={Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary
+ Dependent Variable},
+ journal= apsr,
+ volume= 42,
+ year= 1998,
+ pages={1260-1288}
+}
+
+ at article{BedChrJoh96,
+ author={Edward J. Bedrick and Ronald Christensen and Wesley Johnson},
+ title={A New Perspective on Priors for Generalized Linear models},
+ journal={Journal of the American Statistical Association},
+ volume={91},
+ year={1996},
+ pages={1450-1460},
+ number={436}
+}
+
+ at techreport{BehTod99,
+ author={Jere R. Behrman and Petra E. Todd},
+ title={Randomness in the Experimental Samples of Progresa (Education, Health, and
+ Nutrition Program)},
+ institution={International Food Policy Research Institute},
+ year={1999},
+ month={March},
+ type={Research Report},
+ address={2033 K Street, NW Washington, DC 20006}
+}
+
+ at article{Bell97,
+ author={W.R. Bell},
+ title={{Comparing and Assessing Time Series Methods for Forecasting Age-Specific
+ Fertility and Mortality Rates}},
+ journal={Journal of Official Statistics},
+ volume= 13,
+ year= 1997,
+ pages={279--303},
+ number= 3
+}
+
+ at article{Bello93,
+ author={Abdul Lateef Bello},
+ title={Choosing Among Imputation Techniques for Incomplete Multivariate Data: A
+ Simulation Study},
+ journal={Communications in Statistics A: Theory and Methods},
+ volume={22},
+ year={1993},
+ pages={853-877},
+ number={3}
+}
+
+ at article{BelMon91,
+ author={W.R. Bell and B.C. Monsell},
+ title={Using Principal Components in time Series modeling and Forecasting of Age-Specific
+ Mortality Rates},
+ journal={Proceedings of the American Statistical Association, Social Statistics Section},
+ year= 1991,
+ pages={154--159}
+}
+
+ at article{Beltrami1873,
+ author={E. Beltrami},
+ title={Sulle funzioni bilineari},
+ journal={Giornale di Matematiche ad Uso degli Studenti Delle Universit{\'a}},
+ volume= 11,
+ year= 1873,
+ pages={98--106},
+ note={{An English translation by D. Boley is available as University of Minnesota,
+ Department of Computer Science, Technical Report 90-37, 1990}}
+}
+
+ at article{BenBluLus03,
+ author={Yael Benyamini, et al.},
+ title={Gender differences in the self-rated health-mortality association: Is it
+ poor self-rated health that predicts mortality or excellent self-rated
+ health that predicts survival?},
+ journal={The Gerontologist},
+ volume={43},
+ year={2003},
+ pages={{396-405}},
+ number={3}
+}
+
+ at incollection{Bendix53,
+ author={Bendix, Reinhard},
+ title={Social Stratification and Political Power},
+ booktitle={Class Status and Power},
+ publisher={The Free Press},
+ year= 1953,
+ address={Glencoe, IL},
+ editor={Bendix, Reinhard and Lipset, Seymour Martin}
+}
+
+ at article{BenHumEbe04,
+ author={Maureen Reindl Benjamins},
+ title={Self-Reported Health and Adult Mortality Risk: An Analysis of Cause Specific
+ Mortality},
+ journal={Social Science and Medicine (Forthcoming 2004)}
+}
+
+ at article{benichou95,
+ author={J. Benichou and M. Gail},
+ title={Methods of Inference for Estimates of Absolute Risk Derived From Population-Based
+ Case-Control Studies},
+ journal={Biometrics},
+ volume= 51,
+ year= 1995,
+ pages={182-194}
+}
+
+ at article{BenIdl99,
+ author={Yael Benyamini, and Ellen Idler},
+ title={Community Studies Reporting Association Between Self-Rated Health and Mortality},
+ journal={Research on Aging},
+ volume= 21,
+ year= 1999,
+ pages={{392-401}},
+ number= 3
+}
+
+ at article{BenIdlLev00,
+ author={Yael Benyamini, Ellen Idler, Howard Leventhal, and Elaine A. Leventhal},
+ title={Positive-Affect and Function as Influences on Self-Assessments of Health:
+ Expanding ou View Beyond Illness and Disability},
+ journal={Journal of Gerontology: Psychological Sciences},
+ volume={{55B}},
+ year= 2000,
+ pages={{P107-116}}
+}
+
+ at article{BenLav03,
+ author={Kenneth Benoit and Michael Laver},
+ title={Estimating Irish party policy positions using computer wordscoring: the
+ 2002 election - a research note},
+ journal={Irish Political Studies},
+ volume={18},
+ year={2003},
+ pages={97--107},
+ number={1}
+}
+
+ at article{BenLevLev00,
+ author={Yael Benyamini, et al},
+ title={Gender Differences in Processing Information for Making Self-Assessments
+ of Health },
+ journal={American Psychosomatic Society},
+ volume= 62,
+ year= 2000,
+ pages={{354-64}},
+ number= 2
+}
+
+ at article{BenLevLev99,
+ author={Yael Benyamini, Elaine A. Leventhal, and Howard Leventhal},
+ title={Self-Assessments of Health. What Do People Know that Predicts their Mortality?},
+ journal={Reasearch on Aging},
+ volume= 21,
+ year= 1999,
+ pages={{477-500}},
+ month={{May}},
+ number= 3
+}
+
+ at article{BenLip59,
+ author={Bendix, Reinhard and Lipset, Seymour Martin},
+ title={On the Social Structure of Western Societies: Some Reflections on Comparative
+ Analysis},
+ journal={Berkeley Journal of Sociology},
+ volume= 5,
+ year= 1959,
+ pages={1-15}
+}
+
+ at article{BenSin99,
+ author={S.K. Benara and Padam Singh},
+ title={Validity of Causes of Infant Death by Verbal Autopsy},
+ journal={Indian Journal of Pediatrics},
+ volume={66},
+ year={1999},
+ pages={647-650}
+}
+
+ at article{BenSin99,
+ author={S.K. Benara and Padam Singh},
+ title={Validity of Causes of Infant Death by Verbal Autopsy},
+ journal={Indian Journal of Pediatrics},
+ volume={66},
+ year={1999},
+ pages={647-650}
+}
+
+ at article{BerdeG47,
+ author={Berelson, B. and de Grazia, S.},
+ title={{Detecting Collaboration in Propaganda}},
+ journal={Public Opinion Quarterly},
+ volume={11},
+ year={1947},
+ pages={244--253},
+ number={2}
+}
+
+ at proceedings{BerDegLin88,
+ editor={J. M. Bernardo and M.H. Degroot and D.V. Lindley and A.F.M. Smith},
+ title={Bayesian Statistics 3},
+ publisher={Clarendon Press, Oxford},
+ year={1987},
+ month={June 1-5},
+ organization={Proceedings of the Third Valencia International Meeting}
+}
+
+ at article{Berenson04,
+ author={Robert Berenson},
+ title={The Medicare Chronic Care Improvement Program},
+ journal={The Urban Institute},
+ year={2004},
+ month={May},
+ note={{http://www.urban.org/url.cfm?ID=900714}}
+}
+
+ at article{Berger04,
+ author={Vance W. Berger},
+ title={Selection Bias and Baseline Imbalances in Randomized Trials},
+ journal={Drug Information Journal},
+ volume={38},
+ year={2004},
+ pages={1-2}
+}
+
+ at article{Berger05a,
+ author={Vance W. Berger},
+ title={Quantifying the Magnitude of Baseline Covariate Imbalances Resulting fronmSelection
+ Bias in Randomized Clinical Trials},
+ journal={Biometrical Journal},
+ volume={47},
+ year={2005},
+ pages={119-127},
+ number={2}
+}
+
+ at book{Berger05b,
+ author={Vance W. Berger},
+ title={Selection Bias and covariate Imbalances in Randomized Clinical Trials},
+ publisher={John Wiley \& Sons, Ltd.},
+ year={2005},
+ editor={Stephen Senn and Vic Barnett},
+ series={Statistics in Practice}
+}
+
+ at article{Berger94,
+ author={James Berger},
+ title={An Overview of Robust Bayesian Analysis (With Discussion)},
+ journal={Test},
+ volume= 3,
+ year= 1994,
+ pages={5-124}
+}
+
+ at article{BerHenSav77,
+ author={E. Berndt and D. Hendry and N.E. Savin},
+ title={Conflict Among Criteria for Testing Hypotheses in the Multivariate Linear
+ Regression Model},
+ journal={Econometrica},
+ volume={45},
+ year={1977},
+ pages={1263-1277}
+}
+
+ at article{BerKinKon97,
+ author={Shulamit L. Bernard, Jean E. Kincade, Thomas R. Conrad, et al},
+ title={Predicting Mortality from Community Surveys of Older Adults: The Importance
+ of Self-Rated Functional Ability},
+ journal={Journal of Gerontology: Social Sciences},
+ volume={{52B}},
+ year= 1997,
+ pages={{S155-63}}
+}
+
+ at misc{BerKos03,
+ author={Erik Bergstralh and Jon Kosanke},
+ title={dist, gmatch, and vmatch: SAS Macros},
+ year= 2003,
+ howpublished={Mayo Clinic, Division of Biostatistics},
+ note={{http://mayoresearch.mayo.edu/mayo/research/biostat/sasmacros.cfm}}
+}
+
+ at book{BerKreOve98,
+ author={Mark de Berg and Marc van Krevald and Mark Overmars and Otfried Schwarzkopf},
+ title={Computational Geometry: Algorithms and Applications},
+ publisher={Springer},
+ year= 1998,
+ address={New York},
+ edition={2nd, revised edition}
+}
+
+
+ at book{ImbRub10,
+ author={Guido Imbens and Donald Rubin},
+ title={Causal Inference},
+ year= {2010},
+ note={Unpublished manuscript}
+}
+
+
+ at article{Bernstein32,
+ author={F. Bernstein},
+ title={{\"U}ber eine Methode, die soziologische und bev{\"o}lkerungsstatistische Gliederung
+ von Abstimmungen bei geheimen Wahlverfahren zu ermittlen},
+ journal={Allgemeines Statistisches Archiv},
+ volume= 22,
+ year= 1932,
+ pages={253--256}
+}
+
+ at article{Besag74,
+ author={Julian Besag},
+ title={Spatial Interaction and the Statistical Analysis of Lattice Systems (With
+ Discussion)},
+ journal= jrssb,
+ volume= 36,
+ year= 1974,
+ pages={192-236}
+}
+
+ at article{Besag75,
+ author={Julian Besag},
+ title={Statistical Analysis of Non-Lattice Data},
+ journal={The Statistician},
+ volume= 24,
+ year= 1975,
+ pages={179--195},
+ number= 3
+}
+
+ at article{Besag83,
+ author={Julian E. Besag},
+ title={Discussion of paper by {P}. {S}witzer},
+ journal={Bull. Intern. Statist. Inst.},
+ volume= 50,
+ year= 1983,
+ pages={422-425},
+ number={Bk. 3}
+}
+
+ at article{Besag86,
+ author={Julian Besag},
+ title={On the Statistical Analysis of Dirty Pictures},
+ journal={Journal of the Royal Statistical Society B},
+ volume={48},
+ year={1986},
+ pages={259--302},
+ number={3}
+}
+
+ at article{Besancon05,
+ author={Marie L. Besancon},
+ title={Relative Resources: Inequality in Ethnic Wars, Revolutions, and Genocides},
+ abstract={Political scientists and economists have exhaustively examined the nexus
+ between economic inequality and political conflict (EI-PC nexus) in aggregated
+ civil wars. This article revisits the nexus and its related theories, empirically
+ and parsimoniously testing the effects of inequality on disaggregated intrastate
+ conflicts. The results buttress the notion that traditionally deprived
+ identity groups are more likely to engage in conflict under more economically
+ equal conditions, while class or revolutionary wars fall under the conditions
+ of greater economic inequality and war. Of the three types of conflicts
+ tested - ethnic conflicts, revolutions, and genocides - economic inequality
+ seems to have the most ambiguous bearing on genocides. Support follows
+ for recent findings that political and social equalities are of greater
+ importance in mitigating ethnic violence and that greed factors might exacerbate
+ violence in all civil conflicts, including genocides. The theoretical argument
+ proposes that the context within which intrastate violence takes place
+ affects the requisite level of relative resources needed for the escalation
+ of violence between groups. The results have policy implications for ethnically
+ divided states that are in the process of equalizing their income differential,
+ but neglect the substantial inclusion of all groups within the political
+ process and the distribution of public goods. The social contracts between
+ the governors and the governed then require careful crafting for a peaceful
+ coexistence of diverse identity groups.},
+ journal={The Journal of Peace Research},
+ volume={42},
+ year={2005},
+ pages={393-415},
+ month={July},
+ number={4}
+}
+
+ at article{BesGreHigMen95,
+ author={Julian Besag and Peter Green and David Higdon and Kerrie Mengersen},
+ title={Bayesian Computation and Stochastic Systems (With Discussion)},
+ journal={Statistical Science},
+ volume= 10,
+ year= 1995,
+ pages={3-66},
+ number= 1
+}
+
+ at article{BesHig99,
+ author={Julian Besag and David M. Higdon},
+ title={Bayesian Analysis of Agricultural Field Experiments (With Discussion)},
+ journal= jrssb,
+ volume= 61,
+ year= 1999,
+ pages={691-746},
+ number={4}
+}
+
+ at article{BesKoo95,
+ author={Julian Besag and Charles Kooperberg},
+ title={On Conditional and Intrinsic Autoregressions},
+ journal={Biometrika},
+ volume={82},
+ year= 1995,
+ pages={733-746},
+ number={4}
+}
+
+ at article{BesYorMol91,
+ author={Julian Besag and Jeremy York and Annie Molli{\'e}},
+ title={Bayesian Image Restoration with Two Applications in Spatial Statistics (With
+ Discussion)},
+ journal={Annals of the Institute of Statistical Mathematics},
+ volume= 43,
+ year= 1991,
+ pages={1-59},
+ number= 1
+}
+
+ at article{Bicego97,
+ author={G. Bicego},
+ title={Estimating adult mortality rates in the context of the AIDS epidemic in
+ sub-Saharan Africa: analysis of DHS sibling histories},
+ journal={Health Transition Review},
+ volume= 7,
+ year= 1997,
+ pages={7--22},
+ number={S2}
+}
+
+ at book{Biggs93,
+ author={N.L. Biggs},
+ title={Algebraic Graph Theory},
+ publisher={Cambridge University Press},
+ year= 1993,
+ address={Cambridge, UK},
+ edition={2nd}
+}
+
+ at article{Billordo05a,
+ author={Libia Billordo},
+ title={Publishing in French Political Science Journals},
+ journal={French Politics},
+ volume={3},
+ year={2005},
+ pages={178-186},
+ number={2}
+}
+
+ at article{Billordo05b,
+ author={Libia Billordo},
+ title={Methods Training in French Political Science},
+ journal={French Politics},
+ volume={3},
+ year={2005},
+ pages={352-0357},
+ number={3}
+}
+
+ at article{BinBreEar05,
+ author={J.B. Bingenheimer and R.T. Brennan and F.J. Earls},
+ title={Firearm violence exposure and serious violent behavior},
+ journal={Science},
+ volume={308},
+ year={2005},
+ pages={1323-1326},
+ month={May}
+}
+
+ at book{BisFieHol75,
+ author={Y.M. M. Bishop and S.E. Fienberg and P.W. Holland},
+ title={Multivariate Analysis: Theory and Practice},
+ publisher={MIT Press},
+ year= 1975,
+ address={Cambridge, MA}
+}
+
+ at book{Bishop95,
+ author={Christopher M. Bishop},
+ title={Neural Networks for Pattern Recognition},
+ publisher={Oxford University Press},
+ year= 1995,
+ address={Oxford}
+}
+
+ at article{BisSteWil06,
+ author={Benjamin Bishin and Daniel Stevens and Christian Wilson},
+ title={{Character Counts: Honesty and Fairness in Election 2000}},
+ journal={Public Opinion Quarterly},
+ volume={70},
+ year={2006},
+ pages={235-248},
+ number={2}
+}
+
+ at article{BjoKri99,
+ author={Jakob Bue Bjorner and Tage Sondergaard Kristensen},
+ title={Multi-item Scales for Measuring Global Self Rated Health},
+ journal={Research on Aging},
+ volume= 21,
+ year= 1999,
+ pages={{417-39}},
+ number= 3
+}
+
+ at article{BlaGeo91,
+ author={R.C. Blattberg and E.I. George},
+ title={Shrinkage Estimation of Price and Promotional Elasticities: Seemingly Unrelated
+ Equations},
+ journal= jasa,
+ volume= 86,
+ year= 1991,
+ pages={304--315},
+ month={Jun},
+ number= 414
+}
+
+ at article{BlaRas04,
+ author={Grant Blank and Karsten B. Rasmussen},
+ title={The Data Documentation Initiative: The Value and Significance of a Worldwide
+ Standard.},
+ journal={Social Science Computer Review},
+ volume={22},
+ year={2004},
+ pages={306-318},
+ number={3}
+}
+
+ at article{BlaSmi04,
+ author={Dan A. Black and Jeffrey A. Smith},
+ title={How robust is the evidence on the effects of college quality? Evidence from
+ matching},
+ journal={Journal of Econometrics},
+ volume={121},
+ year={2004},
+ pages={99-124},
+ number={1}
+}
+
+ at article{BloHilRic03,
+ author={Howard S. Bloom and Carolyn J. Hill and James A. Riccio},
+ title={Linking Program Implementation and Effectiveness: Lessons from a Pooled
+ Sample of Welfare-to-Work Experiments},
+ journal={Journal of Policy Analysis and Management},
+ volume={22},
+ year={2003},
+ pages={551-575},
+ number={4}
+}
+
+ at book{Bloom05,
+ title={Learning More from Social Experiments},
+ publisher={Russell Sage Foundation},
+ year={2005},
+ editor={Howard S. Bloom},
+ address={New York}
+}
+
+ at article{BloRicBla07,
+ author={Howard S. Bloom and Lashawn Richburg-Hayes and Alison Black},
+ title={Using Covariates to Improve Precision for Studies that Randomize Schools
+ to Evaluate Educational Interventions},
+ journal={Educational Evaluation and Policy Analysis},
+ year={2007}
+}
+
+ at book{BLS03,
+ author={{Board on Life Sciences}},
+ title={Sharing Publication-Related Data and Materials: Responsibilities of Authorship
+ in the Life Sciences},
+ publisher={National Academies Press},
+ year= 2003,
+ address={Washington, D.C.}
+}
+
+ at article{Blumer48,
+ author = {Herbert Blumer},
+ title = {Public Opinion and Public Opinion Polling},
+ journal = {American Sociological Review},
+ volume = {13},
+ year = {1948},
+ pages = {542-549},
+ month = {October},
+ number = {5}
+}
+
+ at incollection{Bohm84,
+ author={Peter Bohm},
+ title={Are thee Practicable Demand-Revealing Mechanisms?},
+ booktitle={Public Finance and the Quest for Efficiency},
+ publisher={Wayne State University Press},
+ year={1984},
+ address={Detroit},
+ editor={H. Hanusch},
+ pages={127-139}
+}
+
+ at article{BonBarMee94,
+ author={Luc Monneux and Jan J. Barendregt and Karin Meeter and Gouke J. Bonsel and
+ Paul J. van der Maas},
+ title={Estimating Clinical Morbidity Due to Ischemic Heart Disease and Congestive
+ Heart Failure: The Future Rise of Heart Failure},
+ journal={American Journal of Public Health},
+ volume= 84,
+ year= 1994,
+ pages={20-28}
+}
+
+ at unpublished{BonBonJen01,
+ author={Doug Bond and Joe Bond and J. Craig Jenkins and Churl Oh and Charles Lewis
+ Taylor},
+ title={Integrated Data for Events Analysis (IDEA): An Event Form Typology for Automated
+ Events Data Development},
+ note={manuscript, Harvard University},
+ year= 2001
+}
+
+ at article{Bongaarts89,
+ author={John Bongaarts},
+ title={A Model of the Spread of HIV Infection and the Demographic Impact of AIDS},
+ journal={Statistics in Medicine},
+ volume= 8,
+ year= 1989,
+ pages={103--120}
+}
+
+ at techreport{BooMaiSmi02,
+ author={Heather Booth and John Maindonald and Len Smith},
+ title={{Age-Time Interactions in Mortality Projection: Applying Lee-Carter to Australia}},
+ institution={The Australian National University},
+ year= 2002,
+ month={August},
+ type={Working Papers in Demography}
+}
+
+ at article{BooMaiSmi02b,
+ author={Heather Booth and John Maindonald and Len Smith},
+ title={Applying Lee-Carter Under Conditions of Variable Mortality Decline},
+ journal={Population Studies},
+ volume= 56,
+ year= 2002,
+ pages={325--336},
+ number= 3
+}
+
+ at unpublished{BorBorRal01,
+ author={Roman Borisyuk and Galina Borisyuk and Colin Rallings and Michael Thrasher},
+ title={Forecasting the 2001 General Election Result: A Neural Network Approach},
+ note={{http://www.psa.ac.uk/spgrp/epop/forecasting\_genelect2001.htm}},
+ year={2001},
+ annote={authors try to forecast election results; they generate fitted values by
+ 'predicting' the winners of past elections. they do break up their data
+ sets into training and test in the spirit of cross-validation; they also
+ compare their results to logit.}
+}
+
+ at incollection{Borchardt91,
+ author = {Knut Borchardt},
+ title = {Economic Causes for the Collapse of the Weimar
+ Republic},
+ booktitle = {Perspectives on Modern German Economic History and
+ Policy},
+ publisher = {Cambridge University Press},
+ year = 1991,
+ address = {New York},
+ editor = {Knut Borchardt},
+ pages = {161--184}
+}
+
+ at article{borgan95,
+ author={{\O }rnulf Borgan and B. Langgholz and L. Goldstein},
+ title={Methods for the Analysis of Sampled Cohort Data in the Cox Proportional
+ Hazard Model},
+ journal={The Annals of Statistics},
+ volume= 23,
+ year= 1995,
+ number={1749-1778}
+}
+
+ at article{BorMayTur04,
+ author={Robert Boruch and Henry May and Herbert Turner and Julia Lavenberg and Anthony
+ Petrosino and Dorothy de Moya and Jeremy Grimshaw and Ellen Foley },
+ title={Estimating the Effects of Interventions That are Deployed in Many Places:
+ Place-Randomized Trials},
+ journal={American Behavioral Scientist},
+ volume={47},
+ year={2004},
+ pages={608-633},
+ number={5}
+}
+
+ at book{Boruch97,
+ author={Robert F. Boruch},
+ title={Randomized Experiments for Planning and Evaluation},
+ publisher={Sage Publications},
+ year={1997},
+ address={Thousand Oaks}
+}
+
+ at article{BouChaWel01,
+ author={Andrew Boulle and Daniel Chandramohan and Peter Weller},
+ title={A Case Study of Using Artificial Neural Networks for Classifying Cause of
+ Death from Verbal Autopsy},
+ journal={International Journal of Epidemiology},
+ volume={30},
+ year={2001},
+ pages={515-520}
+}
+
+ at unpublished{BowHan05,
+ author={Jake Bowers and Ben Hansen},
+ title={Attributing Effects to A Cluster Randomized Get-Out-The-Vote Campaign: An
+ Application of Randomization Inference Using Full Matching},
+ note={Departments of Political Science and Statistics: University of Michigan},
+ year={2005},
+ month={July}
+}
+
+ at book{BoxHunHun78,
+ author={George E.P. Box and William G. Hunger and J. Stuart Hunter},
+ title={Statistics for Experimenters},
+ publisher={Wiley-Interscience},
+ year={1978},
+ address={New York}
+}
+
+ at article{BoyHonNar01,
+ author={James P. Boyle and Amanda A. Honeycutt and K.M. Venkat Narayan and Thomas
+ J. Hoerger and Linda S. Geiss and Hong Chen and Theodore J. Thompson},
+ title={Projection of Diabetes Burden Through 2050},
+ journal={Diabetes Care},
+ volume= 24,
+ year= 2001,
+ pages={1936--1940},
+ number= 11
+}
+
+ at article{BozBel87,
+ author={J.E. Bozik and W.R. Bell},
+ title={Forecasting Age Specific Fertility Using Principal Components},
+ journal={Proceedings of the Americal Statistical Association, Social Statistics Section},
+ year= 1987,
+ pages={396--401}
+}
+
+ at article{bracken98,
+ author={Michael B. and John C. Bracken},
+ title={Avoidable Systematic Error in Estimating Treatment Effects Must not be Tolerated},
+ journal={British Medical Journal},
+ volume= 317,
+ year= 1998,
+ pages={11-56},
+ month={October 24}
+}
+
+ at article{Brady85,
+ author={Henry E. Brady},
+ title={The Perils of Survey Research: Inter-Personally Incomparable Responses},
+ journal={Political Methodology},
+ volume= 11,
+ year= 1985,
+ pages={269--290},
+ month={June},
+ number={3--4}
+}
+
+ at article{Brady89,
+ author={Henry E. Brady},
+ title={Factor and Ideal Point Analysis for Interpersonally Incomparable Data},
+ journal={Psychometrika},
+ volume= 54,
+ year= 1989,
+ pages={181--202},
+ month={June},
+ number= 2
+}
+
+ at inproceedings{BraHil73,
+ author={William Brass and Kenneth Hill},
+ title={Estimating Adult Mortality in Africa from Orphanhood},
+ booktitle={Proceedings of the International Population Conference Liege},
+ year= 1973,
+ organization={International Union for the Scientific Study of Population}
+}
+
+ at article{BraTuc01,
+ author={Ted Brader and Joshua Tucker},
+ title={The Emergence of Mass Partisanship in Russia, 1993-96},
+ journal={American Journal of Political Science},
+ volume={45},
+ year={2001},
+ pages={69-83}
+}
+
+ at book{BreDay80,
+ author={Norman E. Breslow and N.E. Day },
+ title={Methods in Cancer Research},
+ publisher={Lyon},
+ year= 1980
+}
+
+ at book{BreFriOls84,
+ author={Leo Breiman and Jerome H. Friedman and Richard A. Olshen and Charles J.
+ Stone},
+ title={Classification and Regression Trees},
+ publisher={Chapman \& Hall},
+ year={1984},
+ address={New York, New York}
+}
+
+ at book{Brehm93,
+ author={John Brehm},
+ title={The Phantom Respondents: Opinion Surveys and Political Respresentation},
+ publisher={University of Michigan Press},
+ year={1993},
+ address={Ann Arbor}
+}
+
+ at article{Breiman01,
+ author={Leo Breiman},
+ title={Statistical Modeling: The Two Cultures},
+ journal={Statistical Science},
+ volume={16},
+ year={2001},
+ pages={199-215},
+ month={August},
+ number={3}
+}
+
+ at article{Breslow96,
+ author={Norman E. Breslow},
+ title={Statistics in Epidemiology: The Case-Control Study},
+ journal= jasa,
+ volume= 91,
+ year= 1996,
+ pages={14--28}
+}
+
+ at unpublished{Breyer04,
+ author={L.A. Breyer},
+ title={The Dbacl Text Classifier},
+ note={laird at lbreyer.com},
+ year={04},
+ month={June}
+}
+
+ at unpublished{Breyer04,
+ author={L.A. Breyer},
+ title={The Dbacl Text Classifier},
+ note={laird at lbreyer.com},
+ year={04},
+ month={June}
+}
+
+ at article{BreZie92,
+ author={Hermann Brenner and Hartwig Ziegler},
+ title={Monitoring and Projecting Cancer Incidence in Saarland, Germany, Based on
+ Age-Cohort Analyses},
+ journal={Journal of Epidemiology and Community Health},
+ volume= 46,
+ year= 1992,
+ pages={15--20}
+}
+
+ at book{BroDav91,
+ author={Peter J. Brockwell and Richard A. Davis},
+ title={Time Series: Theory and Methods},
+ publisher={Springer-Verlag},
+ year={1991},
+ edition={2nd}
+}
+
+ at article{BroDenVer02,
+ author={N. Brouhns and M. Denuit and J. Vermunt},
+ title={A Poisson Log-bilinear Regression Approach to the Construction of Projected
+ Lifetables},
+ journal={Insurance: Mathematics and Economics},
+ volume= 31,
+ year= 2002,
+ pages={373--393}
+}
+
+ at article{BroGra00,
+ author={Ron Brookmeyer and Sarah Gray},
+ title={Methods for Projecting the Incidence and Prevalence of Chronic Diseases
+ in Ageing Populations: Application to Alzheimer's Disease},
+ journal={Statistics in Medicine},
+ volume= 19,
+ year= 2000,
+ pages={1481--1493}
+}
+
+ at article{BroHew00,
+ author={M. Brockerhoff and P. Hewett},
+ title={Inequality of child mortality among ethnic groups in sub-Saharan Africa},
+ journal= bull,
+ year={2000},
+ optnumber={1},
+ optvolume={78},
+ optpages={30--41}
+}
+
+ at article{Bronnum-Hansen02,
+ author={Henrik Bronnum-Hansen},
+ title={Predicting the Effect of Prevention of Ischaemic Heart Disease},
+ journal={Scandinavian Journal of Public Health},
+ volume= 30,
+ year= 2002,
+ pages={5--11}
+}
+
+ at article{Bronnum-Hansen99,
+ author={Henrik Bronnum-Hansen},
+ title={How Good is the Prevent Model for Estimating the Health Benefits of Prevention?},
+ journal={Journal of Epidemiology and Community Health},
+ volume= 53,
+ year= 1999,
+ pages={300--305}
+}
+
+ at article{BroSchRot06,
+ author={M. Alan Brookhart and Sebastian Schneeweiss and Kenneth J. Rothman and Robert
+ J. Glynn and Jerry Avorn and Til Sturmer},
+ title={Variable Selection for Propensity Score Models},
+ journal={American Journal of Epidemiology},
+ volume={163},
+ year={2006},
+ pages={1149-1156},
+ month={April}
+}
+
+ at book{Brown58,
+ author={Ralph Brown},
+ title={Loyalty and Security},
+ publisher={Yale University Press},
+ year= 1958,
+ address={New Haven, CT}
+}
+
+ at article{Brown82,
+ author={Brown, Courtney},
+ title={The Nazi Vote: A National Ecological Study},
+ journal={American Political Science Review},
+ volume= 76,
+ year= 1982,
+ pages={285-302},
+ number= 2
+}
+
+ at article{BruFal94,
+ author={Brustein, William and Falter, J{\"u}rgen W.},
+ title={The Sociology of Nazism: An Interest-Based Account},
+ journal={Rationality and Society},
+ volume= 6,
+ year= 1994,
+ pages={369-399},
+ number= 3
+}
+
+ at book{Brustein96,
+ author={William Brustein},
+ title={The Logic of Evil: Social Origins of the Nazi Party, 1925-1933},
+ publisher={Yale University Press},
+ year= 1996
+}
+
+ at article{BruUrd05,
+ author={Helge Brunborg and Henrik Urdal},
+ title={The Demography of Conflict and Violence: An Introduction},
+ abstract={The demography of armed conflict is an emerging field among demographers
+ and peace researchers alike. The articles in this special issue treat demography
+ as both a cause and a consequence of armed conflict, and they carry important
+ policy implications. A study of German-allied countries during World War
+ II addresses the role of refugees and territorial loss in paving the way
+ for genocide. Other articles focusing on the demographic causes of conflict
+ discuss highly contentious issues of whether economic and social inequality,
+ high population pressure on natural resources, and youth bulges and limited
+ migration opportunities can lead to different forms of armed conflict and
+ state failure. The articles on demographic responses to armed conflict
+ analyze the destructiveness of pre-industrial warfare, differences in short-
+ and long-term mortality trends after armed conflict, and migratory responses
+ in war. Another set of articles on demographic responses to war is published
+ simultaneously in the European Journal of Population.},
+ journal={The Journal of Peace Research},
+ volume={42},
+ year={2005},
+ pages={371-374},
+ month={July},
+ number={4}
+}
+
+ at book{BSER02,
+ author={{Board on Earth Sciences and Resources}},
+ title={Geoscience Data and Collections: National Resources in Peril},
+ publisher={National Academies Press},
+ year= 2002,
+ address={Washington, D.C.}
+}
+
+ at article{Buchheim03,
+ author={Christoph Buchheim},
+ title={Die Erholung von der Weltwirtschaftskrise 1932/33 in Deutschland},
+ journal={Jahrbuch fuer Wirtschaftsgeschicht},
+ year={2003},
+ pages={13-26},
+ number={1}
+}
+
+ at article{BurFred01,
+ author={B Burstrom and P Fredlund},
+ title={Self-rated health: Is it as good a predictor of subsequent mortality among
+ adults in lower as well as in higher social classes?},
+ journal={Journal of Epidemiology and Community Health},
+ volume= 55,
+ year= 2001,
+ pages={{836-40}}
+}
+
+ at article{Burgoon06,
+ author={Brian Burgoon},
+ title={On Welfare and Terror},
+ journal={Journal of Conflict Resolution},
+ volume={50},
+ year={2006},
+ pages={176-203},
+ month={April},
+ number={2}
+}
+
+ at article{Burnham72,
+ author={Walter Dean Burnham},
+ title={Political Immunisation and Political Confessionalism: The United States
+ and Weimar Germany},
+ journal={Journal of Interdisciplinary History},
+ volume= 3,
+ year= 1972,
+ pages={1--30}
+}
+
+ at article{Burtless95,
+ author={Gary Burtless},
+ title={The Case for Randomized Field Trials in Economic and Policy Research},
+ journal={The Journal of Economic Perspectives},
+ volume={9},
+ year={1995},
+ pages={63-84},
+ number={2}
+}
+
+ at article{ButBurMit87,
+ author={J.S. Butler, et al.},
+ title={Measurement Error in Self-Reported Health Variables},
+ journal={The Review of Economics and Statistics},
+ volume= 69,
+ year= 1987,
+ pages={{644-50}}
+}
+
+ at techreport{ButCar01,
+ author={C.T.\ Butts and K.M.\ Carley},
+ title={Multivariate Methods for Interstructural Analysis},
+ institution={CASOS working paper, Carnegie Mellon University},
+ year={2001}
+}
+
+ at incollection{Butler51,
+ author={David E. Butler},
+ title={Appendix},
+ booktitle={The British General Election of 1950},
+ publisher={Macmillan},
+ year= 1951,
+ address={London},
+ editor={H.G. Nicholas}
+}
+
+ at article{BuuEyrTenHop03,
+ author={S. van Buuren and S. Eyres and A. Tennant and M. Hopman-Rock},
+ title={Assessing comparability of dressing disability in different countries by
+ response conversion},
+ journal={European Journal of Public Health},
+ volume={13},
+ year={2003},
+ pages={15-19}
+}
+
+ at book{ByaFotHuo06,
+ author = {Peter Byass and Edward Fottrell and Dao Lan Huong
+ and Yemane Berhane and Tumani Corrah and Kathleen
+ Kahn and Lulu Muhe and Do Duc Van},
+ title = {Refining a probabilistic model for interpreting
+ verbal autopsy data},
+ publisher = {Scandinavian Journal of Public Health},
+ year = {34},
+ volume = {2006},
+ pages = {26-31}
+}
+
+ at article{CamJagHar03,
+ author={Michael J. Camasso and Radha Jagannathan and Carol Harvey and Mark Killingsworth},
+ title={The Use of Client Surveys to Guage the Threat of Contamination in Welfare
+ Reform Experiments},
+ journal={Journal of Policy Analysis and Management},
+ volume={22},
+ year={2003},
+ pages={207-223},
+ number={2}
+}
+
+ at book{CamTri98,
+ author={A.C. Cameron and P.K. Trivedi},
+ title={Regression Analysis of Count Data},
+ publisher={Cambridge University Press},
+ year={1998}
+}
+
+ at article{Canner91,
+ author={Paul L. Canner},
+ title={Covariate Adjustment of Treatment Effects in Clinical Trials},
+ journal={Controlled Clinical Trials},
+ volume={12},
+ year={1991},
+ pages={359-366}
+}
+
+ at book{Cantril65,
+ author={Hadley Cantril},
+ title={The Pattern of Human Concerns},
+ publisher={Rutgers University Press},
+ year= 1965,
+ address={New Brunswick, N.J.}
+}
+
+ at article{CapAngFro95,
+ author={Riccardo Capocaccia and Robert De Angelis and Luisa Frova and Milena Sant
+ and Eva Buiatti and Gemma Gatta and Andrea Micheli and Franco Berrino and
+ Alessandro Barchielli and Ettore Conti and Lorenzo Gafa and Arduino Verdecchia},
+ title={Estimation and Projections of Stomach Cancer Trends in Italy},
+ journal={Cancer Causes and Control},
+ volume= 6,
+ year= 1995,
+ pages={339--346}
+}
+
+ at article{CapDeaFro97,
+ author={Riccardo Capocaccia and Roberta De Angelis and Luisa Frova and Gemma Gatta
+ and Milena Sant and Andrea Micheli and Franco Berrino and Ettore Conti
+ and Lorenzo Gafa and Luca Roncucci and Arduino Verdecchia},
+ title={Estimation and Projections of Colorectal Cancer Trends in Italy},
+ journal={International Journal of Epidemiology},
+ volume= 26,
+ year= 1997,
+ pages={924--932},
+ number= 5
+}
+
+ at article{CapVerMic90,
+ author={Riccardo Capocaccia and Arduino Verdecchia and Andrea Micheli and Milena
+ Sant and Gemma Gatta and Franco Berrino},
+ title={Breast Cancer Incidence and Prevalence Estimated from Survival and Mortality},
+ journal={Cancer Causes and Control},
+ volume= 1,
+ year= 1990,
+ pages={23--29}
+}
+
+ at article{CarCha70,
+ author={J.D. Caroll and J. J. Chang},
+ title={Analysis of Individual Differences in Multidimensional Scaling},
+ journal={Psychometrika},
+ volume= 35,
+ year= 1970,
+ pages={283--319},
+ month={September}
+}
+
+ at article{CarGre97,
+ author={J. Douglas Carroll and Paul E. Green},
+ title={Psychometric Methods in Marketing Research: Part II, Multidimensional Scaling},
+ journal={Journal of Marketing Research},
+ volume={XXXIV},
+ year= 1997,
+ pages={193--204},
+ month={May}
+}
+
+ at article{CarKucLom96,
+ author={Raymond J. Carroll and helmut Kuchenhoff and F. Lombard and Leonard A. Stefanski},
+ title={Asymptotics for the SIMEX estimator in structural measurement error models.
+ },
+ journal={Journal of the American Statistical Association},
+ volume={91},
+ year={1996},
+ pages={242-250}
+}
+
+ at book{CarLou00,
+ author={Bardley P. Carlin and Thomas A. Louis},
+ title={Bayes and Empirical Bayes Methods for Data Analysis},
+ publisher={CRC Press},
+ year= 2000,
+ edition={2nd}
+}
+
+ at article{CarMacRup99,
+ author={Raymond J. Carroll and Jeffrey D. Maca and David Ruppert},
+ title={Nonparametric regression in the presence of measurement error},
+ journal={Biometrika},
+ volume={86},
+ year={1999},
+ pages={3},
+ month={541-554}
+}
+
+ at article{Carpenter02,
+ author={Daniel Paul Carpenter},
+ title={Groups, the Media, Agency Waiting Costs, and FDA Drug Approval},
+ journal= ajps,
+ volume= 46,
+ year= 2002,
+ pages={490--505},
+ month={July},
+ number= 2
+}
+
+ at techreport{CarPrs00,
+ author={Lawrence R. Carter and Alexia Prskawetz},
+ title={Examining Structural Shifts in Mortality using the Lee-Carter Method},
+ institution={Bundesinstitut fur Bevolkerungswissenschaften},
+ year= 2000,
+ address={Germany},
+ note={Demographische Vorausschatzungen --- Abhandlungen des Arbeitkreises Bevolkerrungswissenschaftlicher
+ Methoden der Statistischen Woche}
+}
+
+ at article{Carr07,
+ author={David Carr},
+ title={24-Hour Newspaper People},
+ journal={New York Times},
+ year= 2007,
+ month={15 January}
+}
+
+ at article{CavTre94,
+ author={W.B. Cavnar and J.M. Trenkle},
+ title={{N-Gram-Based Text Categorization}},
+ journal={Proceedings of the Third Annual Symposium on Document Analysis and Information
+ Retrival},
+ year={1994},
+ pages={161-175}
+}
+
+ at article{Chafee19,
+ author={Zechariah Chafee},
+ title={Freedom of Speech in War Time},
+ journal= hlr,
+ volume= 32,
+ year={1919},
+ pages={932--??}
+}
+
+ at book{Chafee41,
+ author={Zechariah Chafee},
+ title={Free Speech in the United States},
+ publisher= hup,
+ year= 1941,
+ address={Cambridge, MA}
+}
+
+ at article{ChaMauRod94,
+ author={Daniel Chandramohan and Gillian H. Maude and Laura C. Rodrigues and Richard
+ J. Hayes},
+ title={Verbal Autopsies for Adult Deaths: Issues in their Development and Validation},
+ journal={International Journal of Epidemiology},
+ volume={23},
+ year={1994},
+ pages={213-222},
+ number={2}
+}
+
+ at article{Chamberlain80,
+ author={Gary Chamberlain},
+ title={Analysis of Covariance with Qualitative Data},
+ journal={Review of Economic Studies},
+ volume={XLVII},
+ year= 1980,
+ pages={225-238}
+}
+
+ at article{ChaRodMau98,
+ author={Daniel Chandramohan and Laura C. Rodriques and Gillian H. Maude and Richard
+ Hayes},
+ title={The Validy of Verbal Autopsies for Assessing the Causes of Institutional
+ Maternal Death},
+ journal={Studies in Family Planning},
+ volume={29},
+ year={1998},
+ pages={414-422},
+ month={December},
+ number={4}
+}
+
+ at article{Chase68,
+ author={G.R. Chase},
+ title={On the Efficiency of Matched Pairs in Bernoulli Trials},
+ journal={Biometrika},
+ volume={55},
+ year={1968},
+ pages={365-369},
+ month={July},
+ number={2}
+}
+
+ at article{ChaSetQui01,
+ author={Daniel Chandramohan and Philip Setel and Maria Quigley},
+ title={Effect of misclassification of causes of death in verbal autopsy: can it
+ be adjusted},
+ journal={International Journal of Epidemiology},
+ volume={30},
+ year={2001},
+ pages={509-514}
+}
+
+ at article{ChaSolShi05,
+ author={Daniel Chandramohan and Nadia Soleman and Kenji Shibuya and John Porter},
+ title={Editorial: Ethical issues in the application of verbal autopsies in mortality
+ surveillance systems},
+ journal={Tropical Medicine and International Health},
+ volume={10},
+ year={2005},
+ pages={1087-1089},
+ month={November},
+ number={11}
+}
+
+ at article{CheCumDum03,
+ author={Lee Cheng, et al},
+ title={Health related quality of life in pregeriatric patients with chronic diseases
+ at urban, public supported clinics},
+ journal={Health and Quality of Life Outcomes},
+ volume= 1,
+ year= 2003,
+ pages={{1-8}},
+ month={October},
+ number= 63
+}
+
+ at book{CheRon83,
+ author={G. Shabbir Cheema and Dennis A. Rondinelli},
+ title={Decentralization and Development: Policy Implementation in Developing Countries},
+ publisher={Sage Publications},
+ year={1983},
+ address={Beverly Hills, CA}
+}
+
+ at article{Childers76,
+ author={Thomas Childers},
+ title={The Social Bases of the Nationalist Socialist Vote},
+ journal={Journal of Contemporary History},
+ volume= 11,
+ year= 1976,
+ pages={17-42}
+}
+
+ at book{Childers83,
+ author={Childers, Thomas},
+ title={The Nazi Voter},
+ publisher={University of North Carolina Press},
+ year= 1983
+}
+
+ at article{ChiLwa91,
+ author={J. Chin and S.K. Lwanga},
+ title={Estimation and Projection of Adult AIDS Cases: a Simple Epidemiological
+ Model},
+ journal={Bulletin of the World Health Organization},
+ volume= 69,
+ year= 1991,
+ pages={399--406},
+ number= 4
+}
+
+ at article{ChiZamGra92,
+ author={J.D. Chiphangwi and T.P. Zamaere and W Graham and B. Duncan and T. Kenyon
+ and R. Chinyama},
+ title={Maternal mortality in the Thyolo district of southern Malawi},
+ journal={East African Medical Journal},
+ volume= 69,
+ year= 1992,
+ pages={675--679}
+}
+
+ at article{Chochran53,
+ author={William G. Cochran},
+ title={Matching in Analytical Studies},
+ journal={American Journal of Public Health},
+ volume={43},
+ year={1953},
+ pages={684-691},
+ month={June}
+}
+
+ at book{Christensen96,
+ author={Ronadl Christensen},
+ title={Plane Answers to Complex Questions: The Theory of Linear Models},
+ publisher={Springer-Verlag New York},
+ year={1996},
+ edition={Second}
+}
+
+ at article{Church75,
+ author={Thomas Church},
+ title={Conspiracy Doctrine and Speech Offenses: A Reexamination of Yates v. U.S.
+ from the Perspective of U.S. v. Spock},
+ journal={Cornell Law Review},
+ volume= 60,
+ year={1975},
+ pages={569--??}
+}
+
+ at incollection{ClaBer92,
+ author={David G. Clayton and Luisa Bernardinelli},
+ title={Bayesian Methods For Mapping Disease Risk},
+ booktitle={Geographical and Environmental Epidemiology: Methods for Small-Area Studies},
+ publisher={Oxford University Press},
+ year= 1992,
+ address={Oxford},
+ editor={P. Elliott and J.Cuzick and D. English and R. Stern},
+ pages={205-220}
+}
+
+ at article{ClaJanHob01,
+ author={W. Crawford Clark and Malvin N. Janal and Elaine K. Hoben and J. Douglas
+ Carroll},
+ title={How Separate are the Sensory, Emotional, and Motivational Dimensions of
+ Pain? A Multidimensional Scaling Analysis},
+ journal={Somatosensory and Motor Research},
+ volume= 18,
+ year= 2001,
+ pages={31-39},
+ number= 1
+}
+
+ at article{ClaMarLie04,
+ author={Tim Clark and Sean Martin and Ted Liefeld},
+ title={Globally Distributed Object Identification for Biological Knowledgebases},
+ journal={Briefings in Bioinformatics},
+ volume={5},
+ year={2004},
+ pages={59-71},
+ month={March},
+ number={1}
+}
+
+ at article{Clarkson00,
+ author={Douglas B. Clarkson},
+ title={A Random Effects Individual Difference Multidimensional Scaling Model},
+ journal={Computational Statistics and Data Analysis},
+ volume= 32,
+ year= 2000,
+ pages={337--347},
+ month={January}
+}
+
+ at incollection{Clayton96,
+ author={David G. Clayton},
+ title={Generalized Linear Mixed Models},
+ booktitle={Markov Chain {M}onte {C}arlo in Practice},
+ publisher={Chapman \& Hall},
+ year= 1996,
+ address={London},
+ editor={W.R. Gilks and S. Richardson and D.J. Spiegelhalter},
+ pages={275-301}
+}
+
+ at article{CleDev88,
+ author={W.S. cleveland and S.J. Devlin},
+ title={Locally Weighted Regression: An Approach to Regression Analysis by Local
+ Fitting},
+ journal={Journal of the American Statistical Association},
+ volume={83},
+ year={1988},
+ pages={596-610}
+}
+
+ at book{CleHen98,
+ author={M.P. Clements and D.F. Hendry},
+ title={{Forecasting Economic Time Series}},
+ publisher= cup,
+ year= 1998,
+ address={Cambridge, U.K.}
+}
+
+ at misc{CliJacRiv00,
+ author={Joshua Clinton and Simon Jackman and Douglas Rivers},
+ title={The Statistical Analysis of Legislative Behavior: A Unified Approach},
+ year={2000},
+ howpublished={Paper presented at the Annual Meeting of the Political Methodology Society}
+}
+
+ at unpublished{CliJacRiv02,
+ author={Joshua Clinton and Simon Jackman and Douglas Rivers},
+ title={The Statistical Analysis of Roll Call Data},
+ note={Stanford University},
+ year= 2002
+}
+
+ at article{CloRubSch91,
+ author={Clifford C. Clogg and Donald B. Rubin and Nathaniel Schenker and Bradley
+ Schultz and Lynn Weidman},
+ title={Multiple Imputation of Industry and Occupation Codes in Census Public-Use
+ Samples Using Bayesian Logistic Regression},
+ journal={Journal of the American Statistical Association},
+ volume={86},
+ year={1991},
+ pages={68-78},
+ month={March},
+ number={413}
+}
+
+ at book{CoaDem66,
+ author={Ansley J. Coale and Paul Demeny},
+ title={Regional Model Life Tables and Stable Populations},
+ publisher={Princeton University Press},
+ year= 1966,
+ address={Princeton, N.J.}
+}
+
+ at book{CocCox57,
+ author={WG Cochran and GM Cox},
+ title={Experimental Designs},
+ publisher={Wiley},
+ year={1957},
+ address={New York}
+}
+
+ at article{Cochran65,
+ author={William G. Cochran},
+ title={The Planning of Observational Studies of Human Populations},
+ journal={Journal of the Royal Statistical Society. Series A (General)},
+ volume= 128,
+ year= 1965,
+ pages={234-266},
+ number= 2
+}
+
+ at article{Cochran68,
+ author={Cochran, William G.},
+ title={The effectiveness of adjustment by subclassification in removing bias in
+ observational studies},
+ journal={Biometrics},
+ volume={24},
+ year={1968},
+ pages={295-313}
+}
+
+ at article{CocRub73,
+ author={Cochran, William G. and Rubin, Donald B.},
+ title={Controlling bias in observational studies: A review},
+ journal={Sankhya: The Indian Journal of Statistics, Series A},
+ volume={35, Part 4},
+ year={1973},
+ pages={417-466}
+}
+
+ at article{ColMah93,
+ author = {David Collier and Mahon, Jr., James E.},
+ title = {Conceptual `Stretching' Revisited},
+ journal = apsr,
+ volume = 87,
+ year = 1993,
+ pages = {845-855},
+ month = {December},
+ number = 4
+}
+
+ at article{ColSul02,
+ author={James E. Coleman and Barry Sullivan},
+ title={Enduring and Empowering: The Bill of Rights in the Third Millennium},
+ journal={Law and Contemporary Problems},
+ volume= 65,
+ year={2002},
+ pages={1--??}
+}
+
+ at book{Colton06,
+ author={Timothy Colton},
+ title={Transitional Citizens: Voters and What Influences Them in the New Russia},
+ publisher={Harvard University Press},
+ year={2006 in press},
+ address={Cambridge, MA}
+}
+
+ at article{ComMolGri01,
+ author={Campbell, M.K. and Mollison, J. and Grimshaw, J.M.},
+ title={{Cluster trials in implementation research: estimation of intracluster correlation
+ coefficients and sample size}},
+ journal={Statistics in Medicine},
+ volume={20},
+ year={2001},
+ pages={391--399},
+ number={3}
+}
+
+ at unpublished{Congdon06,
+ author={Peter Congdon},
+ title={A Model Framework for Mortality and Health Data Classified by Age, Area,
+ and Time},
+ note={to be published in Biometrics Peter congdon, Dept. of Geography, Queen Mary
+ (University of London), Mile end Road, London E1 4NS, England p.congdon at qmul.ac.uk},
+ year={2006}
+}
+
+ at book{CooCam79,
+ author={Thomas D. Cook and Donald T. Campbell},
+ title={Quasi-Experimentation: Design and Analysis Issues for Field Settings},
+ publisher={Rand McNally College Publishing Company},
+ year={1979},
+ address={Chicago}
+}
+
+ at article{CooSte94,
+ author={J. Cook and L. Stefanski},
+ title={Simulation-extrapolation estimation in parametric measurement error models},
+ journal={Journal of the American Statistical Asociation},
+ volume={89},
+ year={1994},
+ pages={1314-1328}
+}
+
+ at book{CorCraFox94,
+ title={Transforming State-Society Relations in Mexico},
+ publisher={Center for U.S.-Mexican Studies},
+ year={1994},
+ editor={Wayne A. Cornelius and Ann L. Craig and Jonathan Fox},
+ address={University of California, San Diego},
+ series={U.S.-Mexico contemporary Perspectives Series, 6}
+}
+
+ at InCollection{Cornelius04,
+author = {Wayne A. Cornelius},
+title = {Mobilized Voting in the 2000 Elections: The Changing Efficacy of Vote Buying and Coercion in Mexican Electoral Politics},
+booktitle = {Mexico's Pivotal Democratic Election: Candidates, Voters, and the Presidential Campaign of 2000},
+OPTcrossref = {},
+OPTkey = {},
+OPTpages = {},
+publisher = {Stanford University Press},
+year = {2004},
+editor = {Jorge I. Dom\'{i}nguez and Chappell Lawson},
+OPTvolume = {},
+OPTnumber = {},
+OPTseries = {},
+OPTtype = {},
+OPTchapter = {},
+address = {Stanford and La Jolla, CA},
+OPTedition = {},
+OPTmonth = {},
+OPTnote = {},
+OPTannote = {}
+}
+
+ at article{Cornfield51,
+ author={Jerome Cornfield},
+ title={A Method of Estimating Comparative Rates from Clinical Data: Application
+ to Cancer of the Lung, Breast and Cervix},
+ journal={Journal of the National Cancer Institute},
+ volume= 11,
+ year= 1951,
+ number={1269-1275}
+}
+
+ at article{CowBra96,
+ author={Mary Kathryn Cowles and Bradley P. Carlin},
+ title={Markov Chain Monte Carlo Convergence Diagnotics: A Comparative Review},
+ journal={Journal of the American Statistical Association},
+ volume={91},
+ year={1996},
+ pages={883-904},
+ month={June},
+ number={434}
+}
+
+ at article{Cox52,
+ author={D.R. Cox},
+ title={Some Recent Work on Systematic Experimental Designs},
+ journal={Journal of the Royal Statistical Society. Series B (Methodological)},
+ volume={14},
+ year={1952},
+ pages={211-219},
+ number={2}
+}
+
+ at book{Cox58,
+ author={David R. Cox},
+ title={Planning of Experiments},
+ publisher={John Wiley},
+ year= 1958,
+ address={New York}
+}
+
+ at book{Cox58,
+ author={D.R. Cox},
+ title={The Planning of Experiments},
+ publisher={Wiley},
+ year= 1958
+}
+
+ at article{CoxSke92,
+ author={Brian Cox and D.C.G. Skegg},
+ title={Projections of Cervical Cancer Mortality and Incidence in New Zealand: The
+ Possible Impact of Screening},
+ journal={Journal of Epidemiology and Community Health},
+ volume= 46,
+ year= 1992,
+ pages={373--377}
+}
+
+ at article{CraWoo05,
+ author={Craggs, Richard and Mary McGee Wood},
+ title={{Evaluating Discourse and Dialogue Coding Schemes}},
+ journal={Computational Linguistics},
+ volume={31},
+ year={2005},
+ pages={289-295},
+ number={3}
+}
+
+ at article{CroKen02,
+ author={Thomas F. Crossley and Steven Kennedy},
+ title={The reliability of self-assesses health status},
+ journal={Journal of Health Economics},
+ volume= 21,
+ year= 2002,
+ pages={{643-58}},
+ number= 4
+}
+
+ at article{Crosnoe05,
+ author={Robert Crosnoe},
+ title={Double Disadvantage or Signs of Resilience? The Elementary School Contexts
+ of Children from Mexican Immigrant Families},
+ journal={American Educational Research Journal},
+ volume={42},
+ year={2005},
+ pages={269-303},
+ number={2}
+}
+
+ at unpublished{CruHotImb06,
+ author = {Richard K. Crump and V. Joseph Hotz and Guido
+ W. Imbens and Oscar Mitnik},
+ title = {Moving the Goalposts: Addressing Limited Overlap in
+ Estimation of Average Treatment Effects by Changing
+ the Estimand},
+ note = {Department of Economics, UC Berkeley},
+ year = {2006},
+ month = {September}
+}
+
+ at article{CruHotImb09,
+ title = {{Dealing with limited overlap in estimation of
+ average treatment effects}},
+ author = {Richard K. Crump and V. Joseph Hotz and Guido
+ W. Imbens and Oscar Mitnik},
+ journal = {Biometrika},
+ volume = {96},
+ number = {1},
+ pages = {187},
+ year = {2009}
+}
+
+
+ at article{CuaFor95,
+ author={C.M. Cuadras and J. Fortiana},
+ title={A Continuous Metric Scaling Solution for A Random Variable},
+ journal={Journal of Multivariate Analysis},
+ volume= 52,
+ year= 1995,
+ pages={1--14}
+}
+
+ at article{CuaForOli97,
+ author={C.M. Cuadras and J. Fortiana and F. Oliva},
+ title={The Proximity of an Individual to a Population with Applications to Discriminant
+ Analysis},
+ journal={Journal of Classification},
+ volume= 14,
+ year= 1997,
+ pages={117-136}
+}
+
+ at article{CumMcKWei03,
+ author={Peter Cummings and B. McKnight and NS Weiss},
+ title={Matched-pair cohort methods in traffic crash research},
+ journal={Accident Analysis and Prevention},
+ volume= 35,
+ year= 2003,
+ pages={131--141},
+ note={{http://depts.washington.edu/hiprc/about/topics/web/bike\_prevmat/}}
+}
+
+ at article{CumRoy88,
+ author={W.G. Cumberland and R. M. Royall},
+ title={Does Simple Random Sampling Provide Adequate Balance?},
+ journal={Journal of the Royal Statistical Society. Series B (methodological)},
+ volume={50},
+ year={1988},
+ pages={118-124},
+ number={1}
+}
+
+ at article{Dagostino98,
+ author={Ralph B. {D'Agostino, Jr.}},
+ title={Propensity Score Methods for Bias Reduction in the Comparison of a Treatment
+ to a Non-randomized Control Group},
+ journal={Statistics in Medicine},
+ volume= 17,
+ year= 1998,
+ pages={2265--2281}
+}
+
+ at article{DagRub00,
+ author={Ralph B. {D'Agostino, Jr.} and Donald B. Rubin},
+ title={Estimating and using propensity scores with partially missing data},
+ journal={Journal of the American Statistical Association},
+ volume= 95 ,
+ year= 2000,
+ pages={749-759}
+}
+
+ at article{DalBecHuc98,
+ author={Russell J. Dalton and Paul A. Beck and Robert Huckfeldt},
+ title={Partisan Cues and the Media: Information Flows in the 1992 Presidential
+ election},
+ journal={American Poltical Science Review},
+ volume={92},
+ year={1998},
+ pages={111-126}
+}
+
+ at article{DanGraStu96,
+ author={I. Danel and W. Graham and P Stupp and P. Castillo},
+ title={Applying the sisterhood method for estimating maternal mortality to a health
+ facility-based sample: a comparison with results from a household-based
+ sample},
+ journal={International Journal of Epidemiology},
+ volume= 25,
+ year= 1996,
+ pages={1017--1-22}
+}
+
+ at inbook{DanLaf05,
+ author={Isabel Danel and Gerard M. La Forgia},
+ title={Health Systems Innovation in Central America},
+ chapter={Contracting for Basic Health Care in Rural Guatemala - Comparison of the
+ Performance of Three Delivery Models},
+ year={2005},
+ publisher={The World Bank},
+ address={Washington, DC},
+ editor={Gerard M. La Forgia}
+}
+
+ at unpublished{DasChe01,
+ author={Sanjiv R. Das and Mike Y. Chen},
+ title={Yahoo! for Amazon: Opinion Extraction from Small Talk on the Web},
+ note={Department of Finance Santa Clara University},
+ year={2001},
+ month={August}
+}
+
+ at article{DasKleiKlei94,
+ author={Erik J. Dasbach, PhD, Ronald Klein, MD, Barbara E. K. Klein, MD, and Scot
+ E. Moss, MA},
+ title={Self-Rated Health and Mortality in People with Diabetes},
+ journal={American Journal of Public Health},
+ volume= 84,
+ year= 1994,
+ pages={{1775-79}}
+}
+
+ at article{DavAlbCoo03,
+ author={G.L. Davis and J.E. Albright and S.F. Cook and D.M. Rosenberg},
+ title={Projecting Future Complications of Chornic Hepatitis C in the United States},
+ journal={Liver Transplantation},
+ volume= 9,
+ year= 2003,
+ pages={331--338},
+ number= 4
+}
+
+ at article{DavAlbCoo03,
+ author={G.L. Davis and J.E. Albright and S.F. Cook and D.M. Rosenberg},
+ title={Projecting Future Complications of Chornic Hepatitis C in the United States},
+ journal={Liver Transplantation},
+ volume= 9,
+ year= 2003,
+ pages={331--338},
+ number= 4
+}
+
+ at unpublished{DavLawPen03,
+ author={Kushal Dave and Steve Lawrence and David Pennock},
+ title={Mining the Peanut Gallery: Opinion Extraction and Semantic Classification
+ of Product Reviews},
+ note={Kushal Dave NEC Laboratories America, 4 Independence Way Princeton, NJ 08540,
+ Kushal at nec-labs.com},
+ year={2003},
+ month={May}
+}
+
+ at article{DavManLai98,
+ author={Huw Talfryn Oakley Davies and Tavakoli Manouche and Crombie Kinloch Iain},
+ title={Authors Reply},
+ journal={British Medical Journal},
+ volume= 317,
+ year= 1998,
+ pages={1156-7},
+ month={October 24}
+}
+
+ at article{DavNeaWen98,
+ author={G. Davey-Smith and J.D. Neaton and D. Wentworth and R. Stamler and J. Stamler},
+ title={Mortality differences between black and white men in the USA: contribution
+ of income and other risk factors among men screened for the Multiple Risk
+ Factor Intervention Trial (MRFIT)},
+ journal= lan,
+ volume= 351,
+ year= 1998,
+ pages={934--939},
+ number= 9107
+}
+
+ at article{DawFauMee89,
+ author={Robyn M. Dawes and David Faust and Paul E. Meehl},
+ title={Clinical Versus Actuarial Judgement},
+ journal={Science},
+ volume={243},
+ year={1989},
+ pages={1668-1674},
+ month={March},
+ number={4899}
+}
+
+ at article{Dawid00,
+ author={Philip Dawid},
+ title={Causal Inference Without Counterfactuals (with discussion)},
+ journal= jasa,
+ volume= 95,
+ year= 2000,
+ pages={447-448}
+}
+
+ at incollection{Dawid83,
+ author={A. P. Dawid},
+ title={Invariant Prior Distributions},
+ booktitle={Encyclopedia of Statistical Sciences},
+ publisher={Wiley-Interscience},
+ year= 1983,
+ editor={S. Kotz and S. Johnson and C.B. Read},
+ pages={228--236},
+ volume= 4
+}
+
+ at inbook{DeaGro00,
+ author={Angus Deaton and Mararet Grosh},
+ title={Consumption},
+ chapter={5},
+ year={2000},
+ publisher={The World Bank},
+ pages={91-133},
+ volume={1},
+ series={Designing household survey questionnaires for developing countries: lessons
+ from fifteen years of the Living Standards Measurement Study}
+}
+
+ at techreport{DeaPax04,
+ author={Angus Deaton and Christina Paxson},
+ title={Mortality, Income, and Income Inequality Over Time in the Britain and the
+ United States},
+ institution={National Bureau of Economic Research},
+ year= 2004,
+ address={Cambridge, MA},
+ number= 8534,
+ note={{http://www.nber.org/papers/w8534}}
+}
+
+ at unpublished{DebKee05,
+ author={Suzanna De Boef and Luke Keele},
+ title={Revisiting Dynamic Specification},
+ note={DeBoef: Dept. of Political Science; PA State University, State College,
+ PA 16802; 814-863-9402 sdeboef at psu.edu},
+ year={2005},
+ month={July}
+}
+
+ at book{DeBoor78a,
+ author={C. de Boor},
+ title={A Practical Guide to Splines},
+ publisher={Springer-Verlag},
+ year={1978},
+ address={New York}
+}
+
+ at article{DeeBat03,
+ author={Dorly J. H. Deeg and Peter A. Bath },
+ title={Self-rated health, gender, and mortality in older persons: Introduction
+ to a special section},
+ journal={The Gerontologist},
+ volume={43},
+ year={2003},
+ pages={{369-71}},
+ number={3}
+}
+
+ at article{DeeKey04,
+ author={Thomas S. Dee and Benjamin J. Keys},
+ title={Does Merit Pay Reward Good Teachers? Evidence from a Randomized Experiment},
+ journal={Journal of Policy Analysis and Management},
+ volume={23},
+ year={2004},
+ pages={471-488},
+ number={3}
+}
+
+ at article{Deeks98,
+ author={Jon Deeks},
+ title={Odds Ratio Should be Used Only in Case-Control Studies and Logistic Regression
+ Analyses},
+ journal={British Medical Journal},
+ volume= 317,
+ year= 1998,
+ pages={1155-6},
+ month={October 24}
+}
+
+ at article{DeeZonMaa89,
+ author={Dorly J. H. Deeg, et al},
+ title={Medical and Social Predictors of Longevity in the Elderly: Total Predictive
+ Value and Interdependence},
+ journal={Social Science and Medicine},
+ volume= 29,
+ year= 1989,
+ pages={{1271-80}},
+ number= 11
+}
+
+ at article{Dehejia05,
+ author={Dehejia Rajeev},
+ title={Practical Propensity Score Matching: A Reply to Smith and Todd},
+ journal={Journal of Econometrics},
+ volume={125},
+ year={2005},
+ pages={355-364}
+}
+
+ at article{DehWah02,
+ author={Rajeev H. Dehejia and Sadek Wahba},
+ title={Propensity Score Matching Methods for Non-Experimental Causal Studies},
+ journal={Review of Economics and Statistics},
+ volume={84},
+ year={2002},
+ pages={151-161},
+ number={1}
+}
+
+ at article{DehWah99,
+ author={Rajeev H. Dehejia and Sadek Wahba},
+ title={Causal Effects in Nonexperimental Studies: Re-Evaluating the Evaluation
+ of Training Programs},
+ journal={Journal of the American Statistical Association},
+ volume={94},
+ year={1999},
+ pages={1053-62},
+ month={December},
+ number={448}
+}
+
+ at article{deMGelGry03,
+ author={Scott de Marchi and Christopher F. Gelpi and Jeffrey D. Grynaviski},
+ title={Untangling Neural Nets},
+ journal= apsr,
+ year={2003, forthcoming}
+}
+
+ at article{DemLaiRub77,
+ author={Arthur P. Dempster and N.M. Laird and D.B. Rubin},
+ title={Maximum Likelihood Estimation from Incomplete Data via the EM Algorithm},
+ journal={Journal of the Royal Statistical Association},
+ volume={39},
+ year={1977},
+ pages={1-38}
+}
+
+ at book{DeMoivere1725,
+ title={Annuities on Lives},
+ year= 1725,
+ editor={Abraham DeMoivre},
+ address={London}
+}
+
+ at book{Derthick79,
+ author={Martha Derthick},
+ title={Policymaking for Social Security},
+ publisher={The Brookings Institution},
+ year={1979},
+ address={Washington, DC}
+}
+
+ at article{DeuBufPoy99,
+ author={Sylvie Deuffic and Laurent Buffat and Thierry Poynard and Alain-Jacques
+ Valleron},
+ title={Modeling the Hepatitis C Virus Epidemic in France},
+ journal={Hepatology},
+ volume= 29,
+ year= 1999,
+ pages={1596--1601},
+ number= 5
+}
+
+ at book{DevLor93,
+ author={R. DeVore and G. Lorentz},
+ title={Constructive Approximation},
+ publisher={Springer-Verlag},
+ year= 1993,
+ address={New York}
+}
+
+ at article{DewThuAnd86,
+ author={William G. Dewald and Jerry G. Thursby and Richard G. Anderson},
+ title={Replication in Empirical Economics: The Journal of Money, Credit and Banking
+ Project},
+ journal={American Economic Review},
+ volume={76},
+ year={1986},
+ pages={587-603},
+ month={September},
+ number={4}
+}
+
+ at article{Diamond86,
+ author={Diamond, A.M.},
+ title={{What is a citation worth}},
+ journal={Journal of Human Resources},
+ volume={21},
+ year={1986},
+ pages={200--215},
+ number={2}
+}
+
+ at misc{DiaSek05,
+ author={Alexis Diamond and Jasjeet Sekhon},
+ title={Genetic Matching for Estimating Causal Effects: A New Method of Achieving
+ Balance in Observational Studies},
+ year= 2005 ,
+ howpublished={{http://jsekhon.fas.harvard.edu/}}
+}
+
+ at article{DieGodYu07,
+ author={Daniel Diermeier and Jean-Fran{\c{c}}ois Godbout and Bei Yu and Stefan Kaufmann},
+ title={Language and Ideology in Congress},
+ year={2007},
+ note={Corresponding author, d-diermeier at kellogg.northwestern.edu}
+}
+
+ at article{DieMarKoe95,
+ author={Paula Diehr and Donald C. Martin and Thomas Koepsell and Allen Cheadle},
+ title={Breaking the Matches in a Paired t-Test for Community Interventions When
+ the Number of Pairs is Small},
+ journal={Statistics in Medicine},
+ volume={14},
+ year={1995},
+ pages={1491-1504}
+}
+
+ at article{Dinh02,
+ author={Viet D. Dinh},
+ title={Freedom and Security After September 11},
+ journal={Harvard Journal of Law and Public Policy},
+ volume= 25,
+ year={2002},
+ pages={399--??}
+}
+
+ at unpublished{DinMaz02,
+ author={L. Dini and G. Mazzini},
+ title={Opinion Classification Through Information Extraction},
+ note={Turin, Italy},
+ year={02}
+}
+
+ at article{DipGan04,
+ author={Thomas A. DiPrete and Markus Gangl},
+ title={Assessing Bias in the Estimation of Causal Effects: Rosenbaum Bounds on
+ Matching Estimators and Instrumental Variables Estimation with Imprerfect
+ Instruments},
+ journal={Sociological Methodology},
+ volume={34},
+ year={2004},
+ pages={271-310},
+ month={December}
+}
+
+ at article{DocWei03,
+ author={Henry V. Doctor and Alexander A. Weinreb},
+ title={Estimation of AIDS adult mortality by verbal autopsy in rural Malawi},
+ journal={AIDS},
+ volume={17},
+ year={2003},
+ pages={2509-2513}
+}
+
+ at article{DonDon87,
+ author={Allan Donner and A. Donald},
+ title={Analysis of data arising from a stratified design with the cluster as unit of randomization},
+ journal={Statistics in Medicine},
+ volume={6},
+ year={1987},
+ pages={43-52}
+}
+ at techreport{Dong04,
+ author={Lauren Bin Dong},
+ title={{The Behrens-Fisher Problem: An Empirical Likelihood Approach}},
+ institution={University of Victoria},
+ year= 2004,
+ key={Econometric Working Paper}
+}
+
+ at article{DonHau89,
+ author={Allan Donner and W. Hauck},
+ title={Estimation of a common odds ration in paired-cluster randomization designs},
+ journal={Statistics in Medicine},
+ volume={8},
+ year={1989},
+ pages={599-607}
+}
+
+ at book{DonKla00,
+ author={Allan Donner and Neil Klar},
+ title={Design and Analysis of Cluster Randomization Trials in Health Research},
+ publisher={Arnold},
+ year={2000},
+ address={London}
+}
+
+ at article{DonKla93,
+ author={Allan Donner and Neil Klar},
+ title={Confidence Interval Construction for Effect Measures Arising from Cluster
+ Randomization Trials},
+ journal={Journal of Clinical Epidemiology},
+ volume={46},
+ year={1993},
+ pages={123-131},
+ number={2}
+}
+
+ at article{Donner87,
+ author={Allan Donner},
+ title={Statistical Methodology for Paired Cluster Designs},
+ journal={American Journal of Epidemiology},
+ volume={126},
+ year={1987},
+ pages={972-979},
+ number={5}
+}
+
+ at article{Doorn98,
+ author={Carol Van Doorn },
+ title={Spouse-rated limitations and spouse-rated life expectancy as mortality predictors},
+ journal={Journal of Gerontolofy: Social Sciences},
+ volume={{53B}},
+ year= 1998,
+ pages={{S137-143}}
+}
+
+ at article{Doorn98,
+ author={Carol van Doorn},
+ title={Spouse-Raetd Limitations and Spouse Rated Life Expectancy as Mortality Predictors},
+ journal={Journal of Gerontology},
+ volume={{53B}},
+ year= 1998,
+ pages={{S137-43}},
+ number= 3
+}
+
+ at techreport{DooTra90,
+ author={Fred Doolittle and Linda Traeger},
+ title={Implementing the National JTPA Study},
+ institution={Manpower Demonstration Research Croporation},
+ year={1990},
+ month={April},
+ address={New York}
+}
+
+ at article{Dorsen89,
+ author={Norman Dorsen},
+ title={Here and There: Foreign Affairs and Civil Liberties},
+ journal= ajil,
+ volume= 83,
+ year={1989},
+ pages={840--??}
+}
+
+ at article{DowMan90,
+ author={John E. Dowd and Kenneth G. Manton},
+ title={Forecasting Chronic Disease Risks in Developing Countries},
+ journal={International Journal of Epidemiology},
+ volume= 19,
+ year= 1990,
+ pages={1019--1036},
+ month={May},
+ number= 4
+}
+
+ at book{Doyle06,
+ author={Sir Arthur Conan Doyle},
+ title={A Study in Scarlet},
+ publisher={Adamant Media Corporation},
+ year= 1888
+}
+
+ at article{DoySam00,
+ author={Michael W. Doyle and Nicholas Sambanis},
+ title={International Peacebuilding},
+ journal= apsr,
+ volume= 94,
+ year= 2000,
+ pages={779--801},
+ month={December},
+ number= 4
+}
+
+ at book{DozSch98,
+ title={Roads not Taken: Tales of Alternative History},
+ publisher={Del Rey},
+ year= 1998,
+ editor={Gardner Dozois and Stanley Schmidt},
+ address={New York}
+}
+
+ at article{Drake93,
+ author={C. Drake},
+ title={Effects of misspecification of the propensity score on estimators of treatment
+ effects},
+ journal={Biometrics},
+ volume={49},
+ year={1993},
+ pages={1231-1236}
+}
+
+ at unpublished{DreFar04,
+ author={Daniel W. Drezner and Henry Farrell},
+ title={The Power and Politics of Blogs},
+ note={American Political Science Association, Chicago, Illinois},
+ year={2004},
+ month={August}
+}
+
+ at book{Dueve95,
+ author={Christian de Dueve},
+ title={Vital Dust},
+ publisher={Basic Books},
+ year= 1995
+}
+
+ at article{DunDav53,
+ author={Duncan, O. D. and Davis, B.},
+ title={An Alternative to Ecological Correlation},
+ journal={American Sociological Review},
+ volume= 18,
+ year= 1953,
+ pages={665-666}
+}
+
+ at unpublished{DurRicWar03,
+ author={Stephen D. Durbin and J. Neal Richter and Doug Warner},
+ title={A System for Affective Rating of Texts},
+ note={RightNow Technolgies, Bozeman, MT},
+ year={03}
+}
+
+ at unpublished{DurRicWar03,
+ author={Stephen D. Durbin and J. Neal Richter and Doug Warner},
+ title={A System for Affective Rating of Texts},
+ note={RightNow Technolgies, Bozeman, MT},
+ year={03}
+}
+
+ at article{Easterlin03,
+ author={Richard A. Easterlin},
+ title={Explaining happiness},
+ journal={PNAS},
+ volume={100},
+ year={2003},
+ pages={11176-11183},
+ month={September},
+ number={19}
+}
+
+ at book{Edwards72,
+ author={A.W.F. Edwards},
+ title={Likelihood},
+ publisher={Cambridge University Press},
+ year= 1972,
+ address={New York}
+}
+
+ at article{Edwards91,
+ author={Harry T. Edwards},
+ title={The Judicial Function and the Elusive Goal of Principled Decisionmaking},
+ journal={Wisconsin Law Review},
+ volume= 1991,
+ year={1991},
+ pages={837--??}
+}
+
+ at article{Efron01,
+ author={Brad Efron},
+ title={[statistical Modeling: The Two Cultures]: Comment},
+ journal={Statistical Science},
+ volume={16},
+ year={2001},
+ pages={218-219},
+ month={August},
+ number={3}
+}
+
+ at article{Efron79,
+ author={B. Efron},
+ title={{Bootstrap methods: another look at the jackknife}},
+ journal={Annals of Statistics},
+ volume= 7,
+ year= 1979,
+ pages={1--26}
+}
+
+ at book{Efron82,
+ author={B. Efron},
+ title={{The Jacknife, the Bootstrap, and Other Resampling Plans}},
+ publisher={SIAM},
+ year= 1982,
+ address={Philadelphia}
+}
+
+ at article{Efron87,
+ author={B. Efron},
+ title={Empirical Bayes Confidence Intervals Based on Bootstrap Samples: Comment},
+ journal={Journal of the American Statistical Association},
+ volume={82},
+ year={1987},
+ pages={754},
+ month={September},
+ number={399}
+}
+
+ at article{Efron94,
+ author={Bradley Efron},
+ title={Missing Data, Imputation, and the Bootstrap},
+ journal={Journal of the American Statistical Association},
+ volume={89},
+ year={1994},
+ pages={463-475},
+ month={June},
+ number={426}
+}
+
+ at article{Efron94b,
+ author={Bradley Efron},
+ title={Missing Data, Imputation, and he Bootstrap: Rejoinder},
+ journal={Journal of the American Statistical Association},
+ volume={89},
+ year={1994},
+ pages={478-479},
+ month={June},
+ number={426}
+}
+
+ at book{EfrTib93,
+ author={B. Efron and R. Tibshirani},
+ title={{An Introduction to the Bootstrap}},
+ publisher={Chapmand and Hall},
+ year= 1993,
+ address={London}
+}
+
+ at book{Einstein20,
+ author={Albert Einstein},
+ title={Relativity: The Special and General Theory},
+ publisher={Henry Holt},
+ year= 1920,
+ address={NY}
+}
+
+ at article{EisLaz38,
+ author={P. Eisenberg and Paul F. Lazarsfeld},
+ title={The psychological effects of unemployment},
+ journal={Psychological Bulletin},
+ volume= 35,
+ year= 1938,
+ pages={358--390}
+}
+
+ at article{ElbGilWu05,
+ author={Nabila El-Bassei and Louisa Gilbert and Elwin Wu and Hyun Go and Jennifer
+ Hill},
+ title={Relationship between drug abuse and intimate partner violence: A longitudinal
+ study among women receiving methadone},
+ journal={American Journal of Public Health},
+ volume={95},
+ year={2005},
+ pages={465-470},
+ month={March},
+ number={3}
+}
+
+ at article{Elekes86,
+ author={G. Elekes},
+ title={A Geometric Inequality and the Complexity of Computing Volume},
+ journal={Discrete \& Computational Geometry},
+ volume={1},
+ year={1986},
+ pages={289-292}
+}
+
+ at book{Elster00,
+ author={Jon Elster},
+ title={Ulysses unbound: studies in rationality, precommitment, and constraints},
+ publisher={Cambridge University Press},
+ year={2000},
+ address={New York}
+}
+
+ at inbook{Elster79,
+ author={John Elster},
+ title={Ulysses and the Sirens: studies in rationality and irrationality},
+ chapter={II Imperfect Rationality: Ulysses and the Sirens},
+ year={1979},
+ publisher={Cambridge University Press},
+ pages={36 - 111},
+ address={Cambridge}
+}
+
+ at article{Emerson68,
+ author={Thomas I. Emerson},
+ title={Freedom of Expression in Wartime},
+ journal={University of Pennsylvania Law Review},
+ volume= 116,
+ year={1968},
+ pages={975--1011}
+}
+
+ at book{Emerson70,
+ author={Thomas I. Emerson},
+ title={The System of Freedom of Expression},
+ publisher={Vintage},
+ year= 1970,
+ address={New York}
+}
+
+ at book{Enders04,
+ author={Walter Enders},
+ title={Applied Econometric Time Series},
+ publisher={Wiley},
+ year={2004},
+ edition={2nd}
+}
+
+ at book{EneHin84,
+ author={James M. Enelow and Melvin J. Hinich},
+ title={The Spatial Theory of Voting: An Introduction},
+ publisher={Cambridge University Press},
+ year= 1984,
+ address={New York}
+}
+
+ at article{EoPre92,
+ author={Irma T. Elo and Samuel H. Preston},
+ title={Effects of Early-Life Conditions on Adult Mortality: A Review},
+ journal={1992},
+ volume={58},
+ year={1992},
+ pages={186-212},
+ month={Summer},
+ number={2}
+}
+
+ at article{EpsGonWei01,
+ author={S.A. Epstein and J.J. Gonzales and K. Weinfurt and B Bockeloo and N Yuan
+ and G Chase},
+ title={Are Psychiatrists' Characterists Related to how They Care for Depression
+ in the Medically Ill? Results from a National Case-Vignette Study},
+ journal={Psychosomatics},
+ volume= 42,
+ year= 2001,
+ pages={482--489},
+ month={Nov.--Dec.},
+ number= 6
+}
+
+ at unpublished{EpsOha05,
+ author={David L. Epstein and Sharyn O'Halloran},
+ title={Higher-Order Markov Models},
+ note={Columbia University},
+ year={2005}
+}
+
+ at book{EriMacSti02,
+ author={Robert S. Erikson and Michael B MacKuen and James S. Stimson},
+ title={The Macro Polity},
+ publisher={Cambridge University Press},
+ year= 2002,
+ address={New York}
+}
+
+ at article{EriUndElo01,
+ author={Ingeborg Eriksson, Anna-Lena Unden, and Stig Elofsson},
+ title={Self-Rated Health. Comparisons Between Three Different Measures. Results
+ from a Population Study.},
+ journal={International Epidemiological Association},
+ volume= 30,
+ year= 2001,
+ pages={{326-33}}
+}
+
+ at book{EstGolGur95,
+ author={Daniel C. Esty and Jack Goldstone and Ted Robert Gurr and Pamela T. Surko
+ and Alan N. Unger},
+ title={State Failure Task Force Report},
+ publisher={Science Applications International Corporation},
+ year= 1995,
+ address={McLean, Virginia}
+}
+
+ at book{EstGolGur98,
+ author={Daniel C. Esty and Jack Goldstone and Ted Robert Gurr and Barbara Harff
+ and Pamela T.\ Surko and Alan N.\ Unger and Robert S. Chen },
+ title={The State Failure Task Force Report: Phase II Findings},
+ publisher={Science Applications International Corporation},
+ year= 1998,
+ address={McLean, Virginia}
+}
+
+ at incollection{EstGolGur98b,
+ author={Daniel C. Esty and Jack Goldstone and Ted Robert Gurr and Barbara Harff
+ and Pamela T.\ Surko and Alan N.\ Unger and Robert S.\ Chen},
+ title={The State Failure Project: Early Warning Research for U.S. Foreign Policy
+ Planning},
+ booktitle={Preventive Measures: Building Risk Assessment and Crisis Early Warning System},
+ publisher={Rowman and Littlefield},
+ year={1998b},
+ address={Lanham, Maryland},
+ editor={John L. Davies and Ted Robert Gurr}
+}
+
+ at article{EstGolGur99,
+ author={Daniel C. Esty and Jack Goldstone and Ted Robert Gurr and Barbara Harff
+ and Marc Levy, Geoffrey D.\ Dabelko, Pamela T.\ Surko and Alan N.\ Unge},
+ title={The State Failure Report: Phase II Findings},
+ journal={Environmental Change and Security},
+ volume= 5,
+ year= 1999,
+ month={Summer}
+}
+
+ at article{EtaLehDia04,
+ author={Jean-Francois Etard and Jean-Yves Le Hesran and Aldiouma Diallo and Jean-Pierre
+ diallo and Jean-Louis Ndiaye and Valerie Delaunay},
+ title={Childhood mortality and probably causes of death using verbal autopsy in
+ Niakhar, Senegal, 1989-2000},
+ journal={International Journal of Epidemiology},
+ volume={33},
+ year={2004},
+ pages={1286-1292}
+}
+
+ at book{Eubank88,
+ author={R.L. Eubank},
+ title={Spline Smoothing and Nonparametric Regression},
+ publisher={Marcel Dekker},
+ year={1988},
+ volume={90},
+ address={Basel},
+ series={Statistics, textbooks and monographs}
+}
+
+ at article{Eule87,
+ author={Julian N. Eule},
+ title={Temporal Limits on the Legislative Mandate: Entrenchment and Retroactivity},
+ journal={American Bar Foundation Research Journal},
+ volume={12},
+ year={1987},
+ pages={379-459},
+ number={2/3}
+}
+
+ at book{Everitt05,
+ author={Brian Everitt},
+ title={An R and S-Plus Companion to Multivariate Analysis},
+ publisher={Springer-Verlag},
+ year={2005},
+ address={London}
+}
+
+ at inproceedings{EzzJohKha95,
+ author={T. Ezzati-Rice and W. Johnson and M. Khare and R. Little and D. Rubin and
+ J. Schafer},
+ title={A Simulation Study to Evaluate the Performance of Model-Based Multiple Imputations
+ in NCHS Health Examination Surveys},
+ publisher={Proceedings of the Annual Research conference},
+ address={Washington, D.C.},
+ pages={257-266},
+ organization={Bureau of the Census}
+}
+
+ at manual{FalHae89,
+ author={J{\"u}rgen W. Falter and Dirk H{\"a}nisch},
+ title={Wahl- und Sozialdaten der Kreise und Gemeinden des Deutschen Reiches von
+ 1920 bis 1933},
+ organization={Zentralarchiv f{\"u}r Empirische Sozialforschung},
+ year= 1989,
+ address={Universit{\"a}t zu K{\"o}ln},
+ note={ZA number 8013}
+}
+
+ at article{FalHan99,
+ author={J{\"u}rgen W. Falter and Dirk H{\"a}nisch},
+ title={Wahlerfolge und W{\"a}hlerschaft der NSDAP in {\"O}sterreich von 1927 bis 1932},
+ journal={Zeitgeschichte},
+ volume= 15,
+ year= 1988,
+ pages={223-244},
+ number= 6
+}
+
+ at article{FalLohLin85,
+ author = {J{\"u}rgen W. Falter and Jan-Bernd Lohm{\"o}ller and
+ Andreas Link and Johann de Rijke},
+ title = {Hat Arbeitslosigkeit tats{\"a}chlich den Aufstieg
+ des Nationalsozialismus bewirkt?},
+ journal = {Jahrbuch f{\"u}r National{\"o}konomie und Statistik},
+ volume = 200,
+ year = 1985,
+ pages = {121-136},
+ number = 2
+}
+
+ at incollection{Falter90,
+ author={Falter, J{\"u}rgen},
+ title={The First German Volkspartei: The Social Foundations of the NSDAP},
+ booktitle={Elections, Parties and Political Traditions},
+ publisher={Berg},
+ year= 1990,
+ address={M{\"u}nchen},
+ editor={Rohe, K.}
+}
+
+ at article{Falter90b,
+ author={Falter, J{\"u}rgen W.},
+ title={Arbeiter haben erheblich haeufiger, Angestellte dagegen sehr viel seltener
+ NSDAP gewaehlt als wir lange Zeit angenommen haben},
+ journal={Geschichte und Gesellschaft},
+ volume= 16,
+ year= 1990,
+ pages={536-552},
+ number= 4
+}
+
+ at book{Falter91,
+ author={Falter, J{\"u}rgen},
+ title={Hitlers W{\"a}hler},
+ publisher={Beck},
+ year= 1991,
+ address={M{\"u}nchen}
+}
+
+ at article{FalZin88,
+ author={Falter, J{\"u}rgen W. and Zintl, Reinhard},
+ title={The Economic Crisis of the 1930s and the Nazi Vote},
+ journal={Journal of Interdisciplinary History},
+ volume= 19,
+ year= 1988,
+ pages={55-85},
+ number= 1
+}
+
+ at article{FanFotBer06,
+ author={Mesganaw Fantahun and Edward Fottrell and Yemane Berhane and Stig Wall and
+ Ulf Hogberg and Peter Byass},
+ title={Assessing a new approach to verbal autopsy interpretation in a rural Ethiopian
+ community: the InterVA model},
+ journal={Bulletin of the World Health Organization},
+ volume={84},
+ year={2006},
+ pages={204-210},
+ month={March},
+ number={3}
+}
+
+ at article{FanFotBer06,
+ author={Mesganaw Fantahun and Edward Fottrell and Yemane Berhane and Stig Wall and
+ Ulf Hogberg and Peter Byass},
+ title={Assessing a new approach to verbal autopsy interpretation in a rural Ethiopian
+ community: the InterVA model},
+ journal={Bulletin of the World Health Organization},
+ volume={84},
+ year={2006},
+ pages={204-210},
+ month={March},
+ number={3}
+}
+
+ at article{Fantahun98,
+ author={Mesganaw Fantahun},
+ title={Patters of Childhood Mortality in Three Districts of North Gondar Administrative
+ Zone},
+ journal={Ethiopian Medical Journal},
+ volume={36},
+ year={1998},
+ pages={71-81},
+ number={2}
+}
+
+ at inproceedings{Fay92,
+ author={Robert E. Fay},
+ title={When are Inferences from Multiple Imputation Valid?},
+ year={1992},
+ pages={354-365},
+ organization={Proceedings of Survey Research Methods Section of the American Satistical
+ Association}
+}
+
+ at article{fearon91,
+ author={James D. Fearon},
+ title={Counterfactuals and Hypothesis Testing in Political Science},
+ journal={World Politics},
+ volume= 43,
+ year= 1991,
+ pages={169--195},
+ month={June},
+ number= 2
+}
+
+ at article{Feeney01,
+ author={G. Feeney},
+ title={The Impact of HIV/AIDS on Adult Mortality in Zimbabwe},
+ journal={Population and Development Review},
+ volume= 27,
+ year= 2001,
+ pages={771--980},
+ number= 4
+}
+
+ at unpublished{Fernandezval05,
+ author={Ivan Fernandez-Val},
+ title={Bias Correcion in Panel Data Models with Individual Specific Parameters},
+ note={Boston University},
+ year={2005}
+}
+
+ at unpublished{Fernandezval05,
+ author={Ivan Fernandez-Val},
+ title={Bias Correction in Panel Data Models with Individual Specific Parameters},
+ note={Boston University},
+ year={2005}
+}
+
+ at techreport{FidFreGro92,
+ author={M. Fidrich and J. Frenk and J. Gromicho},
+ title={An efficient algorithm to check whether $0$ belongs to the convex hull of
+ a finite number of {$L_p$}-circles},
+ institution={Econometric Institute},
+ year={1992},
+ type={Report 9204/A},
+ address={Netherlands}
+}
+
+ at book{FieMarStr85,
+ author={Stephen E. Fienberg and Margaret E. Martin and Miron L. Straf},
+ title={Sharing Research Data},
+ publisher={National Academy Press},
+ year={1985}
+}
+
+ at article{Finkel01,
+ author={N.J. Finkel},
+ title={When Principles Collide in Hard Cases},
+ journal={Psychology, Public Policy, and Law},
+ volume= 7,
+ year= 2001,
+ pages={515--560},
+ month={September},
+ number= 3
+}
+
+ at book{Fiorina81,
+ author={Morris P. Fiorina},
+ title={Retrospective Voting in American National Elections},
+ publisher={Yale University Press},
+ year= 1981,
+ address={New Haven}
+}
+
+ at article{FisCobVen98,
+ author={Bonnie S. Fisher and Craig T. Cobane and Thomas M. Vander Ven and Francis
+ T. Cullen},
+ title={How Many Authors Does It Take to Publish an Article? Trends and Patterns
+ in Political Science},
+ journal={PS: Political Science and Politics},
+ volume= 31,
+ year= 1998,
+ pages={847--856},
+ number= 4
+}
+
+ at article{Fish95,
+ author={Steven M. Fish},
+ title={The Advent of Multipartism in Russia, 1993-95},
+ journal={Post Soviet Affairs},
+ volume={11},
+ year={1995},
+ pages={340-383},
+ number={4}
+}
+
+ at book{Fisher35,
+ author={Ronald A. Fisher},
+ title={The Design of Experiments},
+ publisher={Oliver and Boyd},
+ year= 1935,
+ address={London}
+}
+
+ at article{Fisher24,
+ author={Ronald A. Fisher},
+ title={The conditions under which $\chi^2$ measures the discrepancy
+ between observed observation and hypothesis},
+ journal={Journal of the Royal Statistical Society},
+ volume={87},
+ year={1924},
+ pages={442-450}
+}
+
+ at article{Pearson00,
+ author={Karl Pearson},
+ title={On a criterion that a given system of deviations from the
+ probable in the case of a correlated system of variables is such
+ that it can reasonably be supposed to have arisen
+ from random sampling},
+ journal={Philosophical Magazine},
+ volume={50},
+ year={1900},
+ pages={157-175}
+}
+
+
+ at article{FlaBes82,
+ author={Brian R. Flay and J. Allen Best},
+ title={Overcoming Design Problems in Evaluating Health Behavior Programs},
+ journal={Evaluation \& The Health Professions},
+ volume={5},
+ year={1982},
+ pages={43-69},
+ month={March},
+ number={1}
+}
+
+ at article{Foote58,
+ author={Richard J. Foote},
+ title={A Modified {D}oolittle Approach for Multiple and Partial Correlation and
+ Regression},
+ journal= jasa,
+ volume= 53,
+ year= 1958,
+ pages={133-143}
+}
+
+ at article{ForNorAhm95,
+ author={Ian Ford and John Norrie and Susan Ahmadi},
+ title={Model Inconsistency, Illustrated by Cox proortional Hazard Model},
+ journal={Statistics in Medicine},
+ volume={14},
+ year={1995},
+ pages={735-746}
+}
+
+ at book{Fortas68,
+ author={Abe Fortas},
+ title={Concerning Dissent and Civil Disobedience},
+ publisher={Signet},
+ year= 1968,
+ address={New York}
+}
+
+ at article{Franklin89,
+ author={Charles H. Franklin},
+ title={Estimation across Data Sets: Two-Stage Auxiliary Instrumental Variables
+ Estimation},
+ journal={Political Analysis},
+ volume={1},
+ year={1989},
+ pages={1-23},
+ number={1}
+}
+
+ at book{Franzese02,
+ author={R.J. Franzese},
+ title={Macroeconomic policies of developed democracies},
+ publisher={Cambridge University Press},
+ year={2002},
+ address={New York}
+}
+
+ at unpublished{FraRub01,
+ author={Constantine E.\ Frangakis and Donald Rubin},
+ title={The Defining Role of Principal Effects in Comparing Treatments Using General
+ Post-Treatments Using General Post-Treatment Variables: From Surrogate
+ Endpoints to Censoring by Death},
+ note={\url{http://biosun01.biostat.jhsph.edu/~cfrangak}}
+}
+
+ at article{FraRub02,
+ author={Constantine E. Frangakis and Donald B. Rubin},
+ title={Principal stratification in causal inference},
+ journal={Biometrics},
+ volume= 58,
+ year= 2002,
+ pages={21-29}
+}
+
+ at article{FraRubZho02,
+ author={Constantine E. Frangakis and Donald B. Rubin and Ziao-Hua Zhou},
+ title={Clustered Encouragement Designs with Individual Noncompliance: Bayesian
+ Inference with Randomization, and Application to Advance Directive Forms},
+ journal={Biostatistics},
+ volume={3},
+ year={2002},
+ pages={147-164},
+ number={2}
+}
+
+ at article{FreChrKha05,
+ author={James V. Freeman and Parul Christian and Subarna K. Khatry and Ramesh K.
+ Adhikari and Steven C. LeClerq and Joanne Katz and Gary L. Darmstadt},
+ title={Evaluation of neonatal verbal autopsy using physician review versus algorithm-based
+ cause-of-death assignment in rural Nepal},
+ journal={Paediatric and Perinatal Epidemiology},
+ volume={19},
+ year={2005},
+ pages={323-331}
+}
+
+ at article{FreChrKha05,
+ author={James V. Freeman and Parul Christian and Subarna K. Khatry and Ramesh K.
+ Adhikari and Steven C. LeClerq and Joanne Katz and Gary L. Darmstadt},
+ title={Evaluation of neonatal verbal autopsy using physician review versus algorithm-based
+ cause-of-death assignment in rural Nepal},
+ journal={Paediatric and Perinatal Epidemiology},
+ volume={19},
+ year={2005},
+ pages={323-331}
+}
+
+ at article{Freedman08,
+ author = {David A. Freedman},
+ year = 2008,
+ volume = 40,
+ title = {On Regression Adjustments to Experimental Data},
+ journal = {Advances in Applied Mathematics},
+ pages = {180--193}
+}
+
+ at article{Freese07,
+ author={Jeremy Freese},
+ title={Replication Standards for Quantitative Social Science: Why not Sociology},
+ journal={Sociological Methods and Research},
+ year={2007, forthcoming}
+}
+
+ at article{FreGonGom06,
+ author={Julio Frenk and Eduardo Gonz{\'a}lez-Pier and Octavio G{\'o}mez-Dant{\'e}s and Miguel
+ A. Lezana and Felicia Marie Knaul},
+ title={Comprehensive reform to improve health system performance in Mexico},
+ journal={Lancet},
+ volume={268},
+ year={2006},
+ pages={1524-34},
+ month={October}
+}
+
+ at article{FreKleOst98,
+ author={D.A. Freedman and S.P. Klein and M. Ostland and M.R. Roberts},
+ title={Review},
+ journal={Journal of the American Statistical Association},
+ volume= 93,
+ year= 1998,
+ pages={{1518-1522}}
+}
+
+ at article{FreMil04,
+ author={Per G. Fredriksson and Daniel L. Millimet},
+ title={Comparative Politics and Envrionmental Taxation},
+ journal={Journal of Environmental Economics and Management},
+ volume={48},
+ year={2004},
+ pages={705-722}
+}
+
+ at techreport{Frenk04,
+ author={Julio Frenk},
+ title={Fair Financing and Universal Social Protection: the structural reform of
+ the Mexican health system},
+ institution={Secretaria de Salud},
+ year={2004},
+ address={Mexico City}
+}
+
+ at article{Frenk06,
+ author={Julio Frenk},
+ title={Bridging the divide: global lessons from evidence-based health policy in
+ Mexico},
+ journal={Lancet},
+ volume={368},
+ year={2006},
+ pages={954-61},
+ month={September}
+}
+
+
+
+ at article{FreSepGom03,
+ author={Julio Frenk and Jamie Sep{\'u}lveda and Octavio G{\'o}mez-Dant{\'e}s and Felicia Knaul},
+ title={{Evidence-based health policy: three generations of reform in Mexico}},
+ journal={The Lancet},
+ volume={362},
+ year={2003},
+ pages={1667--1671},
+ number={9396}
+}
+
+ at article{FreWec81,
+ author={Frey, Bruno and Weck, Hannelore},
+ title={Hat Arbeitslosigkeit den Aufstieg des Nationalsozialismus bewirkt?},
+ journal={Jahrbuch fuer Nationaloekonomie und Statistik},
+ volume= 196,
+ year= 1981,
+ pages={1-31}
+}
+
+ at book{Friendly00,
+ author={Michael Friendly},
+ title={Visualizing Categorical Data},
+ publisher={SAS Institute},
+ year={2000}
+}
+
+ at unpublished{FriHol05,
+ author={John N. Friedman and Richard T. Holden},
+ title={The Rising Incumbent Advantage: What's Gerrymandering Got to Do With It?},
+ note={Dept. of Economics, Harvard; rholden at fas.harvard.edu},
+ year={2005},
+ month={August}
+}
+
+ at unpublished{FriHol05,
+ author={John N. Friedman and Richard T. Holden},
+ title={The Rising Incumbent Advantage: What's Gerrymandering Got to Do With It?},
+ note={Dept. of Economics, Harvard; rholden at fas.harvard.edu},
+ year={2005},
+ month={August}
+}
+
+ at article{FriKroNew98,
+ author={Linda P. Fried, Md, MPH, Richard A. Kronmal, PhD, Anne B. Newman, MD, PhD,
+ et al},
+ title={Risk Factors for 5-Year Mortality in Older-Adults: The Cardiovascular Health
+ Study},
+ journal={Journal of the American Medical Association},
+ volume= 279,
+ year= 1998,
+ pages={{585-92}}
+}
+
+ at book{Fritzsche98,
+ author={Peter Fritzsche},
+ title={Germans into Nazis},
+ publisher={Harvard University Press},
+ year= 1998,
+ address={Cambridge, MA}
+}
+
+ at unpublished{Frolich02,
+ author={Markus Fr{\"o}lich},
+ title={What is the Value of Knowing the Propensity Score for Estimating Average
+ Treatment Effects?},
+ note={IZA Discussion Paper 548, University of St. Gallen},
+ year= 2002
+}
+
+ at article{Frolich04,
+ author={Markus Fr{\"o}lich},
+ title={Finite Sample Properties of Propensity Score Matching and Weighting Estimators},
+ journal={Review of Econometrics and Statistics},
+ volume= 86,
+ year= 2004,
+ pages={77--90}
+}
+
+ at article{FurLov01,
+ author={A. Furnham and J. Lovett},
+ title={The Perceived Efficacy and Risks of Complementary and Alternative Medicine
+ and Conventional Medicine: A Vignette Study},
+ journal={Journal of Applied Biobehavioral Research},
+ volume= 6,
+ year= 2001,
+ pages={39--63},
+ number= 1
+}
+
+ at article{FylFor91,
+ author={Knut Fylkesnes and Olav Helge Forde},
+ title={The Tromso Study: Predictors of Self-Evaluated Health-Has Society Adopted
+ the Expanded Health Concept?},
+ journal={Social Science and Medicine},
+ volume= 32,
+ year= 1991,
+ pages={{141-46}},
+ number= 2
+}
+
+ at book{GAD01,
+ author={{Government's Actuary Department}},
+ title={National Population Projections: Review of Methodology for Projecting Mortality},
+ publisher={National Statistics Direct, UK},
+ year= 2001,
+ address={London},
+ note={{http://www.statistics.gov.uk/}}
+}
+
+ at article{GaiMarCar96,
+ author={Mitchell H. Gail and Steven D. Mark and Raymond J. Carroll and Sylvan B.
+ Green and David Pee},
+ title={On Design Considerations and Randomization-Based Inference for Community
+ Intervention Trials},
+ journal={Statistics in Medicine},
+ volume={15},
+ year={1996},
+ pages={1069-1092}
+}
+
+ at article{GaiMarCar96,
+ author={Mitchell H. Gail and Steven D. Mark and Raymond j. Carroll and Sylvan B.
+ Green and David Pee},
+ title={On Design Considerations and Randomization-Based Inference for Community
+ Intervention Trials},
+ journal={Statistics in Medicine},
+ volume={15},
+ year={1996},
+ pages={1069-1092}
+}
+
+ at article{GaiWiePia84,
+ author={M.H. Gail and S. Wieand and S. Piantadosi},
+ title={Biased Estimates of Treatment Effect in Randomized Experiements with Nonlinear
+ Regressions and Omitted Covariates},
+ journal={Biometrika},
+ volume={71},
+ year={1984},
+ pages={431-444},
+ month={December},
+ number={3}
+}
+
+ at article{GajPet04,
+ author={Vendhan Gajalakshmi and Richard Peto},
+ title={Verbal autopsy of 80,000 adult deaths in Tamilnadu, South India},
+ journal={BMC Public Health},
+ volume={4},
+ year={2004},
+ month={October},
+ number={47}
+}
+
+ at article{GajPetKan02,
+ author={Vendhan Gajalakshmi and Richard Peto and Santhanakrishnan Kanaka and Sivagurunathan
+ Balasubramanian},
+ title={Verbal autopsy of 48000 adult deaths attributable to medical causes in Chennai
+ (formerly Madras), India},
+ journal={BMC Public Health},
+ volume={2},
+ year={2002}
+}
+
+ at article{GajPetKan02,
+ author={Vendhan Gajalakshmi and Richard Peto and Santhanakrishnan Kanaka and Sivagurunathan
+ Balasubramanian},
+ title={Verbal autopsy of 48000 adult deaths attributable to medical causes in Chennai
+ (formerly Madras), India},
+ journal={BMC Public Health},
+ volume={2},
+ year={2002}
+}
+
+ at article{GakHogLop04,
+ author={Emmanuela Gakidou and Margaret Hogan and Alan D Lopez},
+ title={Adult Mortality: Time for a Reapprasal},
+ journal={International Journal of Epidemiology},
+ volume= 33,
+ year= 2004,
+ pages={710-717},
+ number= 4
+}
+
+ at article{GakLozGon06,
+ author={Emmanuela Gakidou and Rafael Lozano and Eduardo Gonz{\'a}lez-Pier and Jesse
+ Abbott-Klafter and Jeremy T. Barofsky and Chloe Bryson-Cahn and Dennis
+ M. Feehan and Diana K. Lee and Hector Hern{\'a}ndez-Llamas and Christopher
+ J.L. Murray},
+ title={Assessing the effect of the 2001-06 Mexican health reform: an interim report
+ card},
+ journal={Lancet},
+ volume={368},
+ year={2006},
+ pages={1920-35},
+ month={November}
+}
+
+ at book{Gamson92,
+ author={William A. Gamson},
+ title={Talking Politics},
+ publisher={Cambridge University Press},
+ year= 1992,
+ address={New York, NY}
+}
+
+ at techreport{GAO94,
+ author={{U.S. General Accounting Office}},
+ title={Breast conservation versus mastectomy: patient survival in day-to-day medical
+ practice and randomized studies: report to the chairman, Subcommitee on
+ Human Resources and Intergovernmental Relations, Committee on Government
+ Operations, House of Representatives},
+ institution={U.S. General Accounting Office},
+ year= 1994,
+ address={Washington, DC},
+ number={Report GAO-PEMD-95-9}
+}
+
+ at article{GarFri97,
+ author={M. Garenne and F. Friedberg},
+ title={Accuracy of indirect estimates of maternal mortality: a simulation model},
+ journal={Studies in Family Planning},
+ volume= 28,
+ year= 1997,
+ pages={132--142}
+}
+
+ at book{Gasset32,
+ author={Ortega y Gasset, Javier},
+ title={The Revolt of the Masses},
+ publisher={G. Allen \& Unwin},
+ year= 1932,
+ address={London}
+}
+
+ at article{Gastwirth87,
+ author={J. Gastwirth},
+ title={The statistical precision of medical screening procedures: Application to
+ polygraph and AIDS antibodies test data},
+ journal={Statistical Science},
+ volume={2},
+ year={1987},
+ pages={213-222},
+ number={3}
+}
+
+ at book{Geiger32,
+ author={Geiger, Theodor},
+ title={Die soziale Schichtung des deutschen Volkes},
+ publisher={Ferdinand Enke},
+ year={1932},
+ address={Stuttgart}
+}
+
+ at book{GelCarSte03,
+ author={Andrew Gelman and J.B. Carlin and H.S. Stern and D.B. Rubin},
+ title={Bayesian Data Analysis, Second Edition},
+ publisher={Chapman \& Hall},
+ year= 2003
+}
+
+ at book{GelCarSte95,
+ author={Andrew Gelman and J.B. Carlin and H.S. Stern and D.B. Rubin},
+ title={Bayesian Data Analysis},
+ publisher={Chapman and Hall},
+ year= 1995
+}
+
+ at unpublished{GelGri00,
+ author={Christopher Gelpi and Joseph M. Grieco},
+ title={Democracy, Interdependence, and the Liberal Peace},
+ note={{Duke University, http://www.duke.edu/$\sim$gelpi/papers.htm}},
+ year={2000}
+}
+
+ at book{GelHil07,
+ author={Andrew Gelman and Jennifer Hill},
+ title={Data Analysis Using Regression and Multilevel/Hierarchical Models},
+ publisher={Cambridge University Press},
+ year= 2007,
+ address={New York}
+}
+
+ at unpublished{GelHua04,
+ author={Andrew Gelman and Zaiying Huang},
+ title={Estimating incumbency advantage and its varation, as an example of a before-after
+ study},
+ year={2004},
+ month={October}
+}
+
+ at article{Gelman06,
+ author={Andrew Gelman},
+ title={Prior distributions for variance parameters in hierarchical models},
+ journal={Bayesian Analysis},
+ volume={1},
+ year={2006},
+ pages={515-533},
+ number={3}
+}
+
+ at article{GelSmi90,
+ author={{Gelfand, A.E. and Smith, A.F.M.}},
+ title={Sampling-based approach to calculating marginal densities},
+ journal= jasa,
+ volume= 85,
+ year= 1990,
+ pages={398--409}
+}
+
+ at article{GelSmi90,
+ author={A.E. Gelfand and A.F.M. Smith},
+ title={Sampling-based approaches to calculating marginal densities},
+ journal={Journal of the American Statistical Association},
+ volume={85},
+ year={1990},
+ pages={398-409}
+}
+
+ at article{GemGem84,
+ author={Stuart Geman and Donald Geman},
+ title={Stochastic Relaxation, {G}ibbs Distributions, and the {B}ayesian Restoration
+ of Images},
+ journal={I.E.E.E. Transactions: Pattern Analysis and Machine Intelligence},
+ volume= 6,
+ year= 1984,
+ pages={721-741}
+}
+
+ at article{GerGre00,
+ author={Gerber, Alan S. and Green, Donald P.},
+ title={The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout:
+ A Field Experiment},
+ journal={American Political Science Review},
+ volume= 94,
+ year= 2000,
+ pages={653--663},
+ month={September},
+ number= 3
+}
+
+ at inbook{GerGreKap04,
+ author={Alan S. Gerber and Donald P. Green and Edward H. Kaplan},
+ title={The illusion of learning from observational research},
+ chapter={12},
+ year={2004},
+ publisher={Cambridge University Press},
+ pages={251-273},
+ address={Cambridge, United Kingdom},
+ editor={Ian Shapiro and Rogers M. Smith and Tarek e. Masoud}
+}
+
+ at article{GerSchFra94,
+ author={Gerner, Deborah J. and Philip A. Schrodt and Ronald A. Francisco and Judith
+ L. Weddle},
+ title={{Machine Coding of Event Data Using Regional and International Sources}},
+ journal={International Studies Quarterly},
+ volume={38},
+ year={1994},
+ pages={91-119},
+ number={1}
+}
+
+ at unpublished{Gertler00,
+ author={Paul J. Gertler},
+ title={Final Report: The Impact of PROGRESA on Health},
+ note={International Food Policy Research Institute},
+ year={2000},
+ month={November}
+}
+
+ at article{Gertler06,
+ author={P. Gertler},
+ title={Do Conditional Cash Transfers Improve child Health? Evidence from PROGRESA's
+ Control Randomized Experiment},
+ journal={The American Economic Review: Papers and Proceedings},
+ volume={94},
+ year={2006},
+ pages={336-42},
+ number={2}
+}
+
+ at techreport{Geyer05,
+ author={Charles J. Geyer},
+ title={Le Cam Made Simple: Asymptotics of Maximum Likelihood without the LLN or
+ CLT or Sample Size Going to Infinity},
+ institution={University of Minnesota},
+ year={2005},
+ month={May},
+ address={Univ. MN, Twin Cities, School of Statistics}
+}
+
+ at techreport{Geyer05,
+ author={Charles J. Geyer},
+ title={Le Cam Made Simple: Asymptotics of Maximum Likelihood without the LLN or
+ CLT or Sample Size Going to Infinity},
+ institution={University of Minnesota},
+ year={2005},
+ month={May},
+ address={Univ. MN, Twin Cities, School of Statistics}
+}
+
+ at article{GhoHutRus03,
+ author={Hazem Ghobarah and Paul Huth and Bruce Russett},
+ title={Civil Wars Kill and Maim People--Long after the Shooting Stops},
+ journal= apsr,
+ volume= 97,
+ year= 2003,
+ pages={189--202},
+ month={May},
+ number= 2
+}
+
+ at article{GiaPalCap01,
+ author={S. Giampaoli and L. Palmieri and R. Capocaccia and L. Pilotto and D. Vanuzzo},
+ title={Estimating Population-based Incidence and Prevalence of Major Coronary Events},
+ journal={International Journal of Epidemiology},
+ volume= 30,
+ year= 2001,
+ pages={S5--S10}
+}
+
+ at article{Giles06,
+ author={Jim Giles},
+ title={The Trouble with Replication},
+ journal={Nature},
+ volume={442},
+ year={2006},
+ pages={344-347},
+ month={July}
+}
+
+ at book{Gilksetal96,
+ title={Markov Chain Monte Carlo in Practice},
+ publisher={Chapman \& Hall},
+ year= 1996 ,
+ editor={W.R. Gilks and S. Richardson and D.J. Spiegelhalter}
+}
+
+ at book{Gill02,
+ author={Jeff Gill},
+ title={Bayesian Methods for the Social and Behavioral Sciences},
+ publisher={Chapman and Hall},
+ year= 2002,
+ address={London}
+}
+
+ at article{GilWal05,
+ author = {Jeff Gill and Lee Walker},
+ title = {Elicited Priors for Bayesian Model Specification in
+ Political Science Research},
+ journal = {Journal of Politics},
+ volume = 67,
+ year = 2005,
+ pages = {841--872},
+ month = {August},
+ number = 3
+}
+
+ at unpublished{GimHus06,
+ author={James G. Gimpel and Laura Hussey},
+ title={State of the Journal Market 2006-2007: Political Science},
+ note={Univ. of MD, Dept of Gov 3140 Tydings Hall, College Park, MD 20742},
+ year={2006},
+ month={February}
+}
+
+ at techreport{Girosi91,
+ author={F. Girosi},
+ title={Models of noise and robust estimates},
+ institution= mitai,
+ year={1991},
+ type={A.I. Memo},
+ number={1287},
+ note={ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-1287.pdf}
+}
+
+ at article{GlaLevMye03,
+ author={Steve Glazerman and Dan M. Levy and David Myers},
+ title={Nonexperimental versus experimental estimates of earnings impacts},
+ journal={The Annals of the American Academy of Political and Social Science},
+ volume= 589,
+ year= 2003,
+ pages={63-93},
+ month={September}
+}
+
+ at article{GlaMayDec06,
+ author={Steven Glazerman and Daniel Mayer and Paul Decker},
+ title={Alternative Routes to Teaching: The Impacts of Teach for America on Student
+ Achievement and other Outcomes},
+ journal={Journal of Policy Analysis and Management},
+ volume={25},
+ year={2006},
+ pages={75-96},
+ number={1}
+}
+
+ at article{Gleditsch02,
+ author={Kristian Skrede Gleditsch},
+ title={Expanded Trade and GDP Data},
+ journal={Journal of Conflict Resolution},
+ volume={46},
+ year={2002},
+ pages={712-724},
+ month={October},
+ number={5}
+}
+
+ at article{GleJamRay03,
+ author={Nils Petter Gleditsch and Patrick James and James Lee Ray and Bruce Russett},
+ title={Editors' Joint Statement: Minimum Replication Standards for International
+ Relations Journals},
+ journal={International Studies Perspectives},
+ volume= 4,
+ year= 2003,
+ pages={105}
+}
+
+ at article{GleMetStr03,
+ author={Nils Petter Gleditsch and Claire Metelits and Havard Strand},
+ title={Posting Your Data: Will You be Scooped or Will You be Famous?},
+ journal={International Studies Perspectives},
+ volume= 4,
+ year= 2003,
+ pages={89--97}
+}
+
+ at unpublished{Globetti97,
+ author={Suzanne Globetti},
+ title={What We Know About 'Don't Knows': An Analysis of Seven Point Issue Placements},
+ note={Paper presentated at the annual meetings of the Political Methodology Society,
+ Columbus, OH},
+ year={1997}
+}
+
+ at book{GoeZel86,
+ author={Prem K. Goel and Arnold Zellner},
+ title={Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de
+ Finetti},
+ publisher={Elsevier Science Publishers B.V.},
+ year={1986},
+ volume={6},
+ editor={Prem K. Goel and Arnold Zellner}
+}
+
+ at book{Goldberger91,
+ author={Arthur Goldberger},
+ title={A Course in Econometrics},
+ publisher={Harvard University Press},
+ year= 1991
+}
+
+ at article{GolFraEri96,
+ author={Marthe Gold, MD, MPH, Peter Franks, MD, and Pennifer Erickson},
+ title={Assessing the Health of the Nation. The Predictive Validity of a Preference-Based
+ Measure and Self-Rated Health},
+ journal={Medical Care},
+ volume= 34,
+ year= 1996,
+ pages={{163-77}},
+ number= 2
+}
+
+ at article{GolHeaWah79,
+ author={G. Golub and M. Heath and G. Wahba},
+ title={Generalized cross validation as a method for choosing a good ridge parameter},
+ journal={Technometrics},
+ volume= 21,
+ year= 1979,
+ pages={215--224}
+}
+
+ at article{GolIdn83,
+ author={D. Goldfarb and A. Idnani},
+ title={A Numerically Stable Dual Method for Solving Strictly Convex Quadratic Programs},
+ journal={Mathematical Programming},
+ volume={27},
+ year={1983},
+ pages={1-33}
+}
+
+ at book{GolJudMil96,
+ author={Amos Golan and George Judge and Doug Miller},
+ title={Maximum Entropy Econometrics: Robust Estimation With Limited Data},
+ publisher={John Wiley and Sons},
+ year= 1996
+}
+
+ at article{GolLan92,
+ author={Larry Goldstein and Bryan Langholz},
+ title={Asymptotic Theory for Nested Case-Control Sampling in the cox Regression
+ Model},
+ journal={The Annals of Statistics},
+ volume= 20,
+ year= 1992,
+ pages={1903-1928},
+ number= 4
+}
+
+ at article{GolPhiCox01,
+ author={Lee Goldman and Kathryn A. Phillips and Pamela Coxson and Paula A. Goldman
+ and Lawrence Williams and M.G. Myriam Hunink and Milton C. Weinstein},
+ title={The Effect of Risk Factor Reductions Between 1981-1990 on Coronary Heart
+ Disease Incidence, Prevalence, Mortality, and Cost},
+ journal={Journal of the American College of Cardiology},
+ volume={38},
+ year= 2001,
+ pages={1012--1017},
+ number= 4
+}
+
+ at article{GolSchMcC02,
+ author={J. Goldie and L. Schwartz and A. McConnachie and J. Morrison},
+ title={The Impact of Three Years' Ethics Teaching, in an Integrated Medical Curriculum,
+ on Students' Proposed Behavior on Meeting Ethical Dilemmas},
+ journal={Medical Education},
+ volume= 36,
+ year= 2002,
+ pages={489--497},
+ month={May},
+ number= 5
+}
+
+ at article{GomGarLop99,
+ author={Octavio G{\'o}mez-Dant{\'e}s and Francisco Garrido-Latorre and Sergio L{\'o}pez-Moreno
+ and Blanca Villa and Malaqu{\'i}as L{\'o}pez-Cervantes},
+ title={Assessment of the health program for the non-insured population},
+ journal={Revista de Sa{\'u}de P{\'u}blica},
+ volume={33},
+ year={1999},
+ pages={401-412},
+ number={4},
+ note={Evaluaci{\'o}n de programa de salud para poblaci{\'o}nno asegurada}
+}
+
+ at article{Gompertz1825,
+ author={B. Gompertz},
+ title={On the Nature of the Function Expressive of the Law of Mortality},
+ journal={Philosophical Transactions},
+ volume= 27,
+ year= 1825,
+ pages={513--585}
+}
+
+ at article{GonChaLev02,
+ author={Jeffrey S. Gonzales, Gretchen B. Chapman, and Howard Leventhal},
+ title={Gender Differences in the Factors that Affect Self-Assessments of Health},
+ journal={Journal of Applied Behavioral Research},
+ volume= 7,
+ year= 2002,
+ pages={{133-55}}
+}
+
+ at article{GooBlu96,
+ author={Jodi S. Goodman and Terry C. Blum},
+ title={Assessing the Non-random Sampling Effects of Subject Attrition in Longitudinal
+ Research},
+ journal={Journal of Management},
+ volume={22},
+ year={1996},
+ pages={627-652}
+}
+
+ at article{Goodman53,
+ author={Goodman, Leo},
+ title={Ecological Regressions and the Behavior of Individuals},
+ journal={American Sociological Review},
+ volume= 18,
+ year= 1953,
+ pages={663-666}
+}
+
+ at inbook{GooJaGoo03,
+ author={Mary-Jo DelVecchio Good, et al.},
+ title={The Culture of Medicine and Racial, Ethnic, and Class Disparities in Healthcare},
+ year={2003},
+ publisher={The National Academies Press},
+ pages={{594-625}}
+}
+
+ at article{GosWadBel98,
+ author={Stephen C. Goss and Alice Wade and Felicitie Bell and Bernard Dussault},
+ title={Historical and Projected Mortality for Mexico, Canada, and the United States},
+ journal={North American Actuarial Journal},
+ volume= 4,
+ year= 1998,
+ pages={108--126},
+ number= 2
+}
+
+ at article{Gower66,
+ author={J.C. Gower},
+ title={Some Distance Properties of Latent Root and Vector Methods Used in Multivariate
+ Analysis},
+ journal={Biometrika},
+ volume= 53,
+ year= 1966,
+ pages={325--388},
+ month={December},
+ number={3/4}
+}
+
+ at article{Gower71,
+ author={J.C. Gower},
+ title={A General Coefficient of Similarity and Some of its Properties},
+ journal={Biometrics},
+ volume= 27,
+ year= 1971,
+ pages={857--872}
+}
+
+ at article{GraBraSno89,
+ author={W. Graham and W. Brass and R.W. Snow},
+ title={Estimating Maternal Mortality: The Sisterhood Methods},
+ journal={Studies in Family Planning},
+ volume= 20,
+ year= 1989,
+ pages={125--135},
+ number= 125
+}
+
+ at inbook{Graham94,
+ author={Carol Graham},
+ title={Mexico's Solidarity Program in Comparative Context: Demand-based Poverty
+ Alleviation Programs in Latin America, Africa and Eastern Europe},
+ chapter={15},
+ year={1994},
+ publisher={Center for U.S.-Mexican Studies},
+ pages={309-327},
+ series={U.S.-Mexico Contemporary Perspectives Series, 6},
+ address={University of California, San Diego}
+}
+
+ at article{GraPioCha95,
+ author={Mark Grant, Zdzisiaw Piotrowski, and Rick Chappell},
+ title={Self-Reported Health and Survival in the Longitudinal Study of Aging, 1984-1986},
+ journal={Journal of Clinical Epidemiology},
+ volume= 48,
+ year= 1995,
+ pages={{375-87}},
+ number= 3
+}
+
+ at inbook{GraSch06,
+ author={J.W. Graham and J.L. Schafer},
+ title={Statistical Strategies for Small Sample Research},
+ chapter={On the performance of Multiple Imputation for Multivariate Data with Small
+ Sample Size},
+ year={2006 In press},
+ publisher={Sage},
+ address={Thousand Oaks},
+ editor={R. Hoyle}
+}
+
+ at book{GraSmiBar90,
+ author={Ronald H. Gray and Gordon Smith and Peter Barss},
+ title={The Use of Verbal Autopsy Methods to Determine Selected Causes of Death
+ in Children},
+ publisher={International Union for the Scientific Study of Population},
+ year={1990},
+ address={Rue des augustins, 34 ; 4000 Liege (Belgium)},
+ month={February},
+ number={30}
+}
+
+ at book{Graunt1662,
+ author={John Graunt},
+ title={Natural and Political Observations Mentioned in a Following Index, and Made
+ Upon the Bills of Mortality},
+ publisher={John Martyn and James Allestry.},
+ year= 1662,
+ address={London}
+}
+
+ at article{GreChr01,
+ author={Sander Greenland and Ronald Christensen},
+ title={Data Augmentation Priors for Bayesian and Semi-Bayes Analyses of Conditional-logistic
+ and Proportional-Hazards Regression},
+ journal={Statistics in Medicine},
+ volume={20},
+ year={2001},
+ pages={2421-2428}
+}
+
+ at article{Greenland00,
+ author={Sander Greenland},
+ title={When should Epidemiologic Regressions Use Random Coefficients?},
+ journal={Biometrics},
+ volume={56},
+ year={2000},
+ pages={915-921},
+ month={September}
+}
+
+ at article{Greenland01,
+ author={Sander Greenland},
+ title={Putting Background Information About Relative Risks into conjugate Prior
+ Distributions},
+ journal={Biometrics},
+ volume={57},
+ year={2001},
+ pages={663-670},
+ month={September}
+}
+
+ at article{Greenland03,
+ author={Sander Greenland},
+ title={Quantifying biases in causal models: classical confounding vs collider-stratification
+ bias},
+ journal={Epidemiology},
+ volume= 14,
+ year= 2003,
+ pages={300-306},
+ number= 3
+}
+
+ at article{Greenland03b,
+ author={Sander Greenland},
+ title={Generalized Conjugate Priors for Bayesian Analysis of Risk and Survival
+ Regressions},
+ journal={Biometrics},
+ volume={59},
+ year={2003},
+ pages={92-99},
+ month={March}
+}
+
+ at article{greenland81,
+ author={Sander Greenland},
+ title={Multivariate Estimation of Exposure-Specific Incidence From Case-Control
+ Studies},
+ journal={Journal of Chronic Disease},
+ volume= 34,
+ year= 1981,
+ pages={445-453}
+}
+
+ at article{greenland82,
+ author={Sander Greenland},
+ title={On the Need for the Rare Disease Assumption in Case-Control Studies},
+ journal={American Journal of Epidemiology},
+ volume= 116,
+ year= 1982,
+ pages={547-553},
+ number= 3
+}
+
+ at article{greenland87,
+ author={Sander Greenland},
+ title={Interpretation and Choice of Effect Measures in Epidemiologic Analysis},
+ journal={American Journal of Epidemiology},
+ volume= 125,
+ year= 1987,
+ pages={761-768},
+ number= 5
+}
+
+ at article{greenland94,
+ author={Sander Greenland},
+ title={Modeling Risk Ratios from Matched Cohort Data: An Estimating Equation Approach},
+ journal={Applied Statistics},
+ volume= 43,
+ year= 1994,
+ pages={223-232},
+ number= 1
+}
+
+ at incollection{GreGer01,
+ author={Donald P. Green and Alan Gerber},
+ title={Reclaiming the Experimental Tradition in Political Science},
+ booktitle={Political Science: State of the Discipline, III},
+ publisher={APSA},
+ year= 2001,
+ address={Washington, D.C.},
+ editor={Helen Milner and Ira Katznelson}
+}
+
+ at incollection{GreGer02,
+ author={Donald P. Green and Alan S. Gerber},
+ title={Reclaiming the Experimental Tradition in Political Science},
+ booktitle={State of the Discipline},
+ publisher={W.W. Norton \& Company, Inc.},
+ year={2002},
+ address={New York},
+ editor={Helen Milner and Ira Katznelson},
+ pages={805-832},
+ volume={III}
+}
+
+ at misc{GreGre03,
+ author={Grendar, Jr., M. and M. Grendar},
+ title={Maximum Probability/Entropy Translating of Contiguous Categorical Observations
+ into Frequencies},
+ year= 2003 ,
+ howpublished={Working paper, Institute of Mathematics and Computer Science, Mathematical
+ Institute of Slovak Academy of Sciences, Banska Bystrica}
+}
+
+ at article{GreKimYoo01,
+ author={Donald P.\ Green and Soo Yeon Kim and David H.\ Yoon},
+ title={Dirty Pool},
+ journal= io,
+ volume= 55,
+ year= 2001,
+ pages={441--468},
+ month={Spring},
+ number= 2
+}
+
+ at article{GreLuSil04,
+ author={Robert Greevy and Bo Lu and Jeffrey H. Silver and Paul Rosenbaum},
+ title={Optimal multivariate matching before randomization},
+ journal={Biostatistics},
+ volume={5},
+ year={2004},
+ pages={263-275},
+ number={2}
+}
+
+ at article{GreMicRob06,
+ author={David H. Greenberg and Charles Michalopoulos and Philip K. Robins},
+ title={Do Experimental and Nonexperimental Evaluations give Different Answers about
+ the Effectiveness of Government-Funded Training Programs?},
+ journal={Journal of Policy Analysis and Management},
+ volume={25},
+ year={2006},
+ pages={523-552},
+ number={3}
+}
+
+ at misc{Grenander83,
+ author={Ulf Grenander},
+ title={Tutorial in Pattern Theory},
+ year= 1983,
+ howpublished={Technical Report, Division of Applied Mathematics, Brown University}
+}
+
+ at article{GrePeaRob99,
+ author={Sander Greenland and Judea Pearl and James M. Robins},
+ title={Causal Diagrams for Epidemiologic Research},
+ journal={Epidemiology},
+ volume= 10,
+ year= 1999,
+ pages={37--48},
+ month={January},
+ number= 1
+}
+
+ at book{GreShr04,
+ author={David Greenberg and Mark Shroder},
+ title={The Digest of Social Experiments},
+ publisher={Urban Institute Press},
+ year={2004},
+ address={Washington, DC},
+ edition={Third}
+}
+
+ at techreport{GriHilOdo01,
+ author={W. E. Griffiths and R. Carter Hill and C. J. O'Donnell},
+ title={{Including Prior Information in Probit Model Estimation}},
+ institution={Department of Economics},
+ year= 2001,
+ month={September},
+ type={Working Papers},
+ address={University of Melbourne},
+ number= 816
+}
+
+ at book{Grindle04,
+ author={Merilee S. Grindle},
+ title={Despite the Odds},
+ publisher={Princeton University Press},
+ year={2004},
+ address={Princeton, NJ}
+}
+
+ at unpublished{Grindle05,
+ author={Merilee S. Grindle},
+ title={Going Local: Decentralization, Democratization, and the Promise of Good
+ Governance},
+ note={Kennedy School of Government, Harvard University},
+ year={2005},
+ month={July}
+}
+
+ at book{Grindle77,
+ author={Merilee Serrill Grindle},
+ title={Bureaucrats, Politicans, and Peasants in Mexico},
+ publisher={University of California Press},
+ year={1977},
+ address={Berkeley and Los Angeles, California}
+}
+
+ at book{Grindle80,
+ author={Merilee S. Grindle},
+ title={Politics and Policy Implementation in the Third World},
+ publisher={Princeton University Press},
+ year={1980},
+ address={Princeton, NJ}
+}
+
+ at article{GroBri99,
+ author={Wim Groot and Henriette Maassen van den Brink},
+ title={Job Satisfaction and Preference Drift},
+ journal={Economics Letters},
+ volume= 63,
+ year= 1999,
+ pages={363--367}
+}
+
+ at article{GroLevSny99,
+ author={Tim Groseclose and Steven D. Levitt and James Snyder},
+ title={Comparing Interest Group Scores Across Time and Chambers: Adjusted ADA Scores
+ for the U.S. Congress},
+ journal= apsr,
+ volume= 93,
+ year= 1999,
+ pages={33--50},
+ month={March},
+ number= 1
+}
+
+ at article{Groot00,
+ author={Wim Groot},
+ title={Adaptation and Scale of Reference Bias in Self-Assessments of Quality of
+ Life},
+ journal={Journal of Health Economics},
+ volume= 19,
+ year= 2000,
+ pages={403--420},
+ month={June}
+}
+
+ at article{Grossman97,
+ author={Joel B. Grossman},
+ title={The Japanese American Cases and the Vagaries of Constitutional Adjudication
+ in Wartime: An Institutional Perspective},
+ journal={Hawaii Law Review},
+ volume= 19,
+ year={1997},
+ pages={649}
+}
+
+ at article{GroZalLeb00,
+ author={William M. Grove and David H. Zald and Boyd S. Lebow and Beth E. Snitz and
+ Chad Nelson},
+ title={Clinical Versus Mechanical Prediction: A Meta-Analysis},
+ journal={Psychological Assessment},
+ volume={12},
+ year={19-30},
+ pages={1},
+ number={1}
+}
+
+ at unpublished{GruGuhKum05,
+ author={DanielGruhl and R. Guha and Ravi Kumar and Jasmine NOvak and Andrew Tomkins},
+ title={The Predictive Power of Online Chatter},
+ note={Daniel Gruhl IBM Almaden Research Center, 650 Harry Rd. San Jose, CA 95120
+ dgruhl at us.ibm.com},
+ year={2005},
+ month={August}
+}
+
+ at article{GuRos93,
+ author={X.S. Gu and Paul R. Rosenbaum},
+ title={Comparison of multivariate matching methods: structures, distances, and
+ algorithms},
+ journal={Journal of Computational and Graphical Statistics},
+ volume={2},
+ year={1993},
+ pages={405-420}
+}
+
+ at article{GutVan98,
+ author={Sam Gutterman and Irwin T. Vanderhoof},
+ title={Forecasting Changes in Mortality: A Search for a Law of Causes and Effects},
+ journal={North American Actuarial Journal},
+ volume= 4,
+ year= 1998,
+ pages={135--138},
+ number= 2
+}
+
+ at article{Gwatkin00,
+ author={Davidson R. Gwatkin},
+ title={Health Inequalities and the Health of the Poor},
+ journal= bull,
+ year={2000},
+ optnumber={1},
+ optvolume={78},
+ optpages={3--18}
+}
+
+ at article{Gwatkin03,
+ author={Davidson Gwatkin},
+ title={How well do health programmes reach the poor?},
+ journal={Lancet},
+ volume={361},
+ year={2003},
+ pages={540-1},
+ month={February}
+}
+
+ at article{HabBer95,
+ author={J. Haberland and K.E. Bergmann},
+ title={The Lee-Carter Model of the Prognosis of Mortality in Germany},
+ journal={Gesundheitswesen},
+ volume={57},
+ year={1995},
+ pages={674--679},
+ month={October},
+ number={10},
+ note={article in German},
+ annote={Lee-Carter model is applied to West Germany.}
+}
+
+ at article{Haenisch89,
+ author={Dirk H{\"a}nisch},
+ title={Wahl- und Sozialdaten der Kreise und Gemeinden des Deutschen Reiches von
+ 1920 bis 1933},
+ journal={Historical Social Research},
+ volume= 14,
+ year= 1989,
+ pages={39--67},
+ number= 1
+}
+
+ at incollection{Hagtvet80,
+ author={Hagtvet, Bernt},
+ title={The Theory of Mass Society and the Collapse of the Weimar Republic},
+ booktitle={Who Were the Fascists},
+ publisher={Universitetsforlaget},
+ year= 1980,
+ address={Oslo},
+ editor={al., S. U. Larsen et}
+}
+
+ at article{hahn98,
+ author={Jinyong Hahn},
+ title={On the Role of the Propensity Score in Efficient Semiparametric Estimation
+ of Average Treatment Effects},
+ journal={Econometrica},
+ volume={66},
+ year={1998},
+ pages={315-31}
+}
+
+ at unpublished{Hamermesh07,
+ author={Daniel Hamermesh},
+ title={Replication in Economics: Discussion Paper No. 2760},
+ note={{IZA Discussion Paper : http://www.iza.org/publications/dps/}},
+ year={2007},
+ month={April},
+ address={University of Texas at Austin, NBER and IZA; iza at iza.org}
+}
+
+ at book{Hamilton83,
+ author={Hamilton, Richard},
+ title={Who Voted for Hitler?},
+ publisher={Princeton University Press},
+ year= 1983
+}
+
+ at book{Hamilton94,
+ author={James Douglas Hamilton},
+ title={Time Series Analysis},
+ publisher={Princeton University Press},
+ year= 1994,
+ address={Princeton}
+}
+
+ at book{Hammond24,
+ author={C.S. Hammond and Company},
+ title={[Map of] Germany},
+ publisher={C.S. Hammond and Company},
+ year= 1924,
+ address={New York}
+}
+
+ at article{Hand06,
+ author={David J. Hand},
+ title={Classifier Technology and the Illusion of Progress},
+ journal={Statistical Science},
+ volume={21},
+ year={2006},
+ pages={1-14},
+ number={1}
+}
+
+ at article{Hansen04,
+ author={Ben B. Hansen},
+ title={Full Matching in an Observational Study of Coaching for the {SAT}},
+ journal={Journal of the American Statistical Association},
+ volume={99},
+ year={2004},
+ pages={609--618},
+ number={467}
+}
+
+ at misc{Hansen05,
+ author={Ben Hansen},
+ title={Optmatch: Software for Optimal Matching},
+ year= 2005,
+ note={{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}}
+}
+
+ at techreport{Hansen06,
+ author={Ben Hansen},
+ title={Appraising Covariate Balance After Assignment to Treatment by Groups},
+ institution={Statistics Department, University of Michigan},
+ year= 2006,
+ month={{April}},
+ number= 436
+}
+
+ at article{Harding03,
+ author={David J. Harding},
+ title={Counterfactual Models of Neighborhood Effects: The Effect of Neighborhood
+ Poverty on Dropping Out and Teenage Pregnancy},
+ journal={American Journal of Sociology},
+ volume={109},
+ year={2003},
+ pages={676-719},
+ month={November},
+ number={3}
+}
+
+ at article{HarLis04,
+ author={Glenn W. Harrison and John A. List},
+ title={Field Experiments},
+ journal={Journal of Economic Literature},
+ volume={XLII},
+ year={2004},
+ pages={1009-1055}
+}
+
+ at article{HarSie75,
+ author={H.O. Hartley and R.L. Sielken, Jr.},
+ title={A `Super-Population Viewpoint' for Finite Population Sampling},
+ journal={Biometrics},
+ volume={31},
+ year={1975},
+ pages={411-422},
+ month={June},
+ number={2}
+}
+
+ at article{Hartung56,
+ author={Fritz Hartung},
+ title={Zur Geschichte der Weimarer Republik},
+ journal={Historische Zeitschrift},
+ volume= 181,
+ year= 1956,
+ pages={581--591},
+ number= 3
+}
+
+ at book{Harvey91,
+ author={Andrew Harvey},
+ title={Forecasting, Structural Time Series Models and the Kalman Filter},
+ publisher={Cambridge University Press},
+ year= 1991
+}
+
+ at book{Harville97,
+ author={David A. Harville},
+ title={Matrix Algebra from a Statistician's Perspective},
+ publisher={Springer},
+ year= 1997,
+ address={New York}
+}
+
+ at unpublished{HasKanSta05,
+ author={Justine S. Hastings and Thomas J. Kane and Douglas O. Staiger},
+ title={Evaluating a School Choice Lottery: The Importance of Heterogeneous Treatment
+ Effects},
+ note={Hastings - Yale, Kane - Harvard GSE, Staiger, Dartmouth College},
+ year={2005},
+ month={November}
+}
+
+ at unpublished{HasKanStai05b,
+ author={Justine S. Hastings and Thomas J. Kane and Douglas O. Staiger and Jeffrey
+ M. Weinstein},
+ title={Economic Outcomes and the Decision to Vote: The Effect of Randomized School
+ Admissions on Voter Participation},
+ note={Working paper 11794, National Bureau of Economic Research, 1050 Mass. Ave.,
+ Cambridge},
+ year={2005},
+ month={November}
+}
+
+ at unpublished{HasKanStai05c,
+ author={Justine S. Hastings and Thomas J. Kane and Douglas O. Staiger},
+ title={parental Preferences and School Competition: Evidence froma Public School
+ Choice Program},
+ note={National Bureau of Economic Research, 1050 Mass Ave. Camb. Working Paper
+ 11805},
+ year={2005},
+ month={November}
+}
+
+ at book{HasTib90,
+ author = {Hastie, Trevor J. and Tibshirani, Robert},
+ title = {Generalized Additive Models},
+ publisher = {Chapman Hall},
+ year = {1990},
+ address = {London}
+}
+
+ at article{HauDutBeh99,
+ author = {Lene V. Hau and Z. Dutton and C.H. Behroozi and SE
+ Harris},
+ title = {{Light Speed Reduction to 17 Metres per Second in an
+ Ultracold Atomic Gas}},
+ journal = {Nature},
+ volume = {397},
+ year = {1999},
+ pages = {594-598},
+ number = {6720}
+}
+
+ at article{HavNag05,
+ author={Amelia M. Haviland and Daniel S. Nagin},
+ title={Causal Inferences with Group Based Trajectory models},
+ journal={Psychometrika},
+ volume={70},
+ year={2005},
+ pages={557-578},
+ month={September},
+ number={3}
+}
+
+ at book{Hayes87,
+ author={Hayes, Peter},
+ title={Industry and Ideology},
+ publisher={Cambridge University Press},
+ year={1987}
+}
+
+ at article{Haynes02,
+ author={R Brian Haynes},
+ title={What Kind of Evidence is it that Evidence-Based Medicine Advocates Want
+ Health Care Providers and Consumers to Pay Attention to?},
+ journal={BMC Health Services Research},
+ volume= 2,
+ year= 2002,
+ month={March},
+ number= 3,
+ note={{http://www.biomedcentral.com/1472-6963/2/3}}
+}
+
+ at article{HaySchBla96,
+ author={Judith C. Hayes, David Schoenfield, Dan Blazer, and Deborah T. Gold},
+ title={Global Self-Ratings of Health and Mortality: Hazards in North Carolina Piedmont},
+ journal={Journal of Clinical Epidemiology},
+ volume= 49,
+ year= 1996,
+ pages={{969-79}}
+}
+
+ at article{Heberle43,
+ author={Heberle, R.},
+ title={The Political Movements among the Rural People in Schleswig-Holstein, 1918-1932},
+ journal={Journal of Politics},
+ volume= 5,
+ year= 1943,
+ pages={3-26}
+}
+
+ at book{Heberle45,
+ author={Heberle, Rudolf},
+ title={From Democracy to Nazism},
+ publisher={Louisiana State University Press},
+ year= 1945,
+ address={Baton Rouge}
+}
+
+ at article{HecHidTod97,
+ author={James J. Heckman and Hidehiko Hidehiko and Petra Todd},
+ title={Matching as an econometric evaluation estimator: evidence from evaluating
+ a job training programme},
+ journal={Review of Economic Studies},
+ volume= 64,
+ year= 1997,
+ pages={605-654}
+}
+
+ at article{HecIchSmi98,
+ author={James J. Heckman and Hidehiko Ichimura and Jeffrey Smith and Petra Todd},
+ title={Characterizing selection bias using experimental data},
+ journal={Econometrika},
+ volume= 66,
+ year= 1998,
+ pages={1017-1098},
+ number= 5
+}
+
+ at article{HecIchTod97,
+ author = {James Heckman and H. Ichimura and P. Todd},
+ title = {Matching as an Econometric Evaluation Estimator:
+ Evidence from Evaluating a Job Training Program},
+ journal = {Review of Economic Studies},
+ volume = 64,
+ month = {October},
+ year = 1997,
+ pages = {605--654}
+}
+
+ at article{Heckman06,
+ author={James J. Heckman},
+ title={The Scientific Model of Causality},
+ journal={Sociological Methodology},
+ volume={35},
+ year={2006},
+ pages={1-98},
+ month={June},
+ number={1}
+}
+
+ at article{Heckman06b,
+ author={James J. Heckman},
+ title={Rejoinder: Response to Sobel},
+ journal={Sociological Methodology},
+ volume={35},
+ year={2006},
+ pages={135-162},
+ month={June},
+ number={1}
+}
+
+ at article{Heckman76,
+ author={James Heckman},
+ title={The Common Structure of Statistical Models of Truncation, Sample Selection
+ and Limited Dependent Variables, and Simple Estimator for Such Models},
+ journal={Annals of Economic and Social Measurement},
+ volume={5},
+ year={1976},
+ pages={475-492}
+}
+
+ at incollection{Heckman92,
+ author={James J. Heckman},
+ title={Randomization and Social Policy Evaluation},
+ booktitle={Evaluating Welfare and Training Programs},
+ publisher={Harvard University Press},
+ year={1992},
+ editor={Charles F. Manski and Irwin Garfinkel}
+}
+
+ at inbook{HecRob85,
+ author={J. Heckman and R. Robb},
+ title={Longitudional Analysis of Labor Market Data},
+ chapter={Alternative Methods for Evaluating the Impacts of Interventions},
+ year= 1985 ,
+ publisher= cup,
+ editor={J. Heckman and B. Singer}
+}
+
+ at article{HecSmi95,
+ author={James J. Heckman and Jeffrey A. Smith},
+ title={Assessing the Case for Social Experiments},
+ journal={The Journal of Economic Perspectives},
+ volume={9},
+ year={1995},
+ pages={85-110},
+ number={2}
+}
+
+ at article{HecSny97,
+ author={James Heckman and James Snyder},
+ title={Linear Probabilty Models of the Demand for Attributes With an Empirical
+ Application to Estimating the Preferences of Legislators},
+ journal={Rand Journal of Economics},
+ volume= 28,
+ year= 1997,
+ pages={142--189},
+ month={special issue},
+ number= 0
+}
+
+ at article{Heilbronner97,
+ author={Heilbronner, Oded and M{\"u}hlberger, Detlef},
+ title={The Achilles' Heel of German Catholicism: ``Who Voted for Hitler?'' Revisited},
+ journal={European History Quarterly},
+ volume= 27,
+ year= 1997,
+ pages={221-249},
+ number= 2
+}
+
+ at article{HeiRub90,
+ author={Daniel F. Heitjan and Donald Rubin},
+ title={Inference from Coarse Data via Multiple Imputation with Application to Age
+ Heaping},
+ journal= jasa,
+ volume= 85,
+ year= 1990,
+ pages={304--314}
+}
+
+ at article{Heitjan89,
+ author={Daniel F. Heitjan},
+ title={Inference from Grouped Continuous Data: A Review},
+ journal={Statistical Science},
+ volume={4},
+ year={1989},
+ pages={164-183}
+}
+
+ at article{HelPol80,
+ author={L. Heligman and J. H. Pollard},
+ title={The Age Pattern of Mortality},
+ journal={Journal of the Institute of Acturaries},
+ volume= 107,
+ year= 1980,
+ pages={49--80}
+}
+
+ at article{Henderson24,
+ author={R. Henderson},
+ title={A new method of graduation},
+ journal={Transaction of the Actuarial Society of America},
+ volume= 119,
+ year= 1924,
+ pages={457--526}
+}
+
+ at unpublished{Herron98,
+ author={Michael C. Herron},
+ title={Voting Abstention, and Individual Expectations in the 1992 Presidential
+ Election},
+ note={Presented for the Midwest Political Science Assocation conference, Chicago},
+ year={1998}
+}
+
+ at article{HerSek04,
+ author={Michael C. Herron and Jasjeet S. Sekhon},
+ title={Black Candidates and Black Voters: Assessing the Impact of Candidate Race
+ on Uncounted Vote Rates},
+ journal={Journal of Politics},
+ volume= 66,
+ year= 2005,
+ month={forthcoming November},
+ number= 4
+}
+
+ at article{Heymann02,
+ author={Philip B. Heymann},
+ title={Civil Liberties and Human Rights in the Aftermath of September 11},
+ journal={Harvard Journal of Law and Public Policy},
+ volume= 25,
+ year={2002},
+ pages={440--455}
+}
+
+ at article{Hibbs82,
+ author={Douglas Hibbs},
+ title={Economic Outcomes and Political Support for British Governments Among the
+ Occupational Classes},
+ journal={American Political Science Review},
+ volume= 76,
+ year= 1982,
+ pages={259--279},
+ month={June}
+}
+
+ at incollection{Hicks94,
+ author={Alexander M. Hicks},
+ title={{Introduction to Pooling}},
+ booktitle={{The Comparative Political Economy of the Welfare State}},
+ publisher={Cambridge University Press},
+ year= 1994,
+ address={New York},
+ editor={T. Janoski and A. Hicks}
+}
+
+ at misc{HigYam00,
+ author={Dave Higdon and Steve Yamamoto},
+ title={Bayesian Image Analysis in Scanning Magnetoresistance Microscopy},
+ year= 2000,
+ howpublished={Discussion Paper \# 98-35, Institute of Statistics and Decision Sciences,
+ Duke University}
+}
+
+ at article{HilButLor77,
+ author={Lewis E. Hill and Charles E. Butler and Stephen A. Lorenzen},
+ title={Inflation and the Destruction of Democracy: The Case of the Weimar Republic},
+ journal={Journal of Economic Issues},
+ volume= 11,
+ year= 1977,
+ pages={299-314},
+ number= 2
+}
+
+ at unpublished{Hill04,
+ author={Jennifer Hill},
+ title={Reducing bias in treatment effect estimation in observational studies suffering
+ from missing data},
+ note={Columbia University Instititute for Social and Economic Research and Policy
+ (ISERP) Working Paper 04-01},
+ year= 2004
+}
+
+ at article{Hill87,
+ author={Joe R. Hill},
+ title={Empirical Bayes Confidence Intervals Based on Bootstrap Samples: Comment},
+ journal={Journal of the American Statistical Association},
+ volume={82},
+ year={1987},
+ pages={752-754},
+ month={September},
+ number={399}
+}
+
+ at unpublished{HilPurWil07,
+ author={Dustin Hillard and Stephen Purpura and John Wilkerson},
+ title={Bill Titles as Proxies for Bill Content: A Case Study of the Distillation
+ of Meaning from a Political Corpus},
+ note={Prepared for delivery at 2007 annual Meeting of the Midwest Political Science
+ Association, Chicago, IL},
+ year={2007},
+ month={April},
+ address={Univ of WA hillard at u.washington.edu; JFK School of Gov't. stephen_purpura at ksg07.harvard.edu;
+ Univ of WA jwilker at u.washington.edu}
+}
+
+ at article{HilRei06,
+ author={Jennifer Hill and Jerome P. Reiter},
+ title={Interval estimation for treatment effects using propensity score matching},
+ journal={Statistics in Medicine},
+ volume={25},
+ year={2006},
+ pages={2230-2256}
+}
+
+ at incollection{HilReiZan04,
+ author={Jennifer Hill and J. Reiter and Elaine Zanutto},
+ title={A comparison of experimental and observational data analyses},
+ booktitle={Applied Bayesian Modeling and Causal Inference from an Incomplete-Data Perspective},
+ year= 2004 ,
+ editor={Andrew Gelman and Xiao-Li Meng}
+}
+
+ at incollection{HilRubTho99,
+ author={Jennifer Hill and Donald B. Rubin and Neal Thomas},
+ title={The Design of the {N}ew {Y}ork {S}chool {C}hoice {S}cholarship {P}rogram
+ Evaluation},
+ booktitle={Research Designs: Inspired by the Work of Donald Campbell},
+ publisher={Sage},
+ year= 1999,
+ address={Thousand Oaks, CA},
+ editor={L. Bickman},
+ chapter= 7,
+ pages={155--180}
+}
+
+ at article{HilTru77,
+ author={Kenneth Hill and J Trussell},
+ title={Further Developments in Indirect Mortality Estimation},
+ journal={Population Studies},
+ volume= 31,
+ year= 1977,
+ pages={313--334}
+}
+
+ at article{HilWalBro05,
+ author={Jennifer L. Hill and Jane Waldfogel and Jeanne Brooks-Gunn and Wen-Jui Han},
+ title={Maternal Employment and Child Development: A Fresh Look Using Newer Methods},
+ journal={Developmental Psychology},
+ volume={41},
+ year={2005},
+ pages={833-850},
+ number={6}
+}
+
+ at unpublished{Hindman07,
+ author={Matthew Hindman},
+ title={Voice, Equality, and the Internet},
+ note={Book Manuscript},
+ year={2007}
+}
+
+ at book{HinMun94,
+ author={Melvin J. Hinich and Michael C. Munger},
+ title={Ideology and the Theory of Political Choice},
+ publisher={University of Michigan Press},
+ year={1994},
+ address={Ann Arbor}
+}
+
+ at unpublished{HinTsiJoh03,
+ author={Matthew Hindman and Kostas Tsioutsiouliklis and Judy A. Johnson},
+ title={Googlearchy: How a Few Heavily-Linked Sites Dominate Politics on the Web},
+ note={Midwest Political Science Association, Chicago, Illinois},
+ year={2003},
+ month={April}
+}
+
+ at article{HirImbRid03,
+ author = {Keisuke Hirano and Guido W. Imbens and Geert Ridder},
+ title = {Efficient Estimation of Average Treatment Effects
+ Using the Estimated Propensity Score},
+ journal = {Econometrica},
+ volume = 71,
+ year = 2003,
+ pages = {1161--1189},
+ month = {July},
+ number = 4
+}
+
+ at article{HirImbRub00,
+ author = {Keisuke Hirano and Guido W. Imbens and Donald
+ B. Rubin and Xiao-Hua Zhou},
+ title = {Assessing the effect of an influenza vaccine in an
+ encouragement design},
+ journal = {Biostatistics},
+ volume = {1},
+ year = {2000},
+ pages = {69-88},
+ number = {1}
+}
+
+ at article{HirRubZho00,
+ author={Keisuke Hirano and Guido W. Imbens and Donald B. Rubin and Ziao-Hua Zhou},
+ title={Assessing the effect of an influenza vaccine in an encouragement design},
+ journal={Biostatistics},
+ volume={1},
+ year={2000},
+ pages={69-88},
+ number={1}
+}
+
+ at unpublished{Hiscox04,
+ author={Michael J. Hiscox},
+ title={Through a Glass and Darkly: Attitudes Toward International Trade and the
+ Curious Effects of Issue Framing},
+ note={{http://www.experimentcentral.org/data/data.php?pid=136}},
+ year= 2004,
+ address={Chicago},
+ organization={Annual meetings of the American Political Science Association}
+}
+
+ at article{Ho91,
+ author={Ho, Suzanne C.},
+ title={Health and Social Predictors of Mortality in an Elderly Chinese Cohort},
+ journal={American Journal of Epidemiology},
+ volume= 133,
+ year= 1991,
+ pages={{209-21}},
+ number= 9,
+ keywords={aged, cohort studies, health status, mortality, social environment}
+}
+
+ at article{HoeFesVan97,
+ author={Nancy Hoeymans, MSc, eta al},
+ title={Age, time, and cohort effects on functional status and self-rated health
+ in elderly men},
+ journal={American Journal of Public Health},
+ volume= 87,
+ year= 1997,
+ pages={{1620-25}},
+ number= 10
+}
+
+ at article{HoeMadRaf99,
+ author={Jennifer A. Hoeting and David Madigan and Adrian E. Raftery, Adrian E. and
+ Chris T. Volinsky},
+ title={Bayesian Model Averaging: A Tutorial},
+ journal={Statistical Science},
+ volume= 14,
+ year= 1999,
+ pages={382-417},
+ number= 4
+}
+
+ at article{HoeMadRaf99,
+ author={J.A. Hoeting and D. Madigan and Adrian E. Raftery and C. T. Volinsky},
+ title={Bayesian Model Averaging: A Tutorial (with discussion)},
+ journal={Statistical Science},
+ volume= 14,
+ year= 1999,
+ pages={382--417},
+ month={{corrected version at http://www.stat.washington.edu/www/research/online/hoeting1999.pdf}}
+}
+
+ at article{HojSteAab99,
+ author={Lars Hoj and Jakob Stensballe and Peter Aaby},
+ title={Maternal mortality in Guinea-Bissau: the use of verbal autopsy in a mutli-ethnic
+ population},
+ journal={International Journal of Epidemiology},
+ volume={28},
+ year={1999},
+ pages={70-76}
+}
+
+ at article{holland86,
+ author={Paul W. Holland},
+ title={Statistics and Causal Inference},
+ journal={Journal of the American Statistical Association},
+ volume= 81,
+ year= 1986,
+ pages={945--960}
+}
+
+ at inbook{Holmes95,
+ author={Stephen Holmes},
+ title={Passions and constraint: on the theory of liberal democracy},
+ chapter={5 Precommitment and the Paradox of Democracy},
+ year={1995},
+ publisher={University of Chicago Press},
+ pages={134 - 177},
+ address={Chicago}
+}
+
+ at misc{HolMulKal00,
+ author={F.W. Hollmann and T.J. Mulder and J.E. Kallan},
+ title={{Methodology and Assumptions for the Population Projections of the United
+ States: 1999 to 2100}},
+ year= 2000 ,
+ howpublished={Working Paper 38, Population Division, U.S. Bureau of Census}
+}
+
+ at article{HolQuiRap03,
+ author={Harry J. Holzer and John M. Quigley and Steven Raphael},
+ title={Public Transit and the Spatial Distribution of Minority Employment: Evidence
+ from a Natural Experiment},
+ journal={Journal of Policy Analysis and Management},
+ volume={22},
+ year={2003},
+ pages={415-441},
+ number={3}
+}
+
+ at article{Holtfrerich84,
+ author = {Carl-Ludwig Holtfrerich},
+ title = {Zu hohe L{\"o}hne in der Weimarer Republik?
+ Bemerkungen zur Borchardt-These},
+ journal = {Geschichte und Gesellschaft},
+ volume = 10,
+ year = 1984,
+ number = 1
+}
+
+ at book{HolWai93,
+ title={Differential Item Functioning},
+ publisher={Lawrence Erlbaum},
+ year= 1993,
+ editor={Paul W. Holland and Howard Wainer},
+ address={Hillsdale, N.J.}
+}
+
+ at book{HomUga06,
+ title={Decentralizing Health Services in Mexico},
+ publisher={Center for U.S. -Mexican Studies, UCSD},
+ year={2006},
+ editor={N{\'u}ria Homedes and Antonio Ugalde},
+ address={La Jolla, California}
+}
+
+ at book{HopVau02,
+ title={Paleodemography},
+ publisher={Cambridge University Press},
+ year={2002},
+ editor={Robert D. Hoppa and James W. Vaupel},
+ address={Cambridge, UK}
+}
+
+ at article{Horowitz01,
+ author={Joel L. Horowitz},
+ title={{The Bootstrap}},
+ journal={Handbook of Econometrics},
+ volume={5},
+ year={2001},
+ pages={3159-3228},
+ publisher={Elsevier}
+}
+
+ at article{Howell04,
+ author={William G. Howell},
+ title={Dynamic Selection Effects in Means-Tested, Urban School Voucher Programs},
+ journal={Journal of Policy Analysis and Management},
+ volume={23},
+ year={2004},
+ pages={225-250},
+ number={2}
+}
+
+ at article{Hsieh85,
+ author={David A. Hsieh and Charles F. Manski and Daniel McFadden},
+ title={Estimation of Response Probabilities from Augmented Retrospective Observations},
+ journal={Journal of the American Statistical Association},
+ volume= 80,
+ year= 1985,
+ pages={651-652},
+ month={September},
+ number= 391
+}
+
+ at book{Huber81,
+ author={Peter J. Huber},
+ title={Robust Statistics},
+ publisher={Wiley},
+ year={1981}
+}
+
+ at book{HucSpr95,
+ author={R. Robert Huckfeldt and John Sprague},
+ title={Citizens, Politics, and Social Communication},
+ publisher={Cambridge University Press},
+ year= 1995,
+ address={New York, NY}
+}
+
+ at article{HumRogEbe98,
+ author={R.A. Hummer and R.G. Rogers and I.W. Eberstein},
+ title={Sociodemographic Differentials in Adult Mortality: a Review of Analytic
+ Approaches},
+ journal={Population and Development Review},
+ year={1998},
+ optnumber={2},
+ optvolume={24},
+ optpages={553--578}
+}
+
+ at article{IacPor08,
+ author = {Stefano M. Iacus and Giuseppe Porro},
+ title = {Random Recursive Partitioning: a matching method for
+ the estimation of the average treatment effect},
+ journal = {Journal of Applied Econometrics},
+ volume = {24},
+ pages = {163-185},
+ year = {2009}
+}
+
+ at unpublished{IacPor06b,
+ author = {Stefano M. Iacus and Giuseppe Porro},
+ title = {Missing data imputation, matching and other applications
+ of Random Recursive Partitioning},
+ journal = {Computational Statistics and Data Analysis},
+ volume = {52},
+ number = {2},
+ pages = {773-789},
+ year = {2007}
+}
+
+ at article{IbrChe97,
+ author={Joseph G. Ibrahim and Ming-Hui Chen},
+ title={Predictive Variable Selection for the Multivariate Linear Model},
+ journal={Biometrics},
+ volume= 53,
+ year= 1997,
+ pages={465--478},
+ month={June}
+}
+
+ at article{IdlAng90,
+ author={Ellen Idler and Ronald Angel},
+ title={Self-Rated Health and Mortality in the NHANES-I Epidemiologic Follow-Up
+ Study},
+ journal={American Journal of Public Health},
+ volume= 80,
+ year= 1990,
+ pages={{446-52}}
+}
+
+ at article{IdlBen97,
+ author={Ellen L. Idler and Yael Benyamini},
+ title={Self-Rated Health and Mortality: A Review of Twenty-Seven Community Studies},
+ journal={Journal of Health and Social Behavior},
+ volume= 38,
+ year= 1997,
+ pages={{21-37}}
+}
+
+ at article{Idler03,
+ author={Ellen L. Idler},
+ title={Discussion: Gender Differences in Self-Rated Health, in Mortality, and in
+ the Relationship Between the Two. },
+ journal={The Gerontologist},
+ volume= 43,
+ year= 2003,
+ pages={{372-75}},
+ number= 4
+}
+
+ at inbook{Idler92,
+ author={Ellen L. Idler},
+ title={Self-Assessed Health and Mortality: A Review of Studies},
+ chapter= 2,
+ year= 1992,
+ publisher={{John Wiley \& Sons, Ltd.}},
+ pages={{33-54}},
+ volume= 1,
+ journal={International Review of Health Psychology}
+}
+
+ at article{IdlHudLev99,
+ author={Ellen L. Idler, Shawna V. Hudson, and Howard Leventhal},
+ title={The Meanings of Self-Rated Health- A Qualitative and Quantitative Approach},
+ journal={Research on Aging},
+ volume= 21,
+ year= 1999,
+ pages={{458-76}},
+ month={{May}},
+ number= 3
+}
+
+ at article{IdlKas91,
+ author={Ellen L. Idler and Stanislav Kasl},
+ title={Health Perceptions and Survival: Do Global Evaluations of Health Status
+ Really Predict Mortality?},
+ journal={Journal of Gerontology: Social Sciences},
+ volume= 46,
+ year= 191,
+ pages={{S55-65}},
+ number= 2
+}
+
+ at article{IdlKas95,
+ author={Ellen Idler and Stanislav Kasl},
+ title={Self-Ratings of Health: Do they Also Predict Change in Functional Ability?},
+ journal={Journal of Gerontology: Social Sciences},
+ volume={{50B}},
+ year= 1995,
+ pages={{S344-53}},
+ number= 6
+}
+
+ at article{IdlKasLem90,
+ author={Ellen L. Idler, Stanislav V. Kasl, and Jon H. Lemke},
+ title={Self-Evaluated Health and Mortality Among the Elderly in New Haven, Connecticut,
+ and Iowa and Washington Counties, Iowa, 1982-1986},
+ journal={American Journal of Epidemiology},
+ volume= 131,
+ year= 1990,
+ pages={{91-103}}
+}
+
+ at article{IdlRusDav00,
+ author={Ellen Idler, Louise Russell, and Diane Davis},
+ title={Survival, Functional Limitations, and Self-Rated Health in the NHANES I
+ Epidemiological Follow-Up Study, 1992},
+ journal={American Journal of Epidemiology},
+ volume= 152,
+ year= 2000,
+ pages={{874-83}},
+ number= 4
+}
+
+ at article{IhaGen96,
+ author={Ross Ihaka and Robert Gentleman},
+ title={R: A Language for Data Analysis and Graphics},
+ journal={Journal of Computational and Graphical Statistics},
+ volume={5},
+ year={1996},
+ pages={299-314},
+ month={September},
+ number={3}
+}
+
+ at article{ImaDyk04,
+ author={Kosuke Imai and David A. van Dyk},
+ title={Causal Inference with General Treatment Treatment Regimes: Generalizing
+ the Propensity Score},
+ journal= jasa,
+ volume= 99,
+ year= 2004,
+ pages={854--866},
+ month={September},
+ number= 467
+}
+
+ at article{Imai05,
+ author={Kosuke Imai},
+ title={Do Get-Out-The-Vote Calls Reduce Turnout? The Importance of Statistical
+ Methods for Field Experiments},
+ journal= apsr,
+ volume={99},
+ year={2005},
+ pages={283--300},
+ month={May},
+ number={2}
+}
+
+ at techreport{Imai07,
+ author={Imai, Kosuke},
+ title={Randomization-based Inference and Efficiency Analysis in Experiments under
+ the Matched-Pair Design},
+ institution={Department of Politics, Princeton University},
+ year={2007}
+}
+
+ at article{Imbens00,
+ author={Guido W. Imbens},
+ title={The Role of the Propensity Score in Estimating Dose-Response Functions},
+ journal={Biometrika},
+ volume= 87,
+ year={2000},
+ pages={706-710},
+ number= 3
+}
+
+ at article{Imbens03,
+ author={Guido W. Imbens},
+ title={Sensitivity to exogeneity assumptions in program evaluation},
+ journal={American Economic Review},
+ volume= 96,
+ year= 2003,
+ pages={126-132},
+ number= 2
+}
+
+ at article{Imbens04,
+ author={Guido W. Imbens},
+ title={Nonparametric estimation of average treatment effects under exogeneity:
+ a review},
+ journal={Review of Economics and Statistics},
+ volume= 86,
+ year= 2004,
+ pages={4-29},
+ number= 1
+}
+
+ at unpublished{ImbensNDb,
+ author={Imbens, Guido W. },
+ title={Semiparametric Estimation of Average Treatment Effects under Exogeneity:
+ A Review},
+ note={Manuscript, UC Berkeley},
+ year={2003}
+}
+
+ at article{ImbRub97,
+ author={Guido W. Imbens and Donald B. Rubin},
+ title={Bayesian Inference for Causal Effects in Randomized Experiements with Noncompliance},
+ journal={The Annals of Statistics},
+ volume={25},
+ year={1997},
+ pages={305-327},
+ month={February},
+ number={1}
+}
+
+ at unpublished{ImbRubND,
+ author={Guido W. Imbens and Donald B. Rubin},
+ title={Causal Inference},
+ note={Book Manuscript},
+ year= 2002
+}
+
+ at book{IngMcC96,
+ author = {Jorge Inguez and James A. McCann},
+ title = {Democratizing Mexico: Public Opinion and Electoral
+ Choice},
+ address = {Baltimore},
+ publisher = {Johns Hopkins University Press},
+ year = {1996}
+}
+
+ at book{Insua00,
+ author={David R. Insua and Ruggeri Fabrizio},
+ title={Bayesian Analysis},
+ publisher={Springer-Verlag},
+ year= 2000
+}
+
+ at article{IrwJonMun96,
+ author={Julie R. Irwin and Lawrence E. Jones and David Mundo},
+ title={Risk Perception and Victim Perception: The Judgment of HIV Cases},
+ journal={Journal of Behavioral Decision Making},
+ volume= 9,
+ year= 1996,
+ pages={1--22}
+}
+
+ at article{IslRahMah96,
+ author={M. Aminul Islam and M. Mujibur Rahman and D. Mahalanabis and A.K.S. Mahmudur
+ Rahman},
+ title={Death ina Diarrhoeal Cohort of Infants and Young Children Soon After Discharge
+ From Hospital: Risk Factors and Causes by Verbal Autopsy},
+ journal={Journal of Tropical Pediatrics},
+ volume={42},
+ year={1996},
+ pages={342-347},
+ month={December }
+}
+
+ at article{IslRahMah96,
+ author={M. Aminul Islam and M. Mujibur Rahman and D. Mahalanabis and A.K.S. Mahmudur
+ Rahman},
+ title={Death ina Diarrhoeal Cohort of Infants and Young Children Soon After Discharge
+ From Hospital: Risk Factors and Causes by Verbal Autopsy},
+ journal={Journal of Tropical Pediatrics},
+ volume={42},
+ year={1996},
+ pages={342-347},
+ month={December }
+}
+
+ at misc{ISO97,
+ author={ISO},
+ title={The Dublin Core Metadata Element Set},
+ year={1997},
+ note={{http://www.collectionscanada.ca/iso/tc46sc9/standard/690-2e.htm}}
+}
+
+ at article{Iversen01,
+ author={Edwin S. Iversen, Jr.},
+ title={Spatially disaggregated Real Estate Indices},
+ journal={Journal of Business \& Economic Statistics},
+ volume={19},
+ year={2001},
+ pages={341 - 357},
+ month={July},
+ number={3}
+}
+
+ at article{Jackman00,
+ author={Simon Jackman},
+ title={Estimation and Inference via Bayesian Simulation: An Introduction to Markov
+ Chain Monte Carlo},
+ journal={American Journal of Political Science},
+ volume={44},
+ year={2000},
+ pages={375-404},
+ month={April},
+ number={2}
+}
+
+ at techreport{JagRob03,
+ author={Carol Jagger and Dr. Jean-Marie Robine},
+ title={The Health of Adults in the Eurpoean Union},
+ institution={Press and Communication, Unit "Analysis and Public Opinion"},
+ year= 2003,
+ month={{June}}
+}
+
+ at article{JagSpiCla93,
+ author={C. Jagger, N.A. Spiers, and M. Clarke},
+ title={Factors Associated with Decline in Function, Institutionalization and Mortality
+ of Elderly People},
+ journal={Age and Ageing },
+ volume= 22,
+ year= 1993,
+ pages={{190-97}}
+}
+
+ at incollection{James90,
+ author={Harold James},
+ title={Economic Reasons for the Collapse of the Weimar Republic},
+ booktitle={Weimar: Why Did German Democracy Fail?},
+ publisher={Weidenfeld and Nicolson},
+ year= 1990,
+ address={London},
+ pages={30-57}
+}
+
+ at article{JeeGimSug98,
+ author={Sun Ha Jee and Il Soon Kim and Il Suh and Dongchun Shin and Lawrence J Appel},
+ title={Projected Mortality from Lung Cancer in South Korea, 1980-2004},
+ journal={International Journal of Epidemiology},
+ volume= 27,
+ year= 1998,
+ pages={365--369}
+}
+
+ at inproceedings{JefBarRod01,
+ author={William H. Jefferys and Thomas G. Barnes and Raquel Rodrigues and James
+ O. Berger and Peter M{\"u}ller},
+ title={Model Selection for Cepheid Star Oscillations},
+ booktitle={Bayesian methods, with Applications to Science, Policy, and Official Statistics},
+ year={2001},
+ publisher={Official Publications of the European Communities, Luxembourg},
+ editor={E. George and P. Nanopoulos},
+ pages={253 --252}
+}
+
+ at unpublished{JefBarRod06,
+ author={William H. Jefferys and Thomas G. Barnes and Raquel Rodrigues and James
+ O. Berger and Peter Muller},
+ title={Nonparametric Regression with Wavelet Based Priors: Efficient Posterior
+ Simulation for Unequally Spaced Data and Dependent Priors},
+ note={Jefferys, Barnes Rodriques, Univ of Tx at Austin, Berger and Muller, Duke
+ Univ.},
+ year={2006}
+}
+
+ at book{Jeffreys61,
+ author={H. Jeffreys},
+ title={Theory of Probability},
+ publisher={Clarendon Press},
+ year= 1961,
+ address={Oxford},
+ edition={3rd (1st edn., 1939}
+}
+
+ at book{JohAlb99,
+ author={Valen E. Johnson and James H. Albert},
+ title={Ordinal Data Modeling},
+ publisher={Springer},
+ year= 1999,
+ address={New York}
+}
+
+ at article{Johnson01,
+ author={David H. Johnson},
+ title={Sharing Data: It's Time to End Psychology's Guild Approach},
+ journal={Observer (American Psychological Society)},
+ volume= 14,
+ year= 2001,
+ month={October},
+ number= 8,
+ note={{http://www.psychologicalscience.org/observer/1001/data.html}}
+}
+
+ at inbook{Johnson96,
+ author={Wesley O. Johnson},
+ title={Predictive Influence in the Lognormal Survival Essays in Honor of Seymour
+ Geisser},
+ year={1996},
+ publisher={Elsevier},
+ pages={104-121},
+ address={Amsterdam},
+ editor={J. Lee and A. Zellner and W. Johnson}
+}
+
+ at article{Johnson98,
+ author={Timothy P. Johnson},
+ title={Approaches to Equivalence in Cross-Cultural and Cross-National Survey Research},
+ journal={ZUMA Nachrichten Spezial},
+ volume= 3,
+ year= 1998,
+ pages={1--40},
+ month={January}
+}
+
+ at book{JonBau05,
+ author={Bryan D. Jones and Frank R. Baumgartner},
+ title={The Politics of Attention: How Government Prioritizes Problems},
+ publisher={University of Chicago Press},
+ year={2005},
+ address={Chicago, IL}
+}
+
+ at article{JonDagGon04,
+ author={Alison Snow Jones and Ralph B. D'Agostino Jr. and Edward W. Gondolf and
+ Alex Heckert},
+ title={Assessing the Effectof Batterer Program Completion on Reassault Using Propsensity
+ Scores},
+ journal={Journal of Interpersonal Violence},
+ volume={19},
+ year={2004},
+ pages={1002-1020},
+ month={September},
+ number={9}
+}
+
+ at book{Jones88,
+ author = {Jones, Larry Eugene},
+ title = {German Liberalism and the Dissolution of the Weimar
+ Party System},
+ publisher = {The University of North Carolina Press},
+ year = 1988,
+ address = {Chapel Hill and London}
+}
+
+ at unpublished{JonKimSta05,
+ author={Bryan D. Jones and Chang-Jin Kim and Richard Startz},
+ title={A Markov Switching Model of Congressional Partisan Regimes},
+ note={University of Washington, Center for American Politics and Public Policy,
+ Box 353530 Seattle, WA 98195-3530, bdjones at u.washington.edu},
+ year={2005}
+}
+
+ at article{Jordan1874,
+ author={C. Jordan},
+ title={{M{\'e}moire sur les formes bilin{\'e}aires}},
+ journal={Comptes Rendus de l' Acad{\'e}mie des Sciences, Paris},
+ volume= 78,
+ year= 1874,
+ pages={614--617}
+}
+
+ at article{JylGurFer98,
+ author={Maria Jylha, Jack Guralnik, Luigi Jokela, et al},
+ title={Is self-rated health comparable across cultures and genders?},
+ journal={Journal of Gerontology: Social Sciences},
+ volume={{53B}},
+ year= 1998,
+ pages={{S144-52}}
+}
+
+ at incollection{Kadane80,
+ author={Joseph B. Kadane},
+ title={Predictive and Structural Methods for Eliciting Prior Distributions},
+ booktitle={Bayesian Analysis in Econometrics and Statistics},
+ publisher={North-Holland},
+ year= 1980,
+ editor={Arnold Zellner}
+}
+
+ at article{KadDicWin80,
+ author={Joseph B. Kadane and James M. Dickey and Robert L. Winkler and Wayne S.
+ Smith and Stephen C. Peters},
+ title={Interactive Elicitation of Opinion for a Normal Linear Model},
+ journal={Journal of the American Statistical Association},
+ volume={75},
+ year={1980},
+ pages={845-854},
+ month={December},
+ number={372}
+}
+
+ at article{Kahn86,
+ author={Paul W. Kahn},
+ title={Gramm-Rudman and the Capacity of Congress to Control the Future},
+ journal={Hastings Constitutional Law Quarterly},
+ volume={13},
+ year={1986},
+ pages={185-231}
+}
+
+ at article{KahSchSun98,
+ author={Daniel Kahneman and David Schkade and Cass R. Sunstein},
+ title={Shared Outrage and Erratic Awards: The Psychology of Punitive Damages},
+ journal={Journal of Risk and Uncertainty},
+ volume= 16,
+ year= 1998,
+ pages={49--86},
+ month={April}
+}
+
+ at article{KahTolGar00,
+ author={Kathleen Kahn and Stephen M. Tollman and Michel Garenne and John S.S.Gear},
+ title={Validation and Application of Verbal autosies in a Rural Area of South Africa},
+ journal={Tropical Medicine and International Health},
+ volume={5},
+ year={2000},
+ pages={824-831},
+ number={11}
+}
+
+ at article{KalGraBla90,
+ author={Henry D. Kalter and Ronald H. Gary and Robert E. Black and Socorro A. Gultiano},
+ title={Validation of Postmortem Interviews to Ascertain Selected Causes of Death
+ in Children},
+ journal={International Journal of Epidemiology},
+ volume={19},
+ year={1990},
+ pages={380-386},
+ number={2}
+}
+
+ at article{KalHosBur99,
+ author={Henry D. Kalter and Munir Hossain and Gilbert Burnham and Naila Z. Khan
+ and Samir K. Saha and Md Anwar Ali and Robert E. Black},
+ title={Validation of caregiver interviews to diagnose common causes of severe neonatal
+ illness},
+ journal={Paediatric and Perinatal Epidemiology},
+ volume={13},
+ year={1999},
+ pages={99-113}
+}
+
+ at article{KalHosBur99,
+ author={Henry D. Kalter and Munir Hossain and Gilbert Burnham and Naila Z. Khan
+ and Samir K. Saha and Md Anwar Ali and Robert E. Black},
+ title={Validation of caregiver interviews to diagnose common causes of severe neonatal
+ illness},
+ journal={Paediatric and Perinatal Epidemiology},
+ volume={13},
+ year={1999},
+ pages={99-113}
+}
+
+ at article{Kallay84,
+ author={Michael Kallay},
+ title={The Complexity of Incremental Convex Hull Algorithms in Rd.},
+ journal={Informational Processes Letter},
+ volume={19},
+ year={1984},
+ pages={197},
+ number={4}
+}
+
+ at article{Kallay86,
+ author={Michael Kallay},
+ title={Convex Hull Made Easy},
+ journal={Information Processing Letters},
+ volume={22},
+ year={1986},
+ month={March},
+ issue={3}
+}
+
+ at article{Kalter92,
+ author={Henry Kalter},
+ title={The Validation of interviews for estimating morbidity},
+ journal={Health Policy and Planning},
+ volume={7},
+ year={1992},
+ pages={30-39},
+ number={1}
+}
+
+ at article{KapBarLus88,
+ author={George Kaplan, Vita Barell, and Ayala Lusky},
+ title={Subjective State of Health and Survival in Elderly Adults},
+ journal={Journal of Gerontology: Social Sciences},
+ volume= 43,
+ year= 1988,
+ pages={{S114-20}}
+}
+
+ at article{KapGolEve96,
+ author={George A Kaplan, Debbie Goldberg, Susan Everson, et al},
+ title={Perceived Health Status and Morbidity and Mortality: Evidence from the Kuopio
+ Ischaemic Heart Disease Risk Factor Study},
+ journal={International Journal of Epidemiology},
+ volume= 25,
+ year= 1996,
+ pages={{259-65}}
+}
+
+ at article{Karaagaoglu99,
+ author={Ergun Karaagaoglu},
+ title={Estimation of the Prevalence of a Disease from Screening Tests},
+ journal={Tropical Journal of Medical Sciences},
+ volume={29},
+ year={1999},
+ pages={425-430}
+}
+
+ at article{KasCarGel98,
+ author={Robert E. Kass and Bradley P. Carlin and Andrew Gelman and Radford M. Neal},
+ title={Markov chain Monte Carlo in Practice: A Roundtable Discussion},
+ journal={The American Statistician},
+ volume={52},
+ year={1998},
+ pages={93-100},
+ number={2}
+}
+
+ at article{KasWas96,
+ author={Robert E. Kass and Larry Wasserman},
+ title={The Selection of Prior Distributions by Formal Rules},
+ journal={Journal of the American Statistical Association},
+ volume= 91,
+ year= 1996,
+ pages={1343--1370},
+ month={September},
+ number= 435
+}
+
+ at article{KatTri02,
+ author={Neal K. Katyal and Laurence H. Tribe},
+ title={Waging War, Deciding Guilt: Trying the Military Tribunals},
+ journal= ylj,
+ volume= 111,
+ year={2002},
+ pages={1259--1310}
+}
+
+ at article{Kawada03,
+ author={Tomoyuki Kawada},
+ title={Self rated health and life prognosis},
+ journal={Archives of Medical Research},
+ volume= 34,
+ year= 2003,
+ pages={{343-47}}
+}
+
+ at book{Kele72,
+ author={Kele, M.},
+ title={Nazis and Workers},
+ publisher={University of North Carolina Press},
+ year= 1972,
+ address={Chapel Hill}
+}
+
+ at article{Kelly06,
+ author={Kevin Kelly},
+ title={{Scan this Book}},
+ journal={The New York Times Magazine},
+ year={2006},
+ month={October 11}
+}
+
+ at article{KenStu50,
+ author={M.G. Kendall and A. Stuart},
+ title={The Law of the Cubic Proportion in election Results},
+ journal={The British journal of Sociology},
+ volume={1},
+ year={1950},
+ pages={183-196},
+ month={September},
+ number={3}
+}
+
+ at book{Keyfitz68,
+ author={N. Keyfitz},
+ title={Introduction to the Mathematics of Population},
+ publisher={Addison Wesley},
+ year= 1968,
+ address={Reading, MA}
+}
+
+ at article{Keyfitz82,
+ author={N. Keyfitz},
+ title={Choice of Function for mortality Analysis: Effective Forecasting Depends
+ on a Minimum Parameter Representation},
+ journal={Theoretical Population Biology},
+ volume= 21,
+ year= 1982,
+ pages={239--252}
+}
+
+ at unpublished{KimHov04,
+ author={Soo-Min Kim and Eduard Hovy},
+ title={Determining the Sentiment of Opinions},
+ note={Soo-Min Kim Information Sciences Inst. Univ. of Southern Calif. 4676 Admiralty
+ Way, Marina del Rey, CA 90292-6695; skim at isi.edu},
+ year={04}
+}
+
+ at unpublished{KimHov04,
+ author={Soo-Min Kim and Eduard Hovy},
+ title={Determining the Sentiment of Opinions},
+ note={Soo-Min Kim Information Sciences Inst. Univ. of Southern Calif. 4676 Admiralty
+ Way, Marina del Rey, CA 90292-6695; skim at isi.edu},
+ year={04}
+}
+
+ at article{KimWah70,
+ author={G.S. Kimeldorf and G. Wahba},
+ title={A correspondence between {Bayesian} estimation on stochastic processes and
+ smoothing by splines},
+ journal={Ann. Math. Statist.},
+ volume={41},
+ year={1970},
+ pages={495--502},
+ number={2}
+}
+
+ at article{KinAubHer98,
+ author={Hilary King and Ronald E. Aubert and William H. Herman},
+ title={Global Burden of Diabetes, 1995-2025},
+ journal={Diabetes Care},
+ volume= 21,
+ year= 1998,
+ pages={1414--1431}
+}
+
+ at article{Kinder86,
+ author={Donald R. Kinder},
+ title={The Continuing American Dilemma: White Resistance to Racial Change 40 years
+ After Myrdal},
+ journal={Journal of Social Issues},
+ volume={42},
+ year={1986},
+ pages={151-71}
+}
+
+ at book{KinPal93,
+ title={Experimental Foundations of Political Science},
+ publisher={University of Michigan Press},
+ year= 1993,
+ editor={Donald R. Kinder and Thomas R. Palfrey},
+ address={Ann Arbor}
+}
+
+ at article{KinSea81,
+ author={Donald R. Kinder and David O. Sears},
+ title={Prejudice and Politics: Symbolic Racism Versus Racial Threats to the Good
+ Life},
+ journal={Journal of Personality and Social Psychology},
+ volume={40},
+ year={1981},
+ pages={414-31}
+}
+
+ at article{Kirchgaessner85,
+ author={Gebhard Kirchg{\"a}ssner},
+ title={Rationality, Causality and the Relation between Economic Conditions and
+ the Popularity of Parties},
+ journal={European Economic Review},
+ volume= 28,
+ year= 1985,
+ pages={243-268},
+ month={June/July}
+}
+
+ at article{Kish49,
+ author={Kish, Leslie},
+ title={{A Procedure for Objective Respondent Selection within the Household}},
+ journal={Journal of the American Statistical Association},
+ volume={44},
+ year={1949},
+ pages={380--387},
+ number={247}
+}
+
+ at article{KlaDon97,
+ author={Neil Klar and Allan Donner},
+ title={The Merits of Matching in Community Intervention Trials: A Cautionary Tale},
+ journal={Statistics in Medicine},
+ volume={16},
+ year={1997},
+ pages={1753-1764},
+ number={15}
+}
+
+ at article{KlaDon98,
+ author={Neil Klar and Allan Donner},
+ title={Authors' Reply: The Merits of Matching in Community Intervention Trials:
+ A Cautionary Tale},
+ journal={Statistics in Medicine},
+ volume={17},
+ year={1998},
+ pages={2151-2152}
+}
+
+ at article{Klarman97,
+ author={Michael J. Klarman},
+ title={Majoritarian Judicial Review: The Entrenchment Problem},
+ journal={The Georgetown Law Journal},
+ volume={85},
+ year={1997},
+ pages={491-554}
+}
+
+ at article{Klee80,
+ author={Victor Klee},
+ title={On the Complexity of d-Dimensional Voronoi Diagrams},
+ journal={Archive der Mathematik},
+ volume={34},
+ year={1980},
+ pages={75--80}
+}
+
+ at book{KliSmi99,
+ author={Philip A. Klinker and Rogers M. Smith},
+ title={The Unsteady March: The Rise and Decline of Racial Equality in America},
+ publisher={?},
+ year= 1999
+}
+
+ at article{KluBerBra06,
+ author={Klump, J. and Bertelmann, R. and Brase, J. and Diepenbroek, M. and Grobe,
+ H. and H{\"o}ck, H. and Lautenschlager, M. and Schindler, U. and Sens, I. and
+ W{\"a}chter, J.},
+ title={{Data publication in the open access initiative}},
+ journal={Data Science Journal},
+ volume={5},
+ year={2006},
+ pages={79--83},
+ number={0}
+}
+
+ at article{Knorr-Held00,
+ author={Leonhard Knorr-Held},
+ title={Bayesian Modelling of Inseparable Space-Time Variation in Disease Risk},
+ journal={Statistics in Medicine},
+ volume= 19,
+ year= 2000,
+ pages={2555-2567}
+}
+
+ at article{Koch02,
+ author={Koch, Jeffrey M.},
+ title={Gender Stereotypes and Citizens' Impressions of House Candidates' Ideological
+ Orientation},
+ journal={American Journal of Political Science},
+ volume= 46,
+ year= 2002,
+ pages={453--462}
+}
+
+ at article{KohAlt05,
+ author={Isaac S. Kohane and Russ B Altman},
+ title={Health-Information Altruists --- A Potentially Critical Resource},
+ journal={New England Journal of Medicine},
+ volume= 19,
+ year= 2005,
+ pages={2074--2077},
+ month={November},
+ number= 353
+}
+
+ at article{KohAlt05,
+ author={Isaac S. Kohane and Russ B. Altman},
+ title={Health-Information Altruists - A Potentially Critical Resource},
+ journal={New England Journal of Medicine},
+ volume={353},
+ year={2005},
+ pages={2074-2077},
+ month={November},
+ number={19}
+}
+
+ at book{Kolb88,
+ author={Eberhard Kolb},
+ title={The Weimar Republic},
+ publisher={Unwin Hyman},
+ year= 1988,
+ address={London}
+}
+
+ at article{KolBur91,
+ author={Kolbe, R.H. and Burnett, M.S.},
+ title={{Content-Analysis Research: An Examination of Applications with Directives
+ for Improving Research Reliability and Objectivity}},
+ journal={The Journal of Consumer Research},
+ volume={18},
+ year={1991},
+ pages={243--250},
+ number={2}
+}
+
+ at unpublished{KolFinJos06,
+ author={Pranam Kolari and Tim Finin and Anupam Joshi},
+ title={{SVMs for the Blogosphere: Blog Identification and Splog Detection}},
+ note={American Association for Artificial Intelligence Spring Symposium on Computational
+ Approaches to Analyzing Weblogs},
+ year={2006}
+}
+
+ at article{KolVliKap00,
+ author={H. Koivumaa-Honkanen et al},
+ title={Self-Reported Life Satisfaction and 20-Year Mortality in Healthy Finnish
+ Adults},
+ journal={American Journal of Epedimiology},
+ volume= 152,
+ year= 2000,
+ pages={{983-91}}
+}
+
+ at article{KonDes01,
+ author={M.M. Konstantareas and N. Desbois},
+ title={Preschoolers Perceptions of the Unfairness of Maternal Disciplinary Practices},
+ journal={Child Abuse \& Neglect},
+ volume= 25,
+ year= 2001,
+ pages={473--488},
+ month={April},
+ number= 4
+}
+
+ at article{KooVanBon94,
+ author={Marc A. Koopmanschap and Leona Van Roijen and Luc Bonneux and Jan J. Barendregt},
+ title={Current and Future Costs of Cancer},
+ journal={European Journal of Cancer},
+ volume={30A},
+ year= 1994,
+ pages={60--65},
+ number= 1
+}
+
+ at unpublished{KopSch05,
+ author={Moshe Koppel and Jonathan Schler},
+ title={The Importance of Neutral Examples for Learning Sentiment},
+ note={Dept. of Computer Science Bar-Ilan University, Ramat-Gan Israel koppel,schlerj at cs.biu.ac.il},
+ year={05}
+}
+
+ at unpublished{KopSch05,
+ author={Moshe Koppel and Jonathan Schler},
+ title={The Importance of Neutral Examples for Learning Sentiment},
+ note={Dept. of Computer Science Bar-Ilan University, Ramat-Gan Israel koppel,schlerj at cs.biu.ac.il},
+ year={05}
+}
+
+ at article{KorJorLet99,
+ author={A E Kirtne, A F Jorm, Z Jiao, et al},
+ title={Health, Cognitive and psychosocial factors as predictors of mortality in
+ an elderly community sample},
+ journal={Journal of Epidemiology and Communtiy Health },
+ volume= 53,
+ year= 1999,
+ pages={{83-8}}
+}
+
+ at book{Kornhauser59,
+ author={Kornhauser, W.},
+ title={The Politics of Mass Society},
+ publisher={The Free Press},
+ year= 1959,
+ address={New York}
+}
+
+ at article{KorWilGou03,
+ author={Eline L. Korenromp and Brian G. Williams and Eleanor Gouws and christopher
+ Dye and Robert W. Snow},
+ title={Measurement of trends in childhood malaria mortality in Africa: an assessment
+ of progress toward targets based on verbal autopsy},
+ journal={The Lancet Infectious Diseases},
+ volume={3},
+ year={2003},
+ pages={349-58}
+}
+
+ at book{Koshar86,
+ author={Koshar, R.},
+ title={Social Life, Local Politics, and Nazism},
+ publisher={University of North Carolina Press},
+ year= 1986,
+ address={Chapel Hill}
+}
+
+ at article{KosHeiZak05,
+ author={Michael Kosfeld and Markus Heinrichs and Paul J. Zak and Urs Fischbacher
+ and Ernst Fehr},
+ title={Oxytocin Increases Trust in Humans},
+ journal={Nature},
+ volume={435},
+ year={2005},
+ pages={673-676},
+ month={June}
+}
+
+ at article{KraJay94,
+ author={Neal M. Krause, PhD, and Gina M. Jay, PhD},
+ title={What do Global Self-Rated Health Items Measure?},
+ journal={Medical Care},
+ volume= 32,
+ year= 1994,
+ pages={{930-42}}
+}
+
+ at article{KraSha84,
+ author={M.S. Kramer and S.H. Shapiro},
+ title={{Scientific challenges in the application of randomized trials}},
+ journal={Journal of the American Medical Association},
+ volume= 252,
+ year= 1984 ,
+ pages={2739-45},
+ number= 19
+}
+
+ at article{KriBacRob07,
+ author={Samuel Krislov and Charles Backstrom and Leonard Robins},
+ title={When Texans Gerrymander: Much Power, Continuous Politics, Little Law}
+}
+
+ at book{Krippendorff04,
+ author={Krippendorff, D.K.},
+ title={{Content Analysis: An Introduction to Its Methodology}},
+ publisher={Thousand Oaks, CA: Sage},
+ year={2004}
+}
+
+ at article{Kruedener85,
+ author={J{\"u}rgen von Kruedener},
+ title={Die {\"U}berforderung der Weimarer Republic als Sozialstaat},
+ journal={Geschichte und Gesellschaft},
+ volume= 1,
+ year= 1985,
+ pages={358--376},
+ number= 3
+}
+
+ at article{Krueger90,
+ author={Anne O. Krueger},
+ title={Government Failures in Development},
+ journal={The Journal of Economic Perspectives},
+ volume={4},
+ year={1990},
+ pages={9-23},
+ number={3}
+}
+
+ at article{Krueger99,
+ author={Alan Krueger},
+ title={Experimental Estimates of Education Production Functions},
+ journal={Quarterly Journal of Economics},
+ volume={114},
+ year={1999},
+ pages={497-532},
+ month={May},
+ number={2}
+}
+
+ at article{KrzWys86,
+ author={Mical Krzyzanowski and Miroslaw Wysocki},
+ title={The Relation of Thirteen-Year Mortality to Ventilatory Impairment and Other
+ Respiratory Symptoms: The Cracow Study},
+ journal={International Journal of Epidemiology},
+ volume= 15,
+ year= 1986,
+ pages={{56-64}},
+ number= 1
+}
+
+ at article{KucMwaLes06,
+ author={Helmut K{\"u}chenohoff and Samuel M. Mwalili and Emmanuel Lassaffre},
+ title={A General Method for Dealing with Misclassification in Regression: The Misclassification
+ SIMEX},
+ journal={Biometrics},
+ volume={62},
+ year={2006},
+ pages={85-96},
+ month={March}
+}
+
+ at article{KulLei51,
+ author={S. Kullback and R.A. Leibler},
+ title={On Information and Sufficiency},
+ journal={Annals of Mathematical Statistics},
+ volume= 22,
+ year= 1951,
+ pages={79--86},
+ month={March},
+ number= 1
+}
+
+ at unpublished{KumAll04,
+ author={Giridhar Kumaran and James Allan},
+ title={Text Classification and Named Entities for New Event Detection},
+ note={Center for Intelligent Information Retreival, Department of Computer Science,
+ Univ of MA, Amherst},
+ year={2004},
+ month={July}
+}
+
+ at unpublished{KumAll04,
+ author={Giridhar Kumaran and James Allan},
+ title={Text Classification and Named Entities for New Event Detection},
+ note={Center for Intelligent Information Retreival, Department of Computer Science,
+ Univ of MA, Amherst},
+ year={2004},
+ month={July}
+}
+
+ at article{KunGeuvan95,
+ author={Anton Kunst and J.J. Geurts and J. van den Berg},
+ title={International variation in socioeconomic inequalities in self reported health},
+ journal={Journal of Epidemiology and Community Health},
+ year={1995},
+ optnumber={2},
+ optvolume={49},
+ optpages={117--123}
+}
+
+ at article{KunGroMac98,
+ author={A.E. Kunst and F. Gorenhof and J.P. Mackenbach and E.W. Health},
+ title={Occupational class and cause specific mortality in middle aged men in 11
+ European countries: comparison of population based studies. EU Working
+ Group on Socioeconomic Inequalities in Health },
+ journal= bmj,
+ year={1998},
+ optvolume={316},
+ optpages={1636--1642}
+}
+
+ at article{KunGroMac98b,
+ author={Anton Kunst and F. Groenhof and Johann Mackenbach},
+ title={Mortality by occupational class among men 30--64 years in 11 European countries},
+ journal= ssm,
+ volume= 46,
+ year= 1998,
+ pages={1459-1476},
+ number= 11
+}
+
+ at article{Kunsch87,
+ author={Hans R. K{\"u}nsch},
+ title={Intrinsic Autoregressions and Related Models on the Two-Dimensional Lattice},
+ journal={Biometrika},
+ volume= 74,
+ year= 1987,
+ pages={517-524},
+ number= 3
+}
+
+ at article{Kuo01,
+ author={Yen-Hong Kuo},
+ title={Extrapolation of Association Between Two Variables in Four General Medical
+ Journals},
+ year= 2001 ,
+ month={September},
+ note={Forth International Congress on Peer Review in Biomedical Publication},
+ key={Barcelona, Spain}
+}
+
+ at book{kvart86,
+ author={Igal Kvart},
+ title={A Theory of Counterfactuals},
+ publisher={Indianapolis: Hackett Publishing Company},
+ year= 1986
+}
+
+ at article{KwoShuHov06,
+ author={Namhee Kwon and Stuart W. Shulman and Eduard Hovy},
+ title={{Collective Text Analysis for eRulemaking}},
+ journal={7th Annual International Conference on Digital Government Research},
+ year={2006}
+}
+
+ at incollection{LagRus02,
+ author={Monica Lagazio and Bruce Russett},
+ title={A Neural Network Analysis of Militarized International Disputes, 1885-1992:
+ Temporal Stability and Causal Complexity},
+ booktitle={The Scourge of War: New Extensions on an Old Problem},
+ publisher={University of Michigan Press},
+ year={2002},
+ address={Ann Arbor},
+ editor={Paul Diehl},
+ optpages={269--295},
+ optannote={This paper fits a model for Cold War militarized disputes and assess how
+ well the Cold War model fits pre-Cold War data (so-called 'post-diction').
+ So it gets fitted values. Also, runs test and training, and discusses merits
+ of neural net approach.}
+}
+
+ at article{Lahlrl03,
+ author={P. Lahlrl},
+ title={On the Impact of Boostrapping in Survey Sampling and Small Area Estimation},
+ journal={Statistical Science},
+ volume= 18,
+ year= 2003,
+ pages={199-210},
+ number= 2
+}
+
+ at article{LaiLou87,
+ author={Nan M. Laird and Thomas A. Louis},
+ title={Empirical Bayes Confidence Intervals Based on Bootstrap Samples},
+ journal={Journal of the American Statistical Association},
+ volume={82},
+ year={1987},
+ pages={739-750},
+ month={September},
+ number={399}
+}
+
+ at article{LaiLou87b,
+ author={Nan M. Laird and Thomas A. Louis},
+ title={Empirical Bayes Confidence Intervals Based on Bootstrap Samples: Rejoinder},
+ journal={Journal of the American Statistical Association},
+ volume={399},
+ year={1987},
+ pages={756-757},
+ month={September}
+}
+
+ at unpublished{Lakin05,
+ author={Jason Lakin},
+ title={Letting the Outsiders in: Democratization and Health Reform in Mexico},
+ note={American Political Science Association},
+ year= 2005 ,
+ address={Washington D.C.}
+}
+
+ at article{Lalonde86,
+ author={Robert Lalonde},
+ title={Evaluating the Econometric Evaluations of Training Programs},
+ journal={American Economic Review},
+ volume={76},
+ year={1986},
+ pages={604-620}
+}
+
+ at article{Landers05,
+ author={John Landers},
+ title={The Destructiveness of Pre-Industrial Warfare: Political and Technological
+ Determinants},
+ journal={Journal for Peace Research},
+ volume={42},
+ year={2005},
+ pages={455-470},
+ month={July},
+ number={4}
+}
+
+ at article{langholz91,
+ author={B. Langholz and D.C. Thomas},
+ title={Efficiency of Cohort Sampling Designs: Some Surprising Results},
+ journal={Biometrics},
+ volume= 47,
+ year= 1991,
+ pages={1563-1571}
+}
+
+ at article{langholz96,
+ author={Bryan Langholz and Larry Goldstein},
+ title={Risk Set Sampling in Epidemiologic Cohort Studies},
+ journal={Statistical Science},
+ volume= 11,
+ year= 1996,
+ pages={35-53},
+ number= 1
+}
+
+ at article{langholz97,
+ author={Bryan Langholz and Boran {\O }rnulf},
+ title={Estimation of Absolute Risk from Nested Case-Control Data},
+ journal={Biometrics},
+ volume= 53,
+ year= 1997,
+ pages={767-774},
+ month={June}
+}
+
+ at article{LaPalombara68,
+ author={Joseph LaPalombara},
+ title={Macrotheories and Microapplications in Comparative Politics: A Widening
+ Chasm},
+ journal= cp,
+ year= 1968,
+ pages={52--78},
+ month={October}
+}
+
+ at book{Laplace1820,
+ author={P.S. Laplace},
+ title={Philosophical Essays on Probaiblities},
+ publisher={Dover},
+ year={1951, original: 1820},
+ address={New York}
+}
+
+ at article{LaRBanJar79,
+ author={Asenath LaRue, PhD, Lew Bank, MA, Lissy Jarvick, MD, PhD, and Monte Hetland,
+ BA},
+ title={Health in Old Age: How Do Physicians' Ratings and Self-Ratings Compare?},
+ journal={Journal of Gerontology},
+ volume= 34,
+ year= 1979,
+ pages={{687-91}}
+}
+
+ at article{Lassen05,
+ author={David Dreyer Lassen},
+ title={The Effect of Information on Voter Turnout: Evidence from a Natural Experiement},
+ journal={American Journal of Political Science},
+ volume={2005},
+ year={49},
+ pages={103-118},
+ month={January},
+ number={1}
+}
+
+ at article{Lau97,
+ author={Tai-Shing Lau},
+ title={The Latent Class Model for Multiple Binary Screening Tests},
+ journal={Statistics in Medicine},
+ volume={16},
+ year={1997},
+ pages={2283-2295}
+}
+
+ at article{LauIbr95,
+ author={Purushottam W. Laud and Joseph G. Ibrahim},
+ title={Predictive Model Selection},
+ journal= jrssb,
+ volume= 57,
+ year= 1995,
+ pages={247--262},
+ number= 1
+}
+
+ at article{LauIbr96,
+ author={Purushottam W. Laud and Joseph G. Ibrahim},
+ title={Predictive Specification of Prior Model Probabilities in Variable Selection},
+ journal={Biometrika},
+ volume= 83,
+ year= 1996,
+ pages={267--274},
+ number= 2
+}
+
+ at article{LauSmiSta00,
+ author={Jennifer L. Lauby and Philip J. Smith and Michael Stark and Bobbie Person
+ and Janet Adams},
+ title={A Community-Level HIV Prevention Intervention for Inner-City Women: Results
+ of the Women and Infants Demonstration Projects},
+ journal={American Journal of Public Health},
+ volume={90},
+ year={2000},
+ pages={216-222},
+ month={February},
+ number={2}
+}
+
+ at article{LavBenGar03,
+ author={Michael Laver and Kenneth Benoit and John Garry},
+ title={{Extracting Policy Positions from Political Texts Using Words as Data}},
+ journal={American Political Science Review},
+ volume={97},
+ year={2003},
+ pages={311-331},
+ number={2}
+}
+
+ at book{Leamer78,
+ author={Edward Leamer},
+ title={Specification Searches},
+ publisher={Wiley},
+ year= 1978,
+ address={New York}
+}
+
+ at article{Lebow00,
+ author={Richard Ned Lebow},
+ title={What's so Different About a Counterfactual?},
+ journal= wp,
+ volume= 52,
+ year= 2000,
+ pages={550--85},
+ month={July}
+}
+
+ at misc{Lechner00,
+ author={Michael Lechner},
+ title={A note on the common support problem in applied evaluation studies},
+ year= 2000,
+ howpublished={{http://www.siaw.unisg.ch/lechner}},
+ note={University of St. Galen}
+}
+
+ at incollection{Lechner99,
+ author={Michael Lechner},
+ title={Identification and Estimation of Causal Effects of Multiple Treatments under the Conditional Independence Assumption},
+ booktitle={Econometric Evaluation of Labour Market Policies},
+ publisher={Physica},
+ address={Heidelberg},
+ editor={Lechner, M. and Pfeiffer, F.},
+ year= 2001,
+ pages={43--58}
+}
+
+
+
+ at article{LedBre59,
+ author={S. Ledermann and J. Breas},
+ title={{Les Dimensions de la Mortalit{\'e}}},
+ journal={Population},
+ volume= 14,
+ year= 1959,
+ pages={637--682},
+ note={[in French]}
+}
+
+ at article{Lee00,
+ author={Ronald D. Lee},
+ title={The Lee-Carter Method for Forecasting Mortality, with Various Extensions
+ and Applications},
+ journal={North American Actuarial Journal},
+ volume= 4,
+ year= 2000,
+ pages={80--93},
+ number= 1
+}
+
+ at article{Lee00a,
+ author={Ronald D. Lee},
+ title={{Long-Term Projections and the US Social Security System}},
+ journal={Population and Development Review},
+ volume= 26,
+ year= 2000,
+ pages={137--143},
+ month={March},
+ number= 1
+}
+
+ at article{Lee93,
+ author={Ronald D. Lee},
+ title={{Modeling and Forecasting the Time Series of US Fertility: Age Patterns,
+ Range, and Ultimate Level}},
+ journal={International Journal of Forecasting},
+ volume= 9,
+ year= 1993,
+ pages={187--202}
+}
+
+
+ at article{LeeCar92,
+ author = {Ronald D. Lee and Lawrence R. Carter},
+ title = {{Modeling and Forecasting U.S. Mortality}},
+ journal = jasa,
+ volume = 87,
+ year = 1992,
+ month = {September},
+ pages = {659--675},
+ number = 419
+}
+
+ at article{LeeCar92b,
+ author={Ronald D. Lee and Lawrence R. Carter},
+ title={Rejoinder},
+ journal= jasa,
+ volume= 87,
+ year= 1992,
+ pages={674--675},
+ month={September},
+ number= 419
+}
+
+ at article{LeeCarTul95,
+ author={Ronald D. Lee and Lawrence Carter and S. Tuljapurkar},
+ title={Disaggregation in Population Forecasting: Do We Need It? And How to Do it
+ Simply},
+ journal={Mathematical Population Studies},
+ volume={5},
+ year={1995},
+ pages={217--234},
+ month={July},
+ number={3},
+ annote={Authors describe a model for reducing the dimensionality of the forecasting
+ problem by modeling the evolution over time of the age schedules of vital
+ rates, reducing the problem to forecasting a single parameter for fertility
+ and another for mortality. Authors also show how one can fit the model
+ more simply and prepare integrated forecasts for a collection of regions,
+ and discuss alternate approaches to forecasting the estimated indices of
+ fertility and mortality, including state-space methods.}
+}
+
+ at misc{LeeHigBiFerWes00,
+ author={Herbert Lee and David Higdon and Zhuoxin Bi and Marco Ferreira and Mike
+ West},
+ title={Markov Random Field Models for High-Dimensional Parameters in Simulations
+ of Fluid Flow in Porous Media},
+ year= 2000,
+ howpublished={Discussion Paper \#00-35, Institute of Statistics and Decision Sciences,
+ Duke University}
+}
+
+ at book{LeeJohZel96,
+ author={Jack C. Lee and Wesley O. Johnson and Arnold Zellner},
+ title={Modelling and Prediction: Honoring Seymour Geisser},
+ publisher={Springer},
+ year={1996},
+ editor={Jack C. Lee and Wesley O. Johnson and Arnold Zellner}
+}
+
+ at article{LeeMil01,
+ author={Ronald D. Lee and Timothy Miller},
+ title={Evaluating the Performance of the Lee-Carter Approach to Modeling and Forecasting
+ Mortality},
+ journal={Demography},
+ volume= 38,
+ year= 2001,
+ pages={537--549},
+ month={November},
+ number= 4
+}
+
+ at article{LeeRof94,
+ author={Ronald D. Lee and R. Rofman},
+ title={Modeling and Projecting Mortality in Chile},
+ journal={Notas Poblacion},
+ volume={22},
+ year={1994},
+ pages={183--213},
+ month={Jun},
+ number={59},
+ annote={Authors extend the Lee-Carter method to deal with various problems of incomplete
+ data common in Third World populations, and then apply the method to forecast
+ mortality in Chile.}
+}
+
+ at article{LeeSki99,
+ author={Ronald D. Lee and Jonathan Skinner},
+ title={Will Aging Baby Boomers Bust the Federal Budget},
+ journal={Journal of Economic Perspectives},
+ volume= 13,
+ year= 1999,
+ pages={117--140},
+ month={Winter},
+ number= 1
+}
+
+ at article{LeeTul94,
+ author={Ronald D. Lee and S. Tuljapurkar},
+ title={{Stochastic Population Projections for the U.S.: Beyond High, Medium and
+ Low}},
+ journal= jasa,
+ volume= 89,
+ year= 1994,
+ pages={1175--1189},
+ month={December},
+ number= 428
+}
+
+ at article{LeeTul98,
+ author={Ronald D. Lee and S. Tuljapurkar},
+ title={{Uncertain Demographic Futures and Social Security Finances}},
+ journal={American Economic Review: Papers and Proceedings},
+ year= 1998,
+ pages={237--241},
+ month={May}
+}
+
+ at incollection{LeeTul98a,
+ author={Ronald D. Lee and S. Tuljapurkar},
+ title={{Stochastic Forecasts for Social Security}},
+ booktitle={Frontiers in the Economics of Aging},
+ publisher={University of Chicago Press},
+ year= 1998,
+ address={Chicago},
+ editor={David Wise},
+ pages={393--420}
+}
+
+ at techreport{LenFox06,
+ author={Amanda Lenhart and Susannah Fox},
+ title={{Bloggers: A Portrait of the Internet's New Storytellers}},
+ institution={Pew Internet and American Life Project},
+ year= 2006,
+ note={{http://207.21.232.103/pdfs/PIP\%20Bloggers\%20Report\%20July\%2019\%202006.pdf}}
+}
+
+ at book{LenHsu99,
+ author={T. Leonard and J.S.J. Hsu},
+ title={Bayesian Methods},
+ publisher={Cambridge University Press},
+ year= 1999,
+ address={Cambridge}
+}
+
+ at unpublished{LeuSia03,
+ author={E. Leuven and B. Sianesi},
+ title={psmatch2},
+ note={{Stata module to perform full {M}ahalanobis and propensity score matching,
+ common support graphing, and covariate imbalance testing. Available at:
+ http://www1.fee.uva.nl/scholar/mdw/leuven/stata}},
+ year= 2003
+}
+
+ at misc{LeuSia04,
+ author={Edwin Leuven and Barbara Sianesi},
+ title={PSMATCH2: Stata module to perform full Mahalanobis and propensity score
+ matching, common support graphing, and covariate imbalance testing},
+ year= 2004,
+ howpublished={EconPapers},
+ note={{http://econpapers.repec.org/software/bocbocode/S432001.htm}}
+}
+
+ at article{LeuTanLue97,
+ author={Kai-Kuen Leung, Li-Yu Tang, and Bee-Horng Lue},
+ title={Self-Rated Health and Mortality in Chinese Institutional Elderly Persons},
+ journal={Journal of Clinical Epidemiology},
+ volume= 50,
+ year= 1997,
+ pages={{1107-16}},
+ number= 10
+}
+
+ at book{Levinson01,
+ author={Sanford Levinson},
+ title={What is the Constitution's Role in Wartime: Why Free Speech and Other Rights
+ Are Not as Safe as You Might Think},
+ publisher={?},
+ year= 2001
+}
+
+ at article{LevKas70,
+ author={P.S. Levy and E. H. Kass},
+ title={A three population model for sequential screening for Bacteriuria},
+ journal={American Journal of Epidemiology},
+ volume={91},
+ year={1970},
+ pages={148-154}
+}
+
+ at article{Lewis01,
+ author={Jeffrey B. Lewis},
+ title={Estimating Voter Preference Distributions from Individual-Level Voting Data},
+ journal={Political Analysis},
+ volume= 9,
+ year= 2001,
+ pages={275-297},
+ month={June},
+ number= 3
+}
+
+ at article{Lewis03,
+ author={Anthony Lewis},
+ title={Marbury v. Madison v. Ashcroft},
+ journal={New York Times},
+ year={2003},
+ month={February 24, A17}
+}
+
+ at incollection{Lewis05,
+ author={Maureen Lewis},
+ title={Improving Efficiency and Impact in Health Care Services: Lessons from Central
+ America},
+ booktitle={Health Systems Innovation in Central America},
+ publisher={The World Bank},
+ year= 2005,
+ address={Washington, D.C.},
+ editor={Gerard M. La Forgia}
+}
+
+ at inbook{Lewis05,
+ author={Maureen Lewis},
+ title={Health Sysems Innovatin in Central American},
+ chapter={Improving Efficiency and Impact in Health Care Services - Lessons from Central
+ American },
+ year={2005},
+ publisher={The World Bank},
+ address={Washington DC},
+ editor={Gerard M. La Forgia}
+}
+
+ at book{lewis73,
+ author={David K. Lewis},
+ title={Counterfactuals},
+ publisher={Cambridge: Harvard University Press},
+ year= 1973
+}
+
+ at article{Lewis99b,
+ author={John A. Lewis},
+ title={Statistical Principles for Clinical Trials (ICH E9) An Introductory Note
+ on an International Guideline},
+ journal={Statistics in Medicine},
+ volume={18},
+ year={1999},
+ pages={1903-1904}
+}
+
+ at article{LewLev89,
+ author={R.A. Lew and P.S. Levy},
+ title={Estimation of prevalence on the basis of screening tests},
+ journal={Statistics in Medicine},
+ volume={8},
+ year={1989}
+}
+
+ at article{LewLev89,
+ author={Robert A. Lew and Paul S. Levy},
+ title={Estimation of Prevalence on the Basis of Screening Tests},
+ journal={Statistics in Medicine},
+ volume={8},
+ year={1989},
+ pages={1225-1230}
+}
+
+ at book{Li95,
+ author={S.Z. Li},
+ title={Markov Random Field Modeling in Computer Vision},
+ publisher={Springer-Verlag},
+ year= 1995
+}
+
+ at article{LicLipDan92,
+ author={Allen S. Lichter and Marc E. Lippman and David N. Danforth Jr and Teresa
+ d'Angelo and Seth M. Steinberg and Ernest deMoss and Harold D. MacDonald
+ and Cheryl M. Reichert and Maria Merino and Sandra M. Swain and Kenneth
+ Cowan and Lynn H. Gerber and Judith L. Bader and Peggie A. Findlay and
+ Wendy Schain and Catherine R. Gorrell and Karen Straus and Steven A. Rosenberg
+ and Eli Glatstein},
+ title={Mastectomy Versus Breast-Conserving Therapy in the Treatment of Stage I
+ and II Carcinoma of the Breast: A Randomized Trial at the National Cancer
+ Institute},
+ journal={Journal of Clinical Oncology},
+ volume= 10,
+ year= 1992,
+ pages={976-983},
+ month= June ,
+ number= 6
+}
+
+ at article{LilPer94,
+ author={David E. Lilienfeld and Daniel P. Perl},
+ title={Projected Neurodegenerative Disease Mortality among Minorities in the United
+ States},
+ journal={Neuroepidemiology},
+ volume= 13,
+ year= 1994,
+ pages={179--186}
+}
+
+ at article{LinCutZwi02,
+ author={Shin Lin and David L. Cutler and Michael E. Zwick and Aravinda Chakravarti},
+ title={Haplotype Inference in Random Population Samples},
+ journal={American Journal of Human Genetics},
+ volume= 71,
+ year= 2002,
+ pages={1129--1137}
+}
+
+ at book{LinHam97,
+ title={Handbook of Modern Item Response Theory},
+ publisher={Springer},
+ year= 1997,
+ editor={Wim Van Der Linden and Ronald K. Hambleton},
+ address={New York}
+}
+
+ at article{LinLin80,
+ author={Bernard S. Linn and Margaret W. Linn},
+ title={Objective and self-assessed health in the old and very old},
+ journal={Social Science and Medicine },
+ volume={{14A}},
+ year= 1980,
+ pages={{311-15}}
+}
+
+ at article{LinPekWan05,
+ author={Peter K. Lindenauer and Penelope Pekow and Kaijun Wang and Dheeresh K. Mamidi
+ and Benjamin Guierrez and Evan M. Benjamin},
+ title={Perioperative beta-blocker therapy and mortality after major noncardiac
+ surgery},
+ journal={New England Journal of Medicine},
+ volume={353},
+ year={2005},
+ pages={349-361},
+ month={July},
+ number={4}
+}
+
+ at article{LinSmi72,
+ author={D. V. Lindley and A. F. M. Smith},
+ title={{B}ayes Estimates for the Linear Model},
+ journal={Journal of the Royal Statistical Society B},
+ volume= 34,
+ year= 1972,
+ pages={1-41},
+ number= 1
+}
+
+ at book{Lipset63,
+ author={Lipset, Seymour Martin},
+ title={Political Man: The Social Basis of Politics},
+ publisher={Anchor Books},
+ year= 1963,
+ address={Garden City, NY}
+}
+
+ at article{LisMilFre03,
+ author={John A. List and Daniel L. Millimet and Per G. Fredriksson and W. Warren
+ McHone},
+ title={Effects of Environmental Regulations on Manufacturing Plant Births: Evidence
+ from a Propensity Score Matching Estimator},
+ journal={The Review of Economics and Statistics},
+ volume={85},
+ year={2003},
+ pages={944-952},
+ month={November},
+ number={4}
+}
+
+ at article{LitAn04,
+ author={Roderick Little and Hyonggin An},
+ title={Robust Likelihood-Based Analysis of Multivariate Data with Missing Values},
+ journal={Statistica Sinica},
+ volume={14},
+ year={2004},
+ pages={949-968}
+}
+
+ at article{LitAn04,
+ author={Roderick Little and Hyonggin An},
+ title={Robust Likelihood-Based Analysis of Multivariate Data with Missing Values},
+ journal={Statistica Sinica},
+ volume={14},
+ year={2004},
+ pages={949-968}
+}
+
+ at book{LitRub02,
+ author={Roderick J.A. Little and Donald B. Rubin},
+ title={Statistical Analysis with Missing Data, 2nd Edition},
+ publisher={John Wiley and Sons},
+ year={2002},
+ address={New York, New York}
+}
+
+ at article{LitRub89,
+ author={Rodrick J. Little and Donald Rubin},
+ title={The Analysis of Social Science Data with Missing Values},
+ journal={Sociological Methods and Research},
+ volume={18},
+ year={1989},
+ pages={292-326}
+}
+
+ at inbook{LitSch95,
+ author={Rodrick J. Little and N. Schenker},
+ title={Handbook of Statistical Modeling for the Social and Behavioral Sciences},
+ chapter={Missing Data},
+ year={1995},
+ pages={39-75}
+}
+
+ at unpublished{Little05,
+ author={Roderick Little},
+ title={Calibrated Bayes: A Bayes/Frequentist Roadmap},
+ note={University of Michigan},
+ year={2005},
+ month={September}
+}
+
+ at article{Little92,
+ author={Roderick J. Little},
+ title={Regression with Missing X's: A Review},
+ journal={Journal of the American Statistical Association},
+ volume={87},
+ year={1992},
+ pages={1227-1237}
+}
+
+ at unpublished{LiuHuChe05,
+ author={Bing Liu and Minqing Hu and Junsheng Cheng},
+ title={Opinion Observer: Analyzing and Comparing Opinions on the Web},
+ note={Bing Liu Dept of Computer Science; Univ of Illinois at Chicago, 851 south
+ Morgan St. Chicago, IL 60607-7053, liub at cs.uic.edu},
+ year={2005},
+ month={May}
+}
+
+ at article{LiuWonKon94,
+ author={J. Liu and W.H. Wong and A. Kong},
+ title={Covariance Structure of the Gibbs Sampler with Applications to the Comparisons
+ of Estimators and Augmentation Schemes},
+ journal={Biometrika},
+ volume={81},
+ year={1994},
+ pages={27-40}
+}
+
+ at article{LiWen05,
+ author={Quan Li and Ming Wen},
+ title={The Immediate and Lingering Effects of Armed Conflict on Adult Mortality:
+ A Time-Series Cross-National Analysis},
+ abstract={This research investigates the effect of armed conflict on adult mortality
+ across countries and over time. Theoretical mechanisms are specified for
+ how military violence influences adult mortality, both immediately and
+ over time after conflict. The effects of aggregate conflict, interstate
+ and intrastate conflicts, and conflict severity are explored. The Heckman
+ selection model is applied to account for the conflict-induced missing
+ data problem. A pooled analysis across 84 countries for the period from
+ 1961 to 1998 provides broad empirical support for the proposed theoretical
+ expectations across both genders. This study confirms the importance of
+ both the immediate and the lingering effect of military conflict on the
+ mortality of the working-age population. The immediate effect of civil
+ conflict is much stronger than that of the interstate conflict, while the
+ reverse applies to the lingering effect. Both the immediate and the lingering
+ effects of severe conflict are much stronger than those of minor conflict.
+ While men tend to suffer higher mortality immediately from intrastate conflict
+ and severe conflict, women in the long run experience as much mortality
+ owing to the lingering effects of these conflicts. The mortality data show
+ a strong data selection bias caused by military conflict. The research
+ findings highlight the imperative for negotiating peace. Preventing a contest
+ from escalating into a severe conflict can produce noticeable gains in
+ saved human lives.},
+ journal={Journal of Peace Research},
+ volume={42},
+ year={2005},
+ pages={471-492},
+ month={July},
+ number={4}
+}
+
+ at article{LoeFulKag03,
+ author={Susanna Loeb and Bruce Fuller and Sharon Lynn Kagan and Bidemi Carrol},
+ title={How Welfare Reform Affects Young Children: Experimental Findings from Connecticut
+ - A Research Note},
+ journal={Journal of Policy Analysis and Management},
+ volume={22},
+ year={2003},
+ pages={537-550},
+ number={4}
+}
+
+ at article{Londregan00,
+ author={John Londregan},
+ title={Estimating Legislator's Preferred Points},
+ journal={Political Analysis},
+ volume= 8,
+ year= 2000,
+ pages={21--34},
+ month={Winter},
+ number= 1
+}
+
+ at book{Londregan00b,
+ author={John Londregan},
+ title={Legislative Institutions and Ideology in Chile},
+ publisher={Cambridge University Press},
+ year= 2000,
+ address={New York}
+}
+
+ at book{LonFre06,
+ author={J. Scott Long and Jeremy Freese},
+ title={Regression Models for Categorical Dependent Variables Using Stata},
+ publisher={Stata Press},
+ year={2006},
+ address={College Station, TX}
+}
+
+ at article{LooBee46,
+ author={Loomis, C. P. and Beegle, J. A.},
+ title={The Spread of German Nazism in Rural Areas},
+ journal= asr,
+ volume= 11,
+ year= 1946,
+ pages={724-734}
+}
+
+ at book{LopAhmGui00,
+ author = {Alan Lopez and O. Ahmed and M. Guillot and
+ B.D. Ferguson and J.A. Salomon and C.J.L. Murray and
+ K.H. Hill},
+ title = {World Mortality in 2000: Life Tables for 191
+ Countries},
+ publisher = {World Health Organization},
+ year = 2000,
+ address = {Geneva}
+}
+
+ at article{LowAltFer98,
+ author={Robert Lowry and James E. Alt and Karen Ferree},
+ title={Fiscal Policy Outcomes and Electoral Accountability in the American States},
+ journal={American Political Science Review},
+ volume= 92,
+ year= 1998,
+ pages={759-777},
+ month={December}
+}
+
+ at article{Lowenstein2006,
+ author={Daniel H. Lowenstein},
+ title={Vieth's Gap: Has the Supreme Court Gone from Bad to Worse on Partisan Gerrymandering?},
+ journal={Cornell Journal of Law and Public Policy},
+ volume={N:X},
+ year={2006},
+ pages={101-130},
+ month={January}
+}
+
+ at article{LozSolGak06,
+ author = {Rafael Lozano and Patricia Soliz and Emmanuela
+ Gakidou and Jesse Abbott-Klafter and Dennis
+ M. Feehan and Cecilia Vidal and Juan Pablo Ortiz and
+ Christopher J.L. Murray},
+ title = {Benchmarking of performance of Mexican States with
+ Effective Coverage},
+ journal = {The Lancet},
+ volume = {368},
+ year = {2006},
+ pages = {1729-1741},
+ month = {November}
+}
+
+ at article{lubin94,
+ author = {J.H. Lubin and M.H. Gail},
+ title = {Sampling Strategies in Nested Case-Control Studies},
+ journal = {Environmental Health Perspectives},
+ volume = 102,
+ year = 1994,
+ pages = {47-51},
+ number = {suppl 8}
+}
+
+ at article{Lubkemann05,
+ author = {Stephen C. Lubkemann},
+ title = {Migratory Coping in Wartime Mozambique: An
+ Anthropology of Violence and Displacement in
+ Fragmented Wars},
+ journal = {Journal of Peace Research},
+ volume = {42},
+ year = {2005},
+ pages = {493-508},
+ month = {July},
+ number = {4}
+}
+
+ at article{LumHea99,
+ author = {Thomas Lumley and Patrick Heagerty},
+ title = {Weighted Empirical Adaptive Variance Estimators for
+ Correlated Data Regression},
+ journal = {jrssb},
+ volume = {61},
+ year = {1999},
+ pages = {459--477},
+ number = {2}
+}
+
+ at article{LunSmi05,
+ author = {Jennifer Hickes Lundquist and Herbert L. Smith},
+ title = {Family Formation Among Women in the U.S. Military:
+ Evidence from the NLSY},
+ journal = {Journal of Marriage and Family},
+ volume = {67},
+ year = {2005},
+ pages = {1-13},
+ month = {February}
+}
+
+ at article{LuRos04,
+ author={Bo Lu and Paul R. Rosenbaum},
+ title={Optimal Pair Matching With Two Control Groups},
+ journal={Journal of Computational and Graphical Statistics},
+ volume={13},
+ year={2004},
+ pages={422-434},
+ number={2}
+}
+
+ at inbook{Lustig94,
+ author={Nora Lustig},
+ title={Solidarity as a Strategy of Poverty Alleviation},
+ chapter={5},
+ year={1994},
+ publisher={Center for U.S.-Mexican Studies},
+ pages={79-96},
+ series={U.S.-Mexico Contemporary Perspectives Series, 6},
+ address={University of California, San Diego}
+}
+
+ at article{LuZanHor01,
+ author = {Bo Lu and Elaine Zanuto and Robert Hornik and Paul
+ R. Rosenbaum},
+ title = {Matching With Doses in an Observational Study of a
+ Media Campaign Against Drug Abuse},
+ journal = {Journal of the American Statistical Association},
+ volume = {96},
+ year = {2001},
+ pages = {1245-1253},
+ month = {December},
+ number = {456}
+}
+
+ at techreport{LymVar03,
+ author = {Peter Lyman and Hal R. Varian},
+ title = {{How much information 2003}},
+ institution = {University of California},
+ year = 2003,
+ note =
+ {{http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/}}
+}
+
+ at article{Lynch03,
+ author = {Lynch, C.A.},
+ title = {{Institutional Repositories: Essential
+ Infrastructure For Scholarship In The Digital Age}},
+ journal = {portal: Libraries and the Academy},
+ volume = {3},
+ year = {2003},
+ pages = {327--336},
+ number = {2}
+}
+
+ at article{LynMcc92,
+ author = {Henry S. Lynn and Charles E. McCulloch},
+ title = {When Does it Pay to Break the Matches for Analysis
+ of a Matched-Pairs Design?},
+ journal = {Biometrics},
+ volume = {48},
+ year = {1992},
+ pages = {397-409},
+ month = {June}
+}
+
+ at article{MacKunGro99,
+ author = {J.P. Mackenbach and A.E. Kunst and F. Groenhof and
+ J.K. Borgan and G. Costa and F. Faggiano },
+ title = {Socioeconomic inequalities in mortality among women
+ and among men: an international study},
+ journal = {American Journal of Public Health},
+ volume = 89,
+ year = 1999,
+ pages = {1800-1806},
+ number = 12
+}
+
+ at book{Macridis55,
+ author={Roy C. Macridis},
+ title={The Study of Comparative Government},
+ publisher={Doubleday and Co.},
+ year= 1955,
+ address={New York}
+}
+
+ at article{MacRivJur06,
+ author = {Ellen J. MacKenzie and Frederick P. Rivara and
+ Gregory J. Jurkovich and Avery B. Nathens and
+ Katherine P. Frey and Brian L. Egleston and David
+ S. Salkever and Daniel O Scharfstein},
+ title = {A National Evaluation of the Effect of Trauma-Center
+ Care on Mortality},
+ journal = {New England Journal of Medicine},
+ volume = {354},
+ year = {2006},
+ pages = {366-378},
+ month = {January}
+}
+
+ at article{MadDou64,
+ author = {George L. Maddox and Elizabeth B. Douglas},
+ title = {Self-Assessments of Health: A Longitudinal Study of
+ Elderly Subjects},
+ journal = {Journal of Health and Social Behavior },
+ volume = 14,
+ year = 1973,
+ pages = {{87-93}}
+}
+
+ at article{MadNel92,
+ author = {W.R. Madych and S.A. Nelson},
+ title = {Bounds on Multivariate Polynomials and Exponential
+ Error Estimates for Multiquadric Interpolation},
+ journal = {Journal of Approximation Theory},
+ volume = 70,
+ year = 1992,
+ pages = {94--114}
+}
+
+ at article{Malaker86,
+ author={CR Malaker},
+ title={Estimation of Adult Mortality in India: 1971--1981},
+ journal={Demography India},
+ volume= 15,
+ year= 1986,
+ pages={126--136}
+}
+
+ at article{ManKarMar03,
+ author = {Kristiina Manderbacka, et al},
+ title = {The Effect of Point of Reference on the Association
+ Between Self-Rated Health and Mortality},
+ journal = {Social Science and Medicine},
+ volume = 56,
+ year = 2003,
+ pages = {{1447-52}}
+}
+
+ at book{ManSch99,
+ author={Christopher D. Manning and Hinrich Sch{\"u}tze},
+ title={Foundations of Statistical Natural Language Processing},
+ publisher={Massachusetts Institute of Technology},
+ year={1999},
+ address={Cambridge, MA}
+}
+
+ at book{Manski05,
+ author={Charles F. Manski},
+ title={Social choice with Partial Knowledge of Treatment Response},
+ publisher={Princeton University Press},
+ year={2005},
+ series={Econometric Institute Lectures}
+}
+
+ at article{Manski77,
+ author={Charles F. Manski},
+ title={The Estimation of Choice Probabilities from Choice Based Samples},
+ journal={Econometrics},
+ volume= 45,
+ year= 1977,
+ pages={1977--88},
+ month={November},
+ number= 8
+}
+
+ at article{Manski90,
+ author = {Charles F. Manski},
+ title = {The Use of Intentions Data to Predict Behavior: A
+ Best-Case Analysis},
+ journal = {Journal of the American Statistical Association},
+ volume = {85},
+ year = {1990},
+ pages = {934-940},
+ month = {December},
+ number = {412}
+}
+
+ at book{Manski95,
+ author={Charles F. Manski},
+ title={Identification Problems in the Social Sciences},
+ publisher={Harvard University Press},
+ year= 1995
+}
+
+ at inproceedings{manski99,
+ author = {Charles F. Manski},
+ title = {Nonlinear Statistical Inference: Essays in Honor of
+ Takeshi Amemiya},
+ booktitle = {Nonparametric Identification Under Response-Based
+ Sampling},
+ year = 1999 ,
+ publisher = cup,
+ editor = {C. Hsiao and K. Morimune and J. Powell}
+}
+
+ at article{ManSzoKoh01,
+ author = {Mandl, K.D. and Szolovits, P. and Kohane, I.S.},
+ title = {{Public standards and patients' control: how to keep
+ electronic medical records accessible but private}},
+ journal = {BMJ},
+ volume = {322},
+ year = {2001},
+ pages = {283--7},
+ number = {7281}
+}
+
+ at article{ManSzoKoh01,
+ author={Kenneth D. Mandl and Peter Szolovits and Isaac S. Kohane},
+ title={Public standards and patients' control: how to keep electronic medical records
+ accessible but private},
+ journal={British Medical Journal},
+ volume={322},
+ year={2001},
+ pages={283-287},
+ month={February},
+ publisher={British Medical Journal}
+}
+
+ at article{mantel73,
+ author={N. Mantel},
+ title={Synthetic Retrospective Studies and Related Topics},
+ journal={Biometrics},
+ volume= 29,
+ year= 1973,
+ number={479-486}
+}
+
+
+ at article{ManTudDie06,
+ author={Dennis T. Mangano and Julia C. Tudor and Cynthia Dietzel},
+ title={The Risk Associated wtih Aprotinin in Cardiac Surgery},
+ journal={The New England Journal of Medicine},
+ volume={354},
+ year={2006},
+ pages={353-365},
+ month={January},
+ number={4}
+}
+
+ at article{ManRao04,
+ title = {{Community-Based and-Driven Development: A Critical
+ Review}},
+ author = {Mansuri, G. and Rao, V.},
+ journal = {The World Bank Research Observer},
+ volume = {19},
+ number = {1},
+ pages = {1-39},
+ year = {2004}
+}
+
+ at article{MarCamFay91,
+ author = {Elizabeth A. Martin and Pamela C. Campanelli and
+ Robert E. Fay},
+ title = {An Application of Rasch Analysis to Questionnaire
+ Design: Using Vignettes to Study the Meaning of
+ `Work' in the Current Population Survey},
+ journal = {The Statistician},
+ volume = 40,
+ year = 1991,
+ pages = {265--276},
+ month = {September},
+ number = 3
+}
+
+ at article{March57,
+ author={James G. March},
+ title={party Legislative Representation as a Function of Election Results},
+ journal={1957},
+ volume={21},
+ year={1957-1958},
+ pages={521-542},
+ month={Winter},
+ number={4}
+}
+
+ at article{Marcus00,
+ author={George E. Marcus},
+ title={{Emotions in Politics}},
+ journal={Annual Review of Political Science},
+ volume={3},
+ year={2006},
+ pages={221-50}
+}
+
+ at article{Marcus88,
+ author={George E. Marcus},
+ title={{The Structure of Emotional Response: The 1984 Presidential Candidates}},
+ journal={American Political Science Review},
+ volume={82},
+ year={1988},
+ pages={737-761},
+ number={3}
+}
+
+ at article{MarDiePer93,
+ author={Donald C. Martin and Paula Diehr and Edward B. Perrin and Thomas D. Koepsell},
+ title={The Effect of Matching on the Power of Randomized Community Intervention
+ Studies},
+ journal={Statistics in Medicine},
+ volume={12},
+ year={1993},
+ pages={329-338}
+}
+
+ at article{MarHusLob95,
+ author={David Marsh and Khatidja Husein and Melvyn Lobo and Mehboob Ali Shah and
+ Stephen Luby},
+ title={Verbal autopsy in Karachi slums: comparing single and multiple cause of
+ child deaths},
+ journal={Health Policy and Planning},
+ volume={10},
+ year={1995},
+ pages={395-403},
+ number={4}
+}
+
+ at article{MarHusLob95,
+ author={David Marsh and Khatidja Husein and Melvyn Lobo and Mehboob Ali Shah and
+ Stephen Luby},
+ title={Verbal autopsy in Karachi slums: comparing single and multiple cause of
+ child deaths},
+ journal={Health Policy and Planning},
+ volume={10},
+ year={1995},
+ pages={395-403},
+ number={4}
+}
+
+ at book{MarKenBib79,
+ author={K. V. Mardia and J. T. Kent and J. M. Bibby},
+ title={Multivariate Analysis},
+ publisher= ap,
+ year={1979},
+ address={London}
+}
+
+ at article{MarMajRas93,
+ author={David Marsh and Nuzhat Majit and Zeba Rasmussen and Khalid Mateen and Arif
+ Amin Khan},
+ title={Cause-Specific Child Mortality In A Mountainous Community In Pakistan By
+ Verbal Autopsy},
+ journal={Journal of the Pakistan Medical Association},
+ volume={43},
+ year={1993},
+ pages={226-229},
+ month={November},
+ number={11}
+}
+
+ at article{MarMajRas93,
+ author={David Marsh and Nuzhat Majit and Zeba Rasmussen and Khalid Mateen and Arif
+ Amin Khan},
+ title={Cause-Specific Child Mortality In A Mountainous Community In Pakistan By
+ Verbal Autopsy},
+ journal={Journal of the Pakistan Medical Association},
+ volume={43},
+ year={1993},
+ pages={226-229},
+ month={November},
+ number={11}
+}
+
+ at misc{MarQui01,
+ author={Andrew D. Martin and Kevin M. Quinn},
+ title={The Dimensions of {S}upreme {C}ourt Decision Making: Again Revisiting {T}he
+ {J}udicial {M}ind},
+ year= 2001,
+ howpublished={Paper presented at the Annual Meeting of the Midwest Political Science Association}
+}
+
+ at manual{MarQui05,
+ author={Andrew D. Martin and Kevin M. Quinn},
+ title={MCMCpack: Markov chain Monte Carlo (MCMC) Package},
+ year={2005},
+ url={{http://mcmcpack.wustl.edu}}
+}
+
+ at article{MarSadFik03,
+ author={David R. Marsh and Salim Sadruddin and Fariyal F. Fikree and Chitra Krishnan
+ and Gary L. Darmstadt},
+ title={Validation of verbal autopsy to determine the cause of 137 neonatal deaths
+ in Karachi, Pakistan},
+ journal={2003},
+ volume={17},
+ year={2003},
+ pages={132-142}
+}
+
+ at article{MarSadFik03,
+ author={David R. Marsh and Salim Sadruddin and Fariyal F. Fikree and Chitra Krishnan
+ and Gary L. Darmstadt},
+ title={Validation of verbal autopsy to determine the cause of 137 neonatal deaths
+ in Karachi, Pakistan},
+ journal={2003},
+ volume={17},
+ year={2003},
+ pages={132-142}
+}
+
+ at article{Marshall91,
+ author={R.J. Marshall},
+ title={Mapping Disease and Mortality Rates using Empirical Bayes Estimators},
+ journal={Applied Statistics},
+ volume= 40,
+ year= 1991,
+ pages={283--294},
+ number= 2
+}
+
+ at article{MarSmiSta91,
+ author={M.G. Marmot and G.D. Smith and S. Stansfeld and C. Patel and F. North and
+ J. Head and I. White and E. Brunner and A. Feeney},
+ title={Health inequalities among British civil servants: the Whitehall II study
+ [see comments].},
+ journal= lan,
+ year={1991},
+ optvolume={337},
+ optpages={1387--1393}
+}
+
+ at book{Martin92,
+ author={Lisa Martin},
+ title={Coercive Cooperation: Explaining Multilateral Economic Sanctions},
+ publisher={Princeton University Press},
+ year={1992},
+ note={Please inquire with Lisa Martin before publishing results from these data,
+ as this dataset includes errors that have since been corrected.}
+}
+
+ at article{MatFatIno05,
+ author = {Colin D. Mathers and Doris Ma Fat and Mie Inoue and
+ Chalapati Rao and Alan Lopez},
+ title = {Counting the dead and what they died from: an
+ assessment of the global status of cause of death
+ data},
+ journal = {Bulletin of the World Health Organization},
+ volume = 83,
+ year = 2005,
+ pages = {171--177},
+ month = {March},
+ number = 3
+}
+
+ at unpublished{MatLopSte03,
+ author={Colin D. Mathers and Alan Lopez and Claudia Stein and Doris Ma Fat and Chalapati
+ Rao and Mie Unoue and Kenji Shiubuya and Niels Tomijima and Christina Bernard
+ and Hongyi Xu},
+ title={Deaths and Disease Burden by Cause: Global Burden of Disease Estimates for
+ 2001 by World Bank Country Groups},
+ note={Evidence and Information for Policy, World Health Organization, Geneva and
+ School of Population health, University of Queensland, Brisbane, Austrailia},
+ year={2003}
+}
+
+ at unpublished{MatSteFat02,
+ author={Colin D. Mathers and Claudia Stein and Doris Ma Fat and Chalapati Rao and
+ Mie Unoue and Niels Tomijima and Christina Bernard and Alan D. Lopez and
+ Christopher J.L. Murray},
+ title={Global Burden of Disease 2000: Version 2 Methods and Results},
+ note={Global Programme on Evidence for Health Policy Discussion Paper No. 50 World
+ Health Organization},
+ year={2002},
+ month={October}
+}
+
+ at article{MauRos97,
+ author={Gillian H. Maude and David A. Ross},
+ title={The Effect of Different Sensitivity, Specificity and Cause-Specific Mortality
+ Fractions on the Estimation of Differences in Cause-Specific Mortality
+ Rates in Children from Studies Using Verbal Autopsies},
+ journal={International Journal of Epidemiology},
+ volume={26},
+ year={1997},
+ pages={1097-1106},
+ number={5}
+}
+
+ at techreport{Mayaud01,
+ author={Philippe Mayaud},
+ title={Aids-Related Research Projects},
+ institution={The London School of Hygiene \& Tropical Medicine},
+ year={2001},
+ address={London School of Hygiene \& Tropical Medicine www.ishtm.ac.uk}
+}
+
+ at book{McCNel89,
+ author={Peter McCullagh and James A. Nelder},
+ title={Generalized Linear Models},
+ publisher={Chapman \& Hall},
+ year={1989},
+ series={Monograph on Statistics and Applied Probability},
+ edition={2nd},
+ number={37}
+}
+
+ at inbook{McConahay86,
+ author={John B. McConahay},
+ title={Prejudice, Discrimination, and Racism: Theory and Research},
+ chapter={Modern Racism Ambivalence, and the Modern Racism Scale},
+ year={1986},
+ publisher={New York: Academic Press},
+ editor={J. Dovidio and S.L. Gaertner}
+}
+
+ at article{MccRidMor04,
+ author={Daniel F. McCaffrey and Greg Ridgeway and Andrew R. Morral},
+ title={Propensity Score Estimation With Boosted Regression for Evaluating Causal
+ Effects in Observational Studies},
+ journal={Psychological Methods},
+ volume={9},
+ year={2004},
+ pages={403-425},
+ number={4}
+}
+
+ at article{McCShaWan94,
+ author={John McCallum, DPhil, MPhil, Bruce Shadbolt, PhD, and Dong Wang, BSc},
+ title={Self-Rated Health and Survival: A 7-year Follow-Up study of Australian Elderly.},
+ journal={American Journal of Public Health},
+ volume= 84,
+ year= 1994,
+ pages={{1100-05}}
+}
+
+ at unpublished{McDonald06a,
+ author={Michael P. McDonald},
+ title={Seats to Votes Ratios in the United States},
+ note={George Mason University, Dept of Public and International Affairs 4400 University
+ Dr., 3-F4 Fairfax, VA 22030-4444 (703)-993-4191 mmcdon at gmu.edu},
+ year={2006}
+}
+
+ at unpublished{McDonald06b,
+ author={Michael D. McDonald},
+ title={A Standard for Detecting and Remedying Gerrymanders},
+ note={Department of Political Science, Binghamton University- SUNY, Binghamton
+ NY 13902-6000, (617) 777-2946, mdmcd at binghamton.edu},
+ year={2006}
+}
+
+ at article{McKibbin69,
+ author={Ross McKibbin},
+ title={The Myth of the Unemployed: Who did Vote for the Nazis?},
+ journal={Australian Journal of Politics and History},
+ volume= 15,
+ year= 1969,
+ pages={25--40},
+ number= 2
+}
+
+ at book{McLThr97,
+ author={Geoffrey J. McLachlan and Thriyambakam Krishan},
+ title={The EM Algorithm and Extensions},
+ publisher={New York: Wiley}
+}
+
+ at article{McNown92,
+ author={Robert McNown},
+ title={Comment},
+ journal= jasa,
+ volume= 87,
+ year= 1992,
+ pages={671--672},
+ number= 419
+}
+
+ at article{McNRog89,
+ author={Rober McNown and Andrei Rogers},
+ title={Forecasting Mortality: A Parameterized Time Series Approach},
+ journal={Demography},
+ volume= 26,
+ year= 1989,
+ pages={645--660},
+ number= 4
+}
+
+ at article{McNRog92,
+ author={Robert McNown and Andrei Rogers},
+ title={Forecasting Cause-Specific Mortality Using Time Series Methods},
+ journal={International Journal of Forecasting},
+ volume= 8,
+ year= 1992,
+ pages={413--432}
+}
+
+ at unpublished{McqLasLai06,
+ author={Matthew B. McQueen and Jessica Lasky-Su and Nan M. Laird and Christoph Lange},
+ title={Screening and Testing using the Same Data Set: A Testing Strategy for Genome-Wide
+ Association Studies for Case-Control and Case-Cohort Designs},
+ year={2006}
+}
+
+ at article{Mead92,
+ author={A. Mead},
+ title={Review of the Development of Multidimensional Scaling Methods},
+ journal={The Statistician},
+ volume= 41,
+ year= 1992,
+ pages={27--39},
+ month={April},
+ number= 1
+}
+
+ at article{MebSek04,
+ author={Walter Mebane and Jasjeet Sekhon},
+ title={Robust Estimation and Outlier Detection in Overdispersed Multinomial Models
+ of Count Data},
+ journal= ajps,
+ volume= 48,
+ year= 2004,
+ pages={391--410},
+ month={April}
+}
+
+ at article{MeiKarPar04,
+ author={Bettina Meinow et al.},
+ title={The effect of the duration of follow-up in mortality analysis: The temporal
+ pattern of different predictors},
+ journal={Journal of Gerontology: Social Sciences},
+ volume={{59B}},
+ year={2004},
+ pages={{S181-89}},
+ number={3}
+}
+
+ at article{Meng94,
+ author={Xiao-Li Meng},
+ title={Multiple-Imputation Inferences with Uncongenial Sources of input},
+ journal={Statistical Science},
+ volume={9},
+ year={1994},
+ pages={538-573},
+ number={4}
+}
+
+ at article{Meng94b,
+ author={X.L. Meng},
+ title={Posterior Predictive p-Values},
+ journal={Annals of Statistics},
+ volume={22},
+ year={1994},
+ pages={1142-1160},
+ number={3}
+}
+
+ at article{MenRom03,
+ author={Xiao-Li Meng and Marin Romero},
+ title={Discussion: Efficiency and Self-Efficiency with Multiple Imputation Inference},
+ journal={International Statistical Review},
+ volume={71},
+ year={2003},
+ pages={607-618},
+ number={3}
+}
+
+ at article{MenRub92,
+ author={X.L. Meng and Donald Rubin},
+ title={Performing Likelihood Ratio Tests with Multiply-imputed Data Sets},
+ journal={Biometrika},
+ volume={79},
+ year={192},
+ pages={103-111}
+}
+
+ at article{Metetal53,
+ author={N. Metropolis and A. W. Rosenbluth and M. N. Rosenbluth and A. H. Teller
+ and E. Teller},
+ title={Equation of State Calculations by Fast Computing Machines},
+ journal={Journal of Chemical Physics},
+ volume={21},
+ year= 1953,
+ pages={1087-1092}
+}
+
+ at article{MicBloHil04,
+ author={Charles Michalopoulos and Howard S. Bloom and Carolyn J. Hill},
+ title={Can propensity-score methods match the findings from a random assignment
+ evaluation of mandatory welfare-to-work programs?},
+ journal={Review of Economics and Statistics},
+ volume= 56,
+ year= 2004,
+ pages={156-179},
+ number= 1
+}
+
+ at article{Midlarsky05,
+ author={Halbert White},
+ title={A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct
+ Test for Heteroskedasticity},
+ abstract={This study seeks to distinguish between instances where genocide occurred
+ and others where it might have been expected to occur but did not. Territorial
+ loss, a corollary refugee influx, and a resulting contraction of socio-economic
+ space are suggested to provide that distinction. Four analytic perspectives
+ based on emotional reactions, class envy, prospect theory, and territoriality
+ indicate the critical importance of loss. The theory is examined in the
+ context of the mass murder of European Jewry including, of course, Germany
+ and Austria, and all European German allies that allowed an indigenous
+ genocidal impulse, willingness to comply with German genocidal policies,
+ or an ability to resist German pressures for Jewish deportation. Three
+ instances of perpetrating states - Italy, Vichy France, and Romania - emerge
+ from the analysis. The latter two governments willingly collaborated with
+ the Germans in victimizing their own Jewish citizenry, while Italy was
+ on a genocidal path just prior to the German occupation. All five states
+ mentioned above were found to experience considerable territorial loss
+ and a contraction of socio-economic space. Bulgaria and Finland, on the
+ other hand, actually expanded their borders at the start of the war and
+ saved virtually all of their Jewish citizens. The importance of loss is
+ demonstrated not only cross-sectionally, in the comparison between the
+ five victimizers, on the one hand, and Bulgaria and Finland, on the other,
+ but also diachronically, in the changing behavior over time of the genocidal
+ and perpetrating states.},
+ journal={Econometrica},
+ volume={42},
+ year={1980},
+ pages={375-391},
+ month={July},
+ number={4},
+ optnumber={4},
+ optvolume={48},
+ optpages={817--838},
+ optmonth={May}
+}
+
+ at article{Mierendorff30,
+ author={Carl Mierendorff},
+ title={Gesicht und Charakter der nationalsozialistischen Bewegung},
+ journal={Gesellschaft},
+ volume= 7,
+ year= 1930,
+ pages={489--540},
+ number= 1
+}
+
+ at article{MiiVuoOja97,
+ author={Seppo Miilunpalo, Ilkka Vuori, Pekka Oja, Matti Pasanen, and Helka Urponen},
+ title={Self Rated Health as a Health Measure: The Predictive Value of Self-Reported
+ Health Status on the Use of Physician Services and on Mortality in the
+ Working Age Population},
+ journal={Journal of Clinical Epidemiology},
+ volume= 50,
+ year= 1997,
+ pages={{517-28}}
+}
+
+ at misc{MikSuc05,
+ author={Gerome Miklau and Dan Suciu},
+ title={Managing Integrity for Data Exchanged on the Web},
+ year= 2005,
+ month={16--17 June},
+ howpublished={Eighth International Workshop on the Web and Databases, Baltimore},
+ note={{http://webdb2005.uhasselt.be/papers/1-3.pdf}}
+}
+
+ at article{Miller01,
+ author={Tim Miller},
+ title={Increasing Longevity and Medicare Expenditures},
+ journal={Demography},
+ volume= 38,
+ year= 2001,
+ pages={215--226},
+ month={May},
+ number= 2
+}
+
+ at inbook{MilReiHes98,
+ author={Arthur H. Miller and William M. Reisinger and Vicki L. Hesli},
+ title={Elections and Voters in Post-Communist Russia},
+ chapter={Leader Popularity and Party Development in Post-Soviet Russia },
+ year={100-135},
+ publisher={London: Edward Elgar},
+ editor={Matthew Wyman and Stephen White and Sarah Oates}
+}
+
+ at article{MilRob89,
+ author={Miller, Abraham H. and Robbins, James S.},
+ title={Who Did Vote for Hitler? A Reanalysis of the Lipset/Bendix Controversy},
+ journal={Polity},
+ volume= 21,
+ year= 1989,
+ pages={655-677},
+ number= 4
+}
+
+ at article{MilSha94,
+ author={Paul Milgrom and Chris Shannon},
+ title={Monotone Comparative Statics},
+ journal={Econometrica},
+ volume= 62,
+ year= 1994,
+ pages={157--180},
+ month={January},
+ number= 1,
+ annote={introduction of the single crossing property as a way to ensure monotonicity}
+}
+
+ at article{MinRos00,
+ author={Ming, K. and Rosenbaum, Paul R.},
+ title={Substantial gains in bias reduction from matching with a variable number
+ of controls},
+ journal={Biometrics},
+ volume={56},
+ year={2000},
+ pages={118-124}
+}
+
+ at article{MoeWal01,
+ author={Karl Ove Moene and Michael Wallerstein},
+ title={Inequality, Social Insurance, and Redistribution},
+ journal={American Political Science Review},
+ volume={95},
+ year={2001},
+ pages={859-874},
+ month={December},
+ number={4}
+}
+
+ at article{MoeWal03,
+ author={Karl Ove Moene and Michael Wallerstein},
+ title={Earnings Inequality and Welfare Spending: A Disaggregated Analysis},
+ journal={World Politics},
+ volume={55},
+ year={2003},
+ pages={485-516},
+ month={July}
+}
+
+ at book{Mommsen89,
+ author={Hans Mommsen},
+ title={Die verspielte Freiheit --- der Weg der Republik von Weimar in den Untergang,
+ 1918 bis 1933},
+ publisher={Propyl{\"a}en},
+ year= 1989
+}
+
+ at techreport{MonLopGel99,
+ author={Manuel Montes-y-Gomez and Aurelio Lopez-Loez and Alexander F. Gelbukh and
+ Grigori Sidorov and Adolfo Guzman-Arenas},
+ title={Text Mining: New Techniques and Applications},
+ institution={Center for computing Research of the National Polytechnic Institute, Mexico
+ City},
+ year={1999},
+ month={August},
+ address={CIC, IPN, Laboratorio de Lenguaje Natural, Av. Juan de Dios Batiz, Mexico
+ DF.},
+ volume={34}
+}
+
+ at techreport{MonLopGel99b,
+ author={Manuel Montes y Gomez and Aurelio Lopez Lopez and Alexander F. Gelbukh},
+ title={Text Mining as a Social Thermometer},
+ institution={Centro de Investigaci{\'o}n en Computaci{\'o}n},
+ year={1999},
+ address={CIC, IPN Laboratorio de Lenguaje Natural. Ave. Juan de Dios Batiz, Mexico
+ DF}
+}
+
+ at book{MonSam04,
+ title={Decentralization and Democracy in Latin America},
+ publisher={University of Notre Dame Press},
+ year={2004},
+ editor={Alfred P. Montero and David J. Samuels},
+ address={Notre Dame, Indiana}
+}
+
+ at book{Montgomery2001,
+ author={Douglas C. Montgomery},
+ title={Design and Analysis of Experiments},
+ publisher={Wiley},
+ year={2001},
+ address={New York},
+ edition={5th}
+}
+
+ at article{MorBlaTom03,
+ author={Saul S. Morris and Robert E. Black and Lana Tomaskovic},
+ title={Predicting the distribution of under-five deaths by cause in countries without
+ adequate vital registration systems},
+ journal={International Journal of Epidemiology},
+ volume={32},
+ year={2003},
+ pages={1041-1051}
+}
+
+ at book{Morozov84,
+ author={V.A. Morozov},
+ title={Methods for solving incorrectly posed problems},
+ publisher={Springer-Verlag},
+ year= 1984 ,
+ address={Berlin}
+}
+
+ at article{MorSpi99,
+ author={Mary J. Morrissey and Donna Spiegelman},
+ title={Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data:
+ Extensions and Comparisons},
+ journal={Biometrics},
+ volume={55},
+ year={1999},
+ pages={338-344},
+ month={June}
+}
+
+ at unpublished{MorYamTat02,
+ author={Satoshi Morinaga and Kenji Yamanishi and Kenji Tateishi and Toshikazu Fukushima},
+ title={Mining Product Reputations on the Web},
+ note={Satoshi Morinaga and Kenji Yamanishi NEC Corp. 4-1-1 Miyazaki Miyamae Kawasaki
+ Kanagawa 216-8555 Japan Tel: 81-44-856-2143; morinaga at cw.jp.nec.com},
+ year={2002}
+}
+
+ at unpublished{MorYamTat02,
+ author={Satoshi Morinaga and Kenji Yamanishi and Kenji Tateishi and Toshikazu Fukushima},
+ title={Mining Product Reputations on the Web},
+ note={Satoshi Morinaga and Kenji Yamanishi NEC Corp. 4-1-1 Miyazaki Miyamae Kawasaki
+ Kanagawa 216-8555 Japan Tel: 81-44-856-2143; morinaga at cw.jp.nec.com},
+ year={2002}
+}
+
+ at article{MosSha82,
+ author={Jana Mossey MPH, PhD, and Evelyn SHapira, MA},
+ title={Self-Rated Health: A Predictor of Mortality Among the Elderly},
+ journal={American Journal of Public Health},
+ volume= 72,
+ year= 1982,
+ pages={{800-08}}
+}
+
+ at article{moynihan00,
+ author={Ray Moynihan and Lisa Bero and Dennis Ross-Degnan and David Henry and Kriby
+ Lee and Judy Watkins and Connie Mah and Stephen B. Soumerai},
+ title={Coverage by the News Media of the Benefits and Risks of Medications},
+ journal={New England Journal of Medicine},
+ volume= 342,
+ year= 2000,
+ pages={1645-1650},
+ month={June 1},
+ number= 22
+}
+
+ at article{MugLac02,
+ author={Anthony Mughan and Dean Lacy},
+ title={Economic Performance, Job Insecurity, and Electoral Choice},
+ journal={British Journal of Political Science},
+ volume= 32,
+ year= 2002,
+ pages={513--533}
+}
+
+ at book{MurEva03,
+ title={Health Systems Performance Assessment: Debates, Methods and Empiricism},
+ publisher={World Health Organization},
+ year= 2003,
+ editor={Christopher J.L. Murray and David B. Evans},
+ address={Geneva}
+}
+
+ at book{MurLop96,
+ title={The Global Burden of Disease},
+ publisher={Harvard University Press and WHO},
+ year= 1996 ,
+ editor={Christopher L.J.\ Murray and Alan D.\ Lopez}
+}
+
+ at article{MurLop97,
+ author={Christopher J. L. Murray and Alan D. Lopez},
+ title={Mortality by Cause for Eight Regions of the World: Global Burden of Disease
+ Study},
+ journal={The Lancet},
+ volume={349},
+ year={1997},
+ pages={1269-1276}
+}
+
+ at article{murphy69,
+ author={George G. S. Murphy},
+ title={On Counterfactual Propositions},
+ journal={History and Theory},
+ volume= 9,
+ year= 1969,
+ pages={14-38}
+}
+
+ at book{Murphy72,
+ author={Paul L. Murphy},
+ title={The Meaning of Freedom of Speech},
+ publisher={Greenwood},
+ year= 1972,
+ address={Westport, CT}
+}
+
+ at book{Murray98,
+ author={Murray, David M.},
+ title={Design and Analysis of Group-Randomized Trials},
+ publisher={Oxford UP},
+ year={1998},
+ address={New York}
+}
+
+ at book{Mutz98,
+ author={Diana C. Mutz},
+ title={Impersonal Influence: How Perceptions of Mass Collectives Affect Political
+ Attitudes},
+ publisher={Cambridge University Press},
+ year={1998},
+ address={New York, NY}
+}
+
+ at article{MwaLesDec05,
+ author={Samuel M. Mwalili and Emmanuel Lesaffre and Dominique Declerck},
+ title={A Bayesian ordinal logistic regression model to correct for interobserver
+ measurement error in a geographical oral health study},
+ journal={Applied Statistics},
+ volume={54},
+ year={2005},
+ pages={77-93}
+}
+
+ at article{Myerson04,
+ author = {Roger Myerson},
+ year = {2004},
+ title = {Political Economics and the Weimar Disaster},
+ journal = {Journal of Institutional and Theoretical Economics},
+ pages = {187-209},
+ volume = {160}
+}
+
+ at article{Nagler91,
+ author={Jonathan Nagler},
+ title={{The Effect of Registration Laws and Education on U. S. Voter Turnout}},
+ journal= apsr,
+ volume={85},
+ year={1991},
+ pages={1393--1405},
+ number={4}
+}
+
+ at unpublished{NasYi03,
+ author={Tetsuya Nasukawa and Jeonghee Yi},
+ title={Sentiment Analysis: Capturing Favoriability Using Natural Language Processing},
+ note={Tetsuya Nasukawa IBM Research, Tokyo Research Laboratory 1623-14 Shimotsuruma,
+ Yamato-shi, Kanagawa-ken, 242-8502, Japan; nasukawa at jp.ibm.cop},
+ year={2003},
+ month={October}
+}
+
+ at unpublished{NasYi03,
+ author={Tetsuya Nasukawa and Jeonghee Yi},
+ title={Sentiment Analysis: Capturing Favoriability Using Natural Language Processing},
+ note={Tetsuya Nasukawa IBM Research, Tokyo Research Laboratory 1623-14 Shimotsuruma,
+ Yamato-shi, Kanagawa-ken, 242-8502, Japan; nasukawa at jp.ibm.cop},
+ year={2003},
+ month={October}
+}
+
+ at book{National02,
+ author={NIPSSR},
+ title={Population Projections for Japan (January, 2002)},
+ publisher={National Institute of Population and Social Security Research},
+ year= 2002,
+ annote={Life tables for Japan are constructed using the Lee-Carter method.}
+}
+
+ at book{Neuendorf02,
+ author={Neuendorf, K.A.},
+ title={{The Content Analysis Guidebook}},
+ publisher={Thousand Oaks, CA: Sage Publications},
+ year={2002}
+}
+
+ at article{Neuhaus02,
+ author={John M. Neuhaus},
+ title={Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification},
+ journal={Biometrics},
+ volume={58},
+ year={2002},
+ pages={675-683},
+ month={September}
+}
+
+ at article{Neuhaus99,
+ author={John M. Neuhaus},
+ title={Bias and efficiency loss due to misclassified responses in binary regression},
+ journal={Biomtrika},
+ volume={86},
+ year={1999},
+ pages={843-855},
+ number={4}
+}
+
+ at article{neutra78,
+ author={Raymond R. Neutra and Margaret E. Drolette},
+ title={Estimating Exposure-Specific Disease Rates from Case-Control Studies Using
+ Bayes Theorem},
+ journal={American Journal of Epidemiology},
+ volume= 108,
+ year= 1978,
+ pages={214-222},
+ number= 3
+}
+
+ at article{Neyman23,
+ author={J. Neyman},
+ title={On the application of probability theory to agricultural experiments. Essay
+ on Principles. Section 9},
+ journal={Statistical Science},
+ volume={5},
+ year={1923},
+ pages={465-480},
+ note={Translated in 1990, with discussion}
+}
+
+ at article{Neyman23b,
+ author={J. Neyman},
+ title={Statistical Problems in Agricultural Experiments},
+ journal={Journal of the Royal Statistical Association},
+ volume= 2,
+ year= 1923,
+ pages={107--180},
+ number= 2
+}
+
+ at article{Nickerson05,
+ author={David W. Nickerson},
+ title={Scalable Protocols Offer Efficient Design for Field Experiements},
+ journal={Political Analysis},
+ volume={13},
+ year={2005},
+ pages={233-252}
+}
+
+ at article{NieFet86,
+ author={Richard G. Niemi and Patrick Fett},
+ title={The Swing Ratio: An Explanation and an Assessment},
+ journal={Legislative Studies Quarterly},
+ volume={11},
+ year={1986},
+ pages={75-90},
+ month={February},
+ number={1}
+}
+
+ at misc{NISO01,
+ author={NISO},
+ title={The Dublin Core Metadata Element Set},
+ year={2001},
+ note={{http://www.niso.org/standards/resources/Z39-85.pdf}}
+}
+
+ at article{NiyGirPog98,
+ author = {P. Niyogi and F. Girosi and T. Poggio},
+ title = {Incorporating Prior Information in Machine Learning
+ by Creating Virtual061 Examples},
+ journal = {Proceedings of the IEEE.},
+ volume = {86},
+ year = {1998},
+ pages = {2196--2209},
+ number = {11}
+}
+
+ at book{Noakes71,
+ author={Noakes, J.},
+ title={The Nazi Party in Lower Saxony},
+ publisher={Oxford University Press},
+ year= 1971,
+ address={New York}
+}
+
+ at article{NovReaRau06,
+ author={Scott P. Novak and Sean F. Reardon and Stephen W. Raudenbush and Stephen
+ L. Buka},
+ title={Retail tobacco outlet density and youth cigarette smoking: A propensity-modeling
+ approach},
+ journal={American Journal of Public Health},
+ volume={96},
+ year={2006},
+ pages={670-676},
+ month={April},
+ number={4}
+}
+
+ at article{nurminen95,
+ author={Markku Nurminen},
+ title={To Use or Not to Use the Odds Ratio in Epidemiologic Analysis},
+ journal={European Journal of Epidemiology},
+ volume= 11,
+ year= 1995,
+ number={365-371}
+}
+
+ at article{NybPetGai03,
+ author={Hanne Nybo et al},
+ title={Predictors of Mortality in 2249 Nonagenarians-The Danish 1905 Cohort Study},
+ journal={Journal of the American Geriatrics Society},
+ volume= 51,
+ year= 2003,
+ pages={{1365-73}}
+}
+
+ at booklet{OetParHym03,
+ title={The Not So Short Introduction to \LaTeXe\}, author = {Tobias Oetiker, Hubert
+ Partl, Irene Hyma and Elisabeth Schlegl}, year = 2003, note = {{Available
+ at http://www.ctan.org/tex-archive/info/lshort/english/lshort.pdf.}}
+}
+
+ at article{OhaWooMoo90,
+ author={A. O'Hagan and E.G. Woodward and L.C. Moodaley},
+ title={Practical Bayesian Analysis of a Simple Logistic Regression: Predicting
+ Corneal Transplants},
+ journal={Statistics in Medicine},
+ volume={9},
+ year={1990},
+ pages={1091-1101}
+}
+
+ at book{Ohr97a,
+ author = {Dieter Ohr},
+ title = {Nationalsozialistische Propaganda und Weimarer
+ Wahlen: empirische Analysen zur Wirkung von
+ NSDAP-Versammlungen},
+ publisher = {Westdeutscher Verlag},
+ year = 1997,
+ address = {Opladen}
+}
+
+ at article{Ohr97b,
+ author={Dieter Ohr},
+ title={Nationalsozialistische Versammlungspropaganda und Wahlerfolg der NSDAP:
+ eine kausale Beziehung?},
+ journal={Historical Social Research},
+ volume= 22,
+ year= 1997,
+ pages={106--127},
+ number={3/4}
+}
+
+ at article{OkoDev01,
+ author={Ike S. Okosun and G.E. Alan Dever},
+ title={Verbal Autopsy: A Necessary Solution for the Paucity of Mortality Data in
+ the Less-Developed Countries},
+ journal={Ethnicity and Disease},
+ volume={11},
+ year={2001},
+ pages={575-577}
+}
+
+ at article{OkoDev01,
+ author={Ike S. Okosun and G.E. Alan Dever},
+ title={Verbal Autopsy: A Necessary Solution for the Paucity of Mortality Data in
+ the Less-Developed Countries},
+ journal={Ethnicity and Disease},
+ volume={11},
+ year={2001},
+ pages={575-577}
+}
+
+ at article{OloFliAns94,
+ author={John O'Loughlin and Colin Flint and Luc Anselin},
+ title={The Geography of the Nazi Vote: Context, Confession, and Class in the Reichstag
+ Election of 1930},
+ journal={Annals of the Association of American Geographers},
+ volume= 84,
+ year= 1994,
+ pages={351-380}
+}
+
+ at article{O'Loughlin02,
+ author={John O'Loughlin},
+ title={The Electoral Geography of Weimar Germany},
+ journal={Political Analysis},
+ volume= 10,
+ year= 2002,
+ pages={217--243},
+ number= 3
+}
+
+ at article{Oman85,
+ author={Samuel D. Oman},
+ title={Specifying a Prior Distribution in Structured Regression Problems},
+ journal= jasa,
+ volume= 80,
+ year= 1985,
+ pages={190--195},
+ month={March},
+ number= 389
+}
+
+ at article{OneRus97,
+ author={John R. Oneal and Bruce Russett},
+ title={The Classical Liberals Were Right: Democracy, Interdependence, and Conflict,
+ 1950-1985},
+ journal= isq,
+ volume= 41,
+ year= 1997,
+ pages={267--293},
+ month={June},
+ number= 2
+}
+
+ at inproceedings{OrcWoo72,
+ author={T. Orchard and M.A. Woodbury},
+ title={A Missing Information Principle: Theory and Applications},
+ booktitle={Proceedings of the 6th Berkeley Symposium on Mathematical Statistics and
+ Probability},
+ year={1972},
+ publisher={Berkeley: University of California Press},
+ pages={697-715}
+}
+
+ at book{ORourke98,
+ author={Joseph O'Rourke},
+ title={Computational Geometry in C},
+ publisher={Cambridge University Press},
+ year= 1998,
+ address={New York}
+}
+
+ at article{OSS44,
+ author={OSS},
+ title={Greater Germany --- Kreis Boundaries},
+ journal={OSS Map 6289},
+ year= 1944,
+ month={July 1}
+}
+
+ at article{OveMag92,
+ author={John E. Overall and Kevin N. Magee},
+ title={Directional Baseline Differences and Type I Error Probabilities in Randomized
+ Clinical Trials},
+ journal={Journal of Biopharmaceutical Statistics},
+ volume={2},
+ year={1992},
+ pages={189-203},
+ number={2}
+}
+
+ at article{PacPacDuk90,
+ author={Sara Pacque-Margolis and Michel Pacque and Zwannah Dukuly and John Boateng
+ and Hugh R. Taylor},
+ title={Application of the Verbal Autopsy During A Clinical Trial},
+ journal={Social Science Medicine},
+ volume={31},
+ year={1990},
+ pages={585-591},
+ number={5}
+}
+
+ at article{PalPet03,
+ author={Ted Palmer and Anthony Petrosino},
+ title={The "Experimenting Agency". The California Youth Authority Research Division},
+ journal={Evaluation Review},
+ volume={22},
+ year={2003},
+ pages={228-266},
+ month={June},
+ number={3}
+}
+
+ at article{PalPoo87,
+ author={Thomas R. Palfrey and Keith T. Poole},
+ title={The Relationshiop Between Information, Ideology, and Voter Behavior},
+ journal= ajps,
+ volume= 31,
+ year= 1987,
+ pages={511-530},
+ month={August},
+ number= 3
+}
+
+ at article{Palvi41,
+ author={Palvi, Melchior},
+ title={Economic Foundations of the German Totalitarian State},
+ journal={American Journal of Sociology},
+ volume= 46,
+ year= 1941,
+ pages={469-486},
+ number= 4
+}
+
+ at inproceedings{PanLee05,
+ author={Bo Pang and Lillian Lee},
+ title={Seeing stars: Exploiting class relationships for sentiment categorization
+ with respect to rating scales},
+ booktitle={Proceedings of the ACL},
+ year= 2005 ,
+ pages={115--124}
+}
+
+ at article{PanLeeVai02,
+ author={Bo Pang and Lillian Lee and Shivakumar Vaithyanathan},
+ title={{Thumbs Up? Sentiment Classification using Machine Learning Techniques}},
+ journal={Proceedings of the Conference on Empirical Methods in Natural Language Processing},
+ year={2002},
+ pages={79-86}
+}
+
+ at misc{Parsons00,
+ author={Lori S. Parsons},
+ title={Using SAS Software to Perform a Case-Control Match on Propensity Score in
+ an Observational Study},
+ year= 2000,
+ note={{http://www2.sas.com/proceedings/sugi25/25/po/25p225.pdf}},
+ booktitle={SUGI 25},
+ volume={225-25}
+}
+
+ at misc{Parsons01,
+ author={Lori S. Parsons},
+ title={Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching
+ Techniques},
+ year= 2001,
+ note={{http://www2.sas.com/proceedings/sugi26/p214-26.pdf}},
+ booktitle={SUGI 26},
+ volume={214-26}
+}
+
+ at article{ParThoNor92,
+ author={Marti G. Parker, Mat Thorslund, and Marie-Louise Nordstrom},
+ title={Predictors of Mortality for the Oldest Old. A 4-year Follow-up of Community
+ Based Elderly in Sweden},
+ journal={Archives of Gerontology and Geriatrics},
+ volume= 14,
+ year= 1992 ,
+ pages={{227-37}}
+}
+
+ at article{Paskin05,
+ author={Norman Paskin},
+ title={Digital Object Identifiers for Scientific Data},
+ journal={Data Science Journal},
+ volume={28},
+ year={2005},
+ pages={12-20},
+ month={April},
+ note={{http://www.doi.org/topics/050428CODATAarticleDSJ.pdf}}
+}
+
+ at inproceedings{Passchier80,
+ author={N. Passchier},
+ title={The Electoral Geography of the Nazi Landslide},
+ booktitle={Who Were the Fascists?},
+ year= 1980,
+ publisher={Universitetsforlaget},
+ address={Bergen},
+ editor={S.U. Larsen and B. Hagtvet and J.P. Myklebust},
+ pages={283--300}
+}
+
+ at book{Paul90,
+ author={Paul, G.},
+ title={Aufstand der Bilder},
+ publisher={Dietz},
+ year= 1990,
+ address={Bonn}
+}
+
+ at article{PeaLucGla02,
+ author={J.W. Peabody and J. Luck and P. Glassman and S. Jain},
+ title={Measuring What we want to Measure: Using Vignettes in Clinical Education},
+ journal={Journal of Internal Medicine},
+ volume={17, Suppl.},
+ year= 2002,
+ pages={232--232},
+ month={April},
+ number= 1
+}
+
+ at article{Pearce70,
+ author={S.C. Pearce},
+ title={The Efficiency of Block Designs in General},
+ journal={Biometrika},
+ volume={57},
+ year={1970},
+ pages={339-346},
+ month={August},
+ number={2}
+}
+
+ at book{pearl00,
+ author={Judea Pearl},
+ title={Causality: Models, Reasoning, and Inference},
+ publisher= cup,
+ year= 2000
+}
+
+ at article{PecBeeSan02,
+ author={Mark Peceny and Caroline C. Beer and Shannon Sanchez-Terry},
+ title={Dictatorial Peace?},
+ journal= apsr,
+ volume= 96,
+ year= 2002,
+ pages={15--26},
+ number= 1
+}
+
+ at article{PelAsp1996,
+ author={Markku Peltonen and Kjell Asplund},
+ title={Age-Period-Cohort Effects on Stroke Mortality in Sweden 1969-1993 and Forecasts
+ Up to the Year 2004},
+ journal={Stroke},
+ volume= 27,
+ year= 1996,
+ pages={1981-1985},
+ number= 11
+}
+
+ at article{PelAsp96,
+ author={Markku Peltonen and Kjell Asplund},
+ title={Age-Period-Cohort Effects on Stroke Mortality in Sweden 1969-1993 and Forecasts
+ Up to the Year 2003},
+ journal={Stroke},
+ volume={27},
+ year= 1996,
+ pages={1981--1985}
+}
+
+ at article{Perkins00,
+ author={Susan M. Perkins and Wanzhu Tu and Michael G. Underhill and Xiao-Hua Zhou
+ and Michael D. Murray},
+ title={The use of propensity scores in pharmacoepidemiological research},
+ journal={Pharmacoepidemiology and drug safety},
+ volume= 9,
+ year= 2000,
+ pages={93-101}
+}
+
+ at article{Permutt90,
+ author={Thomas Permutt},
+ title={Testing for Imbalance of Covariates in Controlled Experiments},
+ journal={Statistics in Medicine},
+ volume={9},
+ year={90},
+ pages={1455-1462}
+}
+
+ at article{PerTuUnd00,
+ author={Susan M. Perkins and Wanzhu Tu and Michael G. Underhill and Xiao-Hua Zhou
+ and Michael D. Murray},
+ title={The use of propensity scores in pharmacoepidemiologic research},
+ journal={Pharmacoepidemiology and Drug Safety},
+ volume={9},
+ year={2000},
+ pages={93-101}
+}
+
+ at article{PerWilLev02,
+ author={Thomas T. Perls and John Wilmoth and Robin Levenson and Maureen Drinkwater
+ and Melissa Cohen and Hazel Bogan and Erin Joyce and Stephanie Brewster
+ and Louis Kunkel and Annibale Puca},
+ title={Life-long Sustained Mortality Advantage of Siblings of Centenarians},
+ journal= pnas,
+ volume= 99,
+ year= 2002,
+ pages={8442--8447},
+ month={June 11},
+ number= 12
+}
+
+ at article{PetRoeMul06,
+ author={E.D. Peterson and M.T. Roe and J. Mulgund and E.R. DeLong and B.L.Lytle
+ and R.G. Brindis and S.C. Smith Jr. and C.V. Pollack Jr. and L.K. Newby
+ and R.A. Harrington and W.B. Gibler and E.M. Ohman},
+ title={Association between hospital process performance and outcomes among patients
+ with acute coronary syndromes},
+ journal={Journal of the American Medical Association},
+ volume={295},
+ year={2006},
+ pages={1912-1920},
+ month={April}
+}
+
+ at article{PijFesKro93,
+ author={Loek T. J. Pijls, Edith J.M. Feskens, and Daan Kromhout},
+ title={Self-Rated Health, Mortality, and Chronic Diseases in Elderly Men: The Zutphen
+ Study, 1985-1990},
+ journal={American Journal of Epidemiology},
+ volume= 138,
+ year= 1993,
+ pages={{840-48}},
+ number= 10
+}
+
+ at article{PiqMacHic02,
+ author={Alex R. Piquero and Randall Macintosh},
+ title={The Validity of a Self-Reported Delinquency Scale: Comparisons Across Gender,
+ Age, Race, and Place of Residence},
+ journal= smr,
+ volume= 30,
+ year= 2002,
+ pages={492--529},
+ month={May},
+ number= 4
+}
+
+ at book{Placket81,
+ author={R. L. Plackett},
+ title={The Analysis of Categorical Data},
+ publisher={Macmillian},
+ year= 1981,
+ address={New York},
+ edition={2nd}
+}
+
+ at book{Plackett81,
+ author={R.L. Plackett},
+ title={{The Analysis of Categorical Data}},
+ publisher={Griffin},
+ year= 1981,
+ address={London}
+}
+
+ at manual{PluBesCowVin05,
+ author={Martyn Plummer and Nicky Best and Kate Cowles and Karen Vines},
+ title={coda: Output analysis and diagnostics for MCMC},
+ year={2005},
+ url={{http://www-fis.iarc.fr/coda/}}
+}
+
+ at article{PocAssEno02,
+ author={Stuart J. Pocock and Susan E. Assmann and Laura E. Enos and Linda E. Kasen},
+ title={Subgroup analysis covariate adjustment and baseline comparisons in clinical
+ trial reporting: current practice and problems},
+ journal={Statistics in Medicine},
+ volume={21},
+ year={2002},
+ pages={2917-2930}
+}
+
+ at article{PogGamLit88,
+ author={T. Poggio and E.B.\ Gamble and J.J.\ Little},
+ title={Parallel Integration of Vision Modules},
+ journal={Science},
+ volume= 242,
+ year= 1988,
+ pages={436--440},
+ month={21 October}
+}
+
+ at unpublished{Pole06a,
+ author = {Antoinette Pole},
+ title = {Congressional Blogging: Advertising, Credit
+ Claiming, & Position Taking},
+ note = {Presented at the 2006 annual meeting of the American
+ Political Science Association, Philadelphia, PA},
+ address = {Antoinette_Pole at brown.edu}
+}
+
+ at article{Pole06a,
+ author={Antoinette Pole},
+ title={Black Bloggers and the Blogosphere},
+ journal={International Journal of Technology, Knowledge and Society},
+ volume={2},
+ year={2006},
+ number={6}
+}
+
+ at unpublished{Pole07,
+ author={Antoinette Pole},
+ title={Do Blogs Matter? Elite Political Bloggers in American Politics},
+ address={Antoinette_Pole at brown.edu}
+}
+
+ at article{Pollock44,
+ author={Pollock, James K.},
+ title={An Areal Study of the German Electorate, 1930-1933},
+ journal={American Political Science Review},
+ volume= 38,
+ year= 1944,
+ pages={89-95}
+}
+
+ at unpublished{PolMck07,
+ author={Antoinette Pole and Laura McKenna},
+ title={Blogging Alone? Political Participation and the Blogosphere},
+ address={Antoinette_Pole at brown.edu},
+ organization={Brown University}
+}
+
+ at article{PooDan85,
+ author={Keith Poole and R. Steven Daniels},
+ title={Ideology, Party, and Voting in the U.S. Congress, 1959--1980},
+ journal= apsr,
+ volume= 79,
+ year= 1985,
+ pages={373-399},
+ month={June}
+}
+
+ at article{Poole98,
+ author={Keith T. Poole},
+ title={Recovering a Basic Space From a Set of Issue Scales},
+ journal= ajps,
+ volume= 42,
+ year= 1998,
+ pages={954--993},
+ month={July},
+ number= 3
+}
+
+ at article{PooRos91,
+ author={Keith Poole and Howard Rosenthal},
+ title={Patterns of Congressional Voting},
+ journal= ajps,
+ volume= 35,
+ year= 1991,
+ pages={228--278},
+ month={February}
+}
+
+ at book{Popkin94,
+ author={Samuel Popkin},
+ title={The Reasoning Voter: Communication and Persuasion in Presidential Campaigns},
+ publisher={University of Chicago Press},
+ year= 1994,
+ address={Chicago}
+}
+
+ at article{Porter80,
+ author={Porter, M. F.},
+ title={{An algorithm for suffix stripping}},
+ journal={Program},
+ volume={14},
+ year={1980},
+ pages={130-137},
+ number={3}
+}
+
+ at article{PosVer02,
+ author={Eric A. Posner and Adrian Vermeule},
+ title={Legislative Entrenchment: A Reappraisal},
+ journal={The Yale Law Journal},
+ volume={111},
+ year={2002},
+ pages={1665-1705},
+ month={May},
+ number={7}
+}
+
+ at article{Powell01,
+ author={G.N. Powell},
+ title={Workplace Romances between Senior-level Executives and Lower-Level Employees},
+ journal={Human Relations},
+ volume= 54,
+ year= 2001,
+ pages={1519--1544},
+ month={November},
+ number= 11
+}
+
+ at article{PraAit54,
+ author={Prais, S. J. and Aitchison, J.},
+ title={The Grouping of Observations in Regression Analysis},
+ journal={Revue de l'Institut International de Statistique},
+ volume= 22,
+ year= 1954,
+ pages={1-22}
+}
+
+ at book{PreHeuGui01,
+ author={Samuel H. Preston and Patrick Heuveline and Michel Guillot},
+ title={Demography: Measuring and Modeling Population Processes},
+ publisher={Blackwell},
+ year= 2001,
+ address={Oxford, England}
+}
+
+ at article{prentice78,
+ author={R.L. Prentice and N.E. Breslow},
+ title={Retrospective Studies and Failure-time Models},
+ journal={Biometrica},
+ volume= 65,
+ year= 1978,
+ number={153--5}
+}
+
+ at article{prentice79,
+ author={R.L. Prentice and R. Pyke},
+ title={Logistic Disease Incidence Models and Case-control Studies},
+ journal={Biometrica},
+ volume= 63,
+ year= 1979,
+ number={403-411}
+}
+
+ at article{prentice86,
+ author={R.L. Prentice},
+ title={A Case-Cohort Design for Epidemiological Studies and Disease Prevention
+ Trials},
+ journal={Biometrica},
+ volume= 73,
+ year= 1986,
+ number={1-11}
+}
+
+ at incollection{Preston91,
+ author={Samuel H. Preston},
+ title={Demographic Change in the United States, 1970--2050},
+ booktitle={Forecasting the Health of Elderly Populations},
+ publisher={Springer-Verlag},
+ year= 1991,
+ address={New York},
+ editor={K.G. Manton and B.H. Singer and R.M. Suzman},
+ pages={51--77}
+}
+
+ at incollection{Preston93,
+ author={Samuel H. Preston},
+ title={Demographic Change in the United States, 1970--2050},
+ booktitle={Demography and Retirement: The Twenty-First Century},
+ publisher={Praeger Publishers},
+ year= 1993,
+ address={New York},
+ editor={A.M. Rappaport and Sylvester Scheiber},
+ pages={19-48}
+}
+
+ at article{Prinz86,
+ author={Michael Prinz},
+ title={Der unerw{\"u}nschte Stand: Lage und Status der Angestellten im `Dritten Reich'},
+ journal={Historische Zeitschrift},
+ volume= 242,
+ year= 1986,
+ pages={327--359},
+ number= 2
+}
+
+ at article{Prinz89,
+ author={Michael Prinz},
+ title={Angestellte und Nationalsozialismus},
+ journal={Geschichte und Gesellschaft},
+ volume= 15,
+ year= 1989,
+ pages={552--562},
+ number= 4
+}
+
+ at book{Prinz91,
+ author={Von Michael Prinz and Rainer Zitelmann},
+ title={Nationalsozialismus und Modernisierung},
+ publisher={Darmstadt},
+ year={1991}
+}
+
+ at book{PrzAlvChe00,
+ author={Adam Przeworski and Michael e. Alvarez and Jose Antonio Cheibub and Fernando
+ Limongi},
+ title={Democracy and Development: poltical institutions and well-being in the world,
+ 1950-1990},
+ publisher={Cambridge University Press},
+ year={New York, NY},
+ address={The Edinburgh Building, Cambridge CB22RU, UK}
+}
+
+ at incollection{Przeworski05,
+ author={Adam Przeworski},
+ title={Is the Science of Comparative Politics Possible?},
+ booktitle={Oxford Handbook of Comparative Poltics},
+ publisher={Oxford University Press},
+ year={2005},
+ month={August},
+ address={New York},
+ editor={Carles Boix and Susan C. Stokes}
+}
+
+ at article{PrzTeu66,
+ author={Adam Przeworski and Henry Teune},
+ title={Equivalence in Cross-National Research},
+ journal={Public Opinion Quarterly},
+ volume= 30,
+ year={1966--1967},
+ pages={551--568},
+ month={Winter}
+}
+
+ at article{PurHil06,
+ author={Purpura, Stephen and Dustin Hillard},
+ title={{Automated Classification of Congressional Legislation}},
+ journal={Proceedings of the International Conference on Digital Government Research},
+ year={2006}
+}
+
+ at book{Putnam00,
+ author={Robert D. Putnam},
+ title={Bowling Alone: The Collapse and Revival of American Community},
+ publisher={Simon and Schuster},
+ year= 2000,
+ address={New York}
+}
+
+ at article{Quandt72,
+ author={Richard Quandt},
+ title={Methods of Estimating Switching Regressions},
+ journal= jasa,
+ volume= 67,
+ year= 1972,
+ pages={306-310},
+ number= 338
+}
+
+ at article{Quandt74,
+ author={Richard E. Quandt},
+ title={A Stochastic Model of Elections in Two-Party Systems},
+ journal={Journal of theAmerican Statistical Association},
+ volume={69},
+ year={1974},
+ pages={315-324},
+ month={June},
+ number={346}
+}
+
+ at incollection{Qui04,
+ author={Kevin Quinn},
+ title={Ecological Inference in the Presence of Temporal Dependence},
+ booktitle={Ecological Inference: New Methodological Strategies},
+ publisher={Cambridge University Press},
+ year= 2004,
+ address={New York},
+ editor={Gary King and Ori Rosen and Martin A. Tanner}
+}
+
+ at article{QuiArmSno96,
+ author = {M.A. Quigley and J.R.M. Armstrong Schellenberg and
+ R.W. Snow},
+ title = {Algorithms for verbal autopsies: a validation study
+ in Kenyan children},
+ journal = {Bulletin of the World Health Organization},
+ volume = {74},
+ year = {1996},
+ pages = {147-154},
+ number = {2}
+}
+
+ at article{QuiChaSet00,
+ author={Maria A. Quigley and Daniel Chandramohan and Philip Setel and Fred Binka
+ and Laura C. Rodrigues},
+ title={Validity of data-derived algorithms for ascertaining causes of adult death
+ in two African sites using verbal autopsy},
+ journal={Tropical Medicine and International Health},
+ volume={5},
+ year={2000},
+ pages={33-39},
+ month={January},
+ number={1}
+}
+
+ at article{Quigley05,
+ author={Maria A. Quigley},
+ title={Commentary: Verbal Autopsies - from small-scale studies to mortality surveillance
+ systems},
+ journal={International Journal of Epidemiology},
+ volume={34},
+ year={2005},
+ pages={1087-1088}
+}
+
+ at misc{QuiMonCol06,
+ author = {Quinn, K.M. and Monroe, B.L. and Colaresi, M. and
+ Crespin, M.H. and Radev, D.R.},
+ title = {{How To Analyze Political Attention With Minimal
+ Assumptions And Costs}},
+ year = {2006},
+ howpublished = {Annual Meeting of the Society for Political
+ Methodology}
+}
+
+ at misc{Quinn00,
+ author = {Kevin M. Quinn},
+ title = {Flexible Prior Specifications for Factor Analytic
+ Models with an Application to {A}merican Political
+ Ideology},
+ year = 2000,
+ howpublished = {Paper presented at the Annual Meeting of the Midwest
+ Political Science Association}
+}
+
+ at manual{R08,
+ author={{R Development Core Team}},
+ title={R: A Language and Environment for Statistical Computing},
+ organization={R Foundation for Statistical Computing},
+ year={2008},
+ address={Vienna, Austria},
+ note={{ISBN} 3-900051-07-0},
+ url={{http://www.R-project.org}}
+}
+
+ at article{Rabban83,
+ author={David Rabban},
+ title={The Emergence of Modern First Amendment Doctrine},
+ journal={University of Chicago Law Review},
+ volume= 50,
+ year={1983},
+ pages={1207--??}
+}
+
+ at article{RagGri95,
+ author={T.E. Raghunathan and J.E. Grizzle},
+ title={A Split Questionnaire Survey Design},
+ journal={Journal of the American Statistical Association},
+ volume={90},
+ year={1995},
+ pages={54-63}
+}
+
+ at article{Ragsdale91,
+ author={Lyn Ragsdale},
+ title={{Strong Feelings: Emotional Responses to Presidents}},
+ journal={Political Behavior},
+ volume={13},
+ year={1991},
+ pages={33-65},
+ number={1}
+}
+
+ at article{RahJos98,
+ author={Elham Rahme and Lawrence Joseph},
+ title={Estimating the Prevalence of a Rare Disease: Adjusted Maximum Likelihood},
+ journal={The Statistician},
+ volume={47},
+ year={1998},
+ pages={149-158},
+ number={1}
+}
+
+ at article{RakMorHir91,
+ author={William Rakowski, PhD, Vincent Mor, PhD, and Jeffrey Hiris, BA},
+ title={The Association of Self-Rated Health with Two-Year Mortality in a Sample
+ of Well Elderly},
+ journal={Journal of Aging and Health},
+ volume= 3,
+ year= 1991,
+ pages={{527-45}}
+}
+
+ at article{Rao96,
+ author={J.N.K. Rao},
+ title={On Variance Estimation with Imputed Survey Data},
+ journal={Journal of the American Statistical Association},
+ volume={91},
+ year={1996},
+ pages={499-506}
+}
+
+ at article{RauMarSpy06,
+ author={Stephen W. Raudenbush and Andres Martinez and Jessaca Spybrook},
+ title={Strategies for Improving Precision in Group-Randomized Experiments},
+ journal={Educational Evaluation and Policy Analysis},
+ year={2007},
+ address={University of Chicago, University of Michigan}
+}
+
+ at book{Recipes87,
+ author={William H. Press and Saul Teukolsky and William T. Vetterling and Brian
+ P. Flannery},
+ title={Numerical Recipes: the Art of Scientific Computing},
+ publisher={Cambridge University Press},
+ year={1987},
+ address={Cambridge}
+}
+
+ at article{ReeQui97,
+ author={Barnaby C. Reeves and Maria Quigley},
+ title={A Review of Data-Derived Methods for Assigning Causes of Death from Verbal
+ Autopsy Data},
+ journal={International Journal of Epidemiology},
+ volume={26},
+ year={1997},
+ pages={1080-1089},
+ number={5}
+}
+
+ at article{ReeQui97,
+ author={Barnaby C. Reeves and Maria Quigley},
+ title={A Review of Data-Derived Methods for Assigning Causes of Death from Verbal
+ Autopsy Data},
+ journal={International Journal of Epidemiology},
+ volume={26},
+ year={1997},
+ pages={1080-1089},
+ number={5}
+}
+
+ at book{Rehnquist98,
+ author={William H. Rehnquist},
+ title={All the Laws But One: Civil Liberties in Wartime},
+ publisher={Knopf},
+ year= 1998,
+ address={New York}
+}
+
+ at article{ReuLi03,
+ author={Rafael Reuveny and Quan Li},
+ title={The Joint Democracy-Dyadic Conflict Nexus: A Simultaneous Equations Model},
+ journal= isq,
+ volume= 47,
+ year= 2003,
+ pages={325--346},
+ month={September},
+ number= 3
+}
+
+ at article{RieSch93,
+ author={Arthur van Riel and Arthur Schram},
+ title={Weimar Economic Decline, Nazi Economic Recovery, and the Stabilization of
+ Political Dictatorship},
+ journal={Journal of Economic History},
+ volume= 53,
+ year= 1993,
+ pages={71--105},
+ number= 1
+}
+
+ at book{Riffenburgh98,
+ author={R. H. Riffenburgh},
+ title={Statistics in Medicine},
+ publisher={Academic Press},
+ year={1998},
+ address={San Diego}
+}
+
+ at proceedings{RilWie03,
+ editor={Ellen Riloff and Janyce Wiebe},
+ title={Learning Extraction Patterns for Subjective Expressions},
+ publisher={Conference on Empirical Methods in Natural Language Processing},
+ year={2003},
+ address={Ellen Riloff, School of Computing, University of UT, Salt Lake City, UT
+ 84112; riloff at cs.utah.edu}
+}
+
+ at proceedings{RilWie03,
+ editor={Ellen Riloff and Janyce Wiebe},
+ title={Learning Extraction Patterns for Subjective Expressions},
+ publisher={Conference on Empirical Methods in Natural Language Processing},
+ year={2003},
+ address={Ellen Riloff, School of Computing, University of UT, Salt Lake City, UT
+ 84112; riloff at cs.utah.edu}
+}
+
+ at proceedings{RilWieWil03,
+ editor={Ellen Riloff and Janyce Wiebe and Theresa Wilson},
+ title={Learning Subjective Nouns Using Extraction Pattern Bootstrapping},
+ publisher={Seventh CoNLL Conf. Edmonton},
+ year={2003},
+ month={May-June}
+}
+
+ at proceedings{RilWieWil03,
+ editor={Ellen Riloff and Janyce Wiebe and Theresa Wilson},
+ title={Learning Subjective Nouns Using Extraction Pattern Bootstrapping},
+ publisher={Seventh CoNLL Conf. Edmonton},
+ year={2003},
+ month={May-June}
+}
+
+ at book{Ripley96,
+ author={Brian Ripley},
+ title={Pattern Recognition and Neural Networks},
+ publisher={Cambridge Univeristy Press},
+ year={1996}
+}
+
+ at article{Ritschl03,
+ author = {Ritschl, Albrecht},
+ title = {Hat das Dritte Reich wirklich eine ordentliche
+ Besch{\"a}ftigungspolitik betrieben?},
+ journal = {Jahrbuch f{\"u}r Wirtschaftsgeschichte},
+ year = {2003},
+ pages = {{125-40}}
+}
+
+ at article{Ritschl90,
+ author = {Albrecht Ritschl},
+ title = {Zu hohe L{\"o}hne in der Weimarer Republik? Eine
+ Auseinandersetzung mit Holtferichs Berechnungen zur
+ Lohnposition der Arbeitsschaft 1925-1932},
+ journal = {Geschichte und Gesellschaft},
+ volume = {16},
+ year = 1990,
+ pages = {375--402}
+}
+
+ at article{Robins95,
+ author = {J.M. Robins},
+ title = {Discussion of ``Causal diagrams in empirical research'' by J. Pearl},
+ journal = {Biometrika},
+ volume = 82,
+ year = 1995,
+ number = {387-394}
+}
+
+ at article{Robins86,
+ author = {J.M. Robins and M.H. Gail and J.H. Lubin},
+ title = {More on Biased Selection of Controls for
+ Case-Control Analyses of Cohort Studies},
+ journal = {Biometrics},
+ volume = 42,
+ year = 1986,
+ number = {293-299}
+}
+
+ at incollection{Robins99,
+ author={James M. Robins},
+ title={Marginal Structural Models Versus Structural Nested Models as Tools for
+ Causal Inference},
+ booktitle={Statistical Models in Epidemiology: The Environment and Clinical Trials},
+ publisher={Springer-Verlag},
+ year= 1999,
+ address={New York},
+ editor={M.E. Halloran and D. Berry},
+ pages={95-134},
+ volume= 116
+}
+
+ at article{Robins99b,
+ author={James M. Robins},
+ title={Association, Causation, and Marginal Structural Models},
+ journal={Synthese},
+ volume= 121,
+ year={1999b},
+ pages={151--179}
+}
+
+ at article{RobJew91,
+ author={Laurence D. Robinson and Nicholas P. Jewell},
+ title={Some Surprising Results about Covariate Adjustment in Logistic Regression
+ Models},
+ journal={International Statistical Review},
+ volume={59},
+ year={1991},
+ pages={227-240},
+ month={August},
+ number={2}
+}
+
+ at article{RobMar99,
+ author={Noah Jamie Robinson and Ravai Marindo},
+ title={Current Estimates of and Future Projections for Adult Deaths Attributed
+ to HIV Infection in Zimbabwe},
+ journal={Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology},
+ volume= 20,
+ year= 1999,
+ pages={187--194}
+}
+
+ at article{RobRot01,
+ author={James M. Robins and Andrea Rotnitzky},
+ title={Comment on the Peter J. Bickel and Jaimyoung Kwon,, `Inference for semiparametric
+ models: Some questions and an answer'},
+ journal={Statistica Sinica},
+ volume= 11,
+ year= 2001,
+ pages={920--936},
+ number= 4,
+ annote={on double robustness}
+}
+
+ at article{RobRot03,
+ author={James M. Robins and Andrea Rotnitzky},
+ title={Inverse Probability Weighting Estimation in Survival Analysis},
+ journal={Encyclopedia of Biostatistics},
+ year= 2003,
+ note={forthcoming}
+}
+
+ at article{RobRot95,
+ author={J. Robins and A. Rotnitzky},
+ title={Semiparametric efficiency in multivariate regression models with missing
+ data},
+ journal={Journal of the American Statistical Association},
+ volume= 90,
+ year= 1995 ,
+ pages={122-129}
+}
+
+ at article{RobWan00,
+ author={James Robins and Naisyin Wang},
+ title={Inference for Imputation Estimators},
+ journal={Biometrika},
+ volume={87},
+ year={2000},
+ pages={113-124}
+}
+
+ at article{Rogers86,
+ author={Andrei Rogers},
+ title={Parameterized Multistate Population Dynamics and Projections},
+ journal= jasa,
+ volume= 81,
+ year= 1986,
+ pages={48--61}
+}
+
+ at article{RogRay99,
+ author={Andrei Rogers and James Raymer},
+ title={Fitting Observed Demographic Rates with the Multiexponential Model Schedule:
+ An Assessment of Two Estimation Programs},
+ journal={Applied Regional Science Conference},
+ volume= 11,
+ year= 1999,
+ pages={1--10},
+ number= 1
+}
+
+ at book{Rokeach73,
+ author={Milton Rokeach},
+ title={The Nature of Human Values},
+ publisher={Free Press},
+ year= 1973,
+ address={New York}
+}
+
+ at book{Rokeach79,
+ author={Rokeach, M.},
+ title={{Understanding Human Values: Individual and Societal}},
+ publisher={Free Press},
+ year={1979},
+ address={New York}
+}
+
+ at article{RonVanCha98,
+ author={Carine Ronsmans and Anne Marie Vanneste and jyotshamoy Chakraborty and Jereon
+ Van Ginneken},
+ title={A comparison of Three Verbal Autopsy Methods to Ascertain Levels and Causes
+ of Maternal Deaths in Matlab, Bangladesh},
+ journal={International Journal of Epidemiology},
+ volume={27},
+ year={1998},
+ pages={660-666}
+}
+
+ at book{RoseAckerman99,
+ title={Corruption and Government: Causes, Consequences, and Reform},
+ author={Rose-Ackerman, Susan},
+ year={1999},
+ publisher={Cambridge University Press},
+ address={Cambridge, UK}
+}
+
+ at book{Rosenbaum02,
+ author={Rosenbaum, Paul R.},
+ title={Observational Studies, 2nd Edition},
+ publisher={Springer Verlag},
+ year={2002},
+ address={New York, NY}
+}
+
+ at article{Rosenbaum05,
+ author={Paul R. Rosenbaum},
+ title={An exact distribution-free test comparing two multivariate distributions
+ based on adjacency},
+ journal={Journal of the Royal Statisitcal Society B},
+ volume={67},
+ year={2005},
+ pages={515-530}
+}
+
+ at article{Rosenbaum05b,
+ author={Paul R. Rosenbaum},
+ title={Heterogeneity and Causality: Unit Heterogeneity and Design Sensitivity in
+ Observational Studies},
+ journal={The American Statistician},
+ volume={59},
+ year={2005},
+ pages={147-152},
+ month={May},
+ number={2}
+}
+
+ at article{Rosenbaum84,
+ author={Paul Rosenbaum},
+ title={The Consequences of Adjusting for a Concomitant Variable That Has Been Affected
+ by the Treatment},
+ journal= jrssA,
+ volume= 147,
+ year= 1984,
+ pages={656--666},
+ number= 5
+}
+
+ at article{Rosenbaum86,
+ author={Paul R. Rosenbaum},
+ title={Dropping out of high school in the {U}nited {S}tates: an observational study},
+ journal={Journal of Educational Statistics},
+ volume= 11,
+ year= 1986,
+ pages={207-224}
+}
+
+ at article{Rosenbaum89,
+ author={Rosenbaum, Paul R.},
+ title={Optimal matching for observational studies},
+ journal={Journal of the American Statistical Association},
+ volume= 84,
+ year= 1989,
+ pages={{1024--1032}},
+ keywords={Network; Computation}
+}
+
+ at article{Rosenbaum91,
+ author={Paul R. Rosenbaum},
+ title={A Characterization of Optimal Designs for Observational Studies},
+ journal={Journal of the Royal Statistical Society, Series B},
+ volume={53},
+ year={1991},
+ pages={597-610},
+ number={3}
+}
+
+ at article{Rosenbaum91,
+ author={Paul R. Rosenbaum},
+ title={Sensitivity analysis for matched case-control studies},
+ journal={Biometrics},
+ volume= 47,
+ year= 1991,
+ pages={87-100},
+ number= 1
+}
+
+ at article{Rosenbaum99,
+ author={Paul R. Rosenbaum},
+ title={Choice as an alternative to control in observational studies},
+ journal={Statistical Science},
+ volume= 14,
+ year= 1999,
+ pages={259-304},
+ number= 3,
+ note={With discussion and rejoinder.}
+}
+
+ at book{RosHan93,
+ author={Steven J.\ Rosenstone and John M.\ Hansen},
+ title={Mobilization, Participation, and Democracy in America},
+ publisher={MacMillian},
+ year= 1993
+}
+
+ at article{RosHen78,
+ author={Bernard Rosner and Charles H. Hennekens},
+ title={Analytic Methods in Matched Pair Epidemiological Studies},
+ journal={International Journal of Epidemiology},
+ volume={7},
+ year={1978},
+ pages={367-372},
+ number={4}
+}
+
+ at book{RosNoc83,
+ title={Measuring Social Judgements: The Factorial Survey Approach},
+ publisher={Sage},
+ year= 1983,
+ editor={P. H. Rossi and S. L. Nock},
+ address={Beverly Hills, CA}
+}
+
+ at article{RosRub83,
+ author={Paul R. Rosenbaum and Donald B.\ Rubin},
+ title={The Central Role of the Propensity Score in Observational Studies for Causal
+ Effects},
+ journal={Biometrika},
+ volume= 70,
+ year= 1983,
+ pages={41--55}
+}
+
+ at article{RosRub83b,
+ author={Paul R. Rosenbaum and Donald B. Rubin},
+ title={Assessing sensitivity to an unobserved binary covariate in an observational
+ study with binary outcome},
+ journal={Journal of the Royal Statistical Society Series B},
+ volume= 45,
+ year= 1983,
+ pages={212-218},
+ number= 2
+}
+
+ at article{RosRub84,
+ author = {Paul R. Rosenbaum and Donald B. Rubin},
+ title = {Reducing Bias in Observational Studies Using
+ Subclassification on the Propensity Score},
+ journal = jasa,
+ volume = 79,
+ year = 1984,
+ pages = {515--524}
+}
+
+ at article{RosRub85,
+ author={Rosenbaum, Paul R. and Rubin, Donald B.},
+ title={Constructing a Control Group Using Multivariate Matched Sampling Methods
+ That Incorporate the Propensity Score},
+ journal={The American Statistician},
+ volume={39},
+ year={1985},
+ pages={33-38}
+}
+
+ at article{RosRub85b,
+ author={Rosenbaum, P.R. and Rubin, D.B.},
+ title={The Bias Due to Incomplete Matching},
+ journal={Biometrics},
+ volume={41},
+ year={1985},
+ pages={103--116},
+ number={1}
+}
+
+ at article{RosSil01,
+ author={Paul R. Rosenbaum and J.H. Silber},
+ title={Matching and Thick Description in an Observational Study of Mortality After
+ Surgery},
+ journal={Biostatistics},
+ volume= 2,
+ year= 2001,
+ pages={{217--232}}
+}
+
+ at article{Rothman77,
+ author={Kenneth J. Rothman},
+ title={Epidemiologic Methods in Clinical Trials},
+ journal={Cancer},
+ volume={39},
+ year={1977},
+ pages={1771-1775}
+}
+
+ at book{rothman98,
+ author={Kenneth J. Rothman and Sander Greenland},
+ title={Modern Epidemiology},
+ publisher={Philadelphia: Lippincott-Raven},
+ year= 1998,
+ edition={2nd edition}
+}
+
+ at article{RouSto96,
+ author={L. Roussos and W. Stout},
+ title={A Multidimensionality-based DIF Anslysis Paradigm},
+ journal={Applied Psychological Measurement},
+ volume= 20,
+ year= 1996,
+ pages={355--371}
+}
+
+ at article{Rowe05,
+ author={Alexander K Rowe},
+ title={Should verbal autopsy results for malaria be adjusted to improve validity?},
+ journal={International Journal of Epidemiology},
+ volume={34},
+ year={2005},
+ pages={712-13},
+ number={3}
+}
+
+ at article{Roy51,
+ author={A.D. Roy},
+ title={Some Thoughts on the Distribution of Earnings},
+ journal={Oxford Economic Papers},
+ volume= 3,
+ year= 1951,
+ pages={135--146}
+}
+
+ at article{RoyCum85,
+ author={Richard M. Royall and William G. Cumberland},
+ title={Conditional Doverage Properties of Finite Population Confidence Intervals},
+ journal={Journal of the American Statistical Assocation},
+ volume={80},
+ year={1985},
+ pages={355-359},
+ month={June},
+ number={390},
+ tpages={+}
+}
+
+ at article{RubDudVan06,
+ author={Daniel Rubin and Sandrine Dudoit and Mark van der Laan},
+ title={A Method to Increase the Power of Multiple Testing Procedures Through Sample
+ Splitting},
+ journal={Statistical Applications in Genetics and Molecular Biology},
+ volume={5},
+ year={2006},
+ number={1}
+}
+
+ at article{Rubin01,
+ author={Donald B. Rubin},
+ title={Using propensity scores to help design observational studies: Application
+ to the tobacco litigation},
+ journal={Heatlh Services \& Outcomes Research Methodology},
+ volume={2},
+ year={2001},
+ pages={169-188},
+ month={December},
+ number={3-4}
+}
+
+ at unpublished{Rubin04,
+ author={Donald B. Rubin},
+ title={Discussion of ``{P}rinciples for modeling propensity scores in medical research:
+ a systematic literature review"},
+ note={Forthcoming in {\it Pharmacoepidemiology and Drug Safety}. Referenced paper
+ by Weitzen, Lapane, Toledano, Hume, Mor},
+ year= 2004
+}
+
+ at book{Rubin06,
+ author={Donald B. Rubin},
+ title={Matched Sampling for Causal Effects},
+ publisher={Cambridge University Press},
+ year= 2006 ,
+ address={Cambridge, England}
+}
+
+ at article{Rubin80,
+ author={Rubin, Donald B.},
+ title={Comments on ``Randomization Analysis of Experimental Data: The Fisher Randomization Test'', by D. Basu},
+ journal={Journal of the American Statistical Association},
+ volume={75},
+ year={1980},
+ pages={591-593}
+}
+
+
+
+ at article{Rubin73,
+ author={Rubin, Donald B.},
+ title={Matching to remove bias in observational studies},
+ journal={Biometrics},
+ volume={29},
+ year={1973},
+ pages={159-184}
+}
+
+ at article{Rubin73b,
+ author={Rubin, Donald B.},
+ title={The use of matched sampling and regression adjustment to remove bias in
+ observational studies},
+ journal={Biometrics},
+ volume={29},
+ year={1973},
+ pages={185-203}
+}
+
+ at article{rubin74,
+ author={Donald B. Rubin},
+ title={Estimating Causal Effects of Treatments in Randomized and Nonrandomized
+ Studies},
+ journal={Journal of Educational Psychology},
+ volume= 6,
+ year= 1974,
+ pages={688--701}
+}
+
+ at article{Rubin76,
+ author={Donald Rubin},
+ title={Inference and Missing Data},
+ journal={Biometrika},
+ volume={63},
+ year={1976},
+ pages={581-592}
+}
+
+ at article{Rubin77,
+ author={Donald Rubin},
+ title={Formalizing Subjective Notions about the Effect of Nonrespondents in Sample
+ Surveys},
+ journal={Journal of the American Statistical Association},
+ volume={72},
+ year={1977},
+ pages={538-543},
+ month={September},
+ number={359}
+}
+
+ at article{Rubin77b,
+ author={Donald B. Rubin},
+ title={Assignment to Treatment Group on the Basis of a Covariate},
+ journal={Journal of Educational Statistics},
+ volume={2},
+ year={1977},
+ pages={1},
+ number={1-26}
+}
+
+ at article{Rubin78,
+ author={Donald B. Rubin},
+ title={Bayesian inference for causal effects: The role of randomization},
+ journal={The Annals of Statistics},
+ volume={6},
+ year={1978},
+ pages={34-58}
+}
+
+ at article{Rubin79,
+ author={Donald B. Rubin},
+ title={Using Multivariate Matched Sampling and Regression Adjustment to Control
+ Bias in Observational Studies},
+ journal= jasa,
+ volume={74},
+ year={1979},
+ pages={318--328}
+}
+
+ at book{Rubin87,
+ author={Donald B. Rubin},
+ title={Multiple Imputation for Nonresponse in Surveys},
+ publisher={John Wiley},
+ year={1987},
+ address={New York}
+}
+
+ at article{Rubin87b,
+ author={Donald Rubin},
+ title={A Noniterative sampling/importance resampling alternative to the data augmentation
+ algorithm for creating a few imputations when fractions of missing information
+ are modest: the SIR Algorithm, Discussion of Tanner and Wong},
+ journal={Journal of the American Statistical Assocaition},
+ volume={82},
+ year={1987},
+ pages={543-546}
+}
+
+ at article{Rubin91,
+ author={Donald B. Rubin},
+ title={Practical implications of modes of statistical inference for causal effects
+ and the critical role of the assignment mechanism},
+ journal={Biometrics},
+ volume= 47,
+ year= 1991,
+ pages={1213-1234}
+}
+
+ at article{Rubin94,
+ author={Donald B. Rubin},
+ title={Missing Data, Imputation, and the Bootstrap: Comment},
+ journal={Journal of the American Statistical Association},
+ volume={89},
+ year={1994},
+ pages={475-478},
+ month={Jun},
+ number={426}
+}
+
+ at article{Rubin96,
+ author={Donald B. Rubin},
+ title={Multiple Imputation after 18+ Years},
+ journal= jasa,
+ volume= 91,
+ year= 1996,
+ pages={473--489},
+ number= 434
+}
+
+ at article{Rubin96,
+ author={Donald Rubin},
+ title={Multiple Imputation after 18+ Years},
+ journal={Journal of the American Statistical Association},
+ volume={91},
+ year={1996},
+ pages={473-489}
+}
+
+ at article{Rubin97,
+ author={Donald B. Rubin},
+ title={Estimating causal effects from large data sets using propensity scores},
+ journal={Annals of Internal Medicine},
+ volume= 127,
+ year= 1997,
+ pages={757-763}
+}
+
+ at article{RubSch86,
+ author={Donald Rubin and Nathaniel Schenker},
+ title={Multiple Imputation for Interval Estimation for Simple Random Samples with
+ Ignorable Nonresponse},
+ journal= jasa,
+ volume= 81,
+ year= 1986,
+ pages={366-374},
+ number= 394
+}
+
+ at article{RubSch87,
+ author={Donald B. Rubin and Nathaniel Schenker},
+ title={Logit-Based Interval Estimation from Binomial Data Using the Jeffreys Prior},
+ journal={Sociological Methodology},
+ volume={17},
+ year={1987},
+ pages={131-144}
+}
+
+ at inproceedings{RubSch90,
+ author={D.B. Rubin and J.L. Schafer},
+ title={Efficiently Creating Multiple Imputations for Incomplete Multivariate Normal
+ Data},
+ booktitle={Proceedings of the Statistical Computing Section of the American Statistical
+ Association},
+ year={1990},
+ pages={83-88}
+}
+
+ at article{RubStu06,
+ author={Donald B. Rubin and Elizabeth A. Stuart},
+ title={Affinely invariant matching methods with discriminant mixtures of proportional
+ ellipsoidally symmetric distributions},
+ journal={Annals of Statistics},
+ volume= 34,
+ year={2006},
+ pages={1814-1826},
+ number= 4
+}
+
+ at article{RubTho00,
+ author={Donald B. Rubin and Neal Thomas},
+ title={Combining propensity score matching with additional adjustments for prognostic
+ covariates},
+ journal={Journal of the American Statistical Association},
+ volume= 95,
+ year={2000},
+ pages={573-585}
+}
+
+ at article{RubTh,
+ author={Donald B. Rubin and Neal Thomas},
+ title={Characterizing the Effect of Matching Using Linear Propensity Score Methods
+ With Normal Distributions},
+ journal={Biometrika},
+ volume={79},
+ year={1992},
+ pages={797-809}
+}
+
+ at article{RubTho96,
+ author={Donald B. Rubin and Neal Thomas},
+ title={Matching Using Estimated Propensity Scores, Relating Theory to Practice},
+ journal={Biometrics},
+ volume={52},
+ year={1996},
+ pages={249-264}
+}
+
+ at unpublished{RugKimMar03,
+ author = {Theodore W. Ruger and Pauline T. Kim and Andrew
+ D. Martin and Kevin M. Quinn},
+ title = {The Supreme Court Forecasting Project: Legal and
+ Political Science Approaches to Predicting Supreme
+ Court Decision-Making},
+ note = {Washington University in St. Louis},
+ year = 2003
+}
+
+ at book{Rule88,
+ author={Rule, J. B.},
+ title={Theories of Civil Violence},
+ publisher={University of California Press},
+ year= 1988,
+ address={Berkeley}
+}
+
+ at article{RusOneBer03,
+ author={Bruce Russett and John Oneal and Michael L. Berbaum},
+ title={Causes of Peace: Democracy, Interdependence, and International Organizations,
+ 1885--1992},
+ journal= isq,
+ volume= 47,
+ year= 2003,
+ pages={371--393},
+ month={September},
+ number= 3
+}
+
+ at article{SabCanGib05,
+ author={Marc S. Sabatine and Christopher P. Cannon and C. Michael Gibson and Jose
+ L. Lopez-Sendon and Gilles Montalescot and Pierre Theroux and Basil S.
+ Lewis and Sabina A. Murphy and Carolyn H. McCabe and Eugene Braunwald},
+ title={Effect of Clopidogrel Pretreatment Before Percutaneous Coronary Intervention
+ in Patients with ST-Elevation Myoc},
+ journal={Journal of the American Medical Association},
+ volume={294},
+ year={2005},
+ pages={1224-1232},
+ month={September},
+ number={10}
+}
+
+ at article{sackett96,
+ author={D. Sackett, J. Deeks and D. Altman},
+ title={Down with Odds Ratios},
+ journal={Evidence-Based Medicine},
+ volume= 1,
+ year= 1996,
+ pages={164--6},
+ number= 6
+}
+
+ at book{Saldern79,
+ author={Adelheid Saldern},
+ title={Mittelstand im `Dritten Reich'. Handwerker-Einzelh{\"a}ndler - Bauern},
+ publisher={Campus},
+ year= 1979,
+ address={Frankfurt}
+}
+
+ at article{SalWeiHam02,
+ author={Joshua A. Salomon and Milton C. Weinstein and James K. Hammitt and Sue J.
+ Goldie},
+ title={Empirically Calibrated Model of Hepatitis C Virus Infection in the United
+ States},
+ journal={American Journal of Epidemiology},
+ volume= 156,
+ year= 2002,
+ pages={761--773}
+}
+
+ at article{Sambanis01,
+ author={Nicholas Sambanis},
+ title={Do Ethnic and Nonethnic Civil Wars Have the Same Causes?},
+ journal={Journal of Conflict Resolution},
+ volume={45},
+ year={2001},
+ pages={259-82},
+ month={June},
+ number={3}
+}
+
+ at article{SamDoy07,
+ author={Nicholas Sambanis and Michael W. Doyle},
+ title={No Easy Choices: Estimating the Effects of United Nations Peacekeeping (Response
+ to King and Zeng)},
+ journal= isq,
+ year= 2007,
+ month={October}
+}
+
+ at article{SanKliDun06,
+ author={Lisa Sanbonmatsu and Jeffrey R. Kling and Greg J. Duncan and Jeanne Brooks-Gunn},
+ title={Neighborhoods and Academic Achievement: Results From the Moving to Opportunity
+ Experiment},
+ journal={National Bureau of Economic Research, Working Paper Series},
+ year={2006},
+ month={January},
+ number={Working Paper 11909},
+ note={{http://www.nber.org/papers/w11909}}
+}
+
+ at article{SanNaiWhi02,
+ author={H. Babad and C. Sanderson and B. Naidoo and I. White and D. Wang},
+ title={The Development of a Simulation Model of Primary Prevention Strategies for
+ Coronary Heart Disease},
+ journal={Health Care Management Science},
+ volume= 5,
+ year= 2002,
+ pages={269--274},
+ number= 4
+}
+
+ at article{SanRedHan96,
+ author={Robert Sanson-Fisher and Sally Redman and Lynne Hancock and Stehen Halpin
+ and Philip Clarke and Margot Schofield and Robert Burton and Michael Hensley
+ and robert Gibberd and Alexander Reid and Raoul Walsh and Afaf Girgis and
+ Louise Burton and Ann McClintock and Robert Carter and Allan Donner and
+ Sylvan Green },
+ title={Developing methodologies for evaluating community-wide health promotion},
+ journal={Health Promotion International},
+ volume={11},
+ year={1996},
+ pages={227-236},
+ number={3}
+}
+
+ at article{Sartori70,
+ author={Giovanni Sartori},
+ title={Concept Misformation in Comparative Politics},
+ journal= apsr,
+ volume= 64,
+ year= 1970,
+ pages={1033--1053},
+ month={December},
+ number= 4
+}
+
+ at article{Schaback96,
+ author={R. Schaback},
+ title={Approximation by Radia Basis Functions with Finitely Many Centers},
+ journal={Constructive Approximation},
+ volume= 12,
+ year= 1996,
+ pages={331--340}
+}
+
+ at book{Schafer97,
+ author={Joseph L. Schafer},
+ title={Analysis of incomplete multivariate data},
+ publisher={Chapman \& Hall},
+ year={1997},
+ address={London}
+}
+
+ at book{Schattschneider60,
+ title = {{The Semisovereign People}},
+ author = {Schattschneider, E.E.},
+ year = {1960},
+ address = {New York},
+ publisher = {Holt, Rinehart and Winston}
+}
+
+ at article{SchBuc03,
+ author={M. Schneider and J. Buckley},
+ title={Making the grade: comparing DC charter schools to other DC public schools},
+ journal={Educational Evaluation and Policy Analysis},
+ volume={25},
+ year={2003},
+ pages={203-215},
+ number={2}
+}
+
+ at article{SchGer00,
+ author = {Philip Schrodt and Deborah J.\ Gerner},
+ title = {Cluster-Based Early Warning Indicators for Political
+ Change in the Contemporary Levant},
+ journal = apsr,
+ volume = 94,
+ year = 2000,
+ pages = {803--818},
+ number = 4
+}
+
+ at article{Schieder93,
+ author={Wolfgang Schieder},
+ title={Die NSDAP vor 1933},
+ journal={Geschichte und Gesellschaft},
+ volume= 19,
+ year= 1993,
+ pages={141--154},
+ number= 1
+}
+
+ at inproceedings{SchKhaEzz93,
+ author={Joseph L. Schafer and Meena Khare and Trena M. Ezzati-Rice},
+ title={Multiple Imputation of Missing Data in NHANESIII },
+ booktitle={Proceedings of the Annual Research Conference},
+ year={1993},
+ pages={459-487},
+ organization={Washington, D.C., Bureau of the Census}
+}
+
+ at article{SchMalBla94,
+ author={David E. Schoenfeld, et al},
+ title={Self-Rated Health and Mortality in High Functioning Elderly-a Closer Look
+ at Healthy Individuals:MacArthur Field Study of Successful Aging. },
+ journal={Journal of Gerontology},
+ volume= 49,
+ year= 1994,
+ pages={{M109-113}},
+ number= 3
+}
+
+ at article{Schneider04,
+ author={Schneider, B.},
+ title={{Building a Scientific Community: The Need for Replication}},
+ journal={The Teachers College Record},
+ volume={106},
+ year={2004},
+ pages={1471--1483},
+ number={7}
+}
+
+ at techreport{Schochet03,
+ author={Peter Schochet and Sheena McConnell and John Burghardt},
+ title={National Job Corps Study: Findings Using Administrative Earnings Records
+ Data. Final Report},
+ institution={Mathematica Policy Research, Inc.},
+ year={2003},
+ month={October},
+ address={Princeton, NJ}
+}
+
+ at book{Schoenbaum80,
+ author = {Schoenbaum, David},
+ title = {Hitler's Social Revolution},
+ address = {New York},
+ publisher = {Norton},
+ year = {1980}
+}
+
+ at article{Schoenberg46,
+ author={I.J. Schoenberg},
+ title={Contributions to the problem of approximation of equidistant data by analytic
+ functions, Part A: On the problem of smoothing of graduation, a first class
+ of analytic approximation formulae},
+ journal={Quart. Appl. Math.},
+ volume={4},
+ year={1946},
+ pages={45--99}
+}
+
+ at article{SchOls98,
+ author={Joseph L. Schafer and Maren K. Olsen},
+ title={Multiple Imputation for multivariate Missing-Data Problems: A Data Analyst's
+ Perspective},
+ journal={Multivariate Behavioral Research},
+ volume={33},
+ year={1998},
+ pages={545-571},
+ number={4}
+}
+
+ at article{SchSin00,
+ author={Robert E. Schapire and Yoram Singer},
+ title={BoosTexter: A Boosting-based System for Text Categorization},
+ journal={Machine Learning},
+ volume={39},
+ year={2000},
+ pages={135-168},
+ number={2/3}
+}
+
+ at article{SchSin00,
+ author={Robert E. Schapire and Yoram Singer},
+ title={BoosTexter: A Boosting-based System for Text Categorization},
+ journal={Machine Learning},
+ volume={39},
+ year={2000},
+ pages={135-168},
+ number={2/3}
+}
+
+ at article{SchSla01,
+ author={Kenneth Scheve and Matthew Slaughter},
+ title={Labor Market Competition and Individual Preferences over Immigration Policy},
+ journal={Review of Economics and Statistics},
+ volume={83},
+ year={2001},
+ pages={133-145},
+ month={February},
+ number={1},
+ note={Sample data include only the first five of ten multiply imputed data sets.}
+}
+
+ at article{Schuessler99,
+ author={Alexander A. Schuessler},
+ title={Ecological Inference},
+ journal={Proceedings of the National Academy of Sciences},
+ volume= 96,
+ year= 1999,
+ pages={10578-10581},
+ month={September 14},
+ number= 19,
+ note={{http://www.pnas.org/cgi/content/full/96/19/10578}}
+}
+
+ at book{Schumaker81,
+ author={L.L. Schumaker},
+ title={Spline functions: basic theory},
+ publisher={John Wiley and Sons },
+ year= 1981 ,
+ address={New York}
+}
+
+ at article{Schwarz99,
+ author={Norbert Schwarz},
+ title={Self-Reports: How the Questions Shape the Answers},
+ journal={American Psychologist},
+ volume= 54,
+ year= 1999,
+ pages={93--105},
+ number= 2
+}
+
+ at article{SchWolWel99,
+ author={Lisa M. Schwartz, Steven Woloshin Gilbert H. Welch},
+ title={Misunderstanding About the Effects of Race and Sex on Physicians' Referrals
+ for Cardiac Cetheterization},
+ journal={New England Journal of Medicine},
+ volume= 341,
+ year= 1999,
+ pages={279-283},
+ number= 4
+}
+
+ at article{ScoMacCor97,
+ author={William K. Scott, et al},
+ title={Functional Health Status as a Predictor of Mortality in Men and Women Over
+ 65.},
+ journal={Journal of Clinical Epidemiology},
+ volume= 50,
+ year= 1997,
+ pages={{291-96}},
+ number= 3
+}
+
+ at article{SegBurLoo03,
+ author={O. Segura and A. Burdorf and C. Looman},
+ title={Update of Predictions of mortality from Pleural Mesothelioma in the Netherlands},
+ journal={Occupational and Environmental Medicine},
+ volume= 60,
+ year= 2003,
+ pages={50--55},
+ number= 1
+}
+
+ at article{Seidel91,
+ author={Raimund Seidel},
+ title={Small-Dimensional Linear Programming and Convex Hulls Made Easy},
+ journal={Discrete \& Computational Geometry},
+ volume={6},
+ year={1991},
+ pages={{423-434}},
+ issue={5}
+}
+
+ at inbook{SeiMue94,
+ title={Modelling the AIDS Epidemic: Planning, Policy, and Prediction},
+ chapter={Viral Load and Sexual Risk: Epidemiologic and Policy Implications for HIV/AIDS},
+ year={1994},
+ publisher={Raven Press},
+ pages={461--480},
+ address={New York},
+ altauthor={S.T. Seitz and G.E. Mueller},
+ alteditor={E.H. Kaplan and M.L. Brandeau}
+}
+
+ at article{Sekhon08,
+ author = {Jasjeet S. Sekhon},
+ title = {Multivariate and Propensity Score Matching Software
+ with Automated Balance Optimization: The matching
+ Package for R},
+ journal = {Journal of Statistical Software},
+ year = {2008}
+}
+
+ at unpublished{Sekhon04b,
+ author={Jasjeet S. Sekhon},
+ title={The Varying Role of Voter Information across Democratic Societies},
+ note={{http://jsekhon.fas.harvard.edu/papers/SekhonInformation.pdf}},
+ year= 2004
+}
+
+ at article{SekMeb98,
+ author={Jasjeet Singh Sekhon and Mebane, Jr., Walter R.},
+ title={Genetic Optimization Using Derivatives: Theory and Application to Nonlinear
+ Model},
+ journal= pa,
+ volume= 7,
+ year= 1998,
+ pages={187--210},
+ note={{http://jsekhon.fas.harvard.edu/genoud/genoud.pdf}}
+}
+
+ at article{Sen02,
+ author={Amartya Sen},
+ title={Health: Perception Versus Observation},
+ journal={BMJ},
+ volume= 324,
+ year= 2002,
+ pages={860-861},
+ month={13 April}
+}
+
+ at article{Senn00,
+ author={Stephen Senn},
+ title={Consensus and Controversy in Pharmaceutical Statistics},
+ journal={The Statistician},
+ volume={49},
+ year={2000},
+ pages={135-176},
+ number={2}
+}
+
+ at article{Senn04,
+ author={Stephen Senn},
+ title={Controversies concerning randomization and additivity in clinical trials},
+ journal={Statistics in Medicine},
+ volume={23},
+ year={2004},
+ pages={3729-3753}
+}
+
+ at article{Senn04b,
+ author={Stephen Senn},
+ title={Unbalanced Claims for Balance},
+ journal={2004},
+ volume={13},
+ year={2004},
+ pages={14-16},
+ month={June},
+ number={6}
+}
+
+ at article{Senn05,
+ author={Stephen Senn},
+ title={Quantifying the magnitude of baseline covariate imbalances resulting from
+ selection bias in randomized clinical trials - Comment},
+ journal={Biometrical Journal},
+ volume={47},
+ year={2005},
+ pages={133-135},
+ number={2}
+}
+
+ at article{Senn89,
+ author={S. J. Senn},
+ title={Covariate Imbalance and Random Allocation in Clinical Trials},
+ journal={Statistics in Medicine},
+ volume={8},
+ year={1989},
+ pages={467-475}
+}
+
+ at article{Senn93,
+ author={Stephen Senn},
+ title={Baseline Distribution and conditional Size},
+ journal={Journal of Biopharmaceutical Statistics},
+ volume={3},
+ year={1993},
+ pages={265-270},
+ number={2}
+}
+
+ at article{Senn94,
+ author={S.J. Senn},
+ title={Testing for Baseline Balance in Clinical Trials},
+ journal={Statistics in Medicine},
+ volume={13},
+ year={1994},
+ pages={1715-1726}
+}
+
+ at article{Senn96,
+ author={Stephen Senn},
+ title={Baseline Balance and conditional Size: A Reply to Overall Et. Al.},
+ journal={Journal of Biopharmaceutical Statistics},
+ volume={6},
+ year={1996},
+ pages={201-210},
+ number={2}
+}
+
+ at article{SetSanRao05,
+ author={Philip W. Setal and Osman Sankoh and Chalapati Rao and Victoria A. Velkoff
+ and Colin Mathers and Yang Gonghuan and Yusuf Hemed and Prabhat Jha and
+ Alan D. Lopez},
+ title={Sample registriation of vital events with verbal autopsy: a renewed commitment
+ to measuring and monitoring vital statistics},
+ journal={Bulletin of the World Health Organization},
+ volume={83},
+ year={2005},
+ pages={611-617}
+}
+
+ at article{SetSanVel05,
+ author={Philip W. Setel and O. Sankoh and VA Velkoff and C Mathers and Y Gonghuan
+ et al.},
+ title={Sample registration of vital events with verbal autopsy: a renewed commitment
+ to measuring and monitoring vital statistics},
+ journal={Bulletin of the World Health Organization},
+ volume= 83,
+ year= 2005,
+ pages={611-617}
+}
+
+ at article{SetWhiHem06,
+ author={Philip W. Setel and David R. Whiting and Yusuf Hemed and Daniel Chandramohan
+ and Lara J Wolfson and K.G.M.M. Alberti and Alan Lopez},
+ title={Validity of verbal autopsy procedures for determining causes of death in
+ Tanzania.},
+ journal={Tropical Medicine and International Health},
+ volume= 11,
+ year={2006},
+ pages={681--696},
+ number= 5
+}
+
+ at article{ShaBarrCra02,
+ author={Bruce Shadbolt, Jane Barresi, and Paul Craft},
+ title={Self Rated Health as a Predictor of Survival Among Patients with Advanced
+ Cancer},
+ journal={Journal of Clinical Oncology},
+ volume= 20,
+ year= 2002,
+ pages={{2514-19}},
+ month={{May 15}},
+ number= 10
+}
+
+ at article{ShaDavWeg92,
+ author={Said Shahtahmasebi, MSc, Richard Davies, PhD, and G. Clare Wenger PhD},
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+ journal={The Gerontological Society of America},
+ volume= 32,
+ year= 1992,
+ pages={{404-13}},
+ number= 3
+}
+
+ at article{Shahidullah95,
+ author={M. Shahidullah},
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+ journal={Studies in Family Planning},
+ volume= 26,
+ year= 1995,
+ pages={101--106}
+}
+
+ at unpublished{Shalev05,
+ author={Michael Shalev},
+ title={Limits and Alternatives to Multiple Regression in Comparative Research},
+ note={Dept. of Sociology \& Anthropology and Department of Political Science;
+ The Hebrew University of Jerusalem; Israel 91905},
+ year={2005},
+ month={July}
+}
+
+ at book{Shannon49,
+ author={Claude E. Shannon},
+ title={The Mathematical Theory of Communication},
+ publisher={University of Illinois Press},
+ year= 1949,
+ address={Urbana-Champaign}
+}
+
+ at article{ShaSit96,
+ author={Jun Shao and Randy R. Sitter},
+ title={Bootstrap for Imputed Survey Data},
+ journal={Journal of the American Statistical Association},
+ volume={91},
+ year={1996},
+ pages={1278-1288},
+ month={September},
+ number={435}
+}
+
+ at article{Sherman00,
+ author={Robert P. Sherman},
+ title={Tests of Certain Types of Ignorable Nonresponse in Surveys Subject to Item
+ Nonresponse or Attrition},
+ journal={American Journal of Political Science},
+ volume={44},
+ year={2000},
+ pages={356-368},
+ number={2}
+}
+
+ at article{SheSto93,
+ author={R. Shealy and W. Stout},
+ title={A Model-Based Standardization Approach That Separates True Bias/DIF From
+ Group Ability Differences and Detects Test Bias/DIF as Well as Item Bias/DIF},
+ journal={Psychometrika},
+ volume= 58,
+ year= 1993,
+ pages={159--194},
+ month={June},
+ number= 2
+}
+
+ at article{ShiLitPot06,
+ author={M.H. Shishehbor and D. Litaker and C.E. Pothier and M.S. Lauer},
+ title={Association of socioeconomic status with functional capacity, heart rate,
+ recoery, and all-cause mortality},
+ journal={Journal of the American Medical Association},
+ volume={295},
+ year={2006},
+ pages={784-792},
+ month={February}
+}
+
+ at article{ShiSmiDra89,
+ author={M.J. Shipley and P.G. Smith and M. Draimaix},
+ title={Calculation of Power for Matched Pair Studies When Randomization is by Group},
+ journal={International Journal of Epidemiology},
+ volume={18},
+ year={1989},
+ pages={457-461},
+ number={2}
+}
+
+ at article{ShiTes93,
+ author={T. Shiferaw and F. Tessema},
+ title={Maternal mortality in rural communities of Illubabor, Southwestern Ethiopia:
+ as estimated by the `sisterhood method'},
+ journal={Ethiopian Medical Journal},
+ volume= 31,
+ year= 1993,
+ pages={239--249}
+}
+
+ at article{Shively72,
+ author={Shivley, W. Phillips},
+ title={Party Identification and Voting Choice and Voting Stability: The Weimar
+ Case},
+ journal= apsr,
+ volume= 66,
+ year= 1972,
+ pages={1203-1225}
+}
+
+ at article{Sianesi04,
+ author={Barbara Sianesi},
+ title={An evaluation of the {S}wedish system of active labor market programs in
+ the 1990's},
+ journal={Review of Economics and Statistics},
+ volume= 86,
+ year= 2004,
+ pages={133-155},
+ number= 1
+}
+
+ at article{SibFleHil01,
+ author={A.M. Sibai and A. Fletcher and M. Hills and O. Campbell},
+ title={Non-communicable disease mortality rates using the verbal autopsy in a cohort
+ of middle aged and older populations in Beirut during wartime, 1983-93},
+ journal={Journal of Epidemiology and Community Health},
+ volume={55},
+ year={2001},
+ pages={271-276}
+}
+
+ at article{Signorino99,
+ author={Curtis Signorino},
+ title={Strategic Interaction and the Statistical Analysis of International Conflict},
+ journal= apsr,
+ volume= 93,
+ year= 1999,
+ pages={279-298},
+ number= 2
+}
+
+ at article{SigYil03,
+ author={Curtis Signorino and Kuzey Yilmaz},
+ title={Strategic Misspecification in Discrete Choice Models},
+ journal= ajps,
+ year={2003},
+ month={July},
+ note={{http://www.rochester.edu/College/PSC/signorino/papers/Signo00.pdf}}
+}
+
+ at article{SimHop05,
+ author={Beth A. Simmons and Daniel J. Hopkins},
+ title={The Constraining Power of International Treaties: Theory and Methods},
+ journal={American Political Science Review},
+ volume={99},
+ year={2005},
+ pages={623-631},
+ month={November},
+ number={4}
+}
+
+ at article{SimXeo04,
+ author={Simon, Adam F. and Michael Xeons},
+ title={{Dimensional Reduction of Word-Frequency Data as a Substitute for Intersubjective
+ Content Analysis}},
+ journal={Political Analysis},
+ volume={12},
+ year={2004},
+ pages={63-75},
+ number={1}
+}
+
+ at unpublished{Singh00,
+ author={R. Singh},
+ title={Estimation of Adult Mortality from Widowhood Data for India and its Major
+ States},
+ note={Mumbai, India: International Institute for Population Sciences},
+ year={2000}
+}
+
+ at inproceedings{Singh02,
+ author={Abhishek Singh},
+ title={Forecasting Mortality in India},
+ organization={Ninth International Conference of Forum for Interdisciplinary Mathematics
+ on Statistics Combinatorics and Related Areas, University of Allahbad},
+ crossref={Atlas Document #cakd-39},
+ annote={Lee-Carter is used to forecast mortality in India from 2000-2015.}
+}
+
+ at article{Sisson05,
+ author={Scott A. Sisson},
+ title={Transdimensional Markov Chains: A Decade of Progress and Future Perspectives},
+ journal= jasa,
+ volume= 100,
+ year= 2005,
+ pages={1077--1089},
+ month={September},
+ number= 471
+}
+
+ at inproceedings{Sivamurthy87,
+ author={M. Sivamurthy},
+ title={{Principal Components Representation of ASFR: Model of Fertility Estimation
+ and Projection}},
+ booktitle={CDC Research Monograph},
+ year= 1987 ,
+ address={Cairo Demographic Center},
+ pages={655--693}
+}
+
+ at article{Skalaban92,
+ author={Andrew Skalaban},
+ title={Interstate Competition and State Strategies to Deregulate Interstate Banking
+ 1982-1988},
+ journal={Journal of Politics},
+ volume={54},
+ year={1992},
+ pages={793-809},
+ month={August},
+ number={3}
+}
+
+ at InCollection{Skocpol91,
+ author = {Theda Skocpol},
+ title = {Targeting Within Universalism: Politically Viable
+ Policies to Combat Poverty in the United States},
+ booktitle = {The Urban Underclass},
+ pages = {411-436},
+ publisher = {Brookings Institution},
+ editor = {Christopher Jencks and Paul Peterson},
+ address = {Washington, D.C.},
+ year = {1991}
+}
+
+ at proceedings{Skoufias05,
+ title={PROGRESA and Its Impacts on the Welfare of Rural Households in Mexico},
+ year={2005},
+ organization={International Food Policy Research Institute},
+ address={Washington},
+ author={E. Skoufias}
+}
+
+ at unpublished{Small05,
+ author={Dylan S. Small},
+ title={Sensitivity Analysis for Limited Information Linear Simultaneous Equations
+ Models With Overidentifying Restrictions},
+ note={Dept. of Statistics, The Wharton School of the University of Pennsylvania,
+ Philadelphia, PA 19104-6340, dmall at wharton.upenn.edu},
+ year={2005},
+ month={September}
+}
+
+ at book{SmiMor91,
+ author={P.J. Smith and R.H. Morrow},
+ title={Methods for field trials of interventions against tropical diseases: a 'toolbox'},
+ publisher={Oxford University Press},
+ year={1991},
+ address={Oxford}
+}
+
+ at article{Smith03,
+ author={Tom W. Smith},
+ title={Developing Comparable Questions in Cross-National Surveys, in Cross-Cultural
+ Survey Methods},
+ journal={John Wiley and Sons},
+ volume={2003},
+ pages={Janet A. Harkness and Fons J. R. van de Vijver and Peter Ph. Mohler},
+ month={Hoboken, NJ}
+}
+
+ at article{Smith97,
+ author={Smith, H.L.},
+ title={Matching With Multiple Controls to Estimate Treatment Effects in Observational
+ Studies},
+ journal={Sociological Methodology},
+ volume={27},
+ year={1997},
+ pages={325--353},
+ number={1}
+}
+
+ at article{SmiTod05,
+ author={Jeffrey A. Smith and Petra E. Todd},
+ title={Does matching overcome LaLonde's critique of nonexperimental estimators?},
+ journal={Journal of Econometrics},
+ volume= 125,
+ year= 2005,
+ pages={305-353},
+ month={March-April},
+ number={1-2}
+}
+
+ at article{SmiTod05b,
+ author={Jeffrey Smith and Petra Todd},
+ title={Rejoinder},
+ journal={Journal of Econometrics},
+ volume={2005},
+ year={125},
+ pages={365-375}
+}
+
+ at book{SneCoc80,
+ author={George W. Snedecor and William G. Cochran},
+ title={Statistical Methods},
+ publisher={Iowa State University Press},
+ year={1980},
+ address={Ames, IA},
+ edition={7th}
+}
+
+ at book{SniCar97,
+ author={Paul Sniderman and Edward Carmines},
+ title={Reaching Beyond Race},
+ publisher={Harvard University Press},
+ year= 1997,
+ address={Cambridge, MA}
+}
+
+ at article{SniGro96,
+ author={Paul M. Sniderman and Douglas B. Grob},
+ title={Innovations in Experimental Design in Attitude Surveys},
+ journal={Annual Review of Sociology},
+ volume= 22,
+ year= 1996,
+ pages={377-399},
+ month={August}
+}
+
+ at article{Sobel06,
+ author={Michael E. Sobel},
+ title={Discussion: 'The Scientific Model of Causality'},
+ journal={Sociological Methodology},
+ volume={35},
+ year={2006},
+ pages={99-33},
+ month={June},
+ number={1}
+}
+
+ at article{Sobolev38,
+ author={S.L. Sobolev},
+ title={On a theorem of functional analysis},
+ journal={Math. Sbornik},
+ volume= 45,
+ year= 1938,
+ pages={471--496},
+ note={Russian original;AMS Transl. (2),34(1963),39--68}
+}
+
+ at book{SolChaShi05,
+ author={Nadia Soleman and Daniel Chandramohan and Kenji Shibuya},
+ title={WHO Technical Consultation on Verbal Autopsy Tools},
+ publisher={Geneva},
+ year= 2005,
+ note={{http://www.who.int/healthinfo/statistics/mort\_verbalautopsy.pdf}}
+}
+
+ at article{SolChaShi06,
+ author={Nadia Soleman and Daniel Chandramohan and Kenji Shibuya},
+ title={Verbal autopsy: current practices and challenges},
+ journal={Bulletin of the World Health Organization},
+ volume={84},
+ year={2006},
+ pages={239-245},
+ month={March},
+ number={3}
+}
+
+ at article{SolWil92,
+ author={Patricia J. Solomon and Susan R. Wilson},
+ title={Predicting AIDS Deaths and Prevalence in Australia},
+ journal={The Medical Journal of Australia},
+ volume= 157,
+ year= 1992,
+ pages={121--125}
+}
+
+ at article{SomDjuLoe86,
+ author={Alfred Sommer and Edi Djunaedi and A.A. Loeden and Ignatius Tarwotjo and
+ Keith P. West, Jr. and Robert Tilden and Lisa Mele},
+ title={Impact of Vitamin A Supplementation on Childhood Mortality},
+ journal={The Lancet},
+ volume={1},
+ year={1986},
+ pages={1169-1173}
+}
+
+ at article{SomZeg91,
+ author={A. Sommer and SL Zeger},
+ title={{On Estimating Efficacy from Clinical Trials.}},
+ journal={Statistics in Medicine},
+ volume={10},
+ year={1991},
+ pages={45-52},
+ number={1}
+}
+
+ at article{SomZeg91,
+ author={Alfred Sommer and Scott L. Zeger},
+ title={On Estimating Efficacy From Clinical Trials},
+ journal={Statistics in Medicine},
+ volume={10},
+ year={1991},
+ pages={45-52}
+}
+
+ at article{Song01,
+ author={Juwon Song and Thomas R. Belin and Martha B. Lee and Xingyu Gao and Mary
+ Jane Rotheram-Borus},
+ title={Handling baseline differences and missing items in a longitudinal study
+ of {HIV} risk among runaway youths},
+ journal={Health Services and Outcomes Research Methodology},
+ volume= 2,
+ year= 2001,
+ pages={317-329}
+}
+
+ at article{Sorenson88,
+ author={Kirsten Hjort Sorensen},
+ title={State of Health and its Association with Death Among Old People at Three-Years
+ Follow Up},
+ journal={Danish Medical Bulletin},
+ volume= 35,
+ year= 1988,
+ pages={{597-00}}
+}
+
+ at book{Sowa99,
+ author={J. F. Sowa},
+ title={Knowledge Representation: Logical, Philosophical and Computational Foundations},
+ publisher={Brooks Cole},
+ year= 1999
+}
+
+ at unpublished{SpeSun01,
+ author={P. Speckman and D. Sun},
+ title={{Bayesian Nonparametric Regression and Autoregression Priors}},
+ note={{www.stat.missouri.edu/\textasciitilde speckman/report/bnpreg.ps}},
+ year= 2001
+}
+
+ at article{SpiJagCla96,
+ author={Nicola Spiers, Carol Jagger, and Michael Clarke},
+ title={Physical Function and Perceived Health: Cohort Diffrences and Interrelationships
+ in Older People},
+ journal={Joural of Gerontology: Social Sciences},
+ volume={{51B}},
+ year= 1996,
+ pages={{S226-33}}
+}
+
+ at techreport{Sroka06,
+ author={T. Neil Sroka},
+ title={Understanding the Political Influence of Blogs},
+ institution={Graduate School of Political Management, George Washington University},
+ year={2006},
+ month={April},
+ address={The Institue for Politics, Democracy, & the Internet, Graduate School of
+ Political Management, George Washington U, 805 21st St., NW Suite 401,
+ Washington, DC 20052}
+}
+
+ at article{Stachura93,
+ author={Stachura, Peter D.},
+ title={National Socialism and the German Proletariat, 1925-1935: Old Myths and
+ New Perspectives},
+ journal={The Historical Journal},
+ volume= 36,
+ year= 1993,
+ pages={701-718},
+ number= 3
+}
+
+ at article{StaNouHil00,
+ author={C. Stanton and A. Noureddine and K. Hill},
+ title={An Assessment of DHS Maternal Mortality Indicators},
+ journal={Studies in Family Planning},
+ volume= 31,
+ year= 2000,
+ pages={111--123}
+}
+
+ at inbook{StaSeiWay91,
+ author={E.A. Stanley and S.T. Seitz and P.O. Way and P.D. Johnson and T.F. Curry},
+ title={The AIDS Epidemic and its Demographic Consequences},
+ chapter={The iwgAIDS Model for the Heterosexual Spread of HIV and the Demographic
+ Impacts of the AIDS Epidemic},
+ year= 1991,
+ publisher={United Nations and World Health Organization},
+ pages={119--136},
+ series={ST/ESA/SER.A/119},
+ address={New York}
+}
+
+ at article{SteCoo95,
+ author={L.A. Stefanski and J.R. Cook},
+ title={Stimulation-Extrapolation: The Measurement Error Jackknife},
+ journal={Journal of the American Statistical Association},
+ volume={90},
+ year={1995},
+ pages={1247-1256},
+ month={December},
+ number={432}
+}
+
+ at book{Steele04,
+ author={J. Michael Steele},
+ title={The Cauchy-Schwarz Master Class},
+ publisher={Cambridge University Press},
+ year={2004}
+}
+
+ at article{Stefanski92,
+ author={Leonard A. Stefanski},
+ title={Monotone Likelihood Ratio of a "Faulty-Inspection" Distribution},
+ journal={The American Statistician},
+ volume={46},
+ year={1992},
+ pages={110-114},
+ month={May},
+ number={2}
+}
+
+ at article{SteNap00,
+ author={Anita L. Stewart and Anna Napoles-Springer},
+ title={Health-Related Quality of Life Assessments in Diverse Population Groups
+ in the United States},
+ journal={Medical Care},
+ volume= 38,
+ year= 2000,
+ pages={II-102 -- II-124},
+ month={September},
+ number= 9
+}
+
+ at article{Stephan32,
+ author = {Stephan, Werner},
+ title = {Grenzen des nationalsozialistischen
+ Vormarsches. Eine Analyse der Wahlziffern seit der
+ Reichstagswahl 1930},
+ journal = {Zeitschrift f{\"u}r Politik},
+ volume = 21,
+ year = 1932,
+ pages = {570-578}
+}
+
+ at article{Stephan32b,
+ author = {Stephan, Werner},
+ title = {Die Parteien nach den grossen
+ Fr{\"u}hjahrswahlk{\"a}mpfen. Eine Analyse der
+ Wahlziffern des Jahres 1932},
+ journal = {Zeitschrift f{\"u}r Politik},
+ volume = 22,
+ year = 1932,
+ pages = {110-118}
+}
+
+ at article{Stephan33,
+ author = {Stephan, Werner},
+ title = {Die Reichstagswahlen vom 31. Juli 1932},
+ journal = {Zeitschrift f{\"u}r Politik},
+ volume = 22,
+ year = 1933,
+ pages = {353-360}
+}
+
+ at article{Sterk03,
+ author={Stewart E. Sterk},
+ title={Retrenchment on Entrenchment},
+ journal={The George Washington Law Review},
+ volume={71},
+ year={2003},
+ pages={231-254},
+ month={April},
+ number={2}
+}
+
+ at techreport{Stewart92,
+ author={G.W. Stewart},
+ title={{On the Early History of the Singular Value Decomposition}},
+ institution={University of Maryland, College Park},
+ year= 1992,
+ type={Institute for Advanced Computer Studies},
+ number={TR-92-31}
+}
+
+ at article{Stimson85,
+ author={James A.\ Stimson},
+ title={Regression Models in Space and Time: A Statistical Essay},
+ journal= ajps,
+ volume= 29,
+ year= 1985,
+ pages={914--947}
+}
+
+ at book{Stockman86,
+ author={David A. Stockman},
+ title={The Triumph of Politics: How the Reagon Revolution Failed},
+ publisher={Harper \& Row, Publishers},
+ year={1986},
+ address={New York}
+}
+
+ at article{StoDorKoz89,
+ author={M.J. Stones, Brenda Dornan, and Albert Kozma},
+ title={The prediction of mortality in elderly institution residents},
+ journal={Journal of Gerontology: Psychological Sciences},
+ volume= 44,
+ year= 1989,
+ pages={{P72-79}}
+}
+
+ at article{Stogbauer01,
+ author={Christian St{\"o}gbauer},
+ title={The Radicalisation of the German Electorate: Swinging to the Right and the
+ Left in the Twilight of the Weimar Republic},
+ journal={European Review of Economic History},
+ volume= 5,
+ year= 2001,
+ pages={251--280}
+}
+
+ at article{Stone74,
+ author={Stone, M.},
+ title={Cross-Validatory Choice and Assessment of Statistical Prediction},
+ journal= jrssb,
+ volume= 36,
+ year= 1974,
+ pages={111-33},
+ number= 2
+}
+
+ at article{StoRel90,
+ author={Ross M. Stolzenberg and Daniel a. Relles},
+ title={Theory Testing in a World of Constrained Research Design: The Significance
+ of Heckman's Censored Sampling Bias Correction for Nonexperimental Research},
+ journal={Sociological Methods and Research},
+ volume={18},
+ year={1990},
+ pages={395-415},
+ month={May}
+}
+
+ at article{StoWat03,
+ author={James H. Stock and Mark W. Watson},
+ title={Forecasting Output and Inflation: The Role of Asset Prices},
+ journal={Journal of Economic Literature},
+ year={2003},
+ optnumber={3},
+ optvolume={41},
+ optmonth={September}
+}
+
+ at article{StoWay98,
+ author={John Stover and Peter Way},
+ title={Projecting the Impact of AIDS on Mortality},
+ journal={AIDS},
+ volume= 12,
+ year={1998},
+ pages={S29--S39},
+ number={supplement 1}
+}
+
+ at book{Strang88,
+ author={G. Strang},
+ title={{Linear Algebra and Its Applications}},
+ publisher={Saunders},
+ year= 1988
+}
+
+ at phdthesis{Stuart04,
+ author={Stuart, Elizabeth A.},
+ title={Matching methods for estimating causal effects using multiple control groups},
+ school={Department of Statistics, Harvard University},
+ year= 2004
+}
+
+ at article{StuRub07,
+ author={Elizabeth A. Stuart and Donald B. Rubin},
+ title={Matching with multiple control groups with adjustment for group differences},
+ journal={Journal of Educational and Behavioral Statistics},
+ year= 2007,
+ note={Forthcoming}
+}
+
+ at article{SucJor90,
+ author={L. Suchman and B. Jordan},
+ title={Interactional Troubles in Face to Face Survey Interviews (With Comments
+ and Rejoinder)},
+ journal= jasa,
+ volume= 85,
+ year= 1990,
+ pages={232--253},
+ month={March},
+ number= 409
+}
+
+ at article{Sunetal00,
+ author={D. Sun and R. Tsutakawa and H. Kim and Z. He},
+ title={{Spatio-temporal Interaction with Disease Mapping}},
+ journal={Statistics in Medicine},
+ volume= 19,
+ year= 2000,
+ pages={2015--2035}
+}
+
+ at manual{SunReiLan03,
+ author={S. Sun and S. Reilly and L. Lannom and J. Petrone},
+ title={Handle System Protocol (ver 2.1) Specification },
+ organization={RFC 3652 (Informational)},
+ year={2003},
+ note={{http://www.ietf.org/rfc/rfc3652.txt}}
+}
+
+ at article{Super04,
+ author={Nora Super},
+ title={Medicare's Chronic Care Improvement Pilot Program: What is its Potential},
+ journal={National Health Policy Forum Issue Brief},
+ year={2004},
+ pages={1-20},
+ month={May},
+ number={797},
+ note={The George Washinton University, Washington, DC},
+ institution={National Health Policy Form}
+}
+
+ at incollection{Tabeau01,
+ author={Ewa Tabeau},
+ title={A Review of Demographic Forecasting Models for Mortality},
+ booktitle={Forecasting Mortality in Developed Countries},
+ publisher={Kluwer Academic Publishers},
+ year= 2001,
+ address={The Netherlands},
+ editor={Ewa Tabeau, Anneke van de Berg Jeths and Christopher Heathcoate},
+ chapter= 1,
+ pages={1--32}
+}
+
+ at article{TabEkaHuiBos98,
+ author={Ewa Tabeau and Peter Ekamper and Corina Huisman and Alinda Bosch},
+ title={Improving Overall Mortality Forecasts by Analysing Cause-of-Death, Period
+ and Cohort Effects in Trends},
+ journal={European Journal of Population},
+ volume={15},
+ year={1999},
+ pages={153-183}
+}
+
+ at article{TabJetHea03,
+ author={Ewa Tabeau and Aneke van den Berg Jeths and Christopher Heathcote},
+ title={Forecasting Mortality in Developed Countries: Insights from a statistical,
+ demographic and epidemiological perspective.},
+ journal={Journal of European Population},
+ volume={1023},
+ number={10}
+}
+
+ at book{Takeshi85,
+ author={Takeshi Amemiya},
+ title={Advanced Econometrics},
+ publisher={Harvard University Press},
+ year={1985},
+ address={Cambridge}
+}
+
+ at book{Tally00,
+ author={Steve Tally},
+ title={Almost America: From the Colonists to Clinton, A ``What If'' History of
+ the U.S.},
+ publisher={Quill},
+ year= 2000,
+ address={New York}
+}
+
+ at article{TanKumIke03,
+ author={J. Tanaka and H. Humada and K. Ikeda and K. Chayama and M. Mizui and K.
+ Hino and K. Katayama and J. Kumagai and Y. Komiya and Y. Miyakawa and H.
+ Yoshizawa},
+ title={Natural histories of Hepatitis C Virus Infection in Men and Women Simulated
+ by the Markov Model},
+ journal={Journal of Medical Virology},
+ volume= 70,
+ year= 2003,
+ pages={378--386},
+ number= 3
+}
+
+ at book{Tanner96,
+ author={Martin A. Tanner},
+ title={Tools for Statistical Inference: Methods for the Exploration of Posterior
+ Distributions and Likelihood Functions},
+ publisher={Springer-Verlag},
+ year= 1996,
+ address={New York}
+}
+
+ at article{TanWon87,
+ author={M.A. Tanner and W.H. Wong},
+ title={The Calculation of Posterior Distributions by Data Augmentation},
+ journal={Journal of the American Statistical Association},
+ volume={82},
+ year={1987},
+ pages={528-550},
+ month={June}
+}
+
+ at article{Taylor92,
+ author={G. Taylor},
+ title={A Bayesian interpretation of Whittaker-Henderson graduation},
+ journal={Insurance: Mathematics and Economics},
+ volume= 11,
+ year= 1992,
+ pages={7--16}
+}
+
+ at book{Tendler97,
+ author={Judith Tendler},
+ title={Good Government in the Tropics},
+ publisher={The Johns Hopkins University Press},
+ year={1997},
+ address={Baltimore}
+}
+
+ at article{TerKleOce00,
+ author={Jeanne A. Teresi and Marjorie Kleinman and Katja Ocepek-Welikson},
+ title={Modern Psychometric Methods for Detection of Differential Item Functioning:
+ Application to Cognitive Assessment Measures},
+ journal= sim,
+ volume= 19,
+ year= 2000,
+ pages={1651--1683}
+}
+
+ at book{TetBel96,
+ title={Counterfactual Throught Experiments in World Politics},
+ publisher={Princeton University Press},
+ year= 1996,
+ editor={Philip E. Tetlock and A. Belkin},
+ address={Princeton}
+}
+
+ at article{TetLeb01,
+ author={Philip E. Tetlock and Richard Ned Lebow},
+ title={Poking Counterfactual Holes in Covering Laws: Cognitive Styles and Historical
+ Reasoning},
+ journal= apsr,
+ volume= 95,
+ year= 2001,
+ month={December},
+ number= 4
+}
+
+ at book{TetLebPar00,
+ title={Unmaking the West: Counterfactual Explorations of Alternative Histories},
+ publisher={Columbia University Press},
+ year= 2000,
+ editor={Philip E. Tetlock and Ned R. Lebow and G. Parker},
+ address={New York}
+}
+
+ at article{tetlock99,
+ author={Philip E. Tetlock},
+ title={Theory-Driven Reasoning About Plausible Pasts and Probable Futures in World
+ Politics: Are we Prisoners of our Preconceptions?},
+ journal= ajps,
+ volume= 43,
+ year= 1999,
+ pages={335-366},
+ month={April},
+ number= 2
+}
+
+ at book{Thisted88,
+ author={Ronald A. Thisted},
+ title={Elements of Statistical Computing: Numerical Computation},
+ publisher={Chapman and Hall},
+ year= 1988,
+ address={Florida}
+}
+
+ at inproceedings{ThiSteWai93,
+ author={David Thissen and Lynn Steinberg and Howard Wainer},
+ title={Detection of Differential Item Functioning Using the Parameters of the Item
+ Response Models},
+ booktitle={Differential Item Functioning},
+ crossref={HolWai93}
+}
+
+ at article{Thompson05,
+ author={Dennis F. Thompson},
+ title={{Democracy in Time: Popular Sovereignty and Temporal Representation}},
+ journal={Constellations},
+ volume= 12,
+ year= 2005 ,
+ pages={245-261},
+ month={June},
+ number= 2
+}
+
+ at article{Thompson98,
+ author={Simon G. Thompson},
+ title={Letters to the Editor: The Merits of Matching in Community Intervention
+ Trials: A Cautionary Tale},
+ journal={Statistics in Medicine},
+ volume={17},
+ year={1998},
+ pages={2147-2151}
+}
+
+ at article{ThoPanLee06,
+ author={Matt Thomas and Bo Pang and Lillian Lee},
+ title={Get out the vote: Determining support or opposition from Congressional floor-debate
+ transcripts},
+ journal={Proceedings of EMNLP},
+ year= 2006,
+ pages={327--335},
+ note={{http://www.cs.cornell.edu/home/llee/papers/tpl-convote.home.html}}
+}
+
+ at article{ThoSyl82,
+ author={Stuart J. Thorson and Donald A. Sylvan},
+ title={Counterfactuals and the Cuban Missle Crisis},
+ journal={International Studies Quarterly},
+ volume= 26,
+ year= 1982,
+ pages={539--571},
+ number= 4
+}
+
+ at book{Thurstone59,
+ author={L.L. Thurstone},
+ title={The Measurement of Values},
+ publisher={University of Chicago Press},
+ year= 1959,
+ address={Chicago}
+}
+
+ at book{TikArs77,
+ author={A. N. Tikhonov and V. Y. Arsenin},
+ title={Solutions of Ill-posed Problems},
+ publisher={W. H. Winston},
+ year= 1977 ,
+ address={Washington, D.C.}
+}
+
+ at article{Tikhonov63,
+ author={A. N. Tikhonov},
+ title={Solution of incorrectly formulated problems and the regularization method},
+ journal={Soviet Math. Dokl.},
+ volume={4},
+ year= 1963 ,
+ pages={1035--1038}
+}
+
+ at article{Timaeus86,
+ author={Ian Timaeus},
+ title={An Assessment of Methods for Estimating Adult Mortality from Two Sets of
+ Data on Maternal Orphanhood},
+ journal={Demography},
+ volume= 23,
+ year= 1986,
+ pages={435--450}
+}
+
+ at article{Timaeus91,
+ author={Iain Timaeus},
+ title={Measurement of Adult Mortality in Developing Countries: A Comparative Review},
+ journal={Population Index},
+ volume= 57,
+ year= 1991,
+ pages={552-568},
+ number= 4
+}
+
+ at article{Timaeus91b,
+ author={Ian Timaeus},
+ title={Estimation of Adult Mortality from Orphanhood Before and Since Marriage},
+ journal={Population Studies},
+ volume= 45,
+ year={1991b},
+ pages={455--472}
+}
+
+ at article{Timpone98,
+ author={Richard J. Timpone},
+ title={Structure, Behavior, and Voter Turnout in the United States},
+ journal={American Poltical Science Review},
+ volume={92},
+ year={1998},
+ pages={145-158},
+ month={March},
+ number={1}
+}
+
+ at incollection{TimZabAli01,
+ author={Ian M. Timaeus Basia Zaba and Mohammed Ali},
+ title={Estimation of Adult Mortality from Data on Adult Siblings},
+ booktitle={Brass Tacks: Essays in Medical Demography},
+ publisher={Athlone},
+ year= 2001,
+ editor={B. Zaba and J. Blacker},
+ pages={43--66}
+}
+
+ at incollection{Tobler79,
+ author={Waldo Tobler},
+ title={Cellular Geography},
+ booktitle={Philosophy in Geography},
+ publisher={Dordrecht: Reidel},
+ year= 1979,
+ editor={S.\ Gale and G.\ Olssen}
+}
+
+ at article{TodDefOde94,
+ author = {J.E. Todd and A. De Francisco and T.J.D. O'Dempsey
+ and B.M. Greenwood},
+ title = {The limitations of verbal autopsy in a
+ malaria-endemic region},
+ journal = {Annals of Tropical Paediatrics},
+ volume = {14},
+ year = {1994},
+ pages = {31-36}
+}
+
+ at book{Torgerson58,
+ author = {Warren S. Torgerson},
+ title = {Theory and Methods of Scaling},
+ publisher = {Wiley and Sons},
+ year = 1958,
+ address = {New York}
+}
+
+ at incollection{TorRauHer93,
+ author = {Hege Torp and O. Rauum and E. Hernaes and
+ H. Goldstein},
+ title = {The First Norwegian Experiment},
+ booktitle = {Measuring Labour Market Measures: Evaluating the
+ Effects of Active Labour Market Policies},
+ publisher = {Ministry of Labour},
+ year = {1993},
+ address = {Copenhagen, Denmark},
+ editor = {K. Jensen and Per Kongshoj Madsen}
+}
+
+ at article{TruRod90,
+ author = {J. Trussell and G. Rodriguez},
+ title = {A Note on the Sisterhood Estimator of Maternal
+ Mortality},
+ journal = {Studies in Family Planning},
+ volume = 21,
+ year = 1990,
+ pages = {344--346},
+ month = {Nov-Dec},
+ number = 6
+}
+
+ at article{TsuLin86,
+ author = {Robert K. Tsutakawa and Hsin Ying Lin},
+ title = {Bayesian Estimation of Item Response Curves},
+ journal = {Psychometrika},
+ volume = {51},
+ year = {1986},
+ pages = {251-267},
+ month = {June},
+ number = {2}
+}
+
+ at article{TsuMinKey94,
+ author = {Ichiro Tsuji, MD, et al},
+ title = {The Predictive Power of Self-Rated Health,
+ Activities of Daily Living, and Ambulatory Activity
+ for Cause Specific Mortality among the Elderly: A
+ Three-year Follow-up in Urban Japan. },
+ journal = {Journal of the American Geriatric Society},
+ volume = 42,
+ year = 1994,
+ pages = {{153-56}}
+}
+
+ at techreport{Tsutakawa75,
+ author = {Robert K. Tsutakawa},
+ title = {Bayesian Inference for Bioassay},
+ institution = {University of Missouri - Columbia},
+ year = {1975},
+ month = {August},
+ number = {52}
+}
+
+ at article{Tsutakawa84,
+ author = {Robert K. Tsutakawa},
+ title = {Estimation of Two-Parameter Logistic Item Response
+ Curves},
+ journal = {Journal of Educational Statistics},
+ volume = {9},
+ year = {1984},
+ pages = {263-276},
+ number = {4}
+}
+
+ at article{Tsutakawa92,
+ author = {Robert K. Tsutakawa},
+ title = {Moments Under Conjugate Distributions in Bioassay},
+ journal = {Statistics \& Probability Letters},
+ volume = {15},
+ year = {1992},
+ pages = {229-233},
+ month = {October}
+}
+
+ at article{Tsutakawa92b,
+ author = {Robert K. Tsutakawa},
+ title = {Prior Distribution for Item Response Curves},
+ journal = {British Journal of Mathematical and Statistical
+ Psychology},
+ volume = {45},
+ year = {1992},
+ pages = {51-74}
+}
+
+ at article{TulBoe98,
+ author = {Shripad Tuljapurkar and Carl Boe},
+ title = {Mortality Change and Forecasting: How Much and How
+ Little Do We Know?},
+ journal = {North American Actuarial Journal},
+ volume = {2},
+ year = {1998},
+ number = {4},
+ annote = {This paper makes a critical assessment of knowledge
+ about mortality change and the potential of existing
+ work to contribute to the development of useful
+ forecasts in Canada, Mexico, and the United
+ States. Methods of forecasting are reviewed,
+ including the scenario method used by the US Social
+ Security Administration and the time series method
+ of Lee and Carter.}
+}
+
+ at article{TulLiBoe00,
+ author = {S. Tuljapurkar and N. Li and C. Boe},
+ title = {A Universal Pattern of Mortality Decline in the {G7}
+ Countries},
+ journal = {Nature},
+ volume = 405,
+ year = 2000,
+ pages = {789--792},
+ month = {June}
+}
+
+ at article{tumbarello98,
+ author = {M. Tumbarello and E. Tacconelli and K. de Gaetano
+ and F. Ardit and T. Pirronti and R. Claudia and
+ L. Ortona},
+ title = {Bacterial Pneumonia in HIV-Infected Patients:
+ Analysis of Risk Factors and Prognostic Indicators},
+ journal = {Journal of Acquired Immune Deficiency Syndromes and
+ Human Retroviology},
+ volume = 18,
+ year = 1998,
+ number = {39-45}
+}
+
+ at unpublished{TurLit02,
+ author = {P.D. Turney and M.L. Littman},
+ title = {Unsupervised Learning of Semantic Orientation},
+ note = {National Research Council Canada},
+ year = {2002},
+ month = {May}
+}
+
+ at unpublished{TurLit02,
+ author = {P.D. Turney and M.L. Littman},
+ title = {Unsupervised Learning of Semantic Orientation},
+ note = {National Research Council Canada},
+ year = {2002},
+ month = {May}
+}
+
+ at article{TurMat01,
+ author = {G. Turrell and Colin Mathers},
+ title = {Socioeconomic inequalities in all-cause and
+ specific-cause mortality in Australia: 1985--1987
+ and 1995--1997},
+ journal = {International Journal of Epidemiology},
+ volume = 30,
+ year = 2001,
+ pages = {231--239},
+ number = 2
+}
+
+ at book{Turner85,
+ author = {Turner, Henry-Ashbury},
+ title = {German big business and the rise of Hitler},
+ publisher = {Oxford University Press},
+ year = {1985}
+}
+
+ at proceedings{Turney02,
+ editor = {Peter D. Turney},
+ title = {Thumbs Up or Thumbs Down? Semantic Orientation
+ Applied to},
+ publisher = {40th Annual Meeting of the Associatin for
+ Computational Linguistics},
+ year = {2002},
+ month = {July},
+ organization = {Institute for Information Technology},
+ address = {National Research Council of Canada, Ottawa,
+ Ontario, Canada K1A0R6}
+}
+
+ at proceedings{Turney02,
+ editor = {Peter D. Turney},
+ title = {Thumbs Up or Thumbs Down? Semantic Orientation
+ Applied to},
+ publisher = {40th Annual Meeting of the Associatin for
+ Computational Linguistics},
+ year = {2002},
+ month = {July},
+ organization = {Institute for Information Technology},
+ address = {National Research Council of Canada, Ottawa,
+ Ontario, Canada K1A0R6}
+}
+
+ at article{Urdal05,
+ author = {Henrik Urdal},
+ title = {People vs. Malthus: Population Pressure,
+ Environmental Degradation, and Armed Conflict
+ Revisited},
+ journal = {Journal of Peace Research},
+ volume = {42},
+ year = {2005},
+ pages = {417-434},
+ month = {July},
+ number = {4},
+ publisher = {Journal for Peace Research}
+}
+
+ at book{UttLock02,
+ title = {American Political Scientists: a Dictionary},
+ publisher = {Greenwood Press},
+ year = {2002},
+ editor = {Glenn H. Utter and Charles Lockhart},
+ address = {Westport, Conn},
+ edition = {2nd}
+}
+
+ at book{Valentine64,
+ author = {Frederick Albert Valentine},
+ title = {Convex Sets},
+ publisher = {New York, McGraw-Hill},
+ year = {1964}
+}
+
+ at book{Valentine64,
+ author = {Frederick A Valentine},
+ title = {Convex Sets},
+ publisher = {McGraw-Hill},
+ year = 1964,
+ address = {New York}
+}
+
+ at article{VanCor99,
+ author = {Marina Vannucci},
+ title = {Covariance structure of wavelet coefficients: theory
+ and models in a Bayesian perspective},
+ journal = {Journal of the Royal Statistical Society B},
+ volume = {61},
+ year = {1999},
+ pages = {971-986},
+ number = {Part 4}
+}
+
+ at book{Vandeth98,
+ author = {Jan W. van Deth},
+ title = {Comparative Politics, the problem of equivalence},
+ publisher = {Routledge},
+ year = {1998},
+ editor = {Jan W. van Deth},
+ address = {11 New Fetter Lane, London EC4P 4EE}
+}
+
+ at article{vanDoorslaer97,
+ author = {Eddy van Doorslaer},
+ title = {Income-related Inequalities in Health: Some
+ International Comparisons},
+ journal = {Journal of Health Economics},
+ year = {1997},
+ optnumber = {1},
+ optvolume = {16},
+ optpages = {93--112}
+}
+
+ at article{VanWissen01,
+ author = {Leo J.G. van Wissen},
+ title = {Demography of the Firm: A Useful Metaphor?},
+ journal = {European Journal of Population},
+ volume = {18},
+ year = {2002},
+ pages = {263-279}
+}
+
+ at book{Vapnik95,
+ author={Vladimir N. Vapnick},
+ title={The Nature of Statistical Learning Theory},
+ publisher={Springer},
+ year= 1995,
+ address={New York}
+}
+
+ at book{Vapnik98,
+ author={Vladimir N. Vapnik},
+ title={Statistical Learning Theory},
+ publisher={Wiley},
+ year= 1998 ,
+ address={New York}
+}
+
+ at book{VenRip02,
+ author={William N. Venables and Brian D. Ripley},
+ title={Modern Applied Statistics with S},
+ publisher={Springer-Verlag},
+ year={2002},
+ edition={4th}
+}
+
+ at article{VerAngCap02,
+ author={Arduino Verdecchia and Giovanni De Angelis and Riccardo Capocaccia},
+ title={Estimation and Projections of Cancer Prevalence from Cancer Registry Data},
+ journal={Statistics in Medicine},
+ volume= 21,
+ year= 2002,
+ pages={3511--3526}
+}
+
+ at article{VerCapEgi89,
+ author={A. Verdecchia and R. Capocaccia and V. Egidi and A. Golini},
+ title={A Method for the Estimation of Chronic Disease Morbidity and Trends from
+ Mortality Data},
+ journal={Statistics in Medicine},
+ volume= 8,
+ year= 1989,
+ pages={201--216}
+}
+
+ at article{Verrall93,
+ author={R.J. Verrall},
+ title={A state space formulation of Whittaker graduation, with extensions},
+ journal={Insurance: Mathematics and Economics},
+ volume= 13,
+ year= 1993,
+ pages={7--14}
+}
+
+ at article{VerSch77,
+ author={Sidney Verba and Kay Lehman Schlozman},
+ title={Unemployment, Class Consciousness, and Radical Politics: What Didn't Happen
+ in the Thirties},
+ journal={Journal of Politics},
+ volume= 39,
+ year= 1977,
+ pages={291--323},
+ number= 2
+}
+
+ at book{VerSchBra95,
+ author={Sidney Verba and Kay Lehman Schlozman and Henry E. Brady},
+ title={Voice and Equality: Civic Volunteerism in American Politics},
+ publisher={Harvard University Press},
+ year= 1995,
+ address={Cambridge, MA}
+}
+
+ at article{Villalonga04,
+ author={Belen Villalonga},
+ title={Does Diversification Cause the "Diversification Discount"?},
+ journal={Financial Management},
+ volume={33},
+ year={2004},
+ pages={5-27},
+ number={2}
+}
+
+ at article{Voth03,
+ author = {Voth, Hans-Joachim},
+ title = {With a Bang, not a Whimper: Pricking Germany's Stock
+ Market Bubble in 1927 and the Slide into Depression},
+ journal = {Journal of Economic History},
+ volume = 63,
+ year = 2003,
+ pages = {65--99},
+ number = 1
+}
+
+ at article{Voth95,
+ author = {Voth, Hans-Joachim},
+ title = {Did High Wages or High Interest Rates Bring Down the
+ Weimar Republic? A Cointegration Model of Investment
+ in Germany, 1925-1930},
+ journal = {Journal of Economic History},
+ volume = 55,
+ year = 1995,
+ pages = {801--821},
+ month = {December},
+ number = 4
+}
+
+ at article{WacWei82,
+ author={Wacholder, S. and Weinberg, C.R.},
+ title={{Paired versus Two-Sample Design for a Clinical Trial of Treatments with
+ Dichotomous Outcome: Power Considerations}},
+ journal={Biometrics},
+ volume={38},
+ year={1982},
+ pages={801--812},
+ number={3}
+}
+
+ at article{Wagstaff00,
+ author={Adam Wagstaff},
+ title={Socioeconomic inequalities in Child Mortality: Comparisons Across Nine Developing
+ Countries},
+ journal= bull,
+ year={2000},
+ optnumber={1},
+ optvolume={78},
+ optpages={19--29}
+}
+
+ at article{Wahba75,
+ author={G. Wahba},
+ title={Smoothing noisy data by spline functions},
+ journal={Numer. Math},
+ volume= 24,
+ year= 1975,
+ pages={383--393}
+}
+
+ at article{Wahba77,
+ author={G. Wahba},
+ title={Practical approximate solutions to linear operator equations when the data
+ are noisy},
+ journal={SIAM J. Numer. Anal.},
+ volume={14},
+ year={1977}
+}
+
+ at article{Wahba78,
+ author={G. Wahba},
+ title={{Improper Priors, Spline Smoothing and the Problem of Guarding Against Model
+ Errors in Regression}},
+ journal={Journal of the Royal Statistical Society B},
+ volume= 40,
+ year= 1978,
+ pages={364--372},
+ number= 3
+}
+
+ at incollection{Wahba79,
+ author={G. Wahba},
+ title={Smoothing and ill-posed problems},
+ booktitle={Solutions methods for integral equations and applications},
+ publisher={Plenum Press},
+ year= 1979,
+ address={New York},
+ editor={M. Golberg},
+ pages={183--194}
+}
+
+ at inproceedings{Wahba80,
+ author={G. Wahba},
+ title={Spline bases, regularization, and generalized cross-validation for solving
+ approximation problems with large quantities of noisy data},
+ booktitle={Proceedings of the International Conference on Approximation theory in honour
+ of George Lorenz},
+ year={1980},
+ month={January 8--10},
+ publisher={Academic Press},
+ address={Austin, TX},
+ editor={J. Ward and E. Cheney}
+}
+
+ at incollection{Wahba80a,
+ author={G. Wahba},
+ title={Spline bases, regularization, and generalized cross-validation for solving
+ approximation problems with large quantities of noisy data},
+ booktitle={Approximation theory III},
+ publisher={Academic Press},
+ year= 1980,
+ address={New York},
+ editor={W. Cheney},
+ pages={905--912}
+}
+
+ at article{Wahba85,
+ author={G. Wahba},
+ title={A comparison of {GCV} and {GML} for choosing the smoothing parameter in
+ the generalized splines smoothing problem},
+ journal={The Annals of Statistics},
+ volume= 13,
+ year= 1985,
+ pages={1378--1402}
+}
+
+ at book{Wahba90,
+ author={G. Wahba},
+ title={Splines Models for Observational Data},
+ publisher={{Series in Applied Mathematics, Vol. 59, SIAM}},
+ year= 1990 ,
+ address={Philadelphia}
+}
+
+ at techreport{WahLinZha99,
+ author={G. Wahba and Y. Lin and H. Zhang},
+ title={Generalized Approximate Cross Validation for SVM, or, anather way to look
+ at margin-like quantities},
+ institution={Department of Statistics, University of Wisconsin},
+ year={1999},
+ type={Tech. Report},
+ number={1006}
+}
+
+ at misc{Wakefield01,
+ author={Jon Wakefield},
+ title={Ecological Inference for $2 \times 2$ Tables},
+ year= 2001,
+ howpublished={Working Paper \# 12, Center for Statistics and the Social Sciences, University
+ of Washington}
+}
+
+ at article{WalCarXiaGel97,
+ author={Lance A. Waller and Bradley P. Carlin and Hong Xia and Alan E. Gelfand},
+ title={Hierarchical Spatio-Temporal Mapping of Disease Rates},
+ journal= jasa,
+ volume= 92,
+ year= 1996,
+ pages={607-617}
+}
+
+ at article{WalDon94,
+ author={G.E.L. Walraven and P.W.J. van Dongen},
+ title={Assessment of maternal mortality in Tanzania},
+ journal={British Journal of Obstetrics and Gynaecology},
+ volume= 101,
+ year= 1994,
+ pages={414--417}
+}
+
+ at book{Waldron99,
+ author={Jeremy Waldron},
+ title={Law and disagreement},
+ publisher={Oxford University Press},
+ year={1999},
+ address={New York}
+}
+
+ at article{Waletal97,
+ author={L.A. Waller and B.P. Carlin and H. Xia and A.E. Gelfand},
+ title={{Hierarchical Spatio-Temporal Mapping of Disease Rates}},
+ journal= jasa,
+ volume= 92,
+ year= 1997,
+ pages={607--617},
+ number= 438
+}
+
+ at techreport{WalHogHam06,
+ author={Robert Walker and Lesley Hoggart and Gayle Hamilton with Susan Blank},
+ title={Making random assignment happen: Evidence from the UK Employment Retention
+ and Advancement (ERA) demonstration},
+ institution={Department for Work and Pensions, Corporate Document Services},
+ year={2006},
+ month={March},
+ type={research report},
+ note={ISBN 1 84 123981 X, Research Report 330}
+}
+
+ at article{WanRob98,
+ author={Naisyin Wang and James Robins},
+ title={Large-sample theory for parametric multiple imputation procedures},
+ journal={Biometrika},
+ volume={85},
+ year={1998},
+ pages={935-948}
+}
+
+ at article{WanSchAvo05,
+ author={Philip S. Wang and Sebastian Schneeweiss and Jerry Avorn and Michael A.
+ Fischer and Helen Mogun and Daniel H. Solomon and M. Alan Brookhart},
+ title={Risk of Death inelderly Users of Conventional vs. Atypical Antipsychotic
+ Medications},
+ journal={New England Journal of Medicine},
+ volume={353},
+ year={2005},
+ pages={2335-2341},
+ month={December}
+}
+
+ at article{WanSha91,
+ author={Goya Wannamethee and A. G. Shaper },
+ title={Self-assessment of Health Status and Mortality in Middle Aged British Men},
+ journal={International Journal of Epidemiology},
+ volume= 20,
+ year= 1991,
+ pages={{239-45}},
+ number= 1
+}
+
+ at unpublished{WanYanMa06,
+ author={L Wang and G. Yang and J Ma and C Rao and X Wan and AD Lopez},
+ title={Evaluation of the quality of cause of death statistics in rural China using
+ verbal autopsies},
+ year={2006},
+ journal={Journal of Epidemiology and Community Health}
+}
+
+ at book{WapBerBra40,
+ author={Waples, D. and Berelson, B. and Bradshaw, F.R.},
+ title={{What Reading Does to People: A Summary of Evidence on the Social Effects
+ of Reading and a Statement of Problems for Research}},
+ publisher={The University of Chicago Press},
+ year={1940}
+}
+
+ at article{Ware05,
+ author={Helen Ware},
+ title={Demography, Migration and Conflict in the Pacific},
+ abstract={This article explores the relationships between demography and internal
+ conflict in the Pacific Island countries, focusing on the three subregions
+ Polynesia, Micronesia and Melanesia. These countries confront distinctive
+ challenges and opportunities because of their unique cultures and non-militarized
+ status, combined with very small size and remote locations. The use of
+ the MIRAB model of island economies based on migration, remittances, aid
+ and bureaucracy is extended to examine its impact on social cohesion and
+ the avoidance of internal conflict. For Polynesia, MIRAB is found to be
+ a sustainable development strategy. Continuous emigration from Polynesia
+ serves to reduce population pressure and communal tensions. Further, remittance
+ income supports the Polynesian economies, and this also reduces the potential
+ for conflict. For Micronesia, except Kiribati and Nauru, migration access
+ to the USA is assured. In contrast, for the Melanesian countries, there
+ is minimal emigration, rapid population growth and considerable intercommunal
+ tension, which has resulted in several coups and one 'failed state'. Demographic
+ pressure created by rapid population growth results in a lack of employment
+ opportunities for youths (who provide the majority of participators in
+ civil unrest and conflicts) rather than in direct pressure on land and
+ other natural resources.},
+ journal={Journal for Peace Research},
+ volume={42},
+ year={2005},
+ pages={435-454},
+ month={July},
+ number={4}
+}
+
+ at unpublished{WarSivCao05,
+ author={Michael D. Ward and Randolph M. Siverson and Xun Cao},
+ title={Everybody Out of the Pool!},
+ note={Michael Ward, Dept of Politcal Science, Univ of WA, Seattle mdw at u.washington.edu},
+ year={2005},
+ month={August}
+}
+
+ at techreport{WasRoe06,
+ author={Larry Wasserman and Kathryn Roeder},
+ title={Weighted Hypothesis Testing},
+ institution={Carnegie Mellon University},
+ year={2006},
+ month={April}
+}
+
+ at article{Weibe04,
+ author={Janyce M. Wiebe},
+ title={Tracking Point of View in Narrative},
+ journal={Computational Linguistics},
+ volume={20},
+ year={1994},
+ pages={233-287},
+ number={2}
+}
+
+ at article{Weibe04,
+ author={Janyce M. Wiebe},
+ title={Tracking Point of View in Narrative},
+ journal={Computational Linguistics},
+ volume={20},
+ year={1994},
+ pages={233-287},
+ number={2}
+}
+
+ at article{WeiCoxWil87,
+ author={Milton C. Weinstein and Pamela G. Coxson and Lawrence W. Williams and Theodore
+ M. Pass and William B Stason and Lee Goldman},
+ title={Forecasting Coronary Heart Disease Incidence, Mortality, and Cost: The Coronary
+ Heart Disease Policy Model},
+ journal={American Journal of Public Health},
+ volume= 77,
+ year= 1987,
+ pages={1417--1426},
+ number= 11
+}
+
+ at book{Weiss86,
+ author={N.S. Weiss},
+ title={Clinical Epidemiology: the Study of Outcome of Disease},
+ publisher={Oxford University Press, NY},
+ year={1986}
+}
+
+ at book{Weiss86,
+ author={Noel S. Weiss},
+ title={Clinical Epidemiology: The Study of the Outcome of Illness},
+ publisher={Oxford University Press},
+ year={1986},
+ volume={Volume 11},
+ address={New York},
+ series={Monographs in Epidemiology and Biostatistics }
+}
+
+ at article{WeiTan90,
+ author={Greg C. Wei and Martin A. Tanner},
+ title={A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data
+ Augmentation Algorithms},
+ journal={Journal of the American Statistical Association},
+ volume={85},
+ year={1990},
+ pages={699-704},
+ month={September}
+}
+
+ at article{WeiWanIbr97,
+ author={Robert E. Weiss and Yan Wang and Joseph G. Ibrahim},
+ title={Predictive Model Selection for Repeated Measures Random Effects Models Using
+ Bayes Factors},
+ journal={Biometrics},
+ volume= 53,
+ year= 1997,
+ pages={592--602},
+ month={June}
+}
+
+ at article{Wellhofer03,
+ title = {{Democracy and Fascism: Class, Civil Society, and
+ Rational Choice in Italy}},
+ author = {E. Spencer Wellhofer},
+ journal = {American Political Science Review},
+ volume = {97},
+ number = {01},
+ pages = {91--106},
+ year = {2003}
+}
+
+ at article{Werner00,
+ author={Suzanne Werner},
+ title={The Effects of Political Similarity on the Onset of Militarized Disputes,
+ 1816-1985},
+ journal= prq,
+ volume= 53,
+ year= 2000,
+ pages={343--374},
+ month={June}
+}
+
+ at article{Wernette1977,
+ author={Wernette, Dee Richard},
+ title={Quantitative Methods in Studying Political Mobilization in Late Weimar Germany},
+ journal={Historical Methods Newsletter},
+ volume= 10,
+ year= 1977,
+ pages={97-101}
+}
+
+ at book{WesHar97,
+ author={Mike West and Jeff Harrison},
+ title={Bayesian Forecasting and Dynamic Linear Models},
+ publisher={Springer},
+ year= 1997,
+ address={New York}
+}
+
+ at article{WesHarMig85,
+ author={Mike West and P. Jeff Harrison and Helio S. Migon},
+ title={Dynamic Generalized Linear Models and Bayesian Forecasting},
+ journal={Journal of the American Statistical Association},
+ volume={80},
+ year={1985},
+ pages={73-83},
+ month={March},
+ number={389}
+}
+
+ at article{Western95,
+ author={Bruce Western},
+ title={{Concepts and Suggestions for Robust Regression Analysis}},
+ journal={American Journal of Political Science},
+ volume={39},
+ year={1995},
+ pages={786--817},
+ number={3}
+}
+
+ at article{Western98,
+ author={Bruce Western},
+ title={{Causal Heterogeneity in Comparative Research: a Bayesian Hierarchical Modelling
+ Approach}},
+ journal={American Journal of Political Science},
+ volume= 42,
+ year= 1998,
+ pages={1233--1259},
+ month={October},
+ number= 4
+}
+
+ at article{WhiEva96,
+ author={Stephen Whitefield and Geoffrey Evans},
+ title={Support for Democracy and Poltical Opposition in Russia 1993-95},
+ journal={Post Soviet Affairs},
+ volume={12},
+ year={1996},
+ pages={218-52},
+ number={3}
+}
+
+ at book{WhiRosMcA97,
+ author={Stephen White and Richard Rose and Ian McAllister},
+ title={How Russia Votes},
+ publisher={Chatham House Publishers, Inc.},
+ year={1997},
+ address={Chatham, NJ}
+}
+
+ at unpublished{WhiSetCha06,
+ author={David R. Whiting and Philip W. Setel and Daniel Chandramohan and Lara J.
+ Wolfson and Yusuf Hemed and Alan D. Lopez},
+ title={Estimating Cause-Specific Mortality from Community- and Facility-Based Data
+ Sources in Tanzania: Options and Implications for Mortality Burden Estimates},
+ note={Whiting, MEASURE Evaluation, Carolina Population Center, Univ. of NC at
+ Chapel Hill, Dept of Medicine, School of Clinical Medical Sciences, Univ
+ of Newcastle upon Tyne England; david.whiting at ncl.ac.uk},
+ year={2006}
+}
+
+ at article{Whitbeck05,
+ author={Caroline Whitbeck},
+ title={The Responsible Collection, Retention, Sharing, and Interpretation of Data},
+ journal={Online Ethics Center for Engineering and Science},
+ year= 2005,
+ note={{http://onlineethics.org/reseth/mod/data.html}}
+}
+
+ at article{White02,
+ author={Kevin M. White},
+ title={Longevity Advances in High-IncomeCountries, 1955-96},
+ journal={Population and Development Review},
+ volume= 28,
+ year= 2002,
+ pages={59--76},
+ month={March},
+ number= 1
+}
+
+ at article{White80,
+ author={Halbert White},
+ title={A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct
+ Test for Heteroskedasticity},
+ journal={Econometrica},
+ volume={48},
+ year={1980},
+ pages={817--838},
+ month={May},
+ number={4}
+}
+
+ at book{White82,
+ author={Halbert L. White},
+ title={Asymptotic Theory For Econometricians},
+ publisher={Academic Press},
+ year= 1984,
+ address={New York}
+}
+
+ at book{White92,
+ author={Halbert H. White},
+ title={Artificial Neural Networks, Approximation and Learning Theory},
+ publisher={Blackwell},
+ year= 1992,
+ address={Cambridge, MA}
+}
+
+ at article{Whittaker23,
+ author={{Whittaker E.T.}},
+ title={On a New Method of Graduation},
+ journal={Proceedings of the Edinburgh Mathematical Society},
+ volume= 41,
+ year= 1923,
+ pages={63--75}
+}
+
+ at article{WidKub96,
+ author={Widmer, G. and Kubat, M.},
+ title={{Learning in the presence of concept drift and hidden contexts}},
+ journal={Machine Learning},
+ volume={23},
+ year={1996},
+ pages={69--101},
+ number={1},
+ publisher={Springer}
+}
+
+ at unpublished{WieWilBel01,
+ author={Janyce Wiebe and Theresa Wilson and Matthew Bell},
+ title={Identifying Collocations for Recognizing Opinions},
+ note={University of Pittsburgh wiebe, twilson, mbell at cs.pitt.edu},
+ year={2001},
+ month={April}
+}
+
+ at article{WilBerNob01,
+ author={B.P. Will and J.M. Berthelot and K.M. Nobrega and W. Flanagan and W.K. Evans},
+ title={Canada's Population Health Model (POHEM): A Tool for Performing Economic
+ Evaluations of Cancer Control Interventions},
+ journal={European Journal of Cancer},
+ volume= 37,
+ year= 2001,
+ pages={1797--1804}
+}
+
+ at article{WilGouBos02,
+ author={Brian G. Williams and Eleanor Gouws and Cynthia Boschi-Pinto and Jennifer
+ Bryce and Christopher Dye},
+ title={Estimates of world-wide distribution of child deaths from acute respiratory
+ infections},
+ journal={The Lancet Infectious Diseases},
+ volume={2},
+ year={2002},
+ pages={25-32},
+ month={January}
+}
+
+ at article{WilGouBos02,
+ author={Brian G. Williams and Eleanor Gouws and Cynthia Boschi-Pinto and Jennifer
+ Bryce and Christopher Dye},
+ title={Estimates of world-wide distribution of child deaths from acute respiratory
+ infections},
+ journal={The Lancet Infectious Diseases},
+ volume={2},
+ year={2002},
+ pages={25-32},
+ month={January}
+}
+
+ at article{WilHol07,
+ author={Elizabeth Ty Wilde and Robinson Hollister},
+ title={How Close is Close Enough? Evaluating Propensity Score Matching Using Data
+ from a Class-Size Reduction Experiment},
+ journal={Journal of Policy Analysis and Management},
+ volume={26},
+ year={2007},
+ number={3}
+}
+
+ at techreport{Wilmoth93,
+ author={John Wilmoth},
+ title={{Computational Methods for Fitting and Extrapolating the Lee-Carter Model
+ of Mortality Change}},
+ institution={Department of Demography, University of California, Berkeley},
+ year= 1993
+}
+
+ at incollection{Wilmoth96,
+ author={John R. Wilmoth},
+ title={Mortality Projections for Japan: A Comparison of Four Methods},
+ booktitle={Health and Mortality Among Elderly Populations},
+ publisher={Oxford University Press},
+ year= 1996,
+ address={Oxford},
+ editor={G. Caselli and Alan Lopez},
+ pages={266-287}
+}
+
+ at article{Wilmoth98,
+ author={John Wilmoth},
+ title={The Future of Human Longevity: A Demographer's Perspective},
+ journal={Science},
+ volume= 280,
+ year= 1998,
+ pages={395--397},
+ month={April 17},
+ number= 5362
+}
+
+ at article{Wilmoth98b,
+ author={John Wilmoth},
+ title={Is the Pace of Japanese Mortality Decline Converging Towards International
+ Trends?},
+ journal={Population and Development Review},
+ volume= 24,
+ year= 1998,
+ pages={593--600},
+ number= 3
+}
+
+ at article{WinMar92,
+ author={Christopher Winship and Robert D. Mare},
+ title={Models for Sample Selection Bias},
+ journal={Annual Review of Sociology},
+ volume={18},
+ year={1992},
+ pages={327-50}
+}
+
+ at article{WinMor99,
+ author={Christopher Winship and Stephen L. Morgan},
+ title={The Estimation of causal Effects from Observational Data},
+ journal={American Review of Sociology},
+ volume= 25,
+ year= 1999,
+ pages={659--707}
+}
+
+ at article{WinRad94,
+ author={Christopher Winship and Larry Radbill},
+ title={Sampling Weights and Regression Analysis},
+ journal={Sociological Methods and Research},
+ volume={23},
+ year={1994},
+ pages={230-257},
+ month={November},
+ number={2}
+}
+
+ at unpublished{WinSob00,
+ author={Christopher Winship and Michael Sobel},
+ title={Causal Inference in Sociological Studies},
+ note={Harvard University},
+ year= 2000
+}
+
+ at article{WirLin94,
+ author={D.N. Wirawan and M. Linnan},
+ title={The Bali indirect maternal mortality study},
+ journal={Studies in Family Planning},
+ volume= 5,
+ year= 1994,
+ pages={304--309}
+}
+
+ at inbook{WolCalJoh94,
+ author = {F.D. Wolinsky, C.M. Callahan, and R.J. Johnson},
+ title = {Subjective Health Status and Mortality in the
+ Elderly},
+ year = 1994,
+ publisher = {{New York: Springer Publishing Company}},
+ pages = {{13-28}},
+ journal = {Facts and Research in Gerontology}
+}
+
+ at article{WolFir02,
+ author = {Rory Wolfe and David Firth},
+ title = {Modelling Subjective Use of an Ordinal Reponse Scale
+ in a Many Period Crossover Experiment},
+ journal = {Applied Statistics},
+ volume = 51,
+ year = 2002,
+ pages = {245--255},
+ month = {April},
+ number = 2
+}
+
+ at article{WolJoh92,
+ author = {Fredric Wolinsky and Robert Johnson},
+ title = {Perceived Health Status and Mortality Among Older
+ Men and Women},
+ journal = {Journal of Gerontology: Social Sciences},
+ volume = 47,
+ year = 1992,
+ pages = {{S304-12}}
+}
+
+ at article{WolJohStu95,
+ author = {Fredric D. Wolinsky, Robert L. Johnson, and Timothy
+ E. Stump},
+ title = {The Riske of Mortality among Older Adults over an
+ Eight-Year Period},
+ journal = {The Gerontologist},
+ volume = 35,
+ year = 1995,
+ pages = {{150-61}}
+}
+
+ at article{WonBenKof98,
+ author = {John B. Wong and William G. Bennett and Raymond
+ S. Koff and Stephen G. Pauker},
+ title = {Pretreatment Evaluation of Chronic Hepatitis C:
+ Risks Benefits, and Costs},
+ journal = {Journal of the American Medical Association},
+ volume = 280,
+ year = 1998,
+ pages = {2088--2093}
+}
+
+ at article{WonMcqMch00,
+ author = {John B. Wong and Gerladine M. McQuillan and John
+ G. McHutchison and Thierry Poynard},
+ title = {Estimating Future Hepatitis C Morbidity, Mortality,
+ and Costs in the United States},
+ journal = {American Journal of Public Health},
+ volume = 90,
+ year = 2000,
+ pages = {1562--1569},
+ number = 10
+}
+
+ at book{WraPet96,
+ author = {Richard Wrangham and Dale Peterson},
+ title = {Demonic Males},
+ publisher = {Houghton Mifflin},
+ year = 1996
+}
+
+ at book{WuHam00,
+ author = {Chien-Fu Wu and Michael Hamada},
+ title = {Experiments: Planning, analyzing and Parameter
+ Design Optimization},
+ publisher = {Wiley-Interscience},
+ year = {2000},
+ address = {New York}
+}
+
+ at book{WuHam00,
+ author = {Chien-Fu Wu and Michael Hamada},
+ title = {Experiments: Planning, Analyzing, and Parameter
+ Design Optimization},
+ publisher = {Wiley-Interscience},
+ year = {2000},
+ address = {New York}
+}
+
+ at article{WuSch93,
+ author = {Z. Wu and R. Schaback},
+ title = {Local Error Estimates for Radial Basis Function
+ Interpolation of Scattered Data},
+ journal = {Journal of Numerical Analysis},
+ volume = 13,
+ year = 1993,
+ pages = {13--27}
+}
+
+ at article{YanRaoMa05,
+ author = {Gonghuan Yang and Chalapati Rao and Jiemin Ma and
+ Lijun Wang and Xia Wan and Guillermo Dubrovsy and
+ Alan D. Lopez},
+ title = {Validation of verbal autopsy procedures for adult
+ deaths in China},
+ journal = {International Journal of Epidemiology},
+ year = {2005},
+ month = {September},
+ note = {Advance Access published 9/6/05
+ doi:10.1093/ije/dyi181}
+}
+
+ at article{YeeHas03,
+ author = {Yee, T. W. and Hastie, T. J.},
+ title = {Reduced-rank vector generalized linear models},
+ journal = {Statistical Modelling},
+ volume = 3,
+ year = {2003},
+ pages = {15--41},
+ issue = 1
+}
+
+ at article{YeeWil96,
+ author = {T.W. Yee and C.J. Wild},
+ title = {Vector Generalized Additive Models},
+ journal = {Journal of the Royal Statistical Society. Series B
+ (Methodological)},
+ volume = {58},
+ year = {1996},
+ pages = {481--493},
+ number = {3}
+}
+
+ at article{Yoo02,
+ author = {Thomas W. Yoo},
+ title = {Presumed Disloyal: Executive Power Judicial
+ Deference, and the Construction of Race Before and
+ After September 11},
+ journal = {Columbia Human Rights Law Review},
+ volume = 34,
+ year = {2002},
+ pages = {1--??}
+}
+
+ at Article{YacYac06,
+ author = {Jason Webb Yackee and Susan Webb Yackee},
+ title = {A Bias Towards Business? Assessing Interest Group
+ Influence on the U.S. Bureaucracy},
+ journal = {Journal of Politics},
+ year = {2006},
+ OPTkey = {},
+ volume = {68},
+ number = {1},
+ pages = {128-169},
+ OPTmonth = {},
+ OPTnote = {},
+ OPTannote = {}
+}
+
+ at article{YosMagBos03,
+ author = {Hirokazu Yoshikawa and Katherine A. Magnuson and
+ Johannes M. Bos and Jo Ann Hsueh},
+ title = {Effects of Earnings-Supplement Policies on Adult
+ Economic and Middle-Childhood Outcomes Differ for
+ the `Hardest to Employ'},
+ journal = {Child Development},
+ volume = {74},
+ year = {2003},
+ pages = {1500-1521},
+ month = {September/October},
+ number = {5}
+}
+
+ at proceedings{YuHat03,
+ editor={Hong Yu and Vasileios Hatzivassiloglou},
+ title={Towards Answering Opinion Questions: Separating Facts from Opinions and
+ Identifying the Polarity of Opinion Sentences},
+ year={03},
+ organization={2003 Conference on Empirical Methods in Natural Language Processing},
+ address={Hong Yu Dept. of Computer Science columbia Univ. New York, NY 10027 hongyu at cs.columbia.edu}
+}
+
+ at article{YuKeaSly98,
+ author={Elena S. H. Yu, Yin M. Kean, Donal J. Slyman, et al},
+ title={Self Perceived Health and 5-Year Mortality Risks among the Elderly in Shanghai,
+ China},
+ journal={American Journal of Epidemiology},
+ volume= 147,
+ year= 1998,
+ pages={{880-90}}
+}
+
+ at article{yule12,
+ author={G.U. Yule},
+ title={On the Methods of Measuring the Association Between Two Attributes},
+ journal={Journal of the Royal Statistical Society},
+ volume= 75,
+ year= 1912,
+ pages={579--642}
+}
+
+ at book{Zaba86,
+ author={Basia Zaba},
+ title={Measurement of Emigration Using Indirect Techniques. Manual for the collection
+ and analysis of Data on Residence of Relatives},
+ publisher={Ordina Editions, 10, place St. Jacques 4000 Liege Belgium},
+ year={1986},
+ note={Working Group on the Methodology for the Study of International Migration}
+}
+
+ at article{ZabDav96,
+ author={Basia Zaba and Patricia H. David},
+ title={Fertility and the Distribution of Child Mortality Risk Among Women: An Illustrative
+ Analysis},
+ journal={Population Studies},
+ volume= 50,
+ year= 1996,
+ pages={263--278}
+}
+
+ at book{Zaller92,
+ author={John R. Zaller},
+ title={The Nature and Origins of Mass Opinion},
+ publisher={Cambridge University Press},
+ year= 1992,
+ address={New York, NY}
+}
+
+ at article{ZamRouOrh01,
+ author={Asad Zaman and Peter J. Rousseeuw and Mehmet Orhan},
+ title={{Econometric applications of high-breakdown robust regression techniques}},
+ journal={Economics Letters},
+ volume={71},
+ year={2001},
+ pages={1--8}
+}
+
+ at article{Zeileis04,
+ author={Achim Zeileis},
+ title={Econometric Computing with HC and HAC Covariance Matrix Estimators},
+ journal={Journal of Statistical Software},
+ volume={11},
+ year={2004},
+ pages={1--17},
+ number={10},
+ publisher={Wiley}
+}
+
+ at article{Zellner62,
+ author={A. Zellner},
+ title={An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests
+ for Aggregation Bias},
+ journal= jasa,
+ volume= 57,
+ year= 1962,
+ pages={348--368},
+ month={June},
+ number= 298
+}
+
+ at incollection{Zeng00,
+ author={Langche Zeng},
+ title={Neural Network Models for Political Analysis},
+ booktitle={Political Complexity: Nonlinear Models of Politics},
+ publisher={University of Michigan Press},
+ year= 2000,
+ editor={Diana Richards},
+ pages={239--268}
+}
+
+ at article{Zeng99,
+ author={Langche Zeng},
+ title={Classification and Prediction with Neural Network Models},
+ journal= smr,
+ volume= 27,
+ year= 1999,
+ pages={499--524},
+ month={May},
+ number= 4
+}
+
+ at article{zhang98,
+ author={Jun Zhang and F. Kai Yu},
+ title={What's the Relative Risk? A Method of Correcting the Odds Ratio in Cohort
+ Studies of Common Outcomes},
+ journal={New England Journal of Medicine},
+ volume= 280,
+ year= 1998,
+ pages={1690--1},
+ number= 19
+}
+
+ at article{Zhao04,
+ author={Zhong Zhao},
+ title={Using matching to estimate treatment effects: data requirements, matching
+ metrics, and {M}onte {C}arlo evidence},
+ journal={Review of Economics and Statistics},
+ volume= 86,
+ year= 2004,
+ pages={91-107},
+ number= 1
+}
+
+ at article{zocchetti97,
+ author={Carlo Zocchetti, Dario Consonni and Pier Bertazzi},
+ title={Relationship Between Prevalence Rate Ratios and Odds Ratios in Cross-sectional
+ Studies},
+ journal={International Journal of Epidemiology},
+ volume= 26,
+ year= 1997,
+ pages={220--23},
+ number= 1
+}
+
+ at article{Zorn01,
+ author={Christopher Zorn},
+ title={Generalized Estimating Equation Models for Correlated Data: A Review with
+ Applications},
+ journal= ajps,
+ volume= 45,
+ year= 2001,
+ pages={470--490},
+ month={April}
+}
+
+ at article{ZouTepSaa00,
+ author={Shimian Zou and Martin Tepper and Susie El Saadany},
+ title={Prediction of Hepatitis C Burden in Canada},
+ journal={Canadian Journal of Gastroenterology},
+ volume= 14,
+ year= 2000,
+ pages={575--580},
+ month={July/August},
+ number= 7
+}
+
+ at article{KnaArrMen06,
+ author = {Felicia Marie Knaul, H{\'e}ctor Arreola-Ornelas,
+ Oscar M{\'e}ndez-Carniado, Chloe Bryson-Cahn, Jeremy
+ Barofsky, Rachel Maguire, Martha Miranda,},
+ title = {Evidence is good for your health system: policy
+ reform to remedy catastrophic and impoverishing
+ health spending in Mexico},
+ journal = {Lancet},
+ volume = {368},
+ year = {2006},
+ pages = {1828-41},
+ month = {November}
+}
+
+ at article{KnaFre05,
+ author = {Felicia Marie Knaul and Julio Frenk},
+ title = {Health Insurance in Mexico: Achieving Universal
+ Coverage Through Structural Reform},
+ journal = {Health Affairs},
+ volume = {24},
+ number = {6},
+ year = {2005},
+ pages = {1828-41},
+ month = {November}
+}
+
+ at book{Turner79,
+ author={Henry Turner},
+ title={German Big Business and the Rise of Hitler},
+ publisher={Oxford University Press},
+ year= 1979,
+ address={New York}
+}
+
+ at article{CamElbAlt04,
+ author={Campbell, M. and Elbourne, D. and Altman, D.},
+ title={{CONSORT statement: extension to cluster randomised trials}},
+ journal={BMJ},
+ volume={328},
+ year={2004},
+ pages={702--708},
+ number={7441}
+}
+
+ at proceedings{HigGre06,
+ editor={JPT Higgins and S. Green},
+ title={Cochrane Handbook for Systematic Review of Interventions 4.2.5 [updated
+ September 2006]},
+ publisher={John Wiley and Sons},
+ year= 2006,
+ address={Chichester, UK},
+ series={The Cochrane Library},
+ number= 4
+}
+
+ at article{Campbell04,
+ author={Michael J Campbell},
+ title={Editorial: Extending CONSORT to include cluster trials},
+ journal={BMJ},
+ volume= 328,
+ year= 2004,
+ pages={654-655},
+ month={March},
+ note={{http://www.bmj.com/cgi/content/full/328/7441/654\%20?q=y}}
+}
+
+ at techreport{MRC02,
+ author={{Medical Research Council}},
+ title={Cluster Randomized Trials: Methodological and Ethical Considerations},
+ institution={MRC Clinical Trials Series},
+ year= 2002,
+ note={{http://www.mrc.ac.uk/Utilities/Documentrecord/index.htm?d=MRC002406}}
+}
+
+ at article{Cornfield78,
+ author={Jerome Cornfield},
+ title={Randomization by Group: a Formal Analysis},
+ journal={American Journal of Epidemiology},
+ volume={108},
+ year={1978},
+ pages={100},
+ number={2}
+}
+
+ at book{Wood06,
+ author={Simon N. Wood},
+ title={Generalized Additive Models: An Introduction with R},
+ publisher={CRC Press},
+ year= 2006,
+ address={London}
+}
+
+ at article{Wood04,
+ author={Simon N. Wood},
+ title={Stable and efficient multiple smoothing parameter estimation for generalized
+ additive models},
+ journal= jasa,
+ volume= 99,
+ year= 2004,
+ pages={673--686}
+}
+
+ at article{Wood00,
+ author={Simon N. Wood},
+ title={Modeling and Smoothing Parameter Estimation wiht Multiple Quadratic penalties},
+ journal={Journal of the Royal Statistical Society},
+ volume={62},
+ year={2000},
+ pages={413-428},
+ number={2}
+}
+
+ at manual{HamHen05,
+ author={Jeff Hamann and Arne Henningsen},
+ title={systemfit: Simultaneous Equation Systems in R Package},
+ year={2005},
+ url={{http://www.systemfit.org}}
+}
+
+ at article{BenYek01,
+ author={Yoav Benjamini and Daniel Yekutieli},
+ title={The Control of the False Discovery Rate in Multiple Teting under Dependency},
+ journal={The Annals of Statistics},
+ volume={29},
+ year={2001},
+ pages={1165-1188},
+ month={August},
+ number={4}
+}
+
+ at article{BenHoc95,
+ author={Yoav Benjamini and Yosef Hochberg},
+ title={Controlling the False Disvoery Rate: A Practical and Powerful Approach to
+ Multiple Testing},
+ journal={Journal of the Royal Statistical Society, Series B},
+ volume={57},
+ year={1995},
+ pages={289-300},
+ number={1}
+}
+
+ at techreport{WWC06,
+ author={{What Works Clearinghouse}},
+ title={Evidence Standards for Reviewing Studies},
+ institution={Institute for Educational Sciences},
+ year= 2006,
+ note={{http://www.whatworks.ed.gov/reviewprocess/standards.html}}
+}
+
+ at article{RauMarSpy07,
+ author={Stephen W. Raudenbush and Andres Martinez and Jessaca Spybrook},
+ title={Strategies for Improving Precision in Group-Randomized Experiments},
+ journal={Educational Evaluation and Policy Analysis},
+ volume= 29,
+ year= 2007,
+ pages={5--29}
+}
+
+ at article{HorKle07,
+ author={Nicholas J. Horton and Ken P. Kleinman},
+ title={Much Ado About Nothing: A Comparion of Missing Data Methods and Software
+ to Fit Incomplete Data Regression Models},
+ journal={The American Statistician},
+ volume= 61,
+ year= 2007,
+ pages={79--90},
+ month={February},
+ number= 1
+}
+
+ at book{GwaWagYaz05,
+ title={Reaching the Poor},
+ publisher={The World Bank},
+ year= 2005,
+ editor={Davidson R. Gwatkin and Adam Wagstaff and Adbo S. Yazbeck},
+ address={Washington, D.C.}
+}
+
+ at book{wdr04,
+ title={Making Services Work for Poor People: World Development Report, 2004},
+ publisher={Oxford University Press and the World Bank},
+ year= 2003,
+ editor={{World Bank}},
+ address={Washington, D.C.}
+}
+
+ at article{BauLak03,
+ author={Matthew A. Baum and David A. Lake},
+ title={The Political Economy of Growth: Democracy and Human Capital},
+ journal={American Journal of Political Science},
+ volume={47},
+ year={2003},
+ pages={333-347},
+ month={April},
+ number={2}
+}
+
+
+ at book{Lee02,
+ author={Taeku Lee},
+ title={Mobilizing Public Opinion: Black Insurgency and Racial Attitudes in the Civil Rights Era},
+ publisher={University of Chicago Press},
+ year={2002},
+ address={Chicago, IL}
+}
+
+ at book{Herbst93,
+ author={Susan Herbst},
+ title={Numbered Voices: How Opinion Polling Has Shaped American Politics},
+ publisher={University of Chicago Press},
+ year={1993},
+ address={Chicago, IL}
+}
+
+ at book{Ginsberg86,
+ title = {The Captive Public: How Mass Opinion Promotes State
+ Power},
+ author = {Benjamin Ginsberg},
+ year = {1986},
+ publisher = {Basic Books},
+ address = {New York, NY}
+}
+
+ at article{Blumer48,
+ title = {Public Opinion and Public Opinion Polling},
+ author = {Hubert Blumer},
+ journal = {American Sociological Review},
+ volume = {13},
+ number = {5},
+ pages = {542--549},
+ year = {1948}
+}
+
+ at article{Converse87,
+ title = {{Changing Conceptions of Public Opinion in the
+ Political Process}},
+ author = {Philip E. Converse},
+ journal = {The Public Opinion Quarterly},
+ volume = {51},
+ pages = {12--24},
+ year = {1987}
+}
+
+ at article{LakBau01,
+ author = {David A. Lake and Matthew A. Baum},
+ title = {The Invisible Hand of Democracy: Political Control
+ and the Provision of Public Services},
+ journal = {Comparative Political Studies},
+ volume = {34},
+ year = {2001},
+ pages = {587-621},
+ month = {August},
+ number = {6}
+}
+
+ at article{IveSos06,
+ author = {Torben Iversen and David Soskice},
+ title = {Electoral Institutions and the Politics of
+ Coalitions: Why Some Democracies Redistribute More
+ Than Others},
+ journal = apsr,
+ volume = {100},
+ year = {2006},
+ pages = {165-181},
+ month = {May},
+ number = {2}
+}
+
+ at article{Timmons05,
+ author = {Jeffrey F. Timmons},
+ title = {The Fiscal Contract: States, Taxes, and Public
+ Services},
+ journal = {World Politics},
+ volume = {57},
+ year = {2005},
+ pages = {530-567},
+ month = {July},
+ number = {4}
+}
+
+ at article{Ross06,
+ author={Michael Ross},
+ title={Is Democracy Good for the Poor?},
+ journal= ajps,
+ volume={50},
+ year={2006},
+ pages={860-874},
+ month={October},
+ number={4}
+}
+
+ at unpublished{Spence07,
+ author={Matthew J. Spence},
+ title={Do Governments Spend More to Compensate for Openness},
+ note={Working paper.},
+ year= 2007
+}
+
+
+ at article{Rodrik98,
+ author={Dani Rodrik},
+ title={Why Do More Open Economies Have Bigger Governments?},
+ journal={Journal of Political Economy},
+ volume={106},
+ year={1998},
+ pages={997-1032},
+ month={October},
+ number={5}
+}
+
+ at article{Fearon05,
+ author={James D. Fearon},
+ title={Primary Commodity Exports and Civil War},
+ journal={Journal of Conflict Resolution},
+ volume={49},
+ year={2005},
+ pages={483-507},
+ month={August},
+ number={4}
+}
+
+ at article{ColHoe04,
+ author={Paul Collier and Anke Hoeffler},
+ title={Greed and Grievance in Civil War},
+ journal={Oxford Economic Papers},
+ volume={56},
+ year={2004},
+ pages={563-595},
+ month={October},
+ number={4}
+}
+
+ at article{FeaLai03,
+ author = {James D. Fearon and David D. Laitin},
+ title = {Ethnicity, Insurgency, and Civil War},
+ journal = apsr,
+ volume = {97},
+ year = {2003},
+ pages = {75-90},
+ month = {February},
+ number = {1}
+}
+
+ at article{Marinov05,
+ author = {Nikolay Marinov},
+ title = {Do Economic Sanctions Destabilize Country Leaders?},
+ journal = ajps,
+ volume = {49},
+ year = {2005},
+ pages = {564-576},
+ month = {July},
+ number = {3}
+}
+
+ at book{Barro97,
+ author = {Robert J. Barro},
+ title = {Determinants of Economic Growth},
+ publisher = {MIT Press},
+ year = 1997,
+ address = {Cambridge}
+}
+
+ at article{ChaLin01,
+ author = {Chih-Chung Chang and Chih-Jen Lin},
+ title = {{LIBSVM}: a library for support vector machines},
+ year = {2001},
+ note = {{http://www.csie.ntu.edu.tw/~cjlin/libsvm}}
+}
+
+ at article{BosLarGie03,
+ author = {Thomas J. Bossert and Osvaldo Larra{\~n}aga and Ursula
+ Giedion Jos\'e and Jesus Arbelaez and Diana M. Bowser},
+ title = {Descentralizaci{\'o}n y distribuci{\'o}n equitativa
+ de los recursos: evidencia obtenida en Colombia y
+ Chile},
+ journal = {Bulletin of World Health Organization},
+ volume = 81,
+ year = 2003,
+ pages = {95-100},
+ number = 2
+}
+
+ at article{Bowyer04,
+ author = {Tim Bowyer},
+ title = {Popular participation and the State: democratizing
+ the health sector in rural Peru},
+ journal = {International Journal of Health Planning and
+ Management},
+ volume = 19,
+ year = 2003,
+ pages = {131-161},
+}
+ at article{CohPet97,
+ author = {John M. Cohen and Stephen B. Peterson},
+ title = {Administrative Decentalization: A New Framework for
+ Improved Governance, Accountability, and
+ Performance},
+ journal = {CID Development Discussion Paper 582},
+ year = 1997,
+ pages = {1-37},
+ month = {July},
+}
+ at book{DjuMac75,
+ title = {Alternative Approaches to Meeting Basic Health Needs
+ in Developing Countries},
+ publisher = {World Health Organization},
+ year = 1975,
+ editor = {V. Djukanovic and E.P. Mach},
+ address = {Geneva},
+}
+
+ at book{Frenk95,
+ title = {Health and the Economy: Proposals for Progress in
+ the Mexican Health System},
+ publisher = {D.F. Funsalud},
+ year = 1995,
+ editor = {V. Djukanovic and E.P. Mach},
+ address = {Mexico},
+}
+
+ at article{GaiKul02,
+ author = {Raghav Gaiha and Vani Kulkami},
+ title = {Panchayats, Communities, and the Rural Poor in
+ India},
+ journal = {Journal of Asian and African Studies},
+ volume = 37,
+ year = 2002,
+ pages = {131-161},
+}
+ at article{GonLeyAta89,
+ author = {Miguel Gonz\'alez-Block and Ren\'e Leyva and Oscar
+ Zap Ata and Ricardo Loewe and Javier Alag\'on},
+ title = {Health Services Decentralisation in Mexico:
+ Formulation, Implementation and Results of Policy},
+ journal = {Health Policy and Planning},
+ year = 1989,
+ pages = {301-315},
+ volume = 4,
+ month = {July}
+}
+
+ at book{KauNel04,
+ title = {Crucial Needs, Weak Incentives: social sector
+ reform, democratization and globalization in Latin
+ American},
+ publisher = {Wilson Center Press},
+ year = 2004,
+ editor = {Robert R. Kaufman and Joan M. Nelson},
+ address = {Washington}
+}
+
+ at article{Lloyd-Sherlock00,
+ author={Peter Lloyd-Sherlock},
+ title={Failing the needy: public social spending in Latin America},
+ journal={Journal of International Development},
+ year= 2000,
+ pages={101-119},
+ volume= 12,
+ month={July},
+
+}
+ at article{LonFre97,
+ author={Juan Luis Londo\~no and Julio Frenk},
+ title={Structured Pluralism: towards an innovative model for health system reform in Latin American},
+ journal={Health Policy},
+ year=1997,
+ pages={1-36},
+ volume= 41,
+ month={July},
+
+}
+
+ at inbook{Lustig94,
+ author={Nora Lustig},
+ title={Solidarity as a strategy of poverty alleviation},
+ year={1994},
+ publisher={Center for U.S.-Mexican Studies},
+ address={University of California, San Diego},
+ editor={Wayne Cornelius and Ann Craig and Jonathan Fox},
+}
+
+ at article{Prudhomme95,
+ author={R\'emy Prud'homme},
+ title={The Dangers of Decentralization},
+ journal={The World Bank Research Observer},
+ year=1995,
+ volume= 10,
+ month={August},
+ pages={201-220},
+ number= 2,
+}
+
+ at article{RawSheVan04,
+ author = {Laura Rawlings and Lynne Sherburne-Benz and Julie
+ Van Domelen},
+ title = {Evaluating Social Funds: A Cross Country analysis of
+ Community Investments},
+ journal = {World Bank Regional and Sectoral Studies},
+ year = 2004,
+}
+
+ at article{Shah97,
+ author = {Anwar Shah},
+ title = {Fostering Responsive and Accountable Governance:
+ Lessons from Decentralization Experience},
+ journapublisherl ={World Bank},
+ year = 1997,
+ address = {Washington, DC},
+ annote =
+ {{http://www1.worldbank.org/wbiep/decentralization/library3/shah.pdf}},
+}
+
+ at article{Smoke01,
+ author = {Paul Smoke},
+ title = {Fiscal Decentralization in Developing Countries: A
+ Review of Current Concepts and Practice},
+ journal = {Governance and Human Rights Programme Paper No. 2},
+ year = 2001,
+ month = {February},
+ journapublisherl ={UNRISD},
+ address = {Geneva},
+}
+
+ at book{Snyder01,
+ author={Richard Snyder},
+ title={Politics after neoliberalism},
+ publisher={Cambridge University Press},
+ address={Cambridge},
+ year=2001,
+}
+
+ at book{Weyland04,
+ title = {Learning From Foreign Models in Latin American
+ Policy Reform},
+ editor = {Kurt Weyland},
+ publisher = {Woodrow Wilson Center Press},
+ year = 2004,
+ address = {Washington DC},
+}
+
+ at book{Weyland96,
+ author = {Kurt Weyland},
+ title = {Democracy Without Equity: failures of reform in
+ Brazil},
+ address = {Pittsburgh},
+ publisher = {University of Pittsburgh Press},
+ year = 1996,
+}
+ at unpublished{Wallach06,
+ author={Hanna M. Wallach},
+ title={Topic Modeling: Beyond Bag-of-Words},
+ note={{http://www.icml2006.org/icml_documents/camera-ready/123_Topic_Modeling_Beyon.pdf}},
+ year= 2006,
+}
+ at unpublished{WanMccWei07,
+ author = {Xuerui Wang and Andrew MacCallum and Xing Wei},
+ title = {Topical N-grams: Phrase and Topic Discovery, with an
+ Application to Information Retreival},
+ note =
+ {{http://www.cs.umass.edu/%7Exuerui/papers/ngram_tr.pdf}},
+ year = 2007,
+}
+ at unpublished{GriSteBle04,
+ author = {Thomas L. Griffiths and mark Steyvers and David
+ M. Blei and Joshua B. Tenenbaum},
+ title = {Integrating Topics and Syntax},
+ note =
+ {{http://books.nips.cc/papers/files/nips17/NIPS2004_0642.pdf}},
+ year = 2004,
+}
+ at unpublished{ScoMat99,
+ author = {Sam Scott and Stan Matwin},
+ title = {Feature Engineering for Text Classification},
+ note =
+ {{http://www.ldv.uni-trier.de/ldvpage/naumann/textklassifikation/Textklassifikation/scott99feature.pdf}},
+ year = 1999,
+}
+ at unpublished{Sabastiani02,
+ author = {Fabrizio Sebastiani},
+ title = {Machine Learning in Automated Text Categorisation},
+ note =
+ {{http://www.math.tau.ac.il/%7Eshimsh/Text_Domain/ACMCS00.pdf}},
+ year = 2002,
+}
+ at unpublished{BekAll03,
+ author = {Ron Bekkerman and James Allan},
+ title = {Using Bigrams in Text Categorization},
+ note = {{http://ciir.cs.umass.edu/pubfiles/ir-408.pdf}},
+ year = 2003,
+}
+ at unpublished{MosBas04,
+ author = {Alessandro Moschitti and Roberto Basili},
+ title = {Complex Linguistic Features for Text
+ Classification:a comprehensive study},
+ note =
+ {{http://dit.unitn.it/\~moschitt/articles/ECIR2004.pdf}},
+ year = 2004,
+}
+
+ at article{Manor95,
+ title = {{Democratic Decentralization in Africa and Asia}},
+ author = {Manor, J.},
+ journal = {IDS Bulletin},
+ volume = {26},
+ number = {2},
+ pages = {81--88},
+ year = {1995}
+}
+
+ at article{BarMoo90,
+ title = {{Capture and Governance at Local and National
+ Levels}},
+ author = {Bardhan, P. and Mookherjee, D.},
+ journal = {The American Economic Review},
+ volume = {90},
+ number = {2},
+ pages = {135--139},
+ year = {2000}
+}
+
+ at book{SavLevBir06,
+ author = {William D. Savedoff and Ruth Levine and Nancy
+ Birdsall},
+ title = {When Will We Ever Learn? Improving Lives Through
+ Impact Evaluation},
+ publisher = {Center for Global Development},
+ year = 2006,
+ note =
+ {{http://www.cgdev.org/section/initiatives/\_active/evalgap}}
+}
+
+ at article{GonGutSte06,
+ title = {Priority setting for health interventions in
+ Mexico's System of Social Protection in Health},
+ author = {Gonz{\'a}lez-Pier, E. and Guti{\'e}rrez-Delgado,
+ C. and Stevens, G. and Barraza-Llor{\'e}ns, M. and
+ Porras-Condey, R. and Carvalho, N. and Loncich,
+ K. and Dias, R.H. and Kulkarni, S. and Casey, A. and
+ others},
+ journal = {The Lancet},
+ volume = {368},
+ number = {9547},
+ pages = {1608--1618},
+ year = {2006},
+ publisher = {Elsevier}
+}
+
+ at article{DonKla94,
+ author = {Allan Donner and Neil Klar},
+ title = {Cluster Randomization Trials in Epidemiology: Theory
+ and Application},
+ journal = {Journal of Statistical Planning and Inference},
+ volume = {42},
+ year = {1994},
+ pages = {37-56}
+}
+
+ at article{BleLaf07,
+ author = {David M. Blei and John D. Lafferty},
+ title = {A Correlated Topic Model of Science},
+ journal = {The Annals of Applied Statistics},
+ volume = {1},
+ year = {2007},
+ pages = {17-35},
+ number = {1}
+}
+
+ at article{BleNgJor03,
+ author = {David M. Blei and Andrew Y. Ng and Michael
+ I. Jordan},
+ title = {Latent Dirichlet Allocation},
+ journal = {Journal of Machine Learning Research},
+ volume = {3},
+ year = {2003},
+ pages = {993-1022}
+}
+
+ at inproceedings{GolZhu06,
+ author = {Andrew B. Goldberg and Xiaojin Zhu},
+ title = {Seeing Stars When there aren't Many Stars: Graph
+ Based Semi-Supervised Learning for Sentiment
+ Categorization},
+ booktitle = {HLT-NAACL 2006 Workshop on Textgraphs: Graph-based
+ Algorithms for Natural Language Processing},
+ year = {2006},
+ address = {New York, NY},
+ url = {{http://www.cs.wisc.edu/\~jerryzhu/pub/sslsa.pdf}}
+}
+
+ at inproceedings{Turney02,
+ author = {Peter Turney},
+ title = {Thumbs Up or thumbs Down? Semantic Orientation
+ Applied to Unsupervised Classification of Reviews},
+ booktitle = {Proceedings of ACL-02, 40th Annual Meeting of the
+ Assocation for Computational Linguistics},
+ year = {2002},
+ pages = {417-424},
+ address = {Philadelphia, US},
+ url = {{http://www.aclweb.org/anthology/P02-1053.pdf}}
+}
+
+ at inproceedings{YuHat03,
+ author = {Hong Yu and Vasileios Hatzivassiloglou},
+ title = {Towards Answering Opinion Questions: Separating
+ Facts from Opinions and Identifying the Polarity of
+ Opinion Sentences},
+ booktitle = {Proceedings of EMNLP-03, 8th Conference on Empirical
+ Methods in Natural Language Processing},
+ year = {2003},
+ editor = {Michael Collins and Mark Steedman},
+ pages = {129-136},
+ address = {Sapporo, JP},
+ url = {{http://www.aclweb.org/anthology/W03-1017.pdf}}
+}
+
+ at inproceedings{PopEtz05,
+ author = {Ana-Maria Popescu and Oren Etzioni},
+ title = {Extracting Product Features and Opinions from
+ Reviews},
+ booktitle = {Proceedings of HLT-EMNLP-05, the Human Language
+ Technology Conference / Conference on Empirical
+ Methods in Natural Language Processing},
+ year = {2005},
+ pages = {339-346},
+ address = {Vancouver, CA},
+ url = {{http://www.acl.ldc.upenn.edu/H/H05/H05-1043.pdf}}
+}
+
+ at inproceedings{BalPea94,
+ author = {Alexander Balke and Judea Pearl},
+ title = {Counterfactual Probabilities: Computational Methods,
+ Bounds and Applications},
+ booktitle = {Proceedings of the Conference on Uncertainty in
+ Artificial Intelligence (UAI-94)},
+ year = {1994},
+ month = {July},
+ address = {Seattle, WA},
+}
+
+
+ at manual{Bates07,
+ author = {Douglas Bates},
+ title = {lme4: Fit linear and generalized linear
+ mixed-effects models},
+ year = {2007},
+}
+
+
+
+ at book{PinBat00,
+ author = {Jose C. Pinheiro and Douglas M. Bates},
+ title = {Mixed-Effects Models in S and S-PLUS},
+ publisher = {Springer},
+ year = {2000},
+ address = {New York}
+}
+
+ at BOOK{BoxJon04,
+ AUTHOR = {Janet M. Box-Steffensmeier and Bradford S. Jones},
+ TITLE = {Event History Modeling: A Guide for Social
+ Scientists},
+ PUBLISHER = {Cambridge University Press},
+ YEAR = {2004},
+}
+
+ at BOOK{TheGra00,
+ AUTHOR = {Terry M. Therneau and Patricia M. Grambsch},
+ TITLE = {Modeling Survival Data: Extending the Cox Model},
+ PUBLISHER = {Springer},
+ YEAR = {2000},
+}
+
+ at book{Schoenhoven72,
+ author={Klaus Sch\"onhoven},
+ title={Die Bayerische Volkspartei 1924-1932},
+ publisher={Droste},
+ year= 1972,
+ address={D\"usseldorf}
+}
+
+ at article{Geiger30,
+ author={Theodor Geiger},
+ title={Panik im Mittelstand},
+ journal={Die Arbeit},
+ year={1930},
+ pages={637-654},
+ number={10}
+}
+
+ at article{Borchardt79,
+ author = {Knut Borchardt},
+ title = {Zwangslagen und Handlungsspielr\"aume in der
+ gro\ss{}en Wirtschaftskrise der fr\"uhen
+ drei\ss{}iger Jahre: Zur Revision des
+ \"uberlieferten Geschichtsbildes},
+ journal = {Jahrbuch der Bayerischen Akademie der
+ Wissenschaften},
+ year = {1979},
+ pages = {87-132}
+}
+
+ at book{Kruedener90,
+ author = {J{\"u}rgen von Kruedener},
+ title = {Economic Crisis and Political Collapse: The Weimar
+ Republic},
+ publisher = {Berg},
+ year = 1990,
+ address = {Oxford}
+}
+
+ at book{Barkai77,
+ author = {Avraham Barkai},
+ title = {Das Wirtschaftssystem des Nationalsozialismus},
+ publisher = {Berend von Nottbeck},
+ year = 1977,
+ address = {K\"oln}
+}
+
+ at book{Lipset60,
+ author = {Seymour Lipset},
+ title = {Political Man: The Social Bases of Politics},
+ publisher = {Johns Hopkins University Press},
+ address = {Baltimore},
+ year = 1960
+}
+
+ at article{Temin91,
+ author = {Temin,Peter},
+ title = {Soviet and Nazi Planning in the 1930s},
+ journal = {Economic History Review},
+ year = {1991},
+ volume = {44},
+ pages = {573-593}
+}
+
+ at article{Palyi41,
+ author={Palyi, Melchior},
+ title={Economic Foundations of the German Totalitarian State},
+ journal={American Journal of Sociology},
+ volume= 46,
+ year= 1941,
+ pages={469-486},
+ number= 4
+}
+
+ at article{Abelshauser99,
+ author={Abelshauser, Werner},
+ title={Kriegswirtschaft und Wirtschaftswunder},
+ journal={Vierteljahrshefte fuer Zeitgeschichte},
+ year={1999},
+ pages={503-38}
+}
+
+ at book{Kretschmar33,
+ author={Hans Kretschmar},
+ title={Deutsche Agrarprogramme der Nachkriegszeit},
+ publisher={Junker und D\"unnhaupt},
+ year= 1933,
+ address={Berlin}
+}
+
+ at book{Ruppert92,
+ author={Karsten Ruppert},
+ title={Im Dienst am Staat von Weimar: Das Zentrum als regierende Partei in der Weimarer Demokratie 1923-1930},
+ publisher={Droste},
+ year= 1992,
+ address={D\"usseldorf}
+}
+
+ at article{BucSch06,
+ title = {{The Role of Private Property in the Nazi Economy:
+ The Case of Industry}},
+ author = {Christoph Buchheim and Jonas Scherner},
+ journal = {The Journal of Economic History},
+ volume = {66},
+ number = {02},
+ pages = {390--416},
+ year = {2006},
+ publisher = {Cambridge University Press}
+}
+
+ at article{Hemmer35,
+ title = {{Die unsichtbaren Arbeitslosen}},
+ author = {Hemmer, W.},
+ journal = {Statistische Methoden-Soziale Tatsachen. Zeulenroda:
+ Bernhard Sporn, Buchdruckerei und Verlagsanstalt},
+ year = {1935}
+}
+
+ at book{Plum72,
+ author = {Plum, G\"unter},
+ title = {Gesellschaftsstruktur und politisches Bewusstsein in
+ einer katholischen Region 1928-1933: Untersuchung am
+ Beispiel des Regierungsbezirks Aachen},
+ publisher = {Deutsche Verlags-Anstalt},
+ year = 1972,
+ address = {Stuttgart}
+}
+
+ at Article{HerTre07,
+ author = {Claudia Herrera and Jesus Trevi{\~n}o},
+ title = {Aplaza Calderón hasta 2030 la meta sobre un sistema
+ universal de salud},
+ journal = {La Jornada},
+ year = 2007,
+ month = {October 6}
+}
+
+ at Article{Frenk05,
+ author = {Julio Frenk},
+ title = {Sistema de Pr\'{o}teccion Social en Salud, Elementos
+ conceptuales, financieros, y operativos},
+ journal = {Secretaria de Salud},
+ year = 2005,
+ note = {Mexico City}
+}
+
+ at InCollection{Ritschl92,
+ author = {Albrecht Ritschl},
+ title = {Die Wirtschaftspolitik des Dritten Reichs: Ein
+ {\"U}berblick},
+ booktitle = {Deutschland 1933-1945. Neue Studien zur
+ nationalsozialistischen Herrschaft},
+ year = 1992,
+ editor = {Karl-Dietrich Bracher and M. Funke and
+ H.-A. Jacobsen},
+ address = {D\"{u}sseldorf},
+ publisher = {Droste}
+}
+
+ at Book{James86,
+ author = {Harold James},
+ title = {The German Slump: Politics and Economics, 1924-1936},
+ publisher = {Clarendon Press},
+ year = 1986,
+ address = {Oxford}
+}
+ at inbook{Brady04b,
+ author={Henry E. Brady},
+ title={Rethinking Social Inquiry: Diverse Tools, Shared Standards},
+ chapter={Doing Good and Doing Better: How Far Does the Quantitative Template Get Us?},
+ year={2004},
+ publisher={Lanham, MD: Rowman and Littlefield},
+ editor={H.E. Brady and D. Collier}
+}
+ at inbook{Munck04,
+ author={Gerardo L. Munck},
+ title={Rethinking Social Inquiry: Diverse Tools, Shared Standards},
+ chapter={Tools for Qualitative Research},
+ year={2004},
+ publisher={Lanham, MD: Rowman and Littlefield},
+ editor={H.E. Brady and D. Collier}
+}
+ at inbook{Rogowski04,
+ author={Ronald Rogowski},
+ title={Rethinking Social Inquiry: Diverse Tools, Shared Standards},
+ chapter={How Inference in the Social (but Not the Physical) Sciences Neglects Theoretical Anomaly},
+ year={2004},
+ publisher={Lanham, MD: Rowman and Littlefield},
+ editor={H.E. Brady and D. Collier}
+}
+ at inbook{Bartels04,
+ author={Larry M. Bartels},
+ title={Rethinking Social Inquiry: Diverse Tools, Shared Standards},
+ chapter={Some Unfulfilled Promises of Quantitative Imperialism},
+ year={2004},
+ publisher={Lanham, MD: Rowman and Littlefield},
+ editor={H.E. Brady and D. Collier}
+}
+ at inbook{BraColSea04,
+ author={Henry E. Brady and David Collier and Jason Seawright},
+ title={Rethinking Social Inquiry: Diverse Tools, Shared Standards},
+ chapter={Refocusing the Discussion of Methodology},
+ year={2004},
+ publisher={Lanham, MD: Rowman and Littlefield},
+ editor={H.E. Brady and D. Collier}
+}
+ at inbook{ColBraSea04,
+ author={David Collier and Henry E. Brady and Jason Seawright},
+ title={Rethinking Social Inquiry: Diverse Tools, Shared Standards},
+ chapter={Critiques, Responses, and Trade-Offs: Drawing Together the Debate},
+ year={2004},
+ publisher={Lanham, MD: Rowman and Littlefield},
+ editor={H.E. Brady and D. Collier}
+}
+ at inbook{ColBraSea04b,
+ author={David Collier and Henry E. Brady and Jason Seawright},
+ title={Rethinking Social Inquiry: Diverse Tools, Shared Standards},
+ chapter={Sources of Leverage in Causal Inference: Toward an Alternative View of Methodology},
+ year={2004},
+ publisher={Lanham, MD: Rowman and Littlefield},
+ editor={H.E. Brady and D. Collier}
+}
+ at inbook{ColMahSea04,
+ author={David Collier and James Mahoney and Jason Seawright},
+ title={Rethinking Social Inquiry: Diverse Tools, Shared Standards},
+ chapter={Claiming Too Much: Warnings about Selection Bias},
+ year={2004},
+ publisher={Lanham, MD: Rowman and Littlefield},
+ editor={H.E. Brady and D. Collier}
+}
+ at article{Brady04,
+ title = {Symposium: Two Paths to a Science of Politics},
+ author = {Henry E. Brady},
+ journal = {Perspectives on Politics},
+ volume = {2},
+ pages = {295--300},
+ year = {2004}
+}
+ at article{Fischer98,
+ title = {Beyond Empiricism: Policy Inquiry in Postpositivist Perspective},
+ author = {Frank Fischer},
+ journal = {Policy Studies Journal},
+ volume = {26},
+ number = {1},
+ pages = {129--146},
+ year = {1998}
+}
+ at article{ClaGilGol06,
+ title = {A Simple Multivariate Test for Asymmetric Hypotheses},
+ author = {William Roberts Clark and Mihcael J. Gilligan and Matt Golder},
+ journal = {Political Analysis},
+ volume = {14},
+ pages = {311--331},
+ year = {2006}
+}
+ at article{Denrell03,
+ title = {Vicarious Learning, Undersampling of Failure and the Myths of Management},
+ author = {Jerker Denrell},
+ journal = {Organization Science},
+ volume = {14},
+ number = {3},
+ pages = {227--243},
+ year = {2003}
+}
+ at article{Wong02,
+ title = {Did how we learn affect what we learn? Methodological bias, multimethod research and the case of econmic development},
+ author = {Wilson Wong},
+ journal = {The Social Science Journal},
+ volume = {39},
+ pages = {247--264},
+ year = {2002}
+}
+ at article{Tilly01,
+ title = {Mechanisms in Political Processes},
+ author = {Charles Tilly},
+ journal = {Annual Review of Political Science},
+ volume = {4},
+ pages = {21--41},
+ year = {2001}
+}
+ at article{CarPan05,
+ title = {TQCA: A Technique for Adding Temporality to
+ Qualitative Comparative analysis},
+ author = {Neal Caren and Aaron Panofsky},
+ journal = {Sociological Methods Reseaarch},
+ volume = {34},
+ pages = {147},
+ year = {2005}
+}
+ at article{Ebbinghaus05,
+ title = {When Less is More: Selection Problems in a Large-N
+ and Small-N Cross-National Comparisons},
+ author = {Bernhard Ebbinghaus},
+ journal = {International Sociology},
+ volume = {20},
+ number = {2},
+ Month = {June},
+ pages = {133--152},
+ year = {2005}
+}
+ at article{Tarrow95,
+ title = {Bridging the Quantitative-Qualitative Divide in
+ Political Science},
+ author = {Sidney Tarrow},
+ journal = {American Political Science Review},
+ volume = {89},
+ number = {2},
+ Month = {June},
+ pages = {471--474},
+ year = {1995}
+}
+ at article{Tickner05,
+ title = {What Is Your Research Program? Some Feminist Answers
+ to International Relations Methodological Questions},
+ author = {J. Ann Tickner},
+ journal = {International Studies Quarterly},
+ volume = {49},
+ pages = {1--21},
+ year = {2005}
+}
+ at article{Ragin97,
+ title = {Turning the Tables: How Case-Oriented Research
+ Challenges Variable-Oriented Research},
+ author = {Charles C. Ragin},
+ journal = {Comparative Social Research},
+ volume = {16},
+ pages = {27--42},
+ year = {1997}
+}
+ at article{Abelson01,
+ author = {Julia Abelson},
+ title = {Understanding the Role of Contextual Influences on
+ Local Healh-Care Decision Making: Case Study Results
+ from Ontario, Canada},
+ journal = {Social Science & Medicine},
+ volume = {53},
+ year = {2001},
+ pages = {777--793}
+}
+ at article{Achen05,
+ author = {Christopher H. Achen},
+ title = {Two Cheers for Charles Ragin},
+ journal = {Studies in Comparative International Development},
+ volume = {40},
+ year = {2005},
+ pages = {27--32},
+ month = {Spring},
+ number = {1}
+}
+ at article{Adler97,
+ author = {Emanuel Adler},
+ title = {Seizing the Middle Ground: Constructivism in World
+ Politics},
+ journal = {European Journal of International Relations},
+ volume = {3},
+ year = {1997},
+ pages = {319-363},
+ number = {3}
+}
+ at article{Agrawal01,
+ author = {Arun Agrawal},
+ title = {Common Property Institutions and Sustainable
+ Governance of Resources},
+ journal = {World Development},
+ volume = {29},
+ year = {2001},
+ pages = {1649--1672},
+ number = {10}
+}
+ at article{Agrawal03,
+ author = {Arun Agrawal},
+ title = {Sustainable Governance of Common-Pool Resources:
+ Context, Methods, and Politics},
+ journal = {Annual Review of Anthropology},
+ volume = {32},
+ year = {2003},
+ pages = {243--262}
+}
+ at article{AmeHalYou99,
+ author = {Edwin Amenta and Drew Halfmann and Michael P. Young},
+ title = {The Strategies and Contexts of Social
+ Protest:Political Mediation and the Impact of the
+ Townsend Movement in California},
+ journal = {Mobilization: An International Journal},
+ volume = {4},
+ year = {1999},
+ pages = {1--23},
+ number = {1}
+}
+ at unpublished{Andersen03,
+ author = {Svein S. Andersen},
+ title = {On a Cleary Day You Can See the EU. Case Study
+ Methodology in EU Research},
+ note =
+ {{http://www.arena.uio.no/publications/working-papers2003/papers/03_16.xml}},
+ year = 2003,
+}
+ at unpublished{AndWet01,
+ author = {Steinar Andresen and Joergen Wettestad},
+ title = {Case studies of the effectiveness of international
+ environmental regimes: Balancing textbook ideals and
+ feasibility concerns},
+ note = {{http://www.fni.no/doc&.pdf/rapp1901.pdf}},
+ year = {2001}
+}
+ at article{AspSch00,
+ author = {Mark D. Aspinwall and Gerald Schneider},
+ title = {Same Menu, Seperate Tables: The Institutionalist
+ Turn in Political Science and the Study of European
+ Integration},
+ journal = {European Journal of Political Research},
+ volume = {38},
+ year = {2000},
+ pages = {1--36}
+}
+ at article{Bartle00,
+ author = {John Bartle},
+ title = {Political Awareness, Opinion Constraining and the
+ Stablility of Ideological Positions},
+ journal = {Political Studies},
+ volume = {48},
+ year = {2000},
+ pages = {467--484}
+}
+ at article{Bartle03,
+ author = {John Bartle},
+ title = {Partisanship, Performance and Personality: Competing
+ and Complementary Characterizations of the 2001
+ British General Election},
+ journal = {Party Politics},
+ volume = {9},
+ year = {2003},
+ pages = {317--345},
+ number = {3}
+}
+ at article{Beck06,
+ author = {Nathaniel Beck},
+ title = {Causal Process ``Observation'': Oxymoron or Old
+ Wine},
+}
+ at article{Bellin00,
+ author = {Eva Bellin},
+ title = {Contingent Democrats: Industrialists, Labor, and
+ Democratization in Late-Developing Countries},
+ journal = {World Politics},
+ volume = {52},
+ year = {2000},
+ pages = {175-205},
+ month = {January}
+}
+ at article{BelMacTha01,
+ author = {Duncan S.A. Bell and Paul K. MacDonald and Bradley
+ A. Thayer},
+ title = {Start the Evolution Without Us},
+ journal = {International Security},
+ volume = {26},
+ year = {2001},
+ pages = {187--198},
+ month = {Summer},
+ number = {1}
+}
+ at article{BerLebSte00,
+ author = {Steven Bernstein and Richard Ned Lebow and Janice
+ Gross Stein and Steven Weber},
+ title = {God Gave Physics the Easy Problems: Adapting Social
+ Science to an Unpredictable World},
+ journal = {European Journal of International Relations},
+ volume = {6},
+ year = {2000},
+ pages = {43--76},
+ number = {1}
+}
+ at article{Berman97,
+ author = {Sheri Berman},
+ title = {Civil Society and the Collapse of the Weimar
+ Republic},
+ journal = {World Politics},
+ volume = {49},
+ year = {1997},
+ pages = {401--429},
+ number = {3}
+}
+ at article{BlaDob98,
+ author = {Andr\'{e} Blais and Agnieszka Dobrznska},
+ title = {Turnout in electoral democracies},
+ journal = {European Journal of Political Research},
+ volume = {33},
+ year = {1998},
+ pages = {239--261},
+ number = {}
+}
+ at article{BogCatKel00,
+ author = {Laura M. Bogart and Sheryl L. Catz and Jeffrey
+ A. Kelly and Michelle L. Gray-Bernhardt, and Barbara
+ R. Hartmann and Laura L. Otto-Salaj and Kristin
+ L. Hackl and Frederick R. Bloom},
+ title = {Psychosical Issues in the Era of New Aids Treatments
+ from the Perspective of Persons Living with HIV},
+ journal = {2000},
+ volume = {5},
+ year = {2000},
+ pages = {500--516},
+ number = {4}
+}
+
+ at article{Bostrom03,
+ author = {Magnus Bostro{\''m}},
+ title = {How State-Dependent is a Non-State-Driven
+ Rule-Making Project? The Case of Forest
+ Certification in Sweden},
+ journal = {Journal of Envrionmental Policy & Planning},
+ volume = {5},
+ year = {2003},
+ pages = {165--180},
+ number = {2}
+}
+
+ at article{Brady07,
+ author = {Henry E. Brady},
+ title = {Using a Simple Model of Decision-Making to Select
+ and Understand Cases}
+}
+ at article{BraLanHal00,
+ author = {Paul Brace and Laura Langer and Melinda Gann Hall},
+ title = {Measuring the Preferences of State Supreme Court
+ Judges},
+ journal = {The Journal of Politics},
+ volume = {62},
+ year = {2000},
+ pages = {387--413},
+ month = {May},
+ number = {2}
+}
+ at article{BraOhr99,
+ author = {AnnP. Branch and Jakob C. Ohrgaard},
+ title = {Trapped in the Supranational-Intergovernmental
+ Dichotomy: A Respone to Stone Sweet and Sandholtz},
+ journal = {Journal of European Public Policy},
+ volume = {6},
+ year = {1999},
+ pages = {123--143},
+ number = {1}
+}
+ at unpublished{Braumoeller99,
+ author = {Bear F. Braumoeller},
+ title = {Statistical Estimation in the Presence of Multiple
+ Causal Paths},
+ note = {paper prepared of rhte annual meeting of the Midwest
+ Political Science Association, Chicago, IL April
+ 15-17, 1999.},
+ year = {1999}
+}
+ at article{Braumoeller03,
+ author = {Bear F. Braumoeller},
+ title = {Causal Complexity and the Study of Politics},
+ journal = {Political Analysis},
+ volume = {11},
+ year = {2003},
+ pages = {209-233}
+}
+ at article{Brecher99,
+ author = {Michael Brecher},
+ title = {International Studies in the Twentieth Century and
+ Beyond: Flawed Dichotomies, Symthesis, Cumulation:
+ ISA Presidential Address},
+ journal = {International Studies Quarterly},
+ volume = {43},
+ year = {1999},
+ pages = {213-264},
+ month = {June},
+ number = {2}
+}
+ at article{BreKerPet03,
+ author = {Mark D. Brewer and Rogan Kersh and R. Eric Petersen},
+ title = {Assessing Conventional Wisdom about Religion and
+ Politics: A Preliminary View from the pews},
+ journal = {Journal for the Scientific Study of Religion},
+ volume = {42},
+ year = {2003},
+ pages = {125--136},
+ number = {}
+}
+ at article{Burden05,
+ author = {Barry C. Burden},
+ title = {Ralph Nader's Campaign Strategy in the 2000
+ U.S. Presidential Election},
+ journal = {American Politics Research},
+ volume = {33},
+ year = {2005},
+ pages = {672--699},
+ month = {September},
+ number = {5}
+}
+ at article{Buthe02,
+ author = {Tim B\''{u}the},
+ title = {Taking Temporality Seriously: Modeling History and
+ the Use of Narratives as Evidence},
+ journal = {American Political Science Review},
+ volume = {96},
+ year = {2002},
+ pages = {481--493},
+ month = {September},
+ number = {3}
+}
+
+ at article{CamLapRie00,
+ author = {Charles Cameron and John S. Lapinski and Charles
+ R. Riemann},
+ title = {Testing Formal Theories of Political Rhetoric},
+ journal = {The Journal of Politics},
+ volume = {62},
+ year = {2000},
+ pages = {187--205},
+ month = {February},
+ number = {1}
+}
+ at article{Caporaso95,
+ author = {James A. Caporaso},
+ title = {Review: Research Design, Falsification, and the
+ Qualitative-Quantitative Divide},
+ journal = {American Political Science Review},
+ volume = {89},
+ year = {1995},
+ pages = {457--460},
+ month = {June},
+ number = {2}
+}
+ at article{ChiRot03,
+ author = {Fang-Yi Chiou and Lawrence S. Rothenberg},
+ title = {When Pivotal Politics Meets Partisan Politics},
+ journal = {American Journal of Political Science},
+ volume = {47},
+ year = {2003},
+ pages = {503--522},
+ month = {July},
+ number = {3}
+}
+ at article{ClaGilGol06,
+ author = {William Roberts Clark and Michael J. Gilligan and
+ Matt Golder},
+ title = {A Simple Multivariate Test for Asymmetric
+ Hypotheses},
+ journal = {Political Analysis},
+ volume = {14},
+ year = {2006},
+ pages = {311--331}
+}
+ at article{Clarke05,
+ author = {Kevin A. Clarke},
+ title = {The Phantom Menace: Omitted Variable Bias in
+ Econometric Research},
+ journal = {Conflict management and Peace Science},
+ volume = {22},
+ year = {2005},
+ pages = {341--352},
+ number = {4}
+}
+ at article{Collier95,
+ author = {David Collier},
+ title = {Review: Translating Quantitative Methods for
+ Qualitative Researchers: the Case of Selection Bias},
+ journal = {American Political Science Review},
+ volume = {89},
+ year = {1995},
+ pages = {461--466},
+ month = {June},
+ number = {2}
+}
+ at article{Denrell03,
+ author = {Jerker Denrell},
+ title = {Vicarious Learning, Undersampling of Failure, and
+ the Myths of Management},
+ journal = {Organization Science},
+ volume = {14},
+ year = {2003},
+ pages = {227--243},
+ month = {May-June},
+ number = {3}
+}
+ at article{DerBou04,
+ author = {Mark de Rond and Hamid Bouchikhi},
+ title = {On the dialectics of Strategic Alliances},
+ journal = {Organization Science},
+ volume = {15},
+ year = {15},
+ pages = {56-69},
+ month = {January-February},
+ number = {1}
+}
+ at article{DesFinHen00,
+ author = {Laura Desimone and Matia Finn-Stevenson and
+ Christopher Henrich},
+ title = {Whole School Reform in a Lowe-Income African
+ American Community: The Effects of the CoZi Model on
+ Teachers, Parents, and Students},
+ journal = {Urban Education},
+ volume = {35},
+ year = {2000},
+ pages = {269}
+}
+ at article{DeSoysa02,
+ author = {Indra de Soysa},
+ title = {Ecoviolence: Shrinking Pie, or Honey Pot?},
+ journal = {Global Environmental Politics},
+ volume = {2},
+ year = {2002},
+ pages = {1--34},
+ month = {November},
+ number = {4}
+}
+ at article{DeSoysa02b,
+ author = {Indra De Soysa},
+ title = {Paradise Is a Bazaar? Greed, Creed and Governance in
+ Civil War, 1989-99},
+ journal = {Journal of Pece Research},
+ volume = {39},
+ year = {2002},
+ pages = {395--416},
+ number = {4}
+}
+ at article{DicLev99,
+ author = {Jonathan M. Dicicco and Jack S. Levy},
+ title = {Power Shifts and Problem Shifts},
+ journal = {Journal of Conflict Resolution},
+ volume = {43},
+ year = {1999},
+ pages = {675--704},
+ month = {December},
+ number = {6}
+}
+ at article{Dorussen01,
+ author = {Han Dorussen},
+ title = {Mixing Carrots with Sticks: Evaluating the
+ Effectiveness of Positive Incentives},
+ journal = {Journal of Peace Research},
+ volume = {38},
+ year = {2001},
+ pages = {251--262},
+ number = {2}
+}
+ at article{Dowding01,
+ author = {Keith Dowding},
+ title = {There Must Be End to Confusion: Policy Networks,
+ Intellectual Fatigue, and the Need for Political
+ Science Methods Courses in British Universities},
+ journal = {Political Studies},
+ volume = {49},
+ year = {2001},
+ pages = {89--105}
+}
+ at article{Druckman04,
+ author = {James N. Druckman},
+ title = {Political Preference Formation: Competition,
+ Deliberation, and the (Ir)relevance of Framing
+ Effects},
+ journal = {American Political Science Review},
+ volume = {98},
+ year = {2004},
+ pages = {671--686},
+ month = {November},
+ number = {4}
+}
+ at article{Elgie04,
+ author = {Robert Elgie},
+ title = {Semi-Presidentialism: Concepts, Consequences and
+ contesting Explanations},
+ journal = {Political Studies Review},
+ volume = {2},
+ year = {2004},
+ pages = {314--330}
+}
+ at article{ElmElm02,
+ author = {Colin Elman and Miriam Fendius Elman},
+ title = {How Not to Be Lakatos Intolerant: Appraising
+ Progress in IR Research},
+ journal = {International Studies Quarterly},
+ volume = {46},
+ year = {2002},
+ pages = {231--262}
+}
+ at article{BerLor99,
+ author = {Bernard I. Finel and Kristin M. Lord},
+ title = {The Surprising Logic of Transparency},
+ journal = {International Studies Quarterly},
+ volume = {43},
+ year = {1999},
+ pages = {315--339},
+ month = {June},
+ number = {June}
+}
+ at article{Forster98,
+ author = {Anthony Forster},
+ title = {Britain and the Negotiation of the Maastricht
+ Treaty: A Critique of Liberal Intergovernmentalism},
+ journal = {Journal of Common Market Studies},
+ volume = {36},
+ year = {1998},
+ pages = {347--368},
+ month = {September},
+ number = {3}
+}
+ at article{Fricke03,
+ author = {Tom Fricke},
+ title = {Culture and Causality: An Anthropological Comment},
+ journal = {Population and Development Review},
+ volume = {29},
+ year = {2003},
+ pages = {470--479},
+ month = {September},
+ number = {3}
+}
+ at article{From02,
+ author = {Johan From},
+ title = {Decision-making in a complex envrironment: A
+ sociological institutionalist analysis of
+ competition policy decision-making in the European
+ Commission},
+ journal = {Journal of European Public Policy},
+ volume = {9},
+ year = {2002},
+ pages = {219--237},
+ number = {2}
+}
+ at article{Galaz05,
+ author = {Victor Galaz},
+ title = {Social-ecological Resilience and Social Conflict:
+ Institutions and Strategic Adaptation in Swedish
+ Water Management},
+ journal = {Ambio},
+ volume = {34},
+ year = {2005},
+ pages = {567--572},
+ month = {November},
+ number = {7}
+}
+
+ at Article{MadHofKup07,
+ author = {Temina Madon and Karen J. Hofman and Linda Kupfer
+ and Roger I. Glass},
+ title = {Implementation Science},
+ journal = {Science},
+ year = {2007},
+ volume = {318},
+ pages = {1728--1729},
+ month = {14 December}
+}
+
+ at Article{Steele05,
+ author = {J. Michael Steele},
+ title = {Darrell Huff and Fifty Years of \emph{How to Lie
+ With Statistics}},
+ journal = {Statistical Science},
+ year = 2005,
+ volume = 20,
+ number = 3,
+ pages = {205-209}
+}
+
+ at book{Huff54,
+ title = {{How to Lie With Statistics}},
+ author = {Darrell Huff},
+ year = {1954},
+ address = {New York},
+ publisher = {WW Norton \& Company}
+}
+
+ at article{HeiRub91,
+ title = {{Ignorability and Coarse Data}},
+ author = {Heitjan, D.F. and Rubin, D.B.},
+ journal = {The Annals of Statistics},
+ volume = {19},
+ number = {4},
+ pages = {2244--2253},
+ year = {1991}
+}
+
+ at Article{Izenman91,
+ author = {Alan Julian Izenman},
+ title = {Recent developments in nonparametric density estimation},
+ journal = {Journal of the American Statistical Association},
+ year = 1991,
+ volume = 86,
+ number = 413,
+ pages = {205--224}
+}
+
+ at article{Gartzke99,
+ title = {War is in the Error Term},
+ author = {Erik Gartzke},
+ journal = {International Organization},
+ volume = {53},
+ year = {1999},
+ pages = {567--587},
+ month = {Summer},
+ number = {3}
+}
+
+ at article{GauLie04,
+ author = {Varun Gauri and Evan S. Lieberman},
+ title = {Institutions, Social Boundaries, and Epidemics:
+ Explaining Goverment AIDS Policies in Brazil and
+ South Africa}
+}
+
+ at article{GelGri01,
+ author = {Christopher Gelpi and Joseph M. Grieco},
+ title = {Democracy, Leadership Tenure, and the Targeting of
+ Militarized challenges, 1918-1992},
+ journal = {Journal of Conflict Resolution},
+ volume = {45},
+ year = {2001},
+ pages = {794--817},
+ month = {December},
+ number = {6}
+}
+
+ at article{GerBar03,
+ author = {John Gerring and Paul A. Barresi},
+ title = {Putting Ordinary Language to Work: A Min-Max
+ Strategy of Concept Formation in the Social
+ Sciences},
+ journal = {Journal of Theoretical Politics},
+ volume = {15},
+ year = {2003},
+ pages = {201--232},
+ number = {2}
+}
+
+ at article{GerGreKap03,
+ author = {Alan S. Gerber and Donald P. Green and Edward
+ H. Kaplan},
+ title = {The Illusion of Learning from Observational
+ Research},
+ year = {2003}
+}
+
+ at article{GerMcD07,
+ author = {John Gerring and Rose McDermott},
+ title = {An Experimental Template for case Study Research},
+ journal = {American Journal of Political Science},
+ volume = {51},
+ year = {2007},
+ pages = {688--701},
+ month = {July},
+ number = {3}
+}
+
+ at article{Gerring04,
+ author = {John Gerring},
+ title = {What Is a Case Study and What Is It Good for?},
+ journal = {American Political Science Review},
+ volume = {98},
+ year = {2004},
+ pages = {341--354},
+ month = {May},
+ number = {2}
+}
+
+ at article{Gerring05,
+ author = {John Gerring},
+ title = {A unified Framework for The Social Sciences},
+ journal = {Journal of Theoretical Politics},
+ volume = {17},
+ year = {2005},
+ pages = {163--198},
+ number = {2}
+}
+
+ at article{Gilardi01,
+ author = {Fabrizio Gilardi},
+ title = {Policy Credibility and Delegation of Regulatory
+ Competencies or Independent Agencies: A Comparative
+ Empirical Consideration},
+ year = {2001}
+}
+
+ at article{Gilardi02,
+ author = {Fabrizio Gilardi},
+ title = {Policy Credibility and Delegation to Independent
+ Regulatory Agencies: A Comparative Empirical
+ Analysis},
+ journal = {Journal of European Public Policy},
+ volume = {9},
+ year = {2002},
+ pages = {873--893},
+ month = {December},
+ number = {6}
+}
+
+ at article{Glaser02,
+ author = {Barney G. Glaser},
+ title = {Conceptualization: On Theory and Theorizing Using
+ Grounded theory},
+ journal = {International Journal of Qualitative Methods},
+ volume = {1},
+ year = {2002},
+ note = {{Article 3 from http://www.ualberta.ca/~ijqm/}},
+ month = {Spring},
+ number = {2}
+}
+
+ at article{GoeLev05,
+ author = {Gary Goertz and Jack S. Levy},
+ title = {Causal Explanations, Necessary Conditions, and Case
+ Studies: World War I and the End of the Cold War}
+}
+
+ at article{Goerzen05,
+ author = {Anthony Goerzen},
+ title = {Managing Alliance Networks: Emerging Practices of
+ Multinational Corporations},
+ journal = {Academy of management Executive},
+ volume = {19},
+ year = {20},
+ pages = {94--107},
+ number = {2}
+}
+
+ at article{Golder03,
+ author = {Matt Golder},
+ title = {Explaining Variation in the Success of Extreme Right
+ Parties in Western Europe},
+ journal = {Comparative Political Studies},
+ volume = {36},
+ year = {2003},
+ pages = {432--466},
+ month = {May},
+ number = {4}
+}
+ at article{Grendstad99,
+ author = {Gunnar Grendstad},
+ title = {A Political Cultural Map of Europe. A Survey Approach},
+ journal = {GeoJournal},
+ volume = {47},
+ year = {20},
+ pages = {463--475}
+}
+
+ at article{Grigorian05,
+ author = {Arman Grigorian},
+ title = {Third-Party Intervention and Escalation in Kosovo:
+ Does Moral Hazard Explain it?},
+ journal = {Ethnopolitics},
+ volume = {4},
+ year = {2005},
+ pages = {195--213},
+ number = {2}
+}
+
+ at article{Guzzini01,
+ author = {Stefano Guzzini},
+ title = {The Significance and Roles of Teaching Theory in
+ International Relations},
+ journal = {Journal of International Relations and Development},
+ volume = {4},
+ year = {2001},
+ pages = {98--117},
+ number = {2}
+}
+
+ at article{Gwako97,
+ author = {Edwins Laban Moogi Gwako},
+ title = {Conjugal Power in Rural Kenya Families: Its
+ Influence on Women's Decsions About Family Size and
+ Family Planning Practices},
+ journal = {Sex Roles},
+ volume = {36},
+ year = {1997},
+ pages = {127--147},
+ month = {February},
+ number = {3/4}
+}
+
+ at article{Hansen98,
+ author = {Kenneth N. Hansen},
+ title = {Identifying Facets of Democratic Administration: The
+ Empirical Referents of Discourse},
+ journal = {Administration & Society},
+ volume = {30},
+ year = {1998},
+ pages = {443--461},
+ month = {September},
+ number = {4}
+}
+
+ at article{Harcourt00,
+ author = {Bernard E. Harcourt},
+ title = {After the "Social Meaning Turn": Implications for Research Design and methods of Proof in Contemporary Criminal Law Policy Analysis},
+ journal = {Law & Society Review},
+ volume = {34},
+ year = {20},
+ pages = {179--211},
+ number = {1}
+}
+
+ at article{Haverland03,
+ author = {Markus Haverland},
+ title = {Methodological Issues in Europeanisation Research:
+ the `No Variation' problem},
+ note = {prepared for preentation the section
+ 'Europeanisation: Challengs of a New Research
+ Agenda', panl `Europeanisation: Concepts and
+ Methods', ECPR Conference, Marburg, 18-21 September,
+ 2003},
+ year = {2003},
+ pages = {},
+ month = {September}
+}
+
+ at article{Haverland06,
+ author = {Markus Haverland},
+ title = {Does the EU Cause Domestic Developments? Improving
+ Case Selection in Europeanisation Research},
+ journal = {West European Politics},
+ volume = {29},
+ year = {2006},
+ pages = {134--146},
+ month = {January},
+ number = {1}
+}
+
+ at article{Hawkins04,
+ author = {Darren Hawkins},
+ title = {Explaining Costly International Institutions:
+ Persuasion and Enforceable Human Rights Norms},
+ journal = {International Studies Quarterly},
+ volume = {48},
+ year = {2004},
+ pages = {779--804}
+}
+
+ at article{Hay04,
+ author = {Colin Hay},
+ title = {Theory, Stylized Heuristic or Self-Fulfilling
+ Prophecy? The Status of Rational Choice Theory in
+ Public Administration},
+ journal = {Public Administration},
+ volume = {82},
+ year = {2004},
+ pages = {39--62},
+ number = {1}
+}
+
+ at article{HelHer01,
+ author = {Gunther Hellmann and Benjamin Herborth},
+ title = {Democratic Peace and Militarized Interstate Disputes
+ in the Transatlantic Community},
+ note = {Paper prepared for presentation at the 42. Annual
+ Convention of the International Studies Assocation
+ in Chicago, 21\-25 February 2001},
+ year = {2001}
+}
+
+ at article{HelMul03,
+ author = {Gunther Hellmann and Harald M\"{u}ller},
+ title = {Editing (I)nternational (R)elations: A Changing
+ World},
+ journal = {Journal of International Relations and Development},
+ volume = {6},
+ year = {2003},
+ pages = {372--389},
+ month = {December},
+ number = {4}
+}
+ at article{HesLea97,
+ author = {Frederick M. Hess and David L. Leal},
+ title = {Minority Teachers, Minority Students, and College Matriculation: A New Look at the Role-Modeling Hypothesis},
+ journal = {Policy Studies Journal},
+ volume = {25},
+ year = {1997},
+ pages = {235-248},
+ number = {2}
+}
+ at article{HesLea99,
+ author = {Frederick M. Hess and David L. Leal},
+ title = {Computer-Assisted Learning in Urban Classrooms:The
+ Impact of Politics, Race, and Class},
+ journal = {Urban Education},
+ volume = {34},
+ year = {1999},
+ pages = {370}
+}
+ at article{Hess99,
+ author = {Frederick M. Hess},
+ title = {A Political Explanation of Policy Selection: The
+ Case of Urban School Reform},
+ journal = {Policy Studies Journal},
+ volume = {27},
+ pages = {459--473},
+ number = {3}
+}
+ at article{Hite03,
+ author = {Julie M. Hite},
+ title = {Patterns of Multidimensionality Among Embedded Network Ties: A Typology of Relational Embeddedness in Emerging Entrepreneurial Firms},
+ journal = {Strategic Organization},
+ volume = {1},
+ year = {2003},
+ pages = {9--49},
+ number = {1}
+}
+ at article{Hite05,
+ author = {Julie M. Hite},
+ title = {Evolutionary Processes and Paths of Relationally
+ Embedded Network Ties in Emerging Entrepreneurial
+ Firms},
+ journal = {Entrepreneurship, Theory \& Practice},
+ volume = {29},
+ year = {2005},
+ pages = {113--144},
+ month = {January},
+ number = {1}
+}
+ at article{HodHar03,
+ author = {Matthew Hoddie and Caroline Hartzell},
+ title = {Civil War Settlements and the Implementation of
+ Military Power-Sharing Arrangements},
+ journal = {Journal Of Peace Research},
+ volume = {40},
+ year = {2003},
+ pages = {303-320},
+ number = {3}
+}
+ at article{HofOca01,
+ author = {Andrew J. Hoffman and William Ocasio},
+ title = {Not All Events Are Attended Equally: Toward a
+ Middle-Range Theory of Industry Attention to
+ External Events},
+ journal = {Organization Science},
+ volume = {12},
+ year = {2001},
+ pages = {414--434},
+ month = {July-August},
+ number = {4}
+}
+ at article{Hooghe97,
+ author = {Liesbet Hooghe},
+ title = {Serving `Europe' - Political Orientations of Senior
+ Commission Officials},
+ journal = {European Integration Online Papers},
+ volume = {1},
+ year = {1997},
+ note = {{http://eiop.or.at/eiop/texte/1997-008a.htm}},
+ month = {April},
+ number = {8}
+}
+ at article{HowPerVil04,
+ author = {Susan E. Howell and Huey L. Perry and Matthew vile},
+ title = {Black Cities / White Cities: Evaluating the Police},
+ journal = {Political Behavior},
+ volume = {26},
+ year = {2004},
+ pages = {45--68},
+ month = {March},
+ number = {1}
+}
+ at article{JacLan04,
+ author = {Karen Jacobsen and Loren B. Landau},
+ title = {The Dual Imperative in Refugee Research: Some Methodological and Ethical Considerations in Social Science Research on Forced Migration},
+ journal = {Disasters},
+ volume = {27},
+ year = {2003},
+ pages = {185--206},
+ number = {3}
+}
+ at article{JacPhaSwy04,
+ author = {Dirk Jacobs and Karen Phalet and Marc Swyngedouw},
+ title = {Associational Membership and Political Involvement Among Ethnic Minority Groups in Brussels},
+ journal = {Journal of Ethnic and Migration Studies},
+ volume = {30},
+ year = {2004},
+ pages = {543--559},
+ number = {3}
+}
+ at article{JacTil04,
+ author = {Dirk Jacobs and Jean Tillie},
+ title = {Introduction: Social Capital and Political Integration of Migrants},
+ journal = {Journal of Ethnic and Migration Studies},
+ volume = {30},
+ year = {2004},
+ pages = {419--427},
+ number = {3}
+}
+ at article{Johnson04,
+ author = {Craig Johnson},
+ title = {Uncommon Ground: The `Poverty of History' in common Property Discourse},
+ journal = {Development and Change},
+ volume = {35},
+ year = {2004},
+ pages = {407--433},
+ number = {3}
+}
+ at article{JonSte97,
+ author = {Bradford S. Jones and Marco R. Steenbergen},
+ title = {Modeling Multilevel Data Structures},
+ year = {1997},
+ note = {Annual Meetings of the Political Methodology
+ Society}
+}
+ at article{KaaBea99,
+ author = {Juliet Kaarbo and Ryan K. Beasley},
+ title = {A Practical Guide to the Comparative Case Study
+ Method in Political Psychology},
+ journal = {Political Psychology},
+ volume = {20},
+ year = {1999},
+ pages = {369--391},
+ number = {2}
+}
+ at article{KatVomMah05,
+ author = {Aaron Katz and Matthias vom Hau and James Mahoney},
+ title = {Explaining the Great Reversal in Spanish America:
+ Fuzzy-Set Analysis Versus Regression Analysis},
+ journal = {Sociological Methods Research},
+ volume = {33},
+ year = {2005},
+ pages = {539--573}
+}
+
+ at Book{Kenneally07,
+ author = {Christine Kenneally},
+ title = {The First Word: The Search for the Origins of Language},
+ publisher = {Viking},
+ year = 2007,
+ address = {New York}
+}
+
+ at book{Coombs65,
+ title = {A Theory of Data},
+ author = {C.H. Coombs},
+ year = {1965},
+ address = {New York},
+ publisher = {Wiley}
+}
+
+ at article{CEFP06,
+ author = {Centro de Estudios de las Finanzas P{\'u}blicas,
+ C{\'a}mara de Diputados},
+ title = {Gasto en el Sector Salud},
+ journal = {Nota Informativa},
+ volume = {64},
+ month = {September},
+ year = {2006},
+}
+
+ at Book{Ayres07,
+ author = {Ian Ayres},
+ title = {Supercrunchers},
+ publisher = {Random House},
+ year = 2007,
+ address = {New York}
+}
+
+ at Article{Ross06b,
+ author = {Philip E. Ross},
+ title = {The Expert Mind},
+ journal = {Scientific American},
+ year = {2006},
+ month = {August},
+ note = {{http://www.sciam.com/article.cfm?id=the-expert-mind}}
+}
+
+ at article{RubTho92,
+ author = {Donald B. Rubin and Neal Thomas},
+ title = {Affinely Invariant Matching methods with Ellipsoidal
+ Distributions},
+ journal = {Annals of Statistics},
+ volume = {20},
+ number = {2},
+ year = {1992},
+ pages = {1079-1093}
+}
+
+ at article{Rubin76b,
+ author = {Donald B. Rubin},
+ title = {Multivariate Matching Methods that are Equally
+ Percent Bias Reducing, II: Maximums on Bias
+ Reduction for Fixed Sampled Sizes},
+ journal = {Biometrics},
+ volume = {32},
+ year = {1976},
+ pages = {121-132},
+}
+
+ at book{RosRub02,
+ author = {P.R. Rosenbaum and Donald B. Rubin},
+ title = {Observational Studies},
+ Publisher = {Springer},
+ year = {2002},
+ address = {New York}
+}
+
+ at article{RacineLi09,
+ author = {J.S. Racine and Q. Li},
+ title = {Efficient Estimation of Average Treatment Effects With Mixed Categorical and Continuous Data},
+ journal = {Journal of Business and Economic Statistics},
+ volume = {27},
+ number = {2},
+ year = {2009},
+ pages = {203-223}
+}
+
+ at article{Popoviciu35,
+ author = {T. Popoviciu},
+ title = {Sur Les \'Equations Alg\'ebriques Ayant Toutes Leurs
+ Racines R\'eelles},
+ journal = {Mathematica},
+ volume = {9},
+ year = {1935},
+ pages = {129-145}
+}
+
+ at article{Rubin76c,
+ title = {{Multivariate Matching Methods That are Equal
+ Percent Bias Reducing, I: Some Examples}},
+ author = {Donald B. Rubin},
+ journal = {Biometrics},
+ volume = {32},
+ number = {1},
+ pages = {109--120},
+ year = {1976}
+}
+
+ at InCollection{Coleridge1789,
+ author = {Samuel Taylor Coleridge},
+ title = {The Rime of the Ancyent Marinere},
+ booktitle = {Lyrical Ballads},
+ publisher = {Routledge},
+ year = {1789 (1991)},
+ editor = {W. Wordsworth and S. T. Coleridge},
+ address = {London}
+}
+
+ at book{MieBer07,
+ title = {{Permutation Methods: A Distance Function Approach}},
+ author = {Mielke, P.W. and Berry, K.J.},
+ year = {2007},
+ address = {New York},
+ publisher = {Springer}
+}
+
+ at Article{ShiShi07,
+ author = {H. Shimazaki and S. Shinomoto},
+ title = {A Method for Selecting the Bin Size of a Time Histogram},
+ journal = {Neural Computation},
+ year = 2007,
+ volume = 19,
+ number = 6,
+ pages = {1503--1527}
+}
+ at inbook{Ragin04,
+ author = {Charles C. Ragin},
+ title = {Rethinking Social Inquiry},
+ chapter = {Turning the Tables: How Case-Oriented Research
+ CHallenges Variable Oriented Research},
+ year = {2004},
+ publisher = {Rowman and Littlefield Publishers, Inc.},
+ address = {Lanham, MD},
+ editor = {Henry E. Brady and David Collier}
+}
+
+ at unpublished{Ragin07,
+ author = {Charles C. Ragin},
+ title = {Qualitative Comparative Analysis and Fuzzy Sets},
+ note = {Presented for the American Political Science
+ Assocation conference, Chicago},
+ year = {2007}
+}
+
+ at article{BatChe04,
+ title = {{The Impact of Measurement Error on Evaluation
+ Methods Based on Strong Ignorability}},
+ author = {Battistin, E. and Chesher, A.},
+ journal = {Institute for Fiscal Studies, London},
+ year = {2004}
+}
+
+ at book{Agresti90,
+ title = {{Categorical data analysis}},
+ author = {Agresti, A.},
+ year = {1990},
+ address = {New York},
+ publisher = {John Wiley \& Sons, Inc.}
+}
+
+ at book{Scott92,
+ title = {{Multivariate density estimation. Theory, practice and visualization}},
+ author = {Scott, D.W.},
+ year = {1992},
+ address = {New York},
+ publisher = {John Wiley \& Sons, Inc.}
+}
+
+ at article{FDiac81,
+ title = {{On the histogram as a density estimator: $L_2$
+ theory}},
+ author = {Freedman, D. and Diaconis, P.},
+ journal = {Probability Theory and Related Fields},
+ year = {1981},
+ volume = {57},
+ pages = {453-476}
+}
+
+ at article{FreDue07,
+ title = {Clustering by Passing Messages Between Data Points},
+ author = {BJ Frey and D Dueck},
+ journal = {Science},
+ volume = {315},
+ number = {5814},
+ pages = {972},
+ year = {2007}
+}
+
+ at Article{Ruben02,
+ author = {Harold Ruben},
+ title = {A simple conservative and robust solution of the
+ Behrens-Fisher problem},
+ journal = {Sankhya},
+ year = {2002},
+ volume = {64},
+ number = {1},
+ pages = {139--155}
+}
+
+ at Article{FawChaHer93,
+ author = {WW Fawzi and TC Chalmers and MG Herrera and F
+ Mosteller},
+ title = {Vitamin A Supplementation and Child Mortality},
+ journal = {Journal of the American Medical Association},
+ year = {1993},
+ volume = {269},
+ number = {7},
+ pages = {898--903}
+}
+
+ at Article{LavDurKoc05,
+ author = {LM LaVange and TA Durham and GG Koch},
+ title = {Randomization-based Nonparametric Methods for the
+ Analysis of Multicentre Trials},
+ journal = {Statistical Methods in Medical Research},
+ year = {2005},
+ volume = {14},
+ number = {3},
+ pages = {281--301}
+}
+
+ at Article{Little04,
+ author = {RJ Little},
+ title = {To Model or Not to Model? Competing Modes of
+ Inference for Finite Population Sampling},
+ journal = {Journal of the American Statistical Assocation},
+ year = {2004},
+ volume = {99},
+ pages = {546--556}
+}
+
+ at Article{LitRub00,
+ author = {RJ Little and DB Rubin},
+ title = {Causal Effects in Clinical and Epidemiological
+ Studies Via Potential Outcomes: Concepts and
+ Analytical Approaches},
+ journal = {Annual Review of Public Health},
+ year = {2000},
+ volume = {21},
+ pages = {121--145}
+}
+
+ at Article{MalGre02,
+ author = {G Maldanado and S Greenland},
+ title = {Estimating Causal Effects},
+ journal = {International Journal of Epidemiology},
+ year = {2002},
+ volume = {31},
+ pages = {422--429}
+}
+ at Article{DasNewVel03,
+ author = {Mitali Das and Whitney K. Newey and Francis Vella},
+ title = {Nonparametric Estimation of Sample Selection Models},
+ journal = {Review of Economic Studies},
+ year = {2003},
+ volume = {70},
+ pages = {33--58}
+}
+ at Article{AbaDruHer01,
+ author = {Alberto Abadie and David Drukker and Jane Leber Herr
+ and Guido W. Imbens},
+ title = {Implementing Matching Estimators for Average
+ Treatment Effects in Stata},
+ journal = {The Stata Journal},
+ year = {2004},
+ volume = {4},
+ number = {3},
+ pages = {290--311}
+}
+ at Article{DunAuMil03,
+ author = {Joel Dunning and JKK Au and RWJ Millner and AJ
+ Levine},
+ title = {Derivation and Validation of a Clinical Scoring
+ System to Predict the Need for an Intra-Aortic
+ Balloon Pump in Patients Undergoing Adult Cardiac
+ Surgery},
+ journal = {Interactive Cardiovascular and Thoracic Surgery},
+ year = {203},
+ volume = {2},
+ pages = {639--643}
+}
+
+
+
+ at Article{Rubin90,
+ author = {DB Rubin},
+ title = {On the Applicaiton of Probability Theory to
+ Agricultural Experiments. Essay on
+ Principles. Section 9. Comment: Neyman (1923) and
+ Causal Inference in Experiments and Observational
+ Studies},
+ journal = {Statistical Science},
+ year = {1990},
+ volume = {5},
+ number = {4},
+ pages = {472--480}
+}
+ at book{HasTibFri01,
+ title = {{The Elements of Statistical Learning: Data Mining,
+ Inference, and Prediction}},
+ author = {Trevor Hastie and Robert Tibshirani and Jerome
+ Friedman},
+ year = {2001},
+ address = {New York},
+ publisher = {Springer}
+}
+ at book{HasTibFri09,
+ title = {{The Elements of Statistical Learning: Data Mining,
+ Inference, and Prediction, 2nd Ed}},
+ author = {Trevor Hastie and Robert Tibshirani and Jerome
+ Friedman},
+ year = {2009},
+ address = {New York},
+ publisher = {Springer}
+}
+ at Article{WesMukCha00,
+ title = {{Feature selection for SVMs}},
+ author = {J. Weston and S. Mukherjee and O. Chapelle and
+ M. Pontil and T. Poggio and V. Vapnik},
+ journal = {Advances in Neural Information Processing Systems},
+ volume = {13},
+ pages = {668--674},
+ year = {2000}
+}
+ at Article{HsuChaLin03,
+ title = {A practical guide to support vector classification},
+ author = {C.W. Hsu and C.C. Chang and C.J. Lin},
+ journal = {National Taiwan University, Technical Report, July},
+ year = {2003}
+}
+
+ at Article{BraGroMil02,
+ title = {Feature Selection Using Linear Support Vector
+ Machines},
+ author = {Janez Brank and Marko Grobelnik and Natasa
+ Milic-Frayling and Dunja Mladenic},
+ journal = {Microsoft Research, Technical Report},
+ year = {2002}
+}
+ at Article{HilPurWil08,
+ title = {{Computer Assisted Topic Classification for Mixed
+ Methods Social Science Research}},
+ author = {Dustin Hillard and Stephen Purpura and John
+ Wilkerson},
+ journal = {Journal of Information Technology and Politics},
+ year = {2008},
+ volume = {4},
+ number = {4}
+}
+
+
+ at Incollection{Joachims98,
+ author = {Thorsten Joachims},
+ editor = {Claire N\'{e}dellec and C\'{e}line Rouvierol},
+ booktitle = {Machine Learning ECML-98},
+ title = {Text Categorization with Support Vector Machines:
+ Learning with Many Relevant Features},
+ publisher = {Springer},
+ year = 1998,
+ volume = 1398,
+ pages = {127--142}
+}
+
+ at article{AnaAlkLav08,
+ title = {The Role of Fish Oil in Arrhythmia Prevention},
+ author = {Rishi G. Anand and Mohi Alkadri and Carl J. Lavie
+ and Richard V. Milani},
+ journal = {Journal of Cardiopulmonary Rehabilitation and
+ Prevention},
+ year = {2008},
+ volume = {28},
+ pages = {92--98}
+}
+ at article{RosRosSil07,
+ title = {{Minimum Distance Matched Sampling With Fine Balance
+ in an Observational Study of Treatment for Ovarian
+ Cancer}},
+ author = {Rosenbaum, P.R. and Ross, R.N. and Silber, J.H.},
+ journal = {Journal of the American Statistical Association},
+ volume = {102},
+ number = {477},
+ pages = {75--83},
+ year = {2007}
+}
+ at article{AbaGar03,
+ title = {{The Economic Costs of Conflict: A Case Study of the
+ Basque Country}},
+ author = {Abadie, A. and Gardeazabal, J.},
+ journal = {American Economic Review},
+ volume = {93},
+ number = {1},
+ pages = {113--132},
+ year = {2003}
+}
+
+ at Article{IacPor08b,
+ author = {Stefano M. Iacus and Giuseppe Porro},
+ title = {Invariant and Metric Free Proximities for Data
+ Matching: An R Package},
+ journal = {Journal of Statistical Software},
+ year = 2008,
+ volume = 25,
+ number = 11,
+ pages = {1--22}
+}
+
+ at Article{IacPor07,
+ author = {Stefano M. Iacus and Giuseppe Porro},
+ title = {Missing data imputation, matching and other
+ applications of random recursive partitioning},
+ journal = {Computational Statistics and Data Analysis},
+ year = 2007,
+ volume = 52,
+ number = 2,
+ pages = {773--789}
+}
+
+ at article{StuGre08,
+ title = {Using Full Matching to Estimate Causal Effects in
+ Nonexperimental Studies: Examining the Relationship
+ Between Adolescent Marijuana Use and Adult Outcomes},
+ author = {Elizabeth A. Stuart and Kerry M. Green},
+ journal = {Developmental Psychology},
+ volume = {44},
+ number = {2},
+ pages = {395--406},
+ year = {2008}
+}
+
+ at PhdThesis{Moore08,
+ author = {Ryan T. Moore},
+ title = {Political Analysis and Statistical Applications for
+ Social Policy Research},
+ school = {Harvard University},
+ year = {2008},
+ OPTaddress = {},
+ month = {May},
+}
+
+ at Article{Moore08b,
+ author = {Ryan T. Moore},
+ title = {blockTools: Blocking, Assignment, and Diagnosing
+ Interference in Randomized Experiments},
+ journal = { },
+ year = {2008},
+ note =
+ {{http://www.people.fas.harvard.edu/$\sim$rtmoore/software.blockTools.htm}}
+}
+
+ at Incollection{NigOroOla03,
+ author = {Gustavo Nigenda and Emanuel Orozco and Gustavo
+ Olaiz},
+ editor = {Felicia Knaul and Gustavo Nigenda},
+ booktitle = {Caleidoscopia de la Salud},
+ title = {La Importancia de los Medicamentos en la Operacion
+ del Seguro Popular de Salud},
+ publisher = {Funsalud},
+ pages = {263-273},
+ year = {2003}
+}
+
+ at article{ImaVan04,
+ title = {{Causal inference with general treatment regimes:
+ Generalizing the propensity score}},
+ author = {Imai, K. and van Dyk, D.A.},
+ journal = {Journal of the American Statistical Association},
+ volume = {99},
+ number = {467},
+ pages = {854--866},
+ year = {2004}
+}
+ at Book{MorWin07,
+ author = {Stephen L. Morgan and Christopher Winship},
+ title = {Counterfactuals and Causal Inference: Methods and
+ Principles for Social Research},
+ publisher = {Cambridge University Press},
+ year = 2007,
+ address = {Cambridge}
+}
+
+
+ at Article{AbaImb07,
+ author = {Alberto Abadie and Guido W. Imbens},
+ title = {Bias-Corrected Matching Estimators for Average
+ Treatment Effects},
+ journal = { },
+ year = {2007},
+ OPTkey = {},
+ OPTvolume = {},
+ OPTnumber = {},
+ OPTpages = {},
+ OPTmonth = {},
+ note = {{http://ksghome.harvard.edu/~aabadie/research.html}},
+ OPTannote = {}
+}
+ at Book{Jevons1874,
+ author = {W. Stanley Jevons},
+ title = {The Principles of Science: A Treatise on Logic and
+ the Scientific Method},
+ publisher = {MacMillen and Co.},
+ year = 1874,
+ address = {New York}
+}
+
+ at Book{Twain1883,
+ author = {Mark Twain},
+ title = {Life on the Mississippi},
+ publisher = {Chatto and Windus},
+ year = 1883,
+ address = {London}
+}
+
+ at Article{SamMic08,
+ author = {Nicholas Sambanis and Alexander Michaelides},
+ title = {A Comment on Diagnostic Tools for Counterfactual
+ Inference},
+ journal = {Political Analysis},
+ year = {2008},
+ OPTkey = {},
+ OPTvolume = {17},
+ OPTnumber = {1},
+ OPTpages = {},
+ OPTmonth = {},
+ OPTnote = {},
+ OPTannote = {}
+}
+
+ at Article{McAfee02,
+ author = {R. Preston McAfee},
+ title = {Coarse Matching},
+ journal = {Econometrica},
+ year = {2002},
+ volume = {70},
+ number = {5},
+ pages = {2025-2034}
+}
+
+ at Article{Mielke85,
+ author = {Paul W. Mielke Jr.},
+ title = {Geometric Concerns Pertaining to Applications of
+ Statistical Tests in the Atmospheric Sciences},
+ journal = {Journal of the Atmospheric Sciences},
+ year = {1985},
+ volume = {42},
+ number = {12},
+ pages = {1209-1212}
+}
+
+ at Book{Greene08,
+ author = {William H. Greene},
+ title = {Econometric Analysis, 6th Edn.},
+ publisher = {Prentice Hall},
+ year = 2008,
+ address = {New York}
+}
+
+ at Article{LeeMil02,
+ author = {Ronald Lee and Timothy Miller},
+ title = {An Approach to Forecasting Health Expenditures, with
+ Application to the U.S. Medicare System},
+ journal = {Health Services Research},
+ year = {2002},
+ volume = {37},
+ number = {5},
+ pages = {1365-1386}
+}
+
+ at Article{LubBeeBak95,
+ author = {James Lubitz and James Beebe and Colin Baker},
+ title = {New England Journal of Medicine},
+ journal = {Longevity and Medicare Expenditures},
+ year = {1995},
+ volume = {332},
+ number = {15},
+ pages = {999-1003}
+}
+
+ at Article{McKusick99,
+ author = {David McKusick},
+ title = {Demographic Issues in Medicare
+ Reform:Birthrates, Death Rates, and an Aging
+ Population all Affect Medicare's Financing},
+ journal = {Health Affairs},
+ year = {1999},
+ volume = {18},
+ number = {1},
+ pages = {194-207}
+}
+
+ at Article{Miller01,
+ author = {Tim Miller},
+ title = {Increasing Longevity and Medicare Expenditures},
+ journal = {Demography},
+ year = {2001},
+ volume = {38},
+ number = {2},
+ pages = {215-226}
+}
+
+ at unpublished{Caldis08,
+ author = {Todd G. Caldis},
+ title = {The Long-Term Projection Assumptions for Medicare
+ and Aggregate national health Expenditures},
+ note =
+ {{http://www.cms.hhs.gov/ReportsTrustFunds/downloads/projectionmethodology.pdf}},
+ year = {2008}
+}
+ at Article{Hansen08,
+ author = {Ben Hansen},
+ title = {The Prognostic Analogy of the Propensity Score},
+ journal = {Biometrika},
+ year = 2008,
+ volume = 95,
+ number = 2,
+ pages = {481--488}
+}
+
+ at unpublished{GalSmiBla08,
+ author = {Jose Galdo and Jeffrey Smith and Dan Black},
+ title = {Bandwidth Selection and the Estimation of Treatment
+ Effects with Unbalanced Data},
+ note = {University of Michigan},
+ year = {2008}
+}
+ at Article{Pronin08,
+ author = {Emily Pronin},
+ title = {How We See Ourselves and How We See Others},
+ journal = {Science},
+ year = 2008,
+ volume = 320,
+ pages = {1170--1180}
+}
+ at article{GraSci04,
+ title = {{Puzzles, Proverbs, and Omega Matrices: The
+ Scientific and Social Significance of Empirical
+ Implications of Theoretical Models (EITM)}},
+ author = {Granato, Jim and Scioli, Frank},
+ journal = {Perspectives on Politics},
+ volume = {2},
+ number = {02},
+ pages = {313--323},
+ year = {2004},
+ publisher = {Cambridge Univ Press}
+}
+ at book{GeoBen05,
+ title = {{Case Studies and Theory Development in the Social
+ Sciences}},
+ author = {George, A.L. and Bennett, A.},
+ year = {2005},
+ publisher = {Mit Press}
+}
+
+ at article{MarQuiRug04,
+ title = {{Competing Approaches to Predicting Supreme Court
+ Decision Making}},
+ author = {Martin, A.D. and Quinn, K.M. and Ruger, T.W. and
+ Kim, P.T.},
+ journal = {Perspectives on Politics},
+ volume = {2},
+ number = {04},
+ pages = {761--767},
+ year = {2004}
+}
+
+ at Article{Grove05,
+ author = {William M. Grove},
+ title = {Clinical Versus Statistical Prediction: The
+ Contribution of Paul E. Meehl},
+ journal = {Journal of Clinical Psychology},
+ year = 2005,
+ volume = 61,
+ number = 10,
+ pages = {1233--1243}
+}
+
+ at proceedings{DjeSmi08,
+ title = {Heterogeneous Impacts in PROGRESA},
+ address = {Bonn, Germany},
+ author = {Habiba Djebbari and Jeffrey Smith},
+ organization = {IZA},
+ year = {2008}
+}
+ at Book{Meehl54,
+ author = {Paul E. Meehl},
+ title = {Clinical Versus Statistical Prediction: A
+ Theoretical Analysis and a Review of the Evidence},
+ publisher = {University of Minnesota Press},
+ year = 1954,
+ address = {Minneapolis}
+}
+
+ at Book{Rosenstone83,
+ title = {Forecasting Presidential Elections},
+ author = {S.J. Rosenstone},
+ year = {1983},
+ publisher = {Yale University Press New Haven}
+}
+ at Article{AdcCol01,
+ author = {Robert Adcock and David Collier},
+ title = {Measurement Validity: A Shared Standard for
+ Qualitative and Quantitative Research},
+ journal = {American Political Science Review},
+ year = {2001},
+ volume = {95},
+ number = {3},
+ month = {September},
+ pages = {529--546}
+}
+
+ at Article{BatGreLev00,
+ author = {R.H. Bates and A. Greif and M. Levi, and
+ J.L. Rosenthal, and B.R. Weingast},
+ title = {The Analytic Narrative Project},
+ journal = {American Political Science Review},
+ year = {2000},
+ volume = {94},
+ number = {3},
+ pages = {696--702}
+}
+
+ at Article{Carpenter00,
+ author = {Daniel P. Carpenter},
+ title = {What is the Marginal Value of Analytic Narratives?},
+ journal = {Social Science History},
+ year = {2000},
+ volume = {24},
+ number={4},
+ pages = {653-668}
+}
+
+ at Article{Skocpol00,
+ author = {Theda Skocpol},
+ title = {Commentary: Theory Tackles History},
+ journal = {Social Science History},
+ year = {2000},
+ volume = {24},
+ number = {4},
+ pages = {677-684}
+}
+
+ at article{Parikh00,
+ author = {Sunita Parikh},
+ title = {The Strategic Value of Analytic Narratives},
+ journal = {Social Science History},
+ volume = {24},
+ year = {2000},
+ pages = {677--684},
+ number = {4}
+}
+ at article{Mahoney00b,
+ author = {James Mahoney},
+ title = {Path Dependence in Historical Sociology},
+ journal = {Theory and Society},
+ volume = {29},
+ year = {2000},
+ pages = {507--548}
+}
+
+ at article{Pierson00,
+ author = {Paul Pierson},
+ title = {Increasing Returns, Path Dependence, and the Study
+ of Politics},
+ journal = {American Political Science Review},
+ year = {2000},
+ pages = {251--268},
+ month = {June}
+}
+ at article{Mahoney99,
+ author = {James Mahoney},
+ title = {Nominal, Ordinal, and Narrative Appraisal in
+ Macro-Causal Analysis},
+ journal = {American Journal of Sociology},
+ volume = {104},
+ year = {1999},
+ pages = {1154-1196},
+ month = {January},
+ number = {4}
+}
+ at article{Pierson00b,
+ author = {Paul Pierson},
+ title = {Not Just What, but When: Timing and Sequence in
+ Political Processes},
+ journal = {Studies in american Political Development},
+ volume = {14},
+ year = {2000},
+ pages = {72--92},
+ month = {spring}
+}
+
+ at article{ColLev77,
+ author = {David Collier and Steven Levitsky},
+ title = {Democracy with Adjectives: Conceptual Innovation in
+ Comparative Research},
+ journal = {World Politics},
+ volume = {43},
+ year = {1977},
+ pages = {430--451},
+ month = {April},
+ number = {3}
+}
+
+ at article{Elman05,
+ author = {Colin Elman},
+ title = {Explanatory Typologies in Qualitative Studies of
+ International Politics},
+ journal = {International Organization},
+ volume = {59},
+ year = {2005},
+ pages = {293--326},
+ month = {spring},
+ number = {2}
+}
+
+ at article{Lustick96,
+ author = {Ian S. Lustick},
+ title = {History, Historiography, and Political Science:
+ Multiple Historical Records and the Problem of
+ Selection Bias},
+ journal = {American Political Science Review},
+ volume = {90},
+ year = {1996},
+ pages = {605--618},
+ month = {September},
+ number = {3}
+}
+
+ at Article{Campbell05,
+ author = {James E. Campbell},
+ title = {Introduction: Assessments of the 2004 Presidential
+ Vote Forecasts},
+ journal = {PS: Political Science \& Politics},
+ year = {2005},
+ volume = {38},
+ pages = {23--24}
+}
+
+ at article{Lieberman05,
+ author = {Evan S. Lieberman},
+ title = {Nested Analysis as a Mixed-Method Strategy for
+ Comparative Research},
+ journal = {American Political Science Review},
+ volume = {99},
+ year = {2005},
+ pages = {435--452},
+ month = {August},
+ number = {3}
+}
+
+ at techreport{Heckman08,
+ author = {James J. Heckman},
+ title = {Econometric Causality},
+ institution = {National Bureau of Economic Research},
+ year = 2008,
+ address = {Cambridge, MA},
+ number = 13934,
+ note = {{http://www.nber.org/papers/w13934}}
+}
+
+ at article{Hall06,
+ author = {Peter A. Hall},
+ title = {Systematic Process Analysis: When and How to Use It},
+ journal = {European Management Review},
+ volume = {3},
+ year = {2006},
+ pages = {24--31},
+ month = {Spring},
+ number = {1}
+}
+
+ at article{Converse87,
+ title = {Changing Conceptions of Public Opinion in the
+ Political Process},
+ author = {Philip E. Converse},
+ journal = {The Public Opinion Quarterly},
+ volume = {51},
+ pages = {12--24},
+ year = {1987}
+}
+
+ at article{Mahoney08,
+ author = {James Mahoney},
+ title = {Toward a Unified Theory of Causality},
+ journal = {Comparative Political studies},
+ volume = {41},
+ year = {2008},
+ pages = {412--436},
+ month = {April/May},
+ number = {4/5}
+}
+
+ at article{DasNewVel03,
+ title = {Nonparametric Estimation of Sample Selection Models},
+ author = {Mitali Das and Whitney K. Newey and Francis Vella},
+ journal = {Review of Economic Studies},
+ volume = {70},
+ year = {2003},
+ pages = {33--58}
+}
+
+ at article{HerHei04,
+ title = {The Distribution of R&D Subsidies and Its Effect on
+ the Final Outcome of Innovation Policy},
+ author = {Liliana Herrera and Joost Heijs},
+ year = {04},
+ journal = {DRUID Summern conference 2004 on Industrial Dynamic,
+ Innovation and Development,Elsinore, Denmark, June
+ 14-16},
+ year = {2004}
+}
+
+ at article{AbaDruHer01,
+ title = {Implementing Matching Estimators for Average
+ Treatment Effects in Stata},
+ author = {Alberto Abadie and David Drukker and Jane Leber Herr
+ and Guido W. Imbens},
+ journal = {The Stata Journal},
+ volume = {1},
+ year = {2001},
+ pages = {1--18},
+ number = {1}
+}
+
+ at unpublished{BryCapLuc02,
+ title = {Why so Unhappy? The Effect of Union Membership on
+ Job Satisfaction},
+ author = {Alex Bryson and Lorenzo Cappellari and Claudio
+ Lucifora},
+ year = {2002},
+ note = {Policy Studies Institute and Centre for Economic
+ Performance}
+}
+
+ at article{DunAuMil03,
+ title = {Derivation and Validation of a Clinical Scoring
+ System to Predict the Need for an Intra-Aortic
+ Balloon Pump in Patients Undergoing Adult Cardiac
+ Surgery},
+ author = {Joel Dunning and JKK Au and RWJ Millner and AJ
+ Levine},
+ journal = {Interactive Cardiovascular and Thoracic Surgery},
+ volume = {2},
+ year = {2003},
+ pages = {639--643}
+}
+
+
+ at Book{GlaStr99,
+ author = {Barney G. Glaser and Anselm L. Strauss},
+ title = {The Discovery of Grounded theory: Strategies for
+ Qualitative Research},
+ publisher = {Aldine De Gruyter},
+ year = {1999},
+ address = {New York}
+}
+
+ at Book{Tetlock05,
+ author = {Philip E. Tetlock},
+ title = {Expert Political Judgment: How Good Is It? How Can
+ We Know?},
+ publisher = {Princeton University Press},
+ year = {2005},
+ address = {Princeton}
+}
+
+ at Book{Hammersley00,
+ author = {Martyn Hammersley},
+ title = {Taking Sides in Social Research: Essays on
+ Partisanship and Bias},
+ publisher = {Routledge},
+ year = {2000},
+ address = {London and New York}
+}
+
+ at book{Little91,
+ title = {{Varieties of Social Explanation: An Introduction to
+ the Philosophy of Social Science}},
+ author = {Little, Daniel},
+ year = {1991},
+ publisher = {Westview Press}
+}
+
+ at Book{Gerring07,
+ author = {John Gerring},
+ title = {Case Study Research: Principles and Practices},
+ publisher = {Cambridge University Press},
+ year = 2007,
+ address = {New York}
+}
+
+ at article{Goldthorpe01,
+ title = {{Causation, Statistics, and Sociology}},
+ author = {Goldthorpe, J.H.},
+ journal = {European Sociological Review},
+ volume = {17},
+ number = {1},
+ pages = {1--20},
+ year = {2001}
+}
+
+ at book{Gill08,
+ title = {{Bayesian Methods: A Social and Behavioral Sciences
+ Approach, 2nd edition}},
+ author = {Jeff Gill},
+ year = {2008},
+ publisher = {Chapman \& Hall/CRC}
+}
+
+ at book{Elster89,
+ title = {Nuts and Bols for the Social Sciences},
+ author = {Jon Elster},
+ year = {1989},
+ publisher = {Cambridge University Press},
+ address = {Cambridge, New York}
+}
+
+ at book{Hausman98,
+ author = {Daniel M. Hausman},
+ title = {Causal Asymmetries},
+ publisher = {Cambridge University Press},
+ year = {1998},
+ address = {Cambridge UK, New York}
+}
+
+ at book{Collins98,
+ author = {Randall Collins},
+ title = {The Sociology of Philosophies: A Global Theory of
+ Intellecutual Change},
+ publisher = {Belknap Press of Harvard University Press},
+ year = {1998},
+ address = {Cambridge,MA}
+}
+
+ at InCollection{Hall09,
+ author = {Peter A. Hall},
+ title = {Path Dependence},
+ booktitle = {The Future of Political Science: 100 Perspectives},
+ pages = { },
+ publisher = {Routledge},
+ year = {2009, forthcoming},
+ editor = {Gary King and Kay Scholzman and Norman Nie}
+}
+
+ at article{MahGoe06,
+ title = {{A Tale of Two Cultures: Contrasting Quantitative
+ and Qualitative Research}},
+ author = {James Mahoney and Gary Goertz},
+ journal = {Political Analysis},
+ volume = {14},
+ number = {3},
+ pages = {227--249},
+ year = {2006}
+}
+
+ at techreport{Duneier08,
+ author = {Mitchell Duneier},
+ title = {How Not to Lie with Ethnography},
+ institution = {Princeton University},
+ year = {2008},
+ OPTkey = {},
+ OPTvolume = {},
+ OPTnumber = {},
+ OPTpages = {},
+ OPTmonth = {},
+ OPTnote = {},
+ OPTannote = {}
+}
+
+ at Article{BraGoe00,
+ author = {Bear Braumoeller and Gary Goertz},
+ title = {The Methodology of Necessary Conditions},
+ journal = {American Journal of Political Science},
+ year = 2000,
+ volume = 44,
+ number = 4,
+ pages = {844--858},
+ month = {October}
+}
+
+ at techreport{GlyQui08,
+ author = {Adam Glynn and Kevin Quinn},
+ title = {Non-parametric Mechanisms and Causal Modeling},
+ institution = {Harvard},
+ year = 2008
+}
+
+ at InCollection{Goertz03,
+ author = {Gary Goertz},
+ title = {The Substantive Importance of Necessary Condition
+ Hypotheses},
+ booktitle = {Necessary Conditions: Theory, Methodology, and
+ Applications},
+ publisher = {Rowman \& Littlefield},
+ year = 2003,
+ editor = {Gary Goertz and Harvey Starr},
+ address = {Lanham, MD}
+}
+
+ at book{RosAllMcc05,
+ author = {Peter E. Rossi and Greg M. Allenby and Robert
+ McCulloch},
+ title = {Bayesian Statistics and Marketing},
+ publisher = {John Wiley & Sons, Ltd},
+ year = 2005,
+ address = {West Sussex, England}
+}
+
+ at Article{Starfield91,
+ author = {Starfield, B.},
+ title = {Primary care and health. {A} cross-national
+ comparison},
+ journal = {The Journal of the American Medical Association},
+ year = {1991},
+ volume = {226},
+ number = {16},
+ pages = {2268-2271},
+}
+
+
+ at Article{WarMur82,
+ author = {Warner, Kenneth and Murt, Hillary},
+ title = {Imact of the antismoking campaign on smoking
+ prevalence: {A} cohort analysis},
+ journal = {Journal of Public Health Policy},
+ year = 1982,
+ volume = 3,
+ number = 4,
+ pages = {374-390}
+}
+
+ at Article{WilDeeLun00,
+ author = {Wilmoth, J.R. and Deegan, L.J. and Lundstr\"{o}m,
+ H. and Horiuchi, S.},
+ title = {Increase of maximum life-span in {S}weden,
+ 1861-1999},
+ journal = {Science},
+ year = 2000,
+ volume = 289,
+ pages = {2366-2368}
+}
+
+ at Article{Waldron91,
+ author = {Waldron, Ingrid},
+ title = {Patterns and causes of gender differences in
+ smoking},
+ journal = {Social Science and Medicine},
+ year = 1991,
+ volume = 32,
+ number = 9,
+ pages = {989-1005}
+}
+
+ at Article{DolPetBor04,
+ author = {Doll, Richard and Peto, Richard and Boreham, Jillian
+ and Sutherland, Isabelle},
+ title = {Mortality in relation to smoking: 50 years'
+ observations on male {B}ritish doctors},
+ journal = {British Medical Journal},
+ year = {2004},
+ volume = {328},
+ pages = {1519-1527}
+}
+
+ at Article{Doll99,
+ author = {Doll, Richard},
+ title = {Tobacco: A Medical History},
+ journal = {Journal of Urban Health: {B}ulletin of the {New York
+ Academy of Medicine} },
+ year = 1999,
+ volume = 76,
+ number = 3,
+ pages = {989-1005}
+}
+
+ at Article{RouBhoPar98,
+ author = {Routh, Hirak Behari and Bhowmik, Kazal Rekha and
+ Parish, Jennifer and Parish, Lawrence},
+ title = {Historical Aspects of Tobacco Use and Smoking},
+ journal = {Clinics in Dermatology},
+ year = 1998,
+ volume = 16,
+ pages = {539-544}
+}
+
+ at Article{LeeTul97,
+ author = {Lee, Ronald and Tuljapurkar, Shripad},
+ title = {Death and taxes: Longer life, consumption, and
+ Social Security},
+ journal = {Demography},
+ year = 1997,
+ volume = 34,
+ number = 1,
+ pages = {67-81},
+ month = {June}
+}
+
+ at TechReport{SSAHist07,
+ author = {{Social Security Administration Historian's Office}},
+ title = {Social Security A Brief History},
+ institution = {Social Security Administration},
+ year = 2007,
+ number = {21-059},
+ month = {October 2007}
+}
+
+ at Article{Schneider99,
+ author = {Schneider, Edward},
+ title = {Aging in the Third Millenium},
+ journal = {Science},
+ year = 1999,
+ volume = 283,
+ number = 5403,
+ pages = {796-797}
+}
+
+ at Article{FrePla07,
+ author = {Freedland, Stephen and Platz, Elizabeth},
+ title = {Obesity and prostate cancer: making snese out of
+ apparently conflicting data},
+ journal = {Epidemiologic Reviews},
+ year = 2007,
+ volume = 29,
+ number = 1,
+ pages = {88-97}
+}
+
+ at Article{LitWhiKri07,
+ author = {Littman, Alyson and White, Emily and Kristal, Alan},
+ title = {Anthropometrics and prostate cancer risk},
+ journal = {American Journal of Epidemiology},
+ year = 2007,
+ volume = 165,
+ number = 11,
+ pages = {1271-1279}
+}
+
+
+ at Article{YanKelHe07,
+ author = {Yang, Wenjie and Kelly, Tanika and He, Jiang},
+ title = {Genetic Epidemiology of Obesity},
+ journal = {Epidemiologic Reviews},
+ year = 2007,
+ volume = 29,
+ number = 1,
+ pages = {49-61}
+}
+
+ at Article{RisHelKne90,
+ author = {Rissanen, Aila and Heli\"{o}vaara, Markku and Knekt,
+ Paul and Reunanen, Antti and Aromaa, Arpo and
+ Maatela, Jouni},
+ title = {Risk of disability and mortality due to verweight in
+ a {F}innish population},
+ journal = {British Medical Journal},
+ year = 1990,
+ volume = 301,
+ number = {},
+ pages = {835-837}
+}
+
+ at Article{HeiEriEll00,
+ author = {Heitmann, BL and Erikson, H and Ellsinger, B-M and
+ Mikkelsen, KL and Larsson, B},
+ title = {Mortaltiy associated with body fat, fat-free mass
+ and body mass index among 60-year-old {S}wedish men
+ $-$ a 22-year follow-up. {T}he study of mean born in
+ $1913$},
+ journal = {International Journal of Obesity},
+ year = 2000,
+ volume = 24,
+ pages = {33-37}
+}
+
+ at Article{CorMonSom06,
+ author = {Romero-Corral, Abel and Montori, Victor and Somers,
+ Virend and Korinek, Josef and Thomas, Randal and
+ Allison, Thomas and Mookadam, Farouk and
+ Lopez-Jimenez, Francisco},
+ title = {Association of bodyweight with total mortality and
+ with cardiovascular events in coronary artery
+ disease: a systematic review of cohort studies},
+ journal = {The Lancet},
+ year = 2006,
+ volume = 368,
+ pages = {666-678},
+ month = {August}
+}
+
+ at Article{FleGraWil07,
+ author = {Flegal, Katherine and Graubard, Barry and
+ Williamson, David and Gail, Mitchell},
+ title = {Cause-specific excess deaths associated with
+ underweight, overweight, and obesity},
+ journal = {Journal of the American Medical Association},
+ year = 2007,
+ volume = 298,
+ number = 17,
+ pages = {2028-2037}
+}
+
+ at Article{AdaSchHar06,
+ author = {Adams, Kenneth and Schatzkin, Arthur and Harris,
+ Tamara and Kipnis, Victor and Mouw, Traci and
+ Ballard-Barbash, Rachel and Hollenbeck, Albert and
+ Leitzmann, Michael},
+ title = {Overweight, obesity, and mortality in a large
+ prospective cohort of persons $50$ to $71$ years
+ old},
+ journal = {New England Journal of Medicine},
+ year = 2006,
+ volume = 355,
+ number = 8,
+ pages = {763-778}
+}
+
+ at Article{SukSacBod03,
+ author = {Suk, Seung-Han and Sacco, Ralph and Boden-Albala
+ Bernadette and Cheun, Jian and Pittman, John and
+ Elkind, Mitchell and Paik, Myunghee},
+ title = {Abdominal obesity and the risk of ischemic stroke:
+ The {N}orthern {M}anhattan {S}troke {S}tudy},
+ journal = {Stroke},
+ year = 2003,
+ volume = 34,
+ number = 7,
+ pages = {1586-1592},
+ month = {July}
+}
+
+ at Book{Quetelet1842,
+ author = {Qu\'{e}telet, Lambert Adolphe Jacques},
+ title = {A treatise on man and the development of his
+ faculties},
+ publisher = {William and Robert Chambers},
+ year = 1842,
+ address = {Edinburgh},
+}
+
+ at TechReport{ssa07,
+ author = {{The Board of Trustees, Federal Old-Age and
+ Survivors Insurance and Federal Disability Insurance
+ Trust Funds}},
+ title = {The 2007 annual report of the board of trustees of
+ the federal old-age and survivors insurance and
+ federal disability insurance trust funds},
+ institution = {Social Security Administration},
+ year = 2007
+}
+
+ at TechReport{ssa04,
+ author = {{The Board of Trustees, Federal Old-Age and
+ Survivors Insurance and Federal Disability Insurance
+ Trust Funds}},
+ title = {The 2004 annual report of the board of trustees of
+ the federal old-age and survivors insurance and
+ federal disability insurance trust funds},
+ institution = {Social Security Administration},
+ year = 2004
+}
+
+ at TechReport{ssa06,
+ author = {{The Board of Trustees, Federal Old-Age and
+ Survivors Insurance and Federal Disability Insurance
+ Trust Funds}},
+ title = {The 2006 annual report of the board of trustees of
+ the federal old-age and survivors insurance and
+ federal disability insurance trust funds},
+ institution = {Social Security Administration},
+ year = 2006
+}
+
+ at TechReport{USG:05,
+ author = {{United States Government}},
+ title = {Budget of the United States Government, Fiscal Year
+ $2006$},
+ institution = {US Government Printing Office},
+ year = {2005}
+}
+
+ at TechReport{USG:09,
+ author = {{United States Government}},
+ title = {Budget of the United States Government, Fiscal Year
+ $2009$},
+ institution = {US Government Printing Office},
+ year = {2009}
+}
+
+ at TechReport{ShoSunBun87,
+ author = {Shoven, John and Sundberg, Jeffrey and Bunker, John},
+ title = {The social security cost of smoking},
+ institution = {National Bureau of Economic Research},
+ year = 1987,
+ type = {Working Paper Series},
+ number = 2234
+}
+
+ at TechReport{Gravelle98,
+ author = {Gravelle, Jane},
+ title = {The proposed tobacco settlement: who pays for the
+ health costs of smoking?},
+ institution = {Library of Congress, Congressional Research Service},
+ year = 1998,
+ number = {97-1053 E}
+}
+
+ at Article{HaeShiMil56,
+ author = {Haenszel, W and Shimkin, MB and Miller, HP},
+ title = {Tobacco smoking patterns in the {U}nited {S}tates},
+ journal = {Public Health Monograph},
+ year = 1956,
+ volume = 45,
+ pages = {1-105}
+}
+
+ at Article{WalLyeBra91,
+ author = {Waldron, I and Lye, D and Brandon, A},
+ title = {Gender differences in teenage smoking},
+ journal = {Women Health},
+ year = 1991,
+ volume = 17,
+ pages = {63-87}
+}
+
+ at Article{Ferrence88,
+ author = {Ferrence, R},
+ title = {Sex differences in cigarette smoking in {C}anada,
+ 1900-1978: {A} reconstructed cohort study},
+ journal = {Canadian Journal of Public Health},
+ year = 1988,
+ volume = 79,
+ pages = {160-165}
+}
+
+ at Article{Elkind85,
+ author = {Elkind, A},
+ title = {The social definition of women's smoking behavior},
+ journal = {Social Science and Medicine},
+ year = 1985,
+ volume = 20,
+ pages = {1269-1278}
+}
+
+ at Article{LynBro01,
+ author = {Lynch, Scott and Brown, Scott},
+ title = {Reconsidering mortality compression and
+ deceleration: {A}n alternative model of mortality
+ rates},
+ journal = {Demography},
+ year = 2001,
+ volume = 38,
+ number = 1,
+ pages = {79-95}
+}
+
+ at TechReport{Holmer08,
+ author = {Holmer, Martin},
+ title = {SSASIM Guide},
+ institution = {Policy Simulation Group},
+ year = 2008,
+ month = {March}
+}
+
+ at Article{MeySab00,
+ author = {Meyerson, Noah and Sabelhaus, John},
+ title = {Uncertainty in Social Security Trust Fund
+ Projections},
+ journal = {National Tax Journal},
+ year = 2000,
+ volume = 53,
+ number = 3,
+ pages = {515-529}
+}
+
+ at Article{TulLiBoe00,
+ author = {Tuljapurkar, Shripad and Li, Nan and Boe, Carl},
+ title = {A universal pattern of mortality decline in the $G7$
+ countries},
+ journal = {Nature},
+ year = 2000,
+ volume = 405,
+ pages = {789-792}
+}
+
+ at Article{Boyer47,
+ author = {Boyer, Carl},
+ title = {Note on an early graph of statistical data {(Huygens
+ 1669)}},
+ journal = {Isis},
+ year = 1947,
+ volume = 37,
+ number = {3$/$4},
+ pages = {148-149}
+}
+
+ at Book{Vollgraff50,
+ editor = {Johan Adriaan Vollgraff},
+ title = {Oeuvres compl\`{e}tes de Christiaan
+ Huygens. Publi\'{e}es par la Soci\'{e}t\'{e}
+ hollandaise des sciences},
+ publisher = {La Haye: M. Nijhoff},
+ year = 1950
+}
+
+ at TechReport{Wilmoth03,
+ author = {Wilmoth, John R.},
+ title = {Overview and Discussion of the Social Security
+ Mortality Projections},
+ institution = {Technical Panel on Assumptions and Methods, Social Security Advisory Board},
+ year = 2003,
+}
+
+ at TechReport{SSAB07,
+ author = {{Social Security Advisory Board Technical Panel}},
+ title = {2007 Technical Panel on Assumptions and Methods},
+ institution = {Social Security Advisory Board},
+ year = 2007,
+}
+
+ at TechReport{SSAB99,
+ author = {{Social Security Advisory Board Technical Panel}},
+ title = {1999 Technical Panel on Assumptions and Methods},
+ institution = {Social Security Advisory Board},
+ year = 1999,
+}
+
+ at TechReport{SSAB94,
+ author = {{Social Security Advisory Board Technical Panel}},
+ title = {1994 Technical Panel on Assumptions and Methods},
+ institution = {Social Security Advisory Board},
+ year = 1994,
+}
+
+ at TechReport{SSAB91,
+ author = {{Social Security Advisory Board Technical Panel}},
+ title = {1991 Technical Panel on Assumptions and Methods},
+ institution = {Social Security Advisory Board},
+ year = 1991,
+}
+
+ at Article{Alho90,
+ author = {Alho, Juha},
+ title = {Effects of targets and aggregration on the
+ propogation of error in mortality forecasts},
+ journal = {Mathematical Population Studies},
+ year = 1990,
+ volume = 2,
+ pages = {209-227}
+}
+
+
+ at Article{CoaKis86,
+ author = {Coale, Ansley and Kisker, Ellen},
+ title = {Mortality crossover: {R}eality or bad data},
+ journal = {Population Studies},
+ year = 1986,
+ volume = 40,
+ number = 3,
+ pages = {389-401}
+}
+
+ at Article{Parascandola04,
+ author = {Parascandola, Mark},
+ title = {Skepticism, statistical methods, and the cigarette:
+ {A} historical analyis of a methodological debate},
+ journal = {Perspectives in Biology and Medicine},
+ year = 2004,
+ volume = 47,
+ number = 42,
+ pages = {244-261}
+}
+
+ at TechReport{Ball73,
+ author = {Ball, Robert},
+ title = {Hearings before the {S}pecial {C}ommittee on
+ {Aging}},
+ institution = {U.S. Senate, Ninety-Third Congress},
+ year = 1973,
+ number = {Part 1},
+ month = {January},
+ note = {SUDOC:Y4.Ag4:So1/2/pt.1}
+}
+
+ at Article{Olshansky05,
+ author = {Olshansky, S. Jay and Passaro, Douglas and Hershow,
+ Ronald and Layden, Jennifer and Carnes, Bruce and
+ Brody, Jacob and Hayflick, Leonard and Butler,
+ Robert and Allison, David and Ludwig, David},
+ title = {A potential decline in life expectancy in the
+ {U}united {S}tates in the 21st century},
+ journal = {The New England Journal of Medicine},
+ year = 2005,
+ volume = 352,
+ number = 11,
+ pages = {1138-1145}
+}
+
+ at TechReport{ssa117,
+ author = {Cheng, Anthony and Miller, Michael and Morris,
+ Michael and Schultz, Jason and Skirvin, J. Patrick
+ and Walder, Danielle},
+ title = {A stochastic model of the long-range financial
+ status of the {OASDI} program},
+ institution = {Social Security Administration, Office of the Chief
+ Actuary},
+ year = 2004,
+ note = {Actuarial Study No. 117}
+}
+
+
+ at TechReport{ssa116,
+ author = {Bell, Felicitie and Miller, Michael},
+ title = {Life tables for the {United States} social security
+ area 1900-2100},
+ institution = {Social Security Administration, Office of the Chief
+ Actuary},
+ year = 2002,
+ note = {Actuarial Study No. 116}
+}
+
+ at TechReport{ssa120,
+ author = {Bell, Felicitie and Miller, Michael},
+ title = {Life tables for the {United States} social security
+ area 1900-2100},
+ institution = {Social Security Administration, Office of the Chief
+ Actuary},
+ year = 2005,
+ note = {Actuarial Study No. 120}
+}
+
+
+ at Article{Warner78,
+ author = {Warner, Kenneth},
+ title = {Possible increases in the underreporting of
+ cigarette consumption},
+ journal = {Journal of the American Statitical Association},
+ year = 1978,
+ volume = 73,
+ number = 362,
+ pages = {314-318}
+}
+
+ at Article{CanFisBak65,
+ author = {Cannell, C.F. and Fisher, G. and Bakker, T.},
+ title = {Reporting of hospitalization in the health interview
+ survey},
+ journal = {Vital and Health Statistics},
+ year = 1965,
+ volume = 2,
+ number = 6,
+ pages = {i-71}
+}
+
+ at Article{Halley93,
+ author = {Halley, Edmund},
+ title = {An estimate of the degrees of mortality of mankind,
+ drawn from curious tables of the births and funerals
+ at the city of Breslaw; with an attempt to ascertain
+ the price of annuities upon lives},
+ journal = {Philosophical Transactions of the Royal Society of
+ London},
+ year = 1693,
+ volume = 17,
+ pages = {596-610}
+}
+
+
+ at Article{Wright86,
+ author = {Wright, VB},
+ title = {Will quitting smoking help Medicare solve its
+ financial problems?},
+ journal = {Inquiry},
+ year = 1986,
+ volume = 23,
+ number = 1,
+ pages = {76-82}
+}
+
+ at TechReport{HMD08,
+ author = {{University of California, Berkeley (USA)} and {Max
+ Planck Institute for Demographic Research
+ (Germany)}},
+ title = {Human Mortality Database},
+ year = 2008,
+ institution = {\url{http://www.mortality.org}},
+ note = {data downloaded on April 7, 2008}
+}
+
+ at Article{SasIch02,
+ author = {Sascha O.\ Becker and Andrea Ichino},
+ title = {Estimation of average treatment effects based on
+ propensity scores},
+ journal = {The Stata Journal},
+ year = 2002,
+ volume = 2,
+ number = 4,
+ pages = {358--377}
+}
+
+ at Article{JacAdaMul99,
+ author = {Jacobs, David R., Jr and Adachi, Hisashi and Mulder,
+ Ina and Kromhout, Daan and Menotti, Alessandro and
+ Nissinen, Aulikki and Blackburn, Henry},
+ title = {Cigarette Smoking and Mortality Risk:
+ Twenty-five-Year Follow-up of the Seven Countries
+ Study},
+ journal = {Archives of Internal Medicine},
+ year = 1999,
+ volume = 159,
+ number = 7,
+ pages = {733-740}
+}
+
+ at Article{PeeBarWil03,
+ author = {Peeters, Anna and Barendregt, Jan and Willekens,
+ Frans and Mackenbach, Johan and Al Mamun, Abdullah
+ and Bonneux, Luc},
+ title = {Obesity in Adulthood and its Consequences for Life
+ Expectancy: A Life-Table Analysis},
+ journal = {Annals of Internal Medicine},
+ year = 2003,
+ volume = 138,
+ number = {24-32}
+}
+
+ at Article{LynSmi05,
+ author = {Lynch, John and Smith, George Davey},
+ title = {A Life Course Approach to Chronic Disease
+ Epidemiology},
+ journal = {Annual Review of Public Health},
+ year = 2005,
+ volume = 26,
+ number = 1,
+ pages = {1-35},
+}
+
+ at Article{Peace85,
+ author = {Peace, L.R.},
+ title = {A Time Correlation Between Cigarette Smoking and
+ Lung Cancer},
+ journal = {The Statistician},
+ year = 1985,
+ volume = 34,
+ number = 4,
+ pages = {371-381}
+}
+
+
+ at Article{Sturm02,
+ author = {Sturm, Roland},
+ title = {The Effects of Obesity, Smoking, and Drinking on
+ Medical Problems and Cost},
+ journal = {Health Affairs},
+ year = 2002,
+ pages = {245-253},
+ month = {March/April}
+}
+
+ at TechReport{Gutterman08,
+ author = {Gutterman, Sam},
+ title = {Human Behavior: An Impediment to Future Mortality
+ Improvement, A Focus on Obesity and Related Matters},
+ institution = {Society of Actuaries},
+ year = 2008,
+ note = {Living to 100 and Beyond Symposium}
+}
+
+ at Article{BakOlsSor07,
+ author = {Baker, Jennifer and Olsen, Lina and
+ S{\o}rensen,Thorkild},
+ title = {Childhood Body-Mass Index and the Risk of Coronary
+ Heart Disease in Adulthood},
+ journal = {New England Journal of Medicine},
+ year = 2007,
+ volume = 357,
+ number = 23,
+ pages = {2329-2337}
+}
+
+ at Article{LeuSia03,
+ title = {{PSMATCH2: Stata module to perform full Mahalanobis
+ and propensity score matching, common support
+ graphing, and covariate imbalance testing}},
+ author = {Leuven, Edwin and Sianesi, Barbara},
+ journal = {Statistical Software Components},
+ year = 2003
+}
+
+ at Article{Kalton68,
+ author = {G. Kalton},
+ title = {Standardization: A Technique to Control for Extraneous Variables},
+ journal = {Applied Statistics},
+ year = 1968,
+ volume = 17,
+ number = 2,
+ pages = {118--136}
+}
+
+ at Article{Wei82,
+ title = {Interval estimation of location difference with
+ incomplete data},
+ author = {Wei, L. J.},
+ journal = {Biometrika},
+ volume = {69},
+ number = {1},
+ pages = {249--251},
+ year = {1982}
+}
+
+ at article{Moulton04,
+ title = {Covariate-based constrained randomization of
+ group-randomized trials},
+ author = {Moulton, L.H.},
+ journal = {Clinical Trials},
+ volume = {1},
+ number = {3},
+ pages = {297},
+ year = {2004}
+}
+
+ at Unpublished{Wand07,
+ author = {Jonathan Wand},
+ title = {Credible Comparisons Using Interpersonally
+ Incomparable Data: Ranking self-evaluations relative
+ to anchoring vignettes or other common survey
+ questions},
+ year = {2007},
+ note = {http://wand.stanford.edu},
+}
+
+
+ at Unpublished{Buckley08,
+ author = {Jack Buckley},
+ title = {Survey Context Effects in Anchoring Vignettes},
+ year = {2008},
+ note = {http://polmeth.wustl.edu/workingpapers.php},
+}
+
+ at Book{GrzymalaBusse07,
+ author = {Anna Grzymala-Busse},
+ title = {Rebuilding Levithan: Party Competition and State
+ Exploitation in Post-Communist Democracies},
+ publisher = {Cambridge University Press},
+ address = {New York},
+ year = {2007}
+}
+
+ at Unpublished{SoeDelHar07,
+ title = {{Validating the Use of Vignettes for Subjective
+ Threshold Scales}},
+ author = {Arthur Van Soest and Liam Delaney and Colm P. Harmon
+ and Arie Kapteyn and James P. Smith},
+ year = {2007},
+ note = {UCD Geary Institute Working Paper}
+}
+
+ at article{JavRip07,
+ title = {{An" Unfolding" Latent Variable Model for Likert
+ Attitude Data: Drawing Inferences Adjusted for
+ Response Style}},
+ author = {Kristin N. Javaras and Brian D. Ripley},
+ journal = {Journal of the American Statistical Association},
+ volume = {102},
+ number = {478},
+ pages = {454--463},
+ year = {2007},
+ publisher = {American Statistical Association}
+}
+
+ at article{KapSmiSoe07,
+ title = {{Vignettes and Self-Reports of Work Disability in
+ the United States and the Netherlands}},
+ author = {Arie Kapteyn and James P. Smith and Arthur Soest},
+ journal = {American Economic Review},
+ volume = {97},
+ number = {1},
+ pages = {461--473},
+ year = {2007},
+ publisher = {American Economic Association Publications}
+}
+
+ at article{Bowling05,
+ title = {{Just one question: If one question works, why ask
+ several?}},
+ author = {Ann Bowling},
+ journal = {Journal of Epidemiology and Community Health},
+ volume = {59},
+ number = {5},
+ pages = {342},
+ year = {2005}
+}
+
+ at article{DamVasSzw05,
+ title = {{Perception of health state and the use of vignettes
+ to calibrate for socioeconomic status: results of
+ the World Health Survey in Brazil, 2003}},
+ author = {Damacena, G.N. and Vasconcellos, M.T.L. and
+ Szwarcwald, C.L.},
+ journal = {Cadernos de Sa{\'u}de P{\'u}blica},
+ volume = {21},
+ pages = {65--77},
+ year = {2005},
+ publisher = {SciELO Brasil}
+}
+
+
+ at article{HseTan07,
+ title = {{Sun and Water: On a Modulus-Based Measurement of
+ Happiness}},
+ author = {Christopher K. Hsee and Judy Ningyu Tang},
+ journal = {Emotion},
+ volume = {7},
+ pages = {213--218},
+ year = {2007}
+}
+
+ at article{SalTanMur04,
+ title = {{Comparability of self rated health: cross sectional
+ multi-country survey using anchoring vignettes}},
+ author = {Joshua A. Salomon and Ajay Tandon and Christopher
+ J.L. Murray},
+ journal = {British Medical Journal},
+ volume = {328},
+ number = {7434},
+ pages = {258},
+ year = {2004},
+ publisher = {Br Med Assoc}
+}
+
+ at unpublished{AbaDiaHai09,
+ title = {Synthetic Control Methods for Comparative Case
+ Studies: Estimating the Effect of California's
+ Tobacco Control Program},
+ author = {Alberto Abadie and Alexis Diamond and Jens
+ Hainmueller},
+ journal = {Journal of the American Statistical Association},
+ year = {2009, forthcoming}
+}
+
+ at Article{AbaDiaHai09b,
+ author = {Alberto Abadie and Alexis Diamond and Jens
+ Hainmueller},
+ title = {Synth: An R Package for Synthetic Control Methods in
+ Comparative Case Studies},
+ journal = {Journal of Statistical Software},
+ year = {2009, forthcoming}
+}
+
+ at article{LozSolGak07,
+ title = {{Benchmarking of performance of Mexican states with
+ effective coverage}},
+ author = {Rafael Lozano and Patricia Soliz and Emmanuela
+ Gakidou and Jesse Abbott-Klafter and Dennis
+ M. Feehan and Cecilia M. Vidal and Juan Pablo Ortiz
+ and Christopher J. L. Murray},
+ journal = {Salud P{\'u}blica de M{\'e}xico},
+ volume = {49},
+ pages = {53--69},
+ year = {2007},
+ publisher = {SciELO Public Health}
+}
+
+
+ at article{MarPol95,
+ title = {{Diagnostics for Redesigning Survey Questionnaires:
+ Measuring Work in the Current Population Survey}},
+ author = {Elizabeth Martin and Anne E. Polivka},
+ journal = {Public Opinion Quarterly},
+ volume = {59},
+ number = {4},
+ pages = {547--567},
+ year = {1995},
+ publisher = {AAPOR}
+}
+
+ at article{Tourangeau04,
+ title = {{Survey Research and Societal Change}},
+ author = {Roger Tourangeau},
+ journal = {Annual Review of Psychology},
+ volume = {55},
+ number = {1},
+ pages = {775--801},
+ year = {2004},
+ publisher = {Annual Reviews}
+}
+
+ at conference{GerWelKel96,
+ title = {{Who lives here? The use of vignettes in household
+ roster research}},
+ year = {1996},
+ author = {Eleanor R. Gerber and Tracy R. Wellens and Catherine
+ Keeley},
+ booktitle = {Proceedings of the Section on Survey Research
+ Methods, American Statistical Association},
+ pages = {962--967}
+}
+
+
+ at InCollection{Tourangeau91,
+ author = {Roger Tourangeau},
+ title = {Context Effects on Respones to Attitude Questions:
+ Attitudes as Memory Structures},
+ booktitle = {Contextual Effects in Social and Psychological
+ Research},
+ pages = {35-48},
+ publisher = {Springer-Verlag},
+ year = {1991},
+ editor = {Norbert Schwarz and Seymour Sudman},
+ address = {New York}
+}
+
+ at InCollection{Smith91,
+ author = {Tom Smith},
+ title = {Thoughts on the Nature of Context Effects},
+ booktitle = {Contextual Effects in Social and Psychological
+ Research},
+ pages = {163-186},
+ publisher = {Springer-Verlag},
+ year = {1991},
+ editor = {Norbert Schwarz and Seymour Sudman},
+ address = {New York}
+}
+
+
+ at article{NieCraMat91,
+ title = {{Measuring Internal Political Efficacy in the 1988
+ National Election Study}},
+ author = {Richard G. Niemi and Stephen C. Craig and Franco
+ Mattei},
+ journal = {American Political Science Review},
+ volume = {85},
+ number = {4},
+ pages = {1407--1413},
+ year = {1991},
+ publisher = {JSTOR}
+}
+
+ at article{CraNieSil90,
+ title = {{Political efficacy and trust: A report on the NES
+ pilot study items}},
+ author = {Stephen C. Craig and Richard G. Niemi and Glenn
+ E. Silver},
+ journal = {Political Behavior},
+ volume = {12},
+ number = {3},
+ pages = {289--314},
+ year = {1990},
+ publisher = {Springer}
+}
+
+ at article{SteKorCar92,
+ title = {{Arenas and Attitudes: A Note on Political Efficacy
+ in a Federal System}},
+ author = {Marianne C. Stewart and Allan Kornberg and Harold
+ D. Clarke and Alan Acock},
+ journal = {Journal of Politics},
+ volume = {54},
+ number = {1},
+ pages = {179--196},
+ year = {1992},
+ publisher = {JSTOR}
+}
+
+ at book{Oliver01,
+ title = {{Democracy in Suburbia}},
+ author = {J. Eric Oliver},
+ year = {2001},
+ publisher = {Princeton University Press},
+ address = {Princeton, NJ}
+}
+
+ at InCollection{Strack91,
+ author = {Fritz Strack},
+ title = {'Order Effects' in Survey Research: Activation and
+ Information Functions of Preceding Questions},
+ booktitle = {Contextual Effects in Social and Psychological
+ Research},
+ pages = {23-34},
+ publisher = {Springer-Verlag},
+ year = {1991},
+ editor = {Norbert Schwarz and Seymour Sudman},
+ address = {New York},
+}
+
+ at book{Weisberg96,
+ title = {{An introduction to survey research, polling, and
+ data analysis}},
+ author = {Herbert F. Weisberg and Jon A. Krosnick and Bruce
+ D. Bowen},
+ year = {1996},
+ publisher = {Sage Publications},
+ address = {Thousand Oaks, CA}
+}
+
+ at book{Fowler95,
+ title = {{Improving Survey Questions: Design and Evaluation}},
+ author = {Floyd J. Fowler},
+ year = {1995},
+ publisher = {Sage Publications},
+ address = {Thousand Oaks, CA}
+}
+
+ at InCollection{SchHipNoe91,
+ author = {Norbert Schwarz and Hans-J. Hippler and Elisabeth
+ Noelle-Neumann},
+ title = {A Cognitive Model of Response-Order Effects},
+ booktitle = {Contextual Effects in Social and Psychological
+ Research},
+ pages = {187-202},
+ publisher = {Springer-Verlag},
+ year = {1991},
+ editor = {Norbert Schwarz and Seymour Sudman},
+ address = {New York},
+}
+
+
+ at article{SchStrMai91,
+ title = {{Assimilation and Contrast Effects in Part-Whole
+ Question Sequences: A Conversational Logic
+ Analysis}},
+ author = {Norbert Schwarz and Fritz Strack and Hans-Peter Mai},
+ journal = {Public Opinion Quarterly},
+ volume = {55},
+ number = {1},
+ pages = {3--23},
+ year = {1991},
+ publisher = {AAPOR}
+}
+
+ at article{WilKe95,
+ title = {{Part-Whole Question Order Effects: Views of
+ Rurality}},
+ author = {Fern K. Willits and Bin Ke},
+ journal = {Public Opinion Quarterly},
+ volume = {59},
+ number = {3},
+ pages = {392--403},
+ year = {1995},
+ publisher = {AAPOR}
+}
+
+ at article{MccObr88,
+ title = {{Question-Order Effects on the Determinants of
+ Subjective Well-Being}},
+ author = {McKee J. McClendon and David J. O'Brien},
+ journal = {Public Opinion Quarterly},
+ volume = {52},
+ number = {3},
+ pages = {351--364},
+ year = {1988},
+ publisher = {AAPOR}
+}
+
+ at article{Finkel87,
+ title = {{The Effects of Participation on Political Efficacy
+ and Political Support: Evidence from a West German
+ Panel}},
+ author = {Steven E. Finkel},
+ journal = {Journal of Politics},
+ volume = {49},
+ number = {2},
+ pages = {441--464},
+ year = {1987}
+}
+
+ at book{SchPre96,
+ title = {{Questions and Answers in Attitude Surveys:
+ Experiments on Question Form, Wording, and Context}},
+ author = {Howard Schuman and Stanley Presser},
+ year = {1996},
+ publisher = {Sage},
+ address = {Thousand Oaks, CA}
+}
+
+
+ at article{Krosnick99,
+ title = {{Survey Research}},
+ author = {Jon A. Krosnick},
+ journal = {Annual Review of Psychology},
+ volume = {50},
+ number = {1},
+ pages = {537--567},
+ year = {1999},
+ publisher = {Annual Reviews}
+}
+
+ at book{SudBraSch96,
+ title = {{Thinking about Answers: The Application of
+ Cognitive Processes to Survey Methodology}},
+ author = {Seymour Sudman and Norman M. Bradburn and Norbert
+ Schwarz},
+ year = {1996},
+ publisher = {Jossey-Bass Publishers},
+ address = {San Francisco, CA}
+}
+
+ at article{KriWilMos97,
+ title = {{Measuring Social Class U.S. Public Health Research:
+ Concepts, Methodologies, and Guidelines}},
+ author = {N. Krieger and D.R. Williams and M.E. Moss},
+ journal = {Annual Reviews in Public Health},
+ volume = {18},
+ number = {1},
+ pages = {341--378},
+ year = {1997},
+ publisher = {Annual Reviews}
+}
+
+ at book{Payne51,
+ title = {{The Art ofAsking Questions}},
+ author = {Stanley L. Payne},
+ publisher = {Princeton University Press},
+ address = {Princeton, NJ},
+ year = {1951}
+}
+
+ at InCollection{Bradburn83,
+ author = {Norman M. Bradburn},
+ title = {Response Effects},
+ booktitle = {Handbook of Survey Research},
+ pages = {},
+ publisher = {Academic Press},
+ year = {1983},
+ address = {New York, NY},
+ editor = {Peter H. Rossi and James D. Wright and Andy
+ B. Anderson},
+}
+
+
+ at Article{Robins08,
+ author = {James M. Robins},
+ title = {Causal models for estimating the effects of weight
+ gain on mortality},
+ journal = {International Journal of Obesity},
+ year = 2008,
+ volume = 32,
+ pages = {s15--s41}
+}
+
+
+ at Article{OepVau02,
+ author = {Oeppen, Jim and Vaupel, James},
+ title = {Broken Limits to Life Expectancy},
+ journal = {Science},
+ year = 2002,
+ volume = 296,
+ pages = {1029-1031},
+ month = {May}
+}
+
+ at Article{HorWil98,
+ author = {Horiuchi, Shiro and Wilmoth, John},
+ title = {Deceleration in the Age Pattern of Mortality at
+ Older Ages},
+ journal = {Demography},
+ year = 1998,
+ volume = 35,
+ number = 4,
+ pages = {391-412},
+ month = {November}
+}
+
+ at Article{PreWan06,
+ author = {Preston, Samuel and Wang, Haidong},
+ title = {Sex Mortality Differences in the United States: The
+ Role of Cohort Smoking Patterns},
+ journal = {Demography},
+ year = 2006,
+ volume = 43,
+ number = 4,
+ pages = {631-646},
+ month = {November}
+}
+
+ at article{Finkel85,
+ title = {{Reciprocal Effects of Participation and Political
+ Efficacy: A Panel Analysis}},
+ author = {Steven E. Finkel},
+ journal = {American Journal of Political Science},
+ volume = {29},
+ number = {4},
+ pages = {891--913},
+ year = {1985}
+}
+
+ at article{DipGla99,
+ title = {{Incentives and Social Capital: Are Homeowners
+ Better Citizens?}},
+ author = {Denise DiPasquale and Edward L. Glaeser},
+ journal = {Journal of Urban Economics},
+ volume = {45},
+ number = {2},
+ pages = {354--384},
+ year = {1999},
+ publisher = {Elsevier}
+}
+
+ at book{ColRai78,
+ title = {{Social Standing in America: New Dimensions of
+ Class}},
+ author = {Richard P. Coleman and Lee Rainwater},
+ publisher = {Basic Books},
+ address = {New York, NY},
+ year = {1978}
+}
+
+ at article{GruSor98,
+ title = {{Can Class Analysis Be Salvaged?}},
+ author = {David Grusky and Jesper B. Sorensen},
+ journal = {American Journal of Sociology},
+ volume = {103},
+ number = {5},
+ pages = {1187--1234},
+ year = {1998},
+ publisher = {UChicago Press}
+}
+
+
+ at article{Sorensen00,
+ title = {{Toward a Sounder Basis for Class Analysis}},
+ author = {Aage B. Sorensen},
+ journal = {American Journal of Sociology},
+ volume = {105},
+ number = {6},
+ pages = {1523--1558},
+ year = {2000},
+ publisher = {University of Chicago Press}
+}
+
+
+ at book{JacJac91,
+ title = {{Class Awareness in the United States}},
+ author = {Mary R. Jackman and Robert W. Jackman},
+ publisher = {University of California Press},
+ year = {1991},
+ address = {Berkeley, CA}
+}
+
+
+ at article{Fischer03,
+ title = {{The Relative Importance of Income and Race in
+ Determining Residential Outcomes in US Urban Areas,
+ 1970-2000}},
+ author = {Mary J. Fischer},
+ journal = {Urban Affairs Review},
+ volume = {38},
+ number = {5},
+ pages = {669-696},
+ year = {2003}
+}
+
+ at article{SchHipDeu85,
+ title = {{Response Scales: Effects of Category Range on
+ Reported Behavior and Comparative Judgments}},
+ author = {Norbert Schwarz and Hans J. Hippler and Bridget
+ Deutsch and Fritz Strack},
+ journal = {Public Opinion Quarterly},
+ volume = {49},
+ number = {3},
+ pages = {388--395},
+ year = {1985},
+ publisher = {The Trustees of Columbia University}
+}
+
+ at article{JavPopLal08,
+ title = {{Co-occurrence of Binge Eating Disorder with
+ Psychiatric and Medical Disorders.}},
+ author = {Kristin Javaras and Harrison Pope and Justine
+ Lalonde and Jacqueline Roberts and Yael Nillni and
+ Nan Laird and Cynthia Bulik and Scott Crow and Susan
+ McElroy and B. Timothy Walsh and others},
+ journal = {The Journal of Clinical Psychiatry},
+ volume = {69},
+ number = {2},
+ pages = {266-273},
+ year = {2008},
+ publisher = {J Clin Psychiatry}
+}
+
+ at article{SinAdlMar03,
+ title = {{Subjective social status: its determinants and its
+ association with measures of ill-health in the
+ Whitehall II study}},
+ author = {Archana Singh-Manoux and Nancy E. Adler and Michael
+ G. Marmot},
+ journal = {Social Science and Medicine},
+ volume = {56},
+ number = {6},
+ pages = {1321--1333},
+ year = {2003},
+ publisher = {Elsevier}
+}
+
+
+ at Article{RogHumKru05,
+ author = {Rogers, Richard and Hummer, Robert and Krueger, Patrick and Pampel, Fred},
+ title = {Mortality Attributable to Cigarette Smoking in the United States},
+ journal = {Population and Development Review},
+ year = 2005,
+ volume = 31,
+ number = 2,
+ pages = {259-292},
+ month = {June}
+}
+
+ at Article{MehCha08,
+ author = {Mehta, Neil and Chang, Virginia},
+ title = {Mortality Attributable to Obesity Among Middle-Adults in the United States},
+ journal = {Demography},
+ year = {Forthcoming}
+}
+
+ at Article{StuRinAnd04,
+ author = {Sturm, Roland and Ringel, Jeanne and Andreyeva, Tatiana},
+ title = {Increasing Obesity Rates and Disability Trends},
+ journal = {Health Affairs},
+ year = 2004,
+ volume = 23,
+ number = 2,
+ pages = {199-205}
+}
+
+ at Book{SloSmiTay03,
+ author = {Sloan, Frank and Smith, Kerry and Taylor, Donald},
+ title = {The Smoking Puzzle: Information, Risk, Perception, and Choice},
+ publisher = {Harvard University Press},
+ year = 2003,
+ address = {Cambridge, Mass.}
+}
+
+ at Article{RyaZweOra92,
+ author = {Ryan, James and Zwerling, Craig and Orav, Endel John},
+ title = {Occupational Risks Associated with Cigarette Smoking{:} A Prospective Study},
+ journal = {American Journal of Public Health},
+ year = 1992,
+ volume = 82,
+ number = 1,
+ pages = {29-32},
+}
+
+
+ at Article{LevGusVel97,
+ author = {Levine, Phillip and Gustafson, Tara and Velenchik, Ann},
+ title = {More Bad News for Smokers? The Effects of Cigarette Smoking on Wages},
+ journal = {Industrial and Labor Relations Review},
+ year = 1997,
+ volume = 50,
+ number = 3,
+ pages = {493-509}
+}
+
+ at Book{SloOstPic04,
+ author = {Sloan, Frank and Ostermann, Jan and Picone, Gabriel and Conover, Christopher and Taylor, Donald},
+ title = {The Price of Smoking},
+ publisher = {The MIT Press},
+ year = 2004,
+ address = {Cambridge, Mass.}
+}
+
+ at Article{DolPetWhe94,
+ author = {Doll, Richard and Peto, Richard and Wheatley, Keith and Gray, Richard and Sutherland, Isabelle},
+ title = {Mortality in Relation to Smoking: 40 Years' Observations on Male British Doctors},
+ journal = {British Medical Journal},
+ year = 1994,
+ volume = 309,
+ number = 6959,
+ pages = {901-911}
+}
+
+ at book{Mayhew91,
+ title={{Divided We Govern: Party Control, Lawmaking and Investigations}},
+ author={David R. Mayhew},
+ year={1991},
+ publisher={New Haven CT, Yale University Press}
+}
+
+ at article{Sebastiani02, title={{Machine learning in automated text
+categorization}}, author={Fabrizio Sebastiani}, journal={ACM Computing
+Surveys (CSUR)}, volume={34}, number={1}, pages={1--47}, year={2002},
+publisher={ACM New York, NY, USA} }
+
+ at article{PanLee08,
+ title={{Opinion Mining and Sentiment Analysis}},
+ author={Bo Pang and Lillian Lee},
+ journal={Foundations and Trends in Information Retrieval},
+ volume={2},
+ number={1},
+ pages={1--135},
+ year={2008}
+}
+
+
+ at Book{Rudalevige02,
+author = {Andrew Rudalevige},
+title = {Managing the President's Program},
+publisher = {Princeton University Press},
+year = {2002},
+address = {Princeton, NJ}
+}
+
+ at Book{Kellstedt03,
+author = {Paul M. Kellstedt},
+title = {The Mass Media and the Dynamics of American Racial Attitudes},
+publisher = {Cambridge University Press},
+year = {2003},
+address = {New York, NY}
+}
+
+ at Book{Gilens99,
+author = {Martin Gilens},
+title = {Why Americans Hate Welfare},
+publisher = {University of Chicago Press},
+year = {1999},
+address = {Chicago, IL}
+}
+
+ at unpublished{NovRau08,
+ title = {The Intergenerational Transfer of Public Pension
+ Promises},
+ author = {Robert Novy-Marx and Joshua D.Rauh},
+ journal = {Working Paper},
+ year = {2008},
+ note = {http://www.nber.org/papers/w14343}
+}
+
+ at Article{Helmond08,
+ author = {Anne Helmond},
+ title = {How Many Blogs Are There? Is Someone Still
+ Counting?},
+ journal = {The Blog Herald},
+ year = 2008,
+ number = {2/11},
+ note =
+ {http://www.blogherald.com/2008/02/11/how-many-blogs-are-there-is-someone-still-counting/}
+}
+
+ at book{Mendelberg01,
+ title={{The Race Card: Campaign Strategy, Implicit Messages, and the Norm of Equality}},
+ author={Tali Mendelberg},
+ year={2001},
+ publisher={Princeton University Press},
+ address={Princeton, NJ}
+}
+
+ at book{Gerring98,
+ title={{Party Ideologies in America, 1828-1996}},
+ author={John Gerring},
+ year={1998},
+ publisher={Cambridge University Press},
+ address={New York}
+}
+
+ at book{Thompson02,
+ title={{Sampling}},
+ author={Steven K. Thompson},
+ year={2002},
+ publisher={John Wiley and Sons},
+ address={New York}
+}
+
+ at book{HilShi08,
+ title={{The Persuadable Voter: Wedge Issues in Presidential Campaigns}},
+ author={Sunshine Hillygus and Todd G. Shields},
+ year={2008},
+ publisher={Princeton University Press},
+ address={Princeton, NJ}
+}
+
+ at book{BraSudWan04,
+ title = {{Asking Questions: The Definitive Guide to
+ Questionnaire Design}},
+ author = {Norman M. Bradburn and Seymour Sudman and Brian
+ Wansink},
+ year = {2004},
+ publisher = {Jossey-Bass},
+ address = {San Francisco}
+}
+
+ at book{ConPre86,
+ title = {{Survey Questions: Handcrafting the Standardized
+ Questionnaire}},
+ author = {Jean M. Converse and Stanley Presser},
+ year = {1986},
+ publisher = {Sage Publications},
+ address = {Thousand Oaks, CA}
+}
+
+ at book{GroCouLep04,
+ title = {{Survey Methodology}},
+ author = {Robert M. Groves and Mick P. Couper and James
+ M. Lepkowski and Eleanor Singer and Roger
+ Tourangeau},
+ year = {2004},
+ publisher = {Wiley},
+ address = {Hoboken, NJ}
+}
+
+ at article{KriJoh08,
+ title = {{New evidence on cross-country differences in job
+ satisfaction using anchoring vignettes}},
+ author = {Nicolai Kristensen and Edvard Johansson},
+ journal = {Labour Economics},
+ volume = {15},
+ number = {1},
+ pages = {96--117},
+ year = {2008}
+}
+
+ at Article{GupKriPoz08,
+ author = {Nabanita Datta Gupta and Nicolai Kristensen and
+ Dario Pozzoli},
+ title = {External Validation of the Use of Vignettes in
+ Cross-Country Health Studies},
+ journal = { },
+ year = 2008,
+ note = {Danish National Centre for Social Research}
+}
+
+ at article{ColRosQui00,
+ title = {{Prospective validation of a standardized
+ questionnaire for estimating childhood mortality and
+ morbidity due to pneumonia and diarrhoea}},
+ author = {Coldham, C. and Ross, D. and Quigley, M. and Segura,
+ Z. and Chandramohan, D.},
+ journal = {Tropical Medicine \& International Health},
+ volume = {5},
+ number = {2},
+ pages = {134--144},
+ year = {2000}
+}
+
+ at Article{MorDaw06,
+ author = {Morera, Osvaldo and Dawes, Robyn},
+ title = {Clinical and Statistical Prediction After 50 Years:
+ A Dedication to Paul Meehl},
+ journal = {Journal of Behavioral Decision Making},
+ year = 2006,
+ volume = 19,
+ pages = {409-412}
+}
+
+ at article{OlsHayCar02,
+ author = {Olshansky, S. Jay and Hayflick, Leonard and Carnes,
+ Bruce A.},
+ title = {{Position Statement on Human Aging}},
+ journal = {Journal of Gerontology Series A: Biological Sciences and Medical Sciences},
+ volume = {57},
+ number = {8},
+ pages = {B292-297},
+ year = {2002}
+}
+
+ at article{Fries80,
+ author = {Fries, JF},
+ title = {{Aging, natural death, and the compression of
+ morbidity}},
+ journal = {New England Journal of Medicine},
+ volume = {303},
+ number = {3},
+ pages = {130-135},
+ year = {1980}
+}
+
+ at article{WarCarHaw08,
+ author = {Wardle, Jane and Carnell, Susan and Haworth, Claire
+ MA and Plomin, Robert},
+ title = {{Evidence for a strong genetic influence on
+ childhood adiposity despite the force of the
+ obesogenic environment}},
+ journal = {American Journal of Clinical Nutrition},
+ volume = {87},
+ number = {2},
+ pages = {398-404},
+ year = {2008}
+}
+
+ at Article{MokForBow01,
+ author = {Mokdad, Ali and Ford, Earl and Bowman, Barbara and
+ Dietz, William and Vinicor, Frank and Bales,
+ Virginia and Marks, James},
+ title = {Prevalence of Obesity, Diabetes, and Obesity-Related
+ Health Risk Factors, 2001},
+ journal = {Journal of the American Medical Association},
+ year = 2003,
+ volume = 289,
+ pages = {76-79}
+}
+
+ at article{FonRedWan03,
+ author = {Fontaine, Kevin R. and Redden, David T. and Wang,
+ Chenxi and Westfall, Andrew O. and Allison, David
+ B.},
+ title = {{Years of Life Lost Due to Obesity}},
+ journal = {Journal of the American Medical Association},
+ year = 2003,
+ volume = {289},
+ number = {2},
+ pages = {187-193}
+}
+
+ at article{AllWanRed03,
+ author = {Allison, David B. and Wang, Chenxi and Redden, David
+ T. and and Westfall, Andrew O. and Fontaine, Kevin
+ R.},
+ title = {{Obesity and Years of Life Lost$-$Reply}},
+ journal = {Journal of the American Medical Association},
+ year = 2003,
+ volume = {289},
+ number = {14},
+ pages = {1777-1778}
+}
+
+ at article{WesAraOls04,
+ author = {Wessel, Timothy R. and Arant, Christopher B. and
+ Olson, Marian B. and Johnson, B. Delia and Reis,
+ Steven E. and Sharaf, Barry L. and Shaw, Leslee
+ J. and Handberg, Eileen and Sopko, George and
+ Kelsey, Sheryl F. and Pepine, Carl J. and Bairey
+ Merz, C. Noel},
+ title = {{Relationship of Physical Fitness vs Body Mass Index
+ With Coronary Artery Disease and Cardiovascular
+ Events in Women}},
+ journal = {Journal of the American Medical Association},
+ volume = {292},
+ number = {10},
+ pages = {1179-1187},
+ year = {2004}
+}
+
+ at article{BlaChu04,
+ author = {Blair, Steven N. and Church, Tim S.},
+ title = {{The Fitness, Obesity, and Health Equation: Is
+ Physical Activity the Common Denominator?}},
+ journal = {Journal of the American Medical Association},
+ volume = {292},
+ number = {10},
+ pages = {1232-1234},
+ year = {2004}
+}
+
+ at Article{FreSigRaj06,
+ author = {Freedman, Michal and Sigurdson, Alice and Rajaraman,
+ Preetha and Doody, Michele and Linet, Martha and
+ Ron, Elaine},
+ title = {The Mortality Risk of Smoking and Obesity Combined},
+ journal = {American Journal of Preventive Medicine},
+ year = 2006,
+ volume = 31,
+ number = 5,
+ pages = {355-362}
+}
+
+ at Article{GreCheCad05,
+ author = {Gregg, Edward and Cheng, Yiling and Cadwell, Betsy
+ and Imperatore, Giuseppina and Williams, Desmond and
+ Flegal, Katherine and Narayan, Venkat and
+ Williamson, David},
+ title = {Secular Trends in Cardiovascular Disease Risk
+ Factors According to Body Mass Index in US Adults},
+ journal = {Journal of the American Medical Association},
+ year = 2005,
+ volume = 293,
+ number = 15,
+ pages = {1868-1874}
+}
+
+ at Article{MurLopFee07,
+ author = {Christopher J.L.\ Murray and Alan D. Lopez and
+ Dennis M. Feean and Shanon T. Peter and Gonghuan
+ Yang},
+ title = {Validation of the Symptom Pattern Method for
+ Analyzing Verbal Autopsy Data},
+ journal = {PLOS Medicine},
+ year = 2007,
+ volume = 4,
+ number = 11,
+ pages = {1739--1753},
+ month = {November}
+}
+
+ at Article{CurDurEil04,
+ author = {Currie, Iain and Durban, Maria and Eilers, Paul},
+ title = {Smoothing and Forecasting Mortality Rates},
+ journal = {Statistical Modelling},
+ year = 2004,
+ volume = 4,
+ pages = {279-298}
+}
+
+ at Article{KirCur09,
+ author = {Kirkby, James and Currie, Iain },
+ title = {Smooth Models of Mortality with Period Shocks},
+ journal = {Statistical Modelling},
+ year = {Forthcoming}
+}
+
+ at Article{INDEPTH03,
+ author = {{INDEPTH Network}},
+ title = {Standardised Verbal Autopsy Questionnaire},
+ journal = { },
+ note = {{http://indepth-network.org}},
+ year = 2003
+}
+
+ at Article{SetRaoHem06,
+ author = {PW Setel and C Rao and Y Hemed and DR Whiting and G
+ Yang et al.},
+ title = {Core Verbal Autopsy Procedures with Comparative
+ Validation Results from Two Countries},
+ journal = {PLoS Medicine},
+ year = 2006,
+ volume = 3,
+ number = 8,
+ pages = {e268},
+ note = {doi:10.1371/journal.pmed.0030268}
+}
+
+ at Book{WHO07,
+ author = {{World Health Organization}},
+ title = {Verbal Autopsy Standards: Ascertaining and
+ Attributing Causes of Death},
+ publisher = {World Health Organization},
+ address = {Geneva},
+ year = 2007
+}
+
+ at Article{ThaKalBaq08,
+ author = {N Thatte and H D Kalter and A H Baqui and E M
+ Williams and G L Darmstadt},
+ title = {Ascertaining causes of neonatal deaths using verbal
+ autopsy: current methods and challenges},
+ journal = {Journal of Perinatology},
+ year = 2008,
+ pages = {1--8},
+ month = {December}
+}
+
+ at TechReport{LeeAndTul03,
+ author = {Lee, Ronald and Anderson, Michael and Tuljapurkar, Shripad},
+ title = {Stochastic Forecasts of the Social Security Trust Fund},
+ institution = {Center for the Economics and Demography of Aging},
+ year = 2003,
+ type = {2003-0005CL}
+}
+
+ at TechReport{Holmer03,
+ author = {Holmer, Martin},
+ title = {Methods for Stochastic Trust Fund Projection},
+ institution = {Policy Simulation Group},
+ year = 2003,
+ month = {January}
+}
+
+ at TechReport{CBO01,
+ author = {{Congressional Budget Office}},
+ title = {Uncertainty in Social Security's Long-Term Finances: A Stochastic Analysis},
+ institution = {Congressional Budget Office},
+ year = 2001,
+ month = {December}
+}
+
+ at TechReport{BurMan03,
+ author = {Burdick, Clark and Manchester, Joyce},
+ title = {Stochastic Models of the Social Security Trust Funds},
+ institution = {Division of Economic Research, Social Security Administration},
+ year = 2003,
+ type = {Research and Statistics Note},
+ number = {2003-01}
+}
+
+ at Manual{Harrell08,
+ title = {Hmisc: Harrell Miscellaneous},
+ author = {Frank E Harrell Jr and with contributions from many
+ other users.},
+ year = {2008},
+ note = {R package version 3.5-2,
+ http://biostat.mc.vanderbilt.edu/s/Hmisc}
+}
+
+ at article{Heckman90,
+ title = {{Varieties of selection bias}},
+ author = {Heckman, James},
+ volume = {80},
+ number = {2},
+ journal = {The American Economic Review},
+ pages = {313--318},
+ year = {1990}
+}
+
+ at article{TheGol61,
+ title={{On pure and mixed estimation in econometrics}},
+ author={Theil, H. and Goldberger, AS},
+ journal={International Economic Review},
+ volume={2},
+ pages={65--78},
+ year={1961}
+}
+ at Book{Hsiao03,
+ author = {C. Hsiao},
+ title = {Analysis of Panel Data},
+ publisher = {Cambridge University Press},
+ year = 2003,
+ address = {New York}
+}
+
+ at article{BanDhiGho05,
+ title= {{Clustering on the Unit Hypersphere Using von Mises-Fisher Distributions}},
+ author= {Arindam Banerjee and Inderjit Dhillon and Joydeep Ghosh and Suvrit Sra},
+ journal= {Journal of Machine Learning},
+ volume= {6},
+ pages= {1345-1382},
+ year= {2005}
+}
+
+ at article{BleJor06,
+ title= {{Variational Inference for Dirichlet Process Mixtures}},
+ author= {David Blei and Michael Jordan},
+ journal= {Journal of Bayesian Analysis},
+ volume= {1},
+ number = {1},
+ pages= {121--144},
+ year= {2006}
+}
+
+ at article{Cowan00,
+ title= {{The Magical Number 4 in Short Term Memory: A Reconsideration of Mental Storage Capacity}},
+ author= {Nelson Cowan},
+ journal= {Behavioral and Brain Sciences},
+ volume= {24},
+ pages= {87--185},
+ year= {2000}
+}
+
+ at article{Dhillon01,
+ title= {{Co-clustering Documents and Words Using Bipartite Spectral Graph Partitioning}},
+ author= {Inderjit Dhillon},
+ journal= {Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
+ pages= {89--98},
+ year= {2003}
+}
+
+ at article{HoPep02,
+ title= {{Simple Explanation of the No Free Lunch Theorem and Its Implications}},
+ author= {Y Ho and D Pepyne},
+ journal= {Journal of Optimization Theory and Applications},
+ volume= {115},
+ number = {3},
+ pages= {549--570},
+ year= {2002}
+}
+
+ at MISC{JonWilBau09,
+ author = {Bryan Jones and John Wilkerson and Frank
+ Baumgartner},
+ title = {{The Policy Agendas Project}},
+ year = 2009,
+ note = {http://www.policyagendas.org}
+}
+
+
+ at article{JorGhaJaa99,
+ title= {{An Introduction to Variational Methods for Graphical Models}},
+ author= {Michael Jordan and Zoubin Ghahramani and Tommi Jaakkola and Lawrence Saul},
+ journal= {Journal of Machine Learning},
+ volume= {37},
+ pages= {183--233},
+ year= {1999}
+}
+
+ at Book{KauRou90,
+ author = {Leonard Kaufman and Peter Rousseeuw},
+ title = {Finding Groups in Data: An Introduction to Cluster Analysis},
+ publisher = {Wiley},
+ year = {1990},
+ address = {New York}
+}
+
+ at Book{Kohonen01,
+ author = {Teuvo Kohonen},
+ title = {Self-Organizing Maps},
+ publisher = {Springer},
+ year = {2001},
+ address = {New York}
+}
+
+ at MISC{Lewis99,
+ author = {David Lewis},
+ title = {{Reuters -21578 text Categorization Test Collection Distribution 1.0}},
+ year = {1999}
+}
+
+ at Book{Mackay03,
+ author = {David Mackay},
+ title = {Information Theory, Inference, and Learning Algorithms},
+ publisher = {Cambridge University Press},
+ year = {2003},
+ address = {Cambridge}
+}
+
+ at article{Meila07,
+ title= {{Comparing Clusterings: An Information Based Distance}},
+ author= {Marina Meila},
+ journal= {Journal of Multivariate Analysis},
+ volume= {98},
+ number = {5},
+ pages= {873--895},
+ year= {2007}
+}
+
+ at article{Miller56,
+ title= {{The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information}},
+ author= {George Miller},
+ journal= {Psychological Review},
+ volume= {63},
+ pages= {81--97},
+ year= {1956}
+}
+
+ at article{NgJorWei02,
+ title= {{On Spectral Clustering: Analysis and an Algorithm}},
+ author= {Andrew Ng and Michael Jordan and Yair Weiss},
+ journal= {Advances in Neural Information Processing Systems 14: Proceedings of the 2002 Conference},
+ year= {2002}
+}
+
+ at article{Sammon69,
+ title= {{A Nonlinear Mapping for Data Structure Analysis}},
+ author= {John Sammon},
+ journal= {IEEE Transactions on Computers},
+ volume= {18},
+ number = {5},
+ pages= {401--409},
+ year= {1969}
+}
+
+ at article{ShiMal00,
+ title= {{Normalized Cuts and Image Segmentation}},
+ author= {J Shi and J Malik},
+ journal= {IEEE Transactions on Pattern Analysis and Machine Intelligence},
+ volume= {22},
+ number = {8},
+ pages= {888--905},
+ year= {2000}
+}
+
+
+ at Book{Simon57,
+ author = {Herbert Simon},
+ title = {Models of Man},
+ publisher = {Wiley},
+ year = {1957},
+ address = {New York}
+}
+
+ at article{StrGro02,
+ title= {{Cluster Ensembles: A Knowledge Reuse Framework for Combining Multiple Partitions}},
+ author= {Alexander Strehl and Joydeep Grosh},
+ journal= {Journal of Machine Learning Research},
+ volume= {3},
+ pages= {583--617},
+ year= {2002}
+}
+
+ at article{vonLuxburg07,
+ title= {{A Tutorial on Spectral Clustering}},
+ author= {Ulrike von Luxburg},
+ journal= {Statistics and Computing},
+ volume= {17},
+ number = {4},
+ pages= {395--416},
+ year= {2007}
+}
+
+ at Book{Watanabe69,
+ title = {Knowing and Guessing: A Quantitative Study of Inference and Information},
+ author = {Satosi Watanabe},
+ publisher = {Wiley},
+ year = {1969},
+ address = {New York}
+}
+
+ at article{WolMac97,
+ title= {{No Free Lunch Theorems for Optimization}},
+ author= {DH Wolpert and WG Macready},
+ journal= {IEEE Transactions on Evolutionary Computation},
+ volume= {1},
+ number = {1},
+ pages= {67--82},
+ year= {1997}
+}
+
+ at Book{Bailey94,
+ author = {Kenneth D. Bailey},
+ title = {Typologies and taxonomies: an introduction to
+ classification techniques},
+ publisher = {Sage},
+ year = 1994,
+ address = {Beverly Hills}
+}
+
+ at article{Spivey08,
+ title = {{A generalized recurrence for Bell numbers}},
+ author = {Spivey, M.Z.},
+ journal = {J. Integer Sequences},
+ volume = {11},
+ year = {2008}
+}
+
+ at article{FraRaf02,
+ title = {{Model-based clustering, discriminant analysis, and
+ density estimation}},
+ author = {Fraley, C. and Raftery, A.E.},
+ journal = {Journal of the American Statistical Association},
+ volume = {97},
+ number = {458},
+ pages = {611--631},
+ year = {2002}
+}
+
+ at Book{GanMaWu07,
+ author = {Guojun Gan and Chaoqun Ma and Jianhong Wu},
+ title = {Data Clustering: Theory, Algorithms, and Applications},
+ publisher = {Siam},
+ year = 2007,
+ address = {Philadelphia}
+}
+
+ at Article{DiaGoeHol08,
+ author = {Persi Diaconis and Sharad Goel and Susan Holmes},
+ title = {Horseshoes in multidimensional scaling and local kernel methods},
+ journal = {Annals of Applied Statistics},
+ year = 2008,
+ volume = 2,
+ number = 3,
+ pages = {777--807}
+}
+
+ at article{Almon65,
+ title = {{The distributed lag between capital appropriations
+ and expenditures}},
+ author = {Almon, Shirley},
+ journal = {Econometrica: Journal of the Econometric Society},
+ pages = {178--196},
+ year = {1965},
+ publisher = {The Econometric Society}
+}
+ at Book{ManRagSch08,
+ author = {Christopher D. Manning and Prabhakar Raghavan and
+ Hinrich Sch{\"u}tze},
+ title = {Introduction to Information Retrieval},
+ publisher = {Cambridge University Press},
+ year = 2008,
+ address = {NY}
+}
+
+ at article{Mayhew74,
+ title = {{The electoral connection}},
+ author = {Mayhew, D.},
+ journal = {New Haven: Yale University},
+ year = {1974}
+}
+
+ at Book{Fiorina89,
+ author = {Morris Fiorina},
+ title = {Congress, Keystone of the Washington Establishment},
+ publisher = {Yale University Press},
+ year = {1989},
+ address = {New Haven}
+}
+
+ at article{EulKar77,
+ title = {{The Puzzle of Representation: Specifying Components
+ of Responsiveness}},
+ author = {Heiz Eulau and Paul Karps},
+ journal = {Legislative Studies Quarterly},
+ volume = {2},
+ number = {3},
+ pages = {233-254},
+ year = {1977}
+}
+
+ at article{Yiannakis82,
+ title = {{House Members Communication Styles: Newsletters and
+ Press Releases}},
+ author = {Diane Evans Yiannakis},
+ journal = {Journal of Politics},
+ volume = {44},
+ number = {4},
+ pages = {1049-1071},
+ year = {1982}
+}
+
+ at article{MonColQui08,
+ title = {{Fightin' Words: Lexical Feature Selection and
+ Evaluation for Identifying the Content of Political
+ Conflict}},
+ author = {Burt Monroe and Michael Colaresi and Kevin Quinn},
+ journal = {Political Analysis},
+ volume = {16},
+ number = {4},
+ pages = {372-403},
+ year = {2008}
+}
+
+ at Book{TayCri04,
+ author = {John Shawe-Taylor and Nello Cristianini},
+ title = {Kernel Methods for Pattern Analysis},
+ publisher = {Cambridge University Press},
+ year = {2004},
+ address = {Cambridge}
+}
+ at Misc{Billington07,
+ author = {James H. Billington},
+ title = {Testimony to Congress (House Subcommittee on
+ Legislative Branch)},
+ howpublished =
+ {http://www.loc.gov/about/welcome/speeches/digital/digitalage.html},
+ month = {20 March},
+ year = 2007
+}
+
+ at article{AbeLedLew08,
+ title = {{Blown to bits: your life, liberty, and happiness
+ after the digital explosion}},
+ author = {Abelson, H. and Ledeen, K. and Lewis, H.},
+ year = {2008},
+ publisher = {Addison-Wesley Professional}
+}
+ at Article{GilCas09,
+ author = {Jeff Gill and George Casella},
+ title = {Nonparametric Priors for Ordinal Bayesian Social
+ Science Models: Specification and Estimation},
+ journal = {Journal of the American Statistical Association},
+ year = 2009,
+ volume = 104,
+ number = 486,
+ pages = {1--12},
+ month = {June}
+}
+
+ at article{SchGer97,
+ title={{Empirical indicators of crisis phase in the Middle East, 1979-1995}},
+ author={Schrodt, P.A. and Gerner, D.J.},
+ journal={Journal of Conflict Resolution},
+ pages={529--552},
+ year={1997},
+ publisher={Sage Publications}
+}
+
+ at article{Guttman50,
+ title = {{The problem of attitude and opinion measurement}},
+ author = {Guttman, L.},
+ journal = {Measurement and prediction},
+ volume = {4},
+ year = {1950}
+}
+
+
+ at article{MilKub05,
+ title = {{Why the move to free trade? Democracy and trade policy in the developing countries}},
+ author = {Helen Milner and Keiko Kubota},
+ journal = {International Organization},
+ volume = {59},
+ number = {1},
+ pages = {107--143},
+ year = {2005}
+}
+
+
+ at Book{GutTho96,
+ author = {Amy Gutmann and Dennis Thompson},
+ title = {Democracy and Disagreement},
+ publisher = {Harvard University Press},
+ year = {1996},
+ address = {Harvard University Press}
+}
+
+ at Unpublished{MikLavBen08,
+ author = {Slava Mikhaylov and Michael Laver and Kenneth
+ Benoit},
+ title = {Coder Reliability and Misclassification in
+ Comparative Manifesto Project Codings},
+ note = {Paper presented at the Midwest Political Science
+ Association, Chicago},
+ month = {April},
+ year = 2008
+}
+
+ at conference{CarElhNgu06,
+ title={{Meta clustering}},
+ author={Rich Caruana and Mohamed Elhawary and Nam Nguyen and Casey Smith},
+ booktitle={ICDM'06. Sixth International Conference on Data Mining},
+ pages={107--118},
+ year={2006}
+}
+
+ at conference{CarNgu07,
+ title={{Consensus clustering}},
+ author={Rich Caruana and Nam Nguyen},
+ booktitle={ICDM'07. Seventh International Conference on Data Mining},
+ year={2007}
+}
+
+ at conference{FerBro03,
+ title={Random Project for High Dimensional Data Clustering: A Cluster Ensemble Approach},
+ author={Xiaoli Fern and Carla Brodley},
+ booktitle={Proceedings of the Twentieth International Conference on Machine Learning},
+ year={2003}
+}
+
+
+ at conference{LawTopJai04,
+ title={Multi-objective Data Clustering},
+ author={Martin Law and Alexander Topchy and Anil Jain},
+ booktitle={IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
+ year={2004}
+}
+
+
+ at conference{BaeBai06,
+ title={A Novel Approach for the Extraction of an Alternate Clustering of High Quality and High Dissimilarity},
+ author={Eric Bae and James Bailey},
+ booktitle={Proceedings of the IEEE International Conference on Data Mining},
+ year={2006}
+}
+
+
+ at conference{FreJai02,
+ title={Data Clustering using Evidence Accumulation},
+ author={Martin Law and Alexander Topchy and Anil Jain},
+ booktitle={Proceedings of the 16th International Conference on Pattern Recognition},
+ year={2002}
+}
+
+ at conference{GioManTsa05,
+ title={Clustering aggregation},
+ author={A Gionis and H Mannila and P Tsaparas},
+ booktitle={Proceedings of the 21st International Conference on Data Mining},
+ year={2005}
+}
+
+ at conference{TopJaiPun03,
+ title={Combining Multiple Weak Clusterings},
+ author={A Topchy and AK Jain and W Punch},
+ booktitle={Proceedings IEEE International Conference on Data Mining},
+ year={2003}
+}
+
+ at conference{TopJaiPun03b,
+ title={A Mixture Model for Clustering Ensembles},
+ author={A Topchy and AK Jain and W Punch},
+ booktitle={Proceedings SIAM International Conference on Data Mining},
+ year={2004}
+}
+
+ at conference{Kleinberg03,
+ title={An Impossibility Theorem for Clustering},
+ author={Jon Kleinberg},
+ booktitle={Advances in Neural Information Processing Systems Proceedings of the 2002 Conference},
+ pages={463-470},
+ year={2003},
+}
+
+
+
+ at article{Achen78,
+ title={Measuring Representation},
+ author={Chris Achen},
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+ year={1978},
+}
+
+
+ at INCOLLECTION{LazBar65,
+ author= {Paul Lazardsfeld and Allen Barton},
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+}
+
+ at article{Pitman97,
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+ year={1997},
+}
+
+
+ at Article{ZhaSma09,
+ author = {Kai Zhang and Dyland S.\ Small},
+ title = {Comment: The Essential Role of Pair Matching in
+ Cluster-Randomized Experiments, with Application to
+ the Mexican Universal Health Insurance Program},
+ journal = {Statistical Science},
+ year = {2009, forthcoming},
+ OPTkey = {},
+ OPTvolume = {},
+ OPTnumber = {},
+ OPTpages = {},
+ OPTmonth = {},
+ OPTnote = {},
+ OPTannote = {}
+}
+
+ at Article{HilSco09,
+ author = {Jennifer Hill and Marc Scott},
+ title = {Discussion of `The Essential Role of Pair Matching'},
+ journal = {Statistical Science},
+ year = {2009},
+ OPTkey = {},
+ OPTvolume = {},
+ OPTnumber = {},
+ OPTpages = {},
+ OPTmonth = {},
+ OPTnote = {},
+ OPTannote = {}
+}
+
+ at article{Imbens09,
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+}
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+}
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+}
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+}
+
+ at book{Pitkin72,
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+}
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+ at book{Ayres08,
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+}
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+}
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+ at book{McLThr08,
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+}
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+ year={2008}
+}
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+ year = {2009}
+}
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+ VOLUME = {7},
+ YEAR = {2009},
+ NUMBER = {1},
+ PAGES = {6},
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+}
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+ title = {Forecasting the Effects of Obesity and Smoking on Life Expectancy},
+ journal = {The New England Journal of Medicine},
+ year = {2009},
+ OPTkey = {},
+ OPTvolume = {361},
+ OPTnumber = {23},
+ OPTpages = {2252-2260},
+ OPTmonth = {},
+ OPTnote = {},
+ OPTannote = {}
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+}
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+ year = {{1996}},
+ tags = "measurement error, statistics"
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+ tags = "measurement error, statistics"
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+ pages = {{815--835}},
+}
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+}
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+ Astrophysics and Cosmology: proceedings of
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+ year = {2006},
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+}
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+ title = {Comparative effectiveness research: Policy context,
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+ month = {August}
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+}
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+ type = {SFB 649},
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+}
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+}
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+}
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+}
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+}
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+}
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+
+ at article{Fraley98,
+author = {Chris Fraley},
+title = {Algorithms for Model-Based Gaussian Hierarchical Clustering},
+journal = {SIAM Journal of Scientific Computing},
+year = {1998},
+volume = {20},
+number = {1},
+pages = {270-281},
+}
+
+ at article{KoyGraRam05,
+author = {M Koyuturk and A Graham and N Ramakrishnan},
+title = {Compression, Clustering, and Pattern Discovery in Very High-Dimensional Discrete-Attribute Data Sets},
+journal = {IEEE Transactions On Knowledge and Data Engineering},
+year = {2005},
+volume = {17},
+number = {4},
+}
+
+ at article{GuhRasShi00,
+author = {S Guha and R Rastogi and K Shim},
+title = {ROCK: A Robust Clustering Algorithm for Categorical Attributes},
+journal = {Information Science},
+year = {2000},
+volume = {25},
+number = {5},
+}
+
+ at article{HeyKruYoo99,
+author = {LJ Heyer and S Kruglyak and S Yooseph},
+title = {Exploring Expression Data: Identification and Analysis of Coexpressed Genes},
+journal = {Genome Research},
+year = {1999},
+volume = {9},
+pages = {1106-1115},
+}
+
+
+ at article{BroPihDatDat08,
+author = {G Brock and V Pihur and S Datta and S Datta},
+title = {clValid: An R Package for Cluster Validation},
+journal = {Journal of Statistical Software},
+year = {2008},
+volume = {25},
+number = {4},
+}
+
+
+ at article{MeiShi01,
+author = {M Meila and J Shi},
+title = {A Random Walks View of Spectral Segmentation},
+journal = {8th International Workshop on Artificial Intelligence and Statistics (AISTATS)},
+year = {2001},
+}
+
+ at article{DhiMalMod03,
+author = {Inderjit Dhillon and Subramanyam Mallela and Dharmendra Modha},
+title = {Information Theoretic Co-Clustering},
+journal = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
+year = {2003},
+volume = {9},
+}
+
+ at article{Lerman91,
+author = {IC Lerman},
+title = {Foundations of the Likelihood Linkage Analysis Classification Method},
+journal = {Applied Stochastic Models and Data Analysis},
+year = {1991},
+volume = {7},
+pages = {63-76},
+}
+
+ at article{WanQiuZam07,
+author = {S Wang and W Qiu and RH Zamar},
+title = {CLUES: A Non-Parametric Clustering Method Based on Local Shrinking},
+journal = {Computational Statistics \& Data Analysis},
+year = {2007},
+volume = {52},
+number = {1},
+pages = {286-298},
+}
+
+ at misc{Leisch99,
+author = {Friedrich Leisch},
+title = {Bagged Clustering},
+howpublished = {Working Paper 51, Adaptive Information Systems and Modelling in Economics and Management Science},
+month = {August},
+year = {1999},
+}
+
+ at article{Rajesh96,
+author = {Dave Rajesh},
+title = {Fuzzy Shell-Clustering and Applications to Circle Detection in Digital Images},
+journal = {Internationl Journal of General Systems},
+year = {1996},
+volume = {16},
+pages = {343-355},
+}
+
+ at Book{Kullback59,
+author = {Solomon Kullback},
+title = {Information Theory and Statistics},
+publisher = {Dover Publications},
+year = {1959},
+}
+
+ at book{KauRou90,
+author = {L Kaufman and PJ Rousseeuw},
+title = {Finding Groups in Data: An Introduction to Cluster Analysis},
+publisher = {Wiley},
+year = {1990},
+}
+ at Article{Washington08,
+ author = {Ebonya L. Washington},
+ title = {Female Socialization: How Daughters Affect Their
+ Legislator Fathers' Voting on Woman's Issues},
+ journal = {American Economic Review},
+ year = 2008,
+ volume = 98,
+ number = 1,
+ pages = {311-332}
+}
+
+ at Article{Muller59,
+ author = {M.E. Muller},
+ title = {A Note on a Method for Generating Points Uniformly on N -Dimensional Spheres},
+ journal = {Comm. Assoc. Comput. Mach.},
+ year = 1959,
+ volume = 2,
+ pages = {19-20},
+ month = {April}
+}
+
diff --git a/inst/doc/gkpubs.bib b/inst/doc/gkpubs.bib
new file mode 100644
index 0000000..8f252c4
--- /dev/null
+++ b/inst/doc/gkpubs.bib
@@ -0,0 +1,2228 @@
+% A bibtex-format file for papers by or coauthored with Gary King
+%
+% rules used for abbreviations:
+%
+% -if one author: use last name and last 2 digits of the year: King99.
+% -if multiple authors, use 1st 3 letters of each of UP TO the first three
+% authors and the last 2 digits of the year: KinTomWit00.
+% -if necessary add lower-case letters for multiple entries in a year:
+% King02, King02b (the first one should NOT have an 'a' afterwards)
+% -No string abbreviations are used
+%
+% entries are in separate sections (books, articles, software, data)
+% in reverse chronical order
+%
+% copies of all papers, articles, data, and software, and some books,
+% are available at http://gking.harvard.edu/
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Books
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ at book{KinSchNie09,
+ editor = {Gary King and Kay Schlozman and Norman Nie},
+ title = {The Future of Political Science: 100 Perspectives},
+ publisher = {Routledge Press},
+ address = {New York},
+ year = {2009}
+}
+
+ at book{GirKin08,
+ author = {Federico Girosi and Gary King},
+ title = {Demographic Forecasting},
+ publisher = {Princeton University Press},
+ year = {2008},
+ address = {Princeton},
+ note = {{http://gking.harvard.edu/files/smooth/}}
+}
+
+ at book{KinRosTan04,
+ editor = {Gary King and Ori Rosen and Martin A. Tanner},
+ title = {Ecological Inference: New Methodological Strategies},
+ publisher = {Cambridge University Press},
+ year = {2004},
+ address = {New York},
+ note = {{http://gking.harvard.edu/files/abs/ecinf04-abs.shtml}}
+}
+
+ at book{King97,
+ author = {Gary King},
+ title = {A Solution to the Ecological Inference Problem:
+ Reconstructing Individual Behavior from Aggregate
+ Data},
+ publisher = {Princeton University Press},
+ year = {1997},
+ address = {Princeton},
+ note = {{http://gking.harvard.edu/eicamera/kinroot.html}}
+}
+
+ at book{KinKeoVer94,
+ author = {Gary King and Robert O. Keohane and Sidney Verba},
+ title = {Designing Social Inquiry: Scientific Inference in
+ Qualitative Research},
+ publisher = {Princeton University Press},
+ year = {1994},
+ address = {Princeton},
+ note = {{http://www.pupress.princeton.edu/titles/5458.html}}
+}
+
+ at book{King89,
+ author = {Gary King},
+ title = {Unifying Political Methodology: The Likelihood
+ Theory of Statistical Inference},
+ publisher = {Michigan University Press},
+ year = 1989,
+ address = {Ann Arbor}
+}
+
+ at book{KinRag88,
+ author = {Gary King and Lyn Ragsdale},
+ title = {The Elusive Executive: Discovering Statistical
+ Patterns in the Presidency},
+ publisher = {Congressional Quarterly Press},
+ year = {1988},
+ address = {Washington, D.C}
+}
+
+ at book{BraHarKin89,
+ author = {Paul Brace and Christine Harrington and Gary King},
+ title = {The Presidency in American Politics},
+ publisher = {New York University Press},
+ year = {1989},
+ address = {New York and London}
+}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Articles
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ at Article{KinNieCob11,
+ author = {Gary King and Richard Nielsen and Carter Coberley
+ and James Pope and Aaron Wells},
+ title = {Comparative Effectiveness of Matching Methods for
+ Causal Inference},
+ journal = { },
+ year = {2011},
+ OPTkey = {},
+ OPTvolume = {},
+ OPTnumber = {},
+ OPTpages = {},
+ OPTmonth = {},
+ OPTnote = {{http://gking.harvard.edu/files/abs/psparadox-abs.shtml}},
+ OPTannote = {}
+}
+
+ at Article{SteKinShi10,
+ author = {Gretchen Stevens, Gary King, and Kenji Shibuya},
+ title = {Deaths From Heart Failure: Using Coarsened Exact
+ Matching to Correct Cause of Death Statistics},
+ journal = {Population Health Metrics},
+ year = 2010,
+ volume = 8,
+ number = 6,
+ note = {{http://gking.harvard.edu/files/abs/heartfcem-abs.shtml}}
+}
+
+ at Article{GriKin10,
+ author = {Justin Grimmer and Gary King},
+ title = {Quantitative Discovery from Qualitative Information:
+ A General-Purpose Document Clustering Methodology},
+ journal = { },
+ year = {2010},
+ note = {{http://gking.harvard.edu/files/abs/discov-abs.shtml}}
+}
+
+ at Article{GriKin10b,
+ author = {Justin Grimmer and Gary King},
+ title = {A General Purpose Computer-Assisted Document
+ Clustering Methodology},
+ journal = { },
+ year = {2010},
+ note =
+ {{http://gking.harvard.edu/files/abs/discovm-abs.shtml}}
+}
+
+ at Article{IacKinPor11,
+ author = {Stefano M. Iacus and Gary King and Giuseppe Porro},
+ title = {Multivariate Matching Methods That are Monotonic
+ Imbalance Bounding},
+ journal = {Journal of the American Statistical Association},
+ year = {In press},
+ note = {{http://gking.harvard.edu/files/abs/cem-math-abs.shtml}}
+}
+
+ at article{HopKin10,
+ author = {Daniel Hopkins and Gary King},
+ title = {Improving Anchoring Vignettes: Designing Surveys to
+ Correct Interpersonal Incomparability},
+ journal = {Public Opinion Quarterly},
+ year = {2010},
+ pages = {1-22},
+ note = {{http://gking.harvard.edu/files/abs/implement-abs.shtml}}
+}
+
+ at Article{KinLuShi10,
+ author = {Gary King and Ying Lu and Kenji Shibuya},
+ title = {Designing Verbal Autopsy Studies},
+ journal = {Population Health Metrics},
+ year = 2010,
+ volume = 8,
+ number = 19,
+ note = {http://gking.harvard.edu/files/abs/desva-abs.shtml}
+}
+
+ at article{LazPenAda09,
+ author = {Lazer, David and Pentland, Alex and Adamic, Lada and
+ Aral, Sinan and Barabasi, Albert-Laszlo and Brewer,
+ Devon and Christakis, Nicholas and Contractor,
+ Noshir and Fowler, James and Gutmann, Myron and
+ Jebara, Tony and King, Gary and Macy, Michael and
+ Roy, Deb and Van Alstyne, Marshall},
+ title = {{SOCIAL SCIENCE: Computational Social Science}},
+ journal = {Science},
+ volume = {323},
+ number = {5915},
+ pages = {721-723},
+ year = {2009},
+ note = {{http://gking.harvard.edu/files/abs/LazPenAda09-abs.shtml}}
+}
+
+ at Article{AbrBolGut09,
+ author = {Mark Abrahamson and Kenneth A. Bollen and Myron
+ Gutmann and Gary King and Amy M. Pienta},
+ title = {Preserving Data for Long Term Analyses},
+ journal = {Historical Social Research},
+ year = {2009},
+ OPTkey = {},
+ OPTvolume = {},
+ OPTnumber = {},
+ OPTpages = {},
+ OPTmonth = {Summer, forthcoming},
+ OPTnote = {},
+ OPTannote = {}
+}
+
+ at article{KinGakIma09,
+ Author = {Gary King and Emmanuela Gakidou and Kosuke Imai and
+ Jason Lakin and Ryan T. Moore and Clayton Nall and
+ Nirmala Ravishankar and Manett Vargas and Martha
+ Mar{\'i}a T{\'e}llez-Rojo and Juan Eugenio
+ Hern{\'a}ndez {\'A}vila and Mauricio Hern{\'a}ndez
+ {\'A}vila and H{\'e}ctor Hern{\'a}ndez Llamas},
+ title = {Public Policy for the Poor? A Randomised Assessment
+ of the Mexican Universal Health Insurance Programme},
+ journal = {The Lancet},
+ volumne = {373},
+ year = {2009},
+ note = {{http://gking.harvard.edu/files/abs/spi-abs.shtml}}
+}
+
+
+ at Article{KinSon09,
+ author = {Gary King and Samir Soneji},
+ title = {The Future of Death in America},
+ journal = {},
+ year = 2009,
+ note = {{http://gking.harvard.edu/files/abs/mort-abs.shtml}}
+}
+
+
+ at Article{IacKinPor09,
+ author = {Stefano M. Iacus and Gary King and Giuseppe Porro},
+ title = {Causal Inference Without Balance Checking: Coarsened
+ Exact Matching},
+ journal = { },
+ year = 2009,
+ note = {{http://gking.harvard.edu/files/abs/cem-plus-abs.shtml}}
+}
+
+ at article{ImaKinStu08,
+ author = {Kosuke Imai and Gary King and Elizabeth Stuart},
+ title = {Misunderstandings Among Experimentalists and
+ Observationalists about Causal Inference},
+ journal = {Journal of the Royal Statistical Society, {S}eries
+ {A}},
+ volume = {171, part 2},
+ year = {2008},
+ pages = {481--502},
+ note =
+ {{http://gking.harvard.edu/files/abs/matchse-abs.shtml}}
+}
+
+ at article{uImaKinStu08,
+ author = {Kosuke Imai and Gary King and Elizabeth Stuart},
+ title = {Misunderstandings Among Experimentalists and
+ Observationalists about Causal Inference},
+ journal = {Journal of the Royal Statistical Society, {S}eries
+ {A}},
+ volume = {171, part 2},
+ year = {2008},
+ pages = {481--502}
+}
+
+ at article{KinLu08,
+ author = {Gary King and Ying Lu},
+ title = {Verbal Autopsy Methods with Multiple Causes of
+ Death},
+ journal = {Statistical Science},
+ volume = {23},
+ number = {1},
+ year = {2008},
+ pages = {78--91},
+ note = {{http://gking.harvard.edu/files/abs/vamc-abs.shtml}}
+}
+
+ at article{AltKin07,
+ author = {Micah Altman and Gary King},
+ title = {A Proposed Standard for the Scholarly Citation of
+ Quantitative Data },
+ journal = {D-Lib Magazine},
+ volume = {13},
+ year = {2007},
+ month = {March / April},
+ number = {3/4},
+ note = {{http://gking.harvard.edu/files/abs/cite-abs.shtml}}
+}
+
+ at article{GroKin07,
+ author = {Bernard Grofman and Gary King},
+ title = {The Future of Partisan Symmetry as a Judicial Test
+ for Partisan Gerrymandering after LULAC v. Perry},
+ journal = {Election Law Journal},
+ volume = {6},
+ year = {2007},
+ pages = {2-35},
+ month = {January},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/jp-abs.shtml}}
+}
+
+ at article{uHoImaKin07,
+ author = {Daniel Ho and Kosuke Imai and Gary King and
+ Elizabeth Stuart},
+ title = {Matching as Nonparametric Preprocessing for Reducing
+ Model Dependence in Parametric Causal Inference},
+ journal = {Political Analysis},
+ year = {2007},
+ volume = {15},
+ pages = {199--236}
+}
+
+ at article{HoImaKin07,
+ author = {Daniel Ho and Kosuke Imai and Gary King and
+ Elizabeth Stuart},
+ title = {Matching as Nonparametric Preprocessing for Reducing
+ Model Dependence in Parametric Causal Inference},
+ journal = {Political Analysis},
+ year = {2007},
+ volume = {15},
+ pages = {199--236},
+ note = {{http://gking.harvard.edu/files/abs/matchp-abs.shtml}}
+}
+
+ at article{HopKin10b,
+ author = {Daniel Hopkins and Gary King},
+ title = {A Method of Automated Nonparametric Content Analysis
+ for Social Science},
+ journal = {American Journal of Political Science},
+ year = {2010},
+ volume = {54},
+ number = {1},
+ month = {January},
+ pages = {229--247},
+ note = {http://gking.harvard.edu/files/abs/words-abs.shtml}
+}
+
+ at article{ImaKinLau07,
+ author = {Kosuke Imai and Gary King and Olivia Lau},
+ title = {Toward A Common Framework for Statistical Analysis
+ and Development},
+ journal = {Journal of Computational Graphics and Statistics},
+ volume = 17,
+ number = 4,
+ pages = {1--22},
+ year = 2008,
+ note = {{http://gking.harvard.edu/files/abs/z-abs.shtml}}
+}
+
+ at article{ImaKinNal09d,
+ author = {Kosuke Imai and Gary King and Clayton Nall},
+ title = {Matched Pairs and the Future of Cluster-Randomized
+ Experiments: A Rejoinder},
+ journal = {Statistical Science},
+ volume = {24},
+ number = {1},
+ pages = {64--72},
+ year = {2009},
+ note =
+ {{http://gking.harvard.edu/files/abs/cluster-abs.shtml}}
+}
+
+ at article{ImaKinNal09,
+ author = {Kosuke Imai and Gary King and Clayton Nall},
+ title = {The Essential Role of Pair Matching in
+ Cluster-Randomized Experiments, with Application to
+ the Mexican Universal Health Insurance Evaluation},
+ journal = {Statistical Science},
+ volume = {24},
+ number = {1},
+ pages = {29--53},
+ year = {2009},
+ note =
+ {{http://gking.harvard.edu/files/abs/cluster-abs.shtml}}
+}
+
+ at article{uImaKinNal09,
+ author = {Kosuke Imai and Gary King and Clayton Nall},
+ title = {The Essential Role of Pair Matching in
+ Cluster-Randomized Experiments, with Application to
+ the Mexican Universal Health Insurance Evaluation},
+ journal = {Statistical Science},
+ volume = {24},
+ number = {1},
+ pages = {29--53},
+ year = {2009}
+}
+
+ at article{King07,
+ author = {Gary King},
+ title = {An Introduction to the Dataverse Network as an
+ Infrastructure for Data Sharing},
+ journal = {Sociological Methods and Research},
+ year = {2007},
+ volume = {36},
+ number = {2},
+ pages = {173--199},
+ note = {{http://gking.harvard.edu/files/abs/dvn-abs.shtml}}
+}
+
+ at inbook{King09b,
+ author = {Gary King},
+ chapter = {The Changing Evidence Base of Political Science
+ Research},
+ title = {The Future of Political Science: 100 Perspectives},
+ publisher = {Routledge Press},
+ address = {New York},
+ year = {2009, forthcoming},
+ editor = {Gary King and Kay Schlozman and Norman Nie},
+ note = {{http://gking.harvard.edu/files/abs/evbase-abs.shtml}}
+}
+
+ at inbook{King09c,
+ author = {Gary King and Kay Schlozman and Norman Nie},
+ chapter = {An Introduction to the Future of Political Science},
+ title = {The Future of Political Science: 100 Perspectives},
+ publisher = {Routledge Press},
+ address = {New York},
+ year = {2009, forthcoming},
+ editor = {Gary King and Kay Schlozman and Norman Nie}
+}
+
+ at article{KinGakRav07,
+ Author = {Gary King and Emmanuela Gakidou and Nirmala
+ Ravishankar and Ryan T. Moore and Jason Lakin and
+ Manett Vargas and Martha Mar{\'i}a T{\'e}llez-Rojo
+ and Juan Eugenio Hern{\'a}ndez {\'A}vila and
+ Mauricio Hern{\'a}ndez {\'A}vila and H{\'e}ctor
+ Hern{\'a}ndez Llamas},
+ title = {A `Politically Robust' Experimental Design for
+ Public Policy Evaluation, with Application to the
+ Mexican Universal Health Insurance Program},
+ journal = {Journal of Policy Analysis and Management},
+ volume = {26},
+ year = {2007},
+ pages = {479-506},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/spd-abs.shtml}}
+}
+
+ at article{KinWan07,
+ author = {Gary King and Jonathan Wand},
+ title = {Comparing Incomparable Survey Responses: New Tools
+ for Anchoring Vignettes},
+ journal = {Political Analysis},
+ volume = {15},
+ year = {2007},
+ pages = {46-66},
+ month = {Winter},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/c-abs.shtml}}
+}
+
+ at article{KinZen07,
+ author = {Gary King and Langche Zeng},
+ title = {When Can History Be Our Guide? The Pitfalls of
+ Counterfactual Inference},
+ journal = {International Studies Quarterly},
+ year = {2007},
+ pages = {183-210},
+ month = {March},
+ note = {{http://gking.harvard.edu/files/abs/counterf-abs.shtml}}
+}
+
+ at article{uKinZen07,
+ author = {Gary King and Langche Zeng},
+ title = {When Can History Be Our Guide? The Pitfalls of
+ Counterfactual Inference},
+ journal = {International Studies Quarterly},
+ year = {2007},
+ pages = {183-210},
+ month = {March}
+}
+
+ at article{KinZen07b,
+ author = {Gary King and Langche Zeng},
+ title = {Detecting Model Dependence in Statistical Inference:
+ A Response},
+ journal = {International Studies Quarterly},
+ volume = {51},
+ year = {2007},
+ pages = {231-241},
+ month = {March},
+ note = {{http://gking.harvard.edu/files/abs/counterf-abs.shtml}}
+}
+
+ at article{WanKinLau07,
+ author = {Jonathan Wand and Gary King and Olivia Lau},
+ title = {Anchors: Software for Anchoring Vignettes Data},
+ journal = {Journal of Statistical Software},
+ year = {2007, forthcoming}
+}
+
+ at inbook{EpsHoKin06,
+ author = {Lee Epstein and Daniel E. Ho and Gary King and
+ Jeffrey A. Segal},
+ title = {Principles and Practice in American Politics:
+ Classic and Contemporary Readings},
+ chapter = {The Effect of War on the Supreme Court},
+ year = {2006},
+ publisher = {Congressional Quarterly Press},
+ edition = {3rd},
+ address = {Washington, D.C.},
+ editor = {Samuel Kernell and Steven S. Smith},
+ note = {{http://gking.harvard.edu/files/abs/crisis-abs.shtml}}
+}
+
+ at article{GakKin06,
+ author = {Emmanuela Gakidou and Gary King},
+ title = {Death by Survey: Estimating Adult Mortality without
+ Selection Bias from Sibling Survival Data from
+ Sibling Survival Data},
+ journal = {Demography},
+ volume = 43,
+ year = 2006,
+ pages = {569--585},
+ month = {August},
+ number = 3,
+ note = {{http://gking.harvard.edu/files/abs/deathbys-abs.shtml}}
+}
+
+ at article{HonKin10,
+ author = {James Honaker and Gary King},
+ title = {What to do About Missing Values in Time Series
+ Cross-Section Data},
+ journal = {American Journal of Political Science},
+ year = {2010},
+ volume = {54},
+ number = {2},
+ month = {April},
+ pages = {561--581},
+ note = {{http://gking.harvard.edu/files/abs/pr-abs.shtml}}
+}
+
+ at article{King06,
+ author = {Gary King},
+ title = {{Publication, Publication}},
+ journal = {PS: Political Science and Politics},
+ volume = {39},
+ year = {2006},
+ pages = {119--125},
+ month = {January},
+ number = {01},
+ note =
+ {{http://gking.harvard.edu/files/abs/paperspub-abs.shtml}}
+}
+
+ at inbook{KinRosTan06,
+ author = {Gary King and Ori Rosen and Martin Tanner},
+ title = {The New Palgrave Dictionary of Economics},
+ chapter = {Ecological Inference},
+ year = {2006},
+ edition = {2nd},
+ editor = {Larry Blume and Steven N. Durlauf},
+ note =
+ {{http://gking.harvard.edu/files/abs/newintro-abs.shtml}}
+}
+
+ at article{KinZen06,
+ author = {Gary King and Langche Zeng},
+ title = {The Dangers of Extreme Counterfactuals},
+ journal = {Political Analysis},
+ volume = {14},
+ year = 2006,
+ pages = {131--159},
+ number = {2},
+ note =
+ {{http://gking.harvard.edu/files/abs/counterft-abs.shtml}}
+}
+
+ at article{GirKin07,
+ author = {Federico Girosi and Gary King},
+ title = {Understanding the Lee-Carter Mortality Forecasting
+ Method},
+ year = 2007,
+ note = {{http://gking.harvard.edu/files/abs/lc-abs.shtml}}
+}
+
+ at article{EpsHoKin05,
+ author = {Lee Epstein and Daniel E. Ho and Gary King, and
+ Jeffrey A. Segal},
+ title = {The Supreme Court During Crisis: How War Affects
+ only Non-War Cases},
+ journal = {New York University Law Review},
+ volume = {80},
+ year = {2005},
+ pages = {1--116},
+ month = {April},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/crisis-abs.shtml}}
+}
+
+ at article{StoKinZen05,
+ author = {Heather Stoll and Gary King and Langchee Zeng},
+ title = {WhatIf: Software for Evaluating Counterfactuals},
+ journal = {Journal of Statistical Software},
+ volume = {15},
+ year = {2005},
+ number = {4},
+ note = {{http://www.jstatsoft.org/index.php?vol=15}}
+}
+
+ at article{BecKinZen04,
+ author = {Nathaniel Beck and Gary King and Langche Zeng},
+ title = {Theory and Evidence in International Conflict:
+ Response to de Marchi, Gelpi, and Grynaviski},
+ journal = apsr,
+ volume = {98},
+ year = {2004},
+ pages = {379-389},
+ month = {May},
+ number = {2},
+ note =
+ {{http://gking.harvard.edu/files/abs/toe-resp-abs.shtml}}
+}
+
+ at inbook{GelKatKin04,
+ author = {Andrew Gelman and Jonathan Katz and Gary King},
+ title = {Rethinking the Vote: The Politics and Prospects of
+ American Electoreal Reform},
+ chapter = {Chapter 5, Empirically Evaluating the Electoral
+ College},
+ year = {2004},
+ publisher = {Oxford University Press},
+ pages = {75-88},
+ address = {New York},
+ editor = {Ann N. Crigler and Marion R. Just and Edward
+ J. McCaffery},
+ note =
+ {{http://gking.harvard.edu/files/abs/rethink-abs.shtml}}
+}
+
+ at article{GilKin04,
+ author = {Jeff Gill and Gary King},
+ title = {What to do When Your Hessian is Not Invertible:
+ Alternatives to Model Respecification in Nonlinear
+ Estimation},
+ journal = {Sociological Methods and Research},
+ volume = {32},
+ year = {2004},
+ pages = {54-87},
+ month = {August},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/help-abs.shtml}}
+}
+
+ at article{ImaKin04,
+ author = {Kosuke Imai and Gary King},
+ title = {Did Illegal Overseas Absentee Ballots Decide the
+ 2000 U.S. Presidential Election?},
+ journal = {Perspectives on Politics},
+ volume = {2},
+ year = {2004},
+ pages = {537--549},
+ month = {September},
+ number = {3},
+ note =
+ {{http://gking.harvard.edu/files/abs/ballots-abs.shtml}}
+}
+
+ at article{King04,
+ author = {Gary King},
+ title = {EI: A Program for Ecological Inference},
+ journal = {Journal of Statistical Software},
+ volume = {11},
+ year = {2004},
+ number = {7}
+}
+
+ at article{King04b,
+ author = {Gary King},
+ title = {Finding New Information for Ecological Inference
+ Models: A Comment on Jon Wakefield, "Ecological
+ Inference in 2 X 2 Tables"},
+ journal = {Journal of the Royal Statistical Society},
+ volume = {167},
+ year = {2004},
+ pages = {437},
+ number = {Series A}
+}
+
+ at inbook{KinRosTan04b,
+ author = {Gary King and Ori Rosen and Martin Tanner},
+ title = {Ecological Inference: New Methodological Strategies},
+ chapter = {Information in Ecological Inference: An
+ Introduction},
+ year = {2004},
+ publisher = {Cambridge University Press},
+ address = {New York},
+ editor = {Gary King and Ori Rosen and Martin Tanner}
+}
+
+ at inbook{KinZen04,
+ author = {Gary King and Langche Zeng},
+ title = {Encyclopedia of Biopharmaceutical Statistics},
+ chapter = {Inference in Case-Control Studies},
+ year = {2004},
+ publisher = {Marcel Dekker},
+ edition = {2nd},
+ address = {New York},
+ editor = {Shein-Chung Chow},
+ note = {{http://gking.harvard.edu/files/abs/1s-enc-abs.shtml}}
+}
+
+ at article{AdoKin03,
+ author = {Christopher Adolph and Gary King},
+ title = {Analyzing Second Stage Ecological Regressions},
+ journal = {Political Analysis},
+ volume = {11},
+ year = {2003},
+ pages = {65-76},
+ month = {Winter},
+ number = {1}
+}
+
+ at article{AdoKinHer03,
+ author = {Christopher Adolph and Gary King, with Michael
+ C. Herron and Kenneth W. Shotts},
+ title = {A Consensus on Second Stage Analyses in Ecological
+ Inference Models},
+ journal = {Political Analysis},
+ volume = {11},
+ year = {2003},
+ pages = {86--94},
+ month = {Winter},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/akhs-abs.shtml}}
+}
+
+ at article{EpsKin03,
+ author = {Lee Epstein and Gary King},
+ title = {Building An Infrastructure for Empirical Research in
+ the Law [with comments from four law school deans]},
+ journal = {Journal of Legal Education},
+ volume = {53},
+ year = {2003},
+ pages = {311--320},
+ number = {311},
+ note = {{http://gking.harvard.edu/files/abs/infra-abs.shtml}}
+}
+
+ at inbook{GakKin03,
+ author = {Emmanuela Gakidou and Gary King},
+ title = {Health Systems Performance Assessment: Debates,
+ Methods and Empiricism},
+ chapter = {Chapter 36, Determinants of Inequality in Child
+ Survival: Results from 39 Countries},
+ publisher = {World Health Organization},
+ pages = {497-502},
+ address = {Geneva},
+ editor = {Chrisopher Murray and David B. Evans}
+}
+
+ at inbook{GilKin03,
+ author = {Jeff Gill and Gary King},
+ title = {Numerical Issues in Statistical Computing for the
+ Social Scientist},
+ chapter = {Chapter 6, Numerical Issues Involved in Inverting
+ Hessian Matrices},
+ year = {2003},
+ publisher = {John Wiley and Sons, Inc.},
+ pages = {143-176},
+ address = {Hoboken, NJ},
+ editor = {Micah Altman and Jeff Gill and Michael P. McDonald}
+}
+
+ at article{King03,
+ author = {Gary King},
+ title = {The Future of Replication},
+ journal = {International Studies Perspectives},
+ volume = {4},
+ year = {2003},
+ pages = {443--499},
+ month = {February},
+ number = 1,
+ note =
+ {{http://gking.harvard.edu/files/abs/replvdc-abs.shtml}}
+}
+
+ at article{KinLow03,
+ author = {Gary King and Will Lowe},
+ title = {An Automated Information Extraction Tool For
+ International Conflict Data with Performance as Good
+ as Human Coders: A Rare Events Evaluation Design},
+ journal = {International Organization},
+ volume = {57},
+ year = {2003},
+ pages = {617-642},
+ month = {July},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/infoex-abs.shtml}}
+}
+
+ at article{KinMurSal04,
+ author = {Gary King and Christopher J.L. Murray and Joshua
+ A. Salomon and Ajay Tandon},
+ title = {Enhancing the Validity and Cross-cultural
+ Comparability of Measurement in Survey Research},
+ journal = {American Political Science Review},
+ volume = {98},
+ year = {2004},
+ pages = {191--207},
+ month = {February},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/vign-abs.shtml}}
+}
+
+ at article{KinRosTan07,
+ author = {Gary King and Ori Rosen and Martin Tanner and
+ Alexander F. Wagner.},
+ title = {Ordinary Economic Voting Behavior in the
+ Extraordinary Election of Adolf Hitler},
+ year = {2007},
+ note = {{http://gking.harvard.edu/files/abs/naziV-abs.shtml}}
+}
+
+ at inbook{KinZen03,
+ author = {Gary King and Langche Zeng},
+ title = {Inference in Case-Control Studies},
+ year = {2003},
+ publisher = {Marcel Dekker},
+ volume = {2nd edition},
+ address = {New York},
+ editor = {Shein-Chung Chow, ed.},
+ journal = {Encyclopedia of Biopharmaceutical Statistics}
+}
+
+ at article{LowKin03,
+ author = {Will Lowe and Gary King },
+ title = {Some Statistical Methods for Evaluating Information
+ Extraction Systems},
+ journal = {Proceedings of the 10th Conference of the European
+ Chapter of the Association for Computational
+ Linguistics},
+ year = {2003},
+ pages = {19-26}
+}
+
+ at article{TomKinZen03,
+ author = {Michael Tomz and Gary King and Langche Zeng},
+ title = {ReLogit: Rare Events Logistic Regression},
+ journal = {Journal of Statistical Software},
+ volume = {8},
+ year = {2003},
+ number = {2},
+ note = {{http://gking.harvard.edu/stats.shtml#relogit}}
+}
+
+ at article{TomWitKin03,
+ author = {Michael Tomz and Jason Wittenberg and Gary King},
+ title = {CLARIFY: Software for Interpreting and Presenting
+ Statistical Results},
+ journal = {Journal of Statistical Software},
+ volume = {8},
+ year = {2003},
+ number = {1},
+ note = {{http://gking.harvard.edu/stats.shtml}}
+}
+
+ at article{EpsKin02,
+ author = {Lee Epstein and Gary King},
+ title = {The Rules of Inference},
+ journal = {University of Chicago Law Review},
+ volume = {69},
+ year = {2002},
+ pages = {1--209},
+ month = {Winter},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/rules-abs.shtml}}
+}
+
+ at article{EpsKin02b,
+ author = {Lee Epstein and Gary King},
+ title = {Empirical Research and The Goals of Legal
+ Scholarship: A Response},
+ journal = {University of Chicago Law Review},
+ volume = {69},
+ year = {2002},
+ pages = {1--209},
+ month = {Winter},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/rules-abs.shtml}}
+}
+
+ at article{GakKin02,
+ author = {Emmanuela Gakidou and Gary King},
+ title = {Measuring Total Health Inequality: Adding Individual
+ Variation to Group-Level Differences},
+ journal = {BioMed Central: International Journal for Equity in
+ Health},
+ volume = {1},
+ year = 2002,
+ month = {August},
+ number = 3,
+ note = {{http://gking.harvard.edu/files/abs/ebb-abs.shtml}}
+}
+
+ at article{HonKinKat02,
+ author = {James Honaker and Gary King and Jonathan N. Katz},
+ title = {A Fast, Easy, and Efficient Estimator for Multiparty
+ Electoral Data},
+ journal = {Political Analysis},
+ volume = {10},
+ year = {2002},
+ pages = {84--100},
+ month = {Winter},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/trip-abs.shtml}}
+}
+
+ at article{King02b,
+ author = {Gary King},
+ title = {Isolating Spatial Autocorrelation, Aggregation Bias,
+ and Distributional Violations in Ecological
+ Inference},
+ journal = {Political Analysis},
+ volume = {10},
+ year = {2002},
+ pages = {298--300},
+ month = {Summer},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/ac-abs.shtml}}
+}
+
+ at article{KinMur02,
+ author = {Gary King and Christopher J.L. Murray},
+ title = {Rethinking Human Security},
+ journal = {Political Science Quarterly},
+ volume = {116},
+ year = {2002},
+ pages = {585--610},
+ month = {Winter},
+ number = {4},
+ note = {{http://gking.harvard.edu/files/abs/hs-abs.shtml}}
+}
+
+ at article{KinZen02,
+ author = {Gary King and Langche Zeng},
+ title = {Improving Forecasts of State Failure},
+ journal = {World Politics},
+ volume = 53,
+ year = 2002,
+ pages = {623--658},
+ month = {July},
+ number = 4 ,
+ note = {{http://gking.harvard.edu/files/abs/civil-abs.shtml}}
+}
+
+ at article{KinZen02b,
+ author = {Gary King and Langche Zeng},
+ title = {Estimating Risk and Rate Levels, Ratios, and
+ Differences in Case-Control Studies},
+ journal = {Statistics in Medicine},
+ volume = 21,
+ year = 2002,
+ pages = {1409--1427},
+ note = {{http://gking.harvard.edu/files/abs/1s-abs.shtml}}
+}
+
+ at article{MurKinLop02,
+ author = {Christopher J.L. Murray and Gary King and Alan
+ D. Lopez and Niels Tomijima and Etienne Krug},
+ title = {Armed Conflict as a Public Health Problem},
+ journal = {BMJ (British Medical Journal)},
+ volume = {324},
+ year = {2002},
+ pages = {346--349},
+ month = {February 9},
+ note =
+ {{http://gking.harvard.edu/files/abs/armedph-abs.shtml}}
+}
+
+ at article{AltAndDig01a,
+ author = {Micah Altman and Leonid Andreev and Mark Diggory and
+ Gary King and Daniel L. Kiskis and Elizabeth Kolster
+ and M. Krot and Sidney Verba},
+ title = {A Digital Library for the Dissemination and
+ Replication of Quantitative Social Science Research:
+ The Virtual Data Center},
+ journal = {Social Science Computer Review},
+ volume = 19,
+ year = 2001,
+ pages = {458--470},
+ month = {Winter},
+ number = 4,
+ note =
+ {{http://gking.harvard.edu/files/abs/vdcwhitepaper-abs.shtml}}
+}
+
+ at article{AltAndDig01b,
+ author = {Micah Altman and Leonid Andreev and Mark Diggory and
+ Gary King and Daniel L. Kiskis and Elizabeth Kolster
+ and M. Krot and Sidney Verba},
+ title = {An Overview of the Virtual Data Center Project and
+ Software},
+ journal = {JCDL '01: First Joint Conference on Digital
+ Libraries},
+ year = 2001,
+ pages = {203-204},
+ note = {{http://gking.harvard.edu/files/abs/jcdl01-abs.shtml}}
+}
+
+ at article{AltKinSig01,
+ author = {James E. Alt and Gary King and Curtis Signorino},
+ title = {Aggregation Among Binary, Count, and Duration
+ Models: Estimating the Same Quantities from
+ Different Levels of Data},
+ journal = {Political Analysis},
+ volume = {9},
+ year = {2001},
+ pages = {21--44},
+ month = {Winter},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/abcd-abs.shtml}}
+}
+
+ at article{King01,
+ author = {Gary King},
+ title = {Proper Nouns and Methodological Propriety: Pooling
+ Dyads in International Relations Data},
+ journal = {International Organization},
+ volume = {55},
+ year = {2001},
+ pages = {497--507},
+ month = {Fall},
+ number = {2},
+ note = {{http://gking.harvard.edu/files/abs/pool-abs.shtml}}
+}
+
+ at article{KinHonJos01,
+ author = {Gary King and James Honaker and Anne Joseph and
+ Kenneth Scheve},
+ title = {Analyzing Incomplete Political Science Data: An
+ Alternative Algorithm for Multiple Imputation},
+ journal = {American Political Science Review},
+ volume = 95,
+ year = 2001,
+ pages = {49--69},
+ month = {March},
+ number = 1 ,
+ note = {{http://gking.harvard.edu/files/abs/evil-abs.shtml}}
+}
+
+ at article{KinZen01,
+ author = {Gary King and Langche Zeng},
+ title = {Logistic Regression in Rare Events Data},
+ journal = {Political Analysis},
+ volume = 9,
+ year = 2001,
+ pages = {137--163},
+ month = {Spring},
+ number = 2 ,
+ note = {{http://gking.harvard.edu/files/abs/0s-abs.shtml}}
+}
+
+ at article{KinZen01b,
+ author = {Gary King and Langche Zeng},
+ title = {Explaining Rare Events in International Relations},
+ journal = {International Organization},
+ volume = 55,
+ year = 2001,
+ pages = {693--715},
+ month = {Summer},
+ number = 3 ,
+ note = {{http://gking.harvard.edu/files/abs/baby0s-abs.shtml}}
+}
+
+ at article{RosJiaKin01,
+ author = {Ori Rosen and Wenxin Jiang and Gary King and Martin
+ A. Tanner},
+ title = {Bayesian and Frequentist Inference for Ecological
+ Inference: The $R \times C$ Case},
+ journal = {Statistica Neerlandica},
+ volume = 55,
+ year = 2001,
+ pages = {134--156},
+ number = 2 ,
+ note = {{http://gking.harvard.edu/files/abs/rosen-abs.shtml}}
+}
+
+ at article{BecKinZen00,
+ author = {Nathaniel Beck and Gary King and Langche Zeng},
+ title = {Improving Quantitative Studies of International
+ Conflict},
+ journal = {American Political Science Review},
+ volume = 94,
+ year = 2000,
+ pages = {21--36},
+ month = {March},
+ number = 1 ,
+ note = {{http://gking.harvard.edu/files/abs/improv-abs.shtml}}
+}
+
+ at article{King00,
+ author = {Gary King},
+ title = {Geography, Statistics, and Ecological Inference},
+ journal = {Annals of the Association of American Geographers},
+ volume = {90},
+ year = {2000},
+ pages = {601--606},
+ month = {September},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/geog-abs.shtml}}
+}
+
+ at article{KinTomWit00,
+ author = {Gary King and Michael Tomz and Jason Wittenberg},
+ title = {Making the Most of Statistical Analyses: Improving
+ Interpretation and Presentation},
+ journal = {American Journal of Political Science},
+ volume = 44,
+ year = 2000,
+ pages = {341--355},
+ month = {April},
+ number = 2,
+ note = {{http://gking.harvard.edu/files/abs/making-abs.shtml}}
+}
+
+ at article{GelKinLiu99,
+ author = {Andrew Gelman and Gary King and Chuanhai Liu},
+ title = {Not Asked and Not Answered: Multiple Imputation for
+ Multiple Surveys},
+ journal = {Journal of the American Statistical Association},
+ volume = 93,
+ year = 1999,
+ pages = {846--857},
+ month = {September},
+ number = 433 ,
+ note = {{http://gking.harvard.edu/files/abs/not-abs.shtml}}
+}
+
+ at article{GelKinLiu99b,
+ author = {Andrew Gelman and Gary King and Chuanhai Liu},
+ title = {Rejoinder},
+ journal = {Journal of the American Statistical Association},
+ volume = 93,
+ year = 1999,
+ pages = {869--874},
+ month = {September},
+ number = 433 ,
+ note = {{http://gking.harvard.edu/files/abs/not-abs.shtml}}
+}
+
+ at article{KatKin99,
+ author = {Jonathan Katz and Gary King},
+ title = {A Statistical Model for Multiparty Electoral Data},
+ journal = {American Political Science Review},
+ volume = 93,
+ year = 1999,
+ pages = {15--32},
+ month = {March},
+ number = {1},
+ note =
+ {{http://gking.harvard.edu/files/abs/multiparty-abs.shtml}}
+}
+
+ at article{King99,
+ author = {Gary King},
+ title = {The Future of Ecological Inference Research: A Reply
+ to Freedman et al.},
+ journal = {Journal of the American Statistical Association},
+ volume = {94},
+ year = {1999},
+ pages = {352-355},
+ month = {March},
+ number = {445},
+ note = {{http://gking.harvard.edu/files/abs/reply-abs.shtml}}
+}
+
+ at article{KinLav99,
+ author = {Gary King and Michael Laver},
+ title = {Many Publications, but Still No Evidence},
+ journal = {Electoral Studies},
+ volume = {18},
+ year = {1999},
+ pages = {597--598},
+ month = {December},
+ number = {4},
+ note =
+ {{http://gking.harvard.edu/files/abs/manypub-abs.shtml}}
+}
+
+ at article{KinRosTan99,
+ author = {Gary King and Ori Rosen and Martin A. Tanner},
+ title = {Binomial-Beta Hierarchical Models for Ecological
+ Inference},
+ journal = {Sociological Methods and Research},
+ volume = 28,
+ year = 1999,
+ pages = {61--90},
+ month = {August},
+ number = 1 ,
+ note = {{http://gking.harvard.edu/files/abs/binom-abs.shtml}}
+}
+
+ at article{LewKin99,
+ author = {Jeffrey Lewis and Gary King},
+ title = {No Evidence on Directional vs. Proximity Voting},
+ journal = {Political Analysis},
+ volume = {8},
+ year = {1999},
+ pages = {21--33},
+ month = {August},
+ number = {1},
+ note =
+ {{http://gking.harvard.edu/files/abs/spatial-abs.shtml}}
+}
+
+ at article{GelKinBos98,
+ author = {Andrew Gelman and Gary King and John Boscardin},
+ title = {Estimating the Probability of Events that Have Never
+ Occurred: When Is Your Vote Decisive?},
+ journal = {Journal of the American Statistical Association},
+ volume = {93},
+ year = {1998},
+ pages = {1--9},
+ month = {March},
+ number = {441},
+ note =
+ {{http://gking.harvard.edu/files/abs/estimatprob-abs.shtml}}
+}
+
+ at article{KinPal98,
+ author = {Gary King and Bradley Palmquist},
+ title = {The Record of American Democracy, 1984-1990},
+ journal = {Sociological Methods and Research},
+ volume = {26},
+ year = {1998},
+ pages = {424--427},
+ month = {February},
+ number = {3},
+ note = {{http://www.hmdc.harvard.edu/ROAD/}}
+}
+
+ at article{BenKin96,
+ author = {Kenneth Benoit and Gary King},
+ title = {A Preview of EI and EzI: Programs for Ecological
+ Inference},
+ journal = {Social Science Computer Review},
+ volume = {14},
+ year = {1996},
+ pages = {433--438},
+ month = {Winter},
+ number = {4},
+ note =
+ {{http://gking.harvard.edu/files/abs/preview-abs.shtml}}
+}
+
+ at inbook{GelKin96,
+ author = {Andrew Gelman and Gary King},
+ title = {Advantages of Conflictual Redistricting},
+ year = {1996},
+ publisher = {Dartmouth Publishing Company},
+ pages = {207--218 },
+ address = {Aldershot, England},
+ editor = {Iain McLean and David Butler, eds},
+ note = {{http://gking.harvard.edu/files/abs/advant-abs.shtml}},
+ journal = {Fixing the Boundary: Defining and Redefining
+ Single-Member Electoral Districts}
+}
+
+ at inbook{KinBruGel96,
+ author = {Gary King and John Bruce and Andrew Gelman},
+ title = {Racial Fairness in Legislative Redistricting},
+ year = {1996},
+ publisher = {Princeton University Press},
+ editor = {Paul E. Peterson, ed.},
+ note = {{http://gking.harvard.edu/files/abs/racial-abs.shtml}},
+ journal = {Classifying by Race}
+}
+
+ at article{King96,
+ author = {Gary King},
+ title = {Why Context Should Not Count},
+ journal = {Political Geography },
+ volume = {15},
+ year = {1996},
+ pages = {159--164},
+ month = {February},
+ number = {2},
+ note = {{http://gking.harvard.edu/files/abs/contxt-abs.shtml}}
+}
+
+ at article{KinSig96,
+ author = {Gary King and Curtis S. Signorino},
+ title = {The Generalization in the Generalized Event Count
+ Model},
+ journal = {Political Analysis},
+ volume = 6,
+ year = 1996,
+ pages = {225--252},
+ note =
+ {{http://gking.harvard.edu/files/abs/generaliz-abs.shtml}}
+}
+
+ at article{King95,
+ author = {Gary King},
+ title = {Replication, Replication},
+ journal = {PS: Political Science and Politics},
+ volume = {28},
+ year = 1995,
+ pages = {443--499},
+ month = {September},
+ number = 3,
+ note =
+ {{http://gking.harvard.edu/files/abs/replication-abs.shtml}}
+}
+
+ at article{King95b,
+ author = {Gary King},
+ title = {A Revised Proposal, Proposal},
+ journal = {PS: Political Science and Politics},
+ volume = {XXVIII},
+ year = 1995,
+ pages = {494--499},
+ month = {September},
+ number = 3,
+ note =
+ {{http://gking.harvard.edu/files/abs/replication-abs.shtml}}
+}
+
+ at article{KinKeoVer95,
+ author = {Gary King and Robert O. Keohane and Sidney Verba},
+ title = {The Importance of Research Design in Political
+ Science},
+ journal = {American Political Science Review},
+ volume = {89},
+ year = {1995},
+ pages = {454--481 },
+ month = {June},
+ number = {2},
+ note =
+ {{http://gking.harvard.edu/files/abs/kkvresp-abs.shtml}}
+}
+
+ at article{VosGelKin95,
+ author = {D. Steven Voss and Andrew Gelman and Gary King},
+ title = {Pre-Election Survey Methodology: Details From Nine
+ Polling Organizations, 1988 and 1992},
+ journal = {Public Opinion Quarterly},
+ volume = {59},
+ year = {1995},
+ pages = {98--132},
+ month = {Spring},
+ number = {1},
+ note =
+ {{http://gking.harvard.edu/files/abs/preelection-abs.shtml}}
+}
+
+ at article{WinSigKin95,
+ author = {Rainer Winkelmann and Curtis Signorino and Gary
+ King},
+ title = {A Correction for an Underdispersed Event Count
+ Probability Distribution},
+ journal = {Political Analysis},
+ year = {1995},
+ pages = {215--228},
+ note =
+ {{http://gking.harvard.edu/files/abs/correction-abs.shtml}}
+}
+
+ at article{AltKin94,
+ author = {James E. Alt and Gary King},
+ title = {Transfers of Governmental Power: The Meaning of Time
+ Dependence},
+ journal = {Comparative Political Studies},
+ volume = {27},
+ year = {1994},
+ pages = {190--210},
+ month = {July},
+ number = {2},
+ note =
+ {{http://gking.harvard.edu/files/abs/transfers-abs.shtml}}
+}
+
+ at article{GelKin94,
+ author = {Andrew Gelman and Gary King},
+ title = {A Unified Method of Evaluating Electoral Systems and
+ Redistricting Plans},
+ journal = {American Journal of Political Science},
+ volume = 38,
+ year = 1994,
+ pages = {514--554},
+ month = {May},
+ number = 2,
+ note =
+ {{http://gking.harvard.edu/files/abs/writeit-abs.shtml}}
+}
+
+ at article{GelKin94b,
+ author = {Andrew Gelman and Gary King},
+ title = {Enhancing Democracy Through Legislative
+ Redistricting},
+ journal = {American Political Science Review},
+ volume = {88},
+ year = {1994},
+ pages = {541--559},
+ month = {September},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/red-abs.shtml}}
+}
+
+ at incollection{GelKin94c,
+ author = {Andrew Gelman and Gary King},
+ title = {Party Competition and Media Messages in
+ U.S. Presidential Election Campaigns},
+ booktitle = {The Parties Respond: Changes in the American Party
+ System},
+ publisher = {Westview Press},
+ year = 1994,
+ address = {Boulder, Colorado},
+ editor = {L. Sandy Maisel},
+ pages = {255-295},
+ note =
+ {{http://gking.harvard.edu/files/abs/partycomp-abs.shtml}}
+}
+
+ at article{GelKin93,
+ author = {Andrew Gelman and Gary King},
+ title = {Why are American Presidential Election Campaign
+ Polls so Variable when Votes are so Predictable?},
+ journal = {British Journal of Political Science},
+ volume = 23,
+ year = 1993,
+ pages = {409--451},
+ month = {October},
+ number = 1,
+ note =
+ {{http://gking.harvard.edu/files/abs/variable-abs.shtml}}
+}
+
+ at inbook{King93,
+ author = {Gary King},
+ title = {The Methodology of Presidential Research},
+ year = {1993},
+ publisher = {University of Pittsburgh},
+ pages = {387--412},
+ address = {Pittsburgh},
+ editor = {George Edwards, III, John H. Kessel, and Bert
+ A. Rockman, eds.},
+ note =
+ {{http://gking.harvard.edu/files/abs/methpres-abs.shtml}},
+ journal = {Researching the Presidency: Vital Questions, New
+ Approaches}
+}
+
+ at article{KingBruGil93,
+ author = {Gary King and John M. Bruce and Michael Gilligan},
+ title = {The Science of Political Science Graduate
+ Admissions},
+ journal = {PS: Political Science and Politics},
+ volume = {XXVI},
+ year = {1993},
+ pages = {772--778},
+ month = {December},
+ number = {4},
+ note = {{http://gking.harvard.edu/files/abs/admis-abs.shtml}}
+}
+
+ at article{KinLav93,
+ author = {Gary King and Michael Laver},
+ title = {On Party Platforms, Mandates, and Government
+ Spending},
+ journal = {American Political Science Review},
+ volume = {87},
+ year = {1993},
+ pages = {744--750},
+ month = {September},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/hoff-abs.shtml}}
+}
+
+ at article{KinWal93,
+ author = {Gary King and Daniel J. Walsh},
+ title = {Good Research and Bad Research: Extending Zimile's
+ Criticism},
+ journal = {Early Childhood Research Quarterly},
+ volume = {8},
+ year = {1993},
+ pages = {397--401},
+ month = {September},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/good-abs.shtml}}
+}
+
+ at article{King91,
+ author = {Gary King},
+ title = {'Truth' is Stranger than Prediction, More
+ Questionable Than Causal Inference},
+ journal = {American Journal of Political Science},
+ volume = {35},
+ year = {1991},
+ pages = {1047--1053},
+ month = {November},
+ number = {4},
+ note = {{http://gking.harvard.edu/files/abs/truth-abs.shtml}}
+}
+
+ at article{King91b,
+ author = {Gary King},
+ title = {Constituency Service and Incumbency Advantage},
+ journal = {British Journal of Political Science},
+ volume = {21},
+ year = {1991},
+ pages = {119--128},
+ month = {January},
+ number = {1},
+ note =
+ {{http://gking.harvard.edu/files/abs/constit-abs.shtml}}
+}
+
+ at article{King91c,
+ author = {Gary King},
+ title = {On Political Methodology},
+ journal = {Political Analysis},
+ volume = {2},
+ year = {1991},
+ pages = {1--30},
+ note =
+ {{http://gking.harvard.edu/files/abs/polmeth-abs.shtml}}
+}
+
+ at article{King91d,
+ author = {Gary King},
+ title = {Stochastic Variation: A Comment on Lewis-Beck and
+ Skalaban's `The R-Square'},
+ journal = {Political Analysis},
+ volume = {2},
+ year = {1991},
+ pages = {185--200},
+ note = {{http://gking.harvard.edu/files/abs/stoch-abs.shtml}}
+}
+
+ at article{King91e,
+ author = {Gary King},
+ title = {Calculating Standard Errors of Predicted Values
+ based on Nonlinear Functional Forms},
+ journal = {The Political Methodologist},
+ volume = {4},
+ year = {1991},
+ month = {Fall},
+ number = {2}
+}
+
+ at article{KinGel91,
+ author = {Gary King and Andrew Gelman},
+ title = {Systemic Consequences of Incumbency Advantage in the
+ U.S. House},
+ journal = {American Journal of Political Science},
+ volume = 35,
+ year = 1991,
+ pages = {110--138},
+ month = {February},
+ number = 1 ,
+ note =
+ {{http://gking.harvard.edu/files/abs/sysconseq-abs.shtml}}
+}
+
+ at article{AnsKin90,
+ author = {Stephen Ansolabehere and Gary King},
+ title = {Measuring the Consequences of Delegate Selection
+ Rules in Presidential Nominations},
+ journal = {Journal of Politics},
+ volume = {52},
+ year = {1990},
+ pages = {609--621},
+ month = {May},
+ number = {2},
+ note = {{http://gking.harvard.edu/files/abs/pri-abs.shtml}}
+}
+
+ at article{GelKin90,
+ author = {Andrew Gelman and Gary King},
+ title = {Estimating the Electoral Consequences of Legislative
+ Redistricting},
+ journal = {Journal of the American Statistical Association},
+ volume = {85},
+ year = {1990},
+ pages = {274--282},
+ month = {June},
+ number = {410},
+ note = {{http://gking.harvard.edu/files/abs/svstat-abs.shtml}}
+}
+
+ at article{GelKin90b,
+ author = {Andrew Gelman and Gary King},
+ title = {Estimating Incumbency Advantage Without Bias},
+ journal = {American Journal of Political Science},
+ volume = {34},
+ year = {1990},
+ pages = {1142--1164},
+ month = {November},
+ number = {4},
+ note = {{http://gking.harvard.edu/files/abs/inc-abs.shtml}}
+}
+
+ at article{KinAltBur90,
+ author = {Gary King and James Alt and Nancy Burns and Michael
+ Laver},
+ title = {A Unified Model of Cabinet Dissolution in
+ Parliamentary Democracies},
+ journal = {American Journal of Political Science},
+ volume = {34},
+ year = {1990},
+ pages = {846--871},
+ month = {August},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/coal-abs.shtml}}
+}
+
+ at article{King90,
+ author = {Gary King},
+ title = {Electoral Responsiveness and Partisan Bias in
+ Multiparty Democracies},
+ journal = {Legislative Studies Quarterly},
+ volume = {XV},
+ year = {1990},
+ pages = {159--181},
+ month = {May},
+ number = {2},
+ note =
+ {{http://gking.harvard.edu/files/abs/electresp-abs.shtml}}
+}
+
+ at article{GelKin89,
+ author = {Andrew Gelman and Gary King},
+ title = {Electoral Responsiveness in U.S. Congressional
+ Elections, 1946-1986},
+ journal = {Proceedings of the Social Statistics Section,
+ American Statistical Association},
+ year = {1989},
+ pages = {208}
+}
+
+ at article{King89b,
+ author = {Gary King},
+ title = {Representation Through Legislative Redistricting: A
+ Stochastic Model},
+ journal = {American Journal of Political Science},
+ volume = {33},
+ year = {1989},
+ pages = {787--824},
+ month = {November},
+ number = {4},
+ note =
+ {{http://gking.harvard.edu/files/abs/repstoch-abs.shtml}}
+}
+
+ at article{King89c,
+ author = {Gary King},
+ title = {Event Count Models for International Relations:
+ Generalizations and Applications},
+ journal = {International Studies Quarterly},
+ volume = {33},
+ year = {1989},
+ pages = {123--147},
+ month = {June},
+ number = {2},
+ note = {{http://gking.harvard.edu/files/abs/ISQ33-abs.shtml}}
+}
+
+ at article{King89d,
+ author = {Gary King},
+ title = {Variance Specification in Event Count Models: From
+ Restrictive Assumptions to a Generalized Estimator},
+ journal = {American Journal of Political Science},
+ volume = 33,
+ year = 1989,
+ pages = {762--784},
+ month = {August},
+ number = 3 ,
+ note =
+ {{http://gking.harvard.edu/files/abs/varspecec-abs.shtml}}
+}
+
+ at article{King89e,
+ author = {Gary King},
+ title = {A Seemingly Unrelated Poisson Regression Model},
+ journal = {Sociological Methods and Research},
+ volume = {17},
+ year = {1989},
+ pages = {235--255},
+ month = {February},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/SMR17-abs.shtml}}
+}
+
+ at article{King88,
+ author = {Gary King},
+ title = {Statistical Models for Political Science Event
+ Counts: Bias in Conventional Procedures and Evidence
+ for The Exponential Poisson Regression Model},
+ journal = {American Journal of Political Science},
+ volume = 32,
+ year = 1988,
+ pages = {838-863},
+ month = {August},
+ number = 3 ,
+ note = {{http://gking.harvard.edu/files/abs/epr-abs.shtml}}
+}
+
+ at article{BroKin87,
+ author = {Robert X Browning and Gary King},
+ title = {Seats, Votes, and Gerrymandering: Measuring Bias and
+ Representation in Legislative Redistricting},
+ journal = {Law and Policy},
+ volume = {9},
+ year = {1987},
+ pages = {305--322},
+ month = {July},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/LP9-abs.shtml}}
+}
+
+ at article{KinBro87,
+ author = {Gary King and Robert X Browning},
+ title = {Democratic Representation and Partisan Bias in
+ Congressional Elections},
+ journal = {American Political Science Review},
+ volume = {81},
+ year = {1987},
+ pages = {1252--1273},
+ month = {December},
+ number = {4},
+ note = {{http://gking.harvard.edu/files/abs/sv-abs.shtml}}
+}
+
+ at article{King87,
+ author = {Gary King},
+ title = {Presidential Appointments to the Supreme Court:
+ Adding Systematic Explanation to Probabilistic
+ Description},
+ journal = {American Politics Quarterly},
+ volume = {15},
+ year = {1987},
+ pages = {373--386},
+ month = {July},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/sct-abs.shtml}}
+}
+
+ at article{King86,
+ author = {Gary King},
+ title = {How Not to Lie With Statistics: Avoiding Common
+ Mistakes in Quantitative Political Science},
+ journal = {American Journal of Political Science},
+ volume = {30},
+ year = {1986},
+ pages = {666--687},
+ month = {August},
+ number = {3},
+ note = {{http://gking.harvard.edu/files/abs/mist-abs.shtml}}
+}
+
+ at article{King86b,
+ author = {Gary King},
+ title = {The Significance of Roll Calls in Voting Bodies: A
+ Model and Statistical Estimation},
+ journal = {Social Science Research},
+ volume = {15},
+ year = {1986},
+ pages = {135--152},
+ month = {June},
+ note = {{http://gking.harvard.edu/files/abs/SSR15-abs.shtml}}
+}
+
+ at article{King86c,
+ author = {Gary King},
+ title = {Political Parties and Foreign Policy: A
+ Structuralist Approach},
+ journal = {Political Psychology},
+ volume = {7},
+ year = {1986},
+ pages = {83--101},
+ month = {March},
+ number = {1},
+ note = {{http://gking.harvard.edu/files/abs/PP7-abs.shtml}}
+}
+
+ at article{KinMer86,
+ author = {Gary King and Richard Merelman},
+ title = {The Development of Political Activists: A Model of
+ Early Learning},
+ journal = {Social Science Quarterly},
+ volume = {67},
+ year = {1986},
+ pages = {473--490},
+ month = {September},
+ number = {3},
+ note =
+ {{http://gking.harvard.edu/files/abs/poliactiv-abs.shtml}}
+}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Data
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ at article{HopKin09b,
+ author = {Daniel Hopkins and Gary King},
+ title = {Replication Data for: A Method of Automated
+ Nonparametric Content Analysis for Social Science},
+ journal = { },
+ year = 2009,
+ note = {\underline{UNF:3:xlE5stLgKvpeMvxzlLxzEQ==}
+ hdl:1902.1/12898 Murray Research Archive [Distributor]}
+}
+
+
+ at article{KinGakIma09b,
+ Author = {Gary King and Emmanuela Gakidou and Kosuke Imai and
+ Jason Lakin and Clayton Nall and Ryan T. Moore and
+ Nirmala Ravishankar and Manett Vargas and Martha
+ Mar{\'i}a T{\'e}llez-Rojo and Juan Eugenio
+ Hern{\'a}ndez {\'A}vila and Mauricio Hern{\'a}ndez
+ {\'A}vila and H{\'e}ctor Hern{\'a}ndez Llamas},
+ title = {Replication Data for: Public Policy for the Poor? A
+ Randomized Ten-Month Evaluation of the Mexican
+ Universal Health Insurance Program},
+ journal = { },
+ year = {2009},
+ note = {\underline{hdl:1902.1/11044}
+ UNF:3:jeUN9XODtYUp2iUbe8gWZQ== Murray Research
+ Archive [Distributor]}
+}
+
+ at article{ImaKinNal09c,
+ author = {Kosuke Imai and Gary King and Clayton Nall},
+ title = {Replication Data for: The Essential Role of Pair-Matching in
+ Cluster-Randomized Experiments, with Application to
+ the Mexican Universal Health Insurance Evaluation: Rejoinder},
+ journal = { },
+ year = {2009},
+ note = {\underline{hdl:1902.1/12730}
+ UNF:3:CKs4T0iVYxP36LQSMgAkuw== Murray Research
+ Archive [Distributor]}
+}
+
+ at article{ImaKinNal09b,
+ author = {Kosuke Imai and Gary King and Clayton Nall},
+ title = {Replication Data for: The Essential Role of Pair Matching in
+ Cluster-Randomized Experiments, with Application to
+ the Mexican Universal Health Insurance Evaluation},
+ journal = { },
+ year = {2009},
+ note = {\underline{hdl:1902.1/11047}
+ UNF:3:jeUN9XODtYUp2iUbe8gWZQ== Murray Research
+ Archive [Distributor]}
+}
+
+ at Article{KinZen08,
+ author = {Gary King and Langche Zeng},
+ title = {Replication data for: Empirical vs. Theoretical
+ Claims about Extreme Counterfactuals: A Response},
+ journal = { },
+ year = 2008,
+ note = {\underline{hdl:1902.1/11903}, Murray Research
+ Archive [Distributor]}
+}
+
+ at article{GakKin06b,
+ author = {Emmanuela Gakidou and Gary King},
+ title = {Replication data for: Death by Survey: Estimating
+ Adult Mortality without Selection Bias from Sibling
+ Survival Data},
+ year = 2006,
+ note = {{\underline{hdl:1902.1/ZMESWNECZW}
+ Murray Research Archive [Distributor]}}
+}
+
+ at article{GirKin06,
+ author = {Federico Girosi and Gary King},
+ title = {Cause of Death Data},
+ year = {2006},
+ note = {{\underline{hdl:1902.1/UOVMCPSWOL}
+ UNF:3:9JU+SmVyHgwRhAKclQ85Cg== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{HoImaKin06,
+ author = {Daniel E. Ho and Kosuke Imai and Gary King and
+ Elizabeth A. Stuart},
+ title = {Replication Data Set for: Matching as Nonparametric
+ Preprocessing for Reducing Model Dependence in
+ Parametric Causal Inference},
+ year = 2006,
+ note = {{\underline{hdl:1902.1/YVDZEQIYDS}
+ Murray Research Archive [distributor]}}
+}
+
+ at article{KinAlt06,
+ author = {Gary King and James E. Alt and Nancy Burns and
+ Michael Laver},
+ title = {Replication data for: A Unified Model of Cabinet
+ Dissolution in Parliamentary Democracies},
+ year = {2006},
+ note = {{\underline{hdl:1902.1/RMPXNUSBBS}
+ UNF:3:lfKIeFJKgejkOzXEY1i6lw== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{KinZen06b,
+ author = {Gary King and Langche Zeng},
+ title = {Replication Data Set for: When Can History be Our
+ Guide? The Pitfalls of Counterfactual Inference},
+ year = 2006,
+ note = {{\underline{hdl:1902.1/DXRXCFAWPK}
+ Murray Research Archive [distributor]}}
+}
+
+ at article{KinZen06c,
+ author = {Gary King and Langche Zeng},
+ title = {Replication data for: Detecting Model Depedence in
+ Statistical Inference: A Response},
+ year = {2006},
+ note = {{\underline{hdl:1902.1/FGSRBXXIYT}
+ UNF:3:K4/CgnMYDMV6izc5RVOZTA== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{KinZen06d,
+ author = {Gary King and Langche Zeng},
+ title = {Replication data for: When Can History be Our Guide?
+ The Pitfalls of Counterfactual Inference},
+ year = {2006},
+ note = {{\underline{hdl:1902.1/DXRXCFAWPK}
+ UNF:3:DaYlT6QSX9r0D50ye+tXpA== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{KinZen06e,
+ author = {Gary King and Langche Zeng},
+ title = {Replication data for: The Dangers of Extreme
+ Counterfactuals },
+ year = {2006},
+ note = {{\underline{hdl:1902.1/UTVMBVNGMX}
+ UNF:3:ytKKNjK+yR8Pq3H0RcV6eg== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{EpsHoKin05b,
+ author = {Lee Epstein and Daniel E. Ho and Gary King and
+ Jeffrey A. Segal},
+ title = {Replication data for: The Supreme Court During
+ Crisis: How War Affects Only Nonwar Cases},
+ year = {2005},
+ note = {{\underline{hdl:1902.1/RESUDVYWPE}
+ UNF:3:ZmbzFbfqogNM0Gb6CcV52A== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{BecKinZen04b,
+ author = {Nathaniel Beck and Gary King and Langche Zeng},
+ title = {Replication data for: Gelpi and Grynaviski},
+ year = {2004},
+ note = {{\underline{hdl:1902.1/LAAYCJJGDS}
+ UNF:3:N0bEAswAlPPVXCxPOZYyqw== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{King03b,
+ author = {Gary King},
+ title = {10 Million International Dyadic Events},
+ year = {2003},
+ note = {{\underline{hdl:1902.1/FYXLAWZRIA}
+ UNF:3:um06qkr/1tAwpS4roUqAiw== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{KinZen01c,
+ author = {Gary King and Langche Zeng},
+ title = {Replication data for: Explaining Rare Events in
+ International Relations},
+ year = {2001},
+ note = {\underline{hdl:1902.1/OUCBSJKXIC}
+ UNF:3:vyct3c8fMCdWOdp03NUhaA== Murray Research
+ Archive [Distributor]}
+}
+
+ at article{KinZen01d,
+ author = {Gary King and Langche Zeng},
+ title = {Replication data for: Improving Forecats of State
+ Failure},
+ year = {2001},
+ note = {{\underline{hdl:1902.1/RPQIODIANR}
+ UNF:3:CEsbEgPxbxExfYuh2NWwWQ== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{BecKinZen00b,
+ author = {Nathaniel Beck and Gary King and Langche Zeng},
+ title = {Replication data for: Improving Quantitative Studies
+ of International Conflict: A Conjecture},
+ volume = {2000},
+ note = {{\underline{hdl:1902.1/SZKONDGOMF}
+ UNF:3:rYRDzT8dCJ/BR7V9u8fObA== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{KinTomWit00b,
+ author = {Gary King and Michael Tomz and Jason Wittenberg},
+ title = {Replication data for: Making the Most of Statistical
+ Analyses: Improving Interpretation and Presentation},
+ year = {2000},
+ note = {{\underline{hdl:1902.1/QTCABXZZRQ}
+ UNF:3:1VaLflZ/LfB+AISX+hBm1w== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{KatKin99b,
+ author = {Jonathan Katz and Gary King},
+ title = {Replication data for: A Statistical Model of
+ Multiparty Electoral Data},
+ year = {1999},
+ note = {{\underline{hdl:1902.1/QIGTWZYTLZ}
+ UNF:3:gwGcKylle0BKJTGv3Zv4OA== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{GelKinBos98b,
+ author = {Andrew Gelman and Gary King and John Boscardin},
+ title = {Replication data for: Estimating the Probability of
+ Events that have Never Occurred: When is your Vote
+ Decisive},
+ year = {1998},
+ note = {{\underline{hdl:1902.1/NOLXXTUHNZ}
+ UNF:3:ORDulVH6qEb4lsCyDn5W3A== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{King97b,
+ author = {Gary King},
+ title = {Replication data for: A Solution to the Ecological
+ Inference Problem: Reconstructing Individuals
+ Behavior from Aggregate Data},
+ year = {1997},
+ note = {{\underline{hdl:1902.1/LWMMKUTYXS}
+ UNF:3:DRWozWd89+vNLO7lY2AHbg== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{GelKin94d,
+ author = {Andrew Gelman and Gary King},
+ title = {Replication data for: Enhancing Democracy Through
+ Legislative Redistricting},
+ year = {1994},
+ note = {{\underline{hdl:1902.1/BNCOWNVERH}
+ UNF:3:ZXahi7PBFxLRb46sVKOAuQ== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{GelKin94e,
+ author = {Andrew Gelman and Gary King},
+ title = {Replication data for: Unified Methods of Evaluating
+ Electoral Systems and Redistricting Plans: United
+ States House of Representatives adn Ohio State
+ Legislature},
+ year = {1994},
+ note = {{\underline{hdl:1902.1/JWFTSFKOBK}
+ UNF:3:Fi01DWj4Sx+0ZEOEo4TOXA== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{King94,
+ author = {Gary King},
+ title = {Elections to the United States House of
+ Representatives, 1898-1992},
+ year = {1994},
+ note = {{\underline{hdl:1902.1/TQDSSPRDDZ}
+ UNF:3:tD8SznMFjKIxWxOqTQaamQ== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{GelKin93b,
+ author = {Andrew Gelman and Gary King},
+ title = {Replication data for: Why Are American Presidential
+ Election Campaign Polls so Variable When Votes are
+ so Predictable?},
+ year = {1993},
+ note = {{\underline{hdl:1902.1/SBBXEUSSCW}
+ Murray Research Archive [Distributor]}}
+}
+
+ at article{KinLav93b,
+ author = {Gary King and Michael Laver},
+ title = {Replication data for: On Party Platforms, Mandates,
+ and Government Spending},
+ year = {1993},
+ note = {{\underline{hdl:1902.1/XEHYCJAWQD}
+ UNF:3:cwNXuRQ/6Lp72obLkttmGg== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{King91e,
+ author = {Gary King},
+ title = {Replication data for: Constituency Service and
+ Incumbency Advantage},
+ year = {1991},
+ note = {{\underline{hdl:1902.1/JTMXGSZXIZ}
+ UNF:3:IE4ZSAs8ZzUK+fRXNbVvGw== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{King91f,
+ author = {Gary King},
+ title = {Replication data for: On Political Methodology},
+ year = {1991},
+ note = {{\underline{hdl:1902.1/KHTLSQXAEJ}
+ Murray Research Archive [Distributor]}}
+}
+
+ at article{AnsKin90b,
+ author = {Stephen Ansolabehere and Gary King},
+ title = {Replication data for: Measuring the Consequences of
+ Delegate Selection Rules in Presidential
+ Nominations},
+ year = {1990},
+ note = {{\underline{hdl:1902.1/BUJXCEPXQK}
+ UNF:3:OdFPcQcvfO5hc3WJ5ty8vQ== Murray Research
+ Archive [Distributor]}}
+}
+
+ at article{KinBen86,
+ author = {Gary King and Gerald Benjamin},
+ title = {Replication data for: The Stability of Partisan
+ Identification in the U.S. House of Representatives,
+ 1789-1984},
+ year = {1986},
+ note = {{\underline{hdl:1902.1/HINHTJQYFO}
+ Murray Research Archive [Distributor]}}
+}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Software
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+ at Article{IacKinPor09b,
+ author = {Stefano M. Iacus and Gary King and Giuseppe Porro},
+ title = {CEM: Coarsened Exact Matching Software},
+ journal = {Journal of Statistical Software},
+ volume = 30,
+ issue = 9,
+ year = 2009,
+ note = {{http://gking.harvard.edu/cem}}
+}
+
+ at article{WanKinLau07,
+ author = {Jonathan Wand and Gary King and Olivia Lau},
+ title = {Anchors: Software for Anchoring Vignettes Data},
+ journal = {Journal of Statistical Software},
+ year = {2007, forthcoming}
+}
+
+ at Article{HonKinBLa10,
+ author = {James Honaker and Gary King and Matthew Blackwell},
+ title = {Amelia II: A Program for Missing Data},
+ year = 2010,
+ note = {{http://gking.harvard.edu/amelia}}
+}
+
+ at article{ImaKinLau06,
+ author = {Kosuke Imai and Gary King and Olivia Lau},
+ title = {Zelig: Everyone's Statistical Software},
+ year = 2006,
+ note = {{http://gking.harvard.edu/zelig}}
+}
+
+ at article{TomWitKin05,
+ author = {Michael Tomz and Jason Wittenberg and Gary King},
+ title = {CLARIFY: Software for Interpreting and Presenting
+ Statistical Results},
+ year = {1998-2005},
+ note = {{http://gking.harvard.edu/stats.shtml#clarify}}
+}
+
+ at article{HonJosKin98,
+ author = {James Honaker and Anne Joseph and Gary King and
+ Kenneth Scheve and Naunihal Singh.},
+ title = {AMELIA: A Program for Missing Data},
+ year = {1998-2002},
+ note = {{http://gking.harvard.edu/amelia}}
+}
+
+ at article{King98,
+ author = {Gary King},
+ title = {MAXLIK, a set of Gauss programs, annotated for
+ pedagogical purposes, to implement the maximum
+ likelihood models in Unifying Political Methodology:
+ The Likelihood Theory of Statistical Inference},
+ year = {1998},
+ note = {{http://gking.harvard.edu/stats.shtml#maxlik}}
+}
+
+ at article{King96b,
+ author = {Gary King},
+ title = {EI: Program for Ecological Inference},
+ year = {1996-2003},
+ note = {{http://gking.harvard.edu/stats.shtml#ei}}
+}
+
+ at article{GelKin92,
+ author = {Andrew Gelman and Gary King},
+ title = {JudgeIt: A Program for Evaluating Electoral Systems
+ and Redistricting Plans},
+ year = {1992-2002},
+ note = {{http://gking.harvard.edu/stats.shtml#judgeit}}
+}
+
+ at article{HoImaKin07a,
+ author = {Daniel E. Ho and Kosuke Imai and Gary King and
+ Elizabeth A. Stuart},
+ title = {MatchIt: Nonparametric Preprocessing for Parametric
+ Causal Inference},
+ year = {Forthcoming},
+ journal = {Journal of Statistical Software},
+ note = {{http://gking.harvard.edu/matchit}}
+}
+
+ at InCollection{Gelman04,
+ author = {Andrew Gelman},
+ title = {Treatment Effects in Before-After Data},
+ booktitle = {Applied Bayesian Modeling and Causal Inference from
+ an Incomplete Data Perspective},
+ publisher = {Wiley},
+ year = 2004,
+ editor = {Andrew Gelman and Xiao-Li Meng},
+ chapter = 18,
+ address = {London}
+}
+
diff --git a/inst/doc/graphs.tex b/inst/doc/graphs.tex
deleted file mode 100644
index 17e6b44..0000000
--- a/inst/doc/graphs.tex
+++ /dev/null
@@ -1,296 +0,0 @@
-\chapter{Graphing Commands}\label{s:plot}
-
-R, and thus Zelig, can produce exceptionally beautiful plots. Many
-built-in plotting functions exist, including scatter plots, line
-charts, histograms, bar charts, pie charts, ternary diagrams, contour
-plots, and a variety of three-dimensional graphs. If you desire, you
-can exercise a high degree of control to generate just the right
-graphic. Zelig includes several default plots for one-observation
-simulations for each model. To view these plots on-screen, simply
-type {\tt plot(s.out)}, where {\tt s.out} is the output from {\tt
- sim()}. Depending on the model chosen, {\tt plot()} will return
-different plots.
-
-If you wish to create your own plots, this section reviews the most
-basic procedures for creating and saving two-dimensional plots. R
-plots material in two steps:
-\begin{enumerate}
-\item You must call an output device (discussed in \Sref{ss:output}),
- select a type of plot, draw a plotting region, draw axes, and plot
- the given data. At this stage, you may also define axes labels, the
- plot title, and colors for the plotted data. Step one is described
- in \Sref{ss:draw} below.
-\item Optionally, you may add points, lines, text, or a legend to the
- existing plot. These commands are described in \Sref{ss:add}.
-\end{enumerate}
-
-\section{Drawing Plots}\label{ss:draw}
-
-The most generic plotting command is \texttt{plot()}, which
-automatically recognizes the type of R object(s) you are trying to
-plot and selects the best type of plot. The most common graphs
-returned by \texttt{plot()} are as follows:
-\begin{enumerate}
-\item If \texttt{X} is a variable of length $n$, \texttt{plot(X)}
- returns a scatter plot of $(x_i, i)$ for $i = 1, \dots n$. If X is
- unsorted, this procedure produces a messy graph. Use
- \texttt{plot(sort(X))} to arrange the plotted values of $(x_i, i)$
- from smallest to largest.
-\item With two numeric vectors \texttt{X} and \texttt{Y}, both of
- length $n$, \texttt{plot(X, Y)} plots a scatter plot of each point
- $(x_i, y_i)$ for $i = 1, \dots n$. Alternatively, if Z is an object
- with two vectors, \texttt{plot(Z)} also creates a scatter plot.
-\end{enumerate}
-
-Optional arguments specific to \texttt{plot} include:
-\begin{itemize}
-\item \texttt{main} creates a title for the graph, and \texttt{xlab}
- and \texttt{ylab} label the x and y axes, respectively. For
- example,
-\begin{verbatim}
-plot(x, y, main = "My Lovely Plot", xlab = "Explanatory Variable",
- ylab = "Dependent Variable")
-\end{verbatim}
-
-\item \texttt{type} controls the type of plot you request. The
- default is \texttt{plot(x, y, type = "p")}, but you may choose among
- the following types:
- \begin{table}[!h]
- \begin{quote}
- \begin{tabular}{cl}
- {\tt "p"} & points\\
- {\tt "l"} & lines\\
- {\tt "b"} & both points and lines\\
- {\tt "c"} & lines drawn up to but not including the points\\
- {\tt "h"} & histogram\\
- {\tt "s"} & a step function\\
- {\tt "n"} & a blank plotting region ( with the axes specified)
- \end{tabular}
- \end{quote}
- \end{table}
-\item If you choose {\tt type = "p"}, R plots open circles by default.
- You can change the type of point by specifying the {\tt pch}
- argument. For example, \texttt{plot(x, y, type = "p", pch = 19)}
- creates a scatter-plot of filled circles. Other options for {\tt
- pch} include:
- \begin{table}[!h]
- \begin{quote}
- \begin{tabular}{cl}
- 19 & solid circle (a disk) \\
- 20 & smaller solid circle \\
- 21 & circle\\
- 22 & square\\
- 23 & diamond \\
- 24 & triangle pointed up\\
- 25 & triangle pointed down\\
- \end{tabular}
- \end{quote}
- \end{table}
-
-In addition, you can specify your own symbols by using, for example,
-{\tt pch = "*"} or {\tt pch = "."}.
-
-\item If you choose {\tt type = "l"}, R plots solid lines by default.
- Use the optional {\tt lty} argument to set the line type. For
- example, {\tt plot(x, y, type = "l", lty = "dashed")} plots a dashed
- line. Other options are dotted, dotdash, longdash, and twodash.
-
-\item \texttt{col} sets the color of the points, lines, or bars. For
- example, \texttt{plot(x, y, type = "b", pch = 20, lty = "dotted",
- col = "violet")} plots small circles connected by a dotted line,
- both of which are violet. (The axes and labels remain black.) Use
- \texttt{colors()} to see the full list of available colors.
-
-\item \texttt{xlim} and {\tt ylim} set the limits to the $x$-axis and
- $y$-axis. For example, \texttt{plot(x, y, xlim = c(0, 25), ylim =
- c(-15, 5))} sets range of the $x$-axis to [0, 25] and the range of
- the $y$-axis to $[-15, 5]$.
-\end{itemize}
-For additional plotting options, refer to {\tt help(par)}.
-
-\section{Adding Points, Lines, and Legends to Existing Plots}\label{ss:add}
-
-Once you have created a plot, you can \emph{add} points, lines, text,
-or a legend. To place each of these elements, R uses coordinates
-defined in terms of the x-axes and y-axes of the plot area, not
-coordinates defined in terms of the the plotting window or device.
-For example, if your plot has an x-axis with values between $[0,
-100]$, and a y-axis with values between $[50, 75]$, you may add a
-point at $(55,55)$.
-
-\begin{itemize}
-\item \textbf{points()} plots one or more sets of points. Use
- \texttt{pch} with \texttt{points} to add points to an existing plot.
- For example, \texttt{points(P, Q, pch = ".", col = "forest green")}
- plots each $(p_i, q_i)$ as tiny green dots.
-\item \textbf{lines()} joins the specified points with line segments.
- The arguments \texttt{col} and \texttt{lty} may also be used. For
- example, \texttt{lines(X, Y, col = "blue", lty = "dotted")} draws a
- blue dotted line from each set of points $(x_i, y_i)$ to the next.
- Alternatively, \texttt{lines} also takes command output which
- specifies $(x, y)$ coordinates. For example, \texttt{density(Z)}
- creates a vector of $x$ and a vector of $y$, and {\tt
- plot(density(Z))} draws the kernel density function.
-\item \textbf{text()} adds a character string at the specified set of
- $(x,y)$ coordinates. For example, {\tt text(5, 5, labels = "Key
- Point")} adds the label ``Key Point'' at the plot location
- $(5,5)$. You may also choose the font using the {\tt font} option,
- the size of the font relative to the axis labels using the {\tt cex}
- option, and choose a color using the {\tt col} option. The full
- list of options may be accessed using {\tt help(text)}.
-\item \textbf{legend()} places a legend at a specified set of $(x,y)$
- coordinates. Type {\tt demo(vertci)} to see an example for {\tt
- legend()}.
-\end{itemize}
-
-\section{Saving Graphs to Files}\label{ss:output}
-
-By default, R displays graphs in a window on your screen. To save R
-plots to file (to include them in a paper, for example), preface your
-plotting commands with:
-\begin{verbatim}
-> ps.options(family = c("Times"), pointsize = 12)
-> postscript(file = "mygraph.eps", horizontal = FALSE, paper = "special",
- width = 6.25, height = 4)
-\end{verbatim}
-where the \texttt{ps.options()} command sets the font type and size in
-the output file, and the \texttt{postscript} command allows you to
-specify the name of the file as well as several additional options.
-Using {\tt paper = special} allows you to specify the width and height
-of the encapsulated postscript region in inches (6.25 inches long and
-4 inches high, in this case), and the statement \texttt{horizontal =
- FALSE} suppresses R's default landscape orientation. Alternatively,
-you may use {\tt pdf()} instead of {\tt postscript()}. If you wish to
-select postscript options for .pdf output, you may do so using options
-in {\tt pdf()}. For example:
-\begin{verbatim}
-> pdf(file = "mygraph.pdf", width = 6.25, height = 4, family = "Times",
-+ pointsize = 12)
-\end{verbatim}
-
-At the end of every plot, you should close your output device. The
-command \texttt{dev.off()} stops writing and saves the \texttt{.eps}
-or {\tt .pdf} file to your working directory. If you forget to close
-the file, you will write all subsequent plots to the same file,
-overwriting previous plots. You may also use \texttt{dev.off()} to close
-on-screen plot windows.
-
-To write multiple plots to the same file, you can use the following
-options:
-\begin{itemize}
-\item For plots on separate pages in the same {\tt .pdf} document, use
-\begin{verbatim}
-> pdf(file = "mygraph.pdf", width = 6.25, height = 4, family = "Times",
-+ pointsize = 12, onefile = TRUE)
-\end{verbatim}
-\item For multiple plots on one page, initialize either a {\tt .pdf}
-or {\tt .eps} file, then (before any plotting commands) type:
-\begin{verbatim}
-par(mfrow = c(2, 4))
-\end{verbatim}
-This creates a grid that has two rows and four columns. Your plot
-statements will populate the grid going across the first row, then the
-second row, from left to right.
-\end{itemize}
-
-\newpage
-
-\section{Examples}
-
-\subsection{Descriptive Plots: Box-plots}
-
-\begin{center}
-\includegraphics{figs/sample1}
-\end{center}
-Using the sample \texttt{turnout} data set included with Zelig, the
-following commands will produce the graph above.
-\begin{verbatim}
-> library(Zelig) # Loads the Zelig package.
-> data (turnout) # Loads the sample data.
-> boxplot(income ~ educate, # Creates a boxplot with income
-+ data = turnout, col = "grey", pch = ".", # as a function of education.
-+ main = "Income as a Function of Years of Education",
-+ xlab = "Education in Years", ylab = "Income in \$10,000s")
-\end{verbatim}
-
-\newpage
-
-\subsection{Density Plots: A Histogram}
-
-Histograms are easy ways to evaluate the density of a quantity of
-interest.
-
-\begin{center}
-\includegraphics{figs/sample2}
-\end{center}
-
-Here's the code to create this graph:
-\begin{verbatim}
-> library(Zelig) # Loads the Zelig package.
-> data(turnout) # Loads the sample data set.
-> truehist(turnout$income, col = "wheat1", # Calls the main plot, with
-+ xlab = "Annual Income in $10,000s", # options.
-+ main = "Histogram of Income")
-> lines(density(turnout$income)) # Adds the kernel density line.
-\end{verbatim} %$
-
-\newpage
-
-\subsection{Advanced Examples}
-
-The examples above are simple examples which only skim the surface of
-R's plotting potential. We include more advanced, model-specific
-plots in the Zelig demo scripts, and have created functions that
-automate some of these plots, including:
-
-\begin{enumerate}
-
-\item {\bf Ternary Diagrams} describe the predicted probability of a
- categorical dependent variable that has three observed outcomes.
- You may choose to use this plot with the multinomial logit, the
- ordinal logit, or the ordinal probit models (Katz and King,
- 1999).\nocite{KatKin99} See \Sref{ternary} for the sample code, type
- {\tt demo(mlogit)} at the R prompt to run the example, and refer to
- Section \ref{ternary} to add points to a ternary diagram.
-
-\begin{center}
-\includegraphics{figs/ternary}
-\end{center}
-
-\newpage
-
-\item {\bf ROC Plots} summarize how well models for binary dependent
- variables (logit, probit, and relogit) fit the data. The ROC plot
- evaluates the fraction of 0's and 1's correctly predicted for every
- possible threshold value at which the continuous Prob$(Y = 1)$ may
- be realized as a dichotomous prediction. The closer the ROC curve
- is to the upper right corner of the plot, the better the fit of the
- model specification (King and Zeng, 2002\emph{b})\nocite{KinZen02}.
- See Section \ref{ROC} for the sample code, and type {\tt demo(roc)} at the
- R prompt to run the example.
-
-\begin{center}
-\includegraphics{figs/roc}
-\end{center}
-
-\newpage
-
-\item {\bf Vertical Confidence Intervals} may be used for almost any
- model, and describe simulated confidence intervals for any quantity
- of interest while allowing one of the explanatory variables to vary
- over a given range of values (King, Tomz and Wittenberg,
- 2000)\nocite{KinTomWit00}. Type {\tt demo(vertci)} at the R prompt to
- run the example, and {\tt help.zelig(plot.ci)} for the manual page.
-
-\begin{center}
-\includegraphics{figs/vertci}
-\label{plot.vertci}
-\end{center}
-
-\end{enumerate}
-
-%%% Local Variables:
-%%% mode: LaTeX
-%%% TeX-master: "zelig"
-%%% End:
-
diff --git a/inst/doc/html.sty b/inst/doc/html.sty
new file mode 100644
index 0000000..ec942b0
--- /dev/null
+++ b/inst/doc/html.sty
@@ -0,0 +1,200 @@
+% see if we should actually do anything
+\message{Including htmltex ...}
+\expandafter\ifx\csname dohtml\endcsname\relax
+\message{dohtml undefined; skipping ...}
+\else
+\message{dohtml defined; it is \dohtml}
+%%%%%%%%%%%%%%%% BEGIN HTML TEX STUFF %%%%%%%%%%%%%%
+
+% get tex to output the output boxes
+\tracingoutput=1
+\showboxbreadth=10000000
+\showboxdepth=10000000
+\hbadness=10000
+\vbadness=10000
+%\setlength{\textheight}{200in} % one page so footnotes go at the bottom.
+\pretolerance=10000
+
+\makeatletter
+\def\@htmlwrite#1{\special{HTML:#1}}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%% Paragraphs and things %%%%%%%%%%%%%%
+\iftrue
+\message{paragraphs ...}
+\let\@@@par\@@par
+\def\@@par{\@htmlwrite{<p>}\@@@par}
+\@restorepar
+\let\@@@newline\newline
+\def\newline{\@htmlwrite{<br>}\@@@newline}
+\let\@@@cr\cr
+\def\cr{\@htmlwrite{<br>}\@@@cr}
+\let\@@@dblslsh\\
+\def\\{\@htmlwrite{<br>}\@@@dblslsh}
+%\def\discretionary#1#2#3{#3} % didn't work very well
+\def\-{}
+\fi
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%% Pages %%%%%%%%%%%%%%%%%%%%%%%%%
+% For TeX
+%\footline={\@htmlwrite{<HR>}} % Does nothing in LaTeX
+
+% For LaTeX
+\message{pages ...}
+\def\ps at html{
+ \def\@oddfoot{\@htmlwrite{<HR></A>}}
+ \let\@evenfoot\@oddfoot
+ \def\@oddhead{\@htmlwrite{<CENTER><A NAME=PAGE\thepage>\thepage</CENTER><HR>}}
+% \def\@oddhead{\@htmlwrite{<A NAME=PAGE\thepage>}}
+ \let\@evenhead\@oddhead
+}
+
+\pagestyle{html}
+\onecolumn
+\def\twocolumn{}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%% MATH MODE %%%%%%%%%%%%%%%%%%%
+\message{math ...}
+\iffalse
+\let\@dollar=$
+\catcode`\$=13
+\let\@inmath=0
+\def${\ifx\@inmath0\@htmlwrite{<I>}\let\@inmath=1\@dollar\else\@dollar\@htmlwrite{</I>}\let\@inmath=0\fi}
+\fi
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% figures %%%%%%%%%%%%%%%%%%%
+\def\fps at figure{h}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%% Footnotes %%%%%%%%%%%%%%%%%
+\iffalse
+\def\footnote#1{}
+\def\footnotetext[#1]#2{}
+\fi
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%% All kinds of lists %%%%%%%%%%%%%%
+\message{lists ...}
+\iftrue
+\newcount\@indef
+\@indef=0
+\def\description{
+ \@htmlwrite{<DL>}
+ \advance\@indef by 1
+ \ifnum\@indef=1
+ \let\@item=\item
+ \def\item[##1]{\@htmlwrite{<DT>}##1\@htmlwrite{<DD>}}
+ \fi
+ }
+\def\enddescription{
+ \@htmlwrite{</DL>}
+ \advance\@indef by -1
+ \ifnum\@indef=0
+ \let\item\@item
+ \fi}
+\fi
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%% centering %%%%%%%%%%%%%%%%%%%%%%
+\iftrue
+\let\@htmlcenterline\centerline
+\def\centerline#1{
+ \@htmlwrite{<CENTER>}
+ \@htmlcenterline{#1}
+ \@htmlwrite{</CENTER>}}
+\fi
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%% labels %%%%%%%%%%%%%%%%%%%%%%%%%%
+\let\@htmllabel\label
+\let\@htmlref\ref
+\catcode`\#=11
+\def\hash{#}
+\catcode`\#=6
+%\def\label#1{\@htmlwrite{<A NAME=#1></A>}\@htmllabel{#1}}
+
+\def\label#1{\@bsphack\if at filesw {\let\thepage\relax
+ \def\protect{\noexpand\noexpand\noexpand}%
+ \edef\@tempa{\write\@auxout{\string
+ \newlabel{#1}{{\labelname}{\thepage}}}}%
+ \expandafter}\@tempa
+ \if at nobreak \ifvmode\nobreak\fi\fi\fi\@esphack}
+
+%\def\ref#1{\@htmlwrite{<A HREF=\hash#1>}\@htmlref{#1}\@htmlwrite{</A>}}
+\let\@refstepcounter\refstepcounter
+\def\refstepcounter#1{
+ \@refstepcounter{#1}
+ \@htmlwrite{<A NAME=#1\@currentlabel></A>}
+ \def\labelname{
+ \special{HTML:<A HREF=HTMLHASH#1\@currentlabel>}
+ \@currentlabel\special{HTML:</A>}
+ }
+}
+
+\def\labelname{}
+
+% unfortunately eqnarray doesn't use refstepcounter so we'll have
+% to take care of it.
+
+%here's a better fix but it doesn't work
+
+%\let\@@eqnnum\@eqnnum
+%\def\@eqnnum{
+% \@htmlwrite{<A NAME=equation\@currentlabel></A>}
+% \def\labelname{
+% \special{HTML:<A HREF=HTMLHASHequation\@currentlabel>}
+% \@currentlabel\special{HTML:</A>}
+% }
+% \@@eqnnum
+%}
+
+\let\@eqnarray\eqnarray
+\def\eqnarray{
+ \@htmlwrite{<A NAME=equation\@currentlabel></A>}
+ \def\labelname{
+ \special{HTML:<A HREF=HTMLHASHequation\@currentlabel>}
+ \@currentlabel\special{HTML:</A>}
+ }
+ \let\cr\@@@cr
+ \@htmlwrite{<br>}
+ \@eqnarray
+}
+
+%\let\@endeqnarray\endeqnarray
+%\def\endeqnarray{
+% \@htmlwrite{<A NAME=equation\@currentlabel></A>}
+% \def\labelname{
+% \special{HTML:<A HREF=HTMLHASHequation\@currentlabel>}
+% \@currentlabel\special{HTML:</A>}
+% }
+% \@endeqnarray
+% \def\cr{\@htmlwrite{<br>}\@@@cr}
+%}
+
+
+%\let\@@@eqncr\@@eqncr
+%\def\@@eqncr{
+% \@htmlwrite{<A NAME=equation\@currentlabel></A>}
+% \def\labelname{
+% \special{HTML:<A HREF=HTMLHASHequation\@currentlabel>}
+% \@currentlabel\special{HTML:</A>}
+% }
+% \@@@eqncr
+%}
+
+\def\@eqnnum{{
+ \@htmlwrite{<A NAME=equation\@currentlabel></A>}
+ \def\labelname{
+ \special{HTML:<A HREF=HTMLHASHequation\@currentlabel>}
+ \@currentlabel\special{HTML:</A>}}
+ \reset at font\rm (\theequation)
+ \@htmlwrite{<br>}}}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\makeatother
+
+%%%%%%%%%%%%%%% END HTML TEX STUFF %%%%%%%%%%%%%%%%%%%%%%
+\fi
diff --git a/inst/doc/index.shtml b/inst/doc/index.shtml
deleted file mode 100644
index dcc69d0..0000000
--- a/inst/doc/index.shtml
+++ /dev/null
@@ -1,99 +0,0 @@
-<html>
-<head>
-<title>Zelig Software Website</title>
-<script language='Javascript' src='/gking.js'></script></HEAD>
-
-<BODY><script language="Javascript">displayHeader()</script>
-
-<h1>Zelig: Everyone's Statistical Software</h1>
-
-<a href="http://www.princeton.edu/~kimai/">Kosuke Imai</a>,
-<a href="/">Gary King</a>,
-<a href="http://www.olivialau.org">Olivia Lau</a>
-<br /><br />
-<!-- rbuild: replace 'Version:' '</b>' version -->
-<b>Version:3.4-8</b>
-<!-- rbuild: replace 'Zelig_' '.tar.gz' version -->
-<a href="/src/contrib/Zelig_3.4-8.tar.gz">(Download)</a>
-<a href="docs/static/whatsnew.pdf"> (What's new) </a>
-<table border='0' cellpadding='0' cellspacing='8'><tr><td valign='top' width='200'>
-<p !style="float:left; width: 200; margin: 1.5em;"> <a
-href="/zelig/zelig.jpg"><img src="/zelig/zelig2.jpg" width="200"
-height="285" /></a> <br /><small> Zelig is named after a Woody Allen
-movie about a man who had the strange ability to become the physical
-and psychological reflection of anyone he met and thus to fit
-perfectly in any situation.</small></p>
-
-</td><td valign='top'>
-
-<p>Zelig is a single, easy-to-use program that can estimate, help
-interpret, and present the results of a large range of statistical
-methods. It literally <em>is</em> "everyone's statistical software"
-because Zelig uses (<a href="http://www.r-project.org" target="gkingext">R</a>) code
-from many researchers. We also hope it will <em>become</em>
-"everyone's statistical software" for applications, and we have
-designed it so that anyone can use it or add their methods to it.
-Zelig comes with detailed, self-contained documentation that minimizes
-startup costs for Zelig and R, automates graphics and summaries for
-all models, and, with only three simple commands required, generally
-makes the power of R accessible for all users. Zelig also works well
-for teaching, and is designed so that scholars can use the same
-program with students that they use for their research.</p>
-
-<p>Zelig adds considerable infrastructure to improve the use of
-existing methods. It generalizes the program <a
-href="/stats.shtml#clarify">Clarify</a> (for Stata), which translates
-hard-to-interpret coefficients into quantities of interest; combines
-multiply imputed data sets (such as output from <a
-href="/stats.shtml#amelia">Amelia</a>) to deal with missing data;
-automates bootstrapping for all models; uses sophisticated
-nonparametric matching commands which improve parametric procedures
-(via <a href="/matchit/">MatchIt</a>); allows one-line commands to run
-analyses in all designated strata; automates the creation of
-replication data files so that you (or, if you wish, anyone else) can
-replicate the results of your analyses (hence satisfying the <a
-href="/projects/repl.shtml">replication standard</a>); makes it easy
-to evaluate counterfactuals (via <a href="/whatif">WhatIf</a>); and
-allows conditional population and superpopulation inferences. Zelig
-includes many specific methods, based on likelihood, frequentist,
-Bayesian, robust Bayesian, and nonparametric theories of
-inference.</p>
-
-<ul>
- <li><a href="/zelig/docs/index.html">Documentation</a> and <a
-href="/zelig/docs/static/install.pdf">Installation</a></li>
-
-
-<li><a href="/zelig/docs/static/install.pdf">Installation</a>, <a
-href="/zelig/docs/static/faq.pdf">Frequently Asked
-Questions</a></li>
-
-<li>Please send <strong>All</strong> questions, bugs and requests: Zelig Mailing List, <a
-href="http://lists.hmdc.harvard.edu/index.cgi?info=zelig"
-target="gkingext">[Un]Subscribe</a>, or <a
-href="http://lists.hmdc.harvard.edu/lists/zelig/"
-target="gkingext">Browse/Search Archives</a></li>
-
-<li>A paper that describes the advances underlying Zelig software:
-Kosuke Imai, Gary King, and Olivia Lau. <span class="bg">"Toward A
-Common Framework for Statistical Analysis and Development"</span>
- <em>Journal of Computational and Graphical Statistics</em>,
- Vol. 17, No. 4 (December), pp. 892-913 (<span class="bg">Abstract:</span> <a
- href="/files/abs/z-abs.shtml">HTML</a> | <span
- class="bg">Paper:</span> <a href="/files/z.pdf">PDF</a>)
-
-<li><a href="/files/zelig_shortcourse.pdf">Slides used to introduce
-Zelig</a></li>
-
-<li><a class="monitor" href="/zelig/" target="ChangeDetectionWiz"
- onClick="doChangeDetectionPopup()">Be notified of Zelig changes</a></li>
-</ul>
-
-</td>
-</tr>
-</table>
-
-
-
-<script language="Javascript">displayFooter()</script></BODY>
-</html>
diff --git a/inst/doc/install.tex b/inst/doc/install.tex
deleted file mode 100644
index 1204dfc..0000000
--- a/inst/doc/install.tex
+++ /dev/null
@@ -1,462 +0,0 @@
-\chapter{Installation}
-
-To use Zelig, you must install the statistical program R (if it is not
-already installed), the Zelig package, and some R libraries (coda,
-MCMCpack, sandwich, VGAM, and zoo).
-
-Note: In this document, {\tt >} denotes the R prompt.
-
-\subsubsection{If You Know R}
-
-We recommend that you launch R and type
-\begin{verbatim}
-> source("http://gking.harvard.edu/zelig/install.R")
-> library(Zelig)
-\end{verbatim}
-then proceed to \Sref{overview}. For Windows R, you may edit the
-{\tt Rprofile} file to load Zelig automatically at launch (after which you
-will no longer need to type {\tt library(Zelig)} at startup). Simply
-add the line:
-\begin{verbatim}
-options(defaultPackages = c(getOption("defaultPackages"), "Zelig"))
-\end{verbatim}
-
-\subsubsection{If You Are New to R}
-
-If you are new to R, we recommend that you read the following section
-on installation procedures as well as the overview of R syntax and
-usage in \Sref{a:R}.
-
-This distribution works on a variety of platforms, including Windows
-(see \Sref{ss:win}), MacOSX (see \Sref{ss:osx}), and Linux (see
-\Sref{ss:unix}). Alternatively, you may access R from your PC using a
-terminal window or an X-windows tunnel to a Linux or Unix server (see
-\Sref{ss:unix}). Most servers have R installed; if not, contact your
-network administrator.
-
-There are advantages and disadvantages to each type of installation.
-On a personal computer, R is easier to install and launch. Using R
-remotely on a server requires a bit more set-up, but does not tie up
-your local CPU, and allows you to take advantage of the server's speed.
-
-\section{Windows}\label{ss:win}
-
-\subsubsection{Installing R}
-
-Go to the Comprehensive R Archive Network website
-\url{(http://www.r-project.org)} and download the latest
-\hlink{installer for
- Windows}{http://cran.us.r-project.org/bin/windows/base/} at
-\hlink{\url{http://cran.us.r-project.org/bin/windows/base/}}{http://cran.us.r-project.org/bin/windows/base/}
-Double-click the {\tt .exe} file to launch the R installer. We recommend
-that you accept the default installation options if this your first
-installation.
-
-\subsubsection{Installing Zelig}
-
-Once R is installed, you must install the Zelig and VGAM packages. There
-are three ways to do this.
-\begin{enumerate}
-\item We recommend that you start R and then type:
-\begin{verbatim}
-> source("http://gking.harvard.edu/zelig/install.R")
-> library(Zelig)
-\end{verbatim}
-\item Alternatively, you may install each component package individually
-in R:
-\begin{verbatim}
-> install.packages("Zelig")
-> install.packages("zoo")
-> install.packages("sandwich")
-> install.packages("MCMCpack")
-> install.packages("coda")
-> install.packages("lattice")
-> install.packages("mvtnorm")
-> install.packages("VGAM")
-> install.packages("sna")
-> install.packages("systemfit")
-> install.packages("nnet")
-> install.packages("gee")
-> install.packages("mgcv")
-> library(Zelig)
-\end{verbatim}
-Zelig will load the optional libraries whenever their functions are
-needed; it is not necessary to load any package other than Zelig at startup.
-
-\item \label{manual.windows}Alternatively, you may use the drop down
- menus to install Zelig. This requires four steps.
-\begin{enumerate}
-\item \label{win.zelig} Go to \hlink{the Zelig
- website}{http://gking.harvard.edu/bin/windows/contrib/\rvers/} and
- and download the latest release of Zelig. The VGAM, MCMCpack,
-coda, zoo, and sandwich packages are available from
-\hlink{CRAN}{http://cran.r-project.org/src/contrib/PACKAGES.html}. Store these
- \texttt{.zip} files in your R program directory. For example, the
- default R program directory is {\tt C:$\backslash$Program
- Files$\backslash$R$\backslash$\rwvers$\backslash$}.\footnote{Note
- that when updating R to the latest release, the installer does not
- delete previous versions from your {\tt C:$\backslash$Program
- Files$\backslash$R$\backslash$} directory. In this example, the
- subdirectory {\tt $\backslash$\rwvers$\backslash$} stores R version
- \fullrvers. Thus, if you have a different version of R installed,
- you should change the last part of the R program directory file path
- accordingly.}
-\item Start R. From the drop-down menus, select the ``Packages'' menu
- and then the ``Install Files from Local Zip Files'' option.
-\item A window will pop up, allowing you to select one of the
- downloaded files for installation. There is no need to unzip the
- files prior to installation. Repeat and select the other downloaded
- file for installation.
-\item At the R prompt, type \texttt{library(Zelig)} to load the
- functionality described in this manual. Note that Zelig will
- automatically load the other libraries as necessary.
-\end{enumerate}
-
-\item An additional \emph{recommended but optional step} is to set up
- R to load Zelig automatically at launch. (If you skip this step,
- you must type {\tt library(Zelig)} at the beginning of every R
- session.) To automate this process, edit the {\tt Rprofile} file
- located in the R program subdirectory ({\tt C:$\backslash$Program
- Files$\backslash$R$\backslash$\rwvers$\backslash$etc$\backslash$}
- in our example). Using a text editor such as Windows notepad, add
- the following line to the {\tt Rprofile} file:
-\begin{verbatim}
-options(defaultPackages = c(getOption("defaultPackages"), "Zelig"))
-\end{verbatim}
-\end{enumerate}
-
-Zelig is distributed under the \hlink{GNU General Public License,
- Version 2}{http://www.gnu.org/licenses/gpl.txt}. After
-installation, the source code is located in your R library directory,
-which is by default {\tt C:$\backslash$Program
- Files$\backslash$R$\backslash$\rwvers$\backslash$library$\backslash$Zelig$\backslash$}.
-
-\subsubsection{Updating Zelig}
-
-There are two ways to update Zelig.
-\begin{enumerate}
-\item We recommend that you periodically update Zelig at the R prompt
- by typing:
-\begin{verbatim}
-> update.packages()
-> library(Zelig)
-\end{verbatim}
-\item Alternatively, you may use the procedure outlined in
- \Sref{win.zelig} to periodically update Zelig. Simply download the
- latest {\tt .zip} file and follow the four steps.
-\end{enumerate}
-
-\section{MacOS X}\label{ss:osx}
-
-\subsubsection{Installing R}
-
-If you are using MacOS X, you may install the latest version of R
-(\fullrvers\ at this time) from the CRAN website \hlink{
- http://cran.us.r-project.org/bin/macosx/}{http://cran.us.r-project.org/bin/macosx/}.
-At this time, Zelig is not supported for R on MacOS 8.6 through 9.x.
-
-\subsubsection{Installing Zelig}\label{osx.manual}
-
-Once R is installed, you must install the Zelig and VGAM packages.
-There are several ways to do this.
-\begin{enumerate}
-\item {\bf For RAqua}:
- \begin{enumerate}
-
- \item We recommend that you start R, and then type:
-\begin{verbatim}
-> source("http://gking.harvard.edu/zelig/install.R")
-> library(Zelig)
-\end{verbatim}
-(You may ignore the warning messages, unless they say ``Non-zero exit
-status''.)
-\item Alternatively, to avoid the warning messages, you need to install
-each package individually and specify the specific installation path:
-\begin{verbatim}
-> install.packages("Zelig", lib = "~/Library/R/library")
-> install.packages("zoo", lib = "~/Library/R/library")
-> install.packages("sandwich", lib = "~/Library/R/library")
-> install.packages("MCMCpack", lib = "~/Library/R/library")
-> install.packages("coda", lib = "~/Library/R/library")
-> install.packages("lattice", lib = "~/Library/R/library")
-> install.packages("mvtnorm", lib = "~/Library/R/library")
-> install.packages("VGAM", lib = "~/Library/R/library")
-> install.packages("sna", lib = "~/Library/R/library")
-> install.packages("systemfit", lib = "~/Library/R/library")
-> install.packages("nnet", lib = "~/Library/R/library")
-> install.packages("gee", lib = "~/Library/R/library")
-> install.packages("mgcv", lib = "~/Library/R/library")
-> library(Zelig)
-\end{verbatim}
- where \texttt{\~{}/Library/R/library} is the default local library
- directory. Zelig will load the other libraries whenever their functions
- are needed; it is not necessary to load these packages at startup.
-
- \item Alternatively, you may use the drop down menus to install
- Zelig. This requires three steps.
- \begin{enumerate}
- \item Go to \hlink{the Zelig
- website}{http://gking.harvard.edu/src/contrib/} and
- download the latest release of Zelig. The VGAM, MCMCpack,
-coda, zoo, and sandwich packages are available from
-\hlink{CRAN}{http://cran.r-project.org/src/contrib/PACKAGES.html}.
-Save these \texttt{.tar.gz} files in a convenient place.
- \item Start R. From the drop-down menus, select the ``Packages''
- menu and then the ``Install Files from Local Files'' option.
- \item A window will pop up, allowing you to select the one of the
- downloaded files for installation. There is no need to unzip
- the files prior to installation. Repeat and select the other
- downloaded file for installation.
- \end{enumerate}
-
-\end{enumerate}
-
-\item {\bf For command line R:}
- \begin{enumerate}
- \item Before installing command line R, you need to create a local R
- library directory. If you have done so already, you may skip to
- the next step. Otherwise, at the terminal prompt in your home
- directory, type:
-\begin{verbatim}
-% mkdir ~/Library/R ~/Library/R/library
-\end{verbatim}
- \item Modify your configuration file to identify {\tt
- \~{}/Library/R/library} as your R library directory. There are
- two ways of doing this:
- \begin{enumerate}
- \item Open the {\tt .Renviron} file (or create one, if you don't
- have one) and add the following line:
-\begin{verbatim}
-R_LIBS = "~/Library/R/library"
-\end{verbatim}
- \item {\it Alternatively}, you may modify your shell configuration
- file. For a Bash shell, open your {\tt .bashrc} file and add
- the following line:
-\begin{verbatim}
-export R_LIBS="$HOME/Library/R/library"
-\end{verbatim} %$
- \end{enumerate}
- \item Start R and at the prompt, type:
-\begin{verbatim}
-> source("http://gking.harvard.edu/zelig/install.R")
-> library(Zelig)
-\end{verbatim}
-(You may ignore the warning messages, unless they say ``Non-zero exit
-status''.)
-\item Alternatively, to avoid the warning messages, you need to install
-each component package separately and specify the installation path:
-\begin{verbatim}
-> install.packages("Zelig", lib = "~/Library/R/library")
-> install.packages("zoo", lib = "~/Library/R/library")
-> install.packages("sandwich", lib = "~/Library/R/library")
-> install.packages("MCMCpack", lib = "~/Library/R/library")
-> install.packages("coda", lib = "~/Library/R/library")
-> install.packages("lattice", lib = "~/Library/R/library")
-> install.packages("mvtnorm", lib = "~/Library/R/library")
-> install.packages("VGAM", lib = "~/Library/R/library")
-> install.packages("sna", lib = "~/Library/R/library")
-> install.packages("systemfit", lib = "~/Library/R/library")
-> install.packages("nnet", lib = "~/Library/R/library")
-> install.packages("gee", lib = "~/Library/R/library")
-> install.packages("mgcv", lib = "~/Library/R/library")
-> library(Zelig)
-\end{verbatim}
- Although the {\tt lib} argument is optional, we recommend that
- you set it to the default RAqua directory
- (\verb|"~/Library/R/library"|), in case you later decide to
- install the RAqua GUI (which has a different default directory).
- \end{enumerate}
-\end{enumerate}
-
-At the R prompt, type \texttt{library(Zelig)} to load the
-functionality described in this manual. Note that Zelig will
-automatically load the other packages as necessary.
-
-Zelig is distributed under the \hlink{GNU General Public License,
- Version 2}{http://www.gnu.org/licenses/gpl.txt}. After
-installation, the source code is located in your R library directory,
-{\tt \~{}/Library/R/library/Zelig/}.
-
-\subsubsection{Updating Zelig}
-
-There are two ways to update Zelig.
-\begin{enumerate}
-
-\item We recommend that you start R and, at the R prompt, type:
-\begin{verbatim}
-> update.packages()
-\end{verbatim}
-
-\item Alternatively, you may remove an old version by command by
- typing {\tt R CMD REMOVE Zelig} at the terminal prompt. Then
- download and reinstall the package using the installation
- procedures~\Sref{osx.manual} outlined above.
-\end{enumerate}
-
-\section{UNIX and Linux}\label{ss:unix}
-
-\subsubsection{Installing R}
-Type {\tt R} at the terminal prompt (which we denote as {\tt \%} in
-this section) to see if R is available. (Typing \texttt{q()} will
-enable you to quit.) If it is installed, proceed to the next section.
-If it is not installed and you are not the administrator, contact that
-individual, kindly request that they install R on the server, and
-continue to the next section. If you have administrator privileges,
-you may download the latest release at the
-\hlink{CRAN}{http://cran.r-project.org} website. Although
-installation varies according to your Linux distribution, we provide
-an example for Red Hat Linux 9.0 as a guide:
-
-\begin{enumerate}
-
-\item Log in as root.
-
-\item Download the appropriate binary file for Red Hat 9 from CRAN.
- For example, for Red Hat 9 running on the Intel 386 platform, go to
- \url{http://cran.r-project.org/bin/linux/}.
-
-\item Type the following command at the terminal prompt:\newline {\tt
- \% rpm -ivh R-\fullrvers-1.i386.rpm}
-
-\end{enumerate}
-
-\subsubsection{Installing Zelig}\label{sss:unix.library}
-
-Before installing Zelig, you need to create a local R library
-directory. If you have done so already, you can skip to
-\Sref{unix.zelig}. If not, you must do so before proceeding because
-most users do not have authorization to install programs globally.
-Suppose we want the directory to be {\tt \~{}/.R/library}. At the
-terminal prompt in your home directory, type:
-\begin{verbatim}
-% mkdir ~/.R ~/.R/library
-\end{verbatim}
-Now you are ready to install Zelig.\label{unix.zelig} There are two
-ways to proceed.
-
-\begin{enumerate}
-\item Recommended procedure:
- \begin{enumerate}
- \item Open the {\tt \~\,/.Renviron} file (or create it if it does
- not exist) and add the following line:
-\begin{verbatim}
-R_LIBS = "~/.R/library"
-\end{verbatim}
- You only need to perform this step once.
- \item Start R. At the R prompt, type:
-\begin{verbatim}
-> source("http://gking.harvard.edu/zelig/install.R")
-> library(Zelig)
-\end{verbatim}
-(You may ignore the warning messages, unless they say ``Non-zero exit
-status''.)
-\item Alternatively, you can avoid the warning messages by installing each
-component package separately and specifying the installation path:
-\begin{verbatim}
-> install.packages("Zelig", lib = "~/Library/R/library")
-> install.packages("zoo", lib = "~/Library/R/library")
-> install.packages("sandwich", lib = "~/Library/R/library")
-> install.packages("MCMCpack", lib = "~/Library/R/library")
-> install.packages("coda", lib = "~/Library/R/library")
-> install.packages("lattice", lib = "~/Library/R/library")
-> install.packages("mvtnorm", lib = "~/Library/R/library")
-> install.packages("VGAM", lib = "~/Library/R/library")
-> install.packages("sna", lib = "~/Library/R/library")
-> install.packages("systemfit", lib = "~/Library/R/library")
-> install.packages("nnet", lib = "~/Library/R/library")
-> install.packages("gee", lib = "~/Library/R/library")
-> install.packages("mgcv", lib = "~/Library/R/library")
-> library(Zelig)
-\end{verbatim}
-\item Finally, create a {\tt .Rprofile} file in your home directory, containing the line:
-\begin{verbatim}
-library(Zelig)
-\end{verbatim}
-This will load Zelig every time you start R.
- \end{enumerate}
-
-\item \label{unix.manual} Alternatively:
- \begin{enumerate}
- \item Add the local R library directory that you created above ({\tt
- \~\,/.R/library} in the example) to the environmental variable
- {\tt R\_LIBS}.
-% If you do not know how to
-% define an environmental variable, download the script {\tt
-% Rloclib} from the \hlink{Zelig download
-% directory}{http://gking.harvard.edu/R/CRAN/src/contrib/}, and
-% run it by typing at the terminal prompt:
-%\begin{verbatim}
-% bash Rloclib [path]
-%\end{verbatim}
-% where {\rm [path]} is an optional input for the local library
-% directory path. If you don't specify a path, the default value is
-% {\tt \~\,/.R/library}.
- \item Download the latest bundles for Unix from the \hlink{Zelig
- website}{http://gking.harvard.edu/src/contrib/}, and (for
- the VGAM, MCMCpack, coda, sandwich, and zoo packages) from the
- \hlink{CRAN}{http://cran.r-project.org/src/contrib/PACKAGES.html}
- website.
- \item If {\tt XX} is the current version number, at the terminal
- prompt, type:
-\begin{verbatim}
-% R CMD INSTALL Zelig_XX.tar.gz
-% R CMD INSTALL zoo_XX.tar.gz
-% R CMD INSTALL sandwich_XX.tar.gz
-% R CMD INSTALL MCMCpack_XX.tar.gz
-% R CMD INSTALL coda_XX.tar.gz
-% R CMD INSTALL lattice_XX.tar.gz
-% R CMD INSTALL mvtnorm_XX.tar.gz
-% R CMD INSTALL VGAM_XX.tar.gz
-% R CMD INSTALL sna_XX.tar.gz
-% R CMD INSTALL systemfit_XX.tar.gz
-% R CMD INSTALL nnet_XX.tar.gz
-% R CMD INSTALL gee_XX.tar.gz
-% R CMD INSTALL mgcv_XX.tar.gz
-
-% rm Zelig_XX.tar.gz zoo_XX.tar.gz sandwich_XX.tar.gz MCMCpack_XX.tar.gz coda_XX.tar.gz lattice_XX.tar.gz mvtnorm_XX.tar.gz mvtnorm_XX.tar.gz VGAM_XX.tar.gz sna_XX.tar.gz systemfit_XX.tar.gz nnet_XX.tar.gz gee_XX.tar.gz mgcv_XX.tar.gz
-\end{verbatim}
-\item Create a {\tt .Rprofile} file in your home directory, containing the line:
-\begin{verbatim}
-library(Zelig)
-\end{verbatim}
-This will load Zelig every time you start R.
- \end{enumerate}
-\end{enumerate}
-
-Zelig is distributed under the \hlink{GNU General Public License,
- Version 2}{http://www.gnu.org/licenses/gpl.txt}. After
-installation, the source code is located in your R library directory.
-If you followed the example above, this is {\tt ~/.R/library/Zelig/}.
-
-\subsubsection{Updating Zelig} \label{sss:unix.updating}
-
-There are two ways to update Zelig.
-
-\begin{enumerate}
-
-\item We recommend that you start R and, at the R prompt, type:
-\begin{verbatim}
-> update.packages()
-\end{verbatim}
-
-\item Alternatively, you may remove an old version by command by
- typing {\tt R CMD REMOVE Zelig} at the terminal prompt. Then
- download and reinstall the package using the installation
- procedure Section \ref{sss:unix.library} outlined above.
-
-\end{enumerate}
-
-\section{Version Compatability}
-
-In addition to R itself, Zelig also depends on several R packages
-maintained by other development teams. Although we make every effort
-to keep the latest version of Zelig up-to-date with the latest version
-of those packages, there may occasionally be incompatabilities. See
-\ref{table.compat} in the Appendix for a list of packages tested to be
-compatabile with a given Zelig release. You may obtain older versions
-of most packages at \url{http://www.r-project.org}.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/intro.tex b/inst/doc/intro.tex
deleted file mode 100644
index 97c23ad..0000000
--- a/inst/doc/intro.tex
+++ /dev/null
@@ -1,144 +0,0 @@
-\chapter{Introduction}
-
-\section{What Zelig and R Do}
-
-Zelig\footnote{Zelig is named after a Woody Allen movie about a man
- who had the strange ability to become the physical reflection of
- anyone he met --- Scottish, African-American, Indian, Chinese, thin,
- obese, medical doctor, Hassidic rabbi, anything --- and thus to fit
- well in any situation.} is an easy-to-use program that can estimate
-and help interpret the results of an enormous and growing range of
-statistical models. It literally \emph{is} ``everyone's statistical
-software'' because Zelig's unified framework incorporates everyone
-else's (R) code. We also hope it will \emph{become} ``everyone's
-statistical software'' for applications, and we have designed Zelig so
-that anyone can use it or add their models to it.
-
-When you are using Zelig, you are also using
-\hlink{R}{http://www.r-project.com}, a powerful statistical software
-language. You do not need to learn R separately, however, since this
-manual introduces you to R through Zelig, which simplifies
-R and reduces the amount of programming knowledge you need to
-get started. Because so many individuals contribute different
-packages to R (each with their own syntax and documentation),
-estimating a statistical model can be a frustrating experience. Users
-need to know which package contains the model, find the modeling
-command within the package, and refer to the manual page for the
-model-specific arguments. In contrast, Zelig users can skip these
-start-up costs and move directly to data analyses. Using Zelig's
-unified command syntax, you gain the convenience of a packaged
-program, without losing any of the power of R's underlying statistical
-procedures.
-
-In addition to generalizing R packages and making existing methods
-easier to use, Zelig includes infrastructure that can improve all
-existing methods and R programs. Even if you know R, using Zelig
-greatly simplifies your work. It mimics the popular
-\hlink{Clarify}{http://gking.harvard.edu/stats.shtml\#clarify} program
-for Stata (and thus the suggestions of \hlink{King, Tomz, and
- Wittenberg,
- 2000}{http://gking.harvard.edu/files/abs/making-abs.shtml})
-\nocite{KinTomWit00} by translating the raw output of existing statistical
-procedures into quantities that are of direct
-interest to researchers. Instead of trying to interpret coefficients
-parameterized for modeling convenience, Zelig makes it easy to compute
-quantities of real interest: probabilities, predicted values, expected
-values, first differences, and risk ratios, along with confidence
-intervals, standard errors, or full posterior (or sampling) densities
-for all quantities. Zelig extends Clarify by seamlessly integrating
-an option for bootstrapping into the simulation of quantities of
-interest. It also integrates a full suite of nonparametric matching
-methods as a preprocessing step to improve the performance of any
-parametric model for causal inference (see
-\hlink{MatchIt}{http://gking.harvard.edu/matchit}). For missing data,
-Zelig accepts multiply imputed datasets created by
-\hlink{Amelia}{http://gking.harvard.edu/stats.shtml\#amelia} (see
-\hlink{King, Honaker, Joseph, and Scheve,
- 2001}{http://gking.harvard.edu/files/abs/evil-abs.shtml})\nocite{KinHonJos01}
-and other programs, allowing users to analyze them as if they were a
-single, fully observed dataset. Zelig outputs replication data sets
-so that you (and if you wish, anyone else) will always be able to
-replicate the results of your analyses (see \hlink{King,
- 1995}{http://gking.harvard.edu/files/abs/replication-abs.shtml}).\nocite{King95}
-Several powerful Zelig commands also make running multiple analyses
-and recoding variables simple.
-
-Using R in combination with Zelig has several advantages over
-commercial statistical software. R and Zelig are part of the open
-source movement, which is roughly based on the principles of science.
-That is, anyone who adds functionality to open source software or
-wishes to redistribute it (legally) must provide the software
-accompanied by its source free of charge.\footnote{As specified in the
- \hlink{GNU General License v. 2}{http://www.gnu.org/copyleft}
- \url{http://www.gnu.org/copyleft}.} If you find a bug in open
-source software and post a note to the appropriate mailing list, a
-solution you can use will likely be posted quickly by one of the
-thousands of people using the program all over the world. Since you
-can see the source code, you might even be able to fix it yourself.
-In contrast, if something goes wrong with commercial software, you
-have to wait for the programmers at the company to fix it (and
-speaking with them is probably out of the question), and wait for a
-new version to be released.
-
-We find that Zelig makes students and colleagues more amenable to
-using R, since the startup costs are lower, and since the manual and
-software are relatively self-contained. This manual even includes an
-appendix devoted to the basics of advanced R programming, although you
-will not need it to run most procedures in Zelig. A large and growing
-fraction of the world's quantitative methodologists and statisticians
-are moving to R, and the base of programs available for R is quickly
-surpassing all alternatives. In addition to built-in functions, R is
-a complete programming language, which allows you to design new
-functions to suit your needs. R has the dual advantage that you do
-not need to understand how to program to use it, but if it turns out
-that you want to do something more complicated, you do not need to
-learn another program. In addition, methodologists all over the world
-add new functions all the time, so if the function you need wasn't
-there yesterday, it may be available today.
-
-\section{Getting Help}
-
-You may find documentation for Zelig on-line (and hence must be
-on-line to access it). If you are unable to connect to the Internet,
-we recommend that you print the pdf version of this document for your
-reference.
-
-If you are on-line, you may access comprehensive help files for Zelig
-commands and for each of the models. For example, load the Zelig
-library and then type at the R prompt:
-\begin{verbatim}
-> help.zelig(command) # For help with all zelig commands.
-> help.zelig(logit) # For help with the logit model.
-\end{verbatim}
-\label{Rhelp}In addition, {\tt help.zelig()} searches the manual pages
-for R in addition to the Zelig specific pages. On certain rare
-occasions, the name of the help topic in Zelig and in R are identical.
-In these cases, {\tt help.zelig()} will return the Zelig help page by
-default. If you wish to access the R help page, you should use {\tt
- help(topic)}.
-
-In addition, built-in examples with sample data and plots are
-available for each model. For example, type {\tt demo(logit)} to view
-the demo for the logit model. Commented code for each model is
-available under the examples section of each model reference page.
-
-Please direct inquiries and problems about Zelig to our listserv at
-\hlink{zelig at lists.gking.harvard.edu}{mailto:zelig at lists.gking.harvard.edu}. We
-suggest you subscribe to this mailing list while learning and using
-Zelig: go to \hlink{\url{http://lists.hmdc.harvard.edu/index.cgi?info=zelig}}{http://lists.hmdc.harvard.edu/index.cgi?info=zelig}. (You can choose to receive email
-in digest form, so that you will never receive more than one message
-per day.) You can also browse or search our
-\hlink{archive}{http://lists.hmdc.harvard.edu/lists/zelig/} of
-previous messages before posting your query.
-
-\section{How to Cite Zelig}
-
-\input{citeZelig}
-
-To refer to a particular Zelig model, please refer to the ``how to
-cite'' portion at the end of each model documentation section.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "zelig"
-%%% End:
diff --git a/inst/doc/irt1d.Rnw b/inst/doc/irt1d.Rnw
deleted file mode 100644
index 657caa4..0000000
--- a/inst/doc/irt1d.Rnw
+++ /dev/null
@@ -1,287 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/irt1d}
-\include{zinput}
-%\VignetteIndexEntry{ One Dimensional Item Response Mode}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,item response,dichotomous, Gibbs}
-%\VignettePackage{Zelig, stats}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{irt1d}: One Dimensional Item Response Model}
-
-\label{irt1d}
-
-Given several observed dependent variables and an unobserved
-explanatory variable, item response theory estimates the latent
-variable (ideal points). The model is estimated using the Markov
-Chain Monte Carlo algorithm via a Gibbs sampler and data augmentation.
-Use this model if you believe that the ideal points lie in one
-dimension, and see the $k$-dimensional item response model
-(\Sref{irtkd}) for $k$ hypothesized latent variables.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\texttt{irt1d} accepts the following argument:
-\begin{itemize}
-\item \texttt{Y1, Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
-subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and
-so on.
-\end{itemize}
-
-\subsubsection{Additional arguments}
-
-\texttt{irt1d} accepts the following additional arguments for model specification:
-
-\begin{itemize}
-\item \texttt{theta.constraints}: a list specifying possible equality
-or inequality constraints on the ability parameters $\theta$. A
-typical entry takes one of the following forms:
-
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = c}: constrains the ability parameter for the subject named
-\texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = "+"}: constrains the ability parameter for the subject named
-\texttt{varname} to be positive.
-
-\item \texttt{varname = "-"}: constrains the ability parameter for the
-subject named {\tt varname} to be negative.
-
-\end{itemize}
-\end{itemize}
-
-The model also accepts the following arguments to monitor the sampling
-scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{theta.start}: starting values for the subject abilities
-(ideal points), either a scalar or a vector with length equal to the
-number of subjects. If a scalar, that value will be the starting value
-for all subjects. The default is \texttt{NA}, which sets the starting
-values based on an eigenvalue-eigenvector decomposition of the
-agreement score matrix formed from the model response matrix
-(\texttt{cbind(Y1, Y2, ...)}).
-
-\item \texttt{alpha.start}: starting values for the difficulty
-parameters $\alpha$, either a scalar or a vector with length equal to
-the number of the items. If a scalar, the value will be the starting
-value for all $\alpha$. The default is \texttt{NA}, which sets the
-starting values based on a series of probit regressions that condition
-on \texttt{theta.start}.
-
-\item \texttt{beta.start}: starting values for the $\beta$ discrimination
-parameters, either a scalar or a vector with length equal to the
-number of the items. If a scalar, the value will be the starting value
-for all $\beta$. The default is \texttt{NA}, which sets the starting
-values based on a series of probit regressions conditioning on
-\texttt{theta.start}.
-
-\item \texttt{store.item}: defaults to {\tt TRUE}, storing the
-posterior draws of the item parameters. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
-items with no variation before fitting the model.
-
-\end{itemize}
-
-\noindent \texttt{irt1d} accepts the following additional arguments to
-specify prior parameters used in the model:
-
-\begin{itemize}
-
-\item \texttt{t0}: prior mean of the subject abilities
-(ideal points). The default is 0.
-
-\item \texttt{T0}: prior precision of the subject abilities
-(ideal points). The default is 0.
-
-\item \texttt{ab0}: prior mean of $(\alpha, \beta)$. It can be a scalar or
-a vector of length 2. If it takes a scalar value, then the prior means for
-both $\alpha$ and $\beta$ will be set to that value. The default is 0.
-
-\item \texttt{AB0}: prior precision of $(\alpha, \beta)$. It can be
-a scalar or a $2 \times 2$ matrix. If it takes a scalar value,
-then the prior precision will be \texttt{diag(AB0,2)}. The prior precision
-is assumed to be same for all the items. The default is 0.25.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCirt1d)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<Example.data>>=
- data(SupremeCourt)
-names(SupremeCourt) <- c("Rehnquist","Stevens","OConnor","Scalia",
- "Kennedy","Souter","Thomas","Ginsburg","Breyer")
-
-@
-Fitting a one-dimensional item response theory model using \texttt{irt1d}:
-<<Example.zelig>>=
- z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
- Souter, Thomas, Ginsburg, Breyer) ~ NULL,
- data = SupremeCourt, model = "irt1d",
- B0.alpha = 0.2, B0.beta = 0.2, burnin = 500, mcmc = 10000,
- thin = 20, verbose = TRUE)
-@
-
-Checking for convergence before summarizing the estimates:
-<<Example.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<Example.heidel>>=
-heidel.diag(z.out$coefficients)
-@
-<<Example.summary>>=
- summary(z.out)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
-$i=1, \ldots, n$. The choice $Y_{ij}$ is assumed to be determined by
-an unobserved utility $Z_{ij}$, which is a function of the subject $i$'s
-abilities (ideal points) $\theta_i$ and item parameters $\alpha_j$ and
-$\beta_j$ as follows:
-\begin{eqnarray*}
-Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
-\end{eqnarray*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij}) \\
-& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
-
-The error term in the unobserved utility equation is independently
-and identically distributed with
-\begin{eqnarray*}
-\epsilon_{ij} \sim \textrm{Normal}(0, 1).
-\end{eqnarray*}
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\pi_{ij}= \Phi(-\alpha_j + \beta_j' \theta_i),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-normal distribution with mean 0 and variance 1, $\theta_i$ is the
-subject ability (ideal point) parameter, and $\alpha_j$ and $\beta_j$
-are the item parameters. Both subject abilities and item parameters
-are estimated from the model, such that the model is identified by
-placing constraints on the subject ability parameters.
-
-\item The \emph{prior} for $\theta_i$ is given by
-\begin{eqnarray*}
-\theta_i &\sim& \textrm{Normal} \left( t_{0},T_{0}^{-1}\right)
-\end{eqnarray*}
-
-\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
-\begin{eqnarray*}
-(\alpha_j, \beta_j)' &\sim& \textrm{Normal}\left( ab_{0},AB_{0}^{-1}\right)
-\end{eqnarray*}
-where $ab_0$ is a 2-vector of prior means and $AB_0$ is a $2 \times 2$ prior
-precision matrix.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated subject abilities(ideal points). If
-\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and
-$\beta$ are also contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for item response models.
-
-\end{itemize}
-
-\subsection*{How to Cite}
-
-\input{cites/irt1d}
-\input{citeZelig}
-\subsection*{See also}
-The unidimensional item-response function is part of the MCMCpack
-library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The
-convergence diagnostics are part of the CODA library by Martyn
-Plummer, Nicky Best, Kate Cowles, and Karen Vines
-\citep{PluBesCowVin05}. Sample data are adapted from \cite{MarQui05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/irt1d.log b/inst/doc/irt1d.log
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@@ -1,289 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/irtkd}
-\include{zinput}
-%\VignetteIndexEntry{K-Dimensional Item Response Model}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,item response,dichotomous, Gibbs}
-%\VignettePackage{Zelig, stats}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-\section{\texttt{irtkd}: $k$-Dimensional Item Response Theory Model}
-
-\label{irtkd}
-
-Given several observed dependent variables and an unobserved
-explanatory variable, item response theory estimates the latent
-variable (ideal points). The model is estimated using the Markov
-Chain Monte Carlo algorithm, via a combination of Gibbs sampling and
-data augmentation. Use this model if you believe that the ideal
-points lie in $k$ dimensions. See the unidimensional item response
-model (\Sref{irt1d}) for a single hypothesized latent variable.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, dimensions = 1,
- model = "irtkd", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\texttt{irtkd} accepts the following arguments:
-\begin{itemize}
-\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
-subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and so on.
-
-\item \texttt{dimensions}: The number of dimensions in the latent space. The
-default is 1.
-
-\end{itemize}
-
-\subsubsection{Additional arguments}
-
-\texttt{irtkd} accepts the following additional arguments for model specification:
-\begin{itemize}
-\item \texttt{item.constraints}: a list of lists specifying possible
-simple equality or inequality constraints on the item parameters.
-A typical entry has one of the following forms:
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be equal
-to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be negative.
-\end{itemize}
-In a $k$ dimensional model, the first item parameter for item $i$ is
-the difficulty parameter $\alpha_i$, the second item parameter is the
-discrimination parameter on dimension 1, $(\beta_{i,1})$, the third
-item parameter is the discrimination parameter on dimension 2,
-$(\beta_{i,2}),\ldots$, and $(k+1)$th item parameter is the
-discrimination parameter on dimension $k$, $(\beta_{i,k})$. The item
-difficulty parameter($\alpha$) should not be constrained in general.
-\end{itemize}
-
-\noindent \texttt{irtkd} accepts the following additional arguments
-to monitor the sampling scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen. The default
-is \texttt{FALSE}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{alphabeta.start}: starting values for the item parameters
-$\alpha$ and $\beta$, either a scalar or a $(k+1) \times items$
-matrix. If it is a scalar, then that value will be the starting value
-for all the elements of \texttt{alphabeta.start}. The default is
-\texttt{NA} which sets the starting values for the unconstrained
-elements based on a series of proportional odds logistic
-regressions. The starting values for the inequality constrained
-elements are set to be either 1.0 or -1.0 depending on the nature of
-the constraints.
-
-\item \texttt{store.item}: defaults to {\tt FALSE}. If {\tt TRUE} stores the
-posterior draws of the item parameters. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{store.ability}: defaults to {\tt TRUE}, storing the
-posterior draws of the subject abilities. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
-items with no variation before fitting the model.
-\end{itemize}
-
-\noindent \texttt{irtkd} accepts the following additional arguments to
-specify prior parameters used in the model:
-
-\begin{itemize}
-
-\item \texttt{b0}: prior mean of $(\alpha, \beta)$, either as a scalar or
-a vector of compatible length. If a scalar value, then the prior means
-for both $\alpha$ and $\beta$ will be set to that value. The default
-is 0.
-
-\item \texttt{B0}: prior precision for $(\alpha, \beta)$, either a
-scalar or a $(k+1) \times items$ matrix. If a scalar value, the prior
-precision will be a blocked diagonal matrix with elements
-\texttt{diag(B0,items)}. The prior precision is assumed to be same for
-all the items. The default is 0.25.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCirtKd)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<BasicExample.data>>=
- data(SupremeCourt)
-names(SupremeCourt) <- c("Rehnquist","Stevens","OConnor","Scalia",
- "Kennedy","Souter","Thomas","Ginsburg","Breyer")
-@
-Fitting a one-dimensional item response theory model using \texttt{irtkd}:
-<<BasicExample.zelig>>=
- z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy, Souter,
- Thomas, Ginsburg, Breyer) ~ NULL, dimensions = 1,
- data = SupremeCourt, model = "irtkd", B0 = 0.25,
- burnin = 5000, mcmc = 50000,
- thin = 10, verbose = TRUE)
-@
-
-Checking for convergence before summarizing the estimates:
-<<BasicExample.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<BasicExample.heidel>>=
- heidel.diag(z.out$coefficients)
-@
-<<BasicExample.raftery>>=
- raftery.diag(z.out$coefficients)
-@
-<<BasicExample.summary>>=
-summary(z.out)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
-$i = 1, \ldots, n$. The choice $Y_{ij}$ is assumed to be
-determined by unobserved utility $Z_{ij}$, which is a function
-of subject abilities (ideal points) $\theta_i$ and item parameters
-$\alpha_j$ and $\beta_j$,
-\begin{eqnarray*}
-Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
-\end{eqnarray*}
-In the $k$-dimensional item response theory model, each subject's ability is
-represented by a $k$-vector, $\theta_i$. Each item has a difficulty
-parameter $\alpha_j$ and a $k$-dimensional discrimination parameter
-$\beta_j$. In one-dimensional item response theory model, $k = 1$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij})\\
-& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
-
-The error term in the unobserved utility equation has
-a standard normal distribution,
-\begin{eqnarray*}
-\epsilon_{ij} \sim \textrm{Normal}(0, 1).
-\end{eqnarray*}
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{ij} &=& \Phi(-\alpha_j + \beta_j \theta_i),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-normal distribution with mean 0 and variance 1, while $\theta_i$
-contains the $k$-dimensional subject abilities(ideal points), and
-$\alpha_{j}$ and $\beta_j$ are the item parameters. Both subject
-abilities and item parameters need to estimated from the model. The
-model is identified by placing constraints on the item parameters.
-
-\item The \emph{prior} for $\theta_i$ is given by
-\begin{eqnarray*}
-\theta_i &\sim& \textrm{Normal}_k(0, I_k)
-\end{eqnarray*}
-
-\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
-\begin{eqnarray*}
-(\alpha_j, \beta_j)' &\sim& \textrm{Normal}_{k+1} \left( b_{0_j}, B_{0_j}^{-1}\right)
-\end{eqnarray*}
-where $b_{0_j}$ is a $(k+1)$-vector of prior mean and $B_{0_j}$ is a $(k+1)
-\times (k+1)$ prior precision matrix which is assumed to be diagonal.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irtkd", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated subject abilities(ideal points). If
-\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and $\beta$
- are also contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for item response models.
-
-\end{itemize}
-
-\subsection*{How to Cite}
-\input{cites/irtkd}
-\input{citeZelig}
-\subsection*{See also}
-The $k$ dimensional item-response function is part of the MCMCpack
-library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The
-convergence diagnostics are part of the CODA library by Martyn
-Plummer, Nicky Best, Kate Cowles, and Karen Vines
-\citep{PluBesCowVin05}. Sample data are adapted from \cite{MarQui05}.
-
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/irtkd.log b/inst/doc/irtkd.log
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--- a/inst/doc/logit.bayes.Rnw
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@@ -1,280 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/logitBayes}
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Logistic Regression for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,bayes,dichotomous, Metropolis}
-%\VignettePackage{Zelig, MCMCpack}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-\section{\texttt{logit.bayes}: Bayesian Logistic Regression}
-
-\label{logit.bayes}
-
-Logistic regression specifies a dichotomous dependent variable as a
-function of a set of explanatory variables using a random walk
-Metropolis algorithm. For a maximum likelihood implementation, see
-\Sref{logit}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: Metropolis tuning parameter, either
-a positive scalar or a vector of length $k$, where $k$ is the number
-of coefficients. The tuning parameter should be set such that the
-acceptance rate of the Metropolis algorithm is satisfactory (typically
-between 0.20 and 0.5) before using the posterior density for
-inference. The default value is 1.1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The
-default is \texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the maximum
-likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will
-be the prior mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0, which leads to an improper prior.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(logit.bayes)} for more information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<BasicExample.data>>=
- data(turnout)
-@
-Estimating the logistic regression using \texttt{logit.bayes}:
-<<BasicExample.zelig>>=
- z.out <- zelig(vote ~ race + educate, model = "logit.bayes",
- data = turnout, verbose = TRUE)
-@
-Convergence diagnostics before summarizing the estimates:
-<<BasicExample.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<BasicExample.heidel>>=
- heidel.diag(z.out$coefficients)
-@
-<<BasicExample.raftery>>=
- raftery.diag(z.out$coefficients)
-@
-<<BasicExample.summary.zout>>=
-summary(z.out)
-@
-Setting values for the explanatory variables to their sample averages:
-<<BasicExample.setx>>=
- x.out <- setx(z.out)
-@
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-<<BasicExample.sim>>=
- s.out1 <- sim(z.out, x = x.out)
-@
-<<BasicExample.summary.sim>>=
-summary(s.out1)
-@
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in individual's probability of
-voting when education is set to be low (25th percentile) versus
-high (75th percentile) while all the other variables held at their
-default values.
-<<FirstDifferences.setx.high>>=
- x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
-@
-<<FirstDifferences.setx.low>>=
-x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
-@
-<<FirstDifferences.sim>>=
-s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-@
-<<FirstDifferences.summary>>=
-summary(s.out2)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the binary dependent variable for observation $i$ which takes
-the value of either 0 or 1.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \rm{Bernoulli}(\pi_{i})\\
-& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
-\end{eqnarray*}
-where $\pi_{i}=\Pr(Y_{i}=1)$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{i}= \frac{1}{1+\exp(-x_{i} \beta)},
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the logit model are
-simulations of the predicted probability of a success:
-\begin{eqnarray*}
-E(Y) = \pi_{i}= \frac{1}{1 + \exp(-x_{i} \beta)},
-\end{eqnarray*}
-given the posterior draws of $\beta$ from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
-distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference (\texttt{qi\$fd}) for the logit model is defined
-as
-\begin{eqnarray*}
-\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr})is defined as
-\begin{eqnarray*}
-\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "logit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values(probabilities) for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
-simulated from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-
-\subsection* {How to Cite}
-
-\input{cites/logit.bayes}
-\input{citeZelig}
-\subsection*{See also}
-Bayesian logistic regression is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
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-\SweaveOpts{results=hide, prefix.string=vigpics/gam}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Additive Model for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, logit.gam, gam}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-
-\section{{\tt logit.gam}: Generalized Additive Model for Dichotomous Dependent Variables}\label{gam.logit}
-
-This function runs a nonparametric Generalized Additive Model (GAM) for dichotomous dependent variables.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + s(x2), model = "logit.gam", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Where {\tt s()} indicates a variable to be estimated via nonparametric smooth. All variables for which {\tt s()} is not specified, are estimated via standard parametric methods.
-
-\subsubsection{Additional Inputs}
-In addition to the standard inputs, {\tt zelig()} takes the following additional options for GAM models.
-\begin{itemize}
-\item {\tt method}: Controls the fitting method to be used. Fitting methods are selected via a list environment within {\tt method=gam.method()}. See {\tt gam.method()} for details.
-\item {\tt scale}: Generalized Cross Validation (GCV) is used if {\tt scale = 0} (see the ``Model'' section for details) except for Logit models where a Un-Biased Risk Estimator (UBRE) (also see the ``Model'' section for details) is used with a scale parameter assumed to be 1. If {\tt scale} is greater than 1, it is assumed to be the scale parameter/variance and UBRE is used. If {\tt scale} is negative GCV is used.
-\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
-\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
-\item {\tt sp}: A vector of smoothing parameters for each term.
-\item {\tt \ldots}: additional options passed to the {\tt logit.gam} model. See the {\tt mgcv} library for details.
-\end{itemize}
-
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Create some count data:
-
-<<echo=FALSE, results=hide>>=
-library(Zelig)
-<<echo=FALSE, results=hide>>=
-set.seed(0)
-n<-400
-sig<-2
-x0 <- runif(n, 0, 1)
-x1 <- runif(n, 0, 1)
-x2 <- runif(n, 0, 1)
-x3 <- runif(n, 0, 1)
-f0 <- function(x) 2 * sin(pi * x)
-f1 <- function(x) exp(2 * x)
-f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
-f3 <- function(x) 0*x
-f <- f0(x0) + f1(x1) + f2(x2)
-g <- (f-5)/3
-g <- binomial()$linkinv(g)
-y <- rbinom(g,1,g)
-my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
-
-@
-\begin{Schunk}
-\begin{Sinput}
-> set.seed(0); n <- 400; sig <- 2;
-> x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
-> x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
-> g <- (f-5)/3
-> g <- binomial()$linkinv(g)
-> y <- rbinom(g,1,g)
-> my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
-\end{Sinput}
-\end{Schunk}
-
-Estimate the model, summarize the results, and plot nonlinearities:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="logit.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1,residuals=TRUE)
-@
-Note that the {\tt plot()} function can be used after model estimation and before simulation to view the nonlinear relationships in the independent variables:
-\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=6, height=6>>=
-plot(z.out,pages=1,residuals=TRUE)
-@
-\label{fig:plotgam}
-\end{figure}
-
-Set values for the explanatory variables to their default (mean/mode) values, then simulate, summarize and plot quantities of interest:
-<<echo=TRUE, results=hide, fig=FALSE>>=
- x.out <- setx(z.out)
- s.out <- sim(z.out, x = x.out)
- summary(s.out)
- plot(s.out)
-@
-\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low values (20th percentile) and high values (80th percentile) of the explanatory variable {\tt x3} while all the other variables are held at their default (mean/mode) values.
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
-x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
-
-s.out <- sim(z.out, x=x.high, x1=x.low)
-summary(s.out)
-plot(s.out)
-
-@
-%\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-@
-\item Variations in GAM model specification. Note that {\tt setx} and {\tt sim} work as shown in the above examples for any GAM model. As such, in the interest of parsimony, I will not re-specify the simulations of quantities of interest.
-
-An extra ridge penalty (useful with convergence problems):
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
- model="logit.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1,residuals=TRUE)
-
-@
-Set the smoothing parameter for the first term, estimate the rest:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
- model="logit.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1)
-
-@
-Set lower bounds on smoothing parameters:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
- model="logit.gam", data=my.data)
-summary(z.out)
-plot(z.out, pages=1)
-
-@
-A GAM with 3df regression spline term \& 2 penalized terms:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
- model="logit.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1)
-
-@
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-
-
-GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt logit.gam} a logistic link function is used. Specifically, let $Y_{i}$ be the binary dependent variable for observation $i$ which takes the value of either 0 or 1.
-\begin{itemize}
-\item The logistic distribution has \emph{stochastic component}
-\begin{eqnarray*}
-Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
-& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
-\end{eqnarray*}
-where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_{i} = \frac{1}{1 + \exp \left(-x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right)},
-\end{equation*}
-where $x_{i}$ is the vector of covariates, $\beta$ is the vector of coefficients and $f_j(Z_j)$ for $j=1, \ldots J$ is the set of smooth terms..
-\end{itemize}
-
-Generalized additive models (GAMs) are similar in many respects to generalized linear models (GLMs). Specifically, GAMs are generally fit by penalized maximum likelihood estimation and GAMs have (or can have) a parametric component identical to that of a GLM. The difference is that GAMs also include in their linear predictors a specified sum of smooth functions.
-
-In this GAM implementation, smooth functions are represented using penalized regression splines. Two techniques may be used to estimate smoothing parameters: Generalized Cross Validation (GCV),
-\begin{equation}
-n \frac{D}{(n-DF)^2},
-\end{equation}
-or an Un-Biased Risk Estimator (UBRE) (which is effectively just a rescaled AIC),
-\begin{equation}
-\frac{D}{n} + 2 s \frac{DF}{n-s},
-\end{equation}
-where $D$ is the deviance, $n$ is the number of observations, $s$ is the scale parameter, and $DF$ is the effective degrees of freedom of the model. The use of GCV or UBRE can be set by the user with the {\tt scale} command described in the ``Additional Inputs'' section and in either case, smoothing parameters are chosen to minimize the GCV or UBRE score for the model.
-
-Estimation for GAM models proceeds as follows: first, basis functions and a set (one or more) of quadratic penalty coefficient matrices are constructed for each smooth term. Second, a model matrix is is obtained for the parametric component of the GAM. These matrices are combined to produce a complete model matrix and a set of penalty matrices for the smooth terms. Iteratively Reweighted Least Squares (IRLS) is then used to estimate the model; at each iteration of the IRLS, a penalized w [...]
-
-Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
-
-
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the {\tt logit.gam} model are the same as those for the standard logistic regression.
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt logit.gam} model is the mean of simulations from the stochastic component,
-\begin{equation*}
-\pi_{i} = \frac{1}{1 + \exp \left(-x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right)},
-\end{equation*}
-
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit.gam model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
-\end{equation*}
-for $w=\{X, Z\}$.
-\end{itemize}
-
-
-
-\subsubsection{Output Values}
-
-
-
-The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "logit.gam", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IRLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt method}: the fitting method used.
-\item {\tt converged}: logical indicating weather the model converged or not.
-\item {\tt smooth}: information about the smoothed parameters.
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt data}: the input data frame.
-\item {\tt model}: the model matrix used.
-
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt p.coeff}: the coefficients of the parametric components of the model.
-\item {\tt se}: the standard errors of the entire model.
-\item {\tt p.table}: the coefficients, standard errors, and associated $t$ statistics for the parametric portion of the model.
-\item {\tt s.table}: the table of estimated degrees of freedom, estimated rank, $F$ statistics, and $p$-values for the nonparametric portion of the model.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-
-
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/logit.gam}
-\input{citeZelig}
-\subsection* {See also}
-The {\tt logit.gam} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
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@@ -1,348 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/logit_gee}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Estimating Equation for Logistic Regression}
-%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, logistic regression}
-%\VignettePackage{Zelig}
-\begin{document}
-
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{logit.gee}: Generalized Estimating Equation for Logistic Regression}
-\label{logit.gee}
-
-The GEE logit estimates the same model as the standard logistic
-regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables). Unlike in logistic
-regression, GEE logit allows for dependence within clusters, such as
-in longitudinal data, although its use is not limited to just
-panel data. The user must first specify a ``working''
-correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
-the standard errors can be obtained using a robust ``sandwich''
-estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit.gee",
- id = "X3", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\noindent where \texttt{id} is a variable which identifies the clusters. The data should be sorted by \texttt{id} and should be ordered within each cluster when appropriate.
-
-\subsubsection{Additional Inputs}
-
-\begin{itemize}
-\item \texttt{robust}: defaults to \texttt{TRUE}. If \texttt{TRUE}, consistent standard errors are estimated using a ``sandwich'' estimator.
-\end{itemize}
-Use the following arguments to specify the structure of the ``working'' correlations within clusters:
-
-\begin{itemize}
-\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
-\begin{itemize}
-\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard logistic regression. The ``working'' correlation matrix is the identity matrix.
-\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
-\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
- $${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{|t-t'|} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
- If (\texttt{corstr = "stat\_M\_dep"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. Choose this option when the correlations are assumed to be the same for observations of the same $|t-t'|$ periods apart for $|t-t'| \leq m$.
-\begin{center}
-Sample ``working'' correlation for Stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_1 & \alpha_2 & 0 & 0 \\
-\alpha_1 & 1 & \alpha_1 & \alpha_2 & 0 \\
-\alpha_2 & \alpha_1 & 1 & \alpha_1 & \alpha_2 \\
-0 & \alpha_2 & \alpha_1 & 1 & \alpha_1 \\
-0 & 0 & \alpha_2 & \alpha_1 & 1
-\end{array} \right) $
-\end{center}
-
-\item Non-stationary $m$ dependent (\texttt{corstr =
- "non\_stat\_M\_dep"}):
-$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{tt'} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
-If (\texttt{corstr = "non\_stat\_M\_dep"}), you must also specify
-\texttt{Mv} = $m$, where $m$ is the number of periods $t$ of
-dependence. This option relaxes the assumption that the
-correlations are the same for all observations of the same $|t-t'|$
-periods apart.
-\begin{center}
-Sample ``working'' correlation for Non-stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_{12} & \alpha_{13} & 0 & 0 \\
-\alpha_{12} & 1 & \alpha_{23} & \alpha_{24} & 0 \\
-\alpha_{13} & \alpha_{23} & 1 & \alpha_{34} & \alpha_{35} \\
-0 & \alpha_{24} & \alpha_{34} & 1 & \alpha_{45} \\
-0 & 0 & \alpha_{35} & \alpha_{45} & 1
-\end{array} \right) $
-\end{center}
-\item Exchangeable (\texttt{corstr = "exchangeable"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha$, $\forall t, t'$ with $t\ne t'$. Choose this option if the correlations are assumed to be the same for all observations within the cluster.
-\begin{center}
-Sample ``working'' correlation for Exchangeable\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha & \alpha & \alpha \\
-\alpha & 1 & \alpha & \alpha & \alpha \\
-\alpha & \alpha & 1 & \alpha & \alpha \\
-\alpha & \alpha & \alpha & 1 & \alpha \\
-\alpha & \alpha & \alpha & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Stationary $m$th order autoregressive (\texttt{corstr = "AR-M"}): If (\texttt{corstr = "AR-M"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. For example, the first order autoregressive model (AR-1) implies ${\rm cor}(y_{it},
- y_{it'})=\alpha^{|t-t'|}, \forall t, t'$ with $t\ne t'$. In AR-1, observation 1 and observation 2 have a correlation of $\alpha$. Observation 2 and observation 3 also have a correlation of $\alpha$. Observation 1 and observation 3 have a correlation of $\alpha^2$, which is a function of how 1 and 2 are correlated ($\alpha$) multiplied by how 2 and 3 are correlated ($\alpha$). Observation 1 and 4 have a correlation that is a function of the correlation between 1 and 2, 2 and 3, and [...]
-\begin{center}
-Sample ``working'' correlation for Stationary AR-1 ({\tt Mv}=1)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha^2 & \alpha^3 & \alpha^4 \\
-\alpha & 1 & \alpha & \alpha^2 & \alpha^3 \\
-\alpha^2 & \alpha & 1 & \alpha & \alpha^2 \\
-\alpha^3 & \alpha^2 & \alpha & 1 & \alpha \\
-\alpha^4 & \alpha^3 & \alpha^2 & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Unstructured (\texttt{corstr = "unstructured"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha_{tt'}$, $\forall t, t'$ with
- $t\ne t'$. No constraints are placed on the correlations, which are then estimated from the data.
-\end{itemize}
-\item \texttt{Mv}: defaults to 1. It specifies the number of periods of correlation and only needs to be specified when \texttt{corstr} is {\tt "stat\_M\_dep"}, {\tt "non\_stat\_M\_dep"}, or {\tt "AR-M"}.
-\item \texttt{R}: defaults to \texttt{NULL}. It specifies a user-defined correlation matrix rather than estimating it from the data. The argument is used only when \texttt{corstr} is {\tt "fixed"}. The input is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Example with Stationary 3 Dependence}
-
-Attaching the sample turnout dataset:
-<<Example.data>>=
-data(turnout)
-@
-Variable identifying clusters
-<<Example.cluster>>=
-turnout$cluster <- rep(c(1:200),10)
-@
-Sorting by cluster
-<<Example.sort>>=
-sorted.turnout <- turnout[order(turnout$cluster),]
-@
-Estimating parameter values for the logistic regression:
-<<Example.zelig>>=
-z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3)
-@
-Setting values for the explanatory variables to their default values:
-<<Example.setx>>=
-x.out1 <- setx(z.out1)
-@
-Simulating quantities of interest:
-<<Example.sim>>=
-s.out1 <- sim(z.out1, x = x.out1)
-@
-<<Example.summary.sim>>=
-summary(s.out1)
-@
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out1)
-@
-\end{center}
-
-\item {Simulating First Differences}
-
-Estimating the risk difference (and risk ratio) between low education
-(25th percentile) and high education (75th percentile) while all the
-other variables held at their default values.
-
-<<FirstDifference.setx>>=
-x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
-x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
-@
-
-<<FirstDifference.sim>>=
-s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
-@
-<<FirstDifference.summary.sim>>=
-summary(s.out2)
-@
-\begin{center}
-<<label=FirstDifferencePlot,fig=true,echo=true>>=
-plot(s.out2)
-@
-\end{center}
-
-\item Example with Fixed Correlation Structure
-
-User-defined correlation structure
-<<Example2.corr>>=
-corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
-diag(corr.mat) <- 1
-@
-Generating empirical estimates:
-<<Example2.zelig>>=
-z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat)
-@
-Viewing the regression output:
-<<Example2.summary>>=
-summary(z.out2)
-@
-\end{enumerate}
-
-\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the binary dependent variable for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
-where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
-
-\begin{itemize}
-
-\item The \emph{stochastic component} is given by the joint and marginal distributions
-\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \pi_{i})\\
-Y_{it} &\sim& g(y_{it} \mid \pi_{it})
-\end{eqnarray*}
-where $f$ and $g$ are unspecified distributions with means $\pi_{i}$ and $\pi_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-
-\item The \emph{systematic component} is the \textit{mean function}, given by:
-\begin{equation*}
-\pi_{it} = \frac{1}{1 + \exp(-x_{it} \beta)}
-\end{equation*}
-where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
-and $\beta$ is the vector of coefficients.
-
-\item The \textit{variance function} is given by:
-\begin{equation*}
-V_{it} = \pi_{it} (1-\pi_{it})
-\end{equation*}
-
-\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-
-\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item All quantities of interest are for marginal means rather than joint means.
-\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
-\item The expected values ({\tt qi\$ev}) for the GEE logit model are
- simulations of the predicted probability of a success: $$E(Y) =
- \pi_{c}= \frac{1}{1 + \exp(-x_{c} \beta)},$$ given draws of $\beta$ from
- its sampling distribution, where $x_{c}$ is a vector of values, one for
-each independent variable, chosen by the user.
-
-\item The first difference ({\tt qi\$fd}) for the GEE logit model is defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- E[Y_{it}(tr_{it}=0)] \right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the treatment
- ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_{it}(tr_{it}=0)]$,
- the counterfactual expected value of $Y_{it}$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $tr_{it}=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "logit.gee", id, data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_{it}$.
- \item {\tt linear.predictors}: the vector of $x_{it}\beta$
- \item {\tt max.id}: the size of the largest cluster.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $z$-statistics.
- \item {\tt working.correlation}: the ``working'' correlation matrix
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the expected
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection*{How To Cite}
-
-\input{cites/logit.gee}
-\input{citeZelig}
-
-\subsection*{See also}
-The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
-
-\bibliographystyle{asa}
-\bibliography{gk}
-
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-
-
-\end{document}
diff --git a/inst/doc/logit.gee.log b/inst/doc/logit.gee.log
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diff --git a/inst/doc/logit.net.Rnw b/inst/doc/logit.net.Rnw
deleted file mode 100644
index e50c295..0000000
--- a/inst/doc/logit.net.Rnw
+++ /dev/null
@@ -1,183 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/logitnet}
-\include{zinput}
-%\VignetteIndexEntry{Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,least squares,continuous, regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt logit.net}: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
-
-Use network logistic regression analysis for a dependent variable that is a binary valued proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "logit.net", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-data(friendship)
-
-
-
-@
-Estimate model:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(friends ~ advice + prestige + perpower, model = "logit.net", data = friendship)
-summary(z.out)
-
-@
-Setting values for the explanatory variables to their default values:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.out <- setx(z.out)
-
-@
-Simulating quantities of interest from the posterior distribution.
-<<echo=TRUE, results=hide, fig=FALSE>>=
-s.out <- sim(z.out, x = x.out)
-summary(s.out)
-plot(s.out)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE, echo=false,width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low personal power (25th percentile) and high personal power (75th percentile) while all the other variables are held at their default values.
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
-x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.25))
-
-s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-summary(s.out2)
-plot(s.out2)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE,echo=false, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-
-
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-The {\tt logit.net} model performs a logistic regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard logistic regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from t [...]
-
-Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
-& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
-\end{eqnarray*}
-where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
-\end{equation*}
-where $x_{i}$ is the vector of $k$ covariates for observation $i$ and $\beta$ is the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network logistic regression are the same as those for the standard logistic regression.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the {\tt logit.net} model are simulations of the predicted probability of a success:
-\begin{equation*}
-E(Y) = \pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)},
-\end{equation*}
-given draws of $\beta$ from its sampling distribution.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference ({\tt qi\$fd}) for the network logit model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
-\end{equation*}
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "logit.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike\'s Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-
- \subsection* {How to Cite}
-
-\input{cites/logit.net}
-\input{citeZelig}
-\subsection* {See also}
-The network logistic regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netgamma)}. Sample data are fictional.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
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deleted file mode 100644
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--- a/inst/doc/lognorm.Rnw
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@@ -1,287 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/lognormal}
-\include{zinput}
-%\VignetteIndexEntry{Log-Normal Regression for Duration Dependent Variables}
-%\VignetteDepends{Zelig, survival}
-%\VignetteKeyWords{model,log, normal,bounded, regression, time durarion}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt lognorm}: Log-Normal Regression for Duration
-Dependent Variables}\label{lognorm}
-
-The log-normal model describes an event's duration, the dependent
-variable, as a function of a set of explanatory variables. The
-log-normal model may take time censored dependent variables, and
-allows the hazard rate to increase and decrease.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X, model = "lognorm", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Log-normal models require that the dependent variable be in the form
-{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
-For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
-duration (lifetime, for example) of each subject, and the associated
-$c_i$ is a binary variable such that $c_i = 1$ if the duration is not
-censored ({\it e.g.}, the subject dies during the study) or $c_i = 0$
-if the duration is censored ({\it e.g.}, the subject is still alive at
-the end of the study). If $c_i$ is omitted, all Y are assumed to be
-completed; that is, time defaults to 1 for all observations.
-
-\subsubsection{Input Values}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for lognormal regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
-cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "exp", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-<<Example.data>>=
- data(coalition)
-@
-Estimate the model:
-<<Example.zelig>>=
- z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm",
- data = coalition)
-@
-View the regression output:
-<<Example.summary>>=
- summary(z.out)
-@
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-<<Example.setx>>=
- x.low <- setx(z.out, numst2 = 0)
- x.high <- setx(z.out, numst2 = 1)
-@
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt
- qi\$fd}):
-<<Example.sim>>=
- s.out <- sim(z.out, x = x.low, x1 = x.high)
-@
-<<Example.summary>>=
- summary(s.out)
-@
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=true>>=
- plot(s.out)
-@
-\end{center}
-
-\subsubsection{Model}
-
-Let $Y_i^*$ be the survival time for observation $i$ with the density
-function $f(y)$ and the corresponding distribution function
-$F(t)=\int_{0}^t f(y) dy$. This variable might be censored for some
-observations at a fixed time $y_c$ such that the fully observed
-dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c \\
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by the distribution
- of the partially observed variable, $Y^*$. For the lognormal model,
-there are two equivalent representations:
- \begin{eqnarray*}
- Y_i^* \; \sim \; \textrm{LogNormal}(\mu_i, \sigma^2) & \textrm{ or
-} & \log(Y_i^*) \; \sim \; \textrm{Normal}(\mu_i, \sigma^2)
-\end{eqnarray*}
-where the parameters $\mu_i$ and $\sigma^2$ are the mean and variance
-of the Normal distribution. (Note that the output from {\tt zelig()}
-parameterizes {\tt scale}$ = \sigma$.)
-
- In addition, survival models like the lognormal have three additional
-properties. The hazard function $h(t)$ measures the probability of not surviving
- past time $t$ given survival up to $t$. In general, the hazard
- function is equal to $f(t)/S(t)$ where the survival function $S(t) =
- 1 - \int_{0}^t f(s) ds$ represents the fraction still surviving at
- time $t$. The cumulative hazard function $H(t)$ describes the
- probability of dying before time $t$. In general, $H(t)=
-\int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the lognormal model,
-\begin{eqnarray*}
-h(t) &=& \frac{1}{\sqrt{2 \pi} \, \sigma t \, S(t)}
-\exp\left\{-\frac{1}{2 \sigma^2} (\log \lambda t)^2\right\} \\
-S(t) &=& 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \\
-H(t) &=& -\log \left\{ 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \right\}
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function for the Normal
-distribution.
-
-\item The \emph{systematic component} is described as:
-\begin{equation*}
-\mu_i = x_i \beta .
-\end{equation*}
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the lognormal model are
- simulations of the expected duration:
-\begin{equation*}
-E(Y) = \exp\left(\mu_i + \frac{1}{2}\sigma^2 \right),
-\end{equation*}
-given draws of $\beta$ and $\sigma$ from their sampling distributions.
-
-\item The predicted value is a draw from the log-normal distribution
- given simulations of the parameters $(\lambda_i, \sigma)$.
-
-\item The first difference ({\tt qi\$fd}) is
-\begin{equation*}
-\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is \begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) - E[Y_i(t_i=0)] \}, \end{equation*} where $t_i$ is a binary explanatory variable
- defining the treatment ($t_i=1$) and control ($t_i=0$) groups. When
- $Y_i(t_i=1)$ is censored rather than observed, we replace it with a
- simulation from the model given available knowledge of the censoring
- process. Variation in the simulations is due to two factors:
- uncertainty in the imputation process for censored $y_i^*$ and
- uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
- expected value of $Y_i$ for observations in the treatment group,
- under the assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
-\begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i} \sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
-\widehat{Y_i(t_i=0)} \},
-\end{equation*}
-where $t_i$ is a binary explanatory
- variable defining the treatment ($t_i=1$) and control ($t_i=0$)
- groups. When $Y_i(t_i=1)$ is censored rather than observed, we
- replace it with a simulation from the model given available
- knowledge of the censoring process. Variation in the simulations
- are due to two factors: uncertainty in the imputation process for
- censored $y_i^*$ and uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
- for observations in the treatment group, under the assumption that
- everything stays the same except that the treatment indicator is
- switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
- C) \~\, X, model = "lognorm", data)}, then you may examine the
-available information in \texttt{z.out} by using
-\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
- z.out\$coefficients}, and a default summary of information
-through \texttt{summary(z.out)}. Other elements available through
-the {\tt \$} operator are listed below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt icoef}: parameter estimates for the intercept and $\sigma$.
- \item {\tt var}: Variance-covariance matrix.
- \item {\tt loglik}: Vector containing the log-likelihood for the
- model and intercept only (respectively).
- \item {\tt linear.predictors}: the vector of
- $x_{i}\beta$.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item Most of this may be conveniently summarized using {\tt
- summary(z.out)}. From {\tt summary(z.out)}, you may
- additionally extract:
- \begin{itemize}
- \item {\tt table}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by $(\lambda_i, \sigma)$.
- \item {\tt qi\$fd}: the simulated first differences between the
- simulated expected values for {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/lognorm}
-\input{citeZelig}
-
-\subsection* {See also}
-The exponential function is part of the survival library by by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library,
-and \cite{VenRip02}.Sample data are from \cite{KinAltBur90}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
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diff --git a/inst/doc/ls.net.Rnw b/inst/doc/ls.net.Rnw
deleted file mode 100644
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--- a/inst/doc/ls.net.Rnw
+++ /dev/null
@@ -1,193 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/lsnet, eval=true}
-\include{zinput}
-%\VignetteIndexEntry{Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model, network,least squares, regression,continuous, proximity matrix}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F>>=
-pkg <- search()
-
-if(!length(grep("package:Zelig",pkg)))
-library(Zelig)
-@
-
-\section{{\tt ls.net}: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}
-\label{ls.net}
-
-Use network least squares regression analysis to estimate the
-best linear predictor when the dependent variable is a
-continuously-valued proximity matrix (a.k.a.\ sociomatrices, adjacency
-matrices, or matrix representations of directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "ls.net", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example with First Differences
-
-Load sample data and format it for social networkx analysis:
-%library sna required
-<<Examples.data>>=
- data(sna.ex)
-@
-<<Examples.library, echo=false>>=
-if(is.na(match("sna",.packages()))){
-message("Loading package sna...")
-library(sna)
-}
-@
-Estimate model:
-<<Examples.zelig>>=
- z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "ls.net", data = sna.ex)
-
-@
-
-Summarize regression results:
-<<Examples.summary>>=
- summary(z.out)
-@
-
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) for the second
-explanatory variable (Var3).
-<<Examples.setx>>=
- x.high <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.8))
- x.low <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.2))
-@
-Generate first differences for the effect of high versus low values of
-Var3 on the outcome variable.
-<<Examples.sim>>=
- try(s.out <- sim(z.out, x = x.high, x1 = x.low))
- try(summary(s.out))
-@
-\begin{center}
-<<label=ExamplesPlot, fig=true, echo=true>>=
- plot(s.out)
-@
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
-The {\tt ls.net} model performs a least squares regression of the
-sociomatrix $\mathbf{Y}$, a $m \times m$ matrix representing network
-ties, on a set of sociomatrices $\mathbf{X}$. This network regression
-model is a directly analogue to standard least squares regression
-element-wise on the appropriately vectorized matrices. Sociomatrices
-are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
-represent the sociomatrix. The vectorization which produces the $Y$
-vector from the $\mathbf{Y}$ matrix is preformed by simple
-row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
-$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
-element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
-element of $Y$ and so on. Once the input matrices are vectorized,
-standard least squares regression is performed. As such:
-\begin{itemize}
-\item The \emph{stochastic component} is described by a density with
-mean $\mu_{i}$ and the common variance $\sigma^{2}$
-\begin{equation*}
-Y_{i} \sim f(y_{i} | \mu_{i}, \sigma^{2}).
-\end{equation*}
-
-\item The \emph{systematic component} models the conditional mean as
-\begin{equation*}
-\mu_{i} = x_{i}\beta
-\end{equation*}
-where $x_{i}$ is the vector of covariates, and $\beta$ is the vector of coefficients.
-\end{itemize}
-The least squares estimator is the best linear predictor of a
-dependent variable given $x_{i}$, and minimizes the sum of squared
-errors $\sum_{i = 1}^{n} (Y_{i} - x_{i}\beta)^{2}$.
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network least squares regression
-are the same as those for the standard least squares regression.
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations from
-the stochastic component,
-\begin{equation*}
-E(Y) = x_{i}\beta,
-\end{equation*}
-given a draw of $\beta$ from its sampling distribution.
-
-\item The first difference ({\tt qi\$fd}) is:
-\begin{equation*}
-FD = E(Y | x_{1}) - E(Y | x)
-\end{equation*}
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, you run {\tt z.out <- zelig(y ~ x,
-model="ls.net", data)}, then you may examine the available information
-in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by
-using {\tt z.out\$coefficients}, and a default summary of information
-through {\tt summary(z.out)}. Other elements available through the
-{\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-\end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\begin{equation*}
-\hat{\beta} = \left( \sum_{i = 1}^{n} x'_{i}x_{i} \right)^{-1} \sum x_{i}y_{i}
-\end{equation*}
-\item {\tt sigma}: the square root of the estimate variance of the
-random error $\varepsilon$:
-\begin{equation*}
-\hat{\sigma} = \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{n - k}
-\end{equation*}
-\item {\tt r.squared}: the fraction of the variance explained by the model.
-\begin{equation*}
-R^{2} = 1 - \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{\sum (y_{i}
-- \bar{y})^{2}}
-\end{equation*}
-\item {\tt adj.r.squared}: the above $R^{2}$ statistic, penalizing for
-an increased number of explanatory variables.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences (or differences in
-expected values) for the specified values of {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/ls.net}
-\input{citeZelig}
-\subsection* {See also}
-The network least squares regression is part of the sna package by
-Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netlm)}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
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-## OR run Sweave to each file if issued from command line (not called by "make" during R build)
-## 2.remove header and footer or each tex file so that it's ready for inclusion by zelig.tex
-## 3.convert all Rd files to tex file so that they are ready for inclusion by zelig.tex
-##
-
-STATICTEXFILES="acknowledgments.tex robjects.tex programmingstatements.tex graphs.tex matchitref.tex describemymodel.tex install.tex intro.tex Zcommands.tex addingModels.tex newModels.tex syntax.tex faq.tex whatsnew.tex"
-
-## get name of the models and save them in a file
-## for now exclude models which are not complete
-##
-## echo "library(Zelig); models<-setdiff(zeligListModels(),c('aov')); write(models,file='models.txt');" | R --vanilla --slave
-
-## at this point, Sweave should have been run and produced tex files
-for f in `cat models.txt`
- do
- #if this script is called from make, then Rbuild would have
- # run vignettes, otherwise this script should run them.
- # to distinguish this to cases we test the number of params
- # 0 - when called from command line
- # 1 - when called from make
- #
- if [ $# -eq 0 ]
- then
- echo "Sweave(\"$f.Rnw\")" | R --slave
- echo "sweaving $f"
- fi
-
- ## create pdfs for all the tex file produced by sweave
- echo "pdflatexing $f"
- pdflatex $f
- bibtex $f
- pdflatex $f
- pdflatex $f
-
- ## remove header and footer from the tex file so that they are ready
- ## for inclusion in big zelig manual
- perl -i -ne 'print unless /^\\include{zinput}$/ .. /^\\begin{document}$/' $f.tex
- perl -i -p -e 's#\\end{document}# #i' $f.tex
- ## remove 'nobibliography*' line at the beginning
- perl -i -p -e 's#\\nobibliography\*# #i' $f.tex
- ## remove bibstyle and bib database at the end
- perl -i -p -e 's#\\bibliographystyle{asa}# #i' $f.tex
- perl -i -p -e 's#\\bibliography{gk,gkpubs}# #i' $f.tex
- perl -i -p -e 's#\\bibliography{gk}# #i' $f.tex
-done
-
-## create pdf file for a007.Rnw, just to pass the local R CMD check
-pdflatex a007.tex
-
-## run R CMD Rdconv to rd files
-## create commandsRd folder if does not exists
-
-if [ ! -d commandsRd ]
-then
- mkdir commandsRd
-fi
-
-cp zinput.tex commandsRd
-cp Rd.sty commandsRd
-cp upquote.sty commandsRd
-
-Rdfiles=`ls ../../man/*.Rd`
-for rd in ${Rdfiles}
- do
- echo $rd
- newname=`basename ${rd} .Rd`
- echo $newname
- R CMD Rdconv -t=latex ${rd} -o commandsRd/${newname}.tex
-
- ### perl -i -pe 's#HeaderA{#section{{\\tt #i' commandsRd/${newname}.tex
- perl -i -pe 's#HeaderA{(.*)}{(.*)}{(.*)}#section{{\\tt \1}: \2}\\label{ss:\3}#i' commandsRd/${newname}.tex
-
-done
-
-
-## create the big Zelig manual
-pdflatex zelig
-bibtex zelig
-pdflatex zelig
-pdflatex zelig
-pdflatex zelig
-
-
-## time to create pdfs for commands in commandsRd (for garys website help)
-cd commandsRd
-for rd in ${Rdfiles}
-do
- newname=`basename ${rd} .Rd`
- ## add header
- perl -i -pe 'print q{\\include{zinput} \\begin{document}} if $. == 1; close ARGV if eof' ${newname}.tex
- ## add footer
- echo "\\end{document}" >> ${newname}.tex
- ## create pdfs
- pdflatex ${newname}
-done
-cd ..
-
-
-
-# some static tex to pdf
-# put them in a folder first
-
-
-if [ ! -d static ]
-then
- mkdir static
-fi
-
-
-
-for stfile in $STATICTEXFILES
-do
- cp $stfile static
-done
-
-cp zinput.tex static
-cp Rd.sty static
-cp upquote.sty static
-cp -r figs static
-
-cd static
-for stfile in ${STATICTEXFILES}
-do
-
- ## add header
- perl -i -pe 'print q{\\include{zinput} \\begin{document}} if $. == 1; close ARGV if eof' ${stfile}
-
- ## replace \chapter with \title ## it was not a good idea
- ### perl -i -pe 's#\\chapter#\\title#i' ${stfile}
-
- ## add footer
- echo "\\end{document}" >> ${stfile}
- ## create pdfs
- pdflatex ${stfile}
-done
-
-cd ..
-
-## generate html help for models and the full html file
-
-###
-###if [ -f "zeligmodels.html" ]
-###then
-### rm "zeligmodels.html"
-###fi
-
-###echo "<ul id=models>" >> zeligmodels.html
-###for f in `cat models.txt`
-### do
-###echo "<li><a href=$f.pdf>$f</a></li>" >> zeligmodels.html
-###done
-
-###echo "</ul>" >> zeligmodels.html
-
-cat zeligheader.html zeligmodels.html zeligfooter.html > zelig.html
-
-
-
-
-## some other pdf files
-
-
-
-## remove the files with the name of the models
-#rm models.txt
-
-## do some cleanup
-rm -f *.aux *.toc *.out *.blg *.bbl #*.log
-
-## clean commandsRd directory
-rm -f commandsRd/*.aux commandsRd/*.out commandsRd/*.log commandsRd/*.tex
-## clean statis folder
-rm -f static/*.aux static/*.out static/*.log static/*.tex static/Rd.sty static/zinput.*
-
-
-##rm zeligmodels.html
-for f in `cat models.txt`
-do
- rm -f $f.tex
-done
-
-## remove cites dir (created by a007.Rnw
-rm -rf cites
-
-###rm models.txt
-
-#### here is how u delete the \end{document}
-#### perl -i.old -p -e 's#\\end{document}# #i' try
-
-#### delete first 10 lines
-#### perl -i.old -ne 'print unless 1 .. 10' foo.txt
-
-#### \SweaveOpts{result=hide}
-#### perl -i.old -p -e 's#\\include{zinput}#\\SweaveOpts{results=hide}\n\\include{zinput}#i' *.Rnw
-
-### change dependencies line
-### perl -i.old -p -e 's#See Package dependencies#Zelig#i' *.Rnw
-
-#### thats how u remove the header from tex files
-#### perl -i.old -ne 'print unless /^\\include{zinput}$/ .. /^\\begin{document}$/' try
-
-
-### here is how to add some tex at the beginning of each file
-
-### perl -i -ple 'print q{TEXTTOBEADDED} if $. == 1; close ARGV if eof' *.tex
diff --git a/inst/doc/matchitref.tex b/inst/doc/matchitref.tex
deleted file mode 100644
index bed2500..0000000
--- a/inst/doc/matchitref.tex
+++ /dev/null
@@ -1,390 +0,0 @@
-\subsubsection{Syntax}
-\begin{verbatim}
-> m.out <- matchit(formula, data, method = "nearest", verbose = FALSE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\paragraph{Arguments for All Matching Methods}
-
-\begin{itemize}
-
-\item \texttt{formula}: formula used to calculate the distance measure
- for matching. It takes the usual syntax of R formulas, {\tt treat
- \~\ x1 + x2}, where {\tt treat} is a binary treatment indicator,
- and {\tt x1} and {\tt x2} are the pre-treatment covariates. Both the
- treatment indicator and pre-treatment covariates must be contained
- in the same data frame, which is specified as {\tt data} (see
- below). All of the usual R syntax for formulas work here. For
- example, {\tt x1:x2} represents the first order interaction term
- between {\tt x1} and {\tt x2}, and {\tt I(x1 \^\ 2)} represents the
- square term of {\tt x1}. See {\tt help(formula)} for details.
-
-\item \texttt{data}: the data frame containing the variables called in
- {\tt formula}.
-
-\item \texttt{method}: the matching method (default =
- \texttt{"nearest"}, nearest neighbor matching). Currently,
- \texttt{"exact"} (exact matching), \texttt{"full"} (full matching),
- \texttt{"nearest"} (nearest neighbor matching), \texttt{"optimal"}
- (optimal matching), \texttt{"subclass"} (subclassification), and
- \texttt{"genetic"} (genetic matching) are available. Note that
- within each of these matching methods, \MatchIt\ offers a variety of
- options. See below for more details.
-
-\item \texttt{verbose}: a logical value indicating whether to print
- the status of the matching algorithm (default = \texttt{FALSE}).
-\end{itemize}
-
-
-\paragraph{Additional Arguments for Specification of
- Distance Measures}
-\label{subsubsec:inputs-all}
-
-The following arguments specify distance measures that are used for
-matching methods. These arguments apply to all matching methods {\it
- except exact matching}.
-
-\begin{itemize}
-
-\item \texttt{distance}: the method used to estimate the distance
- measure (default = {\tt "logit"}, logistic regression) or a
- numerical vector of user's own distance measure. Before using any
- of these techniques, it is best to understand the theoretical
- groundings of these techniques and to evaluate the results. Most of
- these methods (such as logistic or probit regression) estimate the
- propensity score, defined as the probability of receiving treatment,
- conditional on the covariates. Available methods include:
- \begin{itemize}
- \item {\tt "mahalanobis"}: the Mahalanobis distance measure.
- \item binomial generalized linear models with one of the following
- link functions:
- \begin{itemize}
- \item \texttt{"logit"}: logistic link
- \item {\tt "linear.logit"}: logistic link with linear propensity
- score)\footnote{The linear propensity scores are obtained by
- transforming back onto a linear scale.}
- \item \texttt{"probit"}: probit link
- \item {\tt "linear.probit"}: probit link with linear propensity
- score
- \item {\tt "cloglog"}: complementary log-log link
- \item {\tt "linear.cloglog"}: complementary log-log link with linear
- propensity score
- \item {\tt "log"}: log link
- \item {\tt "linear.log"}: log link with linear propensity score
- \item {\tt "cauchit"} Cauchy CDF link
- \item {\tt "linear.cauchit"} Cauchy CDF link with linear propensity
- score
- \end{itemize}
- \item Choose one of the following generalized additive models (see
- {\tt help(gam)} for more options).
- \begin{itemize}
- \item \texttt{"GAMlogit"}: logistic link
- \item {\tt "GAMlinear.logit"}: logistic link with linear propensity
- score
- \item \texttt{"GAMprobit"}: probit link
- \item {\tt "GAMlinear.probit"}: probit link with linear propensity
- score
- \item {\tt "GAMcloglog"}: complementary log-log link
- \item {\tt "GAMlinear.cloglog"}: complementary log-log link with
- linear propensity score
- \item {\tt "GAMlog"}: log link
- \item {\tt "GAMlinear.log"}: log link with linear propensity score,
- \item {\tt "GAMcauchit"}: Cauchy CDF link
- \item {\tt "GAMlinear.cauchit"}: Cauchy CDF link with linear
- propensity score
-\end{itemize}
-\item \texttt{"nnet"}: neural network model. See {\tt help(nnet)} for
- more options.
-\item \texttt{"rpart"}: classification trees. See {\tt help(rpart)}
- for more options.
- \end{itemize}
-
-\item \texttt{distance.options}: optional arguments for estimating the
- distance measure. The input to this argument should be a list. For
- example, if the distance measure is estimated with a logistic
- regression, users can increase the maximum IWLS iterations by
- \texttt{distance.options = list(maxit = 5000)}. Find additional
- options for general linear models using {\tt help(glm)} or {\tt
- help(family)}, for general additive models using {\tt help(gam)},
- for neutral network models {\tt help(nnet)}, and for classification
- trees {\tt help(rpart)}.
-
-\item \texttt{discard}: specifies whether to discard units that fall
- outside some measure of support of the distance measure (default =
- \texttt{"none"}, discard no units). Discarding units may change the
- quantity of interest being estimated. Enter a logical vector
- indicating which unit should be discarded or choose from the
- following options:
- \begin{itemize}
- \item \texttt{"none"}: no units will be discarded before matching.
- Use this option when the units to be matched are substantially
- similar, such as in the case of matching treatment and control
- units from a field experiment that was close to (but not fully)
- randomized (e.g., \citealt{Imai05}), when caliper matching will
- restrict the donor pool, or when you do not wish to change the
- quantity of interest and the parametric methods to be used
- post-matching can be trusted to extrapolate.
- \item \texttt{"hull.both"}: all units that are not within the convex
- hull will be discarded. We recommend that this option be used
- with observational data sets.
- \item \texttt{"both"}: all units (treated and control) that are
- outside the support of the distance measure will be discarded.
- \item \texttt{"hull.control"}: only control units that are not
- within the convex hull of the treated units will be discarded.
- \item \texttt{"control"}: only control units outside the support of
- the distance measure of the treated units will be discarded. Use
- this option when the average treatment effect on the treated is of
- most interest and when you are unwilling to discard
- non-overlapping treatment units (which would change the quantity
- of interest).
- \item \texttt{"hull.treat"}: only treated units that are not within
- the convex hull of the control units will be discarded.
- \item \texttt{"treat"}: only treated units outside the support of
- the distance measure of the control units will be discarded. Use
- this option when the average treatment effect on the control units
- is of most interest and when unwilling to discard control units.
- \end{itemize}
-
-\item \texttt{reestimate}: If {\tt FALSE} (default), the model for the
- distance measure will not be re-estimated after units are discarded.
- The input must be a logical value. Re-estimation may be desirable
- for efficiency reasons, especially if many units were discarded and
- so the post-discard samples are quite different from the original
- samples.
-
-\end{itemize}
-
-\paragraph{Additional Arguments for Subclassification}
-\label{subsubsec:inputs-subclass}
-
-\begin{itemize}
-\item \texttt{sub.by}: criteria for subclassification. Choose from:
- \texttt{"treat"} (default), the number of treatment units;
- \texttt{"control"}, the number of control units; or \texttt{"all"},
- the total number of units.
-\item \texttt{subclass}: either a scalar specifying the number of
- subclasses, or a vector of probabilities bounded between 0 and 1,
- which create quantiles of the distance measure using the units in
- the group specified by \texttt{sub.by} (default = \texttt{subclass =
- 6}).
-\end{itemize}
-
-\paragraph{Additional Arguments for Nearest Neighbor Matching}
-\label{subsubsec:inputs-nearest}
-
-\begin{itemize}
-\item \texttt{m.order}: the order in which to match treatment units
- with control units.
- \begin{itemize}
- \item {\tt "largest"} (default): matches from the largest value of
- the distance measure to the smallest.
- \item {\tt "smallest"}: matches from the smallest value of the
- distance measure to the largest.
- \item {\tt "random"}: matches in random order.
- \end{itemize}
-\item \texttt{replace}: logical value indicating whether each control
- unit can be matched to more than one treated unit (default = {\tt
- replace = FALSE}, each control unit is used at most once -- i.e.,
- sampling without replacement). For matching with replacement, use
- \texttt{replace = TRUE}.
-\item \texttt{ratio}: the number of control units to match to each
- treated unit (default = {\tt 1}). If matching is done without
- replacement and there are fewer control units than {\tt ratio} times
- the number of eligible treated units (i.e., there are not enough
- control units for the specified method), then the higher ratios will
- have \texttt{NA} in place of the matching unit number in
- \texttt{match.matrix}.
-\item \texttt{exact}: variables on which to perform exact matching
- within the nearest neighbor matching (default = {\tt NULL}, no exact
- matching). If \texttt{exact} is specified, only matches that
- exactly match on the covariates in \texttt{exact} will be allowed.
- Within the matches that match on the variables in \texttt{exact},
- the match with the closest distance measure will be chosen.
- \texttt{exact} should be entered as a vector of variable names
- (e.g., \texttt{exact = c("X1", "X2")}).
-\item \texttt{caliper}: the number of standard deviations of the
- distance measure within which to draw control units (default = {\tt
- 0}, no caliper matching). If a caliper is specified, a control
- unit within the caliper for a treated unit is randomly selected as
- the match for that treated unit. If \texttt{caliper != 0}, there
- are two additional options:
- \begin{itemize}
- \item \texttt{calclosest}: whether to take the nearest available
- match if no matches are available within the \texttt{caliper}
- (default = {\tt FALSE}).
- \item \texttt{mahvars}: variables on which to perform
- Mahalanobis-metric matching within each caliper (default = {\tt
- NULL}). Variables should be entered as a vector of variable
- names (e.g., \texttt{mahvars = c("X1", "X2")}). If
- \texttt{mahvars} is specified without \texttt{caliper}, the
- caliper is set to 0.25.
- \end{itemize}
-\item \texttt{subclass} and \texttt{sub.by}: See the options for
- subclassification for more details on these options. If a
- \texttt{subclass} is specified within \texttt{method = "nearest"},
- the matched units will be placed into subclasses after the nearest
- neighbor matching is completed.
-\end{itemize}
-
-\paragraph{Additional Arguments for Optimal Matching}
-\label{subsubsec:inputs-optimal}
-
-\begin{itemize}
-\item {\tt ratio}: the number of control units to be matched to each
- treatment unit (default = {\tt 1}).
-\item {\tt ...}: additional inputs that can be passed to the {\tt
- fullmatch()} function in the {\tt optmatch} package. See {\tt
- help(fullmatch)} or
- \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html}
- for details.
-\end{itemize}
-
-\paragraph{Additional Arguments for Full Matching}
-\label{subsubsec:inputs-full}
-
-\begin{itemize}
-\item {\tt ...}: additional inputs that can be passed to the {\tt
- fullmatch()} function in the {\tt optmatch} package. See {\tt
- help(fullmatch)} or
- \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html} for details.
-\end{itemize}
-
-\paragraph{Additional Arguments for Genetic Matching}
-\label{subsubsec:inputs-genetic}
-
-The available options are listed below.
-\begin{itemize}
-\item {\tt ratio}: the number of control units to be matched to each
- treatment unit (default = {\tt 1}).
-\item {\tt ...}: additional minor inputs that can be passed to the
- {\tt GenMatch()} function in the {\tt Matching} package. See {\tt
- help(GenMatch)} or
- \hlink{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}
- for details.
-\end{itemize}
-
-\subsubsection{Output Values}
-\label{sec:outputs}
-
-Regardless of the type of matching performed, the \texttt{matchit}
-output object contains the following elements:\footnote{When
- inapplicable or unnecessary, these elements may equal {\tt NULL}.
- For example, when exact matching, {\tt match.matrix = NULL}.}
-
-\begin{itemize}
-\item \texttt{call}: the original {\tt matchit()} call.
-
-\item \texttt{formula}: the formula used to specify the model for
- estimating the distance measure.
-
-\item \texttt{model}: the output of the model used to estimate
- the distance measure. \texttt{summary(m.out\$model)} will give the
- summary of the model where \texttt{m.out} is the output object from
- \texttt{matchit()}.
-
-\item \texttt{match.matrix}: an $n_1 \times$ \texttt{ratio} matrix
- where:
- \begin{itemize}
- \item the row names represent the names of the treatment units
- (which match the row names of the data frame specified in
- \texttt{data}).
- \item each column stores the name(s) of the control unit(s) matched
- to the treatment unit of that row. For example, when the
- \texttt{ratio} input for nearest neighbor or optimal matching is
- specified as 3, the three columns of \texttt{match.matrix}
- represent the three control units matched to one treatment unit).
- \item \texttt{NA} indicates that the treatment unit was not matched.
- \end{itemize}
-
-\item \texttt{discarded}: a vector of length $n$ that displays whether
- the units were ineligible for matching due to common support
- restrictions. It equals \texttt{TRUE} if unit $i$ was discarded,
- and it is set to \texttt{FALSE} otherwise.
-
-\item \texttt{distance}: a vector of length $n$ with the estimated
- distance measure for each unit.
-
-\item \texttt{weights}: a vector of length $n$ with the weights
- assigned to each unit in the matching process. Unmatched units have
- weights equal to $0$. Matched treated units have weight $1$. Each
- matched control unit has weight proportional to the number of
- treatment units to which it was matched, and the sum of the control
- weights is equal to the number of uniquely matched control units.
-
-\item \texttt{subclass}: the subclass index in an ordinal scale from 1
- to the total number of subclasses as specified in \texttt{subclass}
- (or the total number of subclasses from full or exact matching).
- Unmatched units have \texttt{NA}.
-
-\item \texttt{q.cut}: the subclass cut-points that classify the
- distance measure.
-
-\item \texttt{treat}: the treatment indicator from \texttt{data} (the
- left-hand side of \texttt{formula}).
-
-\item \texttt{X}: the covariates used for estimating the distance
- measure (the right-hand side of \texttt{formula}). When applicable,
- \texttt{X} is augmented by covariates contained in \texttt{mahvars}
- and \texttt{exact}.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-If you use \MatchIt, please cite
-\begin{verse}
-\bibentry{HoImaKin07}
-and
-\bibentry{HoImaKin05b}
-\end{verse}
-
-The {\tt convex.hull} discard option is implemented via the {\tt
- WhatIf} package. If you use this option, please cite
-\begin{verse}
-\bibentry{StoKinZen06}
-and either
-\bibentry{KinZen06a}
-or
-\bibentry{KinZen06b}
-\end{verse}
-
-Generalized linear distance measures are implemented via the {\tt
-stats} package. If you use this distance measure, please cite
-\begin{verse}
-\bibentry{VenRip02}
-\end{verse}
-
-Generalized additive distance measures are implemented via the {\tt
- mcgv} package. If you use this distance measure, please cite
-\begin{verse}
-\bibentry{HasTib90}
-\end{verse}
-
-The neural network distance measure is implemented via the {\tt nnet}
-package. If you use this distance measure, please cite
-\begin{verse}
-\bibentry{Ripley96}
-\end{verse}
-
-The classification trees distance measure is implemented via the {\tt
- rpart} package. If you use this distance measure, please cite
-\begin{verse}
-\bibentry{BreFriOls84}
-\end{verse}
-
-Full and optimal matching are implemented via the {\tt optmatch}
-package by Ben Hansen. If you use either of these methods, please cite
-\begin{verse}
-\bibentry{Hansen04}
-\end{verse}
-
-Genetic matching is implemented via the {\tt Matching} package by
-Jasjeet Sekhon. If you use this method, please cite
-\begin{verse}
-\bibentry{DiaSek05}
-\end{verse}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "matchit"
-%%% End:
diff --git a/inst/doc/mlogit.Rnw b/inst/doc/mlogit.Rnw
deleted file mode 100644
index c310a0c..0000000
--- a/inst/doc/mlogit.Rnw
+++ /dev/null
@@ -1,276 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/mlogit}
-\include{zinput}
-%\VignetteIndexEntry{Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}
-%\VignetteDepends{Zelig, VGAM}
-%\VignetteKeyWords{model, bayes,multinomial, logistic regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-\section{{\tt mlogit}: Multinomial Logistic Regression for
-Dependent Variables with Unordered Categorical Values}\label{mlogit}
-Use the multinomial logit distribution to model unordered categorical
-variables. The dependent variable may be in the format of either
-character strings or integer values. See
-%\Sref{mlogit.bayes}
-for a Bayesian version of this model.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "mlogit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Input Values}
-
-If the user wishes to use the same formula across all levels, then
-\verb|formula <- as.factor(Y) ~ X1 + X2| may be used.
-If the user wants to use different formula for each level then the following
-syntax should be used:
-\begin{verbatim}
-formulae <- list(list(id(Y, "apples")~ X1,
- id(Y, "bananas")~ X1 + X2)
-\end{verbatim}
-where Y above is supposed to be a factor variable with levels {apples,bananas,oranges}.
-By default, oranges is the last level and omitted. (You cannot
-specify a different base level at this time.)
-For $J$ equations, there must be $J + 1$ levels.
-
-\subsubsection{Examples} \label{ternary}
-
-\begin{enumerate}
-
-\item {The same formula for each level}
-
-Load the sample data:
-<<Examples.data>>=
- data(mexico)
-@
-Estimate the empirical model:
-<<Examples.zelig>>=
- z.out1 <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
- model = "mlogit", data = mexico)
-@
-Set the explanatory variables to their default values, with {\tt pristr}
-(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
-equal to 3 (strong) in the alternative values:
-<<Examples.setx>>=
- x.weak <- setx(z.out1, pristr = 1)
- x.strong <- setx(z.out1, pristr = 3)
-@
-Generate simulated predicted probabilities {\tt qi\$ev} and differences in
-the predicted probabilities {\tt qi\$fd}:
-<<Examples.sim>>=
- s.out1 <- sim(z.out1, x = x.strong, x1 = x.weak)
-
-@
-<<Examples.summary>>=
-summary(s.out1)
-@
-Generate simulated predicted probabilities {\tt qi\$ev} for the
-alternative values:
-<<Examples.weak>>=
- ev.weak <- s.out1$qi$ev + s.out1$qi$fd
-
-@
-Plot the differences in the predicted probabilities.
-<<Examples.library>>=
- library(vcd)
-@
-%%%%does not work but the demo gives same error message
-%%%funlegend=ternarypoints,legendargs=parpoint
-\begin{center}
-<<label=TernaryPlot,fig=true, echo=true>>=
-ternaryplot(x=s.out1$qi$ev, pch = ".", col = "blue",main = "1988 Mexican Presidential Election")
-
-###ternarypoints(ev.weak, pch = ".", col = "red")
-@
-\end{center}
-\item {Different formula for each level}
-
-Estimate the empirical model:
-<<Levels.zelig>>=
- z.out2 <- zelig(list(id(vote88, "1") ~ pristr + othcok,
- id(vote88, "2") ~ othsocok),
- model = "mlogit", data = mexico)
-@
-Set the explanatory variables to their default values, with {\tt pristr}
-(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
-equal to 3 (strong) in the alternative values:
-<<Levels.setx>>=
- x.weak <- setx(z.out2, pristr = 1)
- x.strong <- setx(z.out2, pristr = 3)
-@
-Generate simulated predicted probabilities {\tt qi\$ev} and differences in
-the predicted probabilities {\tt qi\$fd}:
-<<Levels.sim>>=
- s.out1 <- sim(z.out2, x = x.strong, x1 = x.weak)
-@
-<<Levels.summary>>=
- summary(s.out1)
-@
-Generate simulated predicted probabilities {\tt qi\$ev} for the
-alternative values:
-<<Levels.weak>>=
- ev.weak <- s.out1$qi$ev + s.out1$qi$fd
-@
-Using the vcd package, plot the differences in the predicted probabilities.
-\begin{center}
-<<label=LevelsTernaryPlot,fig=true,echo=true>>=
-ternaryplot(s.out1$qi$ev, pch = ".", col = "blue", main = "1988 Mexican Presidential Election")
-###ternarypoints(ev.weak, pch = ".", col = "red")
-@
-\end{center}
-
-
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i$ be the unordered categorical dependent variable that takes
-one of the values from 1 to $J$, where $J$ is the total number of
-categories.
-
-\begin{itemize}
-\item The stochastic component is given by
- \begin{equation*}
- Y_i \; \sim \; \textrm{Multinomial}(y_{i} \mid \pi_{ij}),
- \end{equation*}
- where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1,\dots,J$.
-
-\item The systemic component is given by:
- \begin{equation*}
- \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
- \exp(x_{i}\beta_{k})},
- \end{equation*}
- where $x_i$ is the vector of explanatory variables for observation
- $i$, and $\beta_j$ is the vector of coefficients for category $j$.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the predicted probability
- for each category:
-\begin{equation*}
- E(Y) \; = \; \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
- \exp(x_{i}\beta_{k})}.
-\end{equation*}
-
-\item The predicted value ({\tt qi\$pr}) is a draw from the
- multinomial distribution defined by the predicted probabilities.
-
-\item The first difference in predicted
- probabilities ({\tt qi\$fd}), for each category is given by:
-\begin{equation*}
-\textrm{FD}_j = \Pr(Y=j \mid x_1) - \Pr(Y=j \mid x) \quad {\rm for}
-\quad j=1,\dots,J.
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "mlogit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
- predictors $x_i \beta_j$.
- \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times J$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
-\end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models,
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection*{How to Cite}
-\input{cites/mlogit}
-\input{citeZelig}
-\subsection*{See also}
-The multinomial logit function is part of the VGAM package by Thomas Yee \citep{YeeHas03}. In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM library. Additional documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.Sample data are from \cite{KinTomWit00}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/mlogit.bayes.Rnw b/inst/doc/mlogit.bayes.Rnw
deleted file mode 100644
index ea999c2..0000000
--- a/inst/doc/mlogit.bayes.Rnw
+++ /dev/null
@@ -1,312 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/mlogitBayes}
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, bayes,multinomial, logistic regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{mlogit.bayes}: Bayesian Multinomial Logistic
-Regression}
-
-\label{mlogit.bayes}
-
-Use Bayesian multinomial logistic regression to model unordered
-categorical variables. The dependent variable may be in the format of
-either character strings or integer values. The model is estimated
-via a random walk Metropolis algorithm or a slice sampler. See
-\Sref{mlogit} for the maximum-likelihood estimation of this model.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "mlogit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-{\tt zelig()} accepts the following arguments for {\tt mlogit.bayes}:
-\begin{itemize}
-\item \texttt{baseline}: either a character string or numeric value
-(equal to one of the observed values in the dependent variable)
-specifying a baseline category. The default value is \texttt{NA}
-which sets the baseline to the first alphabetical or numerical unique
-value of the dependent variable.
-\end{itemize}
-
-The model accepts the following additional arguments to monitor the
-Markov chains:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{mcmc.method}: either {\tt "MH"} or {\tt "slice"}, specifying whether
-to use Metropolis Algorithm or slice sampler. The default value is
-\texttt{"MH"}.
-
-\item \texttt{tune}: tuning parameter for the Metropolis-Hasting step,
-either a scalar or a numeric vector (for $k$ coefficients, enter a $k$
-vector). The tuning parameter should be set such that the acceptance
-rate is satisfactory (between 0.2 and 0.5). The default value is 1.1.
-
-\item \texttt{verbose}: defaults to \texttt{FALSE}.
-If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
-printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or a vector (for $k$ coefficients, enter a $k$
-vector). The default is \texttt{NA} where the maximum likelihood
-estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following arguments to specify the priors for the model:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a scalar or
-vector. If a scalar, that value will be the prior mean for all the
-coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix with the dimensions equal to the number of
-coefficients or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0 which
-leads to an improper prior.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCmnl)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<BasicExample.data>>=
- data(mexico)
-@
-Estimating multinomial logistics regression using \texttt{mlogit.bayes}:
-<<BasicExample.zelig>>=
- z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes",
- data = mexico)
-@
-Checking for convergence before summarizing the estimates:
-<<BasicExample.heidel>>=
- heidel.diag(z.out$coefficients)
-@
-<<BasicExample.raftery>>=
-raftery.diag(z.out$coefficients)
-@
-<<BasicExample.summary>>=
-summary(z.out)
-@ \end{verbatim}
-Setting values for the explanatory variables to their sample averages:
-<<BasicExample.setx>>=
- x.out <- setx(z.out)
-@
-Simulating quantities of interest from the posterior distribution
-given \texttt{x.out}.
-<<BasicExample.sim>>=
- s.out1 <- sim(z.out, x = x.out)
-@
-<<BasicExample.summary.sim>>=
-summary(s.out1)
-@
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in the probabilities of
-voting different candidates when \texttt{pristr} (the strength of the
-PRI) is set to be weak (equal to 1) versus strong (equal to 3)
-while all the other variables held at their default values.
-<<FirstDifferences.setx>>=
- x.weak <- setx(z.out, pristr = 1)
- x.strong <- setx(z.out, pristr = 3)
-@
-<<FirstDifferences.sim>>=
-s.out2 <- sim(z.out, x = x.strong, x1 = x.weak)
-@
-<<FirstDifferences.summary>>=
-summary(s.out2)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the (unordered) categorical dependent variable for observation
-$i$ which takes an integer values $j=1, \ldots, J$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by:
-\begin{eqnarray*}
-Y_{i} &\sim& \textrm{Multinomial}(Y_i \mid \pi_{ij}).
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1, \ldots, J$.
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\pi_{ij}=\frac{\exp(x_i\beta_j)}{\sum_{k=1}^J \exp(x_i\beta_k)},
-\textrm{ for } j=1,\ldots, J-1,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta_j$ is the vector of coefficient for
-category $j$. Category $J$ is assumed to be the baseline category.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta_j \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
-\textrm{ for } j = 1, \ldots, J-1,
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the multinomial logistics
- regression model are the predicted probability of belonging to each
- category:
-\begin{eqnarray*}
-\Pr(Y_i=j)=\pi_{ij}=\frac{\exp(x_i \beta_j)}{\sum_{k=1}^J \exp(x_J
-\beta_k)}, \quad \textrm{ for } j=1,\ldots, J-1,
-\end{eqnarray*}
-and
-\begin{eqnarray*}
-\Pr(Y_i=J)=1-\sum_{j=1}^{J-1}\Pr(Y_i=j)
-\end{eqnarray*}
-given the posterior draws of $\beta_j$ for all categories from the
-MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are the draws of
-$Y_i$ from a multinomial distribution whose parameters are the expected
-values(\texttt{qi\$ev}) computed based on the posterior draws
-of $\beta$ from the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) in category $j$ for the
-multinomial logistic model is defined as
-\begin{eqnarray*}
-\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
-\begin{eqnarray*}
-\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "mlogit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated coefficients $\beta$ for each category except the baseline
-category.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values(probabilities) of
-each of the $J$ categories given the specified values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values drawn from the
-multinomial distribution defined by the expected values(\texttt{qi\$ev})
-given the specified values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values of each of the $J$ categories for the values specified in
-\texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the
-expected values of each of the $J$ categories simulated
-from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-
-
-\subsection* {How to Cite}
-
-\input{cites/mlogit.bayes}
-\input{citeZelig}
-\subsection*{See also}
-Bayesian logistic regression is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/mlogit.bayes.log b/inst/doc/mlogit.bayes.log
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-\chapter{Writing New Models} \label{s:new}
-
-With Zelig, writing a new model in R is straightforward. (If you
-already have a model, see Chapter \ref{c:addingmodels} for how to include it in
-Zelig.) With tools to streamline user inputs, writing a new model
-does not require a lot of programming knowledge, but lets developers
-focus on the model's math. Generally, writing a new statistical
-procedure or model comes in orderly steps:
-\begin{enumerate}
-\item Write down the mathematical model. Define the parameters that
-you need, grouping parameters into convenient vectors or matrices
-whenever possible (this will make your code clearer).
-\item Write the code.
-\item Test the code (usually using Monte Carlo data, where you know
-the true values being estimated ) and make sure
-that it works as expected.
-\item Write some documentation explaining your model and the functions
-that run your model.
-\end{enumerate}
-Somewhere between steps [1] and [2], you will need to translate input
-data into the mathematical notation that you used to write down the
-model. Rather than repeating whole blocks of code, use functions to
-streamline the number of commands that users will need to run your
-model.
-
-With more steps being performed by fewer commands, the inputs to these
-commands become more sophisticated. The structure of those inputs
-actually matters quite a lot. If your function has a convoluted
-syntax, it will be difficult to use, difficult to explain, and
-difficult to document. If your function is easy to use and has an
-intuitive syntax, however, it will be easy to explain and document,
-which will make your procedure more accessible to all users.
-
-\section{Managing Statistical Model Inputs}
-\label{ui}
-
-Most statistical models require a matrix of explanatory variables and
-a matrix of dependent variables. Rather than have users create
-matrices themselves, R has a convenient user interface to create
-matrices of response and explanatory variables on the fly. Users
-simply specify a {\tt formula} in the form of
-\verb|dependent ~ explanatory variables|, and developers use the
-following functions to transform the formula into the appropriate
-matrices. Let {\tt mydata} be a data frame.
-\begin{verbatim}
-> formula <- y ~ x1 + x2 # User input
-
-# Given the formula above, programmers can use the following standard commands
-> D <- model.frame(formula, data = mydata) # Subset & listwise deletion
-> X <- model.matrix(formula, data = D) # Creates X matrix
-> Y <- model.response(D) # Creates Y matrix
-\end{verbatim}
-where
-\begin{itemize}
-\item {\tt D} is a subset of {\tt mydata} that contains only the
-variables specified in the formula ({\tt y}, {\tt x1}, and {\tt x2})
-with listwise deletion performed on the subset data frame;
-\item {\tt X} is a matrix that contains a column of 1's, and the
-explanatory variables {\tt x1} and {\tt x2} from {\tt D}; and
-\item {\tt Y} is a matrix containing the dependent variable(s) from
-{\tt D}.
-\end{itemize}
-Depending on the model, {\tt Y} may be a column vector, matrix, or
-other data structure.
-
-\subsection{Describe the Statistical Model}
-
-After setting up the $X$ matrix, the next step for most models will be
-to identify the corresponding vector of parameters. For a single
-response variable model with no ancillary parameters, the standard R
-interface is quite convenient: given $X$, the model's parameters are
-simply $\beta$.
-
-There are very few models, however, that fall into this category.
-Even Normal regression, for example, has two sets of parameters
-$\beta$ and $\sigma^2$. In order to make the R formula format more
-flexible, Zelig has an additional set of tools that lets you describe
-the inputs to your model (for multiple sets of parameters).
-
-After you have written down the statistical model, identify the
-parameters in your model. With these parameters in mind, the first
-step is to write a {\tt describe.*()} function for your model. If
-your model is called {\tt mymodel}, then the {\tt describe.mymodel()}
-function takes no arguments and returns a list with the following
-information:
-\begin{itemize}
-\item {\tt category}: a character string that describes the dependent
-variable. See \Sref{categories} for the current list of available
-categories.
-\item {\tt parameters}: a list containing parameter sets used in your
-model. For each parameter (e.g., theta), you need to provide the
-following information:
-
-\begin{itemize}
-
-\item {\tt equations}: an integer number of equations for the
-parameter. For parameters that can take, for example, two to four
-equations, use {\tt c(2, 4)}.
-
-\item {\tt tagsAllowed}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a given parameter allows constraints.
-
-\item {\tt depVar}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a parameter requires a corresponding dependent
-variable.
-
-\item {\tt expVar}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a parameter allows explanatory variables.
-
-\end{itemize}
-\end{itemize}
-(See \Sref{describe.mymodel} for examples and additional arguments
-output by {\tt describe.mymodel()}.)
-
-\subsection{Single Response Variable Models: Normal Regression Model}
-\label{normal.regression}
-
-Let's say that you are trying to write a Normal regression model with
-stochastic component
-\begin{displaymath}
-\textrm{Normal}(y_i \mid \mu_i, \sigma^2) \; = \; \frac{1}{\sqrt{2 \pi} \sigma} \, \exp
-\left( -\left( \frac{(y_i - \mu_i)^2}{2 \sigma^2} \right) \right)
-\end{displaymath}
-with scalar variance parameter $\sigma^2 > 0$, and systematic component
-$E(Y_i) = \mu_i = x_i \beta$. This implies two sets of parameters in your
-model, and the following {\tt describe.normal.regression()} function:
-\begin{verbatim}
-describe.normal.regression <- function() {
- category <- "continuous"
- mu <- list(equations = 1, # Systematic component
- tagsAllowed = FALSE,
- depVar = TRUE,
- expVar = TRUE)
- sigma2 <- list(equations = 1, # Scalar ancillary parameter
- tagsAllowed = FALSE,
- depVar = FALSE,
- expVar = FALSE)
- pars <- list(mu = mu, sigma2 = sigma2)
- list(category = category, parameters = pars)
-}
-\end{verbatim}
-
-To find the log-likelihood:
-\begin{eqnarray*}
-\textrm{L }(\beta, \sigma^2 \mid y) & = & \prod_{1=1}^n
- \textrm{Normal}(y_i \mid \mu_i, \sigma^2)\\
- & = & \prod_{i=1}^n (2\pi\sigma^2)^{-1/2}\exp\left(\frac{-(y_i-\mu_i)^2}
-{2\sigma^2}\right)\\
- & = &(2\pi\sigma^2)^{-n/2} \prod_{i=1}^n \exp\left(\frac{-(y_i-\mu_i)^2}
-{2\sigma^2}\right)\\
- & = &(2\pi\sigma^2)^{-n/2} \prod_{i=1}^n \exp\left(\frac{-(y_i-x_i \beta)^2}
-{2\sigma^2}\right)\\
-\ln \textrm{L }(\beta, \sigma^2 \mid y) &=& -\frac{n}{2}\ln(2\pi\sigma^2)-
-\sum_{i=1}^n \frac{(y_i-x_i\beta)^2}{2\sigma^2}\\
- &=& -\frac{n}{2}\ln(2\pi\sigma^2)-
- \frac{1}{2\sigma^2}\sum_{i=1}^n (y_i-x_i\beta)^2 \\
-&\propto& -\frac{1}{2} \left( n \ln\sigma^2 + \frac{\sum_{i=1}^n
-(y_i-x_i\beta)^2}{\sigma^2} \right)
-\end{eqnarray*}
-In R code, this translates to:
-\begin{verbatim}
-ll.normal <- function(par, X, Y, n, terms) {
- beta <- parse.par(par, terms, eqn = "mu") # [1]
- gamma <- parse.par(par, terms, eqn = "sigma2") # [2]
- sigma2 <- exp(gamma)
- -0.5 * (n * log(sigma2) + sum((Y - X %*% beta)^2 / sigma2))
-}
-\end{verbatim}
-At Comment [1] above, we use the function {\tt parse.par()}
-to pull out the vector of parameters {\tt beta} (which relate the
-systematic component $\mu_i$ to the explanatory variables $x_i$). No
-matter how many covariates there are, the {\tt parse.par()} function
-can use {\tt terms} to pull out the appropriate parameters from {\tt par}.
-We also use {\tt parse.par()} at Comment [2] to pull out the scalar
-ancillary parameter that (after transformation) corresponds to the
-$\sigma^2$ parameter.
-
-To optimize this function, simply type:
-\begin{verbatim}
-out <- optim(start.val, ll.normal, control = list(fnscale = -1),
- method = "BFGS", hessian = TRUE, X = X, Y = Y, terms = terms)
-\end{verbatim}
-where
-\begin{itemize}
-\item {\tt start.val} is a vector of starting values for {\tt par}.
-Use {\tt set.start()} to create starting values for all parameters,
-systematic and ancillary, in one step.
-\item {\tt ll.normal} is the log-likelihood function derived above.
-\item {\tt "BFGS"} specifies unconstrained optimization using a
- quasi-Newton method.
-\item {\tt control = list(fnscale = -1)} specifies that R should
- maximize the function (omitting this causes R to minimize the
- function by default).
-\item {\tt hessian = TRUE} instructs R to return the Hessian matrix
- (from which you may calculate the variance-covariance matrix).
-\item {\tt X} and {\tt Y} are the matrix of explanatory variables and
- vector of dependent variables, used in the {\tt ll.normal()}
- function.
-\item {\tt terms} are meta-data constructed from the {\tt
-model.frame()} command.
-\end{itemize}
-Please refer to the R-help for {\tt optim()} for more options.
-
-To make this procedure generalizable, we can write a function that
-takes a user-specified data frame and formula, and optional starting
-values for the optimization procedure:
-\begin{verbatim}
-normal.regression <- function(formula, data, start.val = NULL, ...) {
-
- fml <- parse.formula(formula, model = "normal.regression") # [1]
- D <- model.frame(fml, data = data)
- X <- model.matrix(fml, data = D)
- Y <- model.response(D)
- terms <- attr(D, "terms")
- n <- nrow(X)
-
- start.val <- set.start(start.val, terms)
-
- res <- optim(start.val, ll.normal, method = "BFGS",
- hessian = TRUE, control = list(fnscale = -1),
- X = X, Y = Y, n = n, terms = terms, ...) # [2]
-
- fit <- model.end(res, D) # [3]
- fit$n <- n
- class(fit) <- "normal" # [4]
- fit
-}
-\end{verbatim} %$
-The following comments correspond to the bracketed numbers above:
-\begin{enumerate}
-\item The {\tt parse.formula()} command looks for the {\tt
-describe.normal.regression()} function, which changes the
-user-specified formula into the following format:
-\begin{verbatim}
-list(mu = formula, # where `formula' was specified by the user
- sigma = ~ 1)
-\end{verbatim}
-\item The {\tt \dots} here indicate that if the user enters any
-additional arguments when calling {\tt normal.regression()}, that
-those arguments should go to the {\tt optim()} function.
-\item The {\tt model.end()} function takes the optimized output and
-the listwise deleted data frame {\tt D} and creates an object that
-will work with {\tt setx()}.
-\item Choose a class for your model output so that you will be able to
-write an appropriate {\tt summary()}, {\tt param()}, and {\tt qi()}
-function for your model.
-\end{enumerate}
-
-\subsection{Multivariate models: Bivariate Normal example}\label{bivariate.probit}
-
-Most common models have one systematic component. For $n$
-observations, the systematic component varies over observations $i = 1,
-\dots, n$. In the case of the Normal regression model, the systematic
-component is $\mu_i$ ($\sigma^2$ is not estimated as a function of
-covariates).
-
-In some cases, however, your model may have more than one systematic
-component. In the case of bivariate probit, we have a dependent
-variable $Y_i = (Y_{i1}, Y_{i2})$ observed as (0,0), (1,0), (0,1), or
-(1,1) for $i = 1, \dots, n$. Similar to a single-response probit model,
-the stochastic component is described by two latent (unobserved)
-continuous variables ($Y_{i1}^*$, $Y_{i2}^*$) which follow the
-bivariate Normal distribution:
-\begin{eqnarray*}
- \left ( \begin{array}{c}
- Y_{i1}^* \\
- Y_{i2}^*
- \end{array}
- \right ) &\sim &
- \textrm{Normal} \left \{ \left (
- \begin{array}{c}
- \mu_{i1} \\ \mu_{i2}
- \end{array} \right ), \left( \begin{array}{cc}
- 1 & \rho \\
- \rho & 1
- \end{array} \right) \right\},
-\end{eqnarray*}
-where for $j = 1, 2$, $\mu_{ij}$ is the mean for $Y_{ij}^*$ and $\rho$ is a
-correlation parameter. The following observation mechanism links the
-observed dependent variables, $Y_{ij}$, with these latent variables
-\begin{eqnarray*}
-Y_{ij} & = & \left \{ \begin{array}{cc}
- 1 & {\rm if} \; Y_{ij}^* \ge 0, \\
- 0 & {\rm otherwise.}
- \end{array}
- \right.
-\end{eqnarray*}
-
-The systemic components for each observation are
- \begin{eqnarray*}
- \mu_{ij} & = & x_{ij} \beta_j \quad {\rm for} \quad j = 1, 2, \\
- \rho & = & \frac{\exp(x_{i3} \beta_3) - 1}{\exp(x_{i3} \beta_3) + 1}.
-\end{eqnarray*}
-In the default specification, $\rho$ is a scalar
-(such that $x_{i3}$ only contains an intercept term).
-
-If so, we have two sets of parameters: $\mu_{i} = (\mu_{i1},
-\mu_{i2})$ and $\rho$. This implies the following {\tt
-describe.bivariate.probit()} function:
-\begin{verbatim}
-describe.bivariate.probit <- function() {
- category <- "dichotomous"
- package <- list(name = "mvtnorm", # Required package and
- version = "0.7") # minimum version number
- mu <- list(equations = 2, # Systematic component has 2
- tagsAllowed = TRUE, # required equations
- depVar = TRUE,
- expVar = TRUE),
- rho <- list(equations = 1, # Optional systematic component
- tagsAllowed = FALSE, # (estimated as an ancillary
- depVar = FALSE, # parameter by default)
- expVar = TRUE),
- pars <- parameters(mu = mu, rho = rho)
- list(category = category, package = package, parameters = pars)
-}
-\end{verbatim}
-
-Since users may choose different explanatory variables to parameterize
-$\mu_{i1}$ and $\mu_{i2}$ (and sometimes $\rho$), the model requires a
-minimum of \emph{two} formulas. For example,
-\begin{verbatim}
-formulae <- list(mu1 = y1 ~ x1 + x2, # User input
- mu2 = y2 ~ x2 + x3)
-fml <- parse.formula(formulae, model = "bivariate.probit") # [1]
-D <- model.frame(fml, data = mydata)
-X <- model.matrix(fml, data = D)
-Y <- model.response(D)
-\end{verbatim}
-At comment [1], {\tt parse.formula()} finds the {\tt
-describe.bivariate.probit()} function and parses the formulas
-accordingly.
-
-If $\rho$ takes covariates (and becomes a systematic component rather
-than an ancillary parameter), there can be three sets of explanatory variables:
-\begin{verbatim}
-formulae <- list(mu1 = y1 ~ x1 + x2,
- mu2 = y2 ~ x2 + x3,
- rho = ~ x4 + x5)
-\end{verbatim}
-
-From the perspective of the programmer, a nearly identical
-framework works for both single and multiple equation
-models. The ({\tt parse.formula()}) line changes the class
-of {\tt fml} from {\tt "list"} to {\tt "multiple"} and hence ensures
-that {\tt model.frame()} and {\tt model.matrix()} go to the
-appropriate methods. {\tt D}, {\tt X} , and {\tt Y} are analogous to their
-single equation counterparts above:
-\begin{itemize}
-\item {\tt D} is the subset of {\tt mydata} containing the variables
-{\tt y1}, {\tt y2}, {\tt x1}, {\tt x2}, and {\tt x3} with listwise
-deletion performed on the subset;
-\item {\tt X} is a matrix corresponding to the explanatory variables,
-in one of three forms discussed below (see \Sref{constraints}).
-\item {\tt Y} is an $n \times J$ matrix (where $J=2$ here) with
-columns ({\tt y1}, {\tt y2}) corresponding to the outcome variables on
-the left-hand sides of the formulas.
-\end{itemize}
-
-Given for the bivariate probit probability density described above,
-the likelihood is:
-\begin{displaymath}
-L(\mathbf{\pi} | Y_i) = \prod_{i=1}^n
- \pi_{00}^{\textrm{I}\{Y_i = (0,0)\}}
- \pi_{10}^{\textrm{I}\{Y_i = (1,0)\}}
- \pi_{01}^{\textrm{I}\{Y_i = (0,1)\}}
- \pi_{11}^{\textrm{I}\{Y_i = (1,1)\}}
-\end{displaymath}
-where I is an indicator function and
-\begin{itemize}
-\item $\pi_{00} = \int_{-\infty}^0 \int_{-\infty}^0 \textrm{Normal}(Y_{i1}^*, Y_{i2}^* \mid
-\mu_{i1}, \mu_{i2}, \rho) dY_{i2}^* dY_{i1}^*$
-\item $\pi_{10} = \int_0^{\infty} \int_{-\infty}^0 \textrm{Normal}(Y_{i1}^*, Y_{i2}^* \mid
-\mu_{i1}, \mu_{i2}, \rho) dY_{i2}^* dY_{i1}^*$
-\item $\pi_{01} = \int_{-\infty}^0 \int_0^{\infty} \textrm{Normal}(Y_{i1}^*, Y_{i2}^* \mid
-\mu_{i1}, \mu_{i2}, \rho) dY_{i2}^* dY_{i1}^*$
-\item $\pi_{11} = 1-\pi_{00}-\pi_{10}-\pi_{01}$
-\end{itemize}
-This implies the following log-likelihood:
-\begin{eqnarray*}
-\log L(\mathbf{\pi} | Y_i) &=& \sum_{i=1}^n \textrm{I}\{Y_i = (0,0)\} \log\pi_{00}
-+ \textrm{I}\{Y_i = (1,0)\} \log \pi_{10} \\
-&& \quad \quad + \textrm{I}\{Y_i = (0,1)\} \log \pi_{01}
-+ \textrm{I}\{Y_i = (1,1)\} \log \pi_{11}
-\end{eqnarray*}
-(For the corresponding R code, see \Sref{bivariate.probit.llik} below.)
-
-\section{Easy Ways to Manage Matrices}
-\label{constraints}
-
-Most statistical methods relate explanatory variables $x_i$ to a
-dependent variable of interest $y_i$ for each observation $i = 1,
-\dots, n$. Let $\beta$ be a set of parameters that correspond to each
-column in $X$, which is an $n \times k$ matrix with rows $x_i$. For a
-single equation model, the linear predictor is
-\begin{equation*}
-\eta_i = x_i \beta = \beta_0 + \beta_1 x_{i1} +
-\beta_2 x_{i2} + \dots + \beta_k x_{ik}
-\end{equation*}
-Thus, $\eta$ is the set of $\eta_i$ for $i = 1, \dots, n$ and is
-usually represented as an $n \times 1$ matrix.
-
-For a two equation model such as bivariate probit, the linear
-predictor becomes a matrix with columns corresponding to each
-dependent variable $(y_{1i}, y_{2i})$:
-\begin{equation*}
-\eta_i = (\eta_{i1}, \eta_{i2}) = (x_{i1} \beta_1, x_{i2} \beta_2)
-\end{equation*}
-With $\eta$ as an $n \times 2$ matrix, we now have a few choices as to
-how to create the linear predictor:
-\begin{enumerate}
-\item An {\bf intuitive} layout, which stacks matrices of explanatory
-variables, provides an easy visual representation of the relationship
-between explanatory variables and coefficients;
-\item A {\bf computationally-efficient} layout, which takes advantage
-of computational vectorization; and
-\item A {\bf memory-saving} layout, which reduces the overall size of the
-$X$ and $\beta$ matrices.
-\end{enumerate}
-Using the simple tools described in this section, you can pick the
-best matrix management method for your model.
-
-In addition, the way in which $\eta$ is created also affects the
-way parameters are estimated. Let's say that you want two parameters
-to have the same effect in different equations. By setting up $X$ and
-$\beta$ in a certain way, you can let users set constraints across
-parameters. Continuing the bivariate probit example above, let the
-model specification be:
-\begin{verbatim}
-formulae <- list(mu1 = y1 ~ x1 + x2 + tag(x3, "land"),
- mu2 = y2 ~ x3 + tag(x4, "land"))
-\end{verbatim}
-where {\tt tag()} is a special function that constrains variables to
-have the same effect across equations. Thus, the coefficient for {\tt
-x3} in equation {\tt mu1} is constrained to be equal to the
-coefficient for {\tt x4} in equation {\tt mu2}, and this effect is
-identified as the ``land'' effect in both equations. In order to
-consider constraints across equations, the structure of both $X$ and
-$\beta$ matter.
-
-\subsection{The Intuitive Layout}
-
-A stacked matrix of $X$ and vector $\beta$ is probably the most
-visually intuitive configuration. Let $J = 2$ be the number of
-equations in the bivariate probit model, and let $v_t$ be the total
-number of unique covariates in both equations. Choosing {\tt
-model.matrix(\dots, shape = "stacked")} yields a $(Jn \times v_t) =
-(2n \times 6)$ matrix of explanatory variables. Again, let $x_1$ be
-an $n \times 1$ vector representing variable {\tt x1}, $x_2$ {\tt x2},
-and so forth. Then
-\begin{equation*}
-X = \left (\begin{array}{cccccc}
-1 & 0 & x_1 & x_2 & 0 & x_3 \\
-0 & 1 & 0 & 0 & x_3 & x_4
-\end{array} \right)
-\end{equation*}
-Correspondingly, $\beta$ is a vector with elements
-\begin{equation*}
-(\beta_0^{\mu_1} \; \beta_0^{\mu_2} \; \beta_{x_1}^{\mu_1} \;
-\beta_{x_2}^{\mu_1} \; \beta_{x_3}^{\mu_2} \; \beta_{\textrm{land}})\prime
-\end{equation*}
-where $\beta_0^j$ are the intercept terms for equation $j = \{\mu_1,
-\mu_2\}$. Since $X$ is $(2n \times 6)$ and $\beta$ is $(6 \times 1)$, the
-resulting linear predictor $\eta$ is also stacked into a $(2n \times
-1)$ matrix. Although difficult to manipulate (since observations are
-indexed by $i$ and $2i$ for each $i = 1, \dots, n$ rather than just
-$i$), it is easy to see that we have turned the two equations into one
-big $X$ matrix and one long vector $\beta$, which is directly
-analogous to the familiar single-equation $\eta$.
-
-\subsection{The Computationally-Efficient Layout}
-
-Choosing array $X$ and vector $\beta$ is probably the the most
-computationally-efficient configuration: {\tt
-model.matrix(\dots, shape = "array")} produces an $n \times k_t
-\times J$ array where $J$ is the total number of equations and $k_t$
-is the total number of parameters across all the equations. Since some
-parameter values may be constrained across equations, $k_t \leq
-\sum_{j=1}^J k_j$. If a variable is not in a certain equation, it is
-observed as a vector of 0s. With this option, each $i = 1, \dots, n$
-$x_i$ matrix becomes:
-\begin{equation*}
-\left( \begin{array}{ccccccc}
-1 & 0 & x_{i1} & x_{i2} & 0 & x_{i3} \\
-0 & 1 & 0 & 0 & x_{i3} & x_{i4}
-\end{array} \right)
-\end{equation*}
-By stacking each of these $x_i$ matrices along the first dimension,
-we get $X$ as an array with dimensions $n \times k_t \times J$.
-
-Correspondingly, $\beta$ is a vector with elements
-\begin{equation*}
-(\beta_0^{\mu_1} \; \beta_0^{\mu_2} \; \beta_{x_1}^{\mu_1} \;
-\beta_{x_2}^{\mu_1} \; \beta_{x_3}^{\mu_2} \; \beta_{\textrm{land}})\prime
-\end{equation*}
-To multiply the $X$ array with dimensions $(n \times 6 \times 2)$ and
-the $(6 \times 1)$ $\beta$ vector, we \emph{vectorize} over equations
-as follows:
-\begin{verbatim}
-eta <- apply(X, 3, '%*%', beta)
-\end{verbatim}
-The linear predictor {\tt eta} is therefore a $(n \times 2)$ matrix.
-
-\subsection{The Memory-Efficient Layout}
-
-Choosing a ``compact'' $X$ matrix and matrix $\beta$ is probably the most
-memory-efficient configuration: {\tt model.matrix(\dots,
-shape = "compact")} (the default) produces an $n \times v$ matrix,
-where $v$ is the number of unique variables (5 in this
-case)\footnote{Why 5? In addition to the intercept term (a variable
-which is the same in either equation, and so counts only as one
-variable), the \emph{unique} variables are $x_1$, $x_2$, $x_3$, and
-$x_4$.} in all of the equations. Let $x_1$ be an $n \times 1$ vector
-representing variable {\tt x1}, $x_2$ {\tt x2}, and so forth.
-\begin{eqnarray*}
-X = (1 \; x_1 \; x_2 \; x_3 \; x_4) & & \beta = \left( \begin{array}{cc}
-
- \beta_0^{\mu_1} & \beta_0^{\mu_2} \\
-\beta_{x_1}^{\mu_1} & 0 \\
-\beta_{x_2}^{\mu_1} & 0 \\
-\beta_{\textrm{land}} & \beta_{x_3}^{\mu_2} \\
-0 & \beta_{\textrm{land}}
-\end{array} \right)
-\end{eqnarray*}
-The $\beta_{\textrm{land}}$ parameter is used twice to implement the
-constraint, and the number of empty cells is minimized by implementing
-the constraints in $\beta$ rather than $X$. Furthermore, since $X$ is
-$(n \times 5)$ and $\beta$ is $(5 \times 2)$, $X\beta = \eta$ is $n
-\times 2$.
-
-\subsection{Interchanging the Three Methods}
-\label{bivariate.probit.llik}
-
-Continuing the bivariate probit example above, we only need to modify
-a few lines of code to put these different schemes into effect. Using the
-default (memory-efficient) options, the log-likelihood is:
-\begin{verbatim}
-bivariate.probit <- function(formula, data, start.val = NULL, ...) {
- fml <- parse.formula(formula, model = "bivariate.probit")
- D <- model.frame(fml, data = data)
- X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2")) # [1]
- Xrho <- model.matrix(fml, data = D, eqn = "rho")
- Y <- model.response(D)
- terms <- attr(D, "terms")
- start.val <- set.start(start.val, terms)
- start.val <- put.start(start.val, 1, terms, eqn = "rho")
-
- log.lik <- function(par, X, Y, terms) {
- Beta <- parse.par(par, terms, eqn = c("mu1", "mu2")) # [2]
- gamma <- parse.par(par, terms, eqn = "rho")
- rho <- (exp(Xrho %*% gamma) - 1) / (1 + exp(Xrho %*% gamma))
- mu <- X %*% Beta # [3]
- llik <- 0
- for (i in 1:nrow(mu)){
- Sigma <- matrix(c(1, rho[i,], rho[i,], 1), 2, 2)
- if (Y[i,1]==1)
- if (Y[i,2]==1)
- llik <- llik + log(pmvnorm(lower = c(0, 0), upper = c(Inf, Inf),
- mean = mu[i,], corr = Sigma))
- else
- llik <- llik + log(pmvnorm(lower = c(0, -Inf), upper = c(Inf, 0),
- mean = mu[i,], corr = Sigma))
- else
- if (Y[i,2]==1)
- llik <- llik + log(pmvnorm(lower = c(-Inf, 0), upper = c(0, Inf),
- mean = mu[i,], corr = Sigma))
- else
- llik <- llik + log(pmvnorm(lower = c(-Inf, -Inf), upper = c(0, 0),
- mean = mu[i,], corr = Sigma))
- }
- return(llik)
- }
- res <- optim(start.val, log.lik, method = "BFGS",
- hessian = TRUE, control = list(fnscale = -1),
- X = X, Y = Y, terms = terms, ...)
- fit <- model.end(res, D)
- class(fit) <- "bivariate.probit"
- fit
-}
-\end{verbatim}
-
-If you find that the default (memory-efficient) method isn't the best
-way to run your model, you can use either the intuitive option or the
-computationally-efficient option by changing just a few lines of code
-as follows:
-\begin{itemize}
-\item {\bf Intuitive option} At Comment [1]:
-\begin{verbatim}
-X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2"))
-\end{verbatim}
-and at Comment [2],
-\begin{verbatim}
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-\end{verbatim}
-The line at Comment [3] remains the same as in the original version.
-
-\item {\bf Computationally-efficient option} Replace the line at Comment [1] with
-\begin{verbatim}
-X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
-\end{verbatim}
-At Comment [2]:
-\begin{verbatim}
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-\end{verbatim}
-At Comment [3]:
-\begin{verbatim}
-mu <- apply(X, 3, '%*%', Beta)
-\end{verbatim}
-\end{itemize}
-
-Even if your optimizer calls a C or FORTRAN routine, you can use
-combinations of {\tt model.matrix()} and {\tt parse.par()} to set up
-the data structures that you need to obtain the linear predictor (or
-your model's equivalent) before passing these data structures to your
-optimization routine.
-
diff --git a/inst/doc/normal.Rnw b/inst/doc/normal.Rnw
deleted file mode 100644
index d8aae12..0000000
--- a/inst/doc/normal.Rnw
+++ /dev/null
@@ -1,299 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/normal}
-\include{zinput}
-%\VignetteIndexEntry{Normal Regression for Continuous Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, normal,regression,continuous, least squares}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-\section{{\tt normal}: Normal Regression for Continuous Dependent Variables}
-\label{normal}
-
-The Normal regression model is a close variant of the more standard
-least squares regression model (see \Sref{ls}). Both models specify a
-continuous dependent variable as a linear function of a set of
-explanatory variables. The Normal model reports maximum likelihood
-(rather than least squares) estimates. The two models differ only in
-their estimate for the stochastic parameter $\sigma$.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "normal", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for normal regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item Basic Example with First Differences
-
-Attach sample data:
-<<Examples.data>>=
- data(macro)
-@
-Estimate model:
-<<Examples.zelig>>=
- z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal",
- data = macro)
-@
-Summarize of regression coefficients:
-<<Examples.summary>>=
- summary(z.out1)
-@
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) values for trade:
-<<Examples.setx>>=
- x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8))
- x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2))
-@
-Generate first differences for the effect of high versus low trade on
-GDP:
-<<Examples.sim>>=
- s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
-@
-<<Examples.summary.sim>>=
- summary(s.out1)
-@
-%plot does not work
-A visual summary of quantities of interest:
-\begin{center}
-<<label=ExamplesPlot,fig=true,echo=true>>=
- plot(s.out1)
-@
-\end{center}
-
-\item Using Dummy Variables
-%the code in this section does not work well but there is no demo for this part either
-
-Estimate a model with a dummy variable for each year and country (see
-\ref{factors} for help with dummy variables). Note that you do not
-need to create dummy variables, as the program will automatically
-parse the unique values in the selected variables into dummy
-variables.
-<<Dummy.zelig>>=
- z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(year)
- + as.factor(country), model = "normal", data = macro)
-@
-Set values for the explanatory variables, using the default mean/mode
-variables, with country set to the United States and Japan,
-respectively:
-<<Dummy.setx>>=
-### x.US <- try(setx(z.out2, country = "United States"),silent=T)
-### x.Japan <- try(setx(z.out2, country = "Japan"),silent=T)
-@
-Simulate quantities of interest:
-<<Dummy.sim>>=
-### s.out2 <- try(sim(z.out2, x = x.US, x1 = x.Japan), silent=T)
-@
-<<Dummy.summary>>=
-###try(summary(s.out2))
-@
-%plot does not work
-\begin{center}
-<<label=DummyPlot,fig=true,echo=false>>=
-# plot(s.out2)
-@
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i$ be the continuous dependent variable for observation $i$.
-\begin{itemize}
-\item The \emph{stochastic component} is described by a univariate normal
- model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
- \begin{equation*}
- Y_i \; \sim \; \textrm{Normal}(\mu_i, \sigma^2).
- \end{equation*}
-
-\item The \emph{systematic component} is
- \begin{equation*}
- \mu_i \;= \; x_i \beta,
- \end{equation*}
- where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations
- from the the stochastic component, $$E(Y) = \mu_i = x_i \beta,$$
- given a draw of $\beta$ from its posterior.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from the distribution
- defined by the set of parameters $(\mu_i, \sigma)$.
-
-\item The first difference ({\tt qi\$fd}) is:
-\begin{equation*}
-\textrm{FD}\; = \;E(Y \mid x_1) - E(Y \mid x)
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "normal", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: fitted values. For the normal model,
- these are identical to the {\tt linear predictors}.
- \item {\tt linear.predictors}: fitted values. For the normal
- model, these are identical to {\tt fitted.values}.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by $(\mu_i, \sigma)$.
- \item {\tt qi\$fd}: the simulated first difference in the simulated
- expected values for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/normal}
-\input{citeZelig}
-
-\subsection* {See also}
-
-The normal model is part of the stats package by \citet{VenRip02}.
-Advanced users may wish to refer to \texttt{help(glm)} and
-\texttt{help(family)}, as well as \cite{McCNel89}. Robust standard
-errors are implemented via the sandwich package by \citet{Zeileis04}.
-Sample data are from \cite{KinTomWit00}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/normal.bayes.Rnw b/inst/doc/normal.bayes.Rnw
deleted file mode 100644
index a5d84cc..0000000
--- a/inst/doc/normal.bayes.Rnw
+++ /dev/null
@@ -1,270 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/normalBayes}
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Normal Linear Regression}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, normal,linear, regression,bayes, continuous}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{normal.bayes}: Bayesian Normal Linear Regression}
-\label{normal.bayes}
-
-Use Bayesian regression to specify a continuous dependent variable as
-a linear function of specified explanatory variables. The model is
-implemented using a Gibbs sampler. See \Sref{normal} for the
-maximum-likelihood implementation or \Sref{ls} for the ordinary least
-squares variation.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "normal.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the convergence of the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
-progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA}, which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, which uses the
-least squares estimates as the starting values.
-
-\end{itemize}
-
-Use the following arguments to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will
-be the prior mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCregress)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<BasicExample.data>>=
- data(macro)
-@
-
-Estimating linear regression using \texttt{normal.bayes}:
-<<BasicExample.zelig>>=
-z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.bayes",
- data = macro, verbose = TRUE)
-@
-Checking for convergence before summarizing the estimates:
-<<BasicExample.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<BasicExample.heidel>>=
-heidel.diag(z.out$coefficients)
-@
-<<BasicExample.raftery>>=
-raftery.diag(z.out$coefficients)
-@
-<<BasicExample.summary>>=
-summary(z.out)
-@
-
-Setting values for the explanatory variables to their sample averages:
-<<BasicExample.setx>>=
- x.out <- setx(z.out)
-@
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}:
-<<BasicExample.sim>>=
- s.out1 <- sim(z.out, x = x.out)
-@
-<<BasicExample.summary.sim>>=
-summary(s.out1)
-@
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) trade on GDP:
-<<FirstDifferences.setx>>=
- x.high <- setx(z.out, trade = quantile(macro$trade, prob = 0.8))
- x.low <- setx(z.out, trade = quantile(macro$trade, prob = 0.2))
-@
-Estimating the first difference for the effect of
-high versus low trade on unemployment rate:
-<<FirstDifferences.sim>>=
- s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-@
-<<FirstDifferences.summary.sim>>=
- summary(s.out2)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
-\end{eqnarray*}
-where $\epsilon_{i}=Y_i-\mu_i$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i}= x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are given by
-\begin{eqnarray*}
-\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
-\sigma^{2} & \sim & {\rm InverseGamma}\left( \frac{c_0}{2},
-\frac{d_0}{2} \right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory
-variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
-a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape and
-scale parameters for $\sigma^{2}$. Note that $\beta$ and $\sigma^2$
-are assumed to be \emph{a priori} independent.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the linear regression model are
-calculated as following:
-\begin{eqnarray*}
-E(Y) = x_{i} \beta,
-\end{eqnarray*}
-given posterior draws of $\beta$ based on the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) for the linear regression model
-is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1} \{
-Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "normal.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/normal.bayes}
-\input{citeZelig}
-\subsection*{See also}
-Bayesian normal regression is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/normal.bayes.log b/inst/doc/normal.bayes.log
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deleted file mode 100644
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@@ -1,284 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/gam}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Additive Model for Continuous Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, gam, normal.gam}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt normal.gam}: Generalized Additive Model for Continuous Dependent Variables}\label{gam.normal}
-
-This function runs a nonparametric Generalized Additive Model (GAM) for continuous dependent variables.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + s(x2), model = "normal.gam", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Where {\tt s()} indicates a variable to be estimated via nonparametric smooth. All variables for which {\tt s()} is not specified, are estimated via standard parametric methods.
-
-\subsubsection{Additional Inputs}
-In addition to the standard inputs, {\tt zelig()} takes the following additional options for GAM models.
-\begin{itemize}
-\item {\tt method}: Controls the fitting method to be used. Fitting methods are selected via a list environment within {\tt method=gam.method()}. See {\tt gam.method()} for details.
-\item {\tt scale}: Generalized Cross Validation (GCV) is used if {\tt scale = 0} (see the ``Model'' section for details) except for Normal models where a Un-Biased Risk Estimator (UBRE) (also see the ``Model'' section for details) is used with a scale parameter assumed to be 1. If {\tt scale} is greater than 1, it is assumed to be the scale parameter/variance and UBRE is used. If {\tt scale} is negative GCV is used.
-\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
-\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
-\item {\tt sp}: A vector of smoothing parameters for each term.
-\item {\tt \ldots}: additional options passed to the {\tt normal.gam} model. See the {\tt mgcv} library for details.
-\end{itemize}
-
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example:
-
-Create some data:
-
-\begin{Schunk}
-\begin{Sinput}
-> set.seed(0); n <- 400; sig <- 2;
-> x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
-> x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
-> f0 <- function(x) 2 * sin(pi * x)
-> f1 <- function(x) exp(2 * x)
-> f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 * (10 *
-+ x)^3 * (1 - x)^10
-> f3 <- function(x) 0 * x
-> f <- f0(x0) + f1(x1) + f2(x2)
-> e <- rnorm(n, 0, sig); y <- f + e
-> my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
-\end{Sinput}
-\end{Schunk}
-
-<<echo=FALSE, results=hide>>=
-library(Zelig)
-<<echo=FALSE, results=hide>>=
-set.seed(0); n<-400; sig<-2
-set.seed(0); n <- 400; sig <- 2;
-x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
-x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
-f0 <- function(x) 2 * sin(pi * x)
-f1 <- function(x) exp(2 * x)
-f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 * (10 * x)^3 * (1 - x)^10
-f3 <- function(x) 0 * x
-f <- f0(x0) + f1(x1) + f2(x2)
-e <- rnorm(n, 0, sig); y <- f + e
-my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
-
-@
-Estimate the model, summarize the results, and plot nonlinearities:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="normal.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1,residuals=TRUE)
-@
-Note that the {\tt plot()} function can be used after model estimation and before simulation to view the nonlinear relationships in the independent variables:
-\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=6, height=6>>=
-plot(z.out,pages=1,residuals=TRUE)
-@
-\label{fig:plotgam}
-\end{figure}
-
-Set values for the explanatory variables to their default (mean/mode) values, then simulate, summarize and plot quantities of interest:
-<<echo=TRUE, results=hide, fig=FALSE>>=
- x.out <- setx(z.out)
- s.out <- sim(z.out, x = x.out)
- summary(s.out)
- plot(s.out)
-@
-\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low values (20th percentile) and high values (80th percentile) of the explanatory variable {\tt x3} while all the other variables are held at their default (mean/mode) values.
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
-x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
-
-s.out <- sim(z.out, x=x.high, x1=x.low)
-summary(s.out)
-plot(s.out)
-
-@
-%\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-@
-\item Variations in GAM model specification. Note that {\tt setx} and {\tt sim} work as shown in the above examples for any GAM model. As such, in the interest of parsimony, I will not re-specify the simulations of quantities of interest.
-
-An extra ridge penalty (useful with convergence problems):
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
- model="normal.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1,residuals=TRUE)
-
-@
-Set the smoothing parameter for the first term, estimate the rest:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
- model="normal.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1)
-
-@
-Set lower bounds on smoothing parameters:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
- model="normal.gam", data=my.data)
-summary(z.out)
-plot(z.out, pages=1)
-
-@
-A GAM with 3df regression spline term \& 2 penalized terms:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
- model="normal.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1)
-
-@
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-
-
-GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt normal.gam} a normal link function is used. Specifically, let $Y_{i}$ be the continuous dependent variable for observation $i$.
-\begin{itemize}
-\item The \emph{stochastic component} is described by a univariate normal model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
-\begin{equation*}
-Y_{i} \sim \text{Normal} ( \mu_{i}, \sigma^2).\\
-\end{equation*}
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\mu_i = x_i\beta + \sum_{j=1}^{J} f_j (Z_j).
-\end{equation*}
-where $ x_i$ is the vector of $k$ explanatory variables, $\beta$ is the vector of coefficients and $f_j(Z_j)$ for $j=1, \ldots J$ is the set of smooth terms.
-\end{itemize}
-
-
-Generalized additive models (GAMs) are similar in many respects to generalized linear models (GLMs). Specifically, GAMs are generally fit by penalized maximum likelihood estimation and GAMs have (or can have) a parametric component identical to that of a GLM. The difference is that GAMs also include in their linear predictors a specified sum of smooth functions.
-
-In this GAM implementation, smooth functions are represented using penalized regression splines. Two techniques may be used to estimate smoothing parameters: Generalized Cross Validation (GCV),
-\begin{equation}
-n \frac{D}{(n-DF)^2},
-\end{equation}
-or an Un-Biased Risk Estimator (UBRE) (which is effectively just a rescaled AIC),
-\begin{equation}
-\frac{D}{n} + 2 s \frac{DF}{n-s},
-\end{equation}
-where $D$ is the deviance, $n$ is the number of observations, $s$ is the scale parameter, and $DF$ is the effective degrees of freedom of the model. The use of GCV or UBRE can be set by the user with the {\tt scale} command described in the ``Additional Inputs'' section and in either case, smoothing parameters are chosen to minimize the GCV or UBRE score for the model.
-
-Estimation for GAM models proceeds as follows: first, basis functions and a set (one or more) of quadratic penalty coefficient matrices are constructed for each smooth term. Second, a model matrix is is obtained for the parametric component of the GAM. These matrices are combined to produce a complete model matrix and a set of penalty matrices for the smooth terms. Iteratively Reweighted Least Squares (IRLS) is then used to estimate the model; at each iteration of the IRLS, a penalized w [...]
-
-Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
-
-
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the {\tt normal.gam} model are the same as those for the standard Normal regression.
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt normal.gam} model is the mean of simulations from the stochastic component,
-\begin{equation*}
-E(Y) = \mu_{i} = x_{i}\beta + \sum_{j=1}^{J} f_j (Z_j).
-\end{equation*}
-\item The predicted value ({\tt qi\$pr}) is a draw from the Normal distribution defined by the set of parameters $(\mu_i, \sigma^2)$.
-\item The first difference ({\tt qi\$fd}) for the normal.gam model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
-\end{equation*}
-for $w=\{X, Z\}$.
-\end{itemize}
-
-
-
-\subsubsection{Output Values}
-
-
-
-The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "normal.gam", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IRLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt method}: the fitting method used.
-\item {\tt converged}: logical indicating weather the model converged or not.
-\item {\tt smooth}: information about the smoothed parameters.
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt data}: the input data frame.
-\item {\tt model}: the model matrix used.
-
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt p.coeff}: the coefficients of the parametric components of the model.
-\item {\tt se}: the standard errors of the entire model.
-\item {\tt p.table}: the coefficients, standard errors, and associated $t$ statistics for the parametric portion of the model.
-\item {\tt s.table}: the table of estimated degrees of freedom, estimated rank, $F$ statistics, and $p$-values for the nonparametric portion of the model.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-
-
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/normal.gam}
-\input{citeZelig}
-
-\subsection* {See also}
-The {\tt gam.logit} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
-
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
diff --git a/inst/doc/normal.gam.log b/inst/doc/normal.gam.log
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-\SweaveOpts{results=hide, prefix.string=vigpics/normal_gee}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Estimating Equation for Normal Regression}
-%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, normal regression}
-%\VignettePackage{Zelig}
-\begin{document}
-
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{normal.gee}: Generalized Estimating Equation for Normal Regression}
-\label{normal.gee}
-
-The GEE normal estimates the same model as the standard normal
-regression. Unlike in normal
-regression, GEE normal allows for dependence within clusters, such as
-in longitudinal data, although its use is not limited to just
-panel data. The user must first specify a ``working''
-correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
-the standard errors can be obtained using a robust ``sandwich''
-estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "normal.gee",
- id = "X3", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\noindent where \texttt{id} is a variable which identifies the clusters. The data should be sorted by \texttt{id} and should be ordered within each cluster when appropriate.
-
-\subsubsection{Additional Inputs}
-
-\begin{itemize}
-\item \texttt{robust}: defaults to \texttt{TRUE}. If \texttt{TRUE}, consistent standard errors are estimated using a ``sandwich'' estimator.
-\end{itemize}
-Use the following arguments to specify the structure of the ``working'' correlations within clusters:
-
-\begin{itemize}
-\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
-\begin{itemize}
-\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard normal regression. The ``working'' correlation matrix is the identity matrix.
-\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
-\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
- $${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{|t-t'|} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
- If (\texttt{corstr = "stat\_M\_dep"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. Choose this option when the correlations are assumed to be the same for observations of the same $|t-t'|$ periods apart for $|t-t'| \leq m$.
-\begin{center}
-Sample ``working'' correlation for Stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_1 & \alpha_2 & 0 & 0 \\
-\alpha_1 & 1 & \alpha_1 & \alpha_2 & 0 \\
-\alpha_2 & \alpha_1 & 1 & \alpha_1 & \alpha_2 \\
-0 & \alpha_2 & \alpha_1 & 1 & \alpha_1 \\
-0 & 0 & \alpha_2 & \alpha_1 & 1
-\end{array} \right) $
-\end{center}
-
-\item Non-stationary $m$ dependent (\texttt{corstr =
- "non\_stat\_M\_dep"}):
-$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{tt'} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
-If (\texttt{corstr = "non\_stat\_M\_dep"}), you must also specify
-\texttt{Mv} = $m$, where $m$ is the number of periods $t$ of
-dependence. This option relaxes the assumption that the
-correlations are the same for all observations of the same $|t-t'|$
-periods apart.
-\begin{center}
-Sample ``working'' correlation for Non-stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_{12} & \alpha_{13} & 0 & 0 \\
-\alpha_{12} & 1 & \alpha_{23} & \alpha_{24} & 0 \\
-\alpha_{13} & \alpha_{23} & 1 & \alpha_{34} & \alpha_{35} \\
-0 & \alpha_{24} & \alpha_{34} & 1 & \alpha_{45} \\
-0 & 0 & \alpha_{35} & \alpha_{45} & 1
-\end{array} \right) $
-\end{center}
-\item Exchangeable (\texttt{corstr = "exchangeable"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha$, $\forall t, t'$ with $t\ne t'$. Choose this option if the correlations are assumed to be the same for all observations within the cluster.
-\begin{center}
-Sample ``working'' correlation for Exchangeable\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha & \alpha & \alpha \\
-\alpha & 1 & \alpha & \alpha & \alpha \\
-\alpha & \alpha & 1 & \alpha & \alpha \\
-\alpha & \alpha & \alpha & 1 & \alpha \\
-\alpha & \alpha & \alpha & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Stationary $m$th order autoregressive (\texttt{corstr = "AR-M"}): If (\texttt{corstr = "AR-M"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. For example, the first order autoregressive model (AR-1) implies ${\rm cor}(y_{it},
- y_{it'})=\alpha^{|t-t'|}, \forall t, t'$ with $t\ne t'$. In AR-1, observation 1 and observation 2 have a correlation of $\alpha$. Observation 2 and observation 3 also have a correlation of $\alpha$. Observation 1 and observation 3 have a correlation of $\alpha^2$, which is a function of how 1 and 2 are correlated ($\alpha$) multiplied by how 2 and 3 are correlated ($\alpha$). Observation 1 and 4 have a correlation that is a function of the correlation between 1 and 2, 2 and 3, and [...]
-\begin{center}
-Sample ``working'' correlation for Stationary AR-1 ({\tt Mv}=1)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha^2 & \alpha^3 & \alpha^4 \\
-\alpha & 1 & \alpha & \alpha^2 & \alpha^3 \\
-\alpha^2 & \alpha & 1 & \alpha & \alpha^2 \\
-\alpha^3 & \alpha^2 & \alpha & 1 & \alpha \\
-\alpha^4 & \alpha^3 & \alpha^2 & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Unstructured (\texttt{corstr = "unstructured"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha_{tt'}$, $\forall t, t'$ with
- $t\ne t'$. No constraints are placed on the correlations, which are then estimated from the data.
-\end{itemize}
-\item \texttt{Mv}: defaults to 1. It specifies the number of periods of correlation and only needs to be specified when \texttt{corstr} is {\tt "stat\_M\_dep"}, {\tt "non\_stat\_M\_dep"}, or {\tt "AR-M"}.
-\item \texttt{R}: defaults to \texttt{NULL}. It specifies a user-defined correlation matrix rather than estimating it from the data. The argument is used only when \texttt{corstr} is {\tt "fixed"}. The input is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Example with AR-1 Dependence}
-
-Attaching the sample turnout dataset:
-<<Example.data>>=
-data(macro)
-@
-Estimating model and presenting summary:
-<<Example.zelig>>=
-z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.gee", id = "country", data = macro, robust=TRUE, corstr="AR-M", Mv=1)
-summary(z.out)
-@
-Set explanatory variables to their default (mean/mode) values, with high (80th percentile) and low (20th percentile) values:
-<<Example.setx>>=
-x.high <- setx(z.out, trade = quantile(macro$trade, 0.8))
-x.low <- setx(z.out, trade = quantile(macro$trade, 0.2))
-@
-Generate first differences for the effect of high versus low trade on GDP:
-<<Example.sim>>=
-s.out <- sim(z.out, x = x.high, x1 = x.low)
-<<Example.summary.sim>>=
-summary(s.out)
-@
-Generate a plot of quantities of interest:
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out)
-@
-\end{center}
-\end{enumerate}
-
-\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the continuous dependent variable for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
-where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
-
-\begin{itemize}
-
-\item The \emph{stochastic component} is given by the joint and marginal distributions
-\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \mu_{i})\\
-Y_{it} &\sim& g(y_{it} \mid \mu_{it})
-\end{eqnarray*}
-where $f$ and $g$ are unspecified distributions with means $\mu_{i}$ and $\mu_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-
-\item The \emph{systematic component} is the \textit{mean function}, given by:
-\begin{equation*}
-\mu_{it} = x_{it} \beta
-\end{equation*}
-where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
-and $\beta$ is the vector of coefficients.
-
-\item The \textit{variance function} is given by:
-\begin{equation*}
-V_{it} = 1
-\end{equation*}
-
-\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = 1$ as the $t$th diagonal element (in the case of GEE normal, $A_{i}$ is the identity matrix), $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-
-\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
-
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item All quantities of interest are for marginal means rather than joint means.
-\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
-\item The expected values ({\tt qi\$ev}) for the GEE normal model is the mean of simulations from the stochastic component: $$E(Y) =
- \mu_{c}= x_{c} \beta,$$ given draws of $\beta$ from
- its sampling distribution, where $x_{c}$ is a vector of values, one for
-each independent variable, chosen by the user.
-
-\item The first difference ({\tt qi\$fd}) for the GEE normal model is defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- E[Y_{it}(tr_{it}=0)] \right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the treatment
- ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_{it}(tr_{it}=0)]$,
- the counterfactual expected value of $Y_{it}$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $tr_{it}=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "normal.gee", id, data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\mu_{it}$.
- \item {\tt linear.predictors}: the vector of $x_{it}\beta$
- \item {\tt max.id}: the size of the largest cluster.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $z$-statistics.
- \item {\tt working.correlation}: the ``working'' correlation matrix
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the
- specified values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection*{How To Cite}
-
-\input{cites/normal.gee}
-\input{citeZelig}
-
-\subsection*{See also}
-The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
-
-\bibliographystyle{asa}
-\bibliography{gk}
-
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-
-
-
-\end{document}
diff --git a/inst/doc/normal.gee.log b/inst/doc/normal.gee.log
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diff --git a/inst/doc/normal.net.Rnw b/inst/doc/normal.net.Rnw
deleted file mode 100644
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--- a/inst/doc/normal.net.Rnw
+++ /dev/null
@@ -1,162 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/normalnet}
-\include{zinput}
-%\VignetteIndexEntry{Network Normal Regression for Continuous Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,least squares,continuous, regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt normal.net}: Network Normal Regression for Continuous Proximity Matrix Dependent Variables}
-
-The Network Normal regression model is a close variant of the more standard least squares regression model (see {\tt netlm}). Both models specify a continuous proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs) dependent variable as a linear function of a set of explanatory variables. The network Normal model reports maximum likelihood (rather than least squares) estimates. The two models differ only in their estimate for the stochas [...]
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "normal.net", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following additional options for network normal regression:
-
-\begin{itemize}
-\item {\tt LF}: specifies the link function to be used for the network normal regression. Default is {\tt LF="identity"}, but {\tt LF} can also be set to {\tt "log"} or {\tt "inverse"} by the user.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-data(friendship)
-
-
-
-@
-Estimate model:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(perpower ~ friends + advice + prestige, model = "normal.net", data = friendship)
-summary(z.out)
-
-@
-Setting values for the explanatory variables to their default values:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.out <- setx(z.out)
-
-@
-Simulate fitted values.
-<<echo=TRUE, results=hide, fig=FALSE>>=
-s.out <- sim(z.out, x = x.out)
-summary(s.out)
-plot(s.out)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE,echo=false, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-The {\tt normal.net} model performs a Normal regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard Normal regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the [...]
-
-Let $Y_{i}$ be the continuous dependent variable, produced by vectorizing a continuous proximity matrix, for observation $i$.
-\begin{itemize}
-\item The \emph{stochastic component} is described by a univariate normal model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
-\begin{equation*}
-Y_{i} \sim \text{Normal} ( \mu_{i}, \sigma^2).\\
-\end{equation*}
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\mu_i = x_i\beta.
-\end{equation*}
-where $ x_i$ is the vector of $k$ explanatory variables and $\beta$ is the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network Normal regression are the same as those for the standard Normal regression.
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt normal.net} model is the mean of simulations from the stochastic component,
-\begin{equation*}
-E(Y) = \mu_{i} = x_{i}\beta,
-\end{equation*}
-given a draw of $\beta$ from its posterior.
-
-\item The predicted value ({\tt qi\$pr}) is a draw from the distribution defined by the set of parameters $(\mu_i, \sigma^2)$.
-
-\item The first difference ({\tt qi\$fd}) for the network Normal model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y| x_{1}) - \text{Pr}(Y| x)
-\end{equation*}
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "normal.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the systemic component $\lambda$.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt linear.predictors}: fitted values. For the normal model, these are identical to fitted values.
-\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values drawn from the distribution defined by $(\mu_i, \sigma^2)$.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-
- \subsection* {How to Cite}
-
-\input{cites/normal.net}
-\input{citeZelig}
-\subsection* {See also}
-The network normal regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(normal.net)}. Sample data are fictional.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
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deleted file mode 100644
index 55d8ac7..0000000
--- a/inst/doc/ologit.Rnw
+++ /dev/null
@@ -1,261 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/ologit}
-\include{zinput}
-%\VignetteIndexEntry{Ordinal Logistic Regression for Ordered Categorical Dependent Variables}
-%\VignetteDepends{Zelig, MASS}
-%\VignetteKeyWords{model, logistic,regression,ordinal, ordered,categorical}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-pkg <- search()
-if(!length(grep("package:Zelig",pkg)))
-library(Zelig)
-@
-
-\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
-Categorical Dependent Variables}\label{ologit}
-
-Use the ordinal logit regression model if your dependent variable is
-ordered and categorical, either in the form of integer values or character strings.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-If {\tt Y} takes discrete integer values, the {\tt as.factor()}
-command will order automatically order the values. If {\tt Y} takes
-on values composed of character strings, such as ``strongly agree'',
-``agree'', and ``disagree'', {\tt as.factor()} will order the values
-in the order in which they appear in {\tt Y}. You will need to
-replace your dependent variable with a factored variable prior to
-estimating the model through {\tt zelig()}. See Section \ref{factors}
-for more information on creating ordered factors and Example
-\ref{ord.fact} below.
-
-\subsubsection{Example}
-
-\begin{enumerate}
-
-\item {Creating An Ordered Dependent Variable} \label{ord.fact}
-
-Load the sample data:
-<<Example.data>>=
- data(sanction)
-@
-Create an ordered dependent variable:
-<<Example.factor>>=
- sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
- levels = c("net gain", "little effect",
- "modest loss", "major loss"))
-@
-Estimate the model:
-<<Example.zelig>>=
- z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
-@
-Set the explanatory variables to their observed values:
-<<Example.setx>>=
- x.out <- setx(z.out, fn = NULL)
-@
-Simulate fitted values given {\tt x.out} and view the results:
-<<Example.sim>>=
- s.out <- sim(z.out, x = x.out)
-@
-<<Example.summary>>=
- summary(s.out)
-@
-
-\item {First Differences}
-
-Using the sample data \texttt{sanction}, estimate the empirical model and returning the coefficients:
-<<FirstDifferences.zelig>>=
- z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
- data = sanction)
-@
-<<FirstDifferences.summary>>=
-summary(z.out)
-@
-Set the explanatory variables to their means, with {\tt mil} set
-to 0 (no military action in addition to sanctions) in the baseline
-case and set to 1 (military action in addition to sanctions) in the
-alternative case:
-<<FirstDifferences.setx>>=
- x.low <- setx(z.out, mil = 0)
- x.high <- setx(z.out, mil = 1)
-@
-Generate simulated fitted values and first differences, and view the results:
-<<FirstDifferences.sim>>=
- s.out <- sim(z.out, x = x.low, x1 = x.high)
- summary(s.out)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be the ordered categorical dependent variable for
-observation $i$ that takes one of the integer values from $1$ to $J$
-where $J$ is the total number of categories.
-
-\begin{itemize}
-\item The \emph{stochastic component} begins with an unobserved continuous
- variable, $Y^*_i$, which follows the standard logistic distribution
- with a parameter $\mu_i$,
- \begin{equation*}
- Y_i^* \; \sim \; \textrm{Logit}(y_i^* \mid \mu_i),
- \end{equation*}
- to which we add an observation mechanism
- \begin{equation*}
- Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
- \quad {\rm for} \quad j=1,\dots,J.
- \end{equation*}
- where $\tau_l$ (for $l=0,\dots,J$) are the threshold parameters with
- $\tau_l < \tau_m$ for all $l<m$ and $\tau_0=-\infty$ and
- $\tau_J=\infty$.
-
-\item The \emph{systematic component} has the following form, given
- the parameters $\tau_j$ and $\beta$, and the explanatory variables $x_i$:
- \begin{equation*}
- \Pr(Y \le j) \; = \; \Pr(Y^* \le \tau_j) \; = \frac{\exp(\tau_j -
- x_i \beta)}{1+\exp(\tau_j -x_i \beta)},
- \end{equation*}
- which implies:
- \begin{equation*}
- \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}{1 + \exp(\tau_j -
- x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
- \exp(\tau_{j-1} - x_i \beta)}.
- \end{equation*}
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the ordinal logit model
- are simulations of the predicted probabilities for each category:
-\begin{equation*}
-E(Y = j) \; = \; \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}
-{1 + \exp(\tau_j - x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
- \exp(\tau_{j-1} - x_i \beta)},
-\end{equation*}
-given a draw of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from the logit
- distribution described by $\mu_i$, and observed as one of $J$
- discrete outcomes.
-
-\item The difference in each of the predicted probabilities ({\tt
- qi\$fd}) is given by
- \begin{equation*}
- \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
- j=1,\dots,J.
- \end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "ologit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt zeta}: a vector containing the estimated class
- boundaries $\tau_j$.
- \item {\tt deviance}: the residual deviance.
- \item {\tt fitted.values}: the $n \times J$ matrix of in-sample
- fitted values.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt edf}: the effective degrees of freedom.
- \item {\tt Hessian}: the Hessian matrix.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, and $t$-statistics.
- \end{itemize}
-
- \item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/ologit}
-\input{citeZelig}
-
-\subsection* {See also}
-The ordinal logit model is part of the MASS package by William N. Venable and Brian D. Ripley \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(polr)} as well as \cite{McCNel89}. Sample data are from \cite{Martin92}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
diff --git a/inst/doc/ologit.log b/inst/doc/ologit.log
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-h, portuges, romanian, russian, serbian, slovak, slovene, spanish, swedish, tur
-kish, ukrainian, nohyphenation, loaded.
-\@input{zinput.aux}
-\openout2 = `zinput.aux'.
-
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-\c at chapter=\count80
-\c at section=\count81
-\c at subsection=\count82
-\c at subsubsection=\count83
-\c at paragraph=\count84
-\c at subparagraph=\count85
-\c at figure=\count86
-\c at table=\count87
-\abovecaptionskip=\skip41
-\belowcaptionskip=\skip42
-\bibindent=\dimen102
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-\T1/aer/m/n/12 us-ing \T1/aett/m/n/12 z.out$coefficients\T1/aer/m/n/12 , and a
-de-fault sum-mary of in-for-ma-tion through \T1/aett/m/n/12 summary(z.out)\T1/a
-er/m/n/12 .
- []
-
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-
-]
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-
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diff --git a/inst/doc/ologit.pdf b/inst/doc/ologit.pdf
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diff --git a/inst/doc/ologit.tex b/inst/doc/ologit.tex
new file mode 100644
index 0000000..3e72e7f
--- /dev/null
+++ b/inst/doc/ologit.tex
@@ -0,0 +1,106 @@
+
+\include{zinput}
+%\VignetteIndexEntry{Ordinal Logistic Regression for Ordered Categorical Dependent Variables}
+%\VignetteDepends{Zelig, MASS}
+%\VignetteKeyWords{model, logistic,regression,ordinal, ordered,categorical}
+%\VignettePackage{Zelig}
+\usepackage{Sweave}
+\begin{document}
+\nobibliography*
+
+
+\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
+Categorical Dependent Variables}\label{ologit}
+
+Use the ordinal logit regression model if your dependent variable is
+ordered and categorical, either in the form of integer values or character strings.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+If {\tt Y} takes discrete integer values, the {\tt as.factor()}
+command will order automatically order the values. If {\tt Y} takes
+on values composed of character strings, such as ``strongly agree'',
+``agree'', and ``disagree'', {\tt as.factor()} will order the values
+in the order in which they appear in {\tt Y}. You will need to
+replace your dependent variable with a factored variable prior to
+estimating the model through {\tt zelig()}. See Section \ref{factors}
+for more information on creating ordered factors and Example
+\ref{ord.fact} below.
+
+\subsubsection{Example}
+
+\begin{enumerate}
+
+\item {Creating An Ordered Dependent Variable} \label{ord.fact}
+
+Load the sample data:
+\begin{Schunk}
+\begin{Sinput}
+> data(sanction)
+\end{Sinput}
+\end{Schunk}
+Create an ordered dependent variable:
+\begin{Schunk}
+\begin{Sinput}
+> sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain",
++ "little effect", "modest loss", "major loss"))
+\end{Sinput}
+\end{Schunk}
+Estimate the model:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their observed values:
+\begin{Schunk}
+\begin{Sinput}
+> x.out <- setx(z.out, fn = NULL)
+\end{Sinput}
+\end{Schunk}
+Simulate fitted values given {\tt x.out} and view the results:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.out)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(s.out)
+\end{Sinput}
+\end{Schunk}
+
+\item {First Differences}
+
+Using the sample data \texttt{sanction}, estimate the empirical model and returning the coefficients:
+\begin{Schunk}
+\begin{Sinput}
+> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
++ data = sanction)
+\end{Sinput}
+\end{Schunk}
+\begin{Schunk}
+\begin{Sinput}
+> summary(z.out)
+\end{Sinput}
+\end{Schunk}
+Set the explanatory variables to their means, with {\tt mil} set
+to 0 (no military action in addition to sanctions) in the baseline
+case and set to 1 (military action in addition to sanctions) in the
+alternative case:
+\begin{Schunk}
+\begin{Sinput}
+> x.low <- setx(z.out, mil = 0)
+> x.high <- setx(z.out, mil = 1)
+\end{Sinput}
+\end{Schunk}
+Generate simulated fitted values and first differences, and view the results:
+\begin{Schunk}
+\begin{Sinput}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+> summary(s.out)
\ No newline at end of file
diff --git a/inst/doc/oprobit.Rnw b/inst/doc/oprobit.Rnw
deleted file mode 100644
index 98b1c6b..0000000
--- a/inst/doc/oprobit.Rnw
+++ /dev/null
@@ -1,275 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/oprobit}
-\include{zinput}
-%\VignetteIndexEntry{Ordinal Probit Regression for Ordered Categorical Dependent Variables}
-%\VignetteDepends{Zelig, MASS}
-%\VignetteKeyWords{model, probit,regression,ordinal, ordered,categorical}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt oprobit}: Ordinal Probit Regression for Ordered
-Categorical Dependent Variables}\label{oprobit}
-
-Use the ordinal probit regression model if your dependent variables
-are ordered and categorical. They may take on either integer values
-or character strings. For a Bayesian implementation of this model,
-see \Sref{oprobit.bayes}.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "oprobit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-If {\tt Y} takes discrete integer values, the {\tt as.factor()}
-command will order it automatically. If {\tt Y} takes on values
-composed of character strings, such as ``strongly agree'', ``agree'',
-and ``disagree'', {\tt as.factor()} will order the values in the order
-in which they appear in {\tt Y}. You will need to replace your
-dependent variable with a factored variable prior to estimating the
-model through {\tt zelig()}. See \Sref{factors} for more information
-on creating ordered factors and Example \ref{ord.fact.p} below.
-
-\subsubsection{Example}
-\begin{enumerate}
-\item {Creating An Ordered Dependent Variable} \label{ord.fact.p}
-
-Load the sample data:
-<<Example.data>>=
- data(sanction)
-@
-Create an ordered dependent variable:
-<<Example.factor>>=
- sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
- levels = c("net gain", "little effect",
- "modest loss", "major loss"))
-@
-Estimate the model:
-<<Example.zelig>>=
- z.out <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanction)
- summary(z.out)
-@
-Set the explanatory variables to their observed values:
-<<Example.setx>>=
- x.out <- setx(z.out, fn = NULL)
-@
-Simulate fitted values given {\tt x.out} and view the results:
-<<Example.sim>>=
- s.out <- sim(z.out, x = x.out)
- summary(s.out)
-@
-%plot does not work but is nnot included in the demo
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=false>>=
-# plot(s.out)
-@
-\end{center}
-
-\item {First Differences}
-
-Using the sample data \texttt{sanction}, let us estimate the empirical model and return the coefficients:
-<<FirstDifferences.zelig>>=
- z.out <- zelig(as.factor(cost) ~ mil + coop, model = "oprobit",
- data = sanction)
-@
-<<FirstDifferences.summary>>=
-summary(z.out)
-@
-Set the explanatory variables to their means, with {\tt mil} set
-to 0 (no military action in addition to sanctions) in the baseline
-case and set to 1 (military action in addition to sanctions) in the
-alternative case:
-<<FirstDifferences.setx>>=
- x.low <- setx(z.out, mil = 0)
- x.high <- setx(z.out, mil = 1)
-@
-Generate simulated fitted values and first differences, and view the results:
-<<FirstDifferences.sim>>=
- s.out <- sim(z.out, x = x.low, x1 = x.high)
-@
-<<FirstDifferences.summary.sim>>=
-summary(s.out)
-@
-\begin{center}
-<<label=FirstDifferencesPlot,fig=true,echo=true>>=
- plot(s.out)
-@
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
- Let $Y_i$ be the ordered categorical dependent variable for
- observation $i$ that takes one of the integer values from $1$ to $J$
- where $J$ is the total number of categories.
-\begin{itemize}
-\item The \emph{stochastic component} is described by an unobserved continuous
- variable, $Y^*_i$, which follows the normal distribution with mean
- $\mu_i$ and unit variance
- \begin{equation*}
- Y_i^* \; \sim \; N(\mu_i, 1).
- \end{equation*}
- The observation mechanism is
- \begin{equation*}
- Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
- \quad {\rm for} \quad j=1,\dots,J.
- \end{equation*}
- where $\tau_k$ for $k=0,\dots,J$ is the threshold parameter with the
- following constraints; $\tau_l < \tau_m$ for all $l<m$ and
- $\tau_0=-\infty$ and $\tau_J=\infty$.
-
- Given this observation mechanism, the probability for each category,
- is given by
- \begin{equation*}
- \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i) - \Phi(\tau_{j-1} \mid
- \mu_i) \quad {\rm for} \quad j=1,\dots,J
- \end{equation*}
- where $\Phi(\mu_i)$ is the cumulative distribution function for the
- Normal distribution with mean $\mu_i$ and unit variance.
-
-\item The \emph{systematic component} is given by
- \begin{equation*}
- \mu_i \; = \; x_i \beta
- \end{equation*}
- where $x_i$ is the vector of explanatory variables and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the ordinal probit model
- are simulations of the predicted probabilities for each category:
-\begin{equation*}
- E(Y_i = j) \; = \; \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i)
- - \Phi(\tau_{j-1} \mid \mu_i) \quad {\rm for} \quad j=1,\dots,J,
-\end{equation*}
-given draws of $\beta$ from its posterior.
-
-\item The predicted value ({\tt qi\$pr}) is the observed value of
- $Y_i$ given the underlying standard normal distribution described by
- $\mu_i$.
-
-\item The difference in each of the predicted probabilities ({\tt
- qi\$fd}) is given by
- \begin{equation*}
- \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
- j=1,\dots,J.
- \end{equation*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "oprobit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
- predictors $x_i \beta_j$.
- \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
- \item {\tt zeta}: a vector containing the estimated class boundaries.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times J$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
-\end{itemize}
-
- \item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection*{How to Cite}
-\input{cites/oprobit}
-\input{citeZelig}
-\subsection*{See also}
-The ordinal probit function is part of the VGAM package by Thomas Yee \citep{YeeHas03}. In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM library. Additional documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.Sample data are from \cite{Martin92}
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
diff --git a/inst/doc/oprobit.bayes.Rnw b/inst/doc/oprobit.bayes.Rnw
deleted file mode 100644
index 990b3f9..0000000
--- a/inst/doc/oprobit.bayes.Rnw
+++ /dev/null
@@ -1,307 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/oprobitBayes}
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Ordered Probit Regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model, bayes,regression,ordinal, ordered,categorical}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{oprobit.bayes}: Bayesian Ordered Probit Regression}
-
-\label{oprobit.bayes}
-
-Use the ordinal probit regression model if your dependent variables are ordered and
-categorical. They may take either integer values or character strings. The model
-is estimated using a Gibbs sampler with data augmentation. For a
-maximum-likelihood implementation of this models, see \Sref{oprobit}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "oprobit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-
-\subsubsection{Additional Inputs}
-
-{\tt zelig()} accepts the following arguments to monitor the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item{\texttt{tune}}: tuning parameter for the Metropolis-Hasting step.
-The default value is \texttt{NA} which corresponds to 0.05 divided by
-the number of categories in the response variable.
-
-\item \texttt{verbose}: defaults to {\tt FALSE} If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, which uses the
-maximum likelihood estimates as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will be the prior
-mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with dimensions equal to the number of
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0 which leads to an improper prior.
-\end{itemize}
-
-\noindent Zelig users may wish to refer to \texttt{help(MCMCoprobit)}
-for more information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<BasicExample.data>>=
- data(sanction)
-@
-Estimating ordered probit regression using \texttt{oprobit.bayes}:
-<<BasicExample.zelig>>=
- z.out <- zelig(ncost ~ mil + coop, model = "oprobit.bayes",
- data = sanction, verbose=TRUE)
-@
-
-Creating an ordered dependent variable:
-<<BasicExample.factor>>=
-sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
- levels = c("net gain", "little effect",
- "modest loss", "major loss"))
-@
-
-Checking for convergence before summarizing the estimates:
-<<BasicExample.heidel>>=
-heidel.diag(z.out$coefficients)
-@
-<<BasicExample.raftery>>=
-raftery.diag(z.out$coefficients)
-@
-<<BasicExample.summary>>=
-summary(z.out)
-@
-Setting values for the explanatory variables to their sample averages:
-<<BasicExample.setx>>=
- x.out <- setx(z.out)
-@
-Simulating quantities of interest from the posterior distribution given:
-\texttt{x.out}.
-<<BasicExample.sim>>=
- s.out1 <- sim(z.out, x = x.out)
- summary(s.out1)
-@
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in the probabilities of
-incurring different level of cost when there is no military action versus
-military action while all the other variables held at their
-default values.
-
-<<FirstDifferences.setx>>=
- x.high <- setx(z.out, mil=0)
- x.low <- setx(z.out, mil=1)
-@
-<<FirstDifferences.sim>>=
-s.out2 <- sim(z.out, x = x.high, x1 = x.low)
- summary(s.out2)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_{i}$ be the ordered categorical dependent variable for
-observation $i$ which takes an integer value $j=1, \ldots, J$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by an unobserved
-continuous variable, $Y_i^*$,
-\begin{eqnarray*}
-Y_{i}^* \sim \textrm{Normal}(\mu_i, 1).
-\end{eqnarray*}
-Instead of $Y_i^*$, we observe categorical variable $Y_i$,
-\begin{eqnarray*}
-Y_i = j \quad \textrm{ if } \tau_{j-1} \le Y_i^* \le \tau_j \textrm{
-for } j=1,\ldots, J.
-\end{eqnarray*}
-where $\tau_j$ for $j=0,\ldots, J$ are the threshold parameters with
-the following constraints, $\tau_l < \tau_m$ for $l < m$, and
-$\tau_0=-\infty, \tau_J=\infty$.
-
-The probability of observing $Y_i$ equal to category $j$ is,
-\begin{eqnarray*}
-\Pr(Y_i=j) &=& \Phi(\tau_j \mid \mu_i)-\Phi(\tau_{j-1} \mid \mu_i)
-\textrm{ for } j=1,\ldots, J
-\end{eqnarray*}
-where $\Phi(\cdot \mid \mu_i)$ is the cumulative distribution function
-of the Normal distribution with mean $\mu_i$ and variance 1.
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\mu_{i}= x_i \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the ordered probit model are
-the predicted probability of belonging to each category:
-\begin{eqnarray*}
-\Pr(Y_i=j)= \Phi(\tau_j \mid x_i \beta)-\Phi(\tau_{j-1} \mid x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ and threshold parameters $\tau$
-from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are the observed values of
-$Y_i$ given the observation scheme and the posterior draws of $\beta$
-and cut points $\tau$ from the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) in category $j$ for the
-ordered probit model is defined as
-\begin{eqnarray*}
-\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
-\begin{eqnarray*}
-\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j} \{
-Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of observations in the treatment group that belong to category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of observations in the treatment group that belong to category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "oprobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior
-distribution of the \texttt{coefficients} by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated coefficients $\beta$ and threshold parameters $\tau$.
-Note, element $\tau_1$ is normalized to 0 and is not returned in the
-\texttt{coefficients} object.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values (probabilities) of
-each of the $J$ categories for the specified values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values (observed values)
- for the specified values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values of each of the $J$ categories for the values specified in
-\texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the
-expected values of each of the $J$ categories simulated
-from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-
-\subsection*{How to Cite}
-\input{cites/oprobit.bayes}
-\input{citeZelig}
-\subsection*{See also}
-Bayesian ordinal probit regression is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}.
-The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}.
-
-
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-%%% Local Variables:
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-%%% TeX-master: t
-%%% End:
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diff --git a/inst/doc/oprobit.pdf b/inst/doc/oprobit.pdf
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diff --git a/inst/doc/otherworks.bib b/inst/doc/otherworks.bib
new file mode 100644
index 0000000..6618d8e
--- /dev/null
+++ b/inst/doc/otherworks.bib
@@ -0,0 +1,8 @@
+ at book{FitLaiWar04,
+ author = {Garret Fitzmaurice, Nan Laird and James Ware},
+ title = {Applied Longitudinal Analysis},
+ booktitle = {Applied Longitudinal Analysis},
+ year = {2004},
+ publisher = {John Wiley and Sons}
+}
+
diff --git a/inst/doc/poisson.bayes.Rnw b/inst/doc/poisson.bayes.Rnw
deleted file mode 100644
index bcd0b7b..0000000
--- a/inst/doc/poisson.bayes.Rnw
+++ /dev/null
@@ -1,269 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/poissonBayes}
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Poisson Regression}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, poisson,bayes,count,Metropolis}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{poisson.bayes}: Bayesian Poisson Regression}
-
-\label{poisson.bayes}
-
-Use the Poisson regression model if the observations of your dependent
-variable represents the number of independent events that occur during
-a fixed period of time. The model is fit using a random walk
-Metropolis algorithm. For a maximum-likelihood estimation of this
-model see \Sref{poisson}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "poisson.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following argument to monitor the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: Metropolis tuning parameter, either
-a positive scalar or a vector of length $k$, where $k$ is the number
-of coefficients. The tuning parameter should be set such that the
-acceptance rate of the Metropolis algorithm is satisfactory (typically
-between 0.20 and 0.5). The default value is 1.1.
-
-\item \texttt{verbose}: default to {\tt FALSE}.
-If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
-printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the
-maximum likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will be the prior mean for
-all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCpoisson)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<BasicExample.data>>=
- data(sanction)
-@
-Estimating the Poisson regression using \texttt{poisson.bayes}:
-<<BasicExample.zelig>>=
- z.out <- zelig(num ~ target + coop, model = "poisson.bayes",
- data = sanction, verbose = TRUE)
-@
-Checking convergence diagnostics before summarizing the estimates:
-<<BasicExample.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<BasicExample.heidel>>=
-heidel.diag(z.out$coefficients)
-@
-<<BasicExample.raftery>>=
-raftery.diag(z.out$coefficients)
-@
-<<BasicExample.summary>>=
-summary(z.out)
-@
-Setting values for the explanatory variables to their sample averages:
-<<BasicExample.setx>>=
- x.out <- setx(z.out)
-@
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-<<BasicExample.sim>>=
- s.out1 <- sim(z.out, x = x.out)
-@
-<<BasicExample.summary.sim>>=
-summary(s.out1)
-@
-\item {Simulating First Differences} \\
-Estimating the first difference in the number of countries imposing sanctions
-when the number of targets is set to be its maximum versus its minimum :
-<<FirstDifferences.setx>>=
- x.max <- setx(z.out, target = max(sanction$target))
- x.min <- setx(z.out, target = min(sanction$target))
-@
-<<FirstDifferences.sim>>=
- s.out2 <- sim(z.out, x = x.max, x1 = x.min)
- summary(s.out2)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the number of independent events that occur during
-a fixed time period.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \textrm{Poisson}(\lambda_i)
-\end{eqnarray*}
-where $\lambda_i$ is the mean and variance parameter.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\lambda_{i}= \exp(x_{i} \beta)
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the Poisson model are
-calculated as following:
-\begin{eqnarray*}
-E(Y\mid X) = \lambda_i = \exp(x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ based on the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Poisson
-distribution with parameter $\lambda_i$.
-
-\item The first difference (\texttt{qi\$fd}) for the Poisson model is defined
-as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}\{Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]\},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "poisson.bayes", data)
-\end{verbatim}
-
-\noindent you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
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-\subsection*{See also}
-Bayesian poisson regression is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}.
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-\SweaveOpts{results=hide, prefix.string=vigpics/gam}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Additive Model for Count Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, gam, poisson.gam}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt poisson.gam}: Generalized Additive Model for Count Dependent Variables}\label{gam.poisson}
-
-This function runs a nonparametric Generalized Additive Model (GAM) for count dependent variables.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + s(x2), model = "poisson.gam", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Where {\tt s()} indicates a variable to be estimated via nonparametric smooth. All variables for which {\tt s()} is not specified, are estimated via standard parametric methods.
-
-\subsubsection{Additional Inputs}
-In addition to the standard inputs, {\tt zelig()} takes the following additional options for GAM models.
-\begin{itemize}
-\item {\tt method}: Controls the fitting method to be used. Fitting methods are selected via a list environment within {\tt method=gam.method()}. See {\tt gam.method()} for details.
-\item {\tt scale}: Generalized Cross Validation (GCV) is used if {\tt scale = 0} (see the ``Model'' section for details) except for Poisson models where a Un-Biased Risk Estimator (UBRE) (also see the ``Model'' section for details) is used with a scale parameter assumed to be 1. If {\tt scale} is greater than 1, it is assumed to be the scale parameter/variance and UBRE is used. If {\tt scale} is negative GCV is used.
-\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
-\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
-\item {\tt sp}: A vector of smoothing parameters for each term.
-\item {\tt \ldots}: additional options passed to the {\tt poisson.gam} model. See the {\tt mgcv} library for details.
-\end{itemize}
-
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Create some count data:
-
-<<echo=FALSE, results=hide>>=
-library(Zelig)
-<<echo=FALSE, results=hide>>=
-set.seed(0); n<-400; sig<-2
-x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1); x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
-f0 <- function(x) 2 * sin(pi * x)
-f1 <- function(x) exp(2 * x)
-f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
-f3 <- function(x) 0*x
-f <- f0(x0) + f1(x1) + f2(x2)
-g<-exp(f/4)
-y<-rpois(rep(1,n),g)
-my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
-
-@
-\begin{Schunk}
-\begin{Sinput}
-> set.seed(0); n <- 400; sig <- 2;
-> x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
-> x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
-> f0 <- function(x) 2 * sin(pi * x)
-> f1 <- function(x) exp(2 * x)
-> f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 * (10 *
-+ x)^3 * (1 - x)^10
-> f3 <- function(x) 0 * x
-> f <- f0(x0) + f1(x1) + f2(x2)
-> g <- exp(f/4); y <- rpois(rep(1, n), g)
-> my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
-\end{Sinput}
-\end{Schunk}
-
-Estimate the model, summarize the results, and plot nonlinearities:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="poisson.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1,residuals=TRUE)
-@
-Note that the {\tt plot()} function can be used after model estimation and before simulation to view the nonlinear relationships in the independent variables:
-\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=6, height=6>>=
-plot(z.out,pages=1,residuals=TRUE)
-@
-\label{fig:plotgam}
-\end{figure}
-
-Set values for the explanatory variables to their default (mean/mode) values, then simulate, summarize and plot quantities of interest:
-<<echo=TRUE, results=hide, fig=FALSE>>=
- x.out <- setx(z.out)
- s.out <- sim(z.out, x = x.out)
- summary(s.out)
- plot(s.out)
-@
-\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low values (20th percentile) and high values (80th percentile) of the explanatory variable {\tt x3} while all the other variables are held at their default (mean/mode) values.
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
-x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
-
-s.out <- sim(z.out, x=x.high, x1=x.low)
-summary(s.out)
-plot(s.out)
-
-@
-%\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-@
-\item Variations in GAM model specification. Note that {\tt setx} and {\tt sim} work as shown in the above examples for any GAM model. As such, in the interest of parsimony, I will not re-specify the simulations of quantities of interest.
-
-An extra ridge penalty (useful with convergence problems):
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
- model="poisson.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1,residuals=TRUE)
-
-@
-Set the smoothing parameter for the first term, estimate the rest:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
- model="poisson.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1)
-
-@
-Set lower bounds on smoothing parameters:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
- model="poisson.gam", data=my.data)
-summary(z.out)
-plot(z.out, pages=1)
-
-@
-A GAM with 3df regression spline term \& 2 penalized terms:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
- model="poisson.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1)
-
-@
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-
-
-GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt poisson.gam} a Poisson link function is used. Specifically, let $Y_{i}$ be the dependent variable for observation $i$. $Y_i$ is thus the number of independent events that occur during a fixed time period. This variable can take any non-negative integer.
-\begin{itemize}
-\item The Poisson distribution has \emph{stochastic component}
-\begin{equation*}
-Y_{i} \sim \text{Poisson} ( \lambda_{i}),\\
-\end{equation*}
-where $\lambda_{i}$ is the mean and variance parameter.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\lambda_i = \exp \left(x_i\beta + \sum_{j=1}^{J} f_j(Z_j) \right).
-\end{equation*}
-where $ x_i$ is the vector of explanatory variables, $\beta$ is the vector of coefficients and $f_j(Z_j)$ for $j=1, \ldots J$ is the set of smooth terms.
-\end{itemize}
-
-Generalized additive models (GAMs) are similar in many respects to generalized linear models (GLMs). Specifically, GAMs are generally fit by penalized maximum likelihood estimation and GAMs have (or can have) a parametric component identical to that of a GLM. The difference is that GAMs also include in their linear predictors a specified sum of smooth functions.
-
-In this GAM implementation, smooth functions are represented using penalized regression splines. Two techniques may be used to estimate smoothing parameters: Generalized Cross Validation (GCV),
-\begin{equation}
-n \frac{D}{(n-DF)^2},
-\end{equation}
-or an Un-Biased Risk Estimator (UBRE) (which is effectively just a rescaled AIC),
-\begin{equation}
-\frac{D}{n} + 2 s \frac{DF}{n-s},
-\end{equation}
-where $D$ is the deviance, $n$ is the number of observations, $s$ is the scale parameter, and $DF$ is the effective degrees of freedom of the model. The use of GCV or UBRE can be set by the user with the {\tt scale} command described in the ``Additional Inputs'' section and in either case, smoothing parameters are chosen to minimize the GCV or UBRE score for the model.
-
-Estimation for GAM models proceeds as follows: first, basis functions and a set (one or more) of quadratic penalty coefficient matrices are constructed for each smooth term. Second, a model matrix is is obtained for the parametric component of the GAM. These matrices are combined to produce a complete model matrix and a set of penalty matrices for the smooth terms. Iteratively Reweighted Least Squares (IRLS) is then used to estimate the model; at each iteration of the IRLS, a penalized w [...]
-
-Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
-
-
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the {\tt poisson.gam} model are the same as those for the standard Poisson regression.
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt poisson.gam} model is the mean of simulations from the stochastic component,
-\begin{equation*}
-E(Y) = \lambda_{i} = \exp \left(x_{i}\beta \sum_{j=1}^{J} f_j(Z_j) \right).
-\end{equation*}
-
-
-\item The predicted value ({\tt qi\$pr}) is a random draw from the Poisson distribution defined by mean $\lambda_i$.
-
-\item The first difference ({\tt qi\$fd}) for the poisson.gam model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
-\end{equation*}
-for $w=\{X, Z\}$.
-\end{itemize}
-
-
-
-\subsubsection{Output Values}
-
-
-
-The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "poisson.gam", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IRLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt method}: the fitting method used.
-\item {\tt converged}: logical indicating weather the model converged or not.
-\item {\tt smooth}: information about the smoothed parameters.
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt data}: the input data frame.
-\item {\tt model}: the model matrix used.
-
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt p.coeff}: the coefficients of the parametric components of the model.
-\item {\tt se}: the standard errors of the entire model.
-\item {\tt p.table}: the coefficients, standard errors, and associated $t$ statistics for the parametric portion of the model.
-\item {\tt s.table}: the table of estimated degrees of freedom, estimated rank, $F$ statistics, and $p$-values for the nonparametric portion of the model.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-
-
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/poisson.gam}
-\input{citeZelig}
-\subsection* {See also}
-The {\tt gam.logit} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
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-\SweaveOpts{results=hide, prefix.string=vigpics/poisson_gee}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Estimating Equation for Poisson Regression}
-%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, poisson regression}
-%\VignettePackage{Zelig}
-\begin{document}
-
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{poisson.gee}: Generalized Estimating Equation for Poisson Regression}
-\label{poisson.gee}
-
-The GEE poisson estimates the same model as the standard poisson
-regression (appropriate when your dependent variable represents
-the number of independent events that occur during a fixed period of time). Unlike in poisson
-regression, GEE poisson allows for dependence within clusters, such as
-in longitudinal data, although its use is not limited to just
-panel data. The user must first specify a ``working''
-correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
-the standard errors can be obtained using a robust ``sandwich''
-estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "poisson.gee",
- id = "X3", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\noindent where \texttt{id} is a variable which identifies the clusters. The data should be sorted by \texttt{id} and should be ordered within each cluster when appropriate.
-
-\subsubsection{Additional Inputs}
-
-\begin{itemize}
-\item \texttt{robust}: defaults to \texttt{TRUE}. If \texttt{TRUE}, consistent standard errors are estimated using a ``sandwich'' estimator.
-\end{itemize}
-Use the following arguments to specify the structure of the ``working'' correlations within clusters:
-
-\begin{itemize}
-\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
-\begin{itemize}
-\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard poisson regression. The ``working'' correlation matrix is the identity matrix.
-\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
-\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
- $${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{|t-t'|} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
- If (\texttt{corstr = "stat\_M\_dep"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. Choose this option when the correlations are assumed to be the same for observations of the same $|t-t'|$ periods apart for $|t-t'| \leq m$.
-\begin{center}
-Sample ``working'' correlation for Stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_1 & \alpha_2 & 0 & 0 \\
-\alpha_1 & 1 & \alpha_1 & \alpha_2 & 0 \\
-\alpha_2 & \alpha_1 & 1 & \alpha_1 & \alpha_2 \\
-0 & \alpha_2 & \alpha_1 & 1 & \alpha_1 \\
-0 & 0 & \alpha_2 & \alpha_1 & 1
-\end{array} \right) $
-\end{center}
-
-\item Non-stationary $m$ dependent (\texttt{corstr =
- "non\_stat\_M\_dep"}):
-$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{tt'} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
-If (\texttt{corstr = "non\_stat\_M\_dep"}), you must also specify
-\texttt{Mv} = $m$, where $m$ is the number of periods $t$ of
-dependence. This option relaxes the assumption that the
-correlations are the same for all observations of the same $|t-t'|$
-periods apart.
-\begin{center}
-Sample ``working'' correlation for Non-stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_{12} & \alpha_{13} & 0 & 0 \\
-\alpha_{12} & 1 & \alpha_{23} & \alpha_{24} & 0 \\
-\alpha_{13} & \alpha_{23} & 1 & \alpha_{34} & \alpha_{35} \\
-0 & \alpha_{24} & \alpha_{34} & 1 & \alpha_{45} \\
-0 & 0 & \alpha_{35} & \alpha_{45} & 1
-\end{array} \right) $
-\end{center}
-\item Exchangeable (\texttt{corstr = "exchangeable"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha$, $\forall t, t'$ with $t\ne t'$. Choose this option if the correlations are assumed to be the same for all observations within the cluster.
-\begin{center}
-Sample ``working'' correlation for Exchangeable\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha & \alpha & \alpha \\
-\alpha & 1 & \alpha & \alpha & \alpha \\
-\alpha & \alpha & 1 & \alpha & \alpha \\
-\alpha & \alpha & \alpha & 1 & \alpha \\
-\alpha & \alpha & \alpha & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Stationary $m$th order autoregressive (\texttt{corstr = "AR-M"}): If (\texttt{corstr = "AR-M"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. For example, the first order autoregressive model (AR-1) implies ${\rm cor}(y_{it},
- y_{it'})=\alpha^{|t-t'|}, \forall t, t'$ with $t\ne t'$. In AR-1, observation 1 and observation 2 have a correlation of $\alpha$. Observation 2 and observation 3 also have a correlation of $\alpha$. Observation 1 and observation 3 have a correlation of $\alpha^2$, which is a function of how 1 and 2 are correlated ($\alpha$) multiplied by how 2 and 3 are correlated ($\alpha$). Observation 1 and 4 have a correlation that is a function of the correlation between 1 and 2, 2 and 3, and [...]
-\begin{center}
-Sample ``working'' correlation for Stationary AR-1 ({\tt Mv}=1)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha^2 & \alpha^3 & \alpha^4 \\
-\alpha & 1 & \alpha & \alpha^2 & \alpha^3 \\
-\alpha^2 & \alpha & 1 & \alpha & \alpha^2 \\
-\alpha^3 & \alpha^2 & \alpha & 1 & \alpha \\
-\alpha^4 & \alpha^3 & \alpha^2 & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Unstructured (\texttt{corstr = "unstructured"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha_{tt'}$, $\forall t, t'$ with
- $t\ne t'$. No constraints are placed on the correlations, which are then estimated from the data.
-\end{itemize}
-\item \texttt{Mv}: defaults to 1. It specifies the number of periods of correlation and only needs to be specified when \texttt{corstr} is {\tt "stat\_M\_dep"}, {\tt "non\_stat\_M\_dep"}, or {\tt "AR-M"}.
-\item \texttt{R}: defaults to \texttt{NULL}. It specifies a user-defined correlation matrix rather than estimating it from the data. The argument is used only when \texttt{corstr} is {\tt "fixed"}. The input is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Example with Exchangeable Dependence}
-
-Attaching the sample turnout dataset:
-<<Example.data>>=
-data(sanction)
-@
-Variable identifying clusters
-<<Example.cluster>>=
-sanction$cluster <- c(rep(c(1:15),5),rep(c(16),3))
-@
-Sorting by cluster
-<<Example.sort>>=
-sorted.sanction <- sanction[order(sanction$cluster),]
-@
-Estimating model and presenting summary:
-<<Example.zelig>>=
-z.out <- zelig(num ~ target + coop, model = "poisson.gee", id = "cluster", data = sorted.sanction, robust=TRUE, corstr="exchangeable")
-summary(z.out)
-@
-Set explanatory variables to their default values:
-<<Example.setx>>=
-x.out <- setx(z.out)
-@
-Simulate quantities of interest
-<<Example.sim>>=
-s.out <- sim(z.out, x = x.out)
-summary(s.out)
-@
-Generate a plot of quantities of interest:
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out)
-@
-\end{center}
-\end{enumerate}
-
-\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the dependent variable of the number of independent events for a fixed period of time for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
-where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
-
-\begin{itemize}
-
-\item The \emph{stochastic component} is given by the joint and marginal distributions
-\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \lambda_{i})\\
-Y_{it} &\sim& g(y_{it} \mid \lambda_{it})
-\end{eqnarray*}
-where $f$ and $g$ are unspecified distributions with means $\lambda_{i}$ and $\lambda_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-
-\item The \emph{systematic component} is the \textit{mean function}, given by:
-\begin{equation*}
-\lambda_{it} = \mathrm{exp}(x_{it} \beta)
-\end{equation*}
-where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
-and $\beta$ is the vector of coefficients.
-
-\item The \textit{variance function} is given by:
-\begin{equation*}
-V_{it} = \lambda_{it}
-\end{equation*}
-
-\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = \lambda_{it}$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-
-\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
-
-\end{itemize}
-
-
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item All quantities of interest are for marginal means rather than joint means.
-\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
-\item The expected values ({\tt qi\$ev}) for the GEE poisson model is the mean of simulations from the stochastic component: $$E(Y) =
- \lambda_{c} = \mathrm{exp}(x_{c} \beta),$$ given draws of $\beta$ from
- its sampling distribution, where $x_{c}$ is a vector of values, one for
-each independent variable, chosen by the user.
-
-\item The first difference ({\tt qi\$fd}) for the GEE poisson model is defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- E[Y_{it}(tr_{it}=0)] \right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the treatment
- ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_{it}(tr_{it}=0)]$,
- the counterfactual expected value of $Y_{it}$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $tr_{it}=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "poisson.gee", id, data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\lambda_{it}$.
- \item {\tt linear.predictors}: the vector of $x_{it}\beta$
- \item {\tt max.id}: the size of the largest cluster.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $z$-statistics.
- \item {\tt working.correlation}: the ``working'' correlation matrix
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the
- specified values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection*{How To Cite}
-
-\input{cites/poisson.gee}
-\input{citeZelig}
-
-\subsection*{See also}
-The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{Martin92}. Please inquire with Lisa Martin before publishing
-results from these data, as this dataset includes errors that have since
-been corrected.
-
-\bibliographystyle{asa}
-\bibliography{gk}
-
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-
-\end{document}
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deleted file mode 100644
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--- a/inst/doc/poisson.net.Rnw
+++ /dev/null
@@ -1,251 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/ologit}
-\include{zinput}
-%\VignetteIndexEntry{Ordinal Logistic Regression for Ordered Categorical Dependent Variables}
-%\VignetteDepends{Zelig, MASS}
-%\VignetteKeyWords{model, logistic,regression,ordinal, ordered,categorical}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-pkg <- search()
-if(!length(grep("package:Zelig",pkg)))
-library(Zelig)
-@
-
-\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
-Categorical Dependent Variables}\label{ologit}
-
-Use the ordinal logit regression model if your dependent variable is
-ordered and categorical, either in the form of integer values or character strings.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-If {\tt Y} takes discrete integer values, the {\tt as.factor()}
-command will order automatically order the values. If {\tt Y} takes
-on values composed of character strings, such as ``strongly agree'',
-``agree'', and ``disagree'', {\tt as.factor()} will order the values
-in the order in which they appear in {\tt Y}. You will need to
-replace your dependent variable with a factored variable prior to
-estimating the model through {\tt zelig()}. See Section \ref{factors}
-for more information on creating ordered factors and Example
-\ref{ord.fact} below.
-
-\subsubsection{Example}
-
-\begin{enumerate}
-
-\item {Creating An Ordered Dependent Variable} \label{ord.fact}
-
-Load the sample data:
-<<Example.data>>=
- data(sanction)
-@
-Create an ordered dependent variable:
-<<Example.factor>>=
- sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
- levels = c("net gain", "little effect",
- "modest loss", "major loss"))
-@
-Estimate the model:
-<<Example.zelig>>=
- z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
-@
-Set the explanatory variables to their observed values:
-<<Example.setx>>=
- x.out <- setx(z.out, fn = NULL)
-@
-Simulate fitted values given {\tt x.out} and view the results:
-<<Example.sim>>=
- s.out <- sim(z.out, x = x.out)
-@
-<<Example.summary>>=
- summary(s.out)
-@
-
-\item {First Differences}
-
-Using the sample data \texttt{sanction}, estimate the empirical model and returning the coefficients:
-<<FirstDifferences.zelig>>=
- z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
- data = sanction)
-@
-<<FirstDifferences.summary>>=
-summary(z.out)
-@
-Set the explanatory variables to their means, with {\tt mil} set
-to 0 (no military action in addition to sanctions) in the baseline
-case and set to 1 (military action in addition to sanctions) in the
-alternative case:
-<<FirstDifferences.setx>>=
- x.low <- setx(z.out, mil = 0)
- x.high <- setx(z.out, mil = 1)
-@
-Generate simulated fitted values and first differences, and view the results:
-<<FirstDifferences.sim>>=
- s.out <- sim(z.out, x = x.low, x1 = x.high)
- summary(s.out)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be the ordered categorical dependent variable for
-observation $i$ that takes one of the integer values from $1$ to $J$
-where $J$ is the total number of categories.
-
-\begin{itemize}
-\item The \emph{stochastic component} begins with an unobserved continuous
- variable, $Y^*_i$, which follows the standard logistic distribution
- with a parameter $\mu_i$,
- \begin{equation*}
- Y_i^* \; \sim \; \textrm{Logit}(y_i^* \mid \mu_i),
- \end{equation*}
- to which we add an observation mechanism
- \begin{equation*}
- Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
- \quad {\rm for} \quad j=1,\dots,J.
- \end{equation*}
- where $\tau_l$ (for $l=0,\dots,J$) are the threshold parameters with
- $\tau_l < \tau_m$ for all $l<m$ and $\tau_0=-\infty$ and
- $\tau_J=\infty$.
-
-\item The \emph{systematic component} has the following form, given
- the parameters $\tau_j$ and $\beta$, and the explanatory variables $x_i$:
- \begin{equation*}
- \Pr(Y \le j) \; = \; \Pr(Y^* \le \tau_j) \; = \frac{\exp(\tau_j -
- x_i \beta)}{1+\exp(\tau_j -x_i \beta)},
- \end{equation*}
- which implies:
- \begin{equation*}
- \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}{1 + \exp(\tau_j -
- x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
- \exp(\tau_{j-1} - x_i \beta)}.
- \end{equation*}
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the ordinal logit model
- are simulations of the predicted probabilities for each category:
-\begin{equation*}
-E(Y = j) \; = \; \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}
-{1 + \exp(\tau_j - x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
- \exp(\tau_{j-1} - x_i \beta)},
-\end{equation*}
-given a draw of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from the logit
- distribution described by $\mu_i$, and observed as one of $J$
- discrete outcomes.
-
-\item The difference in each of the predicted probabilities ({\tt
- qi\$fd}) is given by
- \begin{equation*}
- \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
- j=1,\dots,J.
- \end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "ologit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt zeta}: a vector containing the estimated class
- boundaries $\tau_j$.
- \item {\tt deviance}: the residual deviance.
- \item {\tt fitted.values}: the $n \times J$ matrix of in-sample
- fitted values.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt edf}: the effective degrees of freedom.
- \item {\tt Hessian}: the Hessian matrix.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, and $t$-statistics.
- \end{itemize}
-
- \item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/poisson.net}
-\input{citeZelig}
-\subsection* {See also}
-The network normal regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(poisson.net)}. Sample data are fictional.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-
-\end{document}
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deleted file mode 100644
index 91ae461..0000000
--- a/inst/doc/probit.bayes.Rnw
+++ /dev/null
@@ -1,293 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/probitBayes}
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Probit Regression for Dichotomous Dependent Variable}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, probit,bayes, regression,dichotomous}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{probit.bayes}: Bayesian Probit Regression}
-
-\label{probit.bayes}
-
-Use the probit regression model for model binary dependent variables
-specified as a function of a set of explanatory variables. The model
-is estimated using a Gibbs sampler. For other models suitable for
-binary response variables, see Bayesian logistic
-regression(\Sref{logit.bayes}), maximum likelihood logit regression
-(\Sref{logit}), and maximum likelihood probit regression
-(\Sref{probit}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "probit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Using the following arguments to monitor the Markov chains:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the
-maximum likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will be the prior
-mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0, which leads to an improper prior.
-\end{itemize}
-
-Use the following arguments to specify optional output for the model:
-\begin{itemize}
-\item \texttt{bayes.resid}: defaults to {\tt FALSE}. If {\tt TRUE},
-the latent Bayesian residuals for all observations are returned.
-Alternatively, users can specify a vector of observations for which the
-latent residuals should be returned.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCprobit)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<BasicExample.data>>=
- data(turnout)
-@
-Estimating the probit regression using \texttt{probit.bayes}:
-<<BasicExample.zelig>>=
- z.out <- zelig(vote ~ race + educate, model = "probit.bayes",
- data = turnout, verbose = TRUE)
-@
-Checking for convergence before summarizing the estimates:
-<<BasicExample.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<BasicExample.heidel>>=
- heidel.diag(z.out$coefficients)
-@
-<<BasicExample.raftery>>=
-raftery.diag(z.out$coefficients)
-@
-<<BasicExample.summary>>=
-summary(z.out)
-@ \end{verbatim}
-Setting values for the explanatory variables to their sample averages:
-<<BasicExample.setx>>=
- x.out <- setx(z.out)
-@
-Simulating quantities of interest from the posterior distribution given:
-\texttt{x.out}
-<<BasicExample.sim>>=
- s.out1 <- sim(z.out, x = x.out)
-@
-<<BasicExample.summary.sim>>=
-summary(s.out1)
-@
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in individual's probability of
-voting when education is set to be low (25th percentile) versus
-high (75th percentile) while all the other variables are held at their
-default values:
-<<FirstDifferences.setx>>=
- x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
- x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
-@
-<<FirstDifferences.sim>>=
- s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-@
-<<FirstDifferences.summary>>=
-summary(s.out2)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the binary dependent variable for observation $i$ which
-takes the value of either 0 or 1.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \textrm{Bernoulli}(\pi_{i})\\
-& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
-\end{eqnarray*}
-where $\pi_{i}=\Pr(Y_{i}=1)$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{i}= \Phi(x_i \beta),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-Normal distribution with mean 0 and variance 1, $x_{i}$ is the vector
-of $k$ explanatory variables for observation $i$, and $\beta$ is the
-vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0}, B_{0}^{-1} \right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the probit model are
-the predicted probability of a success:
-\begin{eqnarray*}
-E(Y \mid X) = \pi_{i}= \Phi(x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
-distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference (\texttt{qi\$fd}) for the probit model is defined
-as
-\begin{eqnarray*}
-\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr})is defined as
-\begin{eqnarray*}
-\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "probit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{bayes.residuals}: When \texttt{bayes.residual} is \texttt{TRUE}
-or a set of observation numbers is given, this object contains the
-posterior draws of the latent Bayesian residuals of all the observations
-or the observations specified by the user.
-
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values (probabilities) for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
-simulated from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/probit.bayes}
-\input{citeZelig}
-\subsection*{See also}
-Bayesian probit regression is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
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-\SweaveOpts{results=hide, prefix.string=vigpics/gam}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Additive Model for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, gam, probit.gam}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt probit.gam}: Generalized Additive Model for Dichotomous Dependent Variables}
-
-This function runs a nonparametric Generalized Additive Model (GAM) for dichotomous dependent variables.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + s(x2), model = "probit.gam", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Where {\tt s()} indicates a variable to be estimated via nonparametric smooth. All variables for which {\tt s()} is not specified, are estimated via standard parametric methods.
-
-\subsubsection{Additional Inputs}
-In addition to the standard inputs, {\tt zelig()} takes the following additional options for GAM models.
-\begin{itemize}
-\item {\tt method}: Controls the fitting method to be used. Fitting methods are selected via a list environment within {\tt method=gam.method()}. See {\tt gam.method()} for details.
-\item {\tt scale}: Generalized Cross Validation (GCV) is used if {\tt scale = 0} (see the ``Model'' section for details) except for Logit models where a Un-Biased Risk Estimator (UBRE) (also see the ``Model'' section for details) is used with a scale parameter assumed to be 1. If {\tt scale} is greater than 1, it is assumed to be the scale parameter/variance and UBRE is used. If {\tt scale} is negative GCV is used.
-\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
-\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
-\item {\tt sp}: A vector of smoothing parameters for each term.
-\item {\tt \ldots}: additional options passed to the {\tt probit.gam} model. See the {\tt mgcv} library for details.
-\end{itemize}
-
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Create some count data:
-
-<<echo=FALSE, results=hide>>=
-library(Zelig)
-<<echo=FALSE, results=hide>>=
-set.seed(0); n<-400; sig<-2
-x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1); x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
-f0 <- function(x) 2 * sin(pi * x)
-f1 <- function(x) exp(2 * x)
-f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
-f3 <- function(x) 0*x
-f <- f0(x0) + f1(x1) + f2(x2)
-
-g <- (f-5)/3
-g <- binomial()$linkinv(g)
-y <- rbinom(g,1,g)
-my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
-
-@
-\begin{Schunk}
-\begin{Sinput}
-> set.seed(0); n <- 400; sig <- 2;
-> x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
-> x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
-> g <- (f-5)/3
-> g <- binomial()$linkinv(g)
-> y <- rbinom(g,1,g)
-> my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
-\end{Sinput}
-\end{Schunk}
-
-Estimate the model, summarize the results, and plot nonlinearities:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="probit.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1,residuals=TRUE)
-@
-Note that the {\tt plot()} function can be used after model estimation and before simulation to view the nonlinear relationships in the independent variables:
-\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=6, height=6>>=
-plot(z.out,pages=1,residuals=TRUE)
-@
-\label{fig:plotgam}
-\end{figure}
-
-Set values for the explanatory variables to their default (mean/mode) values, then simulate, summarize and plot quantities of interest:
-<<echo=TRUE, results=hide, fig=FALSE>>=
- x.out <- setx(z.out)
- s.out <- sim(z.out, x = x.out)
- summary(s.out)
- plot(s.out)
-@
-\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low values (20th percentile) and high values (80th percentile) of the explanatory variable {\tt x3} while all the other variables are held at their default (mean/mode) values.
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
-x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
-
-s.out <- sim(z.out, x=x.high, x1=x.low)
-summary(s.out)
-plot(s.out)
-
-@
-%\SweaveOpts{echo=false}
-\begin{figure}[here]
-\centering
-<<fig=TRUE, width=12, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-@
-\item Variations in GAM model specification. Note that {\tt setx} and {\tt sim} work as shown in the above examples for any GAM model. As such, in the interest of parsimony, I will not re-specify the simulations of quantities of interest.
-
-An extra ridge penalty (useful with convergence problems):
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
- model="probit.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1,residuals=TRUE)
-
-@
-Set the smoothing parameter for the first term, estimate the rest:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
- model="probit.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1)
-
-@
-Set lower bounds on smoothing parameters:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
- model="probit.gam", data=my.data)
-summary(z.out)
-plot(z.out, pages=1)
-
-@
-A GAM with 3df regression spline term \& 2 penalized terms:
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
- model="probit.gam", data=my.data)
-summary(z.out)
-plot(z.out,pages=1)
-
-@
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-
-
-GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt probit.gam} a normal link function is used. Specifically, let $Y_{i}$ be the binary dependent variable for observation $i$ which takes the value of either 0 or 1.
-\begin{itemize}
-\item The normal distribution has \emph{stochastic component}
-\begin{equation*}
-Y_{i} \sim \text{Bernoulli} ( \pi_{i})\\
-\end{equation*}
-where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_{i} = \pmb{\Phi} \left( x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right),
-\end{equation*}
-where $ \pmb{\Phi}(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance and $f_j(Z_j)$ for $j=1, \ldots J$ is the set of smooth terms.
-\end{itemize}
-
-Generalized additive models (GAMs) are similar in many respects to generalized linear models (GLMs). Specifically, GAMs are generally fit by penalized maximum likelihood estimation and GAMs have (or can have) a parametric component identical to that of a GLM. The difference is that GAMs also include in their linear predictors a specified sum of smooth functions.
-
-In this GAM implementation, smooth functions are represented using penalized regression splines. Two techniques may be used to estimate smoothing parameters: Generalized Cross Validation (GCV),
-\begin{equation}
-n \frac{D}{(n-DF)^2},
-\end{equation}
-or an Un-Biased Risk Estimator (UBRE) (which is effectively just a rescaled AIC),
-\begin{equation}
-\frac{D}{n} + 2 s \frac{DF}{n-s},
-\end{equation}
-where $D$ is the deviance, $n$ is the number of observations, $s$ is the scale parameter, and $DF$ is the effective degrees of freedom of the model. The use of GCV or UBRE can be set by the user with the {\tt scale} command described in the ``Additional Inputs'' section and in either case, smoothing parameters are chosen to minimize the GCV or UBRE score for the model.
-
-Estimation for GAM models proceeds as follows: first, basis functions and a set (one or more) of quadratic penalty coefficient matrices are constructed for each smooth term. Second, a model matrix is is obtained for the parametric component of the GAM. These matrices are combined to produce a complete model matrix and a set of penalty matrices for the smooth terms. Iteratively Reweighted Least Squares (IRLS) is then used to estimate the model; at each iteration of the IRLS, a penalized w [...]
-
-Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
-
-
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the {\tt probit.gam} model are the same as those for the standard normal regression.
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt probit.gam} model is the mean of simulations from the stochastic component,
-\begin{equation*}
-\pi_{i} = \pmb{\Phi} \left( x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right).
-\end{equation*}
-
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference ({\tt qi\$fd}) for the probit.gam model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
-\end{equation*}
-for $w=\{X, Z\}$.
-\end{itemize}
-
-
-
-\subsubsection{Output Values}
-
-
-
-The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "probit.gam", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IRLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt method}: the fitting method used.
-\item {\tt converged}: logical indicating weather the model converged or not.
-\item {\tt smooth}: information about the smoothed parameters.
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt data}: the input data frame.
-\item {\tt model}: the model matrix used.
-
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt p.coeff}: the coefficients of the parametric components of the model.
-\item {\tt se}: the standard errors of the entire model.
-\item {\tt p.table}: the coefficients, standard errors, and associated $t$ statistics for the parametric portion of the model.
-\item {\tt s.table}: the table of estimated degrees of freedom, estimated rank, $F$ statistics, and $p$-values for the nonparametric portion of the model.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-
-
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/probit.gam}
-\input{citeZelig}
-\subsection* {See also}
-The {\tt gam.logit} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
-
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
diff --git a/inst/doc/probit.gam.log b/inst/doc/probit.gam.log
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@@ -1,357 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/probit_gee}
-\include{zinput}
-%\VignetteIndexEntry{Generalized Estimating Equation for Probit Regression}
-%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, probit regression}
-%\VignettePackage{Zelig}
-\begin{document}
-
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{probit.gee}: Generalized Estimating Equation for Probit Regression}
-\label{probit.gee}
-
-The GEE probit estimates the same model as the standard probit
-regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables). Unlike in probit
-regression, GEE probit allows for dependence within clusters, such as
-in longitudinal data, although its use is not limited to just
-panel data. The user must first specify a ``working''
-correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
-the standard errors can be obtained using a robust ``sandwich''
-estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "probit.gee",
- id = "X3", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\noindent where \texttt{id} is a variable which identifies the clusters. The data should be sorted by \texttt{id} and should be ordered within each cluster when appropriate.
-
-\subsubsection{Additional Inputs}
-
-\begin{itemize}
-\item \texttt{robust}: defaults to \texttt{TRUE}. If \texttt{TRUE}, consistent standard errors are estimated using a ``sandwich'' estimator.
-\end{itemize}
-Use the following arguments to specify the structure of the ``working'' correlations within clusters:
-
-\begin{itemize}
-\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
-\begin{itemize}
-\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard probit regression. The ``working'' correlation matrix is the identity matrix.
-\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
-\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
- $${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{|t-t'|} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
- If (\texttt{corstr = "stat\_M\_dep"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. Choose this option when the correlations are assumed to be the same for observations of the same $|t-t'|$ periods apart for $|t-t'| \leq m$.
-\begin{center}
-Sample ``working'' correlation for Stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_1 & \alpha_2 & 0 & 0 \\
-\alpha_1 & 1 & \alpha_1 & \alpha_2 & 0 \\
-\alpha_2 & \alpha_1 & 1 & \alpha_1 & \alpha_2 \\
-0 & \alpha_2 & \alpha_1 & 1 & \alpha_1 \\
-0 & 0 & \alpha_2 & \alpha_1 & 1
-\end{array} \right) $
-\end{center}
-
-\item Non-stationary $m$ dependent (\texttt{corstr =
- "non\_stat\_M\_dep"}):
-$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
- \alpha_{tt'} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
- & |t-t'| > m
- \end{array}\right.$$
-If (\texttt{corstr = "non\_stat\_M\_dep"}), you must also specify
-\texttt{Mv} = $m$, where $m$ is the number of periods $t$ of
-dependence. This option relaxes the assumption that the
-correlations are the same for all observations of the same $|t-t'|$
-periods apart.
-\begin{center}
-Sample ``working'' correlation for Non-stationary 2 dependence ({\tt Mv}=2)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha_{12} & \alpha_{13} & 0 & 0 \\
-\alpha_{12} & 1 & \alpha_{23} & \alpha_{24} & 0 \\
-\alpha_{13} & \alpha_{23} & 1 & \alpha_{34} & \alpha_{35} \\
-0 & \alpha_{24} & \alpha_{34} & 1 & \alpha_{45} \\
-0 & 0 & \alpha_{35} & \alpha_{45} & 1
-\end{array} \right) $
-\end{center}
-\item Exchangeable (\texttt{corstr = "exchangeable"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha$, $\forall t, t'$ with $t\ne t'$. Choose this option if the correlations are assumed to be the same for all observations within the cluster.
-\begin{center}
-Sample ``working'' correlation for Exchangeable\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha & \alpha & \alpha \\
-\alpha & 1 & \alpha & \alpha & \alpha \\
-\alpha & \alpha & 1 & \alpha & \alpha \\
-\alpha & \alpha & \alpha & 1 & \alpha \\
-\alpha & \alpha & \alpha & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Stationary $m$th order autoregressive (\texttt{corstr = "AR-M"}): If (\texttt{corstr = "AR-M"}), you must also specify \texttt{Mv} = $m$, where $m$
-is the number of periods $t$ of dependence. For example, the first order autoregressive model (AR-1) implies ${\rm cor}(y_{it},
- y_{it'})=\alpha^{|t-t'|}, \forall t, t'$ with $t\ne t'$. In AR-1, observation 1 and observation 2 have a correlation of $\alpha$. Observation 2 and observation 3 also have a correlation of $\alpha$. Observation 1 and observation 3 have a correlation of $\alpha^2$, which is a function of how 1 and 2 are correlated ($\alpha$) multiplied by how 2 and 3 are correlated ($\alpha$). Observation 1 and 4 have a correlation that is a function of the correlation between 1 and 2, 2 and 3, and [...]
-\begin{center}
-Sample ``working'' correlation for Stationary AR-1 ({\tt Mv}=1)\\
-\bigskip
-$\left( \begin{array}{ccccc}
-1 & \alpha & \alpha^2 & \alpha^3 & \alpha^4 \\
-\alpha & 1 & \alpha & \alpha^2 & \alpha^3 \\
-\alpha^2 & \alpha & 1 & \alpha & \alpha^2 \\
-\alpha^3 & \alpha^2 & \alpha & 1 & \alpha \\
-\alpha^4 & \alpha^3 & \alpha^2 & \alpha & 1
-\end{array} \right) $
-\end{center}
-\item Unstructured (\texttt{corstr = "unstructured"}): ${\rm
- cor}(y_{it}, y_{it'})=\alpha_{tt'}$, $\forall t, t'$ with
- $t\ne t'$. No constraints are placed on the correlations, which are then estimated from the data.
-\end{itemize}
-\item \texttt{Mv}: defaults to 1. It specifies the number of periods of correlation and only needs to be specified when \texttt{corstr} is {\tt "stat\_M\_dep"}, {\tt "non\_stat\_M\_dep"}, or {\tt "AR-M"}.
-\item \texttt{R}: defaults to \texttt{NULL}. It specifies a user-defined correlation matrix rather than estimating it from the data. The argument is used only when \texttt{corstr} is {\tt "fixed"}. The input is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Example with Stationary 3 Dependence}
-
-Attaching the sample turnout dataset:
-<<Example.data>>=
-data(turnout)
-@
-Variable identifying clusters
-<<Example.cluster>>=
-turnout$cluster <- rep(c(1:200),10)
-@
-Sorting by cluster
-<<Example.sort>>=
-sorted.turnout <- turnout[order(turnout$cluster),]
-@
-Estimating parameter values:
-<<Example.zelig>>=
-z.out1 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3)
-@
-Setting values for the explanatory variables to their default values:
-<<Example.setx>>=
-x.out1 <- setx(z.out1)
-@
-Simulating quantities of interest:
-<<Example.sim>>=
-s.out1 <- sim(z.out1, x = x.out1)
-@
-<<Example.summary.sim>>=
-summary(s.out1)
-@
-\begin{center}
-<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out1)
-@
-\end{center}
-
-<<echo=F, results=hide>>=
-options(width=80)
-@
-
-\item {Simulating First Differences}
-
-Estimating the risk difference (and risk ratio) between low education
-(25th percentile) and high education (75th percentile) while all the
-other variables held at their default values.
-
-<<FirstDifference.setx>>=
-x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
-x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
-@
-
-<<FirstDifference.sim>>=
-s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
-@
-<<FirstDifference.summary.sim>>=
-summary(s.out2)
-@
-\begin{center}
-<<label=FirstDifferencePlot,fig=true,echo=true>>=
-plot(s.out2)
-@
-\end{center}
-
-<<echo=F, results=hide>>=
-options(width=75)
-@
-
-\item Example with Fixed Correlation Structure
-
-User-defined correlation structure
-<<Example2.corr>>=
-corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
-diag(corr.mat) <- 1
-@
-Generating empirical estimates:
-<<Example2.zelig>>=
-z.out2 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat)
-@
-Viewing the regression output:
-<<Example2.summary>>=
-summary(z.out2)
-@
-\end{enumerate}
-
-\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the binary dependent variable for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
-where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
-
-\begin{itemize}
-
-\item The \emph{stochastic component} is given by the joint and marginal distributions
-\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \pi_{i})\\
-Y_{it} &\sim& g(y_{it} \mid \pi_{it})
-\end{eqnarray*}
-where $f$ and $g$ are unspecified distributions with means $\pi_{i}$ and $\pi_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-
-\item The \emph{systematic component} is the \textit{mean function}, given by:
-\begin{equation*}
-\pi_{it} = \Phi(x_{it} \beta)
-\end{equation*}
-where $\Phi(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance, $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
-and $\beta$ is the vector of coefficients.
-
-\item The \textit{variance function} is given by:
-\begin{equation*}
-V_{it} = \pi_{it} (1-\pi_{it})
-\end{equation*}
-
-\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-
-\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
-
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item All quantities of interest are for marginal means rather than joint means.
-\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
-\item The expected values ({\tt qi\$ev}) for the GEE probit model are
- simulations of the predicted probability of a success: $$E(Y) =
- \pi_{c}= \Phi(x_{c} \beta),$$ given draws of $\beta$ from
- its sampling distribution, where $x_{c}$ is a vector of values, one for
-each independent variable, chosen by the user.
-
-\item The first difference ({\tt qi\$fd}) for the GEE probit model is defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- E[Y_{it}(tr_{it}=0)] \right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the treatment
- ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_{it}(tr_{it}=0)]$,
- the counterfactual expected value of $Y_{it}$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $tr_{it}=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "probit.gee", id, data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_{it}$.
- \item {\tt linear.predictors}: the vector of $x_{it}\beta$
- \item {\tt max.id}: the size of the largest cluster.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $z$-statistics.
- \item {\tt working.correlation}: the ``working'' correlation matrix
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the expected
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsection*{How To Cite}
-
-\input{cites/probit.gee}
-\input{citeZelig}
-
-\subsection*{See also}
-The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
-
-\bibliographystyle{asa}
-\bibliography{gk}
-
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-
-
-\end{document}
diff --git a/inst/doc/probit.gee.log b/inst/doc/probit.gee.log
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diff --git a/inst/doc/probit.net.Rnw b/inst/doc/probit.net.Rnw
deleted file mode 100644
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--- a/inst/doc/probit.net.Rnw
+++ /dev/null
@@ -1,179 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/probitnet}
-\include{zinput}
-%\VignetteIndexEntry{Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,least squares,continuous, regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt probit.net}: Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables}
-
-Use network probit regression analysis for a dependent variable that is a binary valued proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "probit.net", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-data(friendship)
-
-
-@
-Estimate model:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(friends ~ advice + prestige + perpower, model = "probit.net", data = friendship)
-summary(z.out)
-
-@
-Setting values for the explanatory variables to their default values:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.out <- setx(z.out)
-
-@
-Simulating quantities of interest from the posterior distribution.
-<<echo=TRUE, results=hide, fig=FALSE>>=
-s.out <- sim(z.out, x = x.out)
-summary(s.out)
-plot(s.out)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE,echo=false, width=6, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-@
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low personal power (25th percentile) and high personal power (75th percentile) while all the other variables are held at their default values.
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
-x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.25))
-
-s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-summary(s.out2)
-plot(s.out2)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE,echo=false, width=6, height=6>>=
-plot(s.out)
-@
-\label{fig:plotgam}
-\end{figure}
-
-\end{enumerate}
-
-
-
-\subsubsection{Model}
-The {\tt probit.net} model performs a probit regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard probit regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the [...]
-
-Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{equation*}
-Y_{i} \sim \text{Bernoulli} ( \pi_{i})\\
-\end{equation*}
-where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_{i} = \pmb{\Phi}(x_{i}\beta).
-\end{equation*}
-where $ \pmb{\Phi}(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network probit regression are the same as those for the standard probit regression.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the {\tt probit.net} model are simulations of the predicted probability of a success:
-\begin{equation*}
-E(Y) = \pi_{i} = \pmb{\Phi}(x_{i}\beta),
-\end{equation*}
-given draws of $\beta$ from its sampling distribution.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference ({\tt qi\$fd}) for the network probit model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
-\end{equation*}
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "probit.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike\'s Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-
- \subsection* {How to Cite}
-
-\input{cites/probit.net}
-\input{citeZelig}
-\subsection* {See also}
-The network probit regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netpoisson)}. Sample data are fictional.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
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-.out \T1/aer/m/n/12 by us-ing \T1/aett/m/n/12 names(z.out)\T1/aer/m/n/12 , see
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- a de-fault sum-mary of in-for-ma-tion through \T1/aett/m/n/12 summary(z.out)\T
-1/aer/m/n/12 .
- []
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deleted file mode 100644
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-\chapter{Programming Statements}
-
-This chapter introduces the main programming commands. These include
-functions, if-else statements, for-loops, and special procedures for
-managing the inputs to statistical models.
-
-\section{Functions}
-
-Functions are either built-in or user-defined sets of encapsulated
-commands which may take any number of arguments. Preface a function
-with the {\tt function} statement and use the {\tt <-}
-operator to assign functions to objects in your workspace.
-
-You may use functions to run the same procedure on different objects
-in your workspace. For example,
-\begin{verbatim}
-check <- function(p, q) {
- result <- (p - q)/q
- result
- }
-\end{verbatim}
-is a simple function with arguments {\tt p} and {\tt q} which
-calculates the difference between the $i$th elements of the vector
-{\tt p} and the $i$th element of the vector {\tt q} as a proportion of
-the $i$th element of {\tt q}, and returns the resulting vector. For
-example, {\tt check(p = 10, q = 2)} returns 4. You may omit the
-descriptors as long as you keep the arguments in the correct order:
-{\tt check(10, 2)} also returns 4. You may also use other objects as
-inputs to the function. If {\tt again = 10} and {\tt really = 2},
-then {\tt check(p = again, q = really)} and {\tt check(again, really)}
-also returns 4.
-
-Because functions run commands as a set, you should make sure that
-each command in your function works by testing each line of the
-function at the R prompt.
-
-\section{If-Statements}
-
-Use {\tt if} (and optionally, {\tt else}) to control the flow of R
-functions. For example, let {\tt x} and {\tt y} be scalar numerical
-values:
-\begin{verbatim}
-if (x == y) { # If the logical statement in the ()'s is true,
- x <- NA # then `x' is changed to `NA' (missing value).
-}
-else { # The `else' statement tells R what to do if
- x <- x^2 # the if-statement is false.
-}
-\end{verbatim}
-As with a function, use {\tt \{} and {\tt \}} to define the set of commands associated
-with each if and else statement. (If you include if
-statements inside functions, you may have multiple sets of nested
-curly braces.)
-
-\section{For-Loops}
-
-Use {\tt for} to repeat (loop) operations. Avoiding loops by using matrix
-or vector commands is usually faster and more elegant, but loops are
-sometimes necessary to assign values. If you are using a loop to
-assign values to a data structure, you must first initialize an empty
-data structure to hold the values you are assigning.
-
-Select a data structure compatible with the type of output your loop
-will generate. If your loop generates a scalar, store it in a vector
-(with the $i$th value in the vector corresponding to the the $i$th run
-of the loop). If your loop generates vector output, store them as
-rows (or columns) in a matrix, where the $i$th row (or column)
-corresponds to the $i$th iteration of the loop. If your output
-consists of matrices, stack them into an array. For list output (such
-as regression output) or output that changes dimensions in each
-iteration, use a list. To initialize these data structures, use:
-\begin{verbatim}
-> x <- vector() # An empty vector of any length.
-> x <- list() # An empty list of any length.
-\end{verbatim}
-The {\tt vector()} and {\tt list()} commands create a vector or list
-of any length, such that assigning {\tt x[5] <- 15} automatically
-creates a vector with 5 elements, the first four of which are empty
-values ({\tt NA}). In contrast, the {\tt matrix()} and {\tt array()}
-commands create data structures that are restricted to their original
-dimensions.
-\begin{verbatim}
-> x <- matrix(nrow = 5, ncol = 2) # A matrix with 5 rows and 2 columns.
-> x <- array(dim = c(5,2,3)) # A 3D array of 3 stacked 5 by 2 matrices.
-\end{verbatim}
-If you attempt to assign a value at $(100, 200, 20)$ to either of
-these data structures, R will return an error message (``subscript is
-out of bounds''). R does not automatically extend the
-dimensions of either a matrix or an array to accommodate additional
-values.
-
-\paragraph{Example 1: Creating a vector with a logical statement}
-\begin{verbatim}
-x <- array() # Initializes an empty data structure.
-for (i in 1:10) { # Loops through every value from 1 to 10, replacing
- if (is.integer(i/2)) { # the even values in `x' with i+5.
- x[i] <- i + 5
- }
-} # Enclose multiple commands in {}.
-\end{verbatim}
-You may use {\tt for()} inside or outside of functions.
-
-\paragraph{Example 2: Creating dummy variables by hand}
-
-\label{dummy}You may also use a loop to create a matrix of dummy
-variables to append to a data frame. For example, to generate fixed
-effects for each state, let's say that you have {\tt mydata} which
-contains {\tt y}, {\tt x1}, {\tt x2}, {\tt x3}, and {\tt state}, with
-{\tt state} a character variable with 50 unique values. There are
-three ways to create dummy variables: 1) with a built-in R command; 2)
-with one loop; or 3) with 2 for loops.
-\begin{enumerate}
-\item R will create dummy variables on the fly from a single variable
-with distinct values.
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2 + x3 + as.factor(state),
- data = mydata, model = "ls")
-\end{verbatim}
-This method returns $k - 1$ indicators for $k$ states.
-
-\item Alternatively, you can use a loop to create dummy variables by
-hand. There are two ways to do this, but both start with the same
-initial commands. Using vector commands, first create an index of for
-the states, and initialize a matrix to hold the dummy variables:
-\begin{verbatim}
-idx <- sort(unique(mydata$state))
-dummy <- matrix(NA, nrow = nrow(mydata), ncol = length(idx))
-\end{verbatim} %$
-Now choose between the two methods.
-\begin{enumerate}
-\item The first method is computationally inefficient, but more intuitive for users not
-accustomed to vector operations. The first loop uses {\tt i} as in
-index to loop through all the rows, and the second loop uses {\tt j}
-to loop through all 50 values in the vector {\tt idx}, which
-correspond to columns 1 through 50 in the matrix {\tt dummy}.
-\begin{verbatim}
-for (i in 1:nrow(mydata)) {
- for (j in 1:length(idx)) {
- if (mydata$state[i,j] == idx[j]) {
- dummy[i,j] <- 1
- }
- else {
- dummy[i,j] <- 0
- }
- }
-}
-\end{verbatim} % $
-Then add the new matrix of dummy variables to your data frame:
-\begin{verbatim}
-names(dummy) <- idx
-mydata <- cbind(mydata, dummy)
-\end{verbatim}
-
-\item As you become more comfortable with vector operations, you can
-replace the double loop procedure above with one loop:
-\begin{verbatim}
-for (j in 1:length(idx)) {
- dummy[,j] <- as.integer(mydata$state == idx[j])
-}
-\end{verbatim} %$
-The single loop procedure evaluates each element in {\tt idx} against the vector {\tt mydata\$state}. This creates a vector of $n$ {\tt
- TRUE}/{\tt FALSE} observations, which you may transform to {\tt
- 1}'s and {\tt 0}'s using {\tt as.integer()}. Assign the resulting
- vector to the appropriate column in {\tt dummy}. Combine the {\tt
-dummy} matrix with the data frame as above to complete the procedure.
-\end{enumerate}
-\end{enumerate}
-
-\paragraph{Example 3: Weighted regression with subsets}
-
-Selecting the {\tt by} option in {\tt zelig()} partitions the data
-frame and then automatically loops the specified model through each
-partition. Suppose that {\tt mydata} is a data frame with variables
-{\tt y}, {\tt x1}, {\tt x2}, {\tt x3}, and {\tt state}, with {\tt
-state} a factor variable with 50 unique values. Let's say that you
-would like to run a weighted regression where each observation is
-weighted by the inverse of the standard error on {\tt x1}, estimated
-for that observation's state. In other words, we need
-to first estimate the model for each of the 50 states, calculate 1 /
-{\sc se}({\tt x1}$_j$) for each state $j = 1, \dots, 50$, and then
-assign these weights to each observation in {\tt mydata}.
-\begin{itemize}
-\item Estimate the model separate for each state using the {\tt by}
-option in {\tt zelig()}:
-\begin{verbatim}
-z.out <- zelig(y ~ x1 + x2 + x3, by = "state", data = mydata, model = "ls")
-\end{verbatim}
-Now {\tt z.out} is a list of 50 regression outputs.
-\item Extract the standard error on {\tt x1} for each of the state
-level regressions.
-\begin{verbatim}
-se <- array() # Initalize the empty data structure.
-for (i in 1:50) { # vcov() creates the variance matrix
- se[i] <- sqrt(vcov(z.out[[i]])[2,2]) # Since we have an intercept, the 2nd
-} # diagonal value corresponds to x1.
-\end{verbatim}
-\item Create the vector of weights.
-\begin{verbatim}
-wts <- 1 / se
-\end{verbatim}
-This vector {\tt wts} has 50 values that correspond to the 50 sets of
-state-level regression output in {\tt z.out}.
-\item To assign the vector of weights to each observation, we need to
-match each observation's state designation to the appropriate state.
-For simplicity, assume that the states are numbered 1 through 50.
-\begin{verbatim}
-mydata$w <- NA # Initalizing the empty variable
-for (i in 1:50) {
- mydata$w[mydata$state == i] <- wts[i]
-}
-\end{verbatim} %$
-We use {\tt mydata\$state} as the index (inside the square brackets)
-to assign values to {\tt mydata\$w}. Thus, whenever state equals 5
-for an observation, the loop assigns the fifth value in the vector
-{\tt wts} to the variable {\tt w} in {\tt mydata}. If we had 500
-observations in {\tt mydata}, we could use this method to match each
-of the 500 observations to the appropriate {\tt wts}.
-
-If the states are character strings instead of integers, we can use a
-slightly more complex version
-\begin{verbatim}
-mydata$w <- NA
-idx <- sort(unique(mydata$state))
-for (i in 1:length(idx) {
- mydata$w[mydata$state == idx[i]] <- wts[i]
-}
-\end{verbatim}
-
-\item Now we can run our weighted regression:
-\begin{verbatim}
-z.wtd <- zelig(y ~ x1 + x2 + x3, weights = w, data = mydata,
- model = "ls")
-\end{verbatim}
-
-\end{itemize}
-
diff --git a/inst/doc/quantile.Rnw b/inst/doc/quantile.Rnw
deleted file mode 100644
index 602e7ec..0000000
--- a/inst/doc/quantile.Rnw
+++ /dev/null
@@ -1,341 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/quantile}
-\include{zinput}
-%\VignetteIndexEntry{Quantile Regression for Continuous Dependent Variables}
-%\VignetteDepends{Zelig, stats, quantreg}
-%\VignetteKeyWords{model, quantile,continuous, regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt quantile}: Quantile Regression for Continuous
-Dependent Variables}
-\label{ls}
-
-Use a linear programming implementation of quantile regression to
-estimate a linear predictor of the $\tau$th conditional quantile of the
-population.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "quantile", data = mydata, tau = 0.5)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for quantile regression:
-\begin{itemize}
-\item {\tt tau}: defaults to 0.5. Specifies the conditional quantile(s)
-that will be estimated. 0.5 corresponds to estimating the conditional
-median, 0.25 and 0.75 correspond to the conditional quartiles, etc. If
-{\tt tau} is a vector, the conditional quantile function at each tau is
-estimated. If tau is set outside of the interval [0,1], zelig returns
-the solution for all possible conditional quantiles given the data, but
-does not support inference on this fit ({\tt setx} and {\tt sim} will fail).
-
-\item {\tt se}: a string value that defaults to "nid". Specifies the
-method by which the covariance matrix of coefficients is estimated during
-the {\tt sim} stage of analysis. {\tt se}
-can take the following values, which are passed to the {\tt summary.rq}
-function from the {\tt quantreg} package. These descriptions are copied from
-the {\tt summary.rq} documentation.
-\begin{itemize}
- \item {\tt "iid"} which presumes that the errors are iid and computes
- an estimate of the asymptotic covariance matrix as in KB(1978).
-
- \item {\tt "nid"} which presumes local (in {\tt tau})
- linearity (in {\tt x}) of the
- the conditional quantile functions and computes a Huber
- sandwich estimate using a local estimate of the sparsity.
-
- \item {\tt "ker"} which uses a kernel estimate of the sandwich
- as proposed by Powell(1990).
-\end{itemize}
-\item {\tt \dots}: additional options passed to {\tt rq} when fitting the model.
-See documentation for {\tt rq} in the {\tt quantreg} package for more information.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example with First Differences
-
-Attach sample data, in this case a dataset pertaining to the efficiency of plants
-that convert ammonia to nitric acid. The dependent variable, stack.loss, is 10 times
-the percentage of ammonia that escaped unconverted:
-<<Examples.data>>=
- data(stackloss)
-@
-Estimate model:
-<<Examples.zelig>>=
- z.out1 <- zelig(stack.loss ~ Air.Flow + Water.Temp + Acid.Conc., model = "quantile",
- data = stackloss, tau=0.5)
-@
-Summarize regression coefficients:
-<<Examples.summary>>=
- summary(z.out1)
-@
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) values for the water temperature
-variable (the variable that indiates the temperature of water in the plant's cooling
-coils):
-<<Examples.setx>>=
- x.high <- setx(z.out1, Water.Temp = quantile(stackloss$Water.Temp, 0.8))
- x.low <- setx(z.out1, Water.Temp = quantile(stackloss$Water.Temp, 0.2))
-@
-Generate first differences for the effect of high versus low water temperature on
-stack loss:
-<<Examples.sim>>=
- s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
-@
-<<Examples.summary.sim>>=
-summary(s.out1)
-@
-\begin{center}
-<<label=ExamplesPlot,fig=true,echo=true,width=5.5,height=4>>=
- plot(s.out1)
-@
-\end{center}
-
-\item Using Dummy Variables
-
-We can estimate a model of unemployment as a function of macroeconomic indicators
-and fixed effects for each country (see \Sref{factors} for help with dummy variables).
-Note that you do not need to create dummy variables, as the program will automatically
-parse the unique values in the selected variable into discrete levels.
-<<Dummy.zelig>>=
- data(macro)
- z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country),
- model = "quantile", tau=0.5, data = macro)
-@
-Set values for the explanatory variables, using the default mean/mode
-values, with country set to the United States and Japan, respectively:
-<<Dummy.setx>>=
- x.US <- setx(z.out2, country = "United States")
- x.Japan <- setx(z.out2, country = "Japan")
-@
-Simulate quantities of interest:
-<<Dummy.sim>>=
- s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
-@
-\begin{center}
-<<label=DummyPlot,fig=true,echo=true, width=5.5, height=4>>=
- plot(s.out2)
-@
-\end{center}
-
-\item Estimating Multiple Quantiles
-
-Using the Engel dataset on food expenditure as a function of income, we can use the
-{\tt "quantile"} model to estimate multiple conditional quantiles:
-<<Multiple.zelig>>=
- data(engel)
- z.out3 <- zelig(foodexp ~ income, model = "quantile", tau=seq(0.1,0.9,by=0.1), data = engel)
-@
-We can summarize the coefficient fits, or plot them to compare them to the least squares
-conditional mean estimator.
-<<Multiple.zelig.summary>>=
- summary(z.out3)
-@
-\begin{center}
-<<label=MultiplePlot,fig=true,echo=true,width=5.5,height=4>>=
- plot(summary(z.out3))
-@
-\end{center}
-Set the value of income to the top quartile and the bottom quartile of
-the income distribution for each fit:
-<<Multiple.setx>>=
- x.bottom <- setx(z.out3, income=quantile(engel$income, 0.25))
- x.top <- setx(z.out3, income=quantile(engel$income, 0.75))
-@
-Simulate quantities of interest for each fit simultaneously:
-<<Multiple.sim>>=
- s.out3 <- sim(z.out3, x = x.bottom, x1 = x.top)
-@
-Summary
-<<Example4.sim.summary>>=
-summary(s.out3)
-@
-
-\end{enumerate}
-
-\subsubsection{Model}
-The quantile estimator is best introduced by considering the sample median
-estimator and comparing it to the sample mean estimator.
-To find the mean of a sample, we solve
-for the quantity $\mu$ which minimizes the sum squared residuals:
-\begin{eqnarray*}
- \mu &=& \arg\min_\mu \sum_i (y_i-\mu)^2
-\end{eqnarray*}
-Estimating a quantile is similar, but we solve for $\xi$ which minimizes the
-sum absolute residuals:
-\begin{eqnarray*}
- \xi &=& \arg\min_\xi \sum_i |y_i-\xi|
-\end{eqnarray*}
-One can confirm the equivalence of these optimization problems and the standard
-mean and median operators by taking the derivative with respect to the argument
-and setting it to zero.
-
-The relationship between quantile regression and ordinary least squares regression
-is analogous to the relationship between the sample median and the sample mean, except
-we are now solving for the conditional median or conditional mean given covariates and
-a linear functional form. The optimization problems for the sample mean and median
-are then easily generalized to optimization problems for
-estimating \emph{conditional} means or medians by replacing $\mu$ or $\xi$
-with a linear combination of covariates $X'\beta$:
-\begin{eqnarray}
- \hat\beta_\mathrm{mean} &=& \arg\min_\beta \sum_i (Y_i-X_i'\beta)^2 \nonumber \\
- \hat\beta_\mathrm{median} &=& \arg\min_\beta \sum_i |Y_i-X_i'\beta| \label{median}
-\end{eqnarray}
-Equation \ref{median} can be generalized to provide any quantile of the conditional
-distribution, not just the median. We do this by weighting the aboslute value function
-asymmetrically in proportion to the requested $\tau$th quantile:
-\begin{eqnarray}
- \hat\beta_{\tau} &=& \arg\max_\beta \sum \rho(Y_i-X_i'\beta) \label{beta}\\
- \rho &=& \tau(1-I(Y-X_i'\beta > 0)) + (1-\tau)I(Y-X_i'\beta > 0) \nonumber
-\end{eqnarray}
-We call the asymmetric absolute value function a ``check function''. This optimization
-problem has no closed form solution and is solved via linear programming.
-
-Equation \ref{beta} now lets us define a conditional quantile estimator.
-Suppose that for a given set of covariates $x$, the response variable $Y$ has as true
-conditional probability distribution $f(\theta | x)$ where $f$ can be any probability
-density function parametrized by a vector of parameters $\theta$.
-This density function defines a value $Q_\tau(\theta|x)$, the true $\tau$th population
-quantile given $x$. We can write our conditional quantile estimator $\hat Q_\tau(\theta|x)$ as:
-\begin{eqnarray*}
-\hat Q_\tau(\theta|x) &=& x'\hat\beta_\tau
-\end{eqnarray*}
-Where $\hat\beta$ is the vector that solves equation \ref{beta}.
-Because we solve for the estimator $\hat Q_\tau$ without constructing a likelihood function,
-it is not straightforward to specify a systematic and stochastic component for conditional
-quantile estimates. However, systematic and stochastic components do emerge asymptotically
-in the large-$n$ limit. Asymptotically, $\hat Q_\tau$ is normally distributed, and can be
-written with \emph{stochastic component}
-\begin{eqnarray*}
- \hat Q_\tau &\sim& \mathcal{N}(\mu, \sigma^2),
-\end{eqnarray*}
-And \emph{systematic components}
-\begin{eqnarray*}
- \mu &=& x'\hat\beta_\tau\\
- \sigma^2 &=& \frac{\tau(1-\tau)}{n f^2},
-\end{eqnarray*}
-Where $n$ is the number of
-datapoints, and $f$ is the true population density at the $\tau$th conditional quantile.
-Zelig uses this asymptotic approximation of stochastic and systematic components in
-simulation and numerically estimates the population density to derive $\sigma^2$. The
-simulation results should thus be treated with caution when using small datasets as both
-this asymptotic approximation and the population density approximation can break down.
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations from the stochastic
-component,
-\begin{equation*}
-E(\hat Q_\tau) = x_i \beta_\tau,
-\end{equation*}
-given a draw of $\beta_\tau$ from its sampling distribution. Variation in the expected
-value distribution comes from estimation uncertainty of $\beta_\tau$.
-
-\item The predicted value ({\tt qi\$pr}) is the result of a single draw from the
-stochastic component given a draw of $\beta_\tau$ from its sampling distribution. The
-distribution of predicted values should be centered around the same place as the
-expected values but have larger variance because it includes both estimation
-uncertainty and fundamental uncertainty.
-
-\item This model does not support conditional prediction.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "ls", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
- \item {\tt zelig()} will return an object {\tt z.out}, which is of class
- {\tt rq} (when {\tt tau} specifies a single quantile), {\tt rqs} (when {\tt tau}
- specifies multiple quantiles), or {\tt rq.process} (when {\tt tau} is a value
- outside of $[0,1]$. The {\tt rq} and {\tt rqs} objects are supported by Zelig's
- prediction utilities, but {\tt rq.process} is not. These objects maintain the
- same functionality that they have in the {\tt quantreg} package -- for example,
- one can call the {\tt summary} or {\tt plot} methods on them directly.
- See documentation for {\tt rq} from package {\tt quantreg} for details.
- The following information can be extracted directly from the {\tt z.out}
- object:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: vector of differences between $Y$ and $X'\beta_\tau$.
- \item {\tt fitted.values}: fitted values given by $X'\beta_\tau$.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item When {\tt zelig} was called with a single {\tt tau} value,
- {\tt summary(z.out)} returns a {\tt summary.rq} object. When {\tt zelig}
- was called with $\tau$ as a vector, {\tt summary(z.out)} returns a list
- of {\tt summary.rq} objects. From each {\tt summary.rq} object you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their 95 percent
- confidence intervals. The user can also obtain standard errors and p-values
- by specifying the {\tt se} argument to {\tt summary}. See documentation
- for {\tt summary.rq} in the {\tt quantreg} package for details.
- \item {\tt rdf}: the residual degrees of freedom.
- \item{\tt cov}: a $k \times k$ matrix of unscaled covariances. To obtain
- this attribute, the user must specify {\tt cov=TRUE} as an argument to
- {\tt summary}. See documentation for {\tt summary.rq} in the
- {\tt quantreg} package for details.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values for the specified
- values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first differences (or
- differences in expected values) for the specified values of {\tt
- x} and {\tt x1}.
- \end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-%#\input{cites/ls}
-\input{citeZelig}
-
-\subsection* {See also}
-The quantile regression package {\tt quantreg} by Richard Koenker. In addition, advanced users may wish to refer to \texttt{help(rq)}, \texttt{help(summary.rq)} and \texttt{help(rq.object)}.
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
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- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
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diff --git a/inst/doc/refman.tex b/inst/doc/refman.tex
deleted file mode 100644
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@@ -1,168 +0,0 @@
-\chapter{Main Commands}\label{s:main}
-
-Help for each command in Zelig and R is available through {\tt
- help.zelig()}. For example, typing {\tt help.zelig(setx)} will
-launch a web browser with the appropriate reference manual page for
-the {\tt setx()} command. (Occasionally, you may need to use, for example, {\tt
- help(print)} rather than {\tt help.zelig(print)}, to access the R
- help page instead of the default Zelig help page.)
-
-\include{commandsRd/zelig}
-\include{commandsRd/setx}
-\include{commandsRd/sim}
-\include{commandsRd/summary}
-\include{commandsRd/plot.zelig}
-\include{commands/print}
-\include{commandsRd/repl}
-
-\chapter{Supplementary Commands}
-
-\include{commands/matchit}
-\include{commandsRd/mi}
-\include{commandsRd/network}
-\include{commandsRd/plot.ci}
-\include{commandsRd/plot.surv}
-\include{commandsRd/rocplot}
-\include{commandsRd/ternaryplot}
-\include{commandsRd/ternarypoints}
-
-\chapter{Models Zelig Can Run}\label{s:model.details}
-
-This section describes the mathematical components of the models
-supported by Zelig, using whenever possible the classification and
-notation of \cite{King89}. Most models have a \emph{stochastic
- component} (probability density given certain parameters) and
-a \emph{systematic component} (deterministic functional form that
-specifies how one or more of the parameters varies over the observed
-values $y_i$ as a function of the explanatory variables $x_i$).
-
-Let $Y_i$ be a random outcome variable, realized as $i = 1, \dots, n$
-observations $y_i$. For the probability density $f(\cdot)$ with
-systematic feature $\theta_i$ varying over $i$ and a scalar ancillary
-parameter $\alpha$ (constant over $i$), the stochastic component is
-given by
-\begin{equation*}
-Y_i \sim f(y_i \mid \theta_i, \alpha).
-\end{equation*}
-
-For a functional form $g(\cdot)$, $k$ explanatory variables $X_i$, and
-effect parameters $\beta$, the systematic component is:
-\begin{equation*}
-\theta_i = g(x_i, \beta).
-\end{equation*}
-
-Using the definitions of \hlink{King, Tomz, and Wittenberg,
- 2000}{http://gking.harvard.edu/files/abs/making-abs.shtml},
- \nocite{KinTomWit00}
-Zelig generates at least two quantities of interest:
-\begin{itemize}
-\item The predicted value is a random draw from the stochastic component
- given random draws of $\beta$ and $\alpha$ from their sampling (or
- posterior) distribution.
-\item The expected value is the \emph{mean} of the stochastic component
- given random draws of $\beta$ and $\alpha$ from their sampling (or
- posterior) distributions. For computational efficiency, Zelig
- deterministically calculates the expected values from the simulated
- parameters whenever possible.
-\end{itemize}
-
-Both the predicted values and expected values produced by Zelig can be
-displayed as histograms or density estimates (to summarize the full
-sampling or posterior density), or summarized with confidence
-intervals (by sorting the simulations and taking the 5th and 95th
-percentile values for a 90\% confidence interval for example),
-standard errors (by taking the standard deviation of the simulations),
-or point estimates (by averaging the simulations). The point estimate
-of predicted and expected values are the same only in linear models.
-In almost all situations, simulations from predicted values have more
-variance than expected values. As the number of simulations increases
-the distribution of the expected values tends toward a constant; the
-distribution of the predicted values does not collapse as the number
-of simulations increases.
-
-\include{aov}
-\include{arima}
-\include{blogit}
-\include{bprobit}
-\include{chopit}
-\include{cloglog.net}
-\include{coxph}
-\include{ei.dynamic}
-\include{ei.hier}
-\include{ei.RxC}
-\include{exp}
-\include{factor.bayes}
-\include{factor.mix}
-\include{factor.ord}
-\include{gamma}
-\include{gamma.gee}
-\include{gamma.mixed}
-\include{gamma.net}
-\include{gamma.survey}
-\include{irt1d}
-\include{irtkd}
-\include{logit}
-\include{logit.bayes}
-\include{logit.gam}
-\include{logit.gee}
-\include{logit.gee}
-\include{logit.mixed}
-\include{logit.mixed}
-\include{logit.net}
-\include{logit.survey}
-\include{lognorm}
-\include{ls}
-\include{ls.mixed}
-\include{ls.net}
-\include{mlogit}
-\include{mlogit.bayes}
-\include{negbin}
-\include{normal}
-\include{normal.bayes}
-\include{normal.gam}
-\include{normal.gee}
-\include{normal.net}
-\include{normal.survey}
-\include{ologit}
-\include{oprobit}
-\include{oprobit.bayes}
-\include{poisson}
-\include{poisson.bayes}
-\include{poisson.gam}
-\include{poisson.gee}
-\include{poisson.mixed}
-\include{poisson.net}
-\include{poisson.survey}
-\include{probit}
-\include{probit.bayes}
-\include{probit.gam}
-\include{probit.gee}
-\include{probit.mixed}
-\include{probit.net}
-\include{probit.survey}
-\include{relogit}
-\include{sur}
-\include{threesls}
-\include{tobit}
-\include{tobit.bayes}
-\include{twosls}
-\include{weibull}
-
-
-\chapter{Commands for Programmers and Contributors}
-
-\include{describe.mymodel}
-\include{commands/model.end}
-\include{commands/model.frame.multiple}
-\include{commands/model.matrix.multiple}
-\include{commands/parse.formula}
-\include{commands/parse.par}
-\include{commands/put.start}
-\include{commands/set.start}
-\include{commands/tag}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "zelig"
-%%% End:
diff --git a/inst/doc/relogit.Rnw b/inst/doc/relogit.Rnw
deleted file mode 100644
index 9076da4..0000000
--- a/inst/doc/relogit.Rnw
+++ /dev/null
@@ -1,426 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/relogit}
-\include{zinput}
-%\VignetteIndexEntry{Rare Events Logistic Regression for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,logistic,regression,dichotomous}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt relogit}: Rare Events Logistic Regression for
-Dichotomous Dependent Variables}
-\label{relogit}
-
-The {\tt relogit} procedure estimates the same model as standard
-logistic regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables; see \Sref{logit}), but
-the estimates are corrected for the bias that occurs when the
-sample is small or the observed events are rare (i.e., if the
-dependent variable has many more 1s than 0s or the reverse). The {\tt
- relogit} procedure also optionally uses prior correction for
-case-control sampling designs.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "relogit", tau = NULL,
- case.correct = c("prior", "weighting"),
- bias.correct = TRUE, robust = FALSE,
- data = mydata, ...)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-The {\tt relogit} procedure supports four optional arguments in
-addition to the standard arguments for {\tt zelig()}. You may
-additionally use:
-\begin{itemize}
-\item {\tt tau}: a vector containing either one or two values for
- $\tau$, the true population fraction of ones. Use, for example,
- {\tt tau = c(0.05, 0.1)} to specify that the lower bound on {\tt
- tau} is 0.05 and the upper bound is 0.1. If left unspecified, only
-finite-sample bias correction is performed, not case-control correction.
-\item {\tt case.correct}: if {\tt tau} is specified, choose a method
-to correct for case-control sampling design: {\tt "prior"} (default)
-or {\tt "weighting"}.
-\item {\tt bias.correct}: a logical value of {\tt TRUE} (default) or
- {\tt FALSE} indicating whether the intercept should be corrected for
- finite sample (rare events) bias.
-\item {\tt robust}: defaults to {\tt FALSE} (except when {\tt
-case.control = "weighting"}; the default in this case becomes {\tt
-robust = TRUE}). If {\tt TRUE} is selected, {\tt zelig()} computes
-robust standard errors via the {\tt sandwich} package (see
-\cite{Zeileis04}). The default type of robust standard error is
-heteroskedastic and autocorrelation consistent (HAC), and assumes that
-observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame) The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-Note that if {\tt tau = NULL, bias.correct = FALSE, robust = FALSE},
-the {\tt relogit} procedure performs a standard logistic regression
-without any correction.
-
-\subsubsection*{Example 1: One Tau with Prior Correction and Bias Correction}
-
-Due to memory and space considerations, the data used here are a
-sample drawn from the full data set used in King and Zeng,
-2001,\nocite{KinZen01b} The proportion of militarized interstate
-conflicts to the absence of disputes is $\tau = 1,042 / 303,772
-\approx 0.00343$. To estimate the model,
-<<Example1.data>>=
- data(mid)
-@
-<<Example1.zelig>>=
- z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
- data = mid, model = "relogit", tau = 1042/303772)
-@
-Summarize the model output:
-<<Example1.summary>>=
- summary(z.out1)
-@
-Set the explanatory variables to their means:
-<<Example1.setx>>=
- x.out1 <- setx(z.out1)
-@
-Simulate quantities of interest:
-<<Example1.sim>>=
- s.out1 <- sim(z.out1, x = x.out1)
- summary(s.out1)
-@
-\begin{center}
-<<label=Example1Plot,fig=true,echo=true>>=
- plot(s.out1)
-@
-\end{center}
-
-\subsubsection*{Example 2: One Tau with Weighting, Robust Standard
-Errors, and Bias Correction}
-
-Suppose that we wish to perform case control correction using
-weighting (rather than the default prior correction). To
-estimate the model:
-<<Example2.zelig>>=
-
- z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
- data = mid, model = "relogit", tau = 1042/303772,
- case.control = "weighting", robust = TRUE)
-@
-Summarize the model output:
-<<Example2.summary>>=
- summary(z.out2)
-@
-Set the explanatory variables to their means:
-<<Example2.setx>>=
- x.out2 <- setx(z.out2)
-@
-Simulate quantities of interest:
-<<Example2.sim>>=
- s.out2 <- sim(z.out2, x = x.out2)
- summary(s.out2)
-@
-
-\subsubsection*{Example 3: Two Taus with Bias Correction and Prior Correction}
-
-Suppose that we did not know that $\tau \approx 0.00343$, but only
-that it was somewhere between $(0.002, 0.005)$. To estimate a model
-with a range of feasible estimates for $\tau$ (using the default prior
-correction method for case control correction):
-<<Example3.zelig>>=
- z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem
- + years, data = mid, model = "relogit",
- tau = c(0.002, 0.005))
-@
-Summarize the model output:
-<<Example3.summary>>=
- summary(z.out2)
-@
-Set the explanatory variables to their means:
-<<Example3.setx>>=
- x.out2 <- setx(z.out2)
-@
-Simulate quantities of interest:
-<<Example3.sim>>=
- s.out <- sim(z.out2, x = x.out2)
-@
-<<Example3.summary.sim>>=
-summary(s.out2)
-@
-\begin{center}
-<<label=Example3Plot,fig=true,echo=true>>=
- plot(s.out2)
-@
-\end{center}
-The cost of giving a range of values for $\tau$ is that point
-estimates are not available for quantities of interest. Instead,
-quantities are presented as confidence intervals with significance
-less than or equal to a specified level (e.g., at least 95\% of the
-simulations are contained in the nominal 95\% confidence interval).
-
-\subsubsection{Model}
-
-\begin{itemize}
-\item Like the standard logistic regression, the \emph{stochastic
- component} for the rare events logistic regression is:
-\begin{equation*}
- Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
-\end{equation*}
-where $Y_i$ is the binary dependent variable, and takes a value of
-either 0 or 1.
-
-\item The \emph{systematic component} is:
- \begin{equation*}
- \pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
- \end{equation*}
-
-
-\item If the sample is generated via a case-control (or choice-based)
- design, such as when drawing all events (or ``cases'') and a sample
- from the non-events (or ``controls'') and going backwards to collect
- the explanatory variables, you must correct for selecting on the
- dependent variable. While the slope coefficients are approximately
- unbiased, the constant term may be significantly biased. Zelig has
-two methods for case control correction:
-\begin{enumerate}
-\item The ``prior correction'' method
-adjusts the intercept term. Let $\tau$ be the true population
-fraction of events, $\bar{y}$ the fraction of events in the sample,
-and $\hat{\beta_0}$ the uncorrected intercept term. The corrected
-intercept $\beta_0$ is:
-\begin{equation*}
-\beta = \hat{\beta_0} - \ln \left[ \bigg( \frac{1 - \tau}{\tau}
- \bigg) \bigg( \frac{\bar{y}}{1 - \bar{y}} \bigg) \right].
-\end{equation*}
-
-\item The ``weighting'' method performs a weighted logistic regression to
-correct for a case-control sampling design. Let the 1 subscript
-denote observations for which the dependent variable is observed as a
-1, and the 0 subscript denote observations for which the dependent
-variable is observed as a 0. Then the vector of weights $w_i$
-\begin{eqnarray*}
-w_1 &=& \frac{\tau}{\bar{y}} \\
-w_0 &=& \frac{(1 - \tau)}{(1 - \bar{y})} \\
-w_i &=& w_1 Y_i + w_0 (1 - Y_i)
-\end{eqnarray*}
-\end{enumerate}
- If $\tau$ is unknown, you may alternatively specify an upper and
- lower bound for the possible range of $\tau$. In this case, the
- {\tt relogit} procedure uses ``robust Bayesian'' methods to generate
- a confidence interval (rather than a point estimate) for each
- quantity of interest. The nominal coverage of the confidence
- interval is at least as great as the actual coverage.
-
-\item By default, estimates of the the coefficients $\beta$ are
- bias-corrected to account for finite sample or rare events bias. In
- addition, quantities of interest, such as predicted probabilities,
- are also corrected of rare-events bias. If $\widehat{\beta}$ are
-the uncorrected logit coefficients and bias($\widehat{\beta}$) is the
-bias term, the corrected coefficients $\tilde{\beta}$ are
-\begin{equation*}
-\widehat{\beta} - \textrm{bias}(\widehat{\beta}) = \tilde{\beta}
-\end{equation*}
-The bias term is
-\begin{equation*}
-\textrm{bias}(\widehat{\beta}) = (X'WX)^{-1} X'W \xi
-\end{equation*}
-where
-\begin{eqnarray*}
-\xi_i &=& 0.5 Q_{ii} \Big( (1 + w-1)\widehat{\pi}_i - w_1 \Big) \\
-Q &=& X(X'WX)^{-1} X' \\
-W = \textrm{diag}\{\widehat{\pi}_i (1 - \widehat{\pi}_i) w_i\}
-\end{eqnarray*}
-where $w_i$ and $w_1$ are given in the ``weighting'' section above.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item For either one or no $\tau$:
- \begin{itemize}
- \item The expected values ({\tt qi\$ev}) for the rare events logit
- are simulations of the predicted probability $$E(Y) = \pi_i =
- \frac{1}{1 + \exp(-x_i \beta)},$$
- given draws of $\beta$ from its posterior.
- \item The predicted value ({\tt qi\$pr}) is a draw from a binomial
- distribution with mean equal to the simulated $\pi_i$.
- \item The first difference ({\tt qi\$fd}) is defined as
- \begin{equation*}
- \textrm{FD} = \Pr(Y = 1 \mid x_1, \tau) - \Pr(Y = 1 \mid x, \tau).
- \end{equation*}
- \item The risk ratio ({\tt qi\$rr}) is defined as
- \begin{equation*}
- \textrm{RR} = \Pr(Y = 1 \mid x_1, \tau) \ / \ \Pr(Y = 1 \mid x, \tau).
- \end{equation*}
- \end{itemize}
- \item For a range of $\tau$ defined by $[\tau_1, \tau_2]$, each of
- the quantities of interest are $n \times 2$ matrices, which report
- the lower and upper bounds, respectively, for a confidence interval
- with nominal coverage at least as great as the actual coverage. At
- worst, these bounds are conservative estimates for the likely range
- for each quantity of interest. Please refer to \hlink{King and
- Zeng
- (2002)}{http://gking.harvard.edu/files/1s.pdf}\nocite{KinZen02b}
- for the specific method of calculating bounded quantities of
- interest.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "relogit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt bias.correct}: {\tt TRUE} if bias correction was
-selected, else {\tt FALSE}.
- \item {\tt prior.correct}: {\tt TRUE} if prior correction was
-selected, else {\tt FALSE}.
- \item {\tt weighting}: {\tt TRUE} if weighting was selected, else
-{\tt FALSE}.
- \item {\tt tau}: the value of {\tt tau} for which case control
-correction was implemented.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_i$.
- \item {\tt linear.predictors}: the vector of $x_{i} \beta$
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
- Note that for a range of $\tau$, each of the above items may be
- extracted from the {\tt "lower.estimate"} and {\tt
- "upper.estimate"} objects in your {\tt zelig} output. Use {\tt
- lower <- z.out\$lower.estimate}, and then {\tt
- lower\$coefficients} to extract the coefficients for the
- empirical estimate generated for the smaller of the two $\tau$.
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values, or predicted
- probabilities, for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from Binomial
- distributions given the predicted probabilities.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the predicted
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Differences with Stata Version}
-The Stata version of ReLogit and the R implementation differ slightly
-in their coefficient estimates due to differences in the matrix
-inversion routines implemented in R and Stata. Zelig uses
-orthogonal-triangular decomposition (through {\tt lm.influence()}) to
-compute the bias term, which is more numerically stable than
-standard matrix calculations.
-
-
-\subsection* {How to Cite}
-
-\input{cites/relogit}
-\input{citeZelig}
-
-\subsection* {See also}
-For more information see \cite{KinZen01b},\cite{KinZen01},\cite{KinZen02b}.
-Sample data are from \cite{KinZen01b}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/relogit.log b/inst/doc/relogit.log
deleted file mode 100644
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--- a/inst/doc/relogit.log
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-\chapter{R Objects}\label{a:R}
-
-In R, objects can have one or more classes, consisting of the class of
-the scalar value and the class of the data structure holding the
-scalar value. Use the {\tt is()} command to determine what an object
-\emph{is}. If you are already familiar with R objects, you may skip
-to \Sref{load.data} for loading data, or \Sref{s:commands} for a
-description of Zelig commands.
-
-\section{Scalar Values}\label{variables}
-
-R uses several classes of scalar values, from which it constructs
-larger data structures. R is highly class-dependent: certain
-operations will only work on certain types of values or certain types
-of data structures. We list the three basic types of scalar values
-here for your reference:
-
-\begin{enumerate}
-\item \textbf{Numeric} is the default value type for most numbers. An
- \texttt{integer} is a subset of the \texttt{numeric} class, and may
- be used as a \texttt{numeric} value. You can perform
- any type of math or logical operation on numeric values,
- including:
-\begin{verbatim}
-> log(3 * 4 * (2 + pi)) # Note that pi is a built-in constant,
- [1] 4.122270 # and log() the natural log function.
-> 2 > 3 # Basic logical operations, including >,
- [1] FALSE # <, >= (greater than or equals),
- # <= (less than or equals), == (exactly
- # equals), and != (not equals).
-> 3 >= 2 && 100 == 1000/10 # Advanced logical operations, including
- [1] TRUE # & (and), && (if and only if), | (or),
- # and || (either or).
-\end{verbatim}
- Note that \texttt{Inf} (infinity), \texttt{-Inf} (negative
- infinity), \texttt{NA} (missing value), and \texttt{NaN} (not a
- number) are special numeric values on which most math operations
- will fail. (Logical operations will work, however.)
-
-\item \textbf{Logical} operations create logical values of either
- \texttt{TRUE} or \texttt{FALSE}. To convert logical values to
- numerical values, use the \texttt{as.integer()} command:
-\begin{verbatim}
-> as.integer(TRUE)
- [1] 1
-> as.integer(FALSE)
- [1] 0
-\end{verbatim}
-\item \textbf{Character} values are text strings. For example,
-\begin{verbatim}
-> text <- "supercalafragilisticxpaladocious"
-> text
-[1] "supercalafragilisticxpaladocious"
-\end{verbatim}
- assigns the text string on the right-hand side of the \texttt{<-} to
- the named object in your workspace. Text strings are primarily used
- with data frames, described in the next section. R always returns
- character strings in quotes.
-\end{enumerate}
-
-\section{Data Structures}
-
-\subsection{Arrays}
-
-Arrays are data structures that consist of only one type of scalar
-value (e.g., a vector of character strings, or a matrix of numeric
-values). The most common versions, one-dimensional and
-two-dimensional arrays, are known as \emph{vectors} and
-\emph{matrices}, respectively.
-
-\subsubsection*{Ways to create arrays}
-\begin{enumerate}
-\item Common ways to create {\bf vectors} (or one-dimensional arrays)
-include:
-\begin{verbatim}
-> a <- c(3, 7, 9, 11) # Concatenates numeric values into a vector
-> a <- c("a", "b", "c") # Concatenates character strings into a vector
-> a <- 1:5 # Creates a vector of integers from 1 to 5 inclusive
-> a <- rep(1, times = 5) # Creates a vector of 5 repeated `1's
-\end{verbatim}
-To manipulate a vector:
-\begin{verbatim}
-> a[10] # Extracts the 10th value from the vector `a'
-> a[5] <- 3.14 # Inserts 3.14 as the 5th value in the vector `a'
-> a[5:7] <- c(2, 4, 7) # Replaces the 5th through 7th values with 2, 4, and 7
-\end{verbatim}
-\emph{Unlike} larger arrays, vectors can be extended without first
-creating another vector of the correct length. Hence,
-\begin{verbatim}
-> a <- c(4, 6, 8)
-> a[5] <- 9 # Inserts a 9 in the 5th position of the vector,
- # automatically inserting an `NA' values position 4
-\end{verbatim}
-
-\item \label{factors} A {\bf factor vector} is a special type of
-vector that allows users to create $j$ indicator variables in one
-vector, rather than using $j$ dummy variables (as in Stata or SPSS). R
-creates this special class of vector from a pre-existing vector {\tt x}
-using the {\tt factor()} command, which separates {\tt x} into levels
-based on the discrete values observed in {\tt x}. These values may be
-either integer value or character strings. For example,
-\begin{verbatim}
-> x <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 9, 9, 9, 9)
-> factor(x)
- [1] 1 1 1 1 1 2 2 2 2 9 9 9 9
- Levels: 1 2 9
-\end{verbatim}
-By default, {\tt factor()} creates unordered factors, which are
- treated as discrete, rather than ordered, levels. Add the optional
- argument {\tt ordered = TRUE} to order the factors in the vector:
-\begin{verbatim}
-> x <- c("like", "dislike", "hate", "like", "don't know", "like", "dislike")
-> factor(x, levels = c("hate", "dislike", "like", "don't know"),
-+ ordered = TRUE)
- [1] like dislike hate like don't know like dislike
-Levels: hate < dislike < like < don't know
-\end{verbatim}
- The {\tt factor()} command orders the levels according to the order in
- the optional argument {\tt levels}. If you omit the levels command,
- R will order the values as they occur in the vector. Thus, omitting
- the {\tt levels} argument sorts the levels as {\tt like < dislike <
- hate < don't know} in the example above. If you omit one or more
- of the levels in the list of levels, R returns levels values of {\tt
- NA} for the missing level(s):
-\begin{verbatim}
-> factor(x, levels = c("hate", "dislike", "like"), ordered = TRUE)
- [1] like dislike hate like <NA> like dislike
-Levels: hate < dislike < like
-\end{verbatim}
-Use factored vectors within data frames for plotting (see
-\Sref{ss:draw}), to set the values of the explanatory variables using
-{\tt setx} (see \Sref{s:main}) and in the ordinal logit and multinomial
-logit models (see \Sref{s:models}).
-
-\item Build {\bf matrices} (or two-dimensional arrays) from vectors
-(one-dimensional arrays). You can create a matrix in two ways:
-\begin{enumerate}
-\item From a vector: Use the command \texttt{matrix(vector, nrow
- =} $k$\texttt{, ncol =} $n$\texttt{)} to create a $k \times n$
- matrix from the vector by filling in the columns from left to
- right. For example,
-\begin{verbatim}
-> matrix(c(1,2,3,4,5,6), nrow = 2, ncol = 3)
- [,1] [,2] [,3] # Note that when assigning a vector to a
- [1,] 1 3 5 # matrix, none of the rows or columns
- [2,] 2 4 6 # have names.
-\end{verbatim}
-\item From two or more vectors of length $k$: Use \texttt{cbind()} to
- combine $n$ vectors vertically to form a $k \times n$ matrix,
- or \texttt{rbind()} to combine $n$ vectors horizontally to form a
- $n \times k$ matrix. For example:
-\begin{verbatim}
-> x <- c(11, 12, 13) # Creates a vector `x' of 3 values.
-> y <- c(55, 33, 12) # Creates another vector `y' of 3 values.
-> rbind(x, y) # Creates a 2 x 3 matrix. Note that row
- [,1] [,2] [,3] # 1 is named x and row 2 is named y,
- x 11 12 13 # according to the order in which the
- y 55 33 12 # arguments were passed to rbind().
-> cbind(x, y) # Creates a 3 x 2 matrix. Note that the
- x y # columns are named according to the
- [1,] 11 55 # order in which they were passed to
- [2,] 12 33 # cbind().
- [3,] 13 12
-\end{verbatim}
-\end{enumerate}
-R supports a variety of matrix functions, including: \texttt{det()},
-which returns the matrix's determinant; \texttt{t()}, which transposes
-the matrix; \texttt{solve()}, which inverts the the matrix; and
-\texttt{\%*\%}, which multiplies two matricies. In addition, the
-\texttt{dim()} command returns the dimensions of your matrix. As with
-vectors, square brackets extract specific values from a matrix and the
-assignment mechanism \texttt{<-} replaces values. For example:
-\begin{verbatim}
-> loo[,3] # Extracts the third column of loo.
-> loo[1,] # Extracts the first row of loo.
-> loo[1,3] <- 13 # Inserts 13 as the value for row 1, column 3.
-> loo[1,] <- c(2,2,3) # Replaces the first row of loo.
-\end{verbatim}
-If you encounter problems replacing rows or columns, make sure that
-the \texttt{dims()} of the vector matches the \texttt{dims()} of the
-matrix you are trying to replace.
-
-\item An \textbf{n-dimensional array} is a set of stacked matrices of identical
- dimensions. For example, you may create a three dimensional array
- with dimensions $(x, y, z)$ by stacking $z$ matrices each with $x$
- rows and $y$ columns.
-\begin{verbatim}
-> a <- matrix(8, 2, 3) # Creates a 2 x 3 matrix populated with 8's.
-> b <- matrix(9, 2, 3) # Creates a 2 x 3 matrix populated with 9's.
-> array(c(a, b), c(2, 3, 2)) # Creates a 2 x 3 x 2 array with the first
- , , 1 # level [,,1] populated with matrix a (8's),
- # and the second level [,,2] populated
- [,1] [,2] [,3] # with matrix b (9's).
- [1,] 8 8 8
- [2,] 8 8 8 # Use square brackets to extract values. For
- # example, [1, 2, 2] extracts the second
- , , 2 # value in the first row of the second level.
- # You may also use the <- operator to
- [,1] [,2] [,3] # replace values.
- [1,] 9 9 9
- [2,] 9 9 9
-\end{verbatim}
-If an array is a one-dimensional vector or two-dimensional matrix, R
- will treat the array using the more specific method.
-\end{enumerate}
-
-Three functions especially helpful for
-arrays:
-\begin{itemize}
-\item {\tt is()} returns both the type of scalar value that populates
-the array, as well as the specific type of array (vector, matrix, or
-array more generally).
-\item {\tt dims()} returns the size of an array, where
-\begin{verbatim}
-> dims(b)
- [1] 33 5
-\end{verbatim}
-indicates that the array is two-dimensional (a matrix), and has 33
-rows and 5 columns.
-\item The single bracket \verb|[ ]| indicates specific values in the
-array. Use commas to indicate the index of the specific values you
-would like to pull out or replace:
-\begin{verbatim}
-> dims(a)
- [1] 14
-> a[10] # Pull out the 10th value in the vector `a'
-> dims(b)
- [1] 33 5
-> b[1:12, ] # Pull out the first 12 rows of `b'
-> c[1, 2] # Pull out the value in the first row, second column of `c'
-> dims(d)
- [1] 1000 4 5
-> d[ , 3, 1] # Pulls out a vector of 1,000 values
-\end{verbatim}
-\end{itemize}
-
-\subsection{Lists}
-
-Unlike arrays, which contain only one type of scalar value, lists
-are flexible data structures that can contain heterogeneous value types
-and heterogeneous data structures. Lists are so flexible that one list
-can contain another list. For example, the list {\tt output} can
-contain {\tt coef}, a vector of regression coefficients; {\tt
-variance}, the variance-covariance matrix; and another list {\tt terms}
-that describes the data using character strings. Use the {\tt
-names()} function to view the named elements in a list, and to extract
-a named element, use
-\begin{verbatim}
-> names(output)
- [1] coefficients variance terms
-> output$coefficients
-\end{verbatim} %$
-For lists where the elements are not named, use double square brackets
-\verb|[[ ]]| to extract elements:
-\begin{verbatim}
-> L[[4]] # Extracts the 4th element from the list `L'
-> L[[4]] <- b # Replaces the 4th element of the list `L' with a matrix `b'
-\end{verbatim}
-
-Like vectors, lists are flexible data structures that can be extended
-without first creating another list of with the correct number of
-elements:
-\begin{verbatim}
-> L <- list() # Creates an empty list
-> L$coefficients <- c(1, 4, 6, 8) # Inserts a vector into the list, and
- # names that vector `coefficients'
- # within the list
-> L[[4]] <- c(1, 4, 6, 8) # Inserts the vector into the 4th position
- # in the list. If this list doesn't
- # already have 4 elements, the empty
- # elements will be `NULL' values
-\end{verbatim} %$
-Alternatively, you can easily create a list using objects that already
-exist in your workspace:
-\begin{verbatim}
-> L <- list(coefficients = k, variance = v) # Where `k' is a vector and
- # `v' is a matrix
-\end{verbatim}
-
-\subsection{Data Frames}
-
-A data frame (or data set) is a special type of list in which each
-variable is constrained to have the same number of observations. A
-data frame may contain variables of different types (numeric,
-integer, logical, character, and factor), so long as each variable has
-the same number of observations.
-
-Thus, a data frame can use both matrix commands and list commands to
-manipulate variables and observations.
-\begin{verbatim}
-> dat[1:10,] # Extracts observations 1-10 and all associated variables
-> dat[dat$grp == 1,] # Extracts all observations that belong to group 1
-> group <- dat$grp # Saves the variable `grp' as a vector `group' in
- # the workspace, not in the data frame
-> var4 <- dat[[4]] # Saves the 4th variable as a `var4' in the workspace
-\end{verbatim}
-
-For a comprehensive introduction to data frames and recoding data, see
-\Sref{load.data}.
-
-\subsection{Identifying Objects and Data Structures}
-
-Each data structure has several \emph{attributes} which describe it.
-Although these attributes are normally invisible to users (e.g., not
-printed to the screen when one types the name of the object), there are
-several helpful functions that display particular attributes:
-\begin{itemize}
-\item For arrays, {\tt dims()} returns the size of each dimension.
-\item For arrays, {\tt is()} returns the scalar value type and
-specific type of array (vector, matrix, array). For more complex data
-structures, {\tt is()} returns the default methods (classes) for that object.
-\item For lists and data frames, {\tt names()} returns the variable
-names, and {\tt str()} returns the variable names and a short
-description of each element.
-\end{itemize}
-For almost all data types, you may use {\tt summary()} to get summary
-statistics.
-
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "zelig"
-%%% End:
diff --git a/inst/doc/rq.Rnw b/inst/doc/rq.Rnw
deleted file mode 100644
index c7a573b..0000000
--- a/inst/doc/rq.Rnw
+++ /dev/null
@@ -1,332 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/rq}
-\include{zinput}
-%\VignetteIndexEntry{Quantile Regression for Continuous Dependent Variables}
-%\VignetteDepends{Zelig, stats, quantreg}
-%\VignetteKeyWords{model, quantile,continuous, regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt rq}: Quantile Regression for Continuous
-Dependent Variables}
-\label{ls}
-
-Use a linear programming implementation of quantile regression to
-estimate a linear predictor of the $\tau$th conditional quantile of the
-population.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "rq", data = mydata, tau = 0.5)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for quantile regression:
-\begin{itemize}
-\item {\tt tau}: defaults to 0.5. Specifies the conditional quantile(s)
-that will be estimated. 0.5 corresponds to estimating the conditional
-median, 0.25 and 0.75 correspond to the conditional quartiles, etc. If
-{\tt tau} is a vector, the conditional quantile function at each tau is
-estimated. If tau is set outside of the interval [0,1], zelig returns
-the solution for all possible conditional quantiles given the data, but
-does not support inference on this fit ({\tt setx} and {\tt sim} will fail).
-
-\item {\tt se}: a string value that defaults to "nid". Specifies the
-method by which the covariance matrix of coefficients is estimated during
-the {\tt sim} stage of analysis. {\tt se}
-can take the following values, which are passed to the {\tt summary.rq}
-function from the {\tt quantreg} package. These descriptions are copied from
-the {\tt summary.rq} documentation.
-\begin{itemize}
- \item {\tt "iid"} which presumes that the errors are iid and computes
- an estimate of the asymptotic covariance matrix as in KB(1978).
-
- \item {\tt "nid"} which presumes local (in {\tt tau})
- linearity (in {\tt x}) of the
- the conditional quantile functions and computes a Huber
- sandwich estimate using a local estimate of the sparsity.
-
- \item {\tt "ker"} which uses a kernel estimate of the sandwich
- as proposed by Powell(1990).
-\end{itemize}
-\item {\tt \dots}: additional options passed to {\tt rq} when fitting the model.
-See documentation for {\tt rq} in the {\tt quantreg} package for more information.
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example with First Differences
-
-Attach sample data, in this case a dataset pertaining to the efficiency of plants
-that convert ammonia to nitric acid. The dependent variable, stack.loss, is 10 times
-the percentage of ammonia that escaped unconverted:
-<<Examples.data>>=
- data(stackloss)
-@
-Estimate model:
-<<Examples.zelig>>=
- z.out1 <- zelig(stack.loss ~ Air.Flow + Water.Temp + Acid.Conc., model = "rq",
- data = stackloss, tau=0.5)
-@
-Summarize regression coefficients:
-<<Examples.summary>>=
- summary(z.out1)
-@
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) values for the water temperature
-variable (the variable that indiates the temperature of water in the plant's cooling
-coils):
-<<Examples.setx>>=
- x.high <- setx(z.out1, Water.Temp = quantile(stackloss$Water.Temp, 0.8))
- x.low <- setx(z.out1, Water.Temp = quantile(stackloss$Water.Temp, 0.2))
-@
-Generate first differences for the effect of high versus low water temperature on
-stack loss:
-<<Examples.sim>>=
- s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
-@
-<<Examples.summary.sim>>=
-summary(s.out1)
-@
-\begin{center}
-<<label=ExamplesPlot,fig=true,echo=true,width=5.5,height=4>>=
- plot(s.out1)
-@
-\end{center}
-
-\item Using Dummy Variables
-
-We can estimate a model of unemployment as a function of macroeconomic indicators
-and fixed effects for each country (see \Sref{factors} for help with dummy variables).
-Note that you do not need to create dummy variables, as the program will automatically
-parse the unique values in the selected variable into discrete levels.
-<<Dummy.zelig>>=
- data(macro)
- z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country),
- model = "rq", tau=0.5, data = macro)
-@
-Set values for the explanatory variables, using the default mean/mode
-values, with country set to the United States and Japan, respectively:
-<<Dummy.setx>>=
- x.US <- setx(z.out2, country = "United States")
- x.Japan <- setx(z.out2, country = "Japan")
-@
-Simulate quantities of interest:
-<<Dummy.sim>>=
- s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
-@
-\begin{center}
-<<label=DummyPlot,fig=true,echo=true, width=5.5, height=4>>=
- plot(s.out2)
-@
-\end{center}
-
-\item Estimating Multiple Quantiles
-
-Using the Engel dataset on food expenditure as a function of income, we can use the
-{\tt "rq"} model to estimate multiple conditional quantiles:
-<<Multiple.zelig>>=
- data(engel)
- z.out3 <- zelig(foodexp ~ income, model = "rq", tau=seq(0.1,0.9,by=0.1), data = engel)
-@
-We can summarize the coefficient fits, or plot them to compare them to the least squares
-conditional mean estimator.
-<<Multiple.zelig.summary>>=
- summary(z.out3)
-@
-\begin{center}
-<<label=MultiplePlot,fig=true,echo=true,width=5.5,height=4>>=
- plot(summary(z.out3))
-@
-\end{center}
-Set the value of income to the top quartile and the bottom quartile of
-the income distribution for each fit:
-<<Multiple.setx>>=
- x.bottom <- setx(z.out3, income=quantile(engel$income, 0.25))
- x.top <- setx(z.out3, income=quantile(engel$income, 0.75))
-@
-Simulate quantities of interest for each fit simultaneously:
-<<Multiple.sim>>=
- s.out3 <- sim(z.out3, x = x.bottom, x1 = x.top)
-@
-Summary
-<<Example4.sim.summary>>=
-summary(s.out3)
-@
-
-\end{enumerate}
-
-\subsubsection{Model}
-The quantile estimator is best introduced by considering the sample median
-estimator and comparing it to the sample mean estimator.
-To find the mean of a sample, we solve
-for the quantity $\mu$ which minimizes the sum squared residuals:
-\begin{eqnarray*}
- \mu &=& \arg\min_\mu \sum_i (y_i-\mu)^2
-\end{eqnarray*}
-Estimating a quantile is similar, but we solve for $\xi$ which minimizes the
-sum absolute residuals:
-\begin{eqnarray*}
- \xi &=& \arg\min_\xi \sum_i |y_i-\xi|
-\end{eqnarray*}
-One can confirm the equivalence of these optimization problems and the standard
-mean and median operators by taking the derivative with respect to the argument
-and setting it to zero.
-
-The relationship between quantile regression and ordinary least squares regression
-is analogous to the relationship between the sample median and the sample mean, except
-we are now solving for the conditional median or conditional mean given covariates and
-a linear functional form. The optimization problems for the sample mean and median
-are then easily generalized to optimization problems for
-estimating \emph{conditional} means or medians by replacing $\mu$ or $\xi$
-with a linear combination of covariates $X'\beta$:
-\begin{eqnarray}
- \hat\beta_\mathrm{mean} &=& \arg\min_\beta \sum_i (Y_i-X_i'\beta)^2 \nonumber \\
- \hat\beta_\mathrm{median} &=& \arg\min_\beta \sum_i |Y_i-X_i'\beta| \label{median}
-\end{eqnarray}
-Equation \ref{median} can be generalized to provide any quantile of the conditional
-distribution, not just the median. We do this by weighting the aboslute value function
-asymmetrically in proportion to the requested $\tau$th quantile:
-\begin{eqnarray}
- \hat\beta_{\tau} &=& \arg\max_\beta \sum \rho(Y_i-X_i'\beta) \label{beta}\\
- \rho &=& \tau(1-I(Y-X_i'\beta > 0)) + (1-\tau)I(Y-X_i'\beta > 0) \nonumber
-\end{eqnarray}
-We call the asymmetric absolute value function a ``check function''. This optimization
-problem has no closed form solution and is solved using linear programming,
-so unlike most {\tt Zelig} models, it is not straightforward to specify a systematic and
-stochastic component for conditional quantile estimates. However, the following systematic
-and stochastic components do emerge asymptotically in the large-$n$ limit.
-
-Let $Q_\tau$ be the true conditional quantile for a given set of covariates $X_i$, and $\hat Q_\tau$
-be the conditional quantile estimator. Then the \emph{stochastic component} is described
-by a density with mean $\hat Q_\tau$ and variance $\sigma^2$:
-\begin{eqnarray*}
- Q_\tau &=& \mathcal{N}(\hat Q_\tau, \sigma^2)
-\end{eqnarray*}
-The \emph{systematic component} models the mean and variance of the above density as:
-\begin{eqnarray*}
- \hat Q_\tau &=& x'\hat\beta_\tau\\
- \sigma^2 &=& \frac{\tau(1-\tau)}{n f^2}
-\end{eqnarray*}
-Where $\hat\beta$ is the vector that solves equation \ref{beta}, $n$ is the number of
-datapoints, and $f$ is the true population density at the $\tau$th conditional quantile.
-Zelig uses this asymptotic approximation of stochastic and systematic components in
-simulation and numerically estimates the population density to derive $\sigma^2$. The
-simulation results should thus be treated with caution when using small datasets as both
-this asymptotic approximation and the population density approximation can break down.
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations from the stochastic
-component,
-\begin{equation*}
-E(\hat Q_\tau) = x_i \beta_\tau,
-\end{equation*}
-given a draw of $\beta_\tau$ from its sampling distribution. Variation in the expected
-value distribution comes from estimation uncertainty of $\beta_\tau$.
-
-\item The predicted value ({\tt qi\$pr}) is the result of a single draw from the
-stochastic component given a draw of $\beta_\tau$ from its sampling distribution. The
-distribution of predicted values should be centered around the same place as the
-expected values but have larger variance because it includes both estimation
-uncertainty and fundamental uncertainty.
-
-\item This model does not support conditional prediction.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "ls", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
- \item {\tt zelig()} will return an object {\tt z.out}, which is of class
- {\tt rq} (when {\tt tau} specifies a single quantile), {\tt rqs} (when {\tt tau}
- specifies multiple quantiles), or {\tt rq.process} (when {\tt tau} is a value
- outside of $[0,1]$. The {\tt rq} and {\tt rqs} objects are supported by Zelig's
- prediction utilities, but {\tt rq.process} is not. These objects maintain the
- same functionality that they have in the {\tt quantreg} package -- for example,
- one can call the {\tt summary} or {\tt plot} methods on them directly.
- See documentation for {\tt rq} from package {\tt quantreg} for details.
- The following information can be extracted directly from the {\tt z.out}
- object:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: vector of differences between $Y$ and $X'\beta_\tau$.
- \item {\tt fitted.values}: fitted values given by $X'\beta_\tau$.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item When {\tt zelig} was called with a single {\tt tau} value,
- {\tt summary(z.out)} returns a {\tt summary.rq} object. When {\tt zelig}
- was called with $\tau$ as a vector, {\tt summary(z.out)} returns a list
- of {\tt summary.rq} objects. From each {\tt summary.rq} object you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their 95 percent
- confidence intervals. The user can also obtain standard errors and p-values
- by specifying the {\tt se} argument to {\tt summary}. See documentation
- for {\tt summary.rq} in the {\tt quantreg} package for details.
- \item {\tt rdf}: the residual degrees of freedom.
- \item{\tt cov}: a $k \times k$ matrix of unscaled covariances. To obtain
- this attribute, the user must specify {\tt cov=TRUE} as an argument to
- {\tt summary}. See documentation for {\tt summary.rq} in the
- {\tt quantreg} package for details.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values for the specified
- values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first differences (or
- differences in expected values) for the specified values of {\tt
- x} and {\tt x1}.
- \end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-%#\input{cites/ls}
-\input{citeZelig}
-
-\subsection* {See also}
-The quantile regression package {\tt quantreg} by Richard Koenker. In addition, advanced users may wish to refer to \texttt{help(rq)}, \texttt{help(summary.rq)} and \texttt{help(rq.object)}.
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
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diff --git a/inst/doc/rq.pdf b/inst/doc/rq.pdf
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-#
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-\SweaveOpts{results=hide, prefix.string=vigpics/sur}
-\include{zinput}
-%\VignetteIndexEntry{Seemingly Unrelated Regression}
-%\VignetteDepends{Zelig}
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- before=search()
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-library(Zelig)
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-
-\section{{\tt sur}: Seemingly Unrelated Regression}
-\label{sur}
-
-\texttt{sur} extends ordinary least squares analysis to estimate system of linear
-equations with correlated error terms. The seemingly unrelated regression model can
-be viewed as a special case of generalized least squares.
-\subsubsection{Syntax}
-\begin{verbatim}
-> fml <- list ("mu1" = Y1 ~ X1,
- "mu2" = Y2 ~ X2,
- "mu3" = Y3 ~ X3)
-> z.out<-zelig(formula = fml, model = "2sls", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-\subsubsection{Inputs}
-\texttt{sur} regression specification has at least $M$ equations
-($M \ge 2$) corresponding to the dependent variables ($Y_1, Y_2, \ldots, Y_M$).
-\begin{itemize}
-\item \texttt{formula}:a list whose elements are formulae corresponding to
-the $M$ equations and their respective dependent and explanatory variables.
-For example, when there are no constraints on the coefficients:
-<<Inputs.list1>>=
- fml <- list ("mu1" = Y1 ~ X1,
- "mu2" = Y2 ~ X2,
- "mu3" = Y3 ~ X3)
-@
-\texttt{"mu1"} is the label for the first equation with Y1 as the dependent variable
-and X1 as the explanatory variable. Similarly \texttt{"mu2"} and \texttt{"mu3"} are the
-labels for the Y2 and Y3 equations.
-\item \texttt{tag}: Users can also put constraints on the coefficients by using
-the special function \texttt{tag}. \texttt{tag} takes two parameters. The first
-parameter is the variable whose coefficient needs to be constrained and the second
-parameter is label for the constrained coefficient. Each label uniquely identifies
-the constrained coefficient. For example:
-<<Inputs.list2>>=
- fml <- list ("mu1" = Y1 ~ tag(Xc,"constrain1")+ X1,
- "mu2" = Y2 ~ tag(Xc,"constrain1")+ X2,
- "mu3" = Y3 ~ X3)
-@
-\end{itemize}
-\subsubsection{Additional Inputs}
-\texttt{sur} takes the following additional inputs for model
-specifications:
-\begin{itemize}
-\item \texttt{TX}: an optional matrix to transform the regressor
-matrix and, hence, also the coefficient vector (see details). Default is \texttt{NULL}.
-\item \texttt{maxiter}: maximum number of iterations.
-\item \texttt{tol}: tolerance level indicating when to stop the iteration.
-\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
-matrix (see details). Default is equal to 1.
-\item \texttt{probdfsys}: use the degrees of freedom of the whole system
-(in place of the degrees of freedom of the single equation to calculate probability
-values for the t-test of individual parameters.
-\item \texttt{solvetol}: tolerance level for detecting linear dependencies when
-inverting a matrix or calculating a determinant. Default is \texttt {solvetol}= \begin{verbatim}.Machine\$double.eps.\end{verbatim}
-\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
-not crucial for the basic estimate (e.g McElroy's $R^2$).
-\end{itemize}
-\subsubsection{Details}
-The matrix \texttt{TX} transforms the regressor matrix ($X$) by $X\ast=X \times TX$. Thus,
-the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the original(stacked)
-vector of all coefficients and $b\ast$ is the new coefficient vector that is estimated instead.
-Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
-used to change the order of the coefficients and also to restrict coefficients (if $TX$ has
-less columns than it has rows).
-If iterated (with \texttt{maxit}>1), the covergence criterion is
-\begin{eqnarray*}
-\sqrt{\frac{\sum_i(b_{i,g}-b_{i,g-1})^2}{\sum_ib_{i,g-1}^2}}< tol
-\end{eqnarray*}
-where $b_{i,g}$ is the ith coefficient of the gth iteration step.
-The formula (\texttt{rcovformula} to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
-of the following (see Judge et al., 1955, p.469):
-if \texttt{rcovformula}= 0:
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
-\end{eqnarray*}
-if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
-\end{eqnarray*}
-if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
-\end{eqnarray*}
-if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
-\end{eqnarray*}
-If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
-for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
-yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
-positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
-(Theil, 1971, p.322).
-\subsubsection{Examples}
- Attaching the example dataset:
-<<Examples.data>>=
- data(grunfeld)
-@
- Formula:
-<<Examples.formula>>=
- formula<-list(mu1=Ige~Fge+Cge,
- mu2=Iw~Fw+Cw)
-@
-Estimating the model using \texttt{sur}:
-<<Examples.zelig>>=
- z.out<-zelig(formula=formula,model="sur",data=grunfeld)
-@
-<<Examples.summary>>=
-summary(z.out)
-@
-
-Set explanatory variables to their default (mean/mode) values
-<<Examples.setx>>=
- x.out <- setx(z.out)
-@
-
-Simulate draws from the posterior distribution:
-<<Examples.sim>>=
- s.out <- sim(z.out,x=x.out)
-@
-<<Examples.summary.sim>>=
-summary(s.out)
-@
-\clearpage
-\subsubsection{Model}
-The basic seemingly unrelated regression model assumes that for each individual
-observation $i$ there are $M$ dependent variables ($Y_{ij}, j=1,\ldots,M$)
-each with its own regression equation:
-\begin{eqnarray*}
-Y_{ij} = X_{ij}'\beta_j + \epsilon_{ij}, \quad {\rm for} \quad i=1,\ldots,N \quad {\rm and} \quad j=1,\ldots,M
-\end{eqnarray*}
-when $X_{ij}$ is a k-vector of explanatory variables, $\beta_j$
-is the coefficients of the explanatory variables,
-\begin{itemize}
-\item The \emph{stochastic component} is:
-\begin{eqnarray*}
-\epsilon_{ij} & \sim & {\cal N}(0, \sigma_{ij})
-\end{eqnarray*}
-where within each $j$ equation, $epsilon_{ij}$ is identically
-and independently distributed for $i=1,\ldots,M$,
-\begin{eqnarray*}
-{\rm Var}(\epsilon_{ij})=\sigma_j \quad {\rm and} \quad {\rm Cov}(\epsilon_{ij}, \epsilon_{i\prime j})= 0, \quad {\rm for} \quad i \neq i\prime,\quad {\rm and} \quad j=1,\ldots,M
-\end{eqnarray*}
-However, the error terms for the \emph{ith} observation can be correlated across equations
-\begin{eqnarray*}
-{\rm Cov}(\epsilon_{ij}, \epsilon_{ij\prime})\neq 0, \quad {\rm for} \quad j \neg j\prime, \quad {\rm and} \quad i=1,\ldots,N
-\end{eqnarray*}
-\item The \emph{systematic component} is:
-\begin{eqnarray*}
-\mu_{ij}= E(Y_ij)= X_{ij}\beta_j, \quad {\rm for} \quad i=1,\ldots,N, \quad {\rm and} \quad j=1,\ldots,M
-\end{eqnarray*}
-\end{itemize}
-\subsubsection{See Also}
-For information about two stage least squares regression, see
-\Sref{2sls} and \texttt{help(2sls)}.
-For information about three stage least squares regression, see
-\Sref{3sls} and \texttt{help(3sls)}.
-\subsubsection{Quantities of Interest}
-\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(formula=fml, model = "sur", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below:
-\begin{itemize}
-\item \texttt{rcovest}: residual covariance matrix used for estimation.
-\item \texttt{mcelr2}: McElroys R-squared value for the system.
-\end{itemize}
-\input{systemfit_output}
-\begin{itemize}
-\item \texttt{maxiter}: maximum number of iterations.
-\item \texttt{tol}: tolerance level indicating when to stop the iteration.
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/sur}
-\input{citeZelig}
-
-\subsection* {See also}
-The sur function is adapted from the \texttt{systemfit} library
-\citep{HamHen05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after <- search()
- torm <- setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
-
diff --git a/inst/doc/sur.log b/inst/doc/sur.log
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-\chapter{Data Analysis Commands}\label{c:R}
-
-\section{Command Syntax}
-
-Once R is installed, you only need to know a few basic elements to get
-started. It's important to remember that R, like any spoken
-language, has rules for proper syntax. Unlike English, however, the
-rules for intelligible R are small in number and quite precise (see
-\Sref{s:syntax}).
-
-\subsection{Getting Started}
-
-\begin{enumerate}
-\item To start R under Linux or Unix, type R at the terminal prompt or
- M-x R under ESS.
-\item The R prompt is {\tt >}.
-\item Type commands and hit enter to execute. (No additional
- characters, such as semicolons or commas, are necessary at the end
- of lines.)
-\item To quit from R, type \texttt{q()} and press enter.
-\item The {\tt \#} character makes R ignore the rest of the line, and
- is used in this document to comment R code.
-\item We highly recommend that you make a separate working directory
- or folder for each project.
-\item Each R session has a workspace, or working memory, to store the
- \emph{objects} that you create or input. These objects may be:
-\begin{enumerate}
-\item \emph{values}, which include numerical, integer, character, and
- logical values;
-\item \emph{data structures} made up of variables (vectors), matrices,
- and data frames; or
-\item \emph{functions} that perform the desired tasks on
- user-specified values or data structures.
-\end{enumerate}
-\end{enumerate}
-
-After starting R, you may at any time use Zelig's built-in help
-function to access on-line help for any command. To see help for all
-Zelig commands, type {\tt help.zelig(command)}, which will take you
-to the help page for all Zelig commands. For help with a specific
-Zelig or R command substitute the name of the command for the generic
-{\tt command}. For example, type {\tt help.zelig(logit)} to view
-help for the logit model.
-
-\subsection{Details}\label{s:syntax}.
-
-Zelig uses the syntax of R, which has several essential elements:
-\begin{enumerate}
-\item R is case sensitive. \texttt{Zelig}, the package or library, is
- not the same as \texttt{zelig}, the command.
-
-\item R functions accept user-defined arguments: while some arguments
- are required, other optional arguments modify the function's default
- behavior. Enclose arguments in parentheses and separate multiple
- arguments with commas. For example, {\tt print(x)} or {\tt print(x,
- digits = 2)} prints the contents of the object {\tt x} using the
- default number of digits or rounds to two digits to the right of the decimal
- point, respectively. You may nest commands as long as each has its
- own set of parentheses: \texttt{log(sqrt(5))} takes the square root
- of 5 and then takes the natural log.
-
-\item The {\tt <-} operator takes the output of the function on the
- right and saves them in the named object on the left. For example,
- {\tt z.out <- zelig(\dots)} stores the output from {\tt zelig()} as
- the object {\tt z.out} in your working memory. You may use {\tt
- z.out} as an argument in other functions, view the output by
- typing {\tt z.out} at the R prompt, or save {\tt z.out} to a file
- using the procedures described in \Sref{s:save}.
-
-\item You may name your objects anything, within a few constraints:
- \begin{itemize}
- \item You may only use letters (in upper or lower case) and
- periods to punctuate your variable names.
- \item You may \emph{not} use any special characters (aside from the
- period) or spaces to punctuate your variable names.
- \item Names cannot begin with numbers. For example, R will not
- let you save an object as \texttt{1997.election} but will let
- you save \texttt{election.1997}.
- \end{itemize}
-
- \item Use the {\tt names()} command to see the contents of R
- objects, and the {\tt \$} operator to extract elements from R
- objects. For example:
-\begin{verbatim}
-# Run least squares regression and save the output in working memory:
-> z.out <- zelig(y ~ x1 + x2, model = "ls", data = mydata)
-# See what's in the R object:
-> names(z.out)
- [1] "coefficients" "residuals" "effects" "rank"
-# Extract and display the coefficients in z.out:
-> z.out$coefficients
-\end{verbatim} %$
-
- \item All objects have a class designation which tells R how to
- treat it in subsequent commands. An object's class is generated
- by the function or mathematical operation that created it.
-
- \item To see a list of all objects in your current workspace, type:
- \texttt{ls()}. You can remove an object permanently from memory
- by typing \texttt{remove(goo)} (which deletes the object {\tt
- goo}), or remove all the objects with {\tt remove(list = ls())}.
-
- \item To run commands in a batch, use a text editor (such as the
- Windows R script editor or emacs) to compose your R commands, and
- save the file with a {\tt .R} file extension in your working
- directory. To run the file, type \texttt{source("Code.R")} at the R
- prompt.
-\end{enumerate}
-
-If you encounter a syntax error, check your spelling, case,
-parentheses, and commas. These are the most common syntax errors, and
-are easy to detect and correct with a little practice. If you
-encounter a syntax error in batch mode, R will tell you the line on
-which the syntax error occurred.
-
-\section{Data Sets}
-
-\subsection{Data Structures}
-
-Zelig uses only three of R's many data structures:
-\begin{enumerate}
-\item A {\bf variable} is a one-dimensional vector of length $n$.
-\item A {\bf data frame} is a rectangular matrix with $n$ rows and $k$
- columns. Each column represents a variable and each row an
- observation. Each variable may have a different class. (See
- \Sref{variable.classes} for a list of classes.) You may refer to
- specific variables from a data frame using, for example, {\tt
- data\$variable}.
-\item A {\bf list} is a combination of different data structures. For
- example, {\tt z.out} contains both {\tt coefficients} (a vector) and
- {\tt data} (a data frame). Use {\tt names()} to view the elements
- available within a list, and the {\tt \$} operator to refer to an
- element in a list.
-\end{enumerate}
-
-For a more comprehensive introduction, including ways to manipulate
-these data structures, please refer to Chapter \ref{a:R}.
-
-\subsection{Loading Data}\label{load.data}
-
-Datasets in Zelig are stored in ``data frames.'' In this section, we
-explain the standard ways to load data from disk into memory, how to
-handle special cases, and how to verify that the data you loaded is
-what you think it is.
-
-\subsubsection*{Standard Ways to Load Data}
-
-Make sure that the data file is saved in your working directory. You
-can check to see what your working directory is by starting R, and
-typing {\tt getwd()}. If you wish to use a different directory as
-your starting directory, use \verb|setwd("dirpath")|, where
-\verb|"dirpath"| is the full directory path of the directory you would
-like to use as your working directory.
-
-After setting your working directory, load data using one of the
-following methods:
-
-\begin{enumerate}
-\item If your dataset is in a \textbf{tab- or space-delimited .txt
- file}, use \texttt{read.table("mydata.txt")}
-\item If your dataset is \textbf{a comma separated table}, use
- \texttt{read.csv("mydata.csv")}.
-\item To import {\bf SPSS, Stata, and other data files}, use the
- foreign package, which automatically preserves field characteristics
- for each variable. Thus, variables classed as dates in Stata are
- automatically translated into values in the date class for R. For
- example:
-\begin{verbatim}
-> library(foreign) # Load the foreign package.
-> stata.data <- read.dta("mydata.dta") # For Stata data.
-> spss.data <- read.spss("mydata.sav", to.data.frame = TRUE) # For SPSS.
-\end{verbatim}
-\item To load data in R format, use {\tt load("mydata.RData")}.
-\item For sample data sets included with R packages such as Zelig, you
- may use the {\tt data()} command, which is a shortcut for loading
- data from the sample data directories. Because the locations of
- these directories vary by installation, it is extremely difficult to
- locate sample data sets and use one of the three preceding methods;
- {\tt data()} searches all of the currently used packages and loads
- sample data automatically. For example:
-\begin{verbatim}
-> library(Zelig) # Loads the Zelig library.
-> data(turnout) # Loads the turnout data.
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection*{Special Cases When Loading Data}
-
-These procedures apply to any of the above {\tt read} commands:
-\begin{enumerate}
-\item If your file uses the \textbf{first row to identify variable
- names}, you should use the option \texttt{header = TRUE} to import
- those field names. For example,
-\begin{verbatim}
-> read.csv("mydata.csv", header = TRUE)
-\end{verbatim}
- will read the words in the first row as the variable names and the
- subsequent rows (each with the same number of values as the first)
- as observations for each of those variables. If you have additional
- characters on the last line of the file or fewer values in one of
- the rows, you need to edit the file before attempting to read the
- data.
-\item The R missing value code is \texttt{NA}. If this value is in
- your data, R will recognize your missing values as such. If you
- have instead used a place-holder value (such as -9) to represent
- missing data, you need to tell R this on loading the data:
-\begin{verbatim}
-> read.table("mydata.tab", header = TRUE, na.strings = "-9")
-\end{verbatim}
- Note: You must enclose your place holder values in quotes.
-\item Unlike Windows, the file extension in R does not determine the
- default method for dealing with the file. For example, if your data
- is tab-delimited, but saved as a {\tt .sav} file,
- \texttt{read.table("mydata.sav")} will load your data into R.
-\end{enumerate}
-
-\subsubsection*{Verifying You Loaded The Data Correctly}
-
-Whichever method you use, try the \texttt{names()}, \texttt{dim()},
-and \texttt{summary()} commands to verify that the data was properly
-loaded. For example,
-\begin{verbatim}
-> data <- read.csv("mydata.csv", header = TRUE) # Read the data.
-> dim(data) # Displays the dimensions of the data frame
-[1] 16000 8 # in rows then columns.
-> data[1:10,] # Display rows 1-10 and all columns.
-> names(data) # Check the variable names.
-[1] "V1" "V2" "V3" # These values indicate that the variables
- # weren't named, and took default values.
-> names(data) <- c("income", "educate", "year") # Assign variable names.
-> summary(data) # Returning a summary for each variable.
-\end{verbatim}
-In this case, the {\tt summary()} command will return the maximum,
-minimum, mean, median, first and third quartiles, as well as the
-number of missing values for each variable.
-
-\subsection{Saving Data}\label{s:save}
-
-Use \texttt{save()} to write data or any object to a file in your
-working directory. For example,
-\begin{verbatim}
-> save(mydata, file = "mydata.RData") # Saves `mydata' to `mydata.RData'
- # in your working directory.
-> save.image() # Saves your entire workspace to
- # the default `.RData' file.
-\end{verbatim}
-R will also prompt you to save your workspace when you use the
-\texttt{q()} command to quit. When you start R again, it will load
-the previously saved workspace. Restarting R will not, however, load
-previously used packages. You must remember to load Zelig at the
-beginning of every R session.
-
-Alternatively, you can recall individually saved objects from .RData
-files using the {\tt load()} command. For example,
-\begin{verbatim}
-> load("mydata.RData")
-\end{verbatim}
-loads the objects saved in the {\tt mydata.RData} file. You may save
-a data frame, a data frame and associated functions, or other R
-objects to file.
-
-\section{Variables}
-
-\subsection{Classes of Variables}
-
-R variables come in several types. Certain Zelig models require
-dependent variables of a certain class of variable. (These are
-documented under the manual pages for each model.) Use {\tt
- class(variable)} to determine the class of a variable or {\tt
- class(data\$variable)} for a variable within a data frame. %$
-
-\subsubsection*{Types of Variables} \label{variable.classes}
-
-For all types of variable (vectors), you may use the {\tt c()} command
-to ``concatenate'' elements into a vector, the {\tt :} operator to
-generate a sequence of integer values, the {\tt seq()} command to
-generate a sequence of non-integer values, or the {\tt rep()} function
-to repeat a value to a specified length. In addition, you may use the
-{\tt <-} operator to save variables (or any other objects) to the
-workspace. For example:
-\begin{verbatim}
-> logic <- c(TRUE, FALSE, TRUE, TRUE, TRUE) # Creates `logic' (5 T/F values).
-> var1 <- 10:20 # All integers between 10 and 20.
-> var2 <- seq(from = 5, to = 10, by = 0.5) # Sequence from 5 to 10 by
- # intervals of 0.5.
-> var3 <- rep(NA, length = 20) # 20 `NA' values.
-> var4 <- c(rep(1, 15), rep(0, 15)) # 15 `1's followed by 15 `0's.
-\end{verbatim}
-For the {\tt seq()} command, you may alternatively specify {\tt
- length} instead of {\tt by} to create a variable with a specific
-number (denoted by the {\tt length} argument) of evenly spaced
-elements.
-
-\begin{enumerate}
-\item \textbf{Numeric} variables are real numbers and the default
- variable class for most dataset values. You can perform any type of
- math or logical operation on numeric values. If {\tt var1} and {\tt
- var2} are numeric variables, we can compute
-\begin{verbatim}
-> var3 <- log(var2) - 2*var1 # Create `var3' using math operations.
-\end{verbatim}
- \texttt{Inf} (infinity), \texttt{-Inf} (negative infinity),
- \texttt{NA} (missing value), and \texttt{NaN} (not a number) are
- special numeric values on which most math operations will fail.
- (Logical operations will work, however.) Use {\tt as.numeric()} to
- transform variables into numeric variables. Integers are a special
- class of numeric variable.
-
-\item \textbf{Logical} variables contain values of either
- \texttt{TRUE} or \texttt{FALSE}. R supports the following logical
- operators: {\tt ==}, exactly equals; {\tt >}, greater than; {\tt <},
- less than; {\tt >=}, greater than or equals; {\tt <=}, less than or
- equals; and {\tt !=}, not equals. The {\tt =} symbol is \emph{not}
- a logical operator. Refer to \Sref{logical} for more detail on
- logical operators. If {\tt var1} and {\tt var2} both have $n$
- observations, commands such as
-\begin{verbatim}
-> var3 <- var1 < var2
-> var3 <- var1 == var2
-\end{verbatim}
- create $n$ {\tt TRUE}/{\tt FALSE} observations such that the $i$th
- observation in {\tt var3} evaluates whether the logical statement is
- true for the $i$th value of {\tt var1} with respect to the $i$th
- value of {\tt var2}. Logical variables should usually be converted
- to integer values prior to analysis; use the {\tt as.integer()}
- command.
-
-\item \textbf{Character} variables are sets of text strings. Note
- that text strings are always enclosed in quotes to denote that the
- string is a value, not an object in the workspace or an argument for
- a function (neither of which take quotes). Variables of class
- character are not normally used in data analysis, but used as
- descriptive fields. If a character variable is used in a
- statistical operation, it must first be transformed into a factored
- variable.
-
-\item \textbf{Factor} variables may contain values consisting of
- either integers or character strings. Use {\tt factor()} or {\tt
- as.factor()} to convert character or integer variables into factor
- variables. Factor variables separate unique values into levels.
- These levels may either be ordered or unordered. In practice, this
- means that including a factor variable among the explanatory
- variables is equivalent to creating dummy variables for each level.
- In addition, some models (ordinal logit, ordinal probit, and
- multinomial logit), require that the dependent variable be a factor
- variable.
-\end{enumerate}
-
-\subsection{Recoding Variables}
-
-Researchers spend a significant amount of time cleaning and recoding
-data prior to beginning their analyses. R has several procedures to
-facilitate the process.
-
-\subsubsection*{Extracting, Replacing, and Generating New Variables}
-
-While it is not difficult to recode variables, the process is prone to
-human error. Thus, we recommend that before altering the data, you
-save your existing data frame using the procedures described
-in~\Sref{s:save}, that you only recode one variable at a time, and
-that you recode the variable outside the data frame and then return it
-to the data frame.
-
-To extract the variable you wish to recode, type:
-\begin{verbatim}
-> var <- data$var1 # Copies `var1' from `data', creating `var'.
-\end{verbatim} %$
-Do \emph{not} sort the extracted variable or delete observations from
-it. If you do, the $i$th observation in {\tt var} will no longer
-match the $i$th observation in {\tt data}.
-
-To replace the variable or generate a new variable in the data frame,
-type: \label{insert}
-\begin{verbatim}
-> data$var1 <- var # Replace `var1' in `data' with `var'.
-> data$new.var <- var # Generate `new.var' in `data' using `var'.
-\end{verbatim}
-
-To remove a variable from a data frame (rather than replacing one
-variable with another):
-\begin{verbatim}
-> data$var1 <- NULL
-\end{verbatim} %$
-
-\subsubsection*{Logical Operators}\label{logical}
-
-R has an intuitive method for recoding variables, which relies on
-logical operators that return statements of {\tt TRUE} and {\tt
- FALSE}. A mathematical operator (such as {\tt ==}, {\tt !=}, {\tt
- >}, {\tt >=} {\tt <}, and {\tt <=}) takes two objects of equal
-dimensions (scalars, vectors of the same length, matrices with the
-same number of rows and columns, or similarly dimensioned arrays) and
-compares every element in the first object to its counterpart in the
-second object.
-
-\begin{itemize}
-\item {\tt ==}: checks that one variable ``exactly equals'' another in
- a list-wise manner. For example:
-\begin{verbatim}
-> x <- c(1, 2, 3, 4, 5) # Creates the object `x'.
-> y <- c(2, 3, 3, 5, 1) # Creates the object `y'.
-> x == y # Only the 3rd `x' exactly equals
- [1] FALSE FALSE TRUE FALSE FALSE # its counterpart in `y'.
-\end{verbatim}
-(The {\tt =} symbol is \emph{not} a logical operator.)
-
-\item {\tt !=}: checks that one variable does not equal the other in a
- list-wise manner. Continuing the example:
-\begin{verbatim}
-> x != y
- [1] TRUE TRUE FALSE TRUE TRUE
-\end{verbatim}
-
-\item {\tt >} ({\tt >=}): checks whether each element in the left-hand
- object is greater than (or equal to) every element in the right-hand
- object. Continuing the example from above:
-\begin{verbatim}
-> x > y # Only the 5th `x' is greater
- [1] FALSE FALSE FALSE FALSE TRUE # than its counterpart in `y'.
-> x >= y # The 3rd `x' is equal to the
- [1] FALSE FALSE TRUE FALSE TRUE # 3rd `y' and becomes TRUE.
-\end{verbatim}
-
-\item {\tt <} ({\tt <=}): checks whether each element in the left-hand
- object is less than (or equal to) every object in the right-hand
- object. Continuing the example from above:
-\begin{verbatim}
-> x < y # The elements 1, 2, and 4 of `x' are
-[1] TRUE TRUE FALSE TRUE FALSE # less than their counterparts in `y'.
-> x <= y # The 3rd `x' is equal to the 3rd `y'
-[1] TRUE TRUE TRUE TRUE FALSE # and becomes TRUE.
-\end{verbatim}
-\end{itemize}
-For two vectors of five elements, the mathematical operators compare
-the first element in {\tt x} to the first element in {\tt y}, the
-second to the second and so forth. Thus, a mathematical comparison of
-{\tt x} and {\tt y} returns a vector of five {\tt TRUE}/{\tt FALSE}
-statements. Similarly, for two matrices with 3 rows and 20 columns
-each, the mathematical operators will return a $3 \times 20$ matrix of
-logical values.
-
-There are additional logical operators which allow you to combine and
-compare logical statements:
-\begin{itemize}
-\item {\tt \&}: is the logical equivalent of ``and'', and evaluates
-one array of logical statements against another in a list-wise manner,
-returning a {\tt TRUE} only if both are true in the same location. For example:
-\begin{verbatim}
-> a <- matrix(c(1:12), nrow = 3, ncol = 4) # Creates a matrix `a'.
-> a
- [,1] [,2] [,3] [,4]
-[1,] 1 4 7 10
-[2,] 2 5 8 11
-[3,] 3 6 9 12
-> b <- matrix(c(12:1), nrow = 3, ncol = 4) # Creates a matrix `b'.
-> b
- [,1] [,2] [,3] [,4]
-[1,] 12 9 6 3
-[2,] 11 8 5 2
-[3,] 10 7 4 1
-> v1 <- a > 3 # Creates the matrix `v1' (T/F values).
-> v2 <- b > 3 # Creates the matrix `v2' (T/F values).
-> v1 & v2 # Checks if the (i,j) value in `v1' and
- [,1] [,2] [,3] [,4] # `v2' are both TRUE. Because columns
-[1,] FALSE TRUE TRUE FALSE # 2-4 of `v1' are TRUE, and columns 1-3
-[2,] FALSE TRUE TRUE FALSE # of `var2' are TRUE, columns 2-3 are
-[3,] FALSE TRUE TRUE FALSE # TRUE here.
-> (a > 3) & (b > 3) # The same, in one step.
-\end{verbatim}
- For more complex comparisons, parentheses may be necessary to
- delimit logical statements.
-
-\item {\tt |}: is the logical equivalent of ``or'', and evaluates in a
-list-wise manner whether either of the
- values are {\tt TRUE}. Continuing the example from above:
-\begin{verbatim}
-> (a < 3) | (b < 3) # (1,1) and (2,1) in `a' are less
- [,1] [,2] [,3] [,4] # than 3, and (2,4) and (3,4) in
-[1,] TRUE FALSE FALSE FALSE # `b' are less than 3; | returns
-[2,] TRUE FALSE FALSE TRUE # a matrix with `TRUE' in (1,1),
-[3,] FALSE FALSE FALSE TRUE # (2,1), (2,4), and (3,4).
-\end{verbatim}
-\end{itemize}
-The {\tt \&\&} (if and only if) and {\tt ||} (either or) operators are
-used to control the command flow within functions. The {\tt \&\&}
-operator returns a {\tt TRUE} only if every element in the comparison
-statement is true; the {\tt ||} operator returns a {\tt TRUE} if any
-of the elements are true. Unlike the {\tt \&} and {\tt |} operators,
-which return arrays of logical values, the {\tt \&\&} and {\tt ||}
-operators return only one logical statement irrespective of the
-dimensions of the objects under consideration. Hence, {\tt \&\&} and
-{\tt ||} are logical operators which are \emph{not} appropriate for
-recoding variables.
-
-\subsubsection*{Coding and Recoding Variables}
-
-R uses vectors of logical statements to indicate how a variable should
-be coded or recoded. For example, to create a new variable {\tt var3}
-equal to 1 if {\tt var1} $<$ {\tt var2} and 0 otherwise:
-\begin{verbatim}
-> var3 <- var1 < var2 # Creates a vector of n T/F observations.
-> var3 <- as.integer(var3) # Replaces the T/F values in `var3' with
- # 1's for TRUE and 0's for FALSE.
-> var3 <- as.integer(var1 < var2) # Combine the two steps above into one.
-\end{verbatim}
-
-In addition to generating a vector of dummy variables, you can also
-refer to specific values using logical operators defined in
-\Sref{logical}. For example:
-\begin{verbatim}
-> v1 <- var1 == 5 # Creates a vector of T/F statements.
-> var1[v1] <- 4 # For every TRUE in `v1', replaces the
- # value in `var1' with a 4.
-> var1[var1 == 5] <- 4 # The same, in one step.
-\end{verbatim}
-The index (inside the square brackets) can be created with reference
-to other variables. For example,
-\begin{verbatim}
-> var1[var2 == var3] <- 1
-\end{verbatim}
-replaces the $i$th value in {\tt var1} with a 1 when the $i$th value
-in {\tt var2} equals the $i$th value in {\tt var3}. If you use {\tt
- =} in place of {\tt ==}, however, you will replace all the values in
-{\tt var1} with 1's because {\tt =} is another way to assign
-variables. Thus, the statement {\tt var2 = var3} is of course true.
-
-Finally, you may also replace any (character, numerical, or logical)
-values with special values (most commonly, {\tt NA}).
-\begin{verbatim}
-> var1[var1 == "don't know"] <- NA # Replaces all "don't know"'s with NA's.
-\end{verbatim}
-
-After recoding the {\tt var1} replace the old {\tt data\$var1} with
-the recoded {\tt var1}: {\tt data\$var1 <- var1}. You may combine the
-recoding and replacement procedures into one step. For example:
-\begin{verbatim}
-> data$var1[data$var1 =< 0] <- -1
-\end{verbatim}
-
-Alternatively, rather than recoding just specific values in variables,
-you may calculate new variables from existing variables. For example,
-\begin{verbatim}
-> var3 <- var1 + 2 * var2
-> var3 <- log(var1)
-\end{verbatim}
-After generating the new variables, use the assignment mechanism {\tt
- <-} to insert the new variable into the data frame.
-
-In addition to generating vectors of dummy variables, you may
-transform a vector into a matrix of dummy indicator variables. For
-example, see \Sref{dummy} to transform a vector of $k$ unique values
-(with $n$ observations in the complete vector) into a $n \times k$
-matrix.
-
-\subsubsection*{Missing Data}
-
-To deal with missing values in some of your variables:
-\begin{enumerate}
-\item You may generate multiply imputed datasets using
- \hlink{Amelia}{http://gking.harvard.edu/stats.shtml\#amelia} (or
- other programs).
-\item You may omit missing values. Zelig models automatically apply
- list-wise deletion, so no action is required to run a model. To
- obtain the total number of observations or produce other summary
- statistics using the analytic dataset, you may manually omit
- incomplete observations. To do so, first create a data frame
- containing only the variables in your analysis. For example:
-\begin{verbatim}
-> new.data <- cbind(data$dep.var, data$var1, data$var2, data$var3)
-\end{verbatim}
- The {\tt cbind()} command ``column binds'' variables into a data
- frame. (A similar command {\tt rbind()} ``row binds'' observations
- with the same number of variables into a data frame.) To omit
- missing values from this new data frame:
-\begin{verbatim}
-> new.data <- na.omit(new.data)
-\end{verbatim}
- If you perform {\tt na.omit()} on the full data frame, you risk
- deleting observations that are fully observed in your experimental
- variables, but missing values in other variables. Creating a new
- data frame containing only your experimental variables usually
- increases the number of observations retained after {\tt na.omit()}.
-\end{enumerate}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "zelig"
-%%% End:
diff --git a/inst/doc/systemfit_output.tex b/inst/doc/systemfit_output.tex
deleted file mode 100644
index a338b02..0000000
--- a/inst/doc/systemfit_output.tex
+++ /dev/null
@@ -1,55 +0,0 @@
-\begin{itemize}
-\item \texttt{method}: Estimation method.
-\item \texttt{g}: number of equations.
-\item \texttt{n}: total number of observations.
-\item \texttt{k}: total number of coefficients.
-\item \texttt{ki}: total number of linear independent coefficients.
-\item \texttt{df}: degrees of freedom of the whole system.
-\item \texttt{iter}: number of iteration steps.
-\item \texttt{b}: vector of all estimated coefficients.
-\item \texttt{t}: $t$ values for $b$.
-\item \texttt{se}: estimated standard errors of $b$.
-\item \texttt{bt}: coefficient vector transformed by $TX$.
-\item \texttt{p}: $p$ values for $b$.
-\item \texttt{bcov}: estimated covariance matrix of $b$.
-\item \texttt{btcov}: covariance matrix of $bt$.
-\item \texttt{rcov}: estimated residual covariance matrix.
-\item \texttt{drcov}: determinant of \texttt{rcov}.
-\item \texttt{rcor}: estimated residual correlation matrix.
-\item \texttt{olsr2}: system OLS R-squared value.
-\item \texttt{y}: vector of all (stacked) endogenous variables.
-\item \texttt{x}: matrix of all (diagonally stacked) regressors.
-\item \texttt{data}: data frame of the whole system (including instruments).
-\item \texttt{TX}: matrix used to transform the regressor matrix.
-\item \texttt{rcovformula}: formula to calculate the estimated residual covariance matrix.
-\item \texttt{probdfsys}: system degrees of freedom to calculate probability values?.
-\item \texttt{solvetol}: tolerance level when inverting a matrix or calculating a determinant.
-\item \texttt{eq}: a list that contains the results that belong to the individual equations.
-\item \texttt{eqnlabel*}: the equation label of the ith equation (from the labels list).
-\item \texttt{formula*}: model formula of the ith equation.
-\item \texttt{n*}: number of observations of the ith equation.
-\item \texttt{k*}: number of coefficients/regressors in the ith equation (including the constant).
-\item \texttt{ki*}: number of linear independent coefficients in the ith equation (including the
-constant differs from k only if there are restrictions that are not cross equation).
-\item \texttt{df*}: degrees of freedom of the ith equation.
-\item \texttt{b*}: estimated coefficients of the ith equation.
-\item \texttt{se*}: estimated standard errors of $b$ of the ith equation.
-\item \texttt{t*}: $t$ values for $b$ of the ith equation.
-\item \texttt{p*}: $p$ values for $b$ of the ith equation.
-\item \texttt{covb*}: estimated covariance matrix of $b$ of the ith equation.
-\item \texttt{y*}: vector of endogenous variable (response values) of the ith equation.
-\item \texttt{x*}: matrix of regressors (model matrix) of the ith equation.
-\item \texttt{data*}: data frame (including instruments) of the ith equation.
-\item \texttt{fitted*}: vector of fitted values of the ith equation.
-\item \texttt{residuals*}: vector of residuals of the ith equaiton.
-\item \texttt{ssr*}: sum of squared residuals of the ith equation.
-\item \texttt{mse*}: estimated variance of the residuals (mean of squared errors) of the ith equation.
-\item \texttt{s2*}: estimated variance of the residents($\hat{sigma}^2$) of the ith equation.
-\item \texttt{rmse*}: estimated standard error of the reiduals (square root of mse) of the ith equation.
-\item \texttt{s*}: estimated standard error of the residuals ($\hat{\sigma})$ of the ith equation.
-\item \texttt{r2*}: R-squared (coefficient of determination).
-\item \texttt{adjr2*}: adjusted R-squared value.
-
-
-
-\end{itemize}
diff --git a/inst/doc/threesls.Rnw b/inst/doc/threesls.Rnw
deleted file mode 100644
index 5acb3a5..0000000
--- a/inst/doc/threesls.Rnw
+++ /dev/null
@@ -1,211 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/threesls}
-\include{zinput}
-%\VignetteIndexEntry{ Three Stage Least Squares}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,continuous, least squares,error term}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt threesls}: Three Stage Least Squares}
-\label{threesls}
-
-\texttt{threesls} is a combination of two stage least squares
-and seemingly unrelated regression. It provides consistent estimates for linear regression models with
-explanatory variables correlated with the error term. It also extends ordinary least squares
-analysis to estimate system of linear equations with correlated error terms
-\subsubsection{Syntax}
-<<Syntax.list>>=
- fml <- list ("mu1" = Y1 ~ X1 + Z1,
- "mu2" = Y2 ~ X2 + Z2,
- "inst1" = Z1 ~ W1 + X1,
- "inst2" = Z2 ~ W2 + X2)
-@
-\begin{verbatim}
-> z.out <- zelig(formula = fml, model = "treesls", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-\subsubsection{Inputs}
-\texttt{threesls} regression specification requires at least two sets of equations. The first set of $M$ euqations
-corresponds to the $M$ dependent variables ($Y_1,\ldots,Y_M$) to be estimated. The second set of equations ($Z$)
-corresponds to the instrumental variables in the $M$ equations.
-\begin{itemize}
-\item \texttt{formula}:a list of the system of equations and instrumental variable
-equations. The system of equations is listed first as \texttt{mu}s. The equations
-for the instrumental variables are listed next as \texttt{inst}s.
-For example:
-<<Inputs.list>>=
- fml <- list ("mu1" = Y1 ~ X1 + Z1,
- "mu2" = Y2 ~ X2 + Z2,
- "inst1" = Z1 ~ W1 + X1,
- "inst2" = Z2 ~ W2 + X2)
-@
-\texttt{"mu1"} is the first equation in the two equation model with \texttt{Y1}
-as the dependent variable and \texttt{X1} and \texttt{Z1} as
-the explanatory variables. \texttt{"mu2"} is the second equation with
-\texttt{Y2} as the dependent variable
-and \texttt{X2} and \texttt{Z2} as the explanatory variables.
-\texttt{Z1} and \texttt{Z2} are also problematic endogenous variables, so
-they are estimated through instruments in the \texttt{"inst1"}
-and \texttt{"inst2"} equations.
-\item \texttt{Y}: dependent variables of interest in the system of equations.
-\item \texttt{Z}: the problematic explanatory variables correlated with
-the error term.
-\item \texttt{W}: exogenous instrument variables used to estimate the
-problematic explanatory variables (\texttt{Z})
-\end{itemize}
-\subsubsection{Additional Inputs}
-\texttt{threesls} takes the following additional inputs for model
-specifications:
-\begin{itemize}
-\item \texttt{TX}: an optional matrix to transform the regressor
-matrix and, hence, also the coefficient vector (see details). Default is \texttt{NULL}.
-\item \texttt{maxiter}: maximum number of iterations.
-\item \texttt{tol}: tolerance level indicating when to stop the iteration.
-\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
-matrix (see details). Default is equal to 1.
-\item \texttt{formulathreesls}: formula for calculating the threesls estimator, one of ``GLS'',
-``IV'', ``GMM'', ``Schmidt'', or ``Eviews'' (see details.)
-\item \texttt{probdfsys}: use the degrees of freedom of the whole system
-(in place of the degrees of freedom of the single equation to calculate probability
-values for the t-test of individual parameters.
-\item \texttt{single.eq.sigma}: use different $\sigma^2$ for each single
-equation to calculate the covariance matrix and the standard errors of the coefficients.
-\item \texttt{solvetol}: tolerance level for detecting linear dependencies when
-inverting a matrix or calculating a determinant. Default is \texttt {solvetol}=.Machine\$double.eps.
-\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
-not crucial for the basic estimate (e.g McElroy's $R^2$).
-\end{itemize}
-\subsubsection{Details}
-The matrix \texttt{TX} transforms the regressor matrix ($X$) by $X\ast=X \times TX$. Thus,
-the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the original(stacked)
-vector of all coefficients and $b\ast$ is the new coefficient vector that is estimated instead.
-Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
-used to change the order of the coefficients and also to restrict coefficients (if $TX$ has
-less columns than it has rows).
-If iterated (with \texttt{maxit}>1), the covergence criterion is
-\begin{eqnarray*}
-\sqrt{\frac{\sum_i(b_{i,g}-b_{i,g-1})^2}{\sum_ib_{i,g-1}^2}} < tol
-\end{eqnarray*}
-where $b_{i,g}$ is the ith coefficient of the gth iteration step.
-The formula (\texttt{rcovformula} to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
-of the following (see Judge et al., 1955, p.469):
-if \texttt{rcovformula}= 0:
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
-\end{eqnarray*}
-if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
-\end{eqnarray*}
-if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
-\end{eqnarray*}
-if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
-\end{eqnarray*}
-If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
-for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
-yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
-positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
-(Theil, 1971, p.322).
-The formulas to calculate the threesls estimator lead to identical results
-if the same instruments are used in all equations. If different instruments
-are used in the different equations, only the GMM-threesls estimator (``GMM'')
-and the threesls estimator proposed by Schmidt (1990) (``Schmidt'') are consistent, whereas
-``GMM'' is efficient relative to ``Schmidt'' (see Schmidt, 1990).
-\subsubsection{Examples}
-Attaching the example dataset:
-<<Examples.data>>=
- data(kmenta)
-@
- Formula:
-<<Examples.list>>=
- formula <- list(mu1= q ~ p + d,
- mu2=q ~ p + f + a,
- inst =~ d + f+ a )
-@
-Estimating the model using \texttt{threesls}:
-<<Examples.zelig>>=
- z.out <- zelig(formula=formula, model ="threesls",data=kmenta)
- summary(z.out)
-@
-
-Set explanatory variables to their default (mean/mode) values
-<<Examples.setx>>=
- x.out <- setx(z.out)
-@
-
-Simulate draws from the posterior distribution:
-<<Examples.sim>>=
- s.out <- sim(z.out, x=x.out)
- summary(s.out)
-@
-Plot the quantities of interest
-\begin{center}
-<<label=Examplesthreesls, fig=true, echo=false>>=
-plot(s.out)
-@
-\end{center}
-\clearpage
-
-\subsubsection{Model}
-\subsubsection{See Also}
-For information about two stage least square regression, see
-\Sref{2sls} and \texttt{help(2sls)}.
-For information about seemingly unrelated regression, see
-\Sref{sur} and \texttt{help(sur)}.
-\subsubsection{Quantities of Interest}
-\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(formula=fml, model = "threesls", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below:
-\begin{itemize}
-\item \texttt{rcovest}: residual covariance matrix used for estimation.
-\item \texttt{mcelr2}: McElroys R-squared value for the system.
-\item \texttt{h}: matrix of all (diagonally stacked) instrumental variables.
-\item \texttt{formulathreesls}: formula for calculating the threesls estimator
-
-\end{itemize}
-\input{systemfit_output}
-\begin{itemize}
-\item \texttt{inst*}: instruments of the ith equation.
-\item \texttt{h*}: matrix of instrumental variables of the ith equation.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/threesls}
-\input{citeZelig}
-
-\subsection* {See also}
-The threesls function is adapted from the \texttt{systemfit} library
-\citep{HamHen05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after <- search()
- torm <- setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
-
diff --git a/inst/doc/threesls.log b/inst/doc/threesls.log
deleted file mode 100644
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diff --git a/inst/doc/tobit.Rnw b/inst/doc/tobit.Rnw
deleted file mode 100644
index 4f77ccd..0000000
--- a/inst/doc/tobit.Rnw
+++ /dev/null
@@ -1,229 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/tobit}
-\include{zinput}
-%\VignetteIndexEntry{Linear regression for Left-Censored Dependet Variable}
-%\VignetteDepends{Zelig, survival}
-%\VignetteKeyWords{model, linear,regression,left-censored,continuous}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{tobit}: Linear Regression for a
-Left-Censored Dependent Variable} \label{tobit}
-
-Tobit regression estimates a linear regression model for a
-left-censored dependent variable, where the dependent variable is
-censored from below. While the classical tobit model has values
-censored at 0, you may select another censoring point. For other
-linear regression models with fully observed dependent variables, see
-Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
-regression (\Sref{normal}), or least squares (\Sref{ls}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
- model = "tobit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments to specify how the
-dependent variable is censored.
-\begin{itemize}
-\item \texttt{below}: (defaults to 0) The point at which the dependent
-variable is censored from below. If any values in the dependent
-variable are observed to be less than the censoring point, it is
-assumed that that particular observation is censored from below at the
-observed value. (See \Sref{tobit.bayes} for a Bayesian
-implementation that supports both left and right censoring.)
- \item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) and the options selected in
-{\tt cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "tobit", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(survreg)} for more
-information.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<Examples.data>>=
- data(tobin)
-@
-Estimating linear regression using \texttt{tobit}:
-<<Examples.zelig>>=
- z.out <- zelig(durable ~ age + quant, model = "tobit", data = tobin)
-@
-Setting values for the explanatory variables to their sample averages:
-<<Examples.setx>>=
- x.out <- setx(z.out)
-@
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-<<Examples.sim>>=
- s.out1 <- sim(z.out, x = x.out)
-@
-<<Examples.summary.sim>>=
-summary(s.out1)
-@
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
-
-<<FirstDifferences.setx>>=
- x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
- x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
-@
-Estimating the first difference for the effect of
-high versus low liquidity ratio on duration(\texttt{durable}):
-<<FirstDifferences.sim>>=
- s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-@
-<<FirstDifferences.sim.summary>>=
-summary(s.out2)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-\begin{itemize}
-\item Let $Y_i^*$ be a latent dependent variable which is distributed with
-\emph{stochastic} component
-\begin{eqnarray*}
-Y_i^* & \sim & \textrm{Normal}(\mu_i, \sigma^2) \\
-\end{eqnarray*}
-where $\mu_i$ is a vector means and $\sigma^2$ is a scalar variance
-parameter. $Y_i^*$ is not directly observed, however. Rather we
-observed $Y_i$ which is defined as:
-\begin{equation*}
-Y_i = \left\{
-\begin{array}{lcl}
-Y_i^* &\textrm{if} & c <Y_i^* \\
-c &\textrm{if} & c \ge Y_i^*
-\end{array}\right.
-\end{equation*}
-where $c$ is the lower bound below which $Y_i^*$ is censored.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i} &=& x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the tobit regression
-model are the same as the expected value of $Y*$:
-\begin{equation*}
-E(Y^* | X) = \mu_{i} = x_{i} \beta
-\end{equation*}
-
-\item The first difference (\texttt{qi\$fd}) for the tobit regression
-model is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y^* \mid x_{1}) - E(Y^* \mid x).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[E[Y^*_{i}(t_{i}=1)]-E[Y^*_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "tobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected value for the specified
- values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
- values given the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-
-
-\subsection*{How to Cite}
-\input{cites/tobit}
-\input{citeZelig}
-\subsection*{See also}
-The tobit function is part of the survival library by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library and
-\cite{VenRip02}.Sample data are from \cite{KinAltBur90}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/tobit.bayes.Rnw b/inst/doc/tobit.bayes.Rnw
deleted file mode 100644
index b34357b..0000000
--- a/inst/doc/tobit.bayes.Rnw
+++ /dev/null
@@ -1,297 +0,0 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/tobitBayes}
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Linear Regression for a Censored Dependent Variable}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, linear,bayes,regression,censored,continuous}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{\texttt{tobit.bayes}: Bayesian Linear Regression for a
-Censored Dependent Variable} \label{tobit.bayes}
-
-Bayesian tobit regression estimates a linear regression model with a
-censored dependent variable using a Gibbs sampler. The dependent
-variable may be censored from below and/or from above. For other
-linear regression models with fully observed dependent variables, see
-Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
-regression (\Sref{normal}), or least squares (\Sref{ls}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
- model = "tobit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments to specify how the
-dependent variable is censored.
-\begin{itemize}
-\item \texttt{below}: point at which the dependent variable is censored
-from below. If the dependent variable is only censored from above, set
-\texttt{below = -Inf}. The default value is 0.
-\item \texttt{above}: point at which the dependent variable is censored
-from above. If the dependent variable is only censored from below, set
-\texttt{above = Inf}. The default value is \texttt{Inf}.
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the convergence of the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, such that the
-least squares estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will be the prior mean for
-all coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCtobit)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-<<Examples.data>>=
- data(tobin)
-@
-Estimating linear regression using \texttt{tobit.bayes}:
-<<Examples.zelig>>=
- z.out <- zelig(durable ~ age + quant, model = "tobit.bayes",
- data = tobin, verbose=TRUE)
-@
-Checking for convergence before summarizing the estimates:
-<<Examples.geweke>>=
- geweke.diag(z.out$coefficients)
-@
-<<Examples.heidel>>=
-heidel.diag(z.out$coefficients)
-@
-<<Examples.raftery>>=
-raftery.diag(z.out$coefficients)
-@
-<<Examples.summary>>=
-summary(z.out)
-@
-Setting values for the explanatory variables to their sample averages:
-<<Examples.setx>>=
- x.out <- setx(z.out)
-@
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-<<Examples.sim>>=
- s.out1 <- sim(z.out, x = x.out)
-@
-<<Examples.summary.sim>>=
-summary(s.out1)
-@
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
-
-<<FirstDifferences.setx>>=
- x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
- x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
-@
-Estimating the first difference for the effect of
-high versus low liquidity ratio on duration(\texttt{durable}):
-<<FirstDifferences.sim>>=
- s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-@
-<<FirstDifferences.summary>>=
-summary(s.out2)
-@
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i^*$ be the dependent variable which is not directly observed. Instead,
-we observe $Y_i$ which is defined as following:
-\begin{equation*}
-Y_i = \left\{
-\begin{array}{lcl}
-Y_i^* &\textrm{if} & c_1<Y_i^*<c_2 \\
-c_1 &\textrm{if} & c_1 \ge Y_i^* \\
-c_2 &\textrm{if} & c_2 \le Y_i^*
-\end{array}\right.
-\end{equation*}
-where $c_1$ is the lower bound below which $Y_i^*$ is censored, and
-$c_2$ is the upper bound above which $Y_i^*$ is censored.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
-\end{eqnarray*}
-where $\epsilon_{i}=Y^*_i-\mu_i$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i}= x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-
-\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are
-given by
-\begin{eqnarray*}
-\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
-\sigma^{2} & \sim & \textrm{InverseGamma} \left( \frac{c_0}{2}, \frac{d_0}{2}
-\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory
-variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
-a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape
-and scale parameters for $\sigma^{2}$. Note that $\beta$ and
-$\sigma^2$ are assumed \emph{a priori} independent.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the tobit regression model is
-calculated as following. Let
-\begin{eqnarray*}
-\Phi_1 &=& \Phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
-\Phi_2 &=& \Phi\left(\frac{(c_2 - x \beta)}{\sigma}\right) \\
-\phi_1 &=& \phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
-\phi_2 &=& \phi\left(\frac{(c_2 - x \beta)}{\sigma}\right)
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the (cumulative) Normal density function and
-$\phi(\cdot)$ is the Normal probability density function of the
-standard normal distribution. Then the expected values are
-\begin{eqnarray*}
-E(Y|x) &=& P(Y^* \le c_1|x) c_1+P(c_1<Y^*<c_2|x) E(Y^* \mid c_1<Y^*<c_2, x)+P(Y^* \ge c_2) c_2 \\
- &=& \Phi_{1}c_1 + x \beta(\Phi_{2}-\Phi_{1}) + \sigma (\phi_1 -\phi_2) + (1-\Phi_2) c_2,
-\end{eqnarray*}
-
-\item The first difference (\texttt{qi\$fd}) for the tobit regression
-model is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid x_{1})-E(Y\mid x).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "tobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected value for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values given the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/tobit.bayes}
-\input{citeZelig}
-\subsection*{See also}
-Bayesian tobit regression is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
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- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
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-\SweaveOpts{results=hide, prefix.string=vigpics/twosls}
-\include{zinput}
-%\VignetteIndexEntry{ Two Stage Least Squares}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,continuous, least squares,instrumental}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt twosls}: Two Stage Least Squares}
-\label{twosls}
-
-\texttt{twosls} provides consistent estimates for linear regression
-models with some explanatory variable correlated with the error term
-using instrumental variables. In this situation, ordinary least
-squares fails to provide consistent estimates. The name two-stage
-least squares stems from the two regressions in the estimation
-procedure. In stage one, an ordinary least squares prediction of the
-instrumental variable is obtained from regressing it on the instrument
-variables. In stage two, the coefficients of interest are estimated
-using ordinary least square after substituting the instrumental
-variable by its predictions from stage one.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> fml <- list("mu1" = Y ~ X + W, "mu2" = W ~ X + Z,
- "inst" = ~ X + Z)
-> z.out <- zelig(formula = fml, model = "twosls", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-\subsubsection{Inputs}
-\texttt{twosls} regression take the following inputs:
-\begin{itemize}
-\item \texttt{formula}: A list of the formula for the main equation,
-the formula for the endogenous variable, and the (one-sided) formula
-for instrumental variables (including covariates). The first object in
-the list \texttt{mu} corresponds to the main regression model needs to
-be estimated. Alternatively, a system of simultaneous equations can be
-used. See the help file of {\tt systemfit} for more information.
-For example:
-<<Inputs.list>>=
- fml <- list("mu1" = Y ~ X + W, "mu2" = W ~ X + Z,
- "inst" = ~ X + Z)
-@
-\begin{itemize}
-\item \texttt{Y}: the dependent variable of interest.
-\item \texttt{X}: the covariate.
-\item \texttt{W}: the endogenous variable.
-\item \texttt{Z}: the exogenous instrumental variable.
-\end{itemize}
-\end{itemize}
-\subsubsection{Additional Inputs}
-\texttt{twosls} takes the following additional inputs for model
-specifications:
-\begin{itemize}
-\item \texttt{TX}: an optional matrix to transform the regressor
-matrix and, hence, also the coefficient vector (see \ref{details}). Default is \texttt{NULL}.
-\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
-matrix (see \ref{details}). Default is equal to 1.
-\item \texttt{probdfsys}: use the degrees of freedom of the whole system
-(in place of the degrees of freedom of the single equation to calculate probability
-values for the t-test of individual parameters.
-\item \texttt{single.eq.sigma}: use different $\sigma^2$ for each single
-equation to calculate the covariance matrix and the standard errors of the coefficients.
-\item \texttt{solvetol}: tolerance level for detecting linear dependencies when
-inverting a matrix or calculating a determinant. Default is \texttt {solvetol}=.Machine\$double.eps.
-\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
-not crucial for the basic estimate (e.g McElroy's $R^2$).
-\end{itemize}
-\subsubsection{Details}
-\label{details}
-\begin{itemize}
-\item \texttt{TX}: The matrix \texttt{TX} transforms the regressor matrix
-($X$) by $X\ast=X \times TX$. Thus,
-the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the
-original(stacked)
-vector of all coefficients and $b\ast$ is the new coefficient vector
-that is estimated instead.
-Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
-used to change the order of the coefficients and also to restrict coefficients (if $TX$ has
-less columns than it has rows).
-\item \texttt{rcovformula}: The formula to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
-of the following (see Judge et al., 1955, p.469):
-if \texttt{rcovformula}= 0:
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
-\end{eqnarray*}
-if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
-\end{eqnarray*}
-if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
-\end{eqnarray*}
-if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
-\begin{eqnarray*}
-\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
-\end{eqnarray*}
-If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
-for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
-yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
-positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
-\end{itemize}
-\subsubsection{Examples}
- Attaching the example dataset:
-<<Examples.data>>=
- data(klein)
-@
- Formula:
-<<Examples.list>>=
- formula <- list(mu1=C~Wtot + P + P1,
- mu2=I~P + P1 + K1,
- mu3=Wp~ X + X1 + Tm,
- inst= ~ P1 + K1 + X1 + Tm + Wg + G)
-@
-Estimating the model using \texttt{twosls}:
-<<Examples.zelig>>=
- z.out<-zelig(formula=formula, model="twosls",data=klein)
- summary(z.out)
-@
-
-Set explanatory variables to their default (mean/mode) values
-<<Examples.setx>>=
- x.out <- setx(z.out)
-@
-
-Simulate draws from the posterior distribution:
-<<Examples.sim>>=
-s.out <-sim(z.out,x=x.out)
- summary(s.out)
-@
-Plot the quantities of interest
-\begin{center}
-<<label=Examplestwosls, fig=true, echo=false>>=
-plot(s.out)
-@
-\end{center}
-
-\clearpage
-\subsubsection{Model}
-Let's consider the following regression model,
-\begin{eqnarray*}
-Y_i=X_i\beta + Z_i\gamma + \epsilon_i, \quad i=1,\ldots,N
-\end{eqnarray*}
-where $Y_i$ is the dependent variable,
-$X_i = (X_{1i},\ldots, X_{Ni})$ is the vector of explanatory variables,
-$\beta$ is the vector of coefficients of the explanatory variables $X_i$,
-$Z_i$
-is the problematic explanatory variable, and $\gamma$ is the coefficient
- of $Z_i$. In the equation, there is a direct dependence of $Z_i$
-on the structural disturbances of $\epsilon$.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-\epsilon_i & \sim & {\cal N}(0, \sigma^2), \quad {\rm and} \quad
-{\rm cov}(Z_i, \epsilon_i) \ne 0,
-\end{eqnarray*}
-\item The \emph{systematic component} is given by:
-\begin{eqnarray*}
-\mu_{i}= E(Y_i)= X_{i}\beta + Z_i\gamma,
-\end{eqnarray*}
-\end{itemize}
-\noindent To correct the problem caused by the correlation of $Z_i$ and $\epsilon$,
-two stage least squares utilizes two steps:
-\begin{itemize}
-\item \emph{Stage 1}: A new instrumental variable $\hat{Z}$ is created
-for $Z_i$ which is the ordinary least squares predictions from regressing
-$Z_i$ on a set of exogenous instruments $W$ and $X$.
-\begin{eqnarray*}
-\widehat{Z_i} = \widetilde{W}_i[(\widetilde{W}^\top\widetilde{W})^{-1}\widetilde{W}^\top Z]
-\end{eqnarray*}
-where $\widetilde{W} = (W,X)$
-\item \emph{Stage 2}: Substitute for $\hat{Z}_i$ for $Z_i$ in the original
-equation, estimate $\beta$ and $\gamma$ by ordinary least squares regression
-of $Y$ on $X$ and $\hat{Z}$ as in the following equation.
-\begin{eqnarray*}
-Y_i=X_i\beta + \widehat{Z_i}\gamma + \epsilon_i, \quad {\rm for}
-\quad i=1,\ldots,N
-\end{eqnarray*}
-\end{itemize}
-\subsubsection{See Also}
-For information about three stage least square regression, see
-\Sref{3sls} and \texttt{help(3sls)}.
-For information about seemingly unrelated regression, see
-\Sref{sur} and \texttt{help(sur)}.
-\subsubsection{Quantities of Interest}
-\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(formula=fml, model = "twosls", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below:
-\begin{itemize}
-\item \texttt{h}: matrix of all (diagonally stacked) instrumental variables.
-\item \texttt{single.eq.sigma}: different $\sigma^2$s for each single equation?.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\end{itemize}
-
-\input{systemfit_output}
-
-\begin{itemize}
-\item \texttt{inst*}: instruments of the ith equation.
-\item \texttt{h*}: matrix of instrumental variables of the ith equation.
-\end{itemize}
-
-\subsection* {How to Cite}
-
-\input{cites/twosls}
-\input{citeZelig}
-
-\subsection* {See also}
-The twosls function is adapted from the \texttt{systemfit} library
-\citep{HamHen05}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after <- search()
- torm <- setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
-
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 131.46 348.71 Tm (20) Tj
-ET
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-ET
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 289.26 348.71 Tm (60) Tj
-ET
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 368.16 348.71 Tm (80) Tj
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-ET
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-0.75 w
-[] 0 d
-1 J
-1 j
-10.00 M
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-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 100.89 240.71 Tm (3) Tj
-ET
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-ET
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-ET
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-ET
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 304.32 240.71 Tm (7) Tj
-ET
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 355.17 240.71 Tm (8) Tj
-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 406.03 240.71 Tm (9) Tj
-ET
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-38.02 258.75 m 35.64 258.75 l S
-38.02 270.14 m 35.64 270.14 l S
-38.02 281.52 m 35.64 281.52 l S
-38.02 292.90 m 35.64 292.90 l S
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-BT
-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 253.19 Tm (0.4) Tj
-ET
-BT
-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 275.96 Tm (0.6) Tj
-ET
-BT
-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 298.73 Tm (0.8) Tj
-ET
-38.02 254.02 m
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-ET
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-ET
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-ET
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-[ 3.00 5.00] 0 d
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-0.000 0.000 0.000 RG
-0.75 w
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-0.75 w
-[] 0 d
-1 J
-1 j
-10.00 M
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-BT
-0.000 0.000 0.000 rg
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-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 100.89 132.71 Tm (3) Tj
-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 151.75 132.71 Tm (4) Tj
-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 202.60 132.71 Tm (5) Tj
-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 253.46 132.71 Tm (6) Tj
-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 304.32 132.71 Tm (7) Tj
-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 355.17 132.71 Tm (8) Tj
-ET
-BT
-/F2 1 Tf 8.00 0.00 -0.00 8.00 406.03 132.71 Tm (9) Tj
-ET
-38.02 152.06 m 38.02 193.77 l S
-38.02 152.06 m 35.64 152.06 l S
-38.02 160.40 m 35.64 160.40 l S
-38.02 168.74 m 35.64 168.74 l S
-38.02 177.09 m 35.64 177.09 l S
-38.02 185.43 m 35.64 185.43 l S
-38.02 193.77 m 35.64 193.77 l S
-BT
-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 146.50 Tm (0.4) Tj
-ET
-BT
-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 171.53 Tm (1.0) Tj
-ET
-38.02 146.02 m
-422.50 146.02 l
-422.50 196.99 l
-38.02 196.99 l
-38.02 146.02 l
-S
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-BT
-0.000 0.000 0.000 rg
-/F3 1 Tf 10.00 0.00 -0.00 10.00 117.92 202.91 Tm [(Estimated Cum) 20 (ulative Hazar) 20 (d Over Time: H\(t|X\))] TJ
-ET
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-/F2 1 Tf 8.00 0.00 -0.00 8.00 222.70 119.40 Tm (time) Tj
-ET
-BT
-/F2 1 Tf 0.00 8.00 -8.00 0.00 17.11 157.50 Tm (cumhaz) Tj
-ET
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-1.000 0.000 0.000 RG
-0.75 w
-[ 3.00 5.00] 0 d
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-1 j
-10.00 M
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-S
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-0.000 0.000 0.000 RG
-0.75 w
-[] 0 d
-1 J
-1 j
-10.00 M
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-ET
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-ET
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-ET
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-/F2 1 Tf 0.00 8.00 -8.00 0.00 30.41 260.59 Tm (35) Tj
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-ET
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-ET
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-
-\section{What's New: Zelig Release Notes}\label{release.notes}
-
-Releases listed as ``stable releases'' have been tested against prior
-versions of Zelig for consistency and accuracy. Testing distributions
-may contain bugs, but are usually replaced by stable releases within a
-few days.
-
-\begin{itemize}
-\item{3.4-8} (Jan 1, 2010): Stable release for R 2.10
-
-Fixed problem with survival regressions and robust standard erros (assuming survival >= 2.35-8)\\
-Fixed vignette documentation\\
-
-\item {3.4-7} (Oct 23, 2009): Stable release for R 2.9.2
-
-Fixed .net models \\
-
-\item {3.4-6} (May 22, 2009): Stable release for R 2.9
-
-Added quantile regression model (quantile) \\
-Changed the digits option (thanks to Matthias Studer) \\
-Removed the old mloglm model (Multinomial Log-Linear Regression)\\
-
-\item {3.4-5} (Mar 13, 2009): A bug fixed in {\tt plot.ci()} (thanks to Ken Benoit) \\
-
-\item {3.4-4} (Mar 4, 2009): weights are incorporated into {\tt ologit}, {\tt oprobit}, and {\tt negbin} models (thanks to Casey Klofstad) \\
-
-\item {3.4-2} (Feb 10, 2009): Small fixes in the Rd files as required
-by new check in CRAN \\
-
-\item {3.4-0} (Jan 2, 2009): Bug-fix release for R 2.8.0
-Changed the Zelig citation \\
-Fixed zelig() signature to ensure that the formals() work properly
-and all arguments remain documented. "save.data" and "cite" were not
-documented (thanks to Micah Altman)\\
-Fixed some typos in model family names (thanks to Kevin Condon)\\
-Fixed the predicted values in gam.* models \\
-Fixed the plot functions in gam.* models \\
-
-\item {3.4-0} (Oct 27, 2008): Stable release for R 2.8.0.
-zelig() now takes a "citation" argument. If "citation" is "true" (default)
-the model citation is printed in each zelig run \\
-Introduced two new elemetns on the describe.mymodel function: authors and year \\
-Fixed the problems with lme4 package. Note that there is still a problem with
-simulation step of "gamma.mixed" model. We are still working on that. \\
-Fixed the bug with "gam" models (wrong predicted values) \\
-Fixed the bug with when zelig model name was provided from a variable (reported from
-Jeroen)
-
-
-\item {\bf 3.3-1} (June 12, 2008): Bug-fix release for R.2.7.0. A bug
- fix for {\rm plot.ci()} so that it works with mixed effects models
- (thanks to Keith Schnakenberg).
-
-\item {\bf 3.3-0} (June 03, 2008): Stable release for R.2.7.0. Updated
- {\rm coxph} so that it handles time-varying covariates (contributed
- by Patrick Lam). A new plot function for survival models
- (contributed by John Graves). First version dependencies are as
- follows: \newline
-\begin{tabular}{ll}\label{table.compact}
- "MASS" & "7.2-41" \\
- "nlme" & "3.1-87" \\
- "survival" & "2.34" \\
- "coda" & "0.13-1" \\
- "sna" & "1.5" \\
- "boot" & "1.2-31" \\
- "nnet" & "7.2-41" \\
- "zoo" & "1.5-0" \\
- "sandwich" & "2.1-0" \\
- "lme4" & "0.99875-9" \\
- "systemfit" & "1.0-2" \\
- "VGAM" & "0.7-5" \\
- "MCMCpack" & "0.8-2" \\
- "mvtnorm" & "0.8-3" \\
- "gee" & "4.13-13" \\
- "mgcv" & "1.3-29" \\
- "anchors" & "1.9-2" \\
- "survey" & "3.6-13"
-\end{tabular}
-
-\item {\bf 3.2-1} (April 10, 2008): Bug-fix release for R.2.6.0-2.6.2.
- Fixed the {\rm setx()} bug for multiply imputed data sets. (Thanks
- to Steve Shewfelt and Keith Schnakenberg.)
-
-\item {\bf 3.2} (April 3, 2008): Stable release for R 2.6.0-2.6.2.
- Adding models for survey data -- {\tt normal.survey}, {\tt
- logit.survey}, {\tt probit.survey}, {\tt poisson.survey}, {\tt
- gamma.survey}. First version dependencies are as
- follows:\newline
-\begin{tabular}{ll}\label{table.compat}
-survey & 3.6-13 \\
-MASS & 7.2-34 \\
-nlme & 3.1-86 \\
-survival & 2.34 \\
-boot & 1.2-30 \\
-nnet & 7.2-34 \\
-zoo & 1.4-0 \\
-sandwich & 2.0-2 \\
-sna & 1.4 \\
-lme4 & 0.99875-9 \\
-coda & 0.12-1 \\
-systemfit & 0.8-5 \\
-VGAM & 0.7-4 \\
-MCMCpack & 0.8-2 \\
-mvtnorm & 0.8-1 \\
-gee & 4.13-13 \\
-mgcv & 1.3-29 \\
-anchors & 2.0
-\end{tabular}
-
-\item {\bf 3.1-1} (January 10, 2008): Bug-fix release for R
- 2.6.0-2.6.1. Fixed bugs, improved the code and the documentation for
- mixed effects models. Thanks to Gregor Gorjanc. Fixed systemfit models
- due to some API changes in systemfit package. Added some other models (including *.mixed models) in plot.ci
-
-\item {\bf 3.1} (November 30, 2007): Stable release for R 2.6.0-2.6.1.
- Adding many new models such as {\tt aov}, {\tt chopit}, {\tt coxph},
- generalized linear mixed-effects models, and gee models. Also,
- several bugs are fixed. First version dependencies are as
- follows:\newline
-\begin{tabular}{ll}\label{table.compat}
-MASS & 7.2-34 \\
-nlme & 3.1-86 \\
-survival & 2.34 \\
-boot & 1.2-30 \\
-nnet & 7.2-34 \\
-zoo & 1.4-0 \\
-sandwich & 2.0-2 \\
-sna & 1.4 \\
-lme4 & 0.99875-9 \\
-coda & 0.12-1 \\
-systemfit & 0.8-5 \\
-VGAM & 0.7-4 \\
-MCMCpack & 0.8-2 \\
-mvtnorm & 0.8-1 \\
-gee & 4.13-13 \\
-mgcv & 1.3-29 \\
-anchors & 2.0
-\end{tabular}
-
-\item {\bf 3.0-1} -- {\bf 3.0-6}: Minor bug fixes. Stable release for
- R 2.5.0-2.5.1.
-
-\item {\bf 3.0} (July 20, 2007): Stable release for R 2.5.0-2.5.1.
- Introducing vignettes for each model. Improving documentation in the
- Zelig web site, improving citation style, improving {\tt
- help.zelig()} function, adding gam models, social network methods,
- logit gee model, adding support for cross-validation procedures and
- diagnostics tools, etc.
-
-\item {\bf 2.8-3} (May 29, 2007): Stable release for R 2.4.0-2.5.0.
-Fixed bugs in {\tt help.zelig()}, and summary for multinomial logit,
-bivariate probit, and bivariate logit with multiple imputation. (Thanks
-to Brant Inman and Javier Marquez.) First version dependencies are as
-follows:\newline
-\begin{tabular}{ll}
- MASS & 7.2-34 \\
- boot & 1.2-27 \\
- VGAM & 0.7-1 \\
- MCMCpack & 0.8-2 \\
- mvtnorm & 0.7-5 \\
- survival & 2.31 \\
- sandwich & 2.0-0 \\
- zoo & 1.2-1 \\
- coda & 0.10-7 \\
- nnet & 7.2-34 \\
- sna & 1.4
-\end{tabular}
-
-\item {\bf 2.8-2} (March 3, 2007): Stable release for R 2.4.0-2.4.1.
-Fixed bug in ARIMA simulation process.
-
-\item {\bf 2.8-1} (February 21, 2007): Stable release for R
- 2.4.0-2.4.1. Made {\tt setx()} compatible with ordred factor variables
- (thanks to Mike Ward and Kirill Kalinin). First order dependencies as
- in version 2.8-1.
-
-\item {\bf 2.8-0} (February 12, 2007): Stable release for R
-2.4.0-2.4.1. Released ARIMA models and network analysis models (least
-squares and logit) for sociomatrices. First level dependencies are as
-follows:\newline
-\begin{tabular}{ll}
- MASS & 7.2-31 \\
- boot & 1.2-27 \\
- VGAM & 0.7-1 \\
- MCMCpack & 0.7-4 \\
- mvtnorm & 0.7-5 \\
- survival & 2.31 \\
- sandwich & 2.0-0 \\
- zoo & 1.2-1 \\
- coda & 0.10-7 \\
- nnet & 7.2-31 \\
- sna & 1.4 \\
-\end{tabular}
-
-\item {\bf 2.7-5} (December 25, 2006): Stable release for R 2.4.0-2.4.1.
-Fixed bug related to {\tt names.default()}, summary for multiple
-imputation methods, and prediction for ordinal response models (thanks to
-Brian Ripley, Chris Lawrence, and Ian Yohai).
-
-\item {\bf 2.7-4} (November 10, 2006): Stable release for R 2.4.0. Fixed
-bugs related to R check.
-
-\item {\bf 2.7-3} (November 9, 2006): Stable release for R 2.4.0. Fixed
-bugs related to R check.
-
-\item {\bf 2.7-2} (November 5, 2006): Stable release for R 2.4.0.
-Temporarily removed {\sc arima} models.
-
-\item {\bf 2.7-1} (November 3, 2006): Stable release for R 2.4.0. Made
- changes regarding the S4 classes in VGAM. The {\sc arima} ({\tt arima})
- model for time series data added by Justin Grimmer. First level
- dependencies are as follows:\newline
-\begin{tabular}{ll}
- MASS & 7.2-29 \\
- boot & 1.2-26 \\
- VGAM & 0.7-1 \\
- MCMCpack & 0.7-4 \\
- mvtnorm & 0.7-5 \\
- survival & 2.29 \\
- sandwich & 2.0-0 \\
- zoo & 1.2-1 \\
- coda & 0.10-7 \\
-\end{tabular}
-
-
-\item {\bf 2.6-5} (September 14, 2006): Stable release for R 2.3.0-2.3.1. Fixed bugs
- in bivariate logit, bivariate probit, multinomial logit, and
- model.matrix.multiple (related to changes in version 2.6-4, but not
- previous versions, thanks to Chris Lawrence). First level
- dependencies are as follows:\newline
-\begin{tabular}{ll}
- MASS & 7.2-27.1 \\
- boot & 1.2-26 \\
- VGAM & 0.6-9 \\
- MCMCpack & 0.7-1 \\
- mvtnorm & 0.7-2 \\
- survival & 2.28 \\
- sandwich & 1.1-1 \\
- zoo & 1.0-6 \\
- coda & 0.10-5 \\
-\end{tabular}
-
-\item {\bf 2.6-4} (September 8, 2006): Stable release for R
-2.3.0-2.3.1. Fixed bugs in {\tt setx()}, and bugs related to {\tt
-multiple} and the multinomial logit model. Added instructions for
-installing Fortran tools for Intel macs. Added the R$\times$C
-ecological inference model. (thanks to Kurt Hornik, Luke Keele, Joerg
-Mueller-Scheessel, and B. Dan Wood)
-
-\item {\bf 2.6-3} (June 19, 2006): Stable release for R 2.0.0-2.3.1.
-Fixed bug in {\sc vdc} interface functions, and {\tt parse.formula()}.
-(thanks to Micah Altman, Christopher N. Lawrence, and Eric Kostello)
-
-\item {\bf 2.6-2} (June 7, 2006): Stable release for R 2.0.0-2.3.1.
-Removed R $\times$ C {\sc ei}. Changed {\tt data = list()} to {\tt
-data = mi()} for multiply-imputed data frames. First level version
-compatibilities are as for version 2.6-1.
-
-\item {\bf 2.6-1} (April 29, 2006): Stable release for R 2.0.0-2.2.1.
-Fixed major bug in ordinal logit and ordinal probit expected value
-simulation procedure (does not affect Bayesian ordinal probit).
-(reported by Ian Yohai) Added the following ecological inference {\sc
-ei} models: Bayesian hierarchical {\sc ei}, Bayesian dynamic {\sc ei},
-and R $\times$ C {\sc ei}. First level version compatibilities (at
-time of release) are as follows:\newline
-\begin{tabular}{ll}
-MASS & 7.2-24 \\
-boot & 1.2-24 \\
-VGAM & 0.6-8 \\
-MCMCpack & 0.7-1 \\
-mvtnorm & 0.7-2 \\
-survival & 2.24 \\
-sandwich & 1.1-1 \\
-zoo & 1.0-6 \\
-coda & 0.10-5 \\
-\end{tabular}
-
-\item {\bf 2.5-4} (March 16, 2006): Stable release for R 2.0.0-2.2.1. Fixed bug
-related to windows build. First-level dependencies are the same as in version
-2.5-1.
-
-\item {\bf 2.5-3} (March 9, 2006): Stable release for R 2.0.0-2.2.1. Fixed bugs
-related to VDC GUI. First level dependencies are the same as in version 2.5-1.
-
-\item {\bf 2.5-2} (February 3, 2006): Stable release for R 2.0.0-2.2.1. Fixed bugs
-related to VDC GUI. First level dependencies are the same as in version 2.5-1.
-
-\item {\bf 2.5-1} (January 31, 2006): Stable release
-for R 2.0.0-2.2.1. Added methods for multiple equation models. Added
-tobit regression. Fixed bugs related to robust estimation and upgrade of sandwich and
-zoo packages. Revised {\tt setx()} to use environments. Added
-{\tt current.packages()} to retrieve version of packages upon which Zelig
-depends. First level version compatibilities (at time of release)
-are as follows: \newline
-\begin{tabular}{ll}
- MASS & 7.2-24 \\
- boot & 1.2-24 \\
- VGAM & 0.6-7 \\
- mvtnorm & 0.7-2 \\
- survival & 2.20 \\
- sandwich & 1.1-0 \\
- zoo & 1.0-4 \\
- MCMCpack & 0.6-6 \\
- coda & 0.10-3 \\
-\end{tabular}
-
-\item \textbf{2.4-7} (December 10, 2005): Stable release for R
-2.0.0-2.2.2. Fixed the environment of {\tt eval()} called within {\tt
- setx.default()} (thanks to Micah Altman).
-
-\item \textbf{2.4-6} (October 27, 2005): Stable release for R
-2.0.0-2.2.2. Fixed bug related to simulation for Bayesian Normal regression.
-
-\item \textbf{2.4-5} (October 18, 2005): Stable release for R 2.0.0-2.2.0.
-Fixed installation instructions.
-
-\item \textbf{2.4-4} (September 29, 2005): Stable release for R
-2.0.0-2.2.0. Fixed {\tt help.zelig()} links.
-
-\item \textbf{2.4-3} (September 29, 2005): Stable release for R
-2.0.0-2.2.0. Revised {\tt matchit()} documentation.
-
-\item \textbf{2.4-2} (August 30, 2005): Stable release for R 2.0.0-2.1.1.
- Fixed bug in {\tt setx()} related to {\tt as.factor()} and {\tt I()}.
-Streamlined {\tt qi.survreg()}.
-
-\item \textbf{2.4-1} (August 15, 2005): Stable release for R 2.0.0-2.1.1. Added the
- following Bayesian models: factor analysis, mixed factor analysis,
- ordinal factor analysis, unidimensional item response theory,
- k-dimensional item response theory, logit, multinomial logit,
- normal, ordinal probit, Poisson, and tobit. Also fixed minor bug in
-formula (long variable names coerced to list).
-
-\item \textbf{2.3-2} (August 5, 2005): Stable release for R 2.0.0-2.1.1.
-Fixed bug in simulation procedure for lognormal model.
-
-\item \textbf{2.3-1} (August 4, 2005): Stable release for R 2.0.0-2.1.1.
-Fixed documentation errors related to model parameterization and code bugs
-related to first differences and conditional prediction for exponential,
-lognormal, and Weibull models. (reported by Alison Post)
-
-\item \textbf{2.2-4} (July 30, 2005): Stable release for R
-2.0.0-2.1.1. Revised relogit, adding option for weighting in addition
-to prior correction. (reported by Martin Pl\"oderl)
-
-\item \textbf{2.2-3} (July 24, 2005): Stable release for R 2.0.0-2.1.1.
-Fixed bug associated with robust standard errors for negative binomial.
-
-\item \textbf{2.2-2} (July 13, 2005): Stable release for R 2.0.0-2.1.1.
-Fixed bug in setx(). (reported by Ying Lu)
-
-\item \textbf{2.2-1} (July 11, 2005): Stable release for R 2.0.0-2.1.0.
-Revised ordinal probit to use MASS library. Added robust standard errors
-for the following regression models: exponential, gamma, logit, lognormal,
-least squares, negative binomial, normal (Gaussian), poisson, probit, and
-weibull.
-
-\item \textbf{2.1-4} (May 22, 2005): Stable release for R 1.9.1-2.1.0. Revised help.zelig()
- to deal with CRAN build of Windows version. Added recode of slots to lists
- in NAMESPACE. Revised {\tt install.R} script to deal with changes to {\tt install.packages()}. (reported by Dan Powers and
-Ying Lu)
-
-\item \textbf{2.1-3} (May 9, 2005): Stable release for R 1.9.1-2.1.0.
-Revised param.lm() function to work with bootstrap simulation. (reported
-by Jens Hainmueller)
-
-\item \textbf{2.1-2} (April 14, 2005): Stable release for R 1.9.1-2.1.0.
-Revised summary.zelig().
-\item \textbf{2.1-1} (April 7, 2005): Stable release for R 1.9.1-2.1.0.
-Fixed bugs in
- NAMESPACE and summary.vglm().
-\item \textbf{2.0-14} (April 5, 2005): Stable release for R
- 1.9.1-2.0.1. Added {\tt summary.vglm()} to ensure the compatibility with VGAM 0.6-2.
-\item \textbf{2.0-13} (March 11, 2005): Stable release for R 1.9.1-2.0.1.
-Fixed bugs in {\tt NAMESPACE} and R-help file for {\tt rocplot()}.
-\item \textbf{2.0-12} (February 20, 2005): Stable release for R
-1.9.1-2.0.1. Added {\tt plot = TRUE} option to {\tt rocplot()}.
-\item \textbf{2.0-11} (January 14, 2005): Stable release for R 1.9.1-2.0.1.
-Changed class name for subsettted models from {\tt "multiple"} to {\tt
-"strata"}, and modified affected functions.
-\item \textbf{2.0-10} (January 5, 2005): Stable release for R 1.9.1 and R
-2.0.0. Fixed bug in ordinal logit simulation procedure. (reported by Ian
-Yohai)
-\item \textbf{2.0-9} (October 21, 2004): Stable release for R 1.9.1
-\emph{and} R 2.0.0 (Linux and Windows). Fixed bug in NAMESPACE file.
-\item \textbf{2.0-8} (October 18, 2004): Stable release for R 1.9.1
-\emph{and} R 2.0.0 (Linux only). Revised for submission to CRAN.
-\item \textbf{2.0-7} (October 14, 2004): Stable release for R 1.9.1 \emph{and} R 2.0.0 (Linux
-only). Fixed bugs in {\tt summary.zelig()}, NAMESPACE, and assorted bugs related to new R
-release. Revised syntax for multiple equation models.
-\item \textbf{2.0-6} (October 4, 2004): Stable release for R 1.9.1.
-Fixed problem with NAMESPACE.
-\item \textbf{2.0-5} (September 25, 2004): Stable release for R 1.9.1.
-Changed installation procedure to source {\tt install.R} from Zelig
-website.
-\item \textbf{2.0-4} (September 22, 2004): Stable release for R 1.9.1. Fixed
-typo in installation directions, implemented NAMESPACE, rationalized {\tt
-summary.zelig()}, and tweaked documentation for least squares.
-\item \textbf{2.0-3} (September 1, 2004): Stable release for
-R 1.9.1. Fixed bug in conditional prediction for survival models.
-\item \textbf{2.0-2} (August 25, 2004): Stable release for R 1.9.1.
-Removed predicted values from {\tt ls}.
-\item \textbf{2.0-1b} (July 16, 2004): Stable release for R 1.9.1. MD5
- checksum problem fixed. Revised {\tt plot.zelig()} command to be a
- generic function with methods assigned by the model. Revised entire
- architecture to accept multiply imputed data sets with strata.
- Added functions to simplify adding models. Completely restructured
- reference manual. Fixed bugs related to conditional prediction in
- setx and summarizing strata in summary.zelig.
- \item \textbf{1.1-2} (June 24, 2004): Stable release for R 1.9.1 (MD5
- checksum problem not fixed, but does not seem to cause problems).
- Fixed bug in {\tt help.zelig()}. (reported by Michael L. Levitan)
-\item \textbf{1.1-1} (June 14, 2004): Stable release for R 1.9.0.
- Revised {\tt zelig()} procedure to use {\tt zelig2model()} wrappers,
- revised {\tt help.zelig()} to use a data file with extension {\tt
- .url.tab}, and revised {\tt setx()} procedure to take a list of
- {\tt fn} to apply to variables, and such that {\tt fn = NULL}
- returns the entire {\tt model.matrix()}.
- \item \textbf{1.0-8} (May 27, 2004): Stable release for R 1.9.0.
- Fixed bug in simulation procedure for survival models. (reported
- by Elizabeth Stuart)
- \item \textbf{1.0-7} (May 26, 2004): Stable release for R 1.9.0. Fixed
- bug in relogit simulation procedure. (reported by Tom Vanwellingham)
- \item \textbf{1.0-6} (May 11, 2004): Stable release for R 1.9.0.
- Fixed bug in setx.default, which had previously failed to ignore
- extraneous variables in data frame. (reported by Steve Purpura)
- \item \textbf{1.0-5} (May 7, 2004): Replaced relogit procedure with
- memory-efficient version. (reported by Tom Vanwellingham)
- \item \textbf{1.0-4} (April 19, 2004): Stable release for R 1.9.0.
- Added vcov.lm method; changed print for summary.relogit.
- \item \textbf{1.0-2} (April 16, 2004): Testing distribution for R 1.9.0.
- \item \textbf{1.0-1} (March, 23, 2004): Stable release for R 1.8.1.
-\end{itemize}
-
-\section{What's Next?}
-
-We have several plans for expanding and improving Zelig. Major
-changes slated for Version 3.0 (and beyond) include:
-
-\begin{itemize}
-\item Hierarchical and multi-level models
-\item Ecological inference models
-\item GEE models
-\item Neural network models
-\item Average treatment effects for everyone (treated and control
-units)
-\item Time-series cross-sectional models (via {\tt nlme})
-\item Generalized boosted regression model (via {\tt gbm})
-\item Saving random seeds to ensure exact replication
-\end{itemize}
-
-If you have suggestions, or packages that you would like to contribute
-to Zelig, please email our listserv at \hlink{zelig at lists.gking.harvard.edu}{mailto:zelig at lists.gking.harvard.edu}.
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-<span id="ztitle">ZELIG: EVERYONE'S STATISTICAL SOFTWARE </span> <br/>
-<span id="authors">
-<a href="" id="kosuke">Kosuke Imai </a> - <a href="" id="gary">Gary King </a> - <a href="" id="olivia">Olivia Lau </a> <br/>
-<span id="authorcontent"></span>
-</span>
-</p>
-<p>[Also available is a downloadable <a href="zelig.pdf"> PDF </a> version of this entire document] </p>
-
-<ul id="menuc">
-<li><a id="collapseall" href=""> collapse all </a> </li>
-<li><a id="expandall" href=""> expand all </a> </li>
-</ul>
-
-<div id="navcontainer">
-
-<ul id="zelig">
- <li class="mainindx"> <a class="kot" href="">Introduction </a>
- <ul id="introduction">
- <li><a href="static/acknowledgments.pdf" title="Acknowledgment">Acknowledgment</a></li>
- <li><a href="static/intro.pdf" title="Introduction">Introduction</a></li>
- </ul>
- </li>
- <li class="mainindx"> <a href="">User's Guide </a>
- <ul id="usersguide">
- <li><a href="static/install.pdf" title="Installation">Installation</a></li>
- <li><a href="static/syntax.pdf" title="Data Analysis Commands">Data Analysis Commands</a></li>
- <li><a href="static/Zcommands.pdf" title="Statistical Commands">Statistical Commands</a></li>
- <li><a href="static/graphs.pdf" title="Graphing commands">Graphing Commands</a></li>
- </ul>
- </li>
- <li class="mainindx"> <a href=""> Advanced Zelig uses </a>
- <ul id="advancedzeliguses">
- <li><a href="static/robjects.pdf" title="Advanced R">R Objects</a></li>
- <li><a href="static/programmingstatements.pdf" title="Advanced R">Programming statements</a></li>
- <li><a href="static/newModels.pdf" title="Writing new Models">Writing new Models</a></li>
- <li><a href="static/addingModels.pdf" title="Adding Models and Methods to Zelig">Adding Models and Methods to Zelig</a></li>
- </ul>
- </li>
- <li class="mainindx"><a href="">Reference Manual </a>
- <ul id="referencemanual">
- <li class="mainindx"><a href="">Main Commands</a>
- <ul id="maincommands">
- <li><a href="commandsRd/Zelig.pdf" title="zelig">zelig: Estimating a Statistical Model</a></li>
- <li><a href="commandsRd/setx.pdf" title="setx">setx: Setting Explanatory Variable Values</a></li>
- <li><a href="commandsRd/sim.pdf" title="sim">sim: Simulating Quantities of Interest</a></li>
- <li><a href="commandsRd/plot.zelig.pdf" title="plot">plot.zelig: Graphing Quantities of Interest</a></li>
- <li><a href="commandsRd/repl.pdf" title="repl">repl: Replicating Analyses</a></li>
- </ul>
- </li>
- <li class="mainindx"><a href="">Supplementary Commands </a>
- <ul id="supplementarycommands">
- <li><a href="static/matchit.pdf" title="">matchit: Create matched data</a></li>
- <li><a href="commandsRd/mi.pdf" title="mi">mi: Bundle multiply imputed data sets as a list</a></li>
- <li><a href="commandsRd/network.pdf" title="network">network: Format matricies into data frame for social network analysis</a></li>
- <li><a href="commandsRd/plot.ci.pdf" title="plot.ci">plot.ci: Plotting Vertical confidence Intervals</a></li>
- <li><a href="commandsRd/plot.surv.pdf" title="plot.surv">plot.surv: Plotting Coefidence Intervals for Survival Curves</a></li>
- <li><a href="commandsRd/rocplot.pdf" title="rocplot">rocplot: Receiver Operator Characteristic Plots</a></li>
- <li><a href="commandsRd/ternaryplot.pdf" title="ternaryplot">ternaryplot: Ternary diagram</a></li>
- <li><a href="commandsRd/ternarypoints.pdf" title="ternarypoints">ternarypoints: Adding Points To Ternary Diagrams</a></li>
- </ul>
- </li>
-
- <li class="mainindx"><a href="">Models Zelig Can Run </a>
-
-<!-- shell script is expected to produce <ul id="models"> <li> </li> ... </ul> -->
-
-
-
-<ul id="models">
-<li class="mainindx"><a href="">Models for Continuous Dependent Variables</a>
- <ul>
- <li><a href="aov.pdf">aov : Fit an Analysis of Variance Model</a></li>
- <li><a href="arima.pdf">arima : Arima models for Time Series Data</a></li>
- <li><a href="factor.bayes.pdf">factor.bayes : Bayesian Factor Analysis</a></li>
- <li><a href="ls.pdf">ls : Least Squares Regression for Continuous Dependent Variables</a></li>
- <li><a href="ls.mixed.pdf">ls.mixed : Mixed effects linear model</a></li>
- <li><a href="ls.net.pdf">ls.net : Social Network Least Squares Regression for Continuous Dependent Variables</a></li>
- <li><a href="normal.pdf">normal : Normal Regression for Continuous Dependent Variables</a></li>
- <li><a href="normal.bayes.pdf">normal.bayes : Bayesian Normal Linear Regression</a></li>
- <li><a href="normal.gam.pdf">normal.gam : Generalized Additive Model for Continuous Dependent Variables</a></li>
- <li><a href="normal.gee.pdf">normal.gee : General Estimating Equation for Normal Regression</a></li>
- <li><a href="normal.net.pdf">normal.net : Social Network Normal Regression for Continuous Dependent Variables</a></li>
- <li><a href="normal.survey.pdf">normal.survey : Survey-Weighted Normal Regression for Continuous Dependent Variables</a></li>
- <li><a href="quantile.pdf">quantile : Quantile Regression for Continuous Dependent Variables</a></li>
- <li><a href="rq.pdf">rq : Quantile Regression for Continuous Dependent Variables</a></li>
- <li><a href="sur.pdf">sur : Seemingly Unrelated Regression</a></li>
- <li><a href="threesls.pdf">threesls : Three Stage Least Squares</a></li>
- <li><a href="tobit.pdf">tobit : Linear regression for Left-Censored Dependent Variable</a></li>
- <li><a href="tobit.bayes.pdf">tobit.bayes : Bayesian Linear Regression for a Censored Dependent Variable</a></li>
- <li><a href="twosls.pdf">twosls : Two Stage Least Squares</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Models for Dichotomous Dependent Variables</a>
- <ul>
- <li><a href="blogit.pdf">blogit : Bivariate Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="bprobit.pdf">bprobit : Bivariate Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="cloglog.net.pdf">cloglog.net : Social Network Complementary Log Log Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="irt1d.pdf">irt1d : One Dimensional Item Response Model</a></li>
- <li><a href="irtkd.pdf">irtkd : K-Dimensional Item Response Model</a></li>
- <li><a href="logit.pdf">logit : Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="logit.bayes.pdf">logit.bayes : Bayesian Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="logit.gam.pdf">logit.gam : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
- <li><a href="logit.gee.pdf">logit.gee : General Estimating Equation for Logistic Regression</a></li>
- <li><a href="logit.mixed.pdf">logit.mixed : Mixed effects logistic model</a></li>
- <li><a href="logit.net.pdf">logit.net : Social Network Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="logit.survey.pdf">logit.survey : Survey-Weighted Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.pdf">probit : Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.bayes.pdf">probit.bayes : Bayesian Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.gam.pdf">probit.gam : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.gee.pdf">probit.gee : General Estimating Equation for Probit Regression</a></li>
- <li><a href="probit.mixed.pdf">probit.mixed : Mixed effects probit model</a></li>
- <li><a href="probit.net.pdf">probit.net : Social Network Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.survey.pdf">probit.survey : Survey-Weighted Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="relogit.pdf">relogit : Rare Events Logistic Regression for Dichotomous Dependent Variables</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Models for Ordinal Dependent Variables</a>
- <ul>
- <li><a href="chopit.pdf">chopit : Compound Hierarchical Ordinal Probit Regression for Survey Vignettes</a></li>
- <li><a href="factor.ord.pdf">factor.ord : Ordinal Data Factor Analysis</a></li>
- <li><a href="ologit.pdf">ologit : Ordinal Logistic Regression for Ordered Categorical Dependent Variables</a></li>
- <li><a href="oprobit.pdf">oprobit : Ordinal Probit Regression for Ordered Categorical Dependent Variables</a></li>
- <li><a href="oprobit.bayes.pdf">oprobit.bayes : Bayesian Ordered Probit Regression</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
- <ul>
- <li><a href="coxph.pdf">coxph : Cox Proportional Hazard Regression for Duration Dependent Variables</a></li>
- <li><a href="exp.pdf">exp : Exponential Regression for Duration Dependent Variables</a></li>
- <li><a href="gamma.pdf">gamma : Gamma Regression for Continuous, Positive Dependent Variables</a></li>
- <li><a href="gamma.gee.pdf">gamma.gee : General Estimating Equation for Gamma Regression</a></li>
- <li><a href="gamma.mixed.pdf">gamma.mixed : Mixed effects gamma model</a></li>
- <li><a href="gamma.net.pdf">gamma.net : Social Network Gamma Regression for Continuous, Positive Dependent Variables</a></li>
- <li><a href="gamma.survey.pdf">gamma.survey : Survey-Weighted Gamma Regression for Continuous, Positive Dependent Variables</a></li>
- <li><a href="lognorm.pdf">lognorm : Log-Normal Regression for Duration Dependent Variables</a></li>
- <li><a href="weibull.pdf">weibull : Weibull Regression for Duration Dependent Variables</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Multinomial Choice Models</a>
- <ul>
- <li><a href="mlogit.pdf">mlogit : Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
- <li><a href="mlogit.bayes.pdf">mlogit.bayes : Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Event Count Models</a>
- <ul>
- <li><a href="negbin.pdf">negbin : Negative Binomial Regression for Event Count Dependent Variables</a></li>
- <li><a href="poisson.pdf">poisson : Poisson Regression for Event Count Dependent Variables</a></li>
- <li><a href="poisson.bayes.pdf">poisson.bayes : Bayesian Poisson Regression</a></li>
- <li><a href="poisson.gam.pdf">poisson.gam : Generalized Additive Model for Event Count Dependent Variables</a></li>
- <li><a href="poisson.gee.pdf">poisson.gee : General Estimating Equation for Poisson Regression</a></li>
- <li><a href="poisson.mixed.pdf">poisson.mixed : Mixed effects poisson model</a></li>
- <li><a href="poisson.net.pdf">poisson.net : Social Network Poisson Regression for Event Count Dependent Variables</a></li>
- <li><a href="poisson.survey.pdf">poisson.survey : Survey-Weighted Poisson Regression for Event Count Dependent Variables</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Models for Mixed Dependent Variables</a>
- <ul>
- <li><a href="factor.mix.pdf">factor.mix : Mixed Data Factor Analysis</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Ecological Inference Models</a>
- <ul>
- <li><a href="ei.dynamic.pdf">ei.dynamic : Quinn's Dynamic Ecological Inference Model</a></li>
- <li><a href="ei.hier.pdf">ei.hier : Hierarchical Ecological Inference Model for 2 x 2 Tables</a></li>
- <li><a href="ei.RxC.pdf">ei.RxC : Hierarchical Multinomial-Dirichlet Ecological Inference Model for R x C Tables</a></li>
- </ul>
-</li>
-</ul>
-<!-- shell script is expected to produce <ul id="models"> <li> </li> ... </ul> -->
- </li>
-
- <li class="mainindx"><a href="">Commands for Programmers and Contributors </a>
- <ul id="contribcommands">
- <li><a href="static/describemymodel.pdf" title="describemymodel">describe: Describe a model's systematic and stochastic parameters</a></li>
- <li><a href="commandsRd/model.end.pdf" title="model.end">model.end: Cleaning up after optimization</a></li>
- <li><a href="commandsRd/model.frame.multiple.pdf" title="model.frame.multiple">model.frame.multiple: Extracting the "environment" of a model formula</a></li>
- <li><a href="commandsRd/model.matrix.multiple.pdf" title="model.matrix.multiple">model.matrix.multiple: Desing matrix for multivariate models</a></li>
- <li><a href="commandsRd/parse.formula.pdf" title="parse.formula">parse.formula: Parsing the inputs</a></li>
- <li><a href="commandsRd/parse.par.pdf" title="parse.par">parse.par: Select and reshape parameter vectors</a></li>
- <li><a href="commandsRd/put.start.pdf" title="put.start">put.start: Set specific starting values for certain parameters</a></li>
- <li><a href="commandsRd/set.start.pdf" title="set.start">set.start: Set starting valus for all parameters</a></li>
-
- </ul>
- </li>
- </ul>
- </li> <!-- end of refman li -->
- <li class="mainindx"> <a href=""> Appendices </a>
- <ul id="Appendices">
- <li><a href="static/whatsnew.pdf" title="faq">What's new? What's next? </a></li>
- <li><a href="static/faq.pdf" title="faq">Frequently Asked Questions</a></li>
- </ul>
- </li>
-
-</ul>
-</div>
-
-<script type="text/javascript">displayFooter()</script>
-</body>
-</html>
-
-
-
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-File: ts1cmtt.fd 1999/05/25 v2.5h Standard LaTeX font definitions
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- [16]
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-[17]
-Overfull \hbox (17.68726pt too wide) in paragraph at lines 159--161
-[]\T1/aer/m/n/12 If your dataset is in a \T1/aer/bx/n/12 tab- or space-delimite
-d .txt file\T1/aer/m/n/12 , use \T1/aett/m/n/12 read.table("mydata.txt")
- []
-
-
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 169--169
- []\T1/aett/m/n/12 > library(foreign) # Load the fore
-ign package.
- []
-
-
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 171--171
- []\T1/aett/m/n/12 > spss.data <- read.spss("mydata.sav", to.data.frame = TRUE)
- # For SPSS.[]
- []
-
-
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 182--182
- []\T1/aett/m/n/12 > library(Zelig) # Loads the Ze
-lig library.
- []
-
-[18] [19]
-Overfull \hbox (5.18848pt too wide) in paragraph at lines 282--282
-[]\T1/aett/m/n/12 > logic <- c(TRUE, FALSE, TRUE, TRUE, TRUE) # Creates \TS1/cm
-tt/m/n/12 `\T1/aett/m/n/12 logic\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 (5 T/F value
-s).[]
- []
-
-
-Overfull \hbox (5.1774pt too wide) in paragraph at lines 283--283
-[]\T1/aett/m/n/12 > var1 <- 10:20 # All integers be
-tween 10 and 20.[]
- []
-
-
-Overfull \hbox (3.7234pt too wide) in paragraph at lines 300--300
- []\T1/aett/m/n/12 > var3 <- log(var2) - 2*var1 # Create \TS1/cmtt/m/n/12
-`\T1/aett/m/n/12 var3\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 using math operations.[
-]
- []
-
-[20]
-Overfull \hbox (5.21063pt too wide) in paragraph at lines 366--366
-[]\T1/aett/m/n/12 > var <- data$var1 # Copies \TS1/cmtt/m/n/12 `\
-T1/aett/m/n/12 var1\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 from \TS1/cmtt/m/n/12 `\T
-1/aett/m/n/12 data\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 , creating \TS1/cmtt/m/n/12
- `\T1/aett/m/n/12 var\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 .[]
- []
-
-
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-
-[21]
-Overfull \hbox (5.21063pt too wide) in paragraph at lines 376--376
-[]\T1/aett/m/n/12 > data$new.var <- var # Generate \TS1/cmtt/m/n/12
- `\T1/aett/m/n/12 new.var\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 in \TS1/cmtt/m/n/12
- `\T1/aett/m/n/12 data\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 using \TS1/cmtt/m/n/12
- `\T1/aett/m/n/12 var\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 .[]
- []
-
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-
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-Overfull \hbox (3.7234pt too wide) in paragraph at lines 402--402
- []\T1/aett/m/n/12 > x == y # Only the 3rd \TS1/c
-mtt/m/n/12 `\T1/aett/m/n/12 x\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 exactly equals[
-]
- []
-
-
-Overfull \hbox (9.89136pt too wide) in paragraph at lines 419--419
- [] \T1/aett/m/n/12 [1] FALSE FALSE FALSE FALSE TRUE # than its counte
-rpart in \TS1/cmtt/m/n/12 `\T1/aett/m/n/12 y\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 .
-[]
- []
-
-
-Overfull \hbox (9.89136pt too wide) in paragraph at lines 429--429
- []\T1/aett/m/n/12 [1] TRUE TRUE FALSE TRUE FALSE # less than their counter
-parts in \TS1/cmtt/m/n/12 `\T1/aett/m/n/12 y\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 .
-[]
- []
-
-[22]
-Overfull \hbox (3.7234pt too wide) in paragraph at lines 461--461
- []\T1/aett/m/n/12 > v1 <- a > 3 # Creates the matrix \TS1/c
-mtt/m/n/12 `\T1/aett/m/n/12 v1\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 (T/F values).[
-]
- []
-
-
-Overfull \hbox (3.7234pt too wide) in paragraph at lines 462--462
- []\T1/aett/m/n/12 > v2 <- b > 3 # Creates the matrix \TS1/c
-mtt/m/n/12 `\T1/aett/m/n/12 v2\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 (T/F values).[
-]
- []
-
-
-Overfull \hbox (3.7234pt too wide) in paragraph at lines 463--463
- []\T1/aett/m/n/12 > v1 & v2 # Checks if the (i,j) value
- in \TS1/cmtt/m/n/12 `\T1/aett/m/n/12 v1\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 and[
-]
- []
-
-
-Overfull \hbox (3.7234pt too wide) in paragraph at lines 464--464
- [] \T1/aett/m/n/12 [,1] [,2] [,3] [,4] # \TS1/cmtt/m/n/12 `\T1/ae
-tt/m/n/12 v2\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 are both TRUE. Because columns[
-]
- []
-
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-Overfull \hbox (9.89136pt too wide) in paragraph at lines 465--465
- []\T1/aett/m/n/12 [1,] FALSE TRUE TRUE FALSE # 2-4 of \TS1/cmtt/m/n/12
-`\T1/aett/m/n/12 v1\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 are TRUE, and columns 1-3
-[]
- []
-
-[23]
-Overfull \hbox (5.1774pt too wide) in paragraph at lines 501--501
-[]\T1/aett/m/n/12 > var3 <- var1 < var2 # Creates a vector of n T
-/F observations.[]
- []
-
-
-Overfull \hbox (5.19955pt too wide) in paragraph at lines 530--530
-[]\T1/aett/m/n/12 > var1[var1 == "don\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 t know"]
- <- NA # Replaces all "don\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 t know"\TS1/cmtt/
-m/n/12 '\T1/aett/m/n/12 s with NA\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 s.[]
- []
-
-[24]) (./Zcommands.tex [25]
-Chapter 4.
-<xymatrix 3x5 368> [26
-
-]
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-51.
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-[27]
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 92--92
- []\T1/aett/m/n/12 > z.out <- zelig(vote ~ race + educate, model = "logit", dat
-a = turnout)[]
- []
-
-
-Overfull \hbox (9.88028pt too wide) in paragraph at lines 96--96
- []\T1/aett/m/n/12 > summary(s.out) # Numer
-ical summary.[]
- []
-
-
-Overfull \hbox (9.88028pt too wide) in paragraph at lines 97--97
- []\T1/aett/m/n/12 > plot(s.out) # Graph
-ical summary.[]
- []
-
-
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 103--103
- []\T1/aett/m/n/12 > z.out <- zelig(vote ~ race + educate, model = "logit", dat
-a = turnout)[]
- []
-
-
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-109.
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-[28]
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 141--141
- []\T1/aett/m/n/12 > z.out <- zelig(vote ~ race + educate, model = "logit", dat
-a = turnout)[]
- []
-
-[29]
-Overfull \hbox (9.88028pt too wide) in paragraph at lines 224--226
-[]\T1/aett/m/n/12 x.out <- setx(z.out, fn = list(numeric = mean, ordered = medi
-an, others =
- []
-
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-e 253.
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-ne 274.
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-[30]
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- []\T1/aett/m/n/12 > z.out <- zelig(vote ~ age + race, model = "logit", data =
-turnout)[]
- []
-
-[31]
-Overfull \hbox (9.88028pt too wide) in paragraph at lines 374--376
-[]\T1/aett/m/n/12 s.out <- sim(z.out, x = x.out, x1 = NULL, num = c(1000, 100),
- bootstrap =
- []
-
-[32]
-Overfull \hbox (0.73853pt too wide) in paragraph at lines 401--412
-[]\T1/aer/m/n/12 Zelig sim-u-lates pa-ram-e-ters from clas-si-cal \T1/aer/m/it/
-12 max-i-mum like-li-hood \T1/aer/m/n/12 mod-els us-ing asymp-
- []
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-[]\T1/aett/m/n/12 # Check that the expected values are identical. You may do t
-his for each qi.[]
- []
-
-
-LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 794.
-
-) (./graphs.tex [39]
-Chapter 5.
-[40
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-] [41]
-Overfull \hbox (5.97571pt too wide) in paragraph at lines 111--111
-[]\T1/aer/bx/n/17.28 Adding Points, Lines, and Leg-ends to Ex-ist-ing Plots
- []
-
-[42] [43] <figs/sample1.pdf, id=786, 451.6875pt x 289.08pt>
-File: figs/sample1.pdf Graphic file (type pdf)
-
-<use figs/sample1.pdf> [44 <./figs/sample1.pdf>]
-<figs/sample2.pdf, id=800, 325.215pt x 252.945pt>
-File: figs/sample2.pdf Graphic file (type pdf)
- <use figs/sample2.pdf>
-Overfull \hbox (5.1774pt too wide) in paragraph at lines 234--234
-[]\T1/aett/m/n/12 > lines(density(turnout$income)) # Adds the kern
-el density line.[]
- []
-
-
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-
-[45 <./figs/sample2.pdf>]
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- <use figs/roc.pdf> [47 <./figs/roc.pdf>]
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-File: figs/vertci.pdf Graphic file (type pdf)
- <use figs/vertci.pdf>) [48 <./figs/vertci.pdf>] [49
-
-] (./robjects.tex
-Chapter 6.
-
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 27--27
- []\T1/aett/m/n/12 > 2 > 3 # Basic logical operations,
-including >,[]
- []
-
-
-Overfull \hbox (9.88028pt too wide) in paragraph at lines 29--29
- [] \T1/aett/m/n/12 # <= (less than or equals)
-, == (exactly[]
- []
-
-
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 31--31
- []\T1/aett/m/n/12 > 3 >= 2 && 100 == 1000/10 # Advanced logical operation
-s, including[]
- []
-
-
-Overfull \hbox (9.88028pt too wide) in paragraph at lines 32--32
- [] \T1/aett/m/n/12 [1] TRUE # & (and), && (if and only
- if), | (or),[]
- []
-
-[50
-
-]
-Overfull \hbox (34.5521pt too wide) in paragraph at lines 78--78
- []\T1/aett/m/n/12 > a <- 1:5 # Creates a vector of integers from
- 1 to 5 inclusive[]
- []
-
-
-Overfull \hbox (3.7234pt too wide) in paragraph at lines 84--84
- []\T1/aett/m/n/12 > a[5] <- 3.14 # Inserts 3.14 as the 5th value in th
-e vector \TS1/cmtt/m/n/12 `\T1/aett/m/n/12 a\TS1/cmtt/m/n/12 '[]
- []
-
-
-Overfull \hbox (34.5521pt too wide) in paragraph at lines 85--85
- []\T1/aett/m/n/12 > a[5:7] <- c(2, 4, 7) # Replaces the 5th through 7th values
- with 2, 4, and 7[]
- []
-
-[51]
-Overfull \hbox (28.38968pt too wide) in paragraph at lines 112--112
- []\T1/aett/m/n/12 > x <- c("like", "dislike", "hate", "like", "don\TS1/cmtt/m/
-n/12 '\T1/aett/m/n/12 t know", "like", "dislike")[]
- []
-
-
-Overfull \hbox (11.05815pt too wide) in paragraph at lines 144--144
- [] \T1/aett/m/n/12 [,1] [,2] [,3] # Note that when assigning a ve
-ctor to a[]
- []
-
-[52]
-Overfull \hbox (17.23718pt too wide) in paragraph at lines 154--154
- []\T1/aett/m/n/12 > y <- c(55, 33, 12) # Creates another vector \TS1/c
-mtt/m/n/12 `\T1/aett/m/n/12 y\TS1/cmtt/m/n/12 ' \T1/aett/m/n/12 of 3 values.[]
-
- []
-
-
-Overfull \hbox (11.05815pt too wide) in paragraph at lines 155--155
- []\T1/aett/m/n/12 > rbind(x, y) # Creates a 2 x 3 matrix. Note
- that row[]
- []
-
-
-Overfull \hbox (11.05815pt too wide) in paragraph at lines 159--159
- []\T1/aett/m/n/12 > cbind(x, y) # Creates a 3 x 2 matrix. Note
- that the[]
- []
-
-
-Overfull \hbox (3.71233pt too wide) in paragraph at lines 176--176
- []\T1/aett/m/n/12 > loo[1,3] <- 13 # Inserts 13 as the value for row
-1, column 3.[]
- []
-
-
-Overfull \hbox (9.88582pt too wide) in paragraph at lines 188--188
- []\T1/aett/m/n/12 > a <- matrix(8, 2, 3) # Creates a 2 x 3 matrix popula
-ted with 8\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 s.[]
- []
-
-
-Overfull \hbox (9.88582pt too wide) in paragraph at lines 189--189
- []\T1/aett/m/n/12 > b <- matrix(9, 2, 3) # Creates a 2 x 3 matrix popula
-ted with 9\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 s.[]
- []
-
-
-Overfull \hbox (16.05377pt too wide) in paragraph at lines 191--191
- [] \T1/aett/m/n/12 , , 1 # level [,,1] populated with m
-atrix a (8\TS1/cmtt/m/n/12 '\T1/aett/m/n/12 s),[]
- []
-
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-Overfull \hbox (16.04823pt too wide) in paragraph at lines 195--195
- [] \T1/aett/m/n/12 [2,] 8 8 8 # Use square brackets to extrac
-t values. For[]
- []
-
-[53]
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- [] \T1/aett/m/n/12 , , 2 # value in the first row of th
-e second level.[]
- []
-
-
-Overfull \hbox (9.89136pt too wide) in paragraph at lines 230--230
- []\T1/aett/m/n/12 > c[1, 2] # Pull out the value in the first row, second
-column of \TS1/cmtt/m/n/12 `\T1/aett/m/n/12 c\TS1/cmtt/m/n/12 '[]
- []
-
-[54]
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-
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-Overfull \hbox (5.1774pt too wide) in paragraph at lines 268--268
-[]\T1/aett/m/n/12 > L[[4]] <- c(1, 4, 6, 8) # Inserts the vector into
-the 4th position[]
- []
-
-
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-
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-Overfull \hbox (11.34535pt too wide) in paragraph at lines 291--291
-[]\T1/aett/m/n/12 > dat[1:10,] # Extracts observations 1-10 and all ass
-ociated variables
- []
-
-[55]) (./programmingstatements.tex [56]
-Chapter 7.
-[57
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-]
-Overfull \hbox (5.1774pt too wide) in paragraph at lines 84--84
-[]\T1/aett/m/n/12 > x <- array(dim = c(5,2,3)) # A 3D array of 3 stacked
-5 by 2 matrices.[]
- []
-
-[58]
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-\T1/aer/m/n/12 The sin-gle loop pro-ce-dure eval-u-ates each el-e-ment in \T1/a
-ett/m/n/12 idx \T1/aer/m/n/12 against the vec-tor \T1/aett/m/n/12 mydata$state\
-T1/aer/m/n/12 .
- []
-
-
-Overfull \hbox (22.21619pt too wide) in paragraph at lines 186--186
- []\T1/aett/m/n/12 z.out <- zelig(y ~ x1 + x2 + x3, by = "state", data = mydata
-, model = "ls")[]
- []
-
-
-Overfull \hbox (28.38414pt too wide) in paragraph at lines 192--192
- []\T1/aett/m/n/12 se <- array() # Initalize the empty
- data structure.[]
- []
-
-
-Overfull \hbox (22.21619pt too wide) in paragraph at lines 193--193
- []\T1/aett/m/n/12 for (i in 1:50) { # vcov() creates the
-variance matrix[]
- []
-
-
-Overfull \hbox (28.38414pt too wide) in paragraph at lines 194--194
- [] \T1/aett/m/n/12 se[i] <- sqrt(vcov(z.out[[i]])[2,2]) # Since we have an in
-tercept, the 2nd[]
- []
-
-
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- []\T1/aett/m/n/12 } # diagonal value corr
-esponds to x1.[]
- []
-
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diff --git a/inst/doc/zelig.pdf b/inst/doc/zelig.pdf
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-\include{zinput}
-\usepackage{Sweave}
-\title{Zelig: Everyone's Statistical Software\thanks{The current
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- \texttt{http://gking.harvard.edu/zelig/}, free of charge and
- open-source (under the terms of the GNU GPL, v. 2).}}
-\author{Kosuke
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- \texttt{KImai at Princeton.Edu}).}
-\and %
-Gary King\thanks{Albert J. Weatherhead III University Professor, Harvard
- University (Institute for Quantitative Social Sciences, 1737 Cambridge
-Street, Harvard University, Cambridge MA 02138;
- \texttt{http://GKing.Harvard.Edu}, \texttt{King at Harvard.Edu}, (617)
- 495-2027).}
-\and %
-Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
- University (1737 Cambridge Street, Cambridge MA 02138;
- \texttt{http://www.people.fas.harvard.edu/\~\,olau},
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-
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-<p class="zheader">
-<span id="ztitle">ZELIG: EVERYONE'S STATISTICAL SOFTWARE </span> <br/>
-<span id="authors">
-<a href="" id="kosuke">Kosuke Imai </a> - <a href="" id="gary">Gary King </a> - <a href="" id="olivia">Olivia Lau </a> <br/>
-<span id="authorcontent"></span>
-</span>
-</p>
-<p>[Also available is a downloadable <a href="zelig.pdf"> PDF </a> version of this entire document] </p>
-
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-<li><a id="expandall" href=""> expand all </a> </li>
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-
-<div id="navcontainer">
-
-<ul id="zelig">
- <li class="mainindx"> <a class="kot" href="">Introduction </a>
- <ul id="introduction">
- <li><a href="static/acknowledgments.pdf" title="Acknowledgment">Acknowledgment</a></li>
- <li><a href="static/intro.pdf" title="Introduction">Introduction</a></li>
- </ul>
- </li>
- <li class="mainindx"> <a href="">User's Guide </a>
- <ul id="usersguide">
- <li><a href="static/install.pdf" title="Installation">Installation</a></li>
- <li><a href="static/syntax.pdf" title="Data Analysis Commands">Data Analysis Commands</a></li>
- <li><a href="static/Zcommands.pdf" title="Statistical Commands">Statistical Commands</a></li>
- <li><a href="static/graphs.pdf" title="Graphing commands">Graphing Commands</a></li>
- </ul>
- </li>
- <li class="mainindx"> <a href=""> Advanced Zelig uses </a>
- <ul id="advancedzeliguses">
- <li><a href="static/robjects.pdf" title="Advanced R">R Objects</a></li>
- <li><a href="static/programmingstatements.pdf" title="Advanced R">Programming statements</a></li>
- <li><a href="static/newModels.pdf" title="Writing new Models">Writing new Models</a></li>
- <li><a href="static/addingModels.pdf" title="Adding Models and Methods to Zelig">Adding Models and Methods to Zelig</a></li>
- </ul>
- </li>
- <li class="mainindx"><a href="">Reference Manual </a>
- <ul id="referencemanual">
- <li class="mainindx"><a href="">Main Commands</a>
- <ul id="maincommands">
- <li><a href="commandsRd/Zelig.pdf" title="zelig">zelig: Estimating a Statistical Model</a></li>
- <li><a href="commandsRd/setx.pdf" title="setx">setx: Setting Explanatory Variable Values</a></li>
- <li><a href="commandsRd/sim.pdf" title="sim">sim: Simulating Quantities of Interest</a></li>
- <li><a href="commandsRd/plot.zelig.pdf" title="plot">plot.zelig: Graphing Quantities of Interest</a></li>
- <li><a href="commandsRd/repl.pdf" title="repl">repl: Replicating Analyses</a></li>
- </ul>
- </li>
- <li class="mainindx"><a href="">Supplementary Commands </a>
- <ul id="supplementarycommands">
- <li><a href="static/matchit.pdf" title="">matchit: Create matched data</a></li>
- <li><a href="commandsRd/mi.pdf" title="mi">mi: Bundle multiply imputed data sets as a list</a></li>
- <li><a href="commandsRd/network.pdf" title="network">network: Format matricies into data frame for social network analysis</a></li>
- <li><a href="commandsRd/plot.ci.pdf" title="plot.ci">plot.ci: Plotting Vertical confidence Intervals</a></li>
- <li><a href="commandsRd/plot.surv.pdf" title="plot.surv">plot.surv: Plotting Coefidence Intervals for Survival Curves</a></li>
- <li><a href="commandsRd/rocplot.pdf" title="rocplot">rocplot: Receiver Operator Characteristic Plots</a></li>
- <li><a href="commandsRd/ternaryplot.pdf" title="ternaryplot">ternaryplot: Ternary diagram</a></li>
- <li><a href="commandsRd/ternarypoints.pdf" title="ternarypoints">ternarypoints: Adding Points To Ternary Diagrams</a></li>
- </ul>
- </li>
-
- <li class="mainindx"><a href="">Models Zelig Can Run </a>
-
-<!-- shell script is expected to produce <ul id="models"> <li> </li> ... </ul> -->
-
-
-
diff --git a/inst/doc/zeligmodels.html b/inst/doc/zeligmodels.html
deleted file mode 100644
index 8c651aa..0000000
--- a/inst/doc/zeligmodels.html
+++ /dev/null
@@ -1,101 +0,0 @@
-<ul id="models">
-<li class="mainindx"><a href="">Models for Continuous Dependent Variables</a>
- <ul>
- <li><a href="aov.pdf">aov : Fit an Analysis of Variance Model</a></li>
- <li><a href="arima.pdf">arima : Arima models for Time Series Data</a></li>
- <li><a href="factor.bayes.pdf">factor.bayes : Bayesian Factor Analysis</a></li>
- <li><a href="ls.pdf">ls : Least Squares Regression for Continuous Dependent Variables</a></li>
- <li><a href="ls.mixed.pdf">ls.mixed : Mixed effects linear model</a></li>
- <li><a href="ls.net.pdf">ls.net : Social Network Least Squares Regression for Continuous Dependent Variables</a></li>
- <li><a href="normal.pdf">normal : Normal Regression for Continuous Dependent Variables</a></li>
- <li><a href="normal.bayes.pdf">normal.bayes : Bayesian Normal Linear Regression</a></li>
- <li><a href="normal.gam.pdf">normal.gam : Generalized Additive Model for Continuous Dependent Variables</a></li>
- <li><a href="normal.gee.pdf">normal.gee : General Estimating Equation for Normal Regression</a></li>
- <li><a href="normal.net.pdf">normal.net : Social Network Normal Regression for Continuous Dependent Variables</a></li>
- <li><a href="normal.survey.pdf">normal.survey : Survey-Weighted Normal Regression for Continuous Dependent Variables</a></li>
- <li><a href="quantile.pdf">quantile : Quantile Regression for Continuous Dependent Variables</a></li>
- <li><a href="rq.pdf">rq : Quantile Regression for Continuous Dependent Variables</a></li>
- <li><a href="sur.pdf">sur : Seemingly Unrelated Regression</a></li>
- <li><a href="threesls.pdf">threesls : Three Stage Least Squares</a></li>
- <li><a href="tobit.pdf">tobit : Linear regression for Left-Censored Dependent Variable</a></li>
- <li><a href="tobit.bayes.pdf">tobit.bayes : Bayesian Linear Regression for a Censored Dependent Variable</a></li>
- <li><a href="twosls.pdf">twosls : Two Stage Least Squares</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Models for Dichotomous Dependent Variables</a>
- <ul>
- <li><a href="blogit.pdf">blogit : Bivariate Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="bprobit.pdf">bprobit : Bivariate Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="cloglog.net.pdf">cloglog.net : Social Network Complementary Log Log Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="irt1d.pdf">irt1d : One Dimensional Item Response Model</a></li>
- <li><a href="irtkd.pdf">irtkd : K-Dimensional Item Response Model</a></li>
- <li><a href="logit.pdf">logit : Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="logit.bayes.pdf">logit.bayes : Bayesian Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="logit.gam.pdf">logit.gam : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
- <li><a href="logit.gee.pdf">logit.gee : General Estimating Equation for Logistic Regression</a></li>
- <li><a href="logit.mixed.pdf">logit.mixed : Mixed effects logistic model</a></li>
- <li><a href="logit.net.pdf">logit.net : Social Network Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="logit.survey.pdf">logit.survey : Survey-Weighted Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.pdf">probit : Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.bayes.pdf">probit.bayes : Bayesian Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.gam.pdf">probit.gam : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.gee.pdf">probit.gee : General Estimating Equation for Probit Regression</a></li>
- <li><a href="probit.mixed.pdf">probit.mixed : Mixed effects probit model</a></li>
- <li><a href="probit.net.pdf">probit.net : Social Network Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.survey.pdf">probit.survey : Survey-Weighted Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="relogit.pdf">relogit : Rare Events Logistic Regression for Dichotomous Dependent Variables</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Models for Ordinal Dependent Variables</a>
- <ul>
- <li><a href="chopit.pdf">chopit : Compound Hierarchical Ordinal Probit Regression for Survey Vignettes</a></li>
- <li><a href="factor.ord.pdf">factor.ord : Ordinal Data Factor Analysis</a></li>
- <li><a href="ologit.pdf">ologit : Ordinal Logistic Regression for Ordered Categorical Dependent Variables</a></li>
- <li><a href="oprobit.pdf">oprobit : Ordinal Probit Regression for Ordered Categorical Dependent Variables</a></li>
- <li><a href="oprobit.bayes.pdf">oprobit.bayes : Bayesian Ordered Probit Regression</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
- <ul>
- <li><a href="coxph.pdf">coxph : Cox Proportional Hazard Regression for Duration Dependent Variables</a></li>
- <li><a href="exp.pdf">exp : Exponential Regression for Duration Dependent Variables</a></li>
- <li><a href="gamma.pdf">gamma : Gamma Regression for Continuous, Positive Dependent Variables</a></li>
- <li><a href="gamma.gee.pdf">gamma.gee : General Estimating Equation for Gamma Regression</a></li>
- <li><a href="gamma.mixed.pdf">gamma.mixed : Mixed effects gamma model</a></li>
- <li><a href="gamma.net.pdf">gamma.net : Social Network Gamma Regression for Continuous, Positive Dependent Variables</a></li>
- <li><a href="gamma.survey.pdf">gamma.survey : Survey-Weighted Gamma Regression for Continuous, Positive Dependent Variables</a></li>
- <li><a href="lognorm.pdf">lognorm : Log-Normal Regression for Duration Dependent Variables</a></li>
- <li><a href="weibull.pdf">weibull : Weibull Regression for Duration Dependent Variables</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Multinomial Choice Models</a>
- <ul>
- <li><a href="mlogit.pdf">mlogit : Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
- <li><a href="mlogit.bayes.pdf">mlogit.bayes : Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Event Count Models</a>
- <ul>
- <li><a href="negbin.pdf">negbin : Negative Binomial Regression for Event Count Dependent Variables</a></li>
- <li><a href="poisson.pdf">poisson : Poisson Regression for Event Count Dependent Variables</a></li>
- <li><a href="poisson.bayes.pdf">poisson.bayes : Bayesian Poisson Regression</a></li>
- <li><a href="poisson.gam.pdf">poisson.gam : Generalized Additive Model for Event Count Dependent Variables</a></li>
- <li><a href="poisson.gee.pdf">poisson.gee : General Estimating Equation for Poisson Regression</a></li>
- <li><a href="poisson.mixed.pdf">poisson.mixed : Mixed effects poisson model</a></li>
- <li><a href="poisson.net.pdf">poisson.net : Social Network Poisson Regression for Event Count Dependent Variables</a></li>
- <li><a href="poisson.survey.pdf">poisson.survey : Survey-Weighted Poisson Regression for Event Count Dependent Variables</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Models for Mixed Dependent Variables</a>
- <ul>
- <li><a href="factor.mix.pdf">factor.mix : Mixed Data Factor Analysis</a></li>
- </ul>
-</li>
-<li class="mainindx"><a href="">Ecological Inference Models</a>
- <ul>
- <li><a href="ei.dynamic.pdf">ei.dynamic : Quinn's Dynamic Ecological Inference Model</a></li>
- <li><a href="ei.hier.pdf">ei.hier : Hierarchical Ecological Inference Model for 2 x 2 Tables</a></li>
- <li><a href="ei.RxC.pdf">ei.RxC : Hierarchical Multinomial-Dirichlet Ecological Inference Model for R x C Tables</a></li>
- </ul>
-</li>
-</ul>
diff --git a/inst/doc/zinput.tex b/inst/doc/zinput.tex
index 528bba1..64c4311 100644
--- a/inst/doc/zinput.tex
+++ b/inst/doc/zinput.tex
@@ -20,15 +20,13 @@
\usepackage{fullpage}
\usepackage{lscape}
\usepackage[all]{xy}
+
+\usepackage{csquotes}
% \usepackage[pdftex, bookmarksopen=true,bookmarksnumbered=true,
% linkcolor=webred]{hyperref}
\bibpunct{(}{)}{;}{a}{}{,}
\newcolumntype{.}{D{.}{.}{-1}}
\newcolumntype{d}[1]{D{.}{.}{#1}}
-\htmladdtonavigation{
- \htmladdnormallink{%
- \htmladdimg{http://gking.harvard.edu/pics/home.gif}}
- {http://gking.harvard.edu/}}
\newcommand{\MatchIt}{{\sc MatchIt}}
\newcommand{\hlink}{\htmladdnormallink}
\newcommand{\Sref}[1]{Section~\ref{#1}}
@@ -37,5 +35,4 @@
\newcommand{\rwvers}{R-2.5.1}
%\renewcommand{\bibentry}{\citealt}
-\bodytext{ BACKGROUND="http://gking.harvard.edu/pics/temple.jpg"}
\setcounter{tocdepth}{2}
--
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