[r-cran-zelig] 62/102: Import Upstream version 3.2-0
Andreas Tille
tille at debian.org
Sun Jan 8 16:59:15 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 4b1cc2bee7c86302ade390c267ac2da67d7ff53e
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:39:49 2017 +0100
Import Upstream version 3.2-0
---
DESCRIPTION | 12 +-
NAMESPACE | 3 +
R/describe.gamma.survey.R | 15 +
R/describe.logit.survey.R | 15 +
R/describe.normal.survey.R | 15 +
R/describe.poisson.survey.R | 15 +
R/describe.probit.survey.R | 15 +
R/param.svyglm.R | 46 +
R/plot.zelig.logit.survey.R | 20 +
R/plot.zelig.poisson.survey.R | 16 +
R/plot.zelig.probit.survey.R | 20 +
R/qi.polr.R | 2 +
R/qi.survreg.R | 4 +-
R/qi.svyglm.R | 82 +
R/zelig2gamma.survey.R | 50 +
R/zelig2logit.survey.R | 50 +
R/zelig2normal.survey.R | 48 +
R/zelig2poisson.survey.R | 50 +
R/zelig2probit.survey.R | 50 +
demo/00Index | 6 +-
demo/gamma.survey.R | 100 +
demo/logit.survey.R | 105 +
demo/normal.survey.R | 101 +
demo/poisson.survey.R | 103 +
demo/probit.survey.R | 106 +
inst/doc/Rplots.ps | 125738 +++++++++--------
inst/doc/aov.pdf | Bin 162173 -> 161795 bytes
inst/doc/arima.pdf | Bin 123131 -> 123069 bytes
inst/doc/blogit.pdf | Bin 152249 -> 147180 bytes
inst/doc/bprobit.pdf | Bin 160685 -> 160032 bytes
inst/doc/chopit.pdf | Bin 108508 -> 108510 bytes
inst/doc/cloglog.net.pdf | Bin 105336 -> 105364 bytes
inst/doc/commands/model.end.aux | 22 +-
inst/doc/commands/model.frame.multiple.aux | 24 +-
inst/doc/commands/model.matrix.multiple.aux | 24 +-
inst/doc/commands/parse.formula.aux | 28 +-
inst/doc/commands/parse.par.aux | 24 +-
inst/doc/commands/put.start.aux | 22 +-
inst/doc/commands/set.start.aux | 24 +-
inst/doc/commands/tag.aux | 24 +-
inst/doc/commandsRd/MatchIt.url.pdf | Bin 19852 -> 19851 bytes
inst/doc/commandsRd/PErisk.pdf | Bin 42257 -> 42257 bytes
inst/doc/commandsRd/SupremeCourt.pdf | Bin 32129 -> 32129 bytes
inst/doc/commandsRd/Weimar.pdf | Bin 25054 -> 25055 bytes
inst/doc/commandsRd/Zelig-package.pdf | Bin 47952 -> 47952 bytes
inst/doc/commandsRd/Zelig.pdf | Bin 41496 -> 41494 bytes
inst/doc/commandsRd/Zelig.url.pdf | Bin 18734 -> 18735 bytes
inst/doc/commandsRd/approval.pdf | Bin 24314 -> 24314 bytes
inst/doc/commandsRd/bivariate.pdf | Bin 31447 -> 31448 bytes
inst/doc/commandsRd/coalition.pdf | Bin 33898 -> 33897 bytes
inst/doc/commandsRd/coalition2.pdf | Bin 34955 -> 34952 bytes
inst/doc/commandsRd/current.packages.pdf | Bin 38487 -> 38488 bytes
inst/doc/commandsRd/dims.pdf | Bin 38332 -> 38331 bytes
inst/doc/commandsRd/eidat.pdf | Bin 20561 -> 20562 bytes
inst/doc/commandsRd/free1.pdf | Bin 40773 -> 40778 bytes
inst/doc/commandsRd/free2.pdf | Bin 40800 -> 40800 bytes
inst/doc/commandsRd/friendship.pdf | Bin 31873 -> 31872 bytes
inst/doc/commandsRd/grunfeld.pdf | Bin 26299 -> 26298 bytes
inst/doc/commandsRd/gsource.pdf | Bin 43875 -> 43875 bytes
inst/doc/commandsRd/help.zelig.pdf | Bin 33301 -> 33299 bytes
inst/doc/commandsRd/hoff.pdf | Bin 33603 -> 33603 bytes
inst/doc/commandsRd/homerun.pdf | Bin 39387 -> 39388 bytes
inst/doc/commandsRd/immigration.pdf | Bin 33052 -> 33055 bytes
inst/doc/commandsRd/klein.pdf | Bin 27454 -> 27456 bytes
inst/doc/commandsRd/kmenta.pdf | Bin 24918 -> 24919 bytes
inst/doc/commandsRd/macro.pdf | Bin 32696 -> 32697 bytes
inst/doc/commandsRd/match.data.pdf | Bin 35999 -> 35998 bytes
inst/doc/commandsRd/mexico.pdf | Bin 33502 -> 33503 bytes
inst/doc/commandsRd/mi.pdf | Bin 38081 -> 38080 bytes
inst/doc/commandsRd/mid.pdf | Bin 37171 -> 37170 bytes
inst/doc/commandsRd/model.end.pdf | Bin 36596 -> 36594 bytes
inst/doc/commandsRd/model.frame.multiple.pdf | Bin 47454 -> 47453 bytes
inst/doc/commandsRd/model.matrix.multiple.pdf | Bin 53332 -> 53331 bytes
inst/doc/commandsRd/network.pdf | Bin 35147 -> 35145 bytes
inst/doc/commandsRd/newpainters.pdf | Bin 34961 -> 34964 bytes
inst/doc/commandsRd/parse.formula.pdf | Bin 50655 -> 50655 bytes
inst/doc/commandsRd/parse.par.pdf | Bin 52104 -> 52102 bytes
inst/doc/commandsRd/plot.ci.pdf | Bin 51972 -> 51973 bytes
inst/doc/commandsRd/plot.zelig.pdf | Bin 40112 -> 40112 bytes
inst/doc/commandsRd/put.start.pdf | Bin 34223 -> 34222 bytes
inst/doc/commandsRd/repl.pdf | Bin 49396 -> 49400 bytes
inst/doc/commandsRd/rocplot.pdf | Bin 50649 -> 50649 bytes
inst/doc/commandsRd/sanction.pdf | Bin 31279 -> 31278 bytes
inst/doc/commandsRd/set.start.pdf | Bin 43070 -> 43070 bytes
inst/doc/commandsRd/setx.pdf | Bin 54742 -> 54743 bytes
inst/doc/commandsRd/sim.pdf | Bin 66025 -> 66020 bytes
inst/doc/commandsRd/sna.ex.pdf | Bin 22893 -> 22890 bytes
inst/doc/commandsRd/summary.zelig.pdf | Bin 40773 -> 40772 bytes
inst/doc/commandsRd/swiss.pdf | Bin 29948 -> 29948 bytes
inst/doc/commandsRd/ternaryplot.pdf | Bin 59266 -> 59269 bytes
inst/doc/commandsRd/ternarypoints.pdf | Bin 35078 -> 35078 bytes
inst/doc/commandsRd/tobin.pdf | Bin 24015 -> 24015 bytes
inst/doc/commandsRd/turnout.pdf | Bin 32997 -> 32996 bytes
inst/doc/commandsRd/user.prompt.pdf | Bin 35231 -> 35228 bytes
inst/doc/commandsRd/voteincome.pdf | Bin 30468 -> 30469 bytes
inst/doc/commandsRd/zeligDepStatus.pdf | Bin 44746 -> 44748 bytes
inst/doc/commandsRd/zeligDepUpdate.pdf | Bin 50252 -> 50258 bytes
inst/doc/commandsRd/zeligVDC.pdf | Bin 46763 -> 46761 bytes
inst/doc/coxph.pdf | Bin 162537 -> 162446 bytes
inst/doc/ei.RxC.pdf | Bin 95157 -> 95156 bytes
inst/doc/ei.dynamic.pdf | Bin 104082 -> 104084 bytes
inst/doc/ei.hier.pdf | Bin 101615 -> 101617 bytes
inst/doc/exp.pdf | Bin 135615 -> 135885 bytes
inst/doc/factor.bayes.pdf | Bin 90091 -> 90100 bytes
inst/doc/factor.mix.pdf | Bin 94647 -> 94648 bytes
inst/doc/factor.ord.pdf | Bin 93058 -> 93063 bytes
inst/doc/gamma.gee.pdf | Bin 131771 -> 131875 bytes
inst/doc/gamma.mixed.pdf | Bin 104080 -> 101784 bytes
inst/doc/gamma.net.pdf | Bin 130465 -> 130873 bytes
inst/doc/gamma.pdf | Bin 133237 -> 133402 bytes
inst/doc/gamma.survey.Rnw | 511 +
inst/doc/gamma.survey.pdf | Bin 0 -> 181397 bytes
inst/doc/index.shtml | 2 +-
inst/doc/irt1d.pdf | Bin 99407 -> 99404 bytes
inst/doc/irtkd.pdf | Bin 101613 -> 101611 bytes
inst/doc/logit.bayes.pdf | Bin 101706 -> 101711 bytes
inst/doc/logit.gam.pdf | Bin 271908 -> 271908 bytes
inst/doc/logit.gee.pdf | Bin 155218 -> 155221 bytes
inst/doc/logit.mixed.pdf | Bin 104004 -> 101864 bytes
inst/doc/logit.net.pdf | Bin 110326 -> 110329 bytes
inst/doc/logit.pdf | Bin 156315 -> 156315 bytes
inst/doc/logit.survey.Rnw | 530 +
inst/doc/logit.survey.pdf | Bin 0 -> 183738 bytes
inst/doc/lognorm.pdf | Bin 136856 -> 136965 bytes
inst/doc/ls.mixed.pdf | Bin 134684 -> 132845 bytes
inst/doc/ls.net.pdf | Bin 107434 -> 107790 bytes
inst/doc/ls.pdf | Bin 184926 -> 185249 bytes
inst/doc/mlogit.bayes.pdf | Bin 103456 -> 103460 bytes
inst/doc/mlogit.pdf | Bin 159584 -> 159580 bytes
inst/doc/mloglm.pdf | Bin 53350 -> 53354 bytes
inst/doc/negbin.pdf | Bin 114756 -> 114924 bytes
inst/doc/normal.bayes.pdf | Bin 104184 -> 104182 bytes
inst/doc/normal.gam.pdf | Bin 267321 -> 267324 bytes
inst/doc/normal.gee.pdf | Bin 131592 -> 131593 bytes
inst/doc/normal.net.pdf | Bin 100525 -> 100529 bytes
inst/doc/normal.pdf | Bin 132237 -> 132235 bytes
inst/doc/normal.survey.Rnw | 521 +
inst/doc/normal.survey.pdf | Bin 0 -> 172392 bytes
inst/doc/ologit.pdf | Bin 101659 -> 101658 bytes
inst/doc/oprobit.bayes.pdf | Bin 106708 -> 106706 bytes
inst/doc/oprobit.pdf | Bin 169454 -> 169650 bytes
inst/doc/poisson.bayes.pdf | Bin 99806 -> 99808 bytes
inst/doc/poisson.gam.pdf | Bin 267778 -> 267777 bytes
inst/doc/poisson.gee.pdf | Bin 123355 -> 123347 bytes
inst/doc/poisson.mixed.pdf | Bin 100953 -> 98813 bytes
inst/doc/poisson.net.pdf | Bin 92077 -> 92073 bytes
inst/doc/poisson.pdf | Bin 111001 -> 111007 bytes
inst/doc/poisson.survey.Rnw | 522 +
inst/doc/poisson.survey.pdf | Bin 0 -> 164506 bytes
inst/doc/probit.bayes.pdf | Bin 102327 -> 102330 bytes
inst/doc/probit.gam.pdf | Bin 267843 -> 267845 bytes
inst/doc/probit.gee.pdf | Bin 155395 -> 155392 bytes
inst/doc/probit.mixed.pdf | Bin 104395 -> 102415 bytes
inst/doc/probit.net.pdf | Bin 105674 -> 105676 bytes
inst/doc/probit.pdf | Bin 99319 -> 99320 bytes
inst/doc/probit.survey.Rnw | 530 +
inst/doc/probit.survey.pdf | Bin 0 -> 182481 bytes
inst/doc/refman.tex | 5 +
inst/doc/relogit.pdf | Bin 146117 -> 145946 bytes
inst/doc/static/Zcommands.pdf | Bin 159935 -> 159934 bytes
inst/doc/static/acknowledgments.pdf | Bin 29857 -> 29857 bytes
inst/doc/static/addingModels.pdf | Bin 87604 -> 87603 bytes
inst/doc/static/advancedR.pdf | Bin 96391 -> 96388 bytes
inst/doc/static/faq.pdf | Bin 104829 -> 104829 bytes
inst/doc/static/graphs.pdf | Bin 173305 -> 173303 bytes
inst/doc/static/install.pdf | Bin 97972 -> 97974 bytes
inst/doc/static/intro.pdf | Bin 69845 -> 69845 bytes
inst/doc/static/newModels.pdf | Bin 135798 -> 135791 bytes
inst/doc/static/syntax.pdf | Bin 93889 -> 93889 bytes
inst/doc/sur.pdf | Bin 99544 -> 99548 bytes
inst/doc/threesls.pdf | Bin 135832 -> 135829 bytes
inst/doc/tobit.bayes.pdf | Bin 103712 -> 103713 bytes
inst/doc/tobit.pdf | Bin 93872 -> 93872 bytes
inst/doc/twosls.pdf | Bin 160232 -> 160441 bytes
inst/doc/vigpics/aov-ExamplesPlot1.eps | 742 +-
inst/doc/vigpics/aov-ExamplesPlot1.pdf | 1048 +-
inst/doc/vigpics/aov-ExamplesPlot2.eps | 1314 +-
inst/doc/vigpics/aov-ExamplesPlot2.pdf | 2066 +-
inst/doc/vigpics/aov-ExamplesPlot3.eps | 822 +-
inst/doc/vigpics/aov-ExamplesPlot3.pdf | 1056 +-
inst/doc/vigpics/arima-Example1Plot.eps | 222 +-
inst/doc/vigpics/arima-Example1Plot.pdf | 252 +-
inst/doc/vigpics/arima-Example2Plot.eps | 786 +-
inst/doc/vigpics/arima-Example2Plot.pdf | 1178 +-
inst/doc/vigpics/arima-Example3Plot.eps | 137 +-
inst/doc/vigpics/arima-Example3Plot.pdf | 210 +-
inst/doc/vigpics/blogit-BasicExamplePlot.eps | 2905 +-
inst/doc/vigpics/blogit-BasicExamplePlot.pdf | 1347 +-
inst/doc/vigpics/blogit-JointExamplePlot.eps | 1794 +-
inst/doc/vigpics/blogit-JointExamplePlot.pdf | 896 +-
inst/doc/vigpics/bprobit-BasicExamplePlot.eps | 3509 +-
inst/doc/vigpics/bprobit-BasicExamplePlot.pdf | 1761 +-
inst/doc/vigpics/bprobit-JointEstimationPlot.eps | 1909 +-
inst/doc/vigpics/bprobit-JointEstimationPlot.pdf | 971 +-
inst/doc/vigpics/cloglognet-007.eps | 730 +-
inst/doc/vigpics/cloglognet-007.pdf | 1083 +-
inst/doc/vigpics/cloglognet-009.eps | 730 +-
inst/doc/vigpics/cloglognet-009.pdf | 1083 +-
inst/doc/vigpics/coxph-ExamplePlot.eps | 1319 +-
inst/doc/vigpics/coxph-ExamplePlot.pdf | 2213 +-
inst/doc/vigpics/coxph-ExamplePlot2.eps | 855 +-
inst/doc/vigpics/coxph-ExamplePlot2.pdf | 1300 +-
inst/doc/vigpics/exp-ExamplePlot.eps | 2087 +-
inst/doc/vigpics/exp-ExamplePlot.pdf | 2958 +-
inst/doc/vigpics/gam-006.pdf | 6 +-
inst/doc/vigpics/gam-008.pdf | 6 +-
inst/doc/vigpics/gam-010.pdf | 6 +-
inst/doc/vigpics/gamma-ExamplePlot.eps | 2266 +-
inst/doc/vigpics/gamma-ExamplePlot.pdf | 3133 +-
inst/doc/vigpics/gamma_gee-ExamplePlot.eps | 1685 +-
inst/doc/vigpics/gamma_gee-ExamplePlot.pdf | 2112 +-
inst/doc/vigpics/gammanet-007.eps | 1551 +-
inst/doc/vigpics/gammanet-007.pdf | 2122 +-
inst/doc/vigpics/gammanet-009.eps | 1551 +-
inst/doc/vigpics/gammanet-009.pdf | 2122 +-
...amplesPlot.eps => gammasurvey-ExistingPlot.eps} | 2263 +-
inst/doc/vigpics/gammasurvey-ExistingPlot.pdf | 1834 +
...amplePlot.eps => gammasurvey-ReplicatePlot.eps} | 2481 +-
inst/doc/vigpics/gammasurvey-ReplicatePlot.pdf | 1828 +
inst/doc/vigpics/lmmixed-ExamplesPlot.eps | 2188 +-
inst/doc/vigpics/lmmixed-ExamplesPlot.pdf | 3117 +-
inst/doc/vigpics/logit-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/logit-FirstDifferencesPlot.pdf | 6 +-
inst/doc/vigpics/logit-ROCPlot.pdf | 6 +-
inst/doc/vigpics/logit_gee-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/logit_gee-FirstDifferencePlot.pdf | 6 +-
inst/doc/vigpics/logitnet-007.pdf | 6 +-
inst/doc/vigpics/logitnet-009.pdf | 6 +-
...xamplePlot.eps => logitsurvey-ExistingPlot.eps} | 2515 +-
inst/doc/vigpics/logitsurvey-ExistingPlot.pdf | 1884 +
inst/doc/vigpics/logitsurvey-ReplicatePlot.eps | 1992 +
inst/doc/vigpics/logitsurvey-ReplicatePlot.pdf | 1877 +
inst/doc/vigpics/lognormal-ExamplePlot.eps | 1937 +-
inst/doc/vigpics/lognormal-ExamplePlot.pdf | 2524 +-
inst/doc/vigpics/ls-DummyPlot.eps | 1360 +-
inst/doc/vigpics/ls-DummyPlot.pdf | 2062 +-
inst/doc/vigpics/ls-Example4Plot.eps | 1457 +-
inst/doc/vigpics/ls-Example4Plot.pdf | 2131 +-
inst/doc/vigpics/ls-ExamplesPlot.eps | 1309 +-
inst/doc/vigpics/ls-ExamplesPlot.pdf | 2027 +-
inst/doc/vigpics/lsnet-ExamplesPlot.eps | 1544 +-
inst/doc/vigpics/lsnet-ExamplesPlot.pdf | 2114 +-
inst/doc/vigpics/mlogit-LevelsTernaryPlot.eps | 1998 +-
inst/doc/vigpics/mlogit-LevelsTernaryPlot.pdf | 2018 +-
inst/doc/vigpics/mlogit-TernaryPlot.eps | 1998 +-
inst/doc/vigpics/mlogit-TernaryPlot.pdf | 2018 +-
inst/doc/vigpics/negbin-Example1Plot.eps | 770 +-
inst/doc/vigpics/negbin-Example1Plot.pdf | 1101 +-
inst/doc/vigpics/normal-ExamplesPlot.pdf | 6 +-
...mma-ExamplePlot.eps => normal-ExistingPlot.eps} | 2276 +-
inst/doc/vigpics/normal-ExistingPlot.pdf | 1839 +
...d-ExamplesPlot.eps => normal-ReplicatePlot.eps} | 2135 +-
inst/doc/vigpics/normal-ReplicatePlot.pdf | 1326 +
inst/doc/vigpics/normal_gee-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/normalnet-007.pdf | 6 +-
inst/doc/vigpics/oprobit-FirstDifferencesPlot.eps | 6984 +-
inst/doc/vigpics/oprobit-FirstDifferencesPlot.pdf | 8311 +-
inst/doc/vigpics/poisson-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/poisson_gee-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/poissonnet-007.pdf | 6 +-
...plesPlot.eps => poissonsurvey-ExistingPlot.eps} | 2207 +-
inst/doc/vigpics/poissonsurvey-ExistingPlot.pdf | 1324 +
...lesPlot.eps => poissonsurvey-ReplicatePlot.eps} | 2172 +-
inst/doc/vigpics/poissonsurvey-ReplicatePlot.pdf | 1322 +
inst/doc/vigpics/probit_gee-ExamplePlot.pdf | 6 +-
.../doc/vigpics/probit_gee-FirstDifferencePlot.pdf | 6 +-
inst/doc/vigpics/probitnet-007.pdf | 6 +-
inst/doc/vigpics/probitnet-009.pdf | 6 +-
...amplePlot.eps => probitsurvey-ExistingPlot.eps} | 2639 +-
inst/doc/vigpics/probitsurvey-ExistingPlot.pdf | 1877 +
inst/doc/vigpics/probitsurvey-ReplicatePlot.eps | 1992 +
inst/doc/vigpics/probitsurvey-ReplicatePlot.pdf | 1877 +
inst/doc/vigpics/relogit-Example1Plot.eps | 676 +-
inst/doc/vigpics/relogit-Example1Plot.pdf | 1046 +-
inst/doc/vigpics/relogit-Example3Plot.eps | 741 +-
inst/doc/vigpics/relogit-Example3Plot.pdf | 1048 +-
inst/doc/vigpics/threesls-Examplesthreesls.eps | 3218 +-
inst/doc/vigpics/threesls-Examplesthreesls.pdf | 4222 +-
inst/doc/vigpics/twosls-Examplestwosls.eps | 4984 +-
inst/doc/vigpics/twosls-Examplestwosls.pdf | 6176 +-
inst/doc/vigpics/weibull-ExamplePlot.eps | 2067 +-
inst/doc/vigpics/weibull-ExamplePlot.pdf | 3011 +-
inst/doc/weibull.pdf | Bin 137629 -> 137165 bytes
inst/doc/zelig.html | 5 +
inst/doc/zelig.pdf | 130721 ++++++++++--------
inst/doc/zelig.tex | 28 +-
inst/doc/zeligmodels.html | 5 +
inst/zideal/zideal.RData | Bin 0 -> 2013 bytes
288 files changed, 245513 insertions(+), 186608 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index dd19ead..d22bea8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: Zelig
-Version: 3.1-1
-Date: 2008-02-12
+Version: 3.2-0
+Date: 2008-04-07
Title: Everyone's Statistical Software
Author: Kosuke Imai <kimai at Princeton.Edu>,
Gary King <king at harvard.edu>,
@@ -22,7 +22,7 @@ Description: Zelig is an easy-to-use program that can estimate, and
translates them into quantities of direct interest.
License: GPL (>= 2)
URL: http://gking.harvard.edu/zelig
-Suggests: VGAM (>= 0.7-1), MCMCpack (>= 0.7-4), mvtnorm, survival,
- sandwich (>= 2.0-0), zoo (>= 1.2-1), coda, nnet, sna, gee,
- systemfit, mgcv
-Packaged: Tue Feb 12 10:11:36 2008; king
+Suggests: VGAM (>= 0.7-1), MCMCpack (>= 0.7-4), mvtnorm, survival,
+ sandwich (>= 2.0-0), zoo (>= 1.2-1), coda, nnet, sna, gee,
+ systemfit, mgcv, lme4, anchors (>= 2.0), survey
+Packaged: Mon Apr 7 11:55:23 2008; king
diff --git a/NAMESPACE b/NAMESPACE
index c62db3b..585d3e0 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -84,6 +84,9 @@ S3method(plot.zelig, oprobit)
S3method(plot.zelig, probit)
S3method(plot.zelig, poisson)
S3method(plot.zelig, relogit)
+S3method(plot.zelig, logit.survey)
+S3method(plot.zelig, probit.survey)
+S3method(plot.zelig, poisson.survey)
S3method(print, arimaSummary)
S3method(print, BetaReg)
S3method(print, coxhazard)
diff --git a/R/describe.gamma.survey.R b/R/describe.gamma.survey.R
new file mode 100644
index 0000000..00601d2
--- /dev/null
+++ b/R/describe.gamma.survey.R
@@ -0,0 +1,15 @@
+describe.gamma.survey<-function(){
+category <- "bounded"
+description <- "Survey-Weighted Gamma Regression for Continuous, Positive Dependent Variables"
+package <-list( name ="survey",
+ version ="3.6-13"
+ )
+parameters<-list(lambda="lambda")
+parameters$lambda<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.logit.survey.R b/R/describe.logit.survey.R
new file mode 100644
index 0000000..d06642f
--- /dev/null
+++ b/R/describe.logit.survey.R
@@ -0,0 +1,15 @@
+describe.logit.survey<-function(){
+category <- "dichotomous"
+description <- "Survey-Weighted Logistic Regression for Dichotomous Dependent Variables"
+package <-list( name ="survey",
+ version ="3.6-13"
+ )
+parameters<-list(pi="pi")
+parameters$pi<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.normal.survey.R b/R/describe.normal.survey.R
new file mode 100644
index 0000000..4fa1197
--- /dev/null
+++ b/R/describe.normal.survey.R
@@ -0,0 +1,15 @@
+describe.normal.survey<-function(){
+category <- "continuous"
+description <- "Survey-Weighted Normal Regression for Continuous Dependent Variables"
+package <-list(name ="survey",
+ version ="3.6-13"
+ )
+parameters<-list(mu="mu")
+parameters$mu<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.poisson.survey.R b/R/describe.poisson.survey.R
new file mode 100644
index 0000000..9e9566d
--- /dev/null
+++ b/R/describe.poisson.survey.R
@@ -0,0 +1,15 @@
+describe.poisson.survey<-function(){
+category <- "count"
+description <- "Survey-Weighted Poisson Regression for Event Count Dependent Variables"
+package <-list( name ="survey",
+ version ="3.6-13"
+ )
+parameters<-list(lambda="lambda")
+parameters$lambda<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.probit.survey.R b/R/describe.probit.survey.R
new file mode 100644
index 0000000..b3a12eb
--- /dev/null
+++ b/R/describe.probit.survey.R
@@ -0,0 +1,15 @@
+describe.probit.survey<-function(){
+category <- "dichotomous"
+description <- "Probit Regression for Dichotomous Dependent Variables"
+package <-list( name ="survey",
+ version ="3.6-13"
+ )
+parameters<-list(mu="mu")
+parameters$mu<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/param.svyglm.R b/R/param.svyglm.R
new file mode 100644
index 0000000..de50d5e
--- /dev/null
+++ b/R/param.svyglm.R
@@ -0,0 +1,46 @@
+param.svyglm <- function(object, num = NULL, bootstrap = FALSE){
+ model <- getzelig(object)
+ if (!bootstrap) {
+ coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
+ if (model == "normal.survey") {
+ df <- object$df.residual
+ sig2 <- summary(object)$dispersion
+ alpha <- sqrt(df*sig2/rchisq(num, df=df))
+ res <- cbind(coef, alpha)
+ }
+ else if (model== "gamma.survey") {
+ shape <- gamma.shape(object)
+ alpha <- rnorm(num, mean = shape$alpha, sd = shape$SE)
+ res <- cbind(coef, alpha)
+ }
+ else if (model == "negbin") {
+ alpha <- object$theta
+ res <- cbind(coef, c(alpha))
+ }
+ else
+ res <- coef
+ }
+ else {
+ coef <- coef(object)
+ if (object$family$family == "gaussian") {
+ alpha <- sum(object$residuals^2)/object$df.residual
+ res <- c(coef, alpha)
+ }
+ else if (object$family$family == "Gamma") {
+ alpha <- gamma.shape(object)$alpha
+ res <- c(coef, alpha)
+ }
+ else if (object$family$family == "neg.bin") {
+ alpha <- object$theta
+ res <- c(coef, alpha)
+ }
+ else
+ res <- coef
+ }
+ res
+}
+
+
+
+
+
diff --git a/R/plot.zelig.logit.survey.R b/R/plot.zelig.logit.survey.R
new file mode 100644
index 0000000..25d7d25
--- /dev/null
+++ b/R/plot.zelig.logit.survey.R
@@ -0,0 +1,20 @@
+plot.zelig.logit.survey <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ pr <- x$qi$pr
+ y0 <- 100 * sum(pr == 0)/length(pr)
+ y1 <- 100 * sum(pr == 1)/length(pr)
+ barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1,
+ names.arg = c("Y = 0", "Y = 1"),
+ xlab = "Percentage of Simulations",
+ main = x$qi.name$pr, xlim = c(0, 100))
+ x$qi$pr <- x$qi.name$pr <- NULL
+ for (i in 1:(k-1)) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.poisson.survey.R b/R/plot.zelig.poisson.survey.R
new file mode 100644
index 0000000..a5acdb7
--- /dev/null
+++ b/R/plot.zelig.poisson.survey.R
@@ -0,0 +1,16 @@
+plot.zelig.poisson.survey <- function(x, xlab = "", user.par = FALSE, ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ truehist(x$qi$pr, main = x$qi.name$pr, x0 = -0.25,
+ xlab = xlab, ylab = "Probability", ...)
+ plot(density(x$qi$ev), main = x$qi.name$ev, xlab = xlab, ...)
+ if (k > 2) {
+ for (i in 3:k)
+ plot(density(x$qi[[i]]), main = x$qi.name[[i]],
+ xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.probit.survey.R b/R/plot.zelig.probit.survey.R
new file mode 100644
index 0000000..d6129b4
--- /dev/null
+++ b/R/plot.zelig.probit.survey.R
@@ -0,0 +1,20 @@
+plot.zelig.probit.survey <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ pr <- x$qi$pr
+ y0 <- 100 * sum(pr == 0)/length(pr)
+ y1 <- 100 * sum(pr == 1)/length(pr)
+ barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1,
+ names.arg = c("Y = 0", "Y = 1"),
+ xlab = "Percentage of Simulations",
+ main = x$qi.name$pr, xlim = c(0, 100))
+ x$qi$pr <- x$qi.name$pr <- NULL
+ for (i in 1:(k-1)) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/qi.polr.R b/R/qi.polr.R
index 6308c7b..1190fba 100644
--- a/R/qi.polr.R
+++ b/R/qi.polr.R
@@ -14,6 +14,7 @@ qi.polr <- function(object, simpar, x, x1 = NULL, y = NULL) {
cuts[i,,] <- t(object$inv.link(eta[i,], sim.zeta[i,]))
tmp0[,(2:k),] <- cuts[,(1:(k-1)),]
ev <- cuts - tmp0
+ if (dim(ev)[3] == 1) ev <- ev[,,1]
pr <- matrix(NA, nrow = num, ncol = nrow(x))
tmp <- matrix(runif(length(cuts[,1,]), 0, 1),
nrow = num , ncol = nrow(x))
@@ -36,6 +37,7 @@ qi.polr <- function(object, simpar, x, x1 = NULL, y = NULL) {
cuts[i,,] <- t(object$inv.link(eta1[i,], sim.zeta[i,]))
tmp0[,(2:k),] <- cuts[,(1:(k-1)),]
ev1 <- cuts - tmp0
+ if (dim(ev1)[3] == 1) ev1 <- ev1[,,1]
qi$fd <- ev1 - ev
qi$rr <- ev1 / ev
qi.name$fd <- "First Differences: P(Y=j|X1)-P(Y=j|X)"
diff --git a/R/qi.survreg.R b/R/qi.survreg.R
index 4d0b535..e91e6fe 100644
--- a/R/qi.survreg.R
+++ b/R/qi.survreg.R
@@ -82,14 +82,14 @@ qi.survreg <- function(object, simpar, x, x1 = NULL, y = NULL) {
count <- 1
while ((sum(idx) < length(idx)) & count < 1001) {
count <- count + 1
- tmp.idx <- which(!idx, arr.ind = TRUE)
+ tmp.idx <- which(!idx, TRUE)
y.imp[tmp.idx] <- pr.surv(model, tmp$theta[tmp.idx],
sim.scale[tmp.idx[,1]], tmp$ev[tmp.idx])
idx[tmp.idx] <- y.imp[tmp.idx] >= y.c[tmp.idx[,2]]
}
if (count == 1001) {
warning(" Maximum number of imputed values (1000) reached for censored Y. \n Using censoring point as observed value, since Pr(Y > Yc | sims) <= 0.001.")
- y.imp[which(idx == 0, arr.ind = TRUE)] <- y.c[which(idx == 0, arr.ind == TRUE)[,2]]
+ y.imp[which(idx == 0, TRUE)] <- y.c[which(idx == 0, TRUE)[,2]]
}
yvar <- matrix(NA, ncol = length(y), nrow = nrow(qi$ev))
yvar[, which(status == 1)] <- y.obs
diff --git a/R/qi.svyglm.R b/R/qi.svyglm.R
new file mode 100644
index 0000000..2adb542
--- /dev/null
+++ b/R/qi.svyglm.R
@@ -0,0 +1,82 @@
+qi.svyglm <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ check <- FALSE
+ model <- getzelig(object)
+ k <- length(getcoef(object))
+ coef <- simpar[,1:k]
+ if (k < ncol(simpar))
+ alpha <- simpar[,(k+1):ncol(simpar)]
+ eta <- coef %*% t(x)
+ theta <- matrix(object$family$linkinv(eta), nrow = nrow(coef))
+ pr <- ev <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta))
+ dimnames(pr) <- dimnames(ev) <- dimnames(theta)
+ if (model %in% c("logit.survey", "probit.survey")) {
+ check <- TRUE
+ ev <- theta
+ for (i in 1:ncol(theta))
+ pr[,i] <- as.character(rbinom(length(ev[,i]), 1, ev[,i]))
+ if (!is.null(y)) {
+ if (NCOL(y) > 1)
+ y <- y[,1]
+ }
+ }
+ else if (model == "normal.survey" ) {
+ ev <- theta
+ for (i in 1:nrow(ev))
+ pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
+ }
+ else if (model == "gamma.survey") {
+ ev <- theta
+ for (i in 1:nrow(ev))
+ pr[i,] <- rgamma(length(ev[i,]), shape = alpha[i], scale = theta[i,]/alpha[i])
+ }
+ else if (model %in% c("poisson.survey")) {
+ ev <- theta
+ for (i in 1:ncol(ev))
+ pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
+ }
+ else if (model == "negbin") {
+ ev <- theta
+ for (i in 1:nrow(ev))
+ pr[i,] <- rnegbin(length(ev[i,]), mu = ev[i,], theta = alpha[i])
+ }
+ qi <- list(ev = ev, pr = pr)
+ qi.name <- list(ev = "Expected Values: E(Y|X)",
+ pr = "Predicted Values: Y|X")
+ if (!is.null(x1)){
+ ev1 <- theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
+ nrow = nrow(coef))
+ qi$fd <- ev1-ev
+ qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ if (model %in% c("logit.survey", "probit.survey")) {
+ qi$rr <- ev1/ev
+ qi.name$rr <- "Risk Ratios: P(Y=1|X1)/P(Y=1|X)"
+ }
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ tmp.ev <- yvar - qi$ev
+ if (check)
+ tmp.pr <- yvar - as.integer(qi$pr)
+ else
+ tmp.pr <- yvar - qi$pr
+# tmp.ev <- qi$tt.ev <- yvar - qi$ev
+# if (check)
+# tmp.pr <- qi$tt.pr <- yvar - as.integer(qi$pr)
+# else
+# tmp.pr <- qi$tt.pr <- yvar - qi$pr
+# qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
+# qi.name$tt.pr <- "Unit Treatment Effect for the Treated: Y - PR"
+ qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi$att.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(simpar))
+ qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
+ qi.name$att.pr <- "Average Treatment Effect for the Treated: Y - PR"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
+
+
+
+
+
+
+
diff --git a/R/zelig2gamma.survey.R b/R/zelig2gamma.survey.R
new file mode 100644
index 0000000..c99f6da
--- /dev/null
+++ b/R/zelig2gamma.survey.R
@@ -0,0 +1,50 @@
+zelig2gamma.survey <- function(formula, model, data, M,
+ weights=NULL,
+ ids=NULL, probs=NULL, strata = NULL,
+ fpc=NULL, nest = FALSE, check.strata = !nest,
+ repweights = NULL,
+ type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL,
+ scale=NULL, rscales=NULL, fpctype="fraction",
+ return.replicates=FALSE,
+ na.action="na.omit", start=NULL, etastart=NULL,
+ mustart=NULL, offset=NULL,
+ model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL,
+ design=NULL, link=NULL){
+
+ mf <- match.call(expand.dots = TRUE)
+ mf$M <- mf$model <- NULL
+ if(is.null(link)){mf$family <- Gamma} else {mf$family <- Gamma(link=mf$link)}
+ mf$link <- NULL
+
+ if(is.null(ids)){ids<-~1}
+
+ if(is.null(repweights)){
+
+ mf$design <- svydesign(data=data, ids=ids, probs=probs,
+ strata=strata, fpc=fpc, nest=nest, check.strata=check.strata,
+ weight=weights)
+
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$rho <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- mf$return.replicates <- NULL
+ mf$data <- NULL
+
+ } else {
+ assign(".survey.prob.weights", weights, envir = globalenv())
+ mf$design <- svrepdesign(data=data, repweights=repweights,
+ type=type, weights=weights, combined.weights=combined.weights,
+ rho=rho, bootstrap.average=bootstrap.average, scale=scale,
+ rscales=rscales, fpctype=fpctype, fpc=fpc)
+ # ...drop extraneous options
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- NULL
+ mf$data <- NULL
+ }
+
+ mf[[1]] <- as.name("svyglm")
+ as.call(mf)
+
+}
\ No newline at end of file
diff --git a/R/zelig2logit.survey.R b/R/zelig2logit.survey.R
new file mode 100644
index 0000000..217a1f0
--- /dev/null
+++ b/R/zelig2logit.survey.R
@@ -0,0 +1,50 @@
+zelig2logit.survey <- function(formula, model, data, M,
+ weights=NULL,
+ ids=NULL, probs=NULL, strata = NULL,
+ fpc=NULL, nest = FALSE, check.strata = !nest,
+ repweights = NULL,
+ type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL,
+ scale=NULL, rscales=NULL, fpctype="fraction",
+ return.replicates=FALSE,
+ na.action="na.omit", start=NULL, etastart=NULL,
+ mustart=NULL, offset=NULL,
+ model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL,
+ design=NULL){
+
+ mf <- match.call(expand.dots = TRUE)
+ mf$M <- mf$model <- NULL
+ mf$family <- binomial(link="logit")
+ mf$link <- NULL
+
+ if(is.null(ids)){ids<-~1}
+
+ if(is.null(repweights)){
+
+ mf$design <- svydesign(data=data, ids=ids, probs=probs,
+ strata=strata, fpc=fpc, nest=nest, check.strata=check.strata,
+ weight=weights)
+
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$rho <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- mf$return.replicates <- NULL
+ mf$data <- NULL
+
+ } else {
+ assign(".survey.prob.weights", weights, envir = globalenv())
+ mf$design <- svrepdesign(data=data, repweights=repweights,
+ type=type, weights=weights, combined.weights=combined.weights,
+ rho=rho, bootstrap.average=bootstrap.average, scale=scale,
+ rscales=rscales, fpctype=fpctype, fpc=fpc)
+ # ...drop extraneous options
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- NULL
+ mf$data <- NULL
+ }
+
+ mf[[1]] <- as.name("svyglm")
+ as.call(mf)
+
+}
\ No newline at end of file
diff --git a/R/zelig2normal.survey.R b/R/zelig2normal.survey.R
new file mode 100644
index 0000000..0262726
--- /dev/null
+++ b/R/zelig2normal.survey.R
@@ -0,0 +1,48 @@
+zelig2normal.survey <- function(formula, model, data, M,
+ weights=NULL,
+ ids=NULL, probs=NULL, strata = NULL,
+ fpc=NULL, nest = FALSE, check.strata = !nest,
+ repweights = NULL,
+ type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL,
+ scale=NULL, rscales=NULL, fpctype="fraction",
+ return.replicates=FALSE,
+ na.action="na.omit", start=NULL, etastart=NULL,
+ mustart=NULL, offset=NULL,
+ model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL,
+ design=NULL){
+
+ mf <- match.call(expand.dots = TRUE)
+ mf$M <- mf$model <- NULL
+
+ if(is.null(ids)){ids<-~1}
+
+ if(is.null(repweights)){
+
+ mf$design <- svydesign(data=data, ids=ids, probs=probs,
+ strata=strata, fpc=fpc, nest=nest, check.strata=check.strata,
+ weight=weights) ### added conventional weights input here
+
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$rho <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- mf$return.replicates <- NULL
+ mf$data <- NULL
+
+ } else {
+ assign(".survey.prob.weights", weights, envir = globalenv()) ### moved global assignment down here
+ mf$design <- svrepdesign(data=data, repweights=repweights,
+ type=type, weights=weights, combined.weights=combined.weights,
+ rho=rho, bootstrap.average=bootstrap.average, scale=scale,
+ rscales=rscales, fpctype=fpctype, fpc=fpc)
+ # ...drop extraneous options
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- NULL
+ mf$data <- NULL
+ }
+
+ mf[[1]] <- as.name("svyglm")
+ as.call(mf)
+
+}
\ No newline at end of file
diff --git a/R/zelig2poisson.survey.R b/R/zelig2poisson.survey.R
new file mode 100644
index 0000000..625c62e
--- /dev/null
+++ b/R/zelig2poisson.survey.R
@@ -0,0 +1,50 @@
+zelig2poisson.survey <- function(formula, model, data, M,
+ weights=NULL,
+ ids=NULL, probs=NULL, strata = NULL,
+ fpc=NULL, nest = FALSE, check.strata = !nest,
+ repweights = NULL,
+ type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL,
+ scale=NULL, rscales=NULL, fpctype="fraction",
+ return.replicates=FALSE,
+ na.action="na.omit", start=NULL, etastart=NULL,
+ mustart=NULL, offset=NULL,
+ model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL,
+ design=NULL, link="log"){
+
+ mf <- match.call(expand.dots = TRUE)
+ mf$M <- mf$model <- NULL
+ mf$family <- poisson(link=link)
+ mf$link <- NULL
+
+ if(is.null(ids)){ids<-~1}
+
+ if(is.null(repweights)){
+
+ mf$design <- svydesign(data=data, ids=ids, probs=probs,
+ strata=strata, fpc=fpc, nest=nest, check.strata=check.strata,
+ weight=weights)
+
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$rho <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- mf$return.replicates <- NULL
+ mf$data <- NULL
+
+ } else {
+ assign(".survey.prob.weights", weights, envir = globalenv())
+ mf$design <- svrepdesign(data=data, repweights=repweights,
+ type=type, weights=weights, combined.weights=combined.weights,
+ rho=rho, bootstrap.average=bootstrap.average, scale=scale,
+ rscales=rscales, fpctype=fpctype, fpc=fpc)
+ # ...drop extraneous options
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- NULL
+ mf$data <- NULL
+ }
+
+ mf[[1]] <- as.name("svyglm")
+ as.call(mf)
+
+}
\ No newline at end of file
diff --git a/R/zelig2probit.survey.R b/R/zelig2probit.survey.R
new file mode 100644
index 0000000..38382ec
--- /dev/null
+++ b/R/zelig2probit.survey.R
@@ -0,0 +1,50 @@
+zelig2probit.survey <- function(formula, model, data, M,
+ weights=NULL,
+ ids=NULL, probs=NULL, strata = NULL,
+ fpc=NULL, nest = FALSE, check.strata = !nest,
+ repweights = NULL,
+ type, combined.weights=FALSE, rho = NULL, bootstrap.average=NULL,
+ scale=NULL, rscales=NULL, fpctype="fraction",
+ return.replicates=FALSE,
+ na.action="na.omit", start=NULL, etastart=NULL,
+ mustart=NULL, offset=NULL,
+ model1=TRUE, method="glm.fit", x=FALSE, y=TRUE, contrasts=NULL,
+ design=NULL){
+
+ mf <- match.call(expand.dots = TRUE)
+ mf$M <- mf$model <- NULL
+ mf$family <- binomial(link="probit")
+ mf$link <- NULL
+
+ if(is.null(ids)){ids<-~1}
+
+ if(is.null(repweights)){
+
+ mf$design <- svydesign(data=data, ids=ids, probs=probs,
+ strata=strata, fpc=fpc, nest=nest, check.strata=check.strata,
+ weight=weights)
+
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$rho <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- mf$return.replicates <- NULL
+ mf$data <- NULL
+
+ } else {
+ assign(".survey.prob.weights", weights, envir = globalenv())
+ mf$design <- svrepdesign(data=data, repweights=repweights,
+ type=type, weights=weights, combined.weights=combined.weights,
+ rho=rho, bootstrap.average=bootstrap.average, scale=scale,
+ rscales=rscales, fpctype=fpctype, fpc=fpc)
+ # ...drop extraneous options
+ mf$weights <- mf$ids <- mf$probs <- mf$strata <- mf$fpc <- NULL
+ mf$nest <- mf$check.strata <- mf$repweights <- mf$type <- NULL
+ mf$combined.weights <- mf$bootstrap.average <- NULL
+ mf$scale <- mf$rscales <- mf$fpctype <- NULL
+ mf$data <- NULL
+ }
+
+ mf[[1]] <- as.name("svyglm")
+ as.call(mf)
+
+}
\ No newline at end of file
diff --git a/demo/00Index b/demo/00Index
index 231b1b9..8fd8ed0 100644
--- a/demo/00Index
+++ b/demo/00Index
@@ -69,7 +69,11 @@ gamma.mixed Mixed effects gamma regression
poisson.mixed Mixed effects poisson regression
probit.mixed Mixed effects probit regression
coxph Cox Proportional Hazard Regression for Duration Dependent Variables
-
+normal.survey Survey-Weighted Normal Regression for Continuous Dependent Variables
+logit.survey Survey-Weighted Logistic Regression for Dichotomous Dependent Variables
+probit.survey Survey-Weighted Probit Regression for Dichotomous Dependent Variables
+poisson.survey Survey-Weighted Poisson Regression for Event-count Dependent Variables
+gamma.survey Survey-Weighted Poisson Regression for Positive Continuous Dependent Variables
diff --git a/demo/gamma.survey.R b/demo/gamma.survey.R
new file mode 100644
index 0000000..1563851
--- /dev/null
+++ b/demo/gamma.survey.R
@@ -0,0 +1,100 @@
+##### Example 1: User has Existing Sample Weights #####
+
+# Attach sample data and variable names:
+data(api)
+
+# In this example, we will estimate a model using
+# the percentages of students who receive subsidized
+# lunch and an indicator for whether schooling is
+# year-round to predict California public schools'
+# academic performance index scores:
+
+z.out1 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey",
+ weights=~pw, data = apistrat)
+summary(z.out1)
+
+# Set explanatory variables to their default (mean/mode) values, and set
+# a high (80th percentile) and low (20th percentile) value for "meals,"
+# the percentage of students who receive subsidized meals:
+
+x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8))
+
+# Generate first differences for the effect of high versus low "meals"
+# on academic performance:
+
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+
+# Generate a second set of fitted values and a plot:
+
+plot(s.out1)
+
+
+
+#### Example 2: User has Details about Complex Survey Design ####
+#### (but not sample weights) ####
+
+# Suppose that the survey house that provided
+# the dataset excluded probability weights
+# but made other details about the survey
+# design available. We can still estimate
+# a model without probability weights that takes
+# instead variables that identify each the stratum
+# and/or cluster from which each observation was
+# selected and the size of the finite sample from
+# which each observation was selected.
+
+z.out2 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey",
+ strata=~stype, fpc=~fpc, data = apistrat)
+summary(z.out2)
+
+# Note that these results are identical to the results obtained
+# when pre-existing sampling weights were used. When sampling
+# weights are omitted, Zelig estimates them automatically for
+# "gamma.survey" models based on the user-defined description
+# of sampling designs. If no description is present, the default
+# assumption is equal probability sampling.
+#
+# setx() and sim() can then be run on z.out2 in the same fashion
+# described in Example 1.
+
+
+
+##### Example 3: User has Replicate Weights #####
+
+# Suppose that the survey house that published
+# these data withheld details about the survey
+# design and instead published replication weights
+
+# For the purpose of illustration, create a set of
+# jk1 replicate weights
+
+jk1reps <- jk1weights(psu=apistrat$dnum)
+
+# Estimate the model regressing api00 on the "meals"
+# "yr.rnd" variables.
+
+z.out3 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey",
+ data = apistrat, repweights=jk1reps$weights,
+ type="JK1")
+summary(z.out3)
+
+# Set the explanatory variable "meals" at high and low values
+
+x.low <- setx(z.out3, meals= quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out3, meals= quantile(apistrat$meals, 0.8))
+
+# Generate first differences for the effect of the high
+# versus low concentrations of poverty on school performance
+
+s.out3 <- sim(z.out3, x=x.high, x1=x.low)
+summary(s.out3)
+
+# Generate a second set of fitted values and a plot:
+
+plot(s.out3)
+
+#### The user should also refer to the gamma model demo, since ####
+#### gamma.survey models can take many of the same options as ####
+#### gamma models. ####
\ No newline at end of file
diff --git a/demo/logit.survey.R b/demo/logit.survey.R
new file mode 100644
index 0000000..55e1546
--- /dev/null
+++ b/demo/logit.survey.R
@@ -0,0 +1,105 @@
+##### Example 1: User has Existing Sample Weights #####
+
+# Attach sample data:
+data(api, package="survey")
+
+# In this example, we will estimate a model using
+# the percentages of students who receive subsidized
+# lunch and the percentage who are new to a school
+# to predict whether each California public school
+# attends classes year round.
+
+z.out1 <- zelig(yr.rnd ~ meals + mobility, model = "logit.survey", weights=~pw, data = apistrat)
+summary(z.out1)
+
+# Set explanatory variables to their default (mean/mode) values, and set
+# a high (80th percentile) and low (20th percentile) value for "meals,"
+# the percentage of students who receive subsidized meals:
+
+x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8))
+
+# Generate first differences for the effect of high versus low "meals"
+# on the probability that a school will hold classes year round:
+
+s.out1 <- sim(z.out1, x=x.low, x1=x.high)
+summary(s.out1)
+
+# Generate a second set of fitted values and a plot:
+
+plot(s.out1)
+
+
+
+#### Example 2: User has Details about Complex Survey Design ####
+#### (but not sample weights) ####
+
+# Suppose that the survey house that provided
+# the dataset excluded probability weights
+# but made other details about the survey
+# design available. We can still estimate
+# a model without probability weights that takes
+# instead variables that identify each the stratum
+# and/or cluster from which each observation was
+# selected and the size of the finite sample from
+# which each observation was selected.
+
+z.out2 <- zelig(yr.rnd ~ meals + mobility, model = "logit.survey", strata=~stype, fpc=~fpc, data = apistrat)
+summary(z.out2)
+
+# The coefficient estimates from this model are identical to
+# point estimates in the previous example, but the standard errors
+# are smaller. When sampling weights are omitted, Zelig estimates
+# them automatically for "normal.survey" models based on the
+# user-defined description of sampling designs. In addition,
+# when user-defined descriptions of the sampling design are
+# entered as inputs, variance estimates are better and standard
+# errors are consequently smaller.
+#
+# setx() and sim() can then be run on z.out2 in the same fashion
+# described in Example 1.
+
+
+
+##### Example 3: User has Replicate Weights #####
+
+# Load data for a model using the number of out-of-hospital
+# cardiac arrests and the number of patients who arrive
+# alive in hospitals to predict whether each hospital
+# has been sued (an indicator variable artificially created
+# here for the purpose of illustration).
+
+data(scd)
+scd$sued <- as.vector(c(0,0,0,1,1,1))
+
+# Again, for the purpose of illustration, create four Balanced
+# Repeated Replicate (BRR) weights:
+
+BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
+c(0,1,0,1,1,0))
+
+# Estimate the model using Zelig:
+
+z.out3 <- zelig(formula=sued ~ arrests + alive , model = "logit.survey",
+ repweights=BRRrep, type="BRR", data=scd)
+summary(z.out3)
+
+# Set the explanatory variables at their means and set
+# arrests at its 20th and 80th quartiles
+
+x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2))
+x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8))
+
+# Generate first differences for the effect of the minimum
+# versus the maximum number of individuals who arrive
+# alive on the probability that a hospital will be sued:
+
+s.out3 <- sim(z.out3, x=x.high, x1=x.low)
+summary(s.out3)
+
+# Generate a second set of fitted values and a plot:
+plot(s.out3)
+
+#### The user should also refer to the logit model demo, since ####
+#### logit.survey models can take many of the same options as ####
+#### logit models. ####
diff --git a/demo/normal.survey.R b/demo/normal.survey.R
new file mode 100644
index 0000000..cb3d278
--- /dev/null
+++ b/demo/normal.survey.R
@@ -0,0 +1,101 @@
+##### Example 1: User has Existing Sample Weights #####
+
+# Attach sample data and variable names:
+data(api)
+
+# In this example, we will estimate a model using
+# the percentages of students who receive subsidized
+# lunch and an indicator for whether schooling is
+# year-round to predict California public schools'
+# academic performance index scores:
+
+z.out1 <- zelig(api00 ~ meals + yr.rnd, model = "normal.survey",
+ weights=~pw, data = apistrat)
+summary(z.out1)
+
+# Set explanatory variables to their default (mean/mode) values, and set
+# a high (80th percentile) and low (20th percentile) value for "meals,"
+# the percentage of students who receive subsidized meals:
+
+x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8))
+
+# Generate first differences for the effect of high versus low "meals"
+# on academic performance:
+
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+
+# Generate a second set of fitted values and a plot:
+
+plot(s.out1)
+
+
+
+#### Example 2: User has Details about Complex Survey Design ####
+#### (but not sample weights) ####
+
+# Suppose that the survey house that provided
+# the dataset excluded probability weights
+# but made other details about the survey
+# design available. We can still estimate
+# a model without probability weights that takes
+# instead variables that identify each the stratum
+# and/or cluster from which each observation was
+# selected and the size of the finite sample from
+# which each observation was selected.
+
+z.out2 <- zelig(api00 ~ meals + yr.rnd, model = "normal.survey",
+ strata=~stype, fpc=~fpc, data = apistrat)
+summary(z.out2)
+
+# Note that these results are identical to the results obtained
+# when pre-existing sampling weights were used. When sampling
+# weights are omitted, Zelig estimates them automatically for
+# "normal.survey" models based on the user-defined description
+# of sampling designs. If no description is present, the default
+# assumption is equal probability sampling.
+#
+# setx() and sim() can then be run on z.out2 in the same fashion
+# described in Example 1.
+
+
+
+##### Example 3: User has Replicate Weights #####
+
+# Load data for a model using the number of out-of-hospital
+# cardiac arrests to predict the number of patients who arrive
+# alive in hospitals:
+
+data(scd)
+
+# Create four Balanced Repeated Replicate (BRR) weights:
+
+BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
+c(0,1,0,1,1,0))
+
+# Estimate the model using Zelig:
+
+z.out3 <- zelig(formula=alive ~ arrests , model = "normal.survey",
+ repweights=BRRrep, type="BRR", data=scd, na.action=NULL)
+summary(z.out3)
+
+# Set the explanatory variable at its minimum and maximum
+
+x.min <- setx(z.out3, arrests = min(scd$alive))
+x.max <- setx(z.out3, arrests = max(scd$alive))
+
+# Generate first differences for the effect of the minimum
+# versus the maximum number of cardiac arrests on the number
+# of people who arrive alive:
+
+s.out3 <- sim(z.out3, x=x.max, x1=x.min)
+summary(s.out3)
+
+# Generate a second set of fitted values and a plot:
+plot(s.out3)
+
+#### The user should also refer to the normal model demo, since ####
+#### normal.survey models can take many of the same options as ####
+#### normal models. ####
+
diff --git a/demo/poisson.survey.R b/demo/poisson.survey.R
new file mode 100644
index 0000000..6b45529
--- /dev/null
+++ b/demo/poisson.survey.R
@@ -0,0 +1,103 @@
+##### Example 1: User has Existing Sample Weights #####
+
+# Attach sample data:
+data(api, package="survey")
+
+# In this example, we will estimate a model using
+# each school's academic performance in 2000 and an
+# indicator for year-round schools to predict the
+# number of students who enrolled in each California school.
+
+z.out1 <- zelig(enroll ~ api99 + yr.rnd , model = "poisson.survey", data = apistrat)
+summary(z.out1)
+
+# Set explanatory variables to their default (mean/mode) values, and set
+# a high (80th percentile) and low (20th percentile) value for the
+# measure of academic performance, "api00":
+
+x.low <- setx(z.out1, api00= quantile(apistrat$api00, 0.2))
+x.high <- setx(z.out1, api00= quantile(apistrat$api00, 0.8))
+
+# Generate first differences for the effect of high versus low "meals"
+# on the probability that a school will hold classes year round:
+
+s.out1 <- sim(z.out1, x=x.low, x1=x.high)
+summary(s.out1)
+
+# Generate a second set of fitted values and a plot:
+
+plot(s.out1)
+
+
+
+#### Example 2: User has Details about Complex Survey Design ####
+#### (but not sample weights) ####
+
+# Suppose that the survey house that provided
+# the dataset excluded probability weights
+# but made other details about the survey
+# design available. We can still estimate
+# a model without probability weights that takes
+# instead variables that identify each the stratum
+# and/or cluster from which each observation was
+# selected and the size of the finite sample from
+# which each observation was selected.
+
+z.out2 <- zelig(enroll ~ api99 + yr.rnd , model = "poisson.survey", data = apistrat,
+ strata=~stype, fpc=~fpc)
+summary(z.out2)
+
+# The coefficient estimates from this model are identical to
+# point estimates in the previous example, but the standard errors
+# are smaller. When sampling weights are omitted, Zelig estimates
+# them automatically for "normal.survey" models based on the
+# user-defined description of sampling designs. In addition,
+# when user-defined descriptions of the sampling design are
+# entered as inputs, variance estimates are better and standard
+# errors are consequently smaller.
+#
+# setx() and sim() can then be run on z.out2 in the same fashion
+# described in Example 1.
+
+
+
+##### Example 3: User has Replicate Weights #####
+
+# Load data for a model using the number of out-of-hospital
+# cardiac arrests to predict the number of patients who arrive
+# alive in hospitals.
+
+data(scd, package="survey")
+
+# For the purpose of illustration, create four Balanced
+# Repeated Replicate (BRR) weights:
+
+BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
+c(0,1,0,1,1,0))
+
+# Estimate the model using Zelig:
+
+z.out3 <- zelig(alive ~ arrests , model = "poisson.survey",
+ repweights=BRRrep, type="BRR", data=scd)
+summary(z.out3)
+
+# Set the explanatory variables at their means and set
+# arrests at its 20th and 80th quartiles
+
+x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2))
+x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8))
+
+# Generate first differences for the effect of the minimum
+# versus the maximum number of individuals who arrive
+# alive on the probability that a hospital will be sued:
+
+s.out3 <- sim(z.out3, x=x.high, x1=x.low)
+summary(s.out3)
+
+# Generate a second set of fitted values and a plot:
+plot(s.out3)
+
+#### The user should also refer to the poisson model demo, since ####
+#### poisson.survey models can take many of the same options as ####
+#### poisson models. ####
+
diff --git a/demo/probit.survey.R b/demo/probit.survey.R
new file mode 100644
index 0000000..61f8c3e
--- /dev/null
+++ b/demo/probit.survey.R
@@ -0,0 +1,106 @@
+##### Example 1: User has Existing Sample Weights #####
+
+# Attach sample data:
+data(api, package="survey")
+
+# In this example, we will estimate a model using
+# the percentages of students who receive subsidized
+# lunch and the percentage who are new to a school
+# to predict whether each California public school
+# attends classes year round.
+
+z.out1 <- zelig(yr.rnd ~ meals + mobility, model = "probit.survey", weights=~pw, data = apistrat)
+summary(z.out1)
+
+# Set explanatory variables to their default (mean/mode) values, and set
+# a high (80th percentile) and low (20th percentile) value for "meals,"
+# the percentage of students who receive subsidized meals:
+
+x.low <- setx(z.out1, meals= quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out1, meals= quantile(apistrat$meals, 0.8))
+
+# Generate first differences for the effect of high versus low "meals"
+# on the probability that a school will hold classes year round:
+
+s.out1 <- sim(z.out1, x=x.low, x1=x.high)
+summary(s.out1)
+
+# Generate a second set of fitted values and a plot:
+
+plot(s.out1)
+
+
+
+#### Example 2: User has Details about Complex Survey Design ####
+#### (but not sample weights) ####
+
+# Suppose that the survey house that provided
+# the dataset excluded probability weights
+# but made other details about the survey
+# design available. We can still estimate
+# a model without probability weights that takes
+# instead variables that identify each the stratum
+# and/or cluster from which each observation was
+# selected and the size of the finite sample from
+# which each observation was selected.
+
+z.out2 <- zelig(yr.rnd ~ meals + mobility, model = "probit.survey", strata=~stype, fpc=~fpc, data = apistrat)
+summary(z.out2)
+
+# The coefficient estimates from this model are identical to
+# point estimates in the previous example, but the standard errors
+# are smaller. When sampling weights are omitted, Zelig estimates
+# them automatically for "normal.survey" models based on the
+# user-defined description of sampling designs. In addition,
+# when user-defined descriptions of the sampling design are
+# entered as inputs, variance estimates are better and standard
+# errors are consequently smaller.
+#
+# setx() and sim() can then be run on z.out2 in the same fashion
+# described in Example 1.
+
+
+
+##### Example 3: User has Replicate Weights #####
+
+# Load data for a model using the number of out-of-hospital
+# cardiac arrests and the number of patients who arrive
+# alive in hospitals to predict whether each hospital
+# has been sued (an indicator variable artificially created
+# here for the purpose of illustration).
+
+data(scd)
+scd$sued <- as.vector(c(0,0,0,1,1,1))
+
+# Again, for the purpose of illustration, create four Balanced
+# Repeated Replicate (BRR) weights:
+
+BRRrep<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
+c(0,1,0,1,1,0))
+
+# Estimate the model using Zelig:
+
+z.out3 <- zelig(formula=sued ~ arrests + alive , model = "probit.survey",
+ repweights=BRRrep, type="BRR", data=scd)
+summary(z.out3)
+
+# Set the explanatory variables at their means and set
+# arrests at its 20th and 80th quartiles
+
+x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2))
+x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8))
+
+# Generate first differences for the effect of the minimum
+# versus the maximum number of individuals who arrive
+# alive on the probability that a hospital will be sued:
+
+s.out3 <- sim(z.out3, x=x.high, x1=x.low)
+summary(s.out3)
+
+# Generate a second set of fitted values and a plot:
+plot(s.out3)
+
+#### The user should also refer to the probit model demo, since ####
+#### probit.survey models can take many of the same options as ####
+#### probit models. ####
+
diff --git a/inst/doc/Rplots.ps b/inst/doc/Rplots.ps
index 1d591e8..b0b4fcf 100644
--- a/inst/doc/Rplots.ps
+++ b/inst/doc/Rplots.ps
@@ -88,7 +88,8 @@ bp
1 setlinejoin
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@@ -0,0 +1,511 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/gammasurvey}
+\include{zinput}
+%\VignetteIndexEntry{Survey-Weighted Gamma Regression for Continuous, Positive Dependent Variables}
+%\VignetteDepends{Zelig, stats, survey}
+%\VignetteKeyWords{model,gamma ,continuous, regression, survey}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+library(survey)
+@
+\section{{\tt gamma.survey}: Survey-Weighted Gamma Regression for Continuous, Positive Dependent Variables}
+\label{gamma.survey}
+
+The survey-weighted Gamma regression model is appropriate for
+survey data obtained using complex sampling techniques, such as
+stratified random or cluster sampling (e.g., not simple random
+sampling). Like the conventional Gamma regression models (see
+\Sref{gamma}), survey-weighted Gamma regression specifies a
+continuous, positive dependent variable as function of a set of explanatory
+variables. The survey-weighted Gamma model reports estimates of
+model parameters identical to conventional Gamma estimates, but uses
+information from the survey design to correct variance estimates.
+
+The {\tt gamma.survey} model accommodates three common types of
+complex survey data. Each method listed here requires selecting
+specific options which are detailed in the ``Additional Inputs''
+section below. \begin{enumerate}
+
+\item \textbf{Survey weights}: Survey data are often published along
+with weights for each observation. For example, if a survey
+intentionally over-samples a particular type of case, weights can be
+used to correct for the over-representation of that type of case in
+the dataset. Survey weights come in two forms:
+\begin{enumerate}
+
+\item \textit{Probability} weights report the probability that each
+case is drawn from the population. For each stratum or cluster,
+this is computed as the number of observations in the sample drawn
+from that group divided by the number of observations in the
+population in the group.
+
+\item \textit{Sampling} weights are the inverse of the probability
+weights.
+
+\end{enumerate}
+
+\item \textbf{Strata/cluster identification}: A complex survey
+dataset may include variables that identify the strata or cluster
+from which observations are drawn. For stratified random sampling
+designs, observations may be nested in different strata. There are
+two ways to employ these identifiers:
+
+\begin{enumerate}
+
+\item Use \textit{finite population corrections} to specify the
+total number of cases in the stratum or cluster from which each
+observation was drawn.
+
+\item For stratified random sampling designs, use the raw strata ids
+to compute sampling weights from the data.
+
+\end{enumerate}
+
+\item \textbf{Replication weights}: To preserve the anonymity of
+survey participants, some surveys exclude strata and cluster ids
+from the public data and instead release only pre-computed replicate
+weights.
+
+\end{enumerate}
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "gamma.survey", 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 {\tt zelig} inputs (see
+\Sref{Zcommands}), survey-weighted Gamma models accept the following
+optional inputs:
+\begin{enumerate}
+
+\item Datasets that include survey weights:.
+
+\begin{itemize}
+
+\item {\tt probs}: An optional formula or numerical vector specifying each
+case's probability weight, the probability that the case was
+selected. Probability weights need not (and, in most cases, will
+not) sum to one. Cases with lower probability weights are weighted
+more heavily in the computation of model coefficients.
+
+\item {\tt weights}: An optional numerical vector specifying each
+case's sample weight, the inverse of the probability that the case
+was selected. Sampling weights need not (and, in most cases, will
+not) sum to one. Cases with higher sampling weights are weighted
+more heavily in the computation of model coefficients.
+
+\end{itemize}
+
+\item Datasets that include strata/cluster identifiers:
+
+\begin{itemize}
+
+\item {\tt ids}: An optional formula or numerical vector identifying the
+cluster from which each observation was drawn (ordered from largest level to smallest level).
+For survey designs that do not involve cluster sampling, {\tt ids} defaults to {\tt NULL}.
+
+\item {\tt fpc}: An optional numerical vector identifying each
+case's frequency weight, the total number of units in the population
+from which each observation was sampled.
+
+\item {\tt strata}: An optional formula or vector identifying the
+stratum from which each observation was sampled. Entries may be
+numerical, logical, or strings. For survey designs that do not
+involve cluster sampling, {\tt strata} defaults to {\tt NULL}.
+
+\item {\tt nest}: An optional logical value specifying whether
+primary sampling unites (PSUs) have non-unique ids across multiple
+strata. {\tt nest=TRUE} is appropriate when PSUs reuse the same
+identifiers across strata. Otherwise, {\tt nest} defaults to {\tt
+FALSE}.
+
+\item {\tt check.strata}: An optional input specifying whether to
+check that clusters are nested in strata. If {\tt check.strata} is
+left at its default, {\tt !nest}, the check is not performed. If
+{\tt check.strata} is specified as {\tt TRUE}, the check is carried
+out.
+
+\end{itemize}
+
+\item Datasets that include replication weights:
+\begin{itemize}
+ \item {\tt repweights}: A formula or matrix specifying
+ replication weights, numerical vectors of weights used
+ in a process in which the sample is repeatedly re-weighted and parameters
+ are re-estimated in order to compute the variance of the model parameters.
+ \item {\tt type}: A string specifying the type of replication weights being used.
+ This input is required if replicate weights are specified. The following types
+ of replication weights are recognized: {\tt"BRR"}, {\tt "Fay"},
+ {\tt "JK1"}, {\tt "JKn"}, {\tt "bootstrap"}, or {\tt "other"}.
+ \item {\tt weights}: An optional vector or formula specifying each case's sample weight,
+ the inverse of the probability that the case was selected. If a survey includes both sampling
+ weights and replicate weights separately for the same survey, both should be included in
+ the model specification. In these cases, sampling weights are used to correct potential biases
+ in in the computation of coefficients and replication weights are used to compute the variance
+ of coefficient estimates.
+ \item {\tt combined.weights}: An optional logical value that
+ should be specified as {\tt TRUE} if the replicate weights include the sampling weights. Otherwise,
+ {\tt combined.weights} defaults to {\tt FALSE}.
+ \item {\tt rho}: An optional numerical value specifying a shrinkage factor
+ for replicate weights of type {\tt "Fay"}.
+ \item {\tt bootstrap.average}: An optional numerical input specifying
+ the number of iterations over which replicate weights of type {\tt "bootstrap"} were averaged.
+ This input should be left as {\tt NULL} for {\tt "bootstrap"} weights that were
+ not were created by averaging.
+\item {\tt scale}: When replicate weights are included,
+ the variance is computed as the sum of squared deviations of the replicates from their mean.
+ {\tt scale} is an optional overall multiplier for the standard deviations.
+\item {\tt rscale}: Like {\tt scale}, {\tt rscale} specifies an
+optional vector of replicate-specific multipliers for the squared
+deviations used in variance computation.
+
+\item {\tt fpc}: For models in which {\tt "JK1"}, {\tt "JKn"}, or
+{\tt "other"} replicates are specified, {\tt fpc} is an optional
+numerical vector (with one entry for each replicate) designating the
+replicates' finite population corrections.
+
+\item {\tt fpctype}: When a finite population correction is included
+as an {\tt fpc} input, {\tt fpctype} is a required input specifying
+whether the input to {\tt fpc} is a sampling fraction ({\tt
+fpctype="fraction"}) or a direct correction ({\tt
+fpctype="correction"}).
+
+\item {\tt return.replicates}: An optional logical value
+specifying whether the replicates should be returned as a component
+of the output. {\tt return.replicates} defaults to {\tt FALSE}.
+
+\end{itemize}
+
+\end{enumerate}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item A dataset that includes survey weights:
+
+Attach the sample data:
+<<Existing.data>>=
+data(api, package="survey")
+@
+
+Suppose that a dataset included a positive and continuous measure of
+public schools' performance ({\tt api00}), a measure of
+the percentage of students at each school who receive subsidized
+meals ({\tt meals}), an indicator for whether each school
+holds classes year round ({\tt year.rnd}), and sampling
+weights computed by the survey house ({\tt pw}). Estimate a model
+that regresses school performance on the {\tt meals} and {\tt year.rnd}
+variables:
+<<Existing.zelig>>=
+z.out1 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey",
+weights=~pw, data = apistrat)
+@
+Summarize regression coefficients:
+<<Existing.summary>>=
+ summary(z.out1)
+@
+Set explanatory variables to their default (mean/mode) values, and
+set a high (80th percentile) and low (20th percentile) value for
+``meals'':
+<<Existing.setx>>=
+x.low <- setx(z.out1, meals=quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out1, meals=quantile(apistrat$meals, 0.8))
+@
+Generate first differences for the
+effect of high versus low concentrations of children receiving
+subsidized meals on the probability of holding school year-round:
+<<Existing.sim>>=
+ s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+@
+<<Existing.summary.sim>>=
+ summary(s.out1)
+@
+Generate a visual summary of the quantities of interest:
+\begin{center}
+<<label=ExistingPlot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item A dataset that includes strata/cluster identifiers:
+
+Suppose that the survey house that provided the dataset used in the
+previous example excluded sampling weights but made other details
+about the survey design available. A user can still estimate a model
+without sampling weights that instead uses inputs that identify the
+stratum and/or cluster to which each observation belongs and the
+size of the finite population from which each observation was drawn.
+
+Continuing the example above, suppose the survey house drew at
+random a fixed number of elementary schools, a fixed number of
+middle schools, and a fixed number of high schools. If the variable
+{\tt stype} is a vector of characters ({\tt "E"} for elementary
+schools, {\tt "M"} for middle schools, and {\tt "H"} for high schools)
+that identifies the type of school each case
+represents and {\tt fpc} is a numerical vector that identifies for
+each case the total number of schools of the same type in the
+population, then the user could estimate the following model:
+
+<<Complex.zelig>>=
+z.out2 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey", strata=~stype, fpc=~fpc, data = apistrat)
+@
+Summarize the regression output:
+<<Complex.output>>=
+summary(z.out2)
+@
+The coefficient estimates for this example are
+identical to the point estimates in the first example, when
+pre-existing sampling weights were used. When sampling weights are
+omitted, they are estimated automatically for {\tt "gamma.survey"}
+models based on the user-defined description of sampling designs.
+
+Moreover, because the user provided information about the survey
+design, the standard error estimates are lower in this example than
+in the previous example, in which the user omitted variables pertaining
+to the details of the complex survey design.
+
+\item A dataset that includes replication weights:
+
+Survey houses sometimes supply
+replicate weights (in lieu of details about the survey design).
+Suppose that the survey house that published these school
+data withheld strata/cluster identifiers and instead
+published replication weights. For the sake
+of illustrating how replicate weights can be used as inputs in {\tt
+gamma.survey} models, create a set of jack-knife
+(JK1) replicate weights:
+<<Replicate.rw>>=
+jk1reps <- jk1weights(psu=apistrat$dnum)
+@
+Again, estimate a model that regresses school performance on
+the {\tt meals} and {\tt year.rnd} variables, using
+the JK1 replicate weights in {\tt jk1reps} to compute standard errors:
+<<Replicate.zelig>>=
+z.out3 <- zelig(api00 ~ meals + yr.rnd, model = "gamma.survey", data = apistrat,
+repweights=jk1reps$weights, type="JK1")
+@
+Summarize the regression coefficients:
+<<Replicate.summary>>=
+ summary(z.out3)
+@
+Set the explanatory variable {\tt meals} at its 20th and 80th quantiles:
+<<Replicate.setx>>=
+x.low <- setx(z.out3, meals= quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out3, meals= quantile(apistrat$meals, 0.8))
+@
+Generate first
+differences for the effect of high versus low
+concentrations of poverty on school performance:
+<<Replicate.sim>>=
+s.out3 <- sim(z.out3, x=x.high, x1=x.low)
+@
+<<Replicate.summary.sim>>=
+ summary(s.out3)
+@
+Generate a visual summary of quantities of interest:
+\begin{center}
+<<label=ReplicatePlot,fig=true,echo=true>>=
+ plot(s.out3)
+@
+\end{center}
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item The Gamma distribution with scale parameter $\alpha$ has a
+\emph{stochastic component}:
+\begin{eqnarray*}
+Y &\sim& \textrm{Gamma}(y_i \mid \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{Variance}
+
+When replicate weights are not used, the variance of the
+coefficients is estimated as
+\[
+\hat{\boldsymbol{\Sigma}} \left[
+ \sum_{i=1}^n
+\frac{(1-\pi_i)}{\pi_i^2}
+(\mathbf{X}_i(Y_i-\mu_i))^\prime(\mathbf{X}_i(Y_i-\mu_i)) + 2
+\sum_{i=1}^n \sum_{j=i+1}^n \frac{(\pi_{ij} - \pi_i\pi_j)}{\pi_i
+\pi_j \pi_{ij}}(\mathbf{X}_i(Y_i-\mu_i))^\prime
+(\mathbf{X}_j(Y_j-\mu_j)) \right] \hat{\boldsymbol{\Sigma}}
+\]
+where ${\pi_i}$ is the probability of case $i$ being sampled,
+$\mathbf{X}_i$ is a vector of the values of the explanatory
+variables for case $i$, $Y_i$ is value of the dependent variable for
+case $i$, $\hat{\mu}_i$ is the predicted value of the dependent
+variable for case $i$ based on the linear model estimates, and
+$\hat{\boldsymbol{\Sigma}}$ is the conventional variance-covariance
+matrix in a parametric glm. This statistic is derived from the
+method for estimating the variance of sums described in \cite{Bin83}
+and the Horvitz-Thompson estimator of the variance of a sum
+described in \cite{HorTho52}.
+
+When replicate weights are used, the model is re-estimated for each
+set of replicate weights, and the variance of each parameter is
+estimated by summing the squared deviations of the replicates from
+their mean.
+
+\subsubsection{Quantities of Interest}
+
+\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: $$E(Y) = \alpha \lambda_i.$$
+\item The predicted values ({\tt qi\$pr}) are draws from the gamma
+ distribution for each given set of parameters $(\alpha, \lambda_i)$.
+\item If {\tt x1} is specified, {\tt sim()} also returns the
+ differences in the expected values ({\tt qi\$fd}), $$E(Y \mid x_1) -
+ E(Y \mid x)$$.
+
+\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 = "gamma.survey", 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}: the vector of fitted values.
+ \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}
+
+\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 a
+ distribution defined by $(\alpha, \lambda_i)$.
+ \item {\tt qi\$fd}: the simulated first difference in the expected
+ values for the specified values 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}
+
+When users estimate {\tt gamma.survey} models with replicate weights in {\tt Zelig}, an
+object called {\tt .survey.prob.weights} is created in the global environment.
+{\tt Zelig} will over-write any existing object with that name, and users
+are therefore advised to re-name any object called {\tt .survey.prob.weights} before using {\tt gamma.survey} models in {\tt Zelig}.
+
+\subsection* {How to Cite}
+
+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}
+\input{citeZelig}
+
+\subsection* {See also}
+
+Survey-weighted linear models and the sample data used in the
+examples above are a part of the {\tt survey} package by Thomas
+Lumley. Users may wish to refer to the help files for the three
+functions that Zelig draws upon when estimating survey-weighted
+models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+help(svrepdesign)}. The Gamma 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}.
+
+
+
+
+
+\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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@@ -12,7 +12,7 @@
<a href="http://www.people.fas.harvard.edu/~olau/">Olivia Lau</a>
<br /><br />
<!-- rbuild: replace 'Version:' '</b>' version -->
-<b>Version:3.1-1</b>
+<b>Version:3.2-0</b>
<table border='0' cellpadding='0' cellspacing='8'><tr><td valign='top' width='200'>
<p !style="float:left; width: 200; margin: 1.5em;"> <a
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+\SweaveOpts{results=hide, prefix.string=vigpics/logitsurvey}
+\include{zinput}
+%\VignetteIndexEntry{Survey-Weighted Logistic Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, stats, survey}
+%\VignetteKeyWords{model,logistic,dichotomous, regression, survey}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+library(survey)
+@
+\section{{\tt logit.survey}: Survey-Weighted Logistic Regression for Dichotomous Dependent Variables}
+\label{logit.survey}
+
+The survey-weighted logistic regression model is appropriate for
+survey data obtained using complex sampling techniques, such as
+stratified random or cluster sampling (e.g., not simple random
+sampling). Like the conventional logistic regression models (see
+\Sref{logit}), survey-weighted logistic regression specifies a
+dichotomous dependent variable as function of a set of explanatory
+variables. The survey-weighted logit model reports estimates of
+model parameters identical to conventional logit estimates, but uses
+information from the survey design to correct variance estimates.
+
+The {\tt logit.survey} model accommodates three common types of
+complex survey data. Each method listed here requires selecting
+specific options which are detailed in the ``Additional Inputs''
+section below. \begin{enumerate}
+
+\item \textbf{Survey weights}: Survey data are often published along
+with weights for each observation. For example, if a survey
+intentionally over-samples a particular type of case, weights can be
+used to correct for the over-representation of that type of case in
+the dataset. Survey weights come in two forms:
+\begin{enumerate}
+
+\item \textit{Probability} weights report the probability that each
+case is drawn from the population. For each stratum or cluster,
+this is computed as the number of observations in the sample drawn
+from that group divided by the number of observations in the
+population in the group.
+
+\item \textit{Sampling} weights are the inverse of the probability
+weights.
+
+\end{enumerate}
+
+\item \textbf{Strata/cluster identification}: A complex survey
+dataset may include variables that identify the strata or cluster
+from which observations are drawn. For stratified random sampling
+designs, observations may be nested in different strata. There are
+two ways to employ these identifiers:
+
+\begin{enumerate}
+
+\item Use \textit{finite population corrections} to specify the
+total number of cases in the stratum or cluster from which each
+observation was drawn.
+
+\item For stratified random sampling designs, use the raw strata ids
+to compute sampling weights from the data.
+
+\end{enumerate}
+
+\item \textbf{Replication weights}: To preserve the anonymity of
+survey participants, some surveys exclude strata and cluster ids
+from the public data and instead release only pre-computed replicate
+weights.
+
+\end{enumerate}
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "logit.survey", 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 {\tt zelig} inputs (see
+\Sref{Zcommands}), survey-weighted logistic models accept the following
+optional inputs:
+\begin{enumerate}
+
+\item Datasets that include survey weights:.
+
+\begin{itemize}
+
+\item {\tt probs}: An optional formula or numerical vector specifying each
+case's probability weight, the probability that the case was
+selected. Probability weights need not (and, in most cases, will
+not) sum to one. Cases with lower probability weights are weighted
+more heavily in the computation of model coefficients.
+
+\item {\tt weights}: An optional numerical vector specifying each
+case's sample weight, the inverse of the probability that the case
+was selected. Sampling weights need not (and, in most cases, will
+not) sum to one. Cases with higher sampling weights are weighted
+more heavily in the computation of model coefficients.
+
+\end{itemize}
+
+\item Datasets that include strata/cluster identifiers:
+
+\begin{itemize}
+
+\item {\tt ids}: An optional formula or numerical vector identifying the
+cluster from which each observation was drawn (ordered from largest level to smallest level).
+For survey designs that do not involve cluster sampling, {\tt ids} defaults to {\tt NULL}.
+
+\item {\tt fpc}: An optional numerical vector identifying each
+case's frequency weight, the total number of units in the population
+from which each observation was sampled.
+
+\item {\tt strata}: An optional formula or vector identifying the
+stratum from which each observation was sampled. Entries may be
+numerical, logical, or strings. For survey designs that do not
+involve cluster sampling, {\tt strata} defaults to {\tt NULL}.
+
+\item {\tt nest}: An optional logical value specifying whether
+primary sampling unites (PSUs) have non-unique ids across multiple
+strata. {\tt nest=TRUE} is appropriate when PSUs reuse the same
+identifiers across strata. Otherwise, {\tt nest} defaults to {\tt
+FALSE}.
+
+\item {\tt check.strata}: An optional input specifying whether to
+check that clusters are nested in strata. If {\tt check.strata} is
+left at its default, {\tt !nest}, the check is not performed. If
+{\tt check.strata} is specified as {\tt TRUE}, the check is carried
+out.
+
+\end{itemize}
+
+\item Datasets that include replication weights:
+\begin{itemize}
+ \item {\tt repweights}: A formula or matrix specifying
+ replication weights, numerical vectors of weights used
+ in a process in which the sample is repeatedly re-weighted and parameters
+ are re-estimated in order to compute the variance of the model parameters.
+ \item {\tt type}: A string specifying the type of replication weights being used.
+ This input is required if replicate weights are specified. The following types
+ of replication weights are recognized: {\tt"BRR"}, {\tt "Fay"},
+ {\tt "JK1"}, {\tt "JKn"}, {\tt "bootstrap"}, or {\tt "other"}.
+ \item {\tt weights}: An optional vector or formula specifying each case's sample weight,
+ the inverse of the probability that the case was selected. If a survey includes both sampling
+ weights and replicate weights separately for the same survey, both should be included in
+ the model specification. In these cases, sampling weights are used to correct potential biases
+ in in the computation of coefficients and replication weights are used to compute the variance
+ of coefficient estimates.
+ \item {\tt combined.weights}: An optional logical value that
+ should be specified as {\tt TRUE} if the replicate weights include the sampling weights. Otherwise,
+ {\tt combined.weights} defaults to {\tt FALSE}.
+ \item {\tt rho}: An optional numerical value specifying a shrinkage factor
+ for replicate weights of type {\tt "Fay"}.
+ \item {\tt bootstrap.average}: An optional numerical input specifying
+ the number of iterations over which replicate weights of type {\tt "bootstrap"} were averaged.
+ This input should be left as {\tt NULL} for {\tt "bootstrap"} weights that were
+ not were created by averaging.
+\item {\tt scale}: When replicate weights are included,
+ the variance is computed as the sum of squared deviations of the replicates from their mean.
+ {\tt scale} is an optional overall multiplier for the standard deviations.
+\item {\tt rscale}: Like {\tt scale}, {\tt rscale} specifies an
+optional vector of replicate-specific multipliers for the squared
+deviations used in variance computation.
+
+\item {\tt fpc}: For models in which {\tt "JK1"}, {\tt "JKn"}, or
+{\tt "other"} replicates are specified, {\tt fpc} is an optional
+numerical vector (with one entry for each replicate) designating the
+replicates' finite population corrections.
+
+\item {\tt fpctype}: When a finite population correction is included
+as an {\tt fpc} input, {\tt fpctype} is a required input specifying
+whether the input to {\tt fpc} is a sampling fraction ({\tt
+fpctype="fraction"}) or a direct correction ({\tt
+fpctype="correction"}).
+
+\item {\tt return.replicates}: An optional logical value
+specifying whether the replicates should be returned as a component
+of the output. {\tt return.replicates} defaults to {\tt FALSE}.
+
+\end{itemize}
+
+\end{enumerate}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item A dataset that includes survey weights:
+
+Attach the sample data:
+<<Existing.data>>=
+data(api, package="survey")
+@
+
+Suppose that a dataset included a dichotomous indicator
+for whether each public school attends classes year round ({\tt yr.rnd}), a measure of
+the percentage of students at each school who receive subsidized
+meals ({\tt meals}), a measure of the percentage of students at
+each school who are new to to the school ({\tt mobility}), and sampling
+weights computed by the survey house ({\tt pw}). Estimate a model
+that regresses the year-round schooling indicator on the {\tt meals} and {\tt mobility}
+variables:
+<<Existing.zelig>>=
+z.out1 <- zelig(yr.rnd ~ meals + mobility, model = "logit.survey", weights=~pw, data = apistrat)
+@
+Summarize regression coefficients:
+<<Existing.summary>>=
+ summary(z.out1)
+@
+Set explanatory variables to their default (mean/mode) values, and
+set a high (80th percentile) and low (20th percentile) value for
+``meals'':
+<<Existing.setx>>=
+x.low <- setx(z.out1, meals=quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out1, meals=quantile(apistrat$meals, 0.8))
+@
+Generate first differences for the
+effect of high versus low concentrations of children receiving
+subsidized meals on the probability of holding school year-round:
+<<Existing.sim>>=
+ s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+@
+<<Existing.summary.sim>>=
+ summary(s.out1)
+@
+Generate a visual summary of the quantities of interest:
+\begin{center}
+<<label=ExistingPlot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item A dataset that includes strata/cluster identifiers:
+
+Suppose that the survey house that provided the dataset used in the
+previous example excluded sampling weights but made other details
+about the survey design available. A user can still estimate a model
+without sampling weights that instead uses inputs that identify the
+stratum and/or cluster to which each observation belongs and the
+size of the finite population from which each observation was drawn.
+
+Continuing the example above, suppose the survey house drew at
+random a fixed number of elementary schools, a fixed number of
+middle schools, and a fixed number of high schools. If the variable
+{\tt stype} is a vector of characters ({\tt "E"} for elementary
+schools, {\tt "M"} for middle schools, and {\tt "H"} for high schools)
+that identifies the type of school each case
+represents and {\tt fpc} is a numerical vector that identifies for
+each case the total number of schools of the same type in the
+population, then the user could estimate the following model:
+
+<<Complex.zelig>>=
+z.out2 <- zelig(yr.rnd ~ meals + mobility, model = "logit.survey", strata=~stype, fpc=~fpc, data = apistrat)
+@
+Summarize the regression output:
+<<Complex.output>>=
+summary(z.out2)
+@
+The coefficient estimates for this example are
+identical to the point estimates in the first example, when
+pre-existing sampling weights were used. When sampling weights are
+omitted, they are estimated automatically for {\tt "logit.survey"}
+models based on the user-defined description of sampling designs.
+
+Moreover, because the user provided information about the survey
+design, the standard error estimates are lower in this example than
+in the previous example, in which the user omitted variables pertaining
+to the details of the complex survey design.
+
+\item A dataset that includes replication weights:
+
+Consider a dataset that includes information for a sample of hospitals
+about the number of out-of-hospital cardiac arrests that each
+hospital treats and the number of patients who arrive alive
+at each hospital:
+<<Replicate.data>>=
+data(scd, package="survey")
+@
+Survey houses sometimes supply
+replicate weights (in lieu of details about the survey design). For the sake
+of illustrating how replicate weights can be used as inputs in {\tt
+logit.survey} models, create a set of balanced repeated replicate
+(BRR) weights and an (artificial) dependent variable to simulate an indicator
+for whether each hospital was sued:
+<<Replicate.rw>>=
+BRRrep<-2*cbind(c(1,0,1,0,1,0),c(1,0,0,1,0,1), c(0,1,1,0,0,1),c(0,1,0,1,1,0))
+scd$sued <- as.vector(c(0,0,0,1,1,1))
+@
+Estimate a model that regresses the indicator for hospitals that were
+sued on the number of patients who arrive alive in
+each hospital and the number of cardiac arrests that each hospital treats, using
+the BRR replicate weights in {\tt BRRrep} to compute standard errors.
+<<Replicate.zelig>>=
+z.out3 <- zelig(formula=sued ~ arrests + alive , model = "logit.survey",
+ repweights=BRRrep, type="BRR", data=scd)
+@
+Summarize the regression coefficients:
+<<Replicate.summary>>=
+ summary(z.out3)
+@
+Set {\tt alive} at its mean and set {\tt arrests} at its 20th and 80th quantiles:
+<<Replicate.setx>>=
+x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2))
+x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8))
+@
+Generate first
+differences for the effect of high versus low cardiac arrests
+on the probability that a hospital will be sued:
+<<Replicate.sim>>=
+s.out3 <- sim(z.out3, x=x.high, x1=x.low)
+@
+<<Replicate.summary.sim>>=
+ summary(s.out3)
+@
+Generate a visual summary of quantities of interest:
+\begin{center}
+<<label=ReplicatePlot,fig=true,echo=true>>=
+ plot(s.out3)
+@
+\end{center}
+
+
+\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}(y_i \mid \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{equation*}
+\pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
+\end{equation*}
+where $x_i$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Variance}
+
+When replicate weights are not used, the variance of the
+coefficients is estimated as
+\[
+\hat{\boldsymbol{\Sigma}} \left[
+ \sum_{i=1}^n
+\frac{(1-\pi_i)}{\pi_i^2}
+(\mathbf{X}_i(Y_i-\mu_i))^\prime(\mathbf{X}_i(Y_i-\mu_i)) + 2
+\sum_{i=1}^n \sum_{j=i+1}^n \frac{(\pi_{ij} - \pi_i\pi_j)}{\pi_i
+\pi_j \pi_{ij}}(\mathbf{X}_i(Y_i-\mu_i))^\prime
+(\mathbf{X}_j(Y_j-\mu_j)) \right] \hat{\boldsymbol{\Sigma}}
+\]
+where ${\pi_i}$ is the probability of case $i$ being sampled,
+$\mathbf{X}_i$ is a vector of the values of the explanatory
+variables for case $i$, $Y_i$ is value of the dependent variable for
+case $i$, $\hat{\mu}_i$ is the predicted value of the dependent
+variable for case $i$ based on the linear model estimates, and
+$\hat{\boldsymbol{\Sigma}}$ is the conventional variance-covariance
+matrix in a parametric glm. This statistic is derived from the
+method for estimating the variance of sums described in \cite{Bin83}
+and the Horvitz-Thompson estimator of the variance of a sum
+described in \cite{HorTho52}.
+
+When replicate weights are used, the model is re-estimated for each
+set of replicate weights, and the variance of each parameter is
+estimated by summing the squared deviations of the replicates from
+their mean.
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the survey-weighted logit model are
+ simulations of the predicted probability of a success: $$E(Y) =
+ \pi_i= \frac{1}{1 + \exp(-x_i \beta)},$$ 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 survey-weighted 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 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 = "logit.survey", 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}: 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 data}: the name of the input data frame.
+ \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 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 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.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+When users estimate {\tt logit.survey} models with replicate weights in {\tt Zelig}, an
+object called {\tt .survey.prob.weights} is created in the global environment.
+{\tt Zelig} will over-write any existing object with that name, and users
+are therefore advised to re-name any object called {\tt .survey.prob.weights} before using {\tt logit.survey} models in {\tt Zelig}.
+
+\subsection* {How to Cite}
+
+To cite the \emph{logit.survey} Zelig model:
+\begin{verse}
+Nicholas Carnes. 2008. ``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}
+\input{citeZelig}
+
+\subsection* {See also}
+
+Survey-weighted linear models and the sample data used in the
+examples above are a part of the {\tt survey} package by Thomas
+Lumley. Users may wish to refer to the help files for the three
+functions that Zelig draws upon when estimating survey-weighted
+models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+help(svrepdesign)}. The logit 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}.
+
+
+
+
+
+\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/normal}
+\include{zinput}
+%\VignetteIndexEntry{Survey-Weighted Normal Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats, survey}
+%\VignetteKeyWords{model, normal, regression, continuous, least squares, survey-weight}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+library(survey)
+@
+\section{{\tt normal.survey}: Survey-Weighted Normal Regression for Continuous Dependent Variables}
+\label{normal.survey}
+
+The survey-weighted Normal regression model is appropriate for
+survey data obtained using complex sampling techniques, such as
+stratified random or cluster sampling (e.g., not simple random
+sampling). Like the least squares and Normal regression models (see
+\Sref{ls} and \Sref{normal}), survey-weighted Normal regression
+specifies a continuous dependent variable as a linear function of a
+set of explanatory variables. The survey-weighted normal model
+reports estimates of model parameters identical to least squares or
+Normal regression estimates, but uses information from the survey
+design to correct variance estimates.
+
+The {\tt normal.survey} model accommodates three common types of
+complex survey data. Each method listed here requires selecting
+specific options which are detailed in the ``Additional Inputs''
+section below. \begin{enumerate}
+
+\item \textbf{Survey weights}: Survey data are often published along
+with weights for each observation. For example, if a survey
+intentionally over-samples a particular type of case, weights can be
+used to correct for the over-representation of that type of case in
+the dataset. Survey weights come in two forms:
+\begin{enumerate}
+
+\item \textit{Probability} weights report the probability that each
+case is drawn from the population. For each stratum or cluster,
+this is computed as the number of observations in the sample drawn
+from that group divided by the number of observations in the
+population in the group.
+
+\item \textit{Sampling} weights are the inverse of the probability
+weights.
+
+\end{enumerate}
+
+\item \textbf{Strata/cluster identification}: A complex survey
+dataset may include variables that identify the strata or cluster
+from which observations are drawn. For stratified random sampling
+designs, observations may be nested in different strata. There are
+two ways to employ these identifiers:
+
+\begin{enumerate}
+
+\item Use \textit{finite population corrections} to specify the
+total number of cases in the stratum or cluster from which each
+observation was drawn.
+
+\item For stratified random sampling designs, use the raw strata ids
+to compute sampling weights from the data.
+
+\end{enumerate}
+
+\item \textbf{Replication weights}: To preserve the anonymity of
+survey participants, some surveys exclude strata and cluster ids
+from the public data and instead release only pre-computed replicate
+weights.
+
+\end{enumerate}
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "normal.survey", 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 {\tt zelig} inputs (see
+\Sref{Zcommands}), survey-weighted Normal models accept the following
+optional inputs:
+\begin{enumerate}
+
+\item Datasets that include survey weights:
+
+\begin{itemize}
+
+\item {\tt probs}: An optional formula or numerical vector specifying each
+case's probability weight, the probability that the case was
+selected. Probability weights need not (and, in most cases, will
+not) sum to one. Cases with lower probability weights are weighted
+more heavily in the computation of model coefficients.
+
+\item {\tt weights}: An optional numerical vector specifying each
+case's sample weight, the inverse of the probability that the case
+was selected. Sampling weights need not (and, in most cases, will
+not) sum to one. Cases with higher sampling weights are weighted
+more heavily in the computation of model coefficients.
+
+\end{itemize}
+
+\item Datasets that include strata/cluster identifiers:
+
+\begin{itemize}
+
+\item {\tt ids}: An optional formula or numerical vector identifying the
+cluster from which each observation was drawn (ordered from largest level to smallest level).
+For survey designs that do not involve cluster sampling, {\tt ids} defaults to {\tt NULL}.
+
+\item {\tt fpc}: An optional numerical vector identifying each
+case's frequency weight, the total number of units in the population
+from which each observation was sampled.
+
+\item {\tt strata}: An optional formula or vector identifying the
+stratum from which each observation was sampled. Entries may be
+numerical, logical, or strings. For survey designs that do not
+involve cluster sampling, {\tt strata} defaults to {\tt NULL}.
+
+\item {\tt nest}: An optional logical value specifying whether
+primary sampling unites (PSUs) have non-unique ids across multiple
+strata. {\tt nest=TRUE} is appropriate when PSUs reuse the same
+identifiers across strata. Otherwise, {\tt nest} defaults to {\tt
+FALSE}.
+
+\item {\tt check.strata}: An optional input specifying whether to
+check that clusters are nested in strata. If {\tt check.strata} is
+left at its default, {\tt !nest}, the check is not performed. If
+{\tt check.strata} is specified as {\tt TRUE}, the check is carried
+out.
+
+\end{itemize}
+
+\item Datasets that include replication weights:
+\begin{itemize}
+ \item {\tt repweights}: A formula or matrix specifying
+ replication weights, numerical vectors of weights used
+ in a process in which the sample is repeatedly re-weighted and parameters
+ are re-estimated in order to compute the variance of the model parameters.
+ \item {\tt type}: A string specifying the type of replication weights being used.
+ This input is required if replicate weights are specified. The following types
+ of replication weights are recognized: {\tt"BRR"}, {\tt "Fay"},
+ {\tt "JK1"}, {\tt "JKn"}, {\tt "bootstrap"}, or {\tt "other"}.
+ \item {\tt weights}: An optional vector or formula specifying each case's sample weight,
+ the inverse of the probability that the case was selected. If a survey includes both sampling
+ weights and replicate weights separately for the same survey, both should be included in
+ the model specification. In these cases, sampling weights are used to correct potential biases
+ in in the computation of coefficients and replication weights are used to compute the variance
+ of coefficient estimates.
+ \item {\tt combined.weights}: An optional logical value that
+ should be specified as {\tt TRUE} if the replicate weights include the sampling weights. Otherwise,
+ {\tt combined.weights} defaults to {\tt FALSE}.
+ \item {\tt rho}: An optional numerical value specifying a shrinkage factor
+ for replicate weights of type {\tt "Fay"}.
+ \item {\tt bootstrap.average}: An optional numerical input specifying
+ the number of iterations over which replicate weights of type {\tt "bootstrap"} were averaged.
+ This input should be left as {\tt NULL} for {\tt "bootstrap"} weights that were
+ not were created by averaging.
+\item {\tt scale}: When replicate weights are included,
+ the variance is computed as the sum of squared deviations of the replicates from their mean.
+ {\tt scale} is an optional overall multiplier for the standard deviations.
+\item {\tt rscale}: Like {\tt scale}, {\tt rscale} specifies an
+optional vector of replicate-specific multipliers for the squared
+deviations used in variance computation.
+
+\item {\tt fpc}: For models in which {\tt "JK1"}, {\tt "JKn"}, or
+{\tt "other"} replicates are specified, {\tt fpc} is an optional
+numerical vector (with one entry for each replicate) designating the
+replicates' finite population corrections.
+
+\item {\tt fpctype}: When a finite population correction is included
+as an {\tt fpc} input, {\tt fpctype} is a required input specifying
+whether the input to {\tt fpc} is a sampling fraction ({\tt
+fpctype="fraction"}) or a direct correction ({\tt
+fpctype="correction"}).
+
+\item {\tt return.replicates}: An optional logical value
+specifying whether the replicates should be returned as a component
+of the output. {\tt return.replicates} defaults to {\tt FALSE}.
+
+\end{itemize}
+
+\end{enumerate}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item A dataset that includes survey weights:
+
+Attach the sample data:
+<<Existing.data>>=
+data(api, package="survey")
+@
+
+Suppose that a dataset included a continuous measure of
+public schools' performance ({\tt api00}), a measure of
+the percentage of students at each school who receive subsidized
+meals ({\tt meals}), an indicator for whether each school
+holds classes year round ({\tt year.rnd}), and sampling
+weights computed by the survey house ({\tt pw}). Estimate a model
+that regresses school performance on the {\tt meals} and {\tt year.rnd}
+variables:
+<<Existing.zelig>>=
+z.out1 <- zelig(api00 ~ meals + yr.rnd, model = "normal.survey", weights=~pw,
+data = apistrat)
+@
+Summarize regression coefficients:
+<<Existing.summary>>=
+ summary(z.out1)
+@
+Set explanatory variables to their default (mean/mode) values, and
+set a high (80th percentile) and low (20th percentile) value for
+``meals'':
+<<Existing.setx>>=
+x.low <- setx(z.out1, meals=quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out1, meals=quantile(apistrat$meals, 0.8))
+@
+Generate first differences for the
+effect of high versus low concentrations of children receiving
+subsidized meals on academic performance:
+<<Existing.sim>>=
+ s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+@
+<<Existing.summary.sim>>=
+ summary(s.out1)
+@
+Generate a visual summary of the quantities of interest:
+\begin{center}
+<<label=ExistingPlot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item A dataset that includes strata/cluster identifiers:
+
+Suppose that the survey house that provided the dataset used in the
+previous example excluded sampling weights but made other details
+about the survey design available. A user can still estimate a model
+without sampling weights that instead uses inputs that identify the
+stratum and/or cluster to which each observation belongs and the
+size of the finite population from which each observation was drawn.
+
+Continuing the example above, suppose the survey house drew at
+random a fixed number of elementary schools, a fixed number of
+middle schools, and a fixed number of high schools. If the variable
+{\tt stype} is a vector of characters ({\tt "E"} for elementary
+schools, {\tt "M"} for middle schools, and {\tt "H"} for high schools)
+that identifies the type of school each case
+represents and {\tt fpc} is a numerical vector that identifies for
+each case the total number of schools of the same type in the
+population, then the user could estimate the following model:
+
+<<Complex.zelig>>=
+z.out2 <- zelig(api00 ~ meals + yr.rnd,
+model = "normal.survey", strata=~stype, fpc=~fpc, data = apistrat)
+@
+Summarize the regression output:
+<<Complex.output>>=
+summary(z.out2)
+@
+The coefficient estimates for this example are
+identical to the point estimates in the first example, when
+pre-existing sampling weights were used. When sampling weights are
+omitted, they are estimated automatically for {\tt "normal.survey"}
+models based on the user-defined description of sampling designs.
+
+Moreover, because the user provided information about the survey
+design, the standard error estimates are lower in this example than
+in the previous example, in which the user omitted variables pertaining
+to the details of the complex survey design.
+
+\item A dataset that includes replication weights:
+
+Consider a dataset that includes information for a sample of hospitals
+that includes counts of the number of out-of-hospital cardiac arrests that each
+hospital treats and the number of patients who arrive alive
+at each hospital:
+<<Replicate.data>>=
+data(scd, package="survey")
+@
+Survey houses sometimes supply
+replicate weights (in lieu of details about the survey design). For the sake
+of illustrating how replicate weights can be used as inputs in {\tt
+normal.survey} models, create a set of balanced repeated replicate
+(BRR) weights:
+<<Replicate.rw>>=
+BRRrep<-2*cbind(c(1,0,1,0,1,0),c(1,0,0,1,0,1), c(0,1,1,0,0,1),c(0,1,0,1,1,0))
+@
+Estimate a model that regresses counts of patients who arrive alive in
+each hospital on the number of cardiac arrests that each hospital treats, using
+the BRR replicate weights in {\tt BRRrep} to compute standard errors.
+<<Replicate.zelig>>=
+z.out3 <- zelig(alive ~ arrests , model = "poisson.survey",
+ repweights=BRRrep, type="BRR", data=scd)
+@
+Summarize the regression coefficients:
+<<Replicate.summary>>=
+ summary(z.out3)
+@
+Set {\tt arrests} at its 20th and 80th quantiles:
+<<Replicate.setx>>=
+x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2))
+x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8))
+@
+Generate first
+differences for the effect of minimal versus maximal cardiac arrests
+on numbers of patients who arrive alive:
+<<Replicate.sim>>=
+s.out3<- sim(z.out3, x=x.low, x1=x.high)
+@
+<<Replicate.summary.sim>>=
+ summary(s.out3)
+@
+Generate a visual summary of quantities of interest:
+\begin{center}
+<<label=ReplicatePlot,fig=true,echo=true>>=
+ plot(s.out3)
+@
+\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{Variance}
+
+When replicate weights are not used, the variance of the
+coefficients is estimated as
+\[
+\hat{\boldsymbol{\Sigma}} \left[
+ \sum_{i=1}^n
+\frac{(1-\pi_i)}{\pi_i^2}
+(\mathbf{X}_i(Y_i-\mu_i))^\prime(\mathbf{X}_i(Y_i-\mu_i)) + 2
+\sum_{i=1}^n \sum_{j=i+1}^n \frac{(\pi_{ij} - \pi_i\pi_j)}{\pi_i
+\pi_j \pi_{ij}}(\mathbf{X}_i(Y_i-\mu_i))^\prime
+(\mathbf{X}_j(Y_j-\mu_j)) \right] \hat{\boldsymbol{\Sigma}}
+\]
+where ${\pi_i}$ is the probability of case $i$ being sampled,
+$\mathbf{X}_i$ is a vector of the values of the explanatory
+variables for case $i$, $Y_i$ is value of the dependent variable for
+case $i$, $\hat{\mu}_i$ is the predicted value of the dependent
+variable for case $i$ based on the linear model estimates, and
+$\hat{\boldsymbol{\Sigma}}$ is the conventional variance-covariance
+matrix in a parametric glm. This statistic is derived from the
+method for estimating the variance of sums described in \cite{Bin83}
+and the Horvitz-Thompson estimator of the variance of a sum
+described in \cite{HorTho52}.
+
+When replicate weights are used, the model is re-estimated for each
+set of replicate weights, and the variance of each parameter is
+estimated by summing the squared deviations of the replicates from
+their mean.
+
+\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.survey", 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 survey-weighted normal model,
+ these are identical to the {\tt linear predictors}.
+ \item {\tt linear.predictors}: fitted values. For the survey-weighted 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}
+
+When users estimate {\tt normal.survey} models with replicate weights in {\tt Zelig}, an
+object called {\tt .survey.prob.weights} is created in the global environment.
+{\tt Zelig} will over-write any existing object with that name, and users
+are therefore advised to re-name any object called {\tt .survey.prob.weights} before using {\tt normal.survey} models in {\tt Zelig}.
+
+\subsection* {How to Cite}
+
+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}
+\input{citeZelig}
+
+\subsection* {See also}
+
+Survey-weighted linear models and the sample data used in the
+examples above are a part of the {\tt survey} package by Thomas
+Lumley. Users may wish to refer to the help files for the three
+functions that Zelig draws upon when estimating survey-weighted
+models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+help(svrepdesign)}. 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}.
+
+
+
+
+
+\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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@@ -0,0 +1,522 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/poissonsurvey}
+\include{zinput}
+%\VignetteIndexEntry{Survey-Weighted Poisson Regression for Event Count Dependent Variables}
+%\VignetteDepends{Zelig, stats, survey}
+%\VignetteKeyWords{model,poisson,event,regression, survey}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+library(survey)
+@
+\section{{\tt poisson.survey}: Survey-Weighted Poisson Regression for Event Count Dependent Variables}
+\label{poisson.survey}
+
+The survey-weighted poisson regression model is appropriate for
+survey data obtained using complex sampling techniques, such as
+stratified random or cluster sampling (e.g., not simple random
+sampling). Like the conventional poisson regression model (see
+\Sref{poisson}), survey-weighted poisson regression specifies a
+dependent variable representing the number of independent events
+that occur during a fixed period of time as function of a set of
+explanatory variables. The survey-weighted poisson model reports
+estimates of model parameters identical to conventional poisson
+estimates, but uses information from the survey design to correct
+variance estimates.
+
+The {\tt poisson.survey} model accommodates three common types of
+complex survey data. Each method listed here requires selecting
+specific options which are detailed in the ``Additional Inputs''
+section below. \begin{enumerate}
+
+\item \textbf{Survey weights}: Survey data are often published along
+with weights for each observation. For example, if a survey
+intentionally over-samples a particular type of case, weights can be
+used to correct for the over-representation of that type of case in
+the dataset. Survey weights come in two forms:
+\begin{enumerate}
+
+\item \textit{Probability} weights report the probability that each
+case is drawn from the population. For each stratum or cluster,
+this is computed as the number of observations in the sample drawn
+from that group divided by the number of observations in the
+population in the group.
+
+\item \textit{Sampling} weights are the inverse of the probability
+weights.
+
+\end{enumerate}
+
+\item \textbf{Strata/cluster identification}: A complex survey
+dataset may include variables that identify the strata or cluster
+from which observations are drawn. For stratified random sampling
+designs, observations may be nested in different strata. There are
+two ways to employ these identifiers:
+
+\begin{enumerate}
+
+\item Use \textit{finite population corrections} to specify the
+total number of cases in the stratum or cluster from which each
+observation was drawn.
+
+\item For stratified random sampling designs, use the raw strata ids
+to compute sampling weights from the data.
+
+\end{enumerate}
+
+\item \textbf{Replication weights}: To preserve the anonymity of
+survey participants, some surveys exclude strata and cluster ids
+from the public data and instead release only pre-computed replicate
+weights.
+
+\end{enumerate}
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "poisson.survey", 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 {\tt zelig} inputs (see
+\Sref{Zcommands}), survey-weighted poisson models accept the following
+optional inputs:
+\begin{enumerate}
+
+\item Datasets that include survey weights:.
+
+\begin{itemize}
+
+\item {\tt probs}: An optional formula or numerical vector specifying each
+case's probability weight, the probability that the case was
+selected. Probability weights need not (and, in most cases, will
+not) sum to one. Cases with lower probability weights are weighted
+more heavily in the computation of model coefficients.
+
+\item {\tt weights}: An optional numerical vector specifying each
+case's sample weight, the inverse of the probability that the case
+was selected. Sampling weights need not (and, in most cases, will
+not) sum to one. Cases with higher sampling weights are weighted
+more heavily in the computation of model coefficients.
+
+\end{itemize}
+
+\item Datasets that include strata/cluster identifiers:
+
+\begin{itemize}
+
+\item {\tt ids}: An optional formula or numerical vector identifying the
+cluster from which each observation was drawn (ordered from largest level to smallest level).
+For survey designs that do not involve cluster sampling, {\tt ids} defaults to {\tt NULL}.
+
+\item {\tt fpc}: An optional numerical vector identifying each
+case's frequency weight, the total number of units in the population
+from which each observation was sampled.
+
+\item {\tt strata}: An optional formula or vector identifying the
+stratum from which each observation was sampled. Entries may be
+numerical, logical, or strings. For survey designs that do not
+involve cluster sampling, {\tt strata} defaults to {\tt NULL}.
+
+\item {\tt nest}: An optional logical value specifying whether
+primary sampling unites (PSUs) have non-unique ids across multiple
+strata. {\tt nest=TRUE} is appropriate when PSUs reuse the same
+identifiers across strata. Otherwise, {\tt nest} defaults to {\tt
+FALSE}.
+
+\item {\tt check.strata}: An optional input specifying whether to
+check that clusters are nested in strata. If {\tt check.strata} is
+left at its default, {\tt !nest}, the check is not performed. If
+{\tt check.strata} is specified as {\tt TRUE}, the check is carried
+out.
+
+\end{itemize}
+
+\item Datasets that include replication weights:
+\begin{itemize}
+ \item {\tt repweights}: A formula or matrix specifying
+ replication weights, numerical vectors of weights used
+ in a process in which the sample is repeatedly re-weighted and parameters
+ are re-estimated in order to compute the variance of the model parameters.
+ \item {\tt type}: A string specifying the type of replication weights being used.
+ This input is required if replicate weights are specified. The following types
+ of replication weights are recognized: {\tt"BRR"}, {\tt "Fay"},
+ {\tt "JK1"}, {\tt "JKn"}, {\tt "bootstrap"}, or {\tt "other"}.
+ \item {\tt weights}: An optional vector or formula specifying each case's sample weight,
+ the inverse of the probability that the case was selected. If a survey includes both sampling
+ weights and replicate weights separately for the same survey, both should be included in
+ the model specification. In these cases, sampling weights are used to correct potential biases
+ in in the computation of coefficients and replication weights are used to compute the variance
+ of coefficient estimates.
+ \item {\tt combined.weights}: An optional logical value that
+ should be specified as {\tt TRUE} if the replicate weights include the sampling weights. Otherwise,
+ {\tt combined.weights} defaults to {\tt FALSE}.
+ \item {\tt rho}: An optional numerical value specifying a shrinkage factor
+ for replicate weights of type {\tt "Fay"}.
+ \item {\tt bootstrap.average}: An optional numerical input specifying
+ the number of iterations over which replicate weights of type {\tt "bootstrap"} were averaged.
+ This input should be left as {\tt NULL} for {\tt "bootstrap"} weights that were
+ not were created by averaging.
+\item {\tt scale}: When replicate weights are included,
+ the variance is computed as the sum of squared deviations of the replicates from their mean.
+ {\tt scale} is an optional overall multiplier for the standard deviations.
+\item {\tt rscale}: Like {\tt scale}, {\tt rscale} specifies an
+optional vector of replicate-specific multipliers for the squared
+deviations used in variance computation.
+
+\item {\tt fpc}: For models in which {\tt "JK1"}, {\tt "JKn"}, or
+{\tt "other"} replicates are specified, {\tt fpc} is an optional
+numerical vector (with one entry for each replicate) designating the
+replicates' finite population corrections.
+
+\item {\tt fpctype}: When a finite population correction is included
+as an {\tt fpc} input, {\tt fpctype} is a required input specifying
+whether the input to {\tt fpc} is a sampling fraction ({\tt
+fpctype="fraction"}) or a direct correction ({\tt
+fpctype="correction"}).
+
+\item {\tt return.replicates}: An optional logical value
+specifying whether the replicates should be returned as a component
+of the output. {\tt return.replicates} defaults to {\tt FALSE}.
+
+\end{itemize}
+
+\end{enumerate}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item A dataset that includes survey weights:
+
+Attach the sample data:
+<<Existing.data>>=
+data(api, package="survey")
+@
+
+Suppose that a dataset included a variable reporting the number of
+times a new student enrolled during the previous school year ({\tt enroll}),
+a measure of each school's academic performance ({\tt api99}),
+an indicator for whether each school holds classes year round ({\tt year.rnd}), and sampling
+weights computed by the survey house ({\tt pw}). Estimate a model
+that regresses {\tt enroll} on {\tt api99} and {\tt year.rnd}:
+<<Existing.zelig>>=
+z.out1 <- zelig(enroll ~ api99 + yr.rnd , model = "poisson.survey",
+weights=~pw, data = apistrat)
+@
+Summarize regression coefficients:
+<<Existing.summary>>=
+ summary(z.out1)
+@
+Set explanatory variables to their default (mean/mode) values, and
+set a high (80th percentile) and low (20th percentile) value for
+the measure of academic performance:
+<<Existing.setx>>=
+x.low <- setx(z.out1, api99= quantile(apistrat$api99, 0.2))
+x.high <- setx(z.out1, api99= quantile(apistrat$api99, 0.8))
+@
+Generate first differences for the
+effect of high versus low concentrations of children receiving
+subsidized meals on the probability of holding school year-round:
+<<Existing.sim>>=
+s.out1 <- sim(z.out1, x=x.low, x1=x.high)
+@
+<<Existing.summary.sim>>=
+ summary(s.out1)
+@
+Generate a visual summary of the quantities of interest:
+\begin{center}
+<<label=ExistingPlot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item A dataset that includes strata/cluster identifiers:
+
+Suppose that the survey house that provided the dataset used in the
+previous example excluded sampling weights but made other details
+about the survey design available. A user can still estimate a model
+without sampling weights that instead uses inputs that identify the
+stratum and/or cluster to which each observation belongs and the
+size of the finite population from which each observation was drawn.
+
+Continuing the example above, suppose the survey house drew at
+random a fixed number of elementary schools, a fixed number of
+middle schools, and a fixed number of high schools. If the variable
+{\tt stype} is a vector of characters ({\tt "E"} for elementary
+schools, {\tt "M"} for middle schools, and {\tt "H"} for high schools)
+that identifies the type of school each case
+represents and {\tt fpc} is a numerical vector that identifies for
+each case the total number of schools of the same type in the
+population, then the user could estimate the following model:
+
+<<Complex.zelig>>=
+z.out2 <- zelig(enroll ~ api99 + yr.rnd , model = "poisson.survey", data = apistrat,
+ strata=~stype, fpc=~fpc)
+@
+Summarize the regression output:
+<<Complex.output>>=
+summary(z.out2)
+@
+The coefficient estimates for this example are
+identical to the point estimates in the first example, when
+pre-existing sampling weights were used. When sampling weights are
+omitted, they are estimated automatically for {\tt "poisson.survey"}
+models based on the user-defined description of sampling designs.
+
+Moreover, because the user provided information about the survey
+design, the standard error estimates are lower in this example than
+in the previous example, in which the user omitted variables pertaining
+to the details of the complex survey design.
+
+\item A dataset that includes replication weights:
+
+Consider a dataset that includes information for a sample of hospitals
+about the number of out-of-hospital cardiac arrests that each
+hospital treats and the number of patients who arrive alive
+at each hospital:
+<<Replicate.data>>=
+data(scd, package="survey")
+@
+
+Survey houses sometimes supply
+replicate weights (in lieu of details about the survey design). For the sake
+of illustrating how replicate weights can be used as inputs in {\tt
+normal.survey} models, create a set of balanced repeated replicate
+(BRR) weights:
+<<Replicate.rw>>=
+BRRrep<-2*cbind(c(1,0,1,0,1,0),c(1,0,0,1,0,1), c(0,1,1,0,0,1),c(0,1,0,1,1,0))
+@
+Estimate a model that regresses the count of patients who arrived alive at
+the hospital last year on the number of patients treated for cardiac arrests, using
+the BRR replicate weights in {\tt BRRrep} to compute standard errors:
+<<Replicate.zelig>>=
+z.out3 <- zelig(alive ~ arrests , model = "poisson.survey",
+repweights=BRRrep, type="BRR", data=scd)
+summary(z.out3)
+@
+Summarize the regression coefficients:
+<<Replicate.summary>>=
+ summary(z.out3)
+@
+Set the explanatory variable {\tt arrests} at its 20th and 80th quantiles:
+<<Replicate.setx>>=
+x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2))
+x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8))
+@
+Generate first
+differences for the effect of high versus low cardiac arrests
+on the count of patients who arrive alive:
+<<Replicate.sim>>=
+s.out3 <- sim(z.out3, x=x.high, x1=x.low)
+@
+<<Replicate.summary.sim>>=
+ summary(s.out3)
+@
+Generate a visual summary of quantities of interest:
+\begin{center}
+<<label=ReplicatePlot,fig=true,echo=true>>=
+ plot(s.out3)
+@
+\end{center}
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be 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 \; \textrm{Poisson}(\lambda_i),
+ \end{equation*}
+ where $\lambda_i$ is the mean and variance parameter.
+
+\item The \emph{systematic component} is
+ \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{Variance}
+
+When replicate weights are not used, the variance of the
+coefficients is estimated as
+\[
+\hat{\boldsymbol{\Sigma}} \left[
+ \sum_{i=1}^n
+\frac{(1-\pi_i)}{\pi_i^2}
+(\mathbf{X}_i(Y_i-\mu_i))^\prime(\mathbf{X}_i(Y_i-\mu_i)) + 2
+\sum_{i=1}^n \sum_{j=i+1}^n \frac{(\pi_{ij} - \pi_i\pi_j)}{\pi_i
+\pi_j \pi_{ij}}(\mathbf{X}_i(Y_i-\mu_i))^\prime
+(\mathbf{X}_j(Y_j-\mu_j)) \right] \hat{\boldsymbol{\Sigma}}
+\]
+where ${\pi_i}$ is the probability of case $i$ being sampled,
+$\mathbf{X}_i$ is a vector of the values of the explanatory
+variables for case $i$, $Y_i$ is value of the dependent variable for
+case $i$, $\hat{\mu}_i$ is the predicted value of the dependent
+variable for case $i$ based on the linear model estimates, and
+$\hat{\boldsymbol{\Sigma}}$ is the conventional variance-covariance
+matrix in a parametric glm. This statistic is derived from the
+method for estimating the variance of sums described in \cite{Bin83}
+and the Horvitz-Thompson estimator of the variance of a sum
+described in \cite{HorTho52}.
+
+When replicate weights are used, the model is re-estimated for each
+set of replicate weights, and the variance of each parameter is
+estimated by summing the squared deviations of the replicates from
+their mean.
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the stochastic component, $$E(Y) = \lambda_i = \exp(x_i
+ \beta),$$ given draws of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is a random draw from the
+ poisson distribution defined by mean $\lambda_i$.
+
+\item The first difference in the expected values ({\tt qi\$fd}) is given by:
+\begin{equation*}
+\textrm{FD} \; = \; E(Y | 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 = "poisson.survey", 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}: a vector of the fitted values for the systemic
+ component $\lambda$.
+ \item {\tt linear.predictors}: a 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}
+
+\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 given the
+ specified values of {\tt x}.
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distributions defined by $\lambda_i$.
+ \item {\tt qi\$fd}: the simulated first differences in the expected
+ values given 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.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+When users estimate {\tt poisson.survey} models with replicate weights in {\tt Zelig}, an
+object called {\tt .survey.prob.weights} is created in the global environment.
+{\tt Zelig} will over-write any existing object with that name, and users
+are therefore advised to re-name any object called {\tt .survey.prob.weights} before using {\tt poisson.survey} models in {\tt Zelig}.
+
+
+\subsection* {How to Cite}
+
+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}
+\input{citeZelig}
+
+\subsection* {See also}
+
+Survey-weighted linear models and the sample data used in the
+examples above are a part of the {\tt survey} package by Thomas
+Lumley. Users may wish to refer to the help files for the three
+functions that Zelig draws upon when estimating survey-weighted
+models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+help(svrepdesign)}. The poisson 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}.
+
+
+
+
+
+\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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@@ -0,0 +1,530 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/probitsurvey}
+\include{zinput}
+%\VignetteIndexEntry{Survey-Weighted Probit Regression for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, stats, survey}
+%\VignetteKeyWords{model,probit ,dichotomous, regression, survey}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+library(survey)
+@
+\section{{\tt probit.survey}: Survey-Weighted Probit Regression for Dichotomous Dependent Variables}
+\label{probit.survey}
+
+The survey-weighted probit regression model is appropriate for
+survey data obtained using complex sampling techniques, such as
+stratified random or cluster sampling (e.g., not simple random
+sampling). Like the conventional probit regression models (see
+\Sref{probit}), survey-weighted probit regression specifies a
+dichotomous dependent variable as function of a set of explanatory
+variables. The survey-weighted probit model reports estimates of
+model parameters identical to conventional probit estimates, but uses
+information from the survey design to correct variance estimates.
+
+The {\tt probit.survey} model accommodates three common types of
+complex survey data. Each method listed here requires selecting
+specific options which are detailed in the ``Additional Inputs''
+section below. \begin{enumerate}
+
+\item \textbf{Survey weights}: Survey data are often published along
+with weights for each observation. For example, if a survey
+intentionally over-samples a particular type of case, weights can be
+used to correct for the over-representation of that type of case in
+the dataset. Survey weights come in two forms:
+\begin{enumerate}
+
+\item \textit{Probability} weights report the probability that each
+case is drawn from the population. For each stratum or cluster,
+this is computed as the number of observations in the sample drawn
+from that group divided by the number of observations in the
+population in the group.
+
+\item \textit{Sampling} weights are the inverse of the probability
+weights.
+
+\end{enumerate}
+
+\item \textbf{Strata/cluster identification}: A complex survey
+dataset may include variables that identify the strata or cluster
+from which observations are drawn. For stratified random sampling
+designs, observations may be nested in different strata. There are
+two ways to employ these identifiers:
+
+\begin{enumerate}
+
+\item Use \textit{finite population corrections} to specify the
+total number of cases in the stratum or cluster from which each
+observation was drawn.
+
+\item For stratified random sampling designs, use the raw strata ids
+to compute sampling weights from the data.
+
+\end{enumerate}
+
+\item \textbf{Replication weights}: To preserve the anonymity of
+survey participants, some surveys exclude strata and cluster ids
+from the public data and instead release only pre-computed replicate
+weights.
+
+\end{enumerate}
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "probit.survey", 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 {\tt zelig} inputs (see
+\Sref{Zcommands}), survey-weighted probit models accept the following
+optional inputs:
+\begin{enumerate}
+
+\item Datasets that include survey weights:.
+
+\begin{itemize}
+
+\item {\tt probs}: An optional formula or numerical vector specifying each
+case's probability weight, the probability that the case was
+selected. Probability weights need not (and, in most cases, will
+not) sum to one. Cases with lower probability weights are weighted
+more heavily in the computation of model coefficients.
+
+\item {\tt weights}: An optional numerical vector specifying each
+case's sample weight, the inverse of the probability that the case
+was selected. Sampling weights need not (and, in most cases, will
+not) sum to one. Cases with higher sampling weights are weighted
+more heavily in the computation of model coefficients.
+
+\end{itemize}
+
+\item Datasets that include strata/cluster identifiers:
+
+\begin{itemize}
+
+\item {\tt ids}: An optional formula or numerical vector identifying the
+cluster from which each observation was drawn (ordered from largest level to smallest level).
+For survey designs that do not involve cluster sampling, {\tt ids} defaults to {\tt NULL}.
+
+\item {\tt fpc}: An optional numerical vector identifying each
+case's frequency weight, the total number of units in the population
+from which each observation was sampled.
+
+\item {\tt strata}: An optional formula or vector identifying the
+stratum from which each observation was sampled. Entries may be
+numerical, logical, or strings. For survey designs that do not
+involve cluster sampling, {\tt strata} defaults to {\tt NULL}.
+
+\item {\tt nest}: An optional logical value specifying whether
+primary sampling unites (PSUs) have non-unique ids across multiple
+strata. {\tt nest=TRUE} is appropriate when PSUs reuse the same
+identifiers across strata. Otherwise, {\tt nest} defaults to {\tt
+FALSE}.
+
+\item {\tt check.strata}: An optional input specifying whether to
+check that clusters are nested in strata. If {\tt check.strata} is
+left at its default, {\tt !nest}, the check is not performed. If
+{\tt check.strata} is specified as {\tt TRUE}, the check is carried
+out.
+
+\end{itemize}
+
+\item Datasets that include replication weights:
+\begin{itemize}
+ \item {\tt repweights}: A formula or matrix specifying
+ replication weights, numerical vectors of weights used
+ in a process in which the sample is repeatedly re-weighted and parameters
+ are re-estimated in order to compute the variance of the model parameters.
+ \item {\tt type}: A string specifying the type of replication weights being used.
+ This input is required if replicate weights are specified. The following types
+ of replication weights are recognized: {\tt"BRR"}, {\tt "Fay"},
+ {\tt "JK1"}, {\tt "JKn"}, {\tt "bootstrap"}, or {\tt "other"}.
+ \item {\tt weights}: An optional vector or formula specifying each case's sample weight,
+ the inverse of the probability that the case was selected. If a survey includes both sampling
+ weights and replicate weights separately for the same survey, both should be included in
+ the model specification. In these cases, sampling weights are used to correct potential biases
+ in in the computation of coefficients and replication weights are used to compute the variance
+ of coefficient estimates.
+ \item {\tt combined.weights}: An optional logical value that
+ should be specified as {\tt TRUE} if the replicate weights include the sampling weights. Otherwise,
+ {\tt combined.weights} defaults to {\tt FALSE}.
+ \item {\tt rho}: An optional numerical value specifying a shrinkage factor
+ for replicate weights of type {\tt "Fay"}.
+ \item {\tt bootstrap.average}: An optional numerical input specifying
+ the number of iterations over which replicate weights of type {\tt "bootstrap"} were averaged.
+ This input should be left as {\tt NULL} for {\tt "bootstrap"} weights that were
+ not were created by averaging.
+\item {\tt scale}: When replicate weights are included,
+ the variance is computed as the sum of squared deviations of the replicates from their mean.
+ {\tt scale} is an optional overall multiplier for the standard deviations.
+\item {\tt rscale}: Like {\tt scale}, {\tt rscale} specifies an
+optional vector of replicate-specific multipliers for the squared
+deviations used in variance computation.
+
+\item {\tt fpc}: For models in which {\tt "JK1"}, {\tt "JKn"}, or
+{\tt "other"} replicates are specified, {\tt fpc} is an optional
+numerical vector (with one entry for each replicate) designating the
+replicates' finite population corrections.
+
+\item {\tt fpctype}: When a finite population correction is included
+as an {\tt fpc} input, {\tt fpctype} is a required input specifying
+whether the input to {\tt fpc} is a sampling fraction ({\tt
+fpctype="fraction"}) or a direct correction ({\tt
+fpctype="correction"}).
+
+\item {\tt return.replicates}: An optional logical value
+specifying whether the replicates should be returned as a component
+of the output. {\tt return.replicates} defaults to {\tt FALSE}.
+
+\end{itemize}
+
+\end{enumerate}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item A dataset that includes survey weights:
+
+Attach the sample data:
+<<Existing.data>>=
+data(api, package="survey")
+@
+
+Suppose that a dataset included a dichotomous indicator
+for whether each public school attends classes year round ({\tt yr.rnd}), a measure of
+the percentage of students at each school who receive subsidized
+meals ({\tt meals}), a measure of the percentage of students at
+each school who are new to to the school ({\tt mobility}), and sampling
+weights computed by the survey house ({\tt pw}). Estimate a model
+that regresses the year-round schooling indicator on the {\tt meals} and {\tt mobility}
+variables:
+<<Existing.zelig>>=
+z.out1 <- zelig(yr.rnd ~ meals + mobility, model = "probit.survey", weights=~pw, data = apistrat)
+@
+Summarize regression coefficients:
+<<Existing.summary>>=
+ summary(z.out1)
+@
+Set explanatory variables to their default (mean/mode) values, and
+set a high (80th percentile) and low (20th percentile) value for
+``meals'':
+<<Existing.setx>>=
+x.low <- setx(z.out1, meals=quantile(apistrat$meals, 0.2))
+x.high <- setx(z.out1, meals=quantile(apistrat$meals, 0.8))
+@
+Generate first differences for the
+effect of high versus low concentrations of children receiving
+subsidized meals on the probability of holding school year-round:
+<<Existing.sim>>=
+ s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+@
+<<Existing.summary.sim>>=
+ summary(s.out1)
+@
+Generate a visual summary of the quantities of interest:
+\begin{center}
+<<label=ExistingPlot,fig=true,echo=true>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item A dataset that includes strata/cluster identifiers:
+
+Suppose that the survey house that provided the dataset used in the
+previous example excluded sampling weights but made other details
+about the survey design available. A user can still estimate a model
+without sampling weights that instead uses inputs that identify the
+stratum and/or cluster to which each observation belongs and the
+size of the finite population from which each observation was drawn.
+
+Continuing the example above, suppose the survey house drew at
+random a fixed number of elementary schools, a fixed number of
+middle schools, and a fixed number of high schools. If the variable
+{\tt stype} is a vector of characters ({\tt "E"} for elementary
+schools, {\tt "M"} for middle schools, and {\tt "H"} for high schools)
+that identifies the type of school each case
+represents and {\tt fpc} is a numerical vector that identifies for
+each case the total number of schools of the same type in the
+population, then the user could estimate the following model:
+
+<<Complex.zelig>>=
+z.out2 <- zelig(yr.rnd ~ meals + mobility, model = "probit.survey", strata=~stype, fpc=~fpc, data = apistrat)
+@
+Summarize the regression output:
+<<Complex.output>>=
+summary(z.out2)
+@
+The coefficient estimates for this example are
+identical to the point estimates in the first example, when
+pre-existing sampling weights were used. When sampling weights are
+omitted, they are estimated automatically for {\tt "probit.survey"}
+models based on the user-defined description of sampling designs.
+
+Moreover, because the user provided information about the survey
+design, the standard error estimates are lower in this example than
+in the previous example, in which the user omitted variables pertaining
+to the details of the complex survey design.
+
+\item A dataset that includes replication weights:
+
+Consider a dataset that includes information for a sample of hospitals
+about the number of out-of-hospital cardiac arrests that each
+hospital treats and the number of patients who arrive alive
+at each hospital:
+<<Replicate.data>>=
+data(scd, package="survey")
+@
+Survey houses sometimes supply
+replicate weights (in lieu of details about the survey design). For the sake
+of illustrating how replicate weights can be used as inputs in {\tt
+probit.survey} models, create a set of balanced repeated replicate
+(BRR) weights and an (artificial) dependent variable to simulate an indicator
+for whether each hospital was sued:
+<<Replicate.rw>>=
+BRRrep<-2*cbind(c(1,0,1,0,1,0),c(1,0,0,1,0,1), c(0,1,1,0,0,1),c(0,1,0,1,1,0))
+scd$sued <- as.vector(c(0,0,0,1,1,1))
+@
+Estimate a model that regresses the indicator for hospitals that were
+sued on the number of patients who arrive alive in
+each hospital and the number of cardiac arrests that each hospital treats, using
+the BRR replicate weights in {\tt BRRrep} to compute standard errors.
+<<Replicate.zelig>>=
+z.out3 <- zelig(formula=sued ~ arrests + alive , model = "probit.survey",
+ repweights=BRRrep, type="BRR", data=scd)
+@
+Summarize the regression coefficients:
+<<Replicate.summary>>=
+ summary(z.out3)
+@
+Set {\tt alive} at its mean and set {\tt arrests} at its 20th and 80th quantiles:
+<<Replicate.setx>>=
+x.low <- setx(z.out3, arrests = quantile(scd$arrests, .2))
+x.high <- setx(z.out3, arrests = quantile(scd$arrests,.8))
+@
+Generate first
+differences for the effect of high versus low cardiac arrests
+on the probability that a hospital will be sued:
+<<Replicate.sim>>=
+s.out3 <- sim(z.out3, x=x.high, x1=x.low)
+@
+<<Replicate.summary.sim>>=
+ summary(s.out3)
+@
+Generate a visual summary of quantities of interest:
+\begin{center}
+<<label=ReplicatePlot,fig=true,echo=true>>=
+ plot(s.out3)
+@
+\end{center}
+
+
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the observed 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{equation*}
+Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
+\end{equation*}
+where $\pi_i=\Pr(Y_i=1)$.
+
+\item The \emph{systematic component} is
+\begin{equation*}
+ \pi_i \; = \; \Phi (x_i \beta)
+\end{equation*}
+where $\Phi(\mu)$ is the cumulative distribution function of the
+Normal distribution with mean 0 and unit variance.
+\end{itemize}
+
+\subsubsection{Variance}
+
+When replicate weights are not used, the variance of the
+coefficients is estimated as
+\[
+\hat{\boldsymbol{\Sigma}} \left[
+ \sum_{i=1}^n
+\frac{(1-\pi_i)}{\pi_i^2}
+(\mathbf{X}_i(Y_i-\mu_i))^\prime(\mathbf{X}_i(Y_i-\mu_i)) + 2
+\sum_{i=1}^n \sum_{j=i+1}^n \frac{(\pi_{ij} - \pi_i\pi_j)}{\pi_i
+\pi_j \pi_{ij}}(\mathbf{X}_i(Y_i-\mu_i))^\prime
+(\mathbf{X}_j(Y_j-\mu_j)) \right] \hat{\boldsymbol{\Sigma}}
+\]
+where ${\pi_i}$ is the probability of case $i$ being sampled,
+$\mathbf{X}_i$ is a vector of the values of the explanatory
+variables for case $i$, $Y_i$ is value of the dependent variable for
+case $i$, $\hat{\mu}_i$ is the predicted value of the dependent
+variable for case $i$ based on the linear model estimates, and
+$\hat{\boldsymbol{\Sigma}}$ is the conventional variance-covariance
+matrix in a parametric glm. This statistic is derived from the
+method for estimating the variance of sums described in \cite{Bin83}
+and the Horvitz-Thompson estimator of the variance of a sum
+described in \cite{HorTho52}.
+
+When replicate weights are used, the model is re-estimated for each
+set of replicate weights, and the variance of each parameter is
+estimated by summing the squared deviations of the replicates from
+their mean.
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The expected value ({\tt qi\$ev}) is a simulation of predicted
+ probability of success $$E(Y) = \pi_i = \Phi(x_i
+ \beta),$$ given a draw of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is a draw from a Bernoulli
+ distribution with mean $\pi_i$.
+
+\item The first difference ({\tt qi\$fd}) in expected values 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 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 = "probit.survey", 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}: 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 data}: the name of the input data frame.
+ \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 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 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.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+When users estimate {\tt probit.survey} models with replicate weights in {\tt Zelig}, an
+object called {\tt .survey.prob.weights} is created in the global environment.
+{\tt Zelig} will over-write any existing object with that name, and users
+are therefore advised to re-name any object called {\tt .survey.prob.weights} before using {\tt probit.survey} models in {\tt Zelig}.
+
+\subsection* {How to Cite}
+
+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}
+\input{citeZelig}
+
+\subsection* {See also}
+
+Survey-weighted linear models and the sample data used in the
+examples above are a part of the {\tt survey} package by Thomas
+Lumley. Users may wish to refer to the help files for the three
+functions that Zelig draws upon when estimating survey-weighted
+models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+help(svrepdesign)}. The probit 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}.
+
+
+
+
+
+\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.survey.pdf b/inst/doc/probit.survey.pdf
new file mode 100644
index 0000000..cc26a51
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index eb3f8f5..44e7cc4 100644
--- a/inst/doc/refman.tex
+++ b/inst/doc/refman.tex
@@ -97,6 +97,7 @@ of simulations increases.
\include{gamma.gee}
\include{gamma.mixed}
\include{gamma.net}
+\include{gamma.survey}
\include{irt1d}
\include{irtkd}
\include{logit}
@@ -107,6 +108,7 @@ of simulations increases.
\include{logit.mixed}
\include{logit.mixed}
\include{logit.net}
+\include{logit.survey}
\include{lognorm}
\include{ls}
\include{ls.mixed}
@@ -120,6 +122,7 @@ of simulations increases.
\include{normal.gam}
\include{normal.gee}
\include{normal.net}
+\include{normal.survey}
\include{ologit}
\include{oprobit}
\include{oprobit.bayes}
@@ -129,12 +132,14 @@ of simulations increases.
\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}
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diff --git a/inst/doc/weibull.pdf b/inst/doc/weibull.pdf
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<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="sur.pdf">sur : Seemingly Unrelated Regression</a></li>
<li><a href="threesls.pdf">threesls : Three Stage Least Squares</a></li>
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<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>
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<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 : Probit Regression for Dichotomous Dependent Variables</a></li>
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<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>
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<li><a href="poisson.net.pdf">poisson.net : Social Network Poisson Regression for Event Count Dependent Variables</a></li>
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<li class="mainindx"><a href="">Models for Mixed Dependent Variables</a>
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+/Size 6176
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diff --git a/inst/doc/zelig.tex b/inst/doc/zelig.tex
index 6e89f5a..3c5ce08 100644
--- a/inst/doc/zelig.tex
+++ b/inst/doc/zelig.tex
@@ -21,7 +21,7 @@ Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
\texttt{OLau at Fas.Harvard.Edu}).}}
% rbuild: replace 'Version ' '\\' Version
-\date{Version 3.1-1\\ \today}
+\date{Version 3.2-0\\ \today}
\begin{document}
\maketitle
@@ -75,6 +75,32 @@ few days.
\begin{itemize}
+\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
diff --git a/inst/doc/zeligmodels.html b/inst/doc/zeligmodels.html
index f1386d9..96b8f8a 100644
--- a/inst/doc/zeligmodels.html
+++ b/inst/doc/zeligmodels.html
@@ -12,6 +12,7 @@
<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="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>
@@ -32,12 +33,14 @@
<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 : 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>
@@ -58,6 +61,7 @@
<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>
@@ -77,6 +81,7 @@
<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>
diff --git a/inst/zideal/zideal.RData b/inst/zideal/zideal.RData
new file mode 100644
index 0000000..7e674c3
Binary files /dev/null and b/inst/zideal/zideal.RData differ
--
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