[r-cran-zelig] 58/102: Import Upstream version 3.1-0
Andreas Tille
tille at debian.org
Sun Jan 8 16:58:54 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 88e1fe6c731376262313550f9016f00c12713e09
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:39:39 2017 +0100
Import Upstream version 3.1-0
---
DESCRIPTION | 10 +-
INDEX | 9 +
NAMESPACE | 53 +-
R/MIsimulation.R | 34 +-
R/MLutils.R | 51 +
R/callnetlogit.R | 8 +-
R/chopitcode.R | 245 +
R/describe.aov.R | 17 +
R/describe.arima.R | 38 +-
R/describe.chopit.R | 28 +
R/describe.coxph.R | 17 +
R/describe.gam.logit.R | 2 +-
R/describe.gam.normal.R | 2 +-
R/describe.gam.poisson.R | 2 +-
R/describe.gam.probit.R | 2 +-
R/describe.gamma.gee.R | 15 +
R/describe.gamma.mixed.R | 29 +
R/describe.lm.mixed.R | 29 +
R/describe.logit.gee.R | 30 +-
R/describe.logit.mixed.R | 29 +
R/describe.netcloglog.R | 6 +-
R/describe.netgamma.R | 6 +-
R/describe.netlogit.R | 6 +-
R/describe.netls.R | 4 +-
R/describe.netnormal.R | 6 +-
R/describe.netpoisson.R | 6 +-
R/describe.netprobit.R | 2 +-
R/describe.normal.gee.R | 15 +
R/describe.poisson.gee.R | 15 +
R/describe.poisson.mixed.R | 29 +
R/{describe.logit.gee.R => describe.probit.gee.R} | 30 +-
R/describe.probit.mixed.R | 29 +
R/eiRxC.R | 9 +-
R/netbinom.R | 2 +-
R/netgamma.R | 4 +-
R/netlogit.zelig.R | 10 +-
R/netnormal.R | 4 +-
R/netpoisson.R | 4 +-
R/param.gam.R | 2 +-
R/param.gee.R | 12 +
R/param.glm.R | 9 +-
R/param.lm.R | 5 +-
R/param.mixed.R | 45 +
R/param.netglm.R | 10 +-
R/param.relogit.R | 2 +-
R/param.zaov.R | 46 +
R/plot.zelig.2sls.R | 15 -
R/plot.zelig.R | 8 +-
R/plot.zelig.coxph.R | 30 +
R/{plot.zelig.3sls.R => plot.zelig.default.R} | 2 +-
R/plot.zelig.exp.R | 12 -
R/plot.zelig.gam.logit.R | 2 +-
R/plot.zelig.gam.poisson.R | 2 +-
R/plot.zelig.gam.probit.R | 2 +-
R/plot.zelig.gamma.R | 12 -
...ot.zelig.netnormal.R => plot.zelig.gamma.gee.R} | 4 +-
R/plot.zelig.logit.gee.R | 37 +-
R/plot.zelig.lognorm.R | 12 -
R/plot.zelig.ls.R | 12 -
R/plot.zelig.netcloglog.R | 2 +-
R/plot.zelig.netgamma.R | 12 -
R/plot.zelig.netlogit.R | 2 +-
R/plot.zelig.netls.R | 12 -
R/plot.zelig.netpoisson.R | 2 +-
R/plot.zelig.netprobit.R | 2 +-
R/plot.zelig.normal.R | 12 -
....zelig.gam.normal.R => plot.zelig.normal.gee.R} | 2 +-
...zelig.netpoisson.R => plot.zelig.poisson.gee.R} | 5 +-
R/plot.zelig.probit.gee.R | 20 +
R/plot.zelig.sur.R | 14 -
R/plot.zelig.threesls.R | 15 -
R/plot.zelig.twosls.R | 15 -
R/plot.zelig.weibull.R | 12 -
R/plot.zeliglist.R | 21 +
R/print.coxhazard.R | 8 +
R/print.zaovlist.R | 12 +
R/qi.BetaReg.R | 2 +
R/qi.aov.R | 10 +
R/qi.coxph.R | 241 +
R/qi.eiRxC.R | 22 +-
R/qi.gam.R | 15 +-
R/qi.gee.R | 90 +-
R/qi.glm.R | 18 +-
R/qi.lm.R | 2 +
R/qi.mixed.R | 137 +
R/qi.netglm.R | 19 +-
R/qi.netlm.R | 2 +
R/qi.netlogit.R | 12 +-
R/qi.polr.R | 2 +-
R/qi.relogit.R | 8 +-
R/qi.survreg.R | 19 +-
R/qi.zmlm.R | 71 +
R/repl.zelig.R | 1 +
R/setx.MI.R | 16 +-
R/{setx.default.R => setx.coxph.R} | 136 +-
R/setx.default.R | 25 +-
R/setx.zaovlist.R | 9 +
R/sim.cond.R | 10 +-
R/sim.counter.R | 2 +-
R/{sim.default.R => sim.coxph.R} | 17 +-
R/sim.default.R | 8 +
R/{sim.ei.RxC.R => sim.eiRxC.R} | 118 +-
R/sim.netglm.R | 2 +-
R/sim.setx.MI.R | 11 +-
R/sim.setx.strata.R | 2 +-
R/sim.zaovlist.R | 119 +
R/summarize.coxhazard.R | 3 +
R/summary.coxph.naive.R | 12 +
R/summary.coxph.robust.R | 15 +
R/summary.gee.naive.R | 23 +-
R/summary.gee.robust.R | 23 +-
R/summary.setx.cond.R | 29 +-
R/summary.zaov.R | 26 +
R/summary.zelig.R | 2 +
R/summary.zelig.strata.R | 18 +-
R/summary.zeliglist.R | 33 +
R/terms.zaov.R | 13 +
R/vcov.gee.naive.R | 10 +-
R/vcov.gee.robust.R | 8 +-
R/vcov.netlogit.R | 2 +-
R/vcov.zmlm.R | 4 +
R/vdc.R | 9 +-
R/zelig.default.R | 161 +-
R/zelig2MCMC.R | 594 +-
R/zelig2aov.R | 11 +
R/zelig2blogit.R | 9 +-
R/zelig2bprobit.R | 10 +-
R/zelig2coxph.R | 24 +
R/zelig2exp.R | 31 +-
R/zelig2gam.logit.R | 10 +-
R/zelig2gam.normal.R | 10 +-
R/zelig2gam.poisson.R | 10 +-
R/zelig2gam.probit.R | 10 +-
R/{zelig2logit.gee.R => zelig2gamma.gee.R} | 26 +-
R/zelig2gamma.mixed.R | 17 +
R/zelig2lm.mixed.R | 15 +
R/zelig2logit.gee.R | 26 +-
R/zelig2logit.mixed.R | 17 +
R/zelig2lognorm.R | 31 +-
R/zelig2mlogit.R | 29 +-
R/zelig2netcloglog.R | 10 +-
R/zelig2netgamma.R | 13 +-
R/zelig2netlogit.R | 10 +-
R/zelig2netls.R | 10 +-
R/zelig2netnormal.R | 12 +-
R/zelig2netpoisson.R | 12 +-
R/zelig2netprobit.R | 2 +-
R/{zelig2logit.gee.R => zelig2normal.gee.R} | 26 +-
R/{zelig2logit.gee.R => zelig2poisson.gee.R} | 26 +-
R/zelig2poisson.mixed.R | 17 +
R/{zelig2logit.gee.R => zelig2probit.gee.R} | 26 +-
R/zelig2probit.mixed.R | 19 +
R/zelig2sur.R | 22 +-
R/zelig2threesls.R | 42 +-
R/zelig2tobit.R | 96 +-
R/zelig2twosls.R | 49 +-
R/zelig2weibull.R | 12 +-
R/zelig3aov.R | 9 +
R/{zelig3gee.R => zelig3coxph.R} | 30 +-
R/zelig3gee.R | 29 +-
R/zelig3ls.R | 3 +
R/zelig3mixed.R | 6 +
R/zelig4gee.R | 46 +
R/zelig4glm.R | 75 +
R/zvcClient.R | 10 +-
data/bivariate.tab | 80 +
data/coalition2.txt | 315 +
data/free1.tab | 451 +
data/free2.tab | 451 +
data/homerun.txt | 315 +
data/voteincome.txt | 1501 +
demo/00Index | 36 +-
demo/aov.R | 47 +
demo/bivariate.probit.R | 63 +-
demo/chopit.R | 36 +
demo/{netcloglog.R => cloglog.net.R} | 2 +-
demo/coxph.R | 58 +
demo/ei.RxC.R | 4 +-
demo/gamma.gee.R | 43 +
demo/gamma.mixed.R | 26 +
demo/gamma.negvalues.R | 35 +
demo/{netgamma.R => gamma.net.R} | 2 +-
demo/logit.R | 11 +-
demo/{gam.logit.R => logit.gam.R} | 6 +-
demo/logit.gee.R | 3 +-
demo/logit.mixed.R | 23 +
demo/{netlogit.R => logit.net.R} | 2 +-
demo/ls.mixed.R | 23 +
demo/{netls.R => ls.net.R} | 2 +-
demo/mi.R | 2 +-
demo/{gam.normal.R => normal.gam.R} | 10 +-
demo/normal.gee.R | 36 +
demo/{netnormal.R => normal.net.R} | 2 +-
demo/normal.regression.R | 31 +-
demo/{gam.poisson.R => poisson.gam.R} | 4 +-
demo/poisson.gee.R | 39 +
demo/poisson.mixed.R | 21 +
demo/{netpoisson.R => poisson.net.R} | 2 +-
demo/{gam.probit.R => probit.gam.R} | 6 +-
demo/{logit.gee.R => probit.gee.R} | 7 +-
demo/probit.mixed.R | 23 +
demo/{netprobit.R => probit.net.R} | 2 +-
demo/repl.R | 39 +
inst/doc/Rplots.ps | 451790 +++++++++---------
inst/doc/aov.Rnw | 256 +
inst/doc/aov.pdf | Bin 0 -> 161891 bytes
inst/doc/arima.pdf | Bin 123134 -> 123057 bytes
inst/doc/blogit.pdf | Bin 161291 -> 153114 bytes
inst/doc/bprobit.pdf | Bin 149975 -> 165549 bytes
inst/doc/chopit.Rnw | 391 +
inst/doc/chopit.pdf | Bin 0 -> 108509 bytes
inst/doc/{netcloglog.Rnw => cloglog.net.Rnw} | 20 +-
inst/doc/cloglog.net.pdf | Bin 0 -> 105366 bytes
inst/doc/commands/model.end.aux | 20 +-
inst/doc/commands/model.frame.multiple.aux | 22 +-
inst/doc/commands/model.matrix.multiple.aux | 22 +-
inst/doc/commands/parse.formula.aux | 26 +-
inst/doc/commands/parse.par.aux | 22 +-
inst/doc/commands/put.start.aux | 20 +-
inst/doc/commands/set.start.aux | 22 +-
inst/doc/commands/tag.aux | 22 +-
inst/doc/commandsRd/MatchIt.url.pdf | Bin 19852 -> 19853 bytes
inst/doc/commandsRd/PErisk.pdf | Bin 42256 -> 42257 bytes
inst/doc/commandsRd/SupremeCourt.pdf | Bin 32130 -> 32130 bytes
inst/doc/commandsRd/Weimar.pdf | Bin 25053 -> 25053 bytes
inst/doc/commandsRd/Zelig-package.pdf | Bin 47953 -> 49587 bytes
inst/doc/commandsRd/Zelig-package.tex | 74 +-
inst/doc/commandsRd/Zelig.pdf | Bin 41492 -> 41493 bytes
inst/doc/commandsRd/Zelig.url.pdf | Bin 18734 -> 18736 bytes
inst/doc/commandsRd/approval.pdf | Bin 24315 -> 24314 bytes
inst/doc/commandsRd/bivariate.pdf | Bin 0 -> 31446 bytes
inst/doc/commandsRd/bivariate.tex | 22 +
inst/doc/commandsRd/coalition.pdf | Bin 33899 -> 33900 bytes
inst/doc/commandsRd/coalition2.pdf | Bin 0 -> 34955 bytes
inst/doc/commandsRd/coalition2.tex | 31 +
inst/doc/commandsRd/current.packages.pdf | Bin 38490 -> 38488 bytes
inst/doc/commandsRd/dims.pdf | Bin 38328 -> 38330 bytes
inst/doc/commandsRd/eidat.pdf | Bin 20561 -> 20565 bytes
inst/doc/commandsRd/free1.pdf | Bin 0 -> 40772 bytes
inst/doc/commandsRd/free1.tex | 96 +
inst/doc/commandsRd/free2.pdf | Bin 0 -> 40801 bytes
inst/doc/commandsRd/free2.tex | 96 +
inst/doc/commandsRd/friendship.pdf | Bin 31868 -> 31871 bytes
inst/doc/commandsRd/grunfeld.pdf | Bin 26298 -> 26302 bytes
inst/doc/commandsRd/gsource.pdf | Bin 43875 -> 43874 bytes
inst/doc/commandsRd/help.zelig.pdf | Bin 33300 -> 33300 bytes
inst/doc/commandsRd/hoff.pdf | Bin 33604 -> 33603 bytes
inst/doc/commandsRd/homerun.pdf | Bin 0 -> 39387 bytes
inst/doc/commandsRd/homerun.tex | 26 +
inst/doc/commandsRd/immigration.pdf | Bin 33055 -> 33057 bytes
inst/doc/commandsRd/klein.pdf | Bin 27455 -> 27453 bytes
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inst/doc/commandsRd/macro.pdf | Bin 32697 -> 32697 bytes
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inst/doc/commandsRd/mexico.pdf | Bin 33503 -> 33501 bytes
inst/doc/commandsRd/mi.pdf | Bin 38082 -> 38080 bytes
inst/doc/commandsRd/mid.pdf | Bin 37170 -> 37171 bytes
inst/doc/commandsRd/model.end.pdf | Bin 36596 -> 36594 bytes
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inst/doc/ei.RxC.Rnw | 18 +-
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inst/doc/exp.pdf | Bin 135856 -> 135612 bytes
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inst/doc/factor.mix.pdf | Bin 94650 -> 94647 bytes
inst/doc/factor.ord.pdf | Bin 93058 -> 93057 bytes
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inst/doc/{logit.gee.Rnw => gamma.gee.Rnw} | 207 +-
inst/doc/gamma.gee.pdf | Bin 0 -> 132000 bytes
inst/doc/gamma.mixed.Rnw | 188 +
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inst/doc/{netgamma.Rnw => gamma.net.Rnw} | 18 +-
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inst/doc/index.shtml | 2 +-
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...-ExamplesPlot.eps => gamma_gee-ExamplePlot.eps} | 1615 +-
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.../vigpics/{netgamma-007.eps => gammanet-007.eps} | 1518 +-
inst/doc/vigpics/gammanet-007.pdf | 1256 +
.../vigpics/{netgamma-009.eps => gammanet-009.eps} | 1518 +-
inst/doc/vigpics/gammanet-009.pdf | 1256 +
...l-ExamplesPlot.eps => lmmixed-ExamplesPlot.eps} | 2173 +-
inst/doc/vigpics/lmmixed-ExamplesPlot.pdf | 1837 +
inst/doc/vigpics/logit-ExamplePlot.eps | 771 +-
inst/doc/vigpics/logit-ExamplePlot.pdf | 1047 +-
inst/doc/vigpics/logit-FirstDifferencesPlot.eps | 1919 +-
inst/doc/vigpics/logit-FirstDifferencesPlot.pdf | 3066 +-
inst/doc/vigpics/logit-ROCPlot.pdf | 6 +-
inst/doc/vigpics/logit_gee-ExamplePlot.eps | 948 +-
inst/doc/vigpics/logit_gee-ExamplePlot.pdf | 1155 +-
inst/doc/vigpics/logit_gee-FirstDifferencePlot.eps | 2282 +-
inst/doc/vigpics/logit_gee-FirstDifferencePlot.pdf | 3338 +-
.../vigpics/{netlogit-007.eps => logitnet-007.eps} | 757 +-
inst/doc/vigpics/logitnet-007.pdf | 734 +
.../vigpics/{netlogit-009.eps => logitnet-009.eps} | 757 +-
inst/doc/vigpics/logitnet-009.pdf | 734 +
inst/doc/vigpics/lognormal-ExamplePlot.eps | 2252 +-
inst/doc/vigpics/lognormal-ExamplePlot.pdf | 2988 +-
inst/doc/vigpics/ls-DummyPlot.eps | 2205 +-
inst/doc/vigpics/ls-DummyPlot.pdf | 2204 +-
inst/doc/vigpics/ls-Example4Plot.eps | 2565 +
inst/doc/vigpics/ls-Example4Plot.pdf | 2419 +
inst/doc/vigpics/ls-ExamplesPlot.eps | 2186 +-
inst/doc/vigpics/ls-ExamplesPlot.pdf | 2185 +-
...tls-ExamplesPlot.eps => lsnet-ExamplesPlot.eps} | 1568 +-
inst/doc/vigpics/lsnet-ExamplesPlot.pdf | 1257 +
inst/doc/vigpics/mlogit-LevelsTernaryPlot.eps | 1998 +-
inst/doc/vigpics/mlogit-LevelsTernaryPlot.pdf | 2018 +-
inst/doc/vigpics/mlogit-TernaryPlot.eps | 2000 +-
inst/doc/vigpics/mlogit-TernaryPlot.pdf | 2020 +-
...git-ExamplePlot.eps => negbin-Example1Plot.eps} | 853 +-
inst/doc/vigpics/negbin-Example1Plot.pdf | 748 +
inst/doc/vigpics/netcloglog-007.pdf | 731 -
inst/doc/vigpics/netcloglog-009.pdf | 731 -
inst/doc/vigpics/netgamma-007.pdf | 1255 -
inst/doc/vigpics/netgamma-009.pdf | 1255 -
inst/doc/vigpics/netlogit-007.pdf | 732 -
inst/doc/vigpics/netlogit-009.pdf | 732 -
inst/doc/vigpics/netls-ExamplesPlot.pdf | 1256 -
inst/doc/vigpics/netnormal-007.pdf | 1253 -
inst/doc/vigpics/netpoisson-007.pdf | 747 -
inst/doc/vigpics/netprobit-007.pdf | 732 -
inst/doc/vigpics/netprobit-009.pdf | 732 -
inst/doc/vigpics/normal-ExamplesPlot.eps | 2139 +-
inst/doc/vigpics/normal-ExamplesPlot.pdf | 3095 +-
...ExamplesPlot.eps => normal_gee-ExamplePlot.eps} | 1617 +-
inst/doc/vigpics/normal_gee-ExamplePlot.pdf | 1260 +
.../{netnormal-007.eps => normalnet-007.eps} | 1503 +-
inst/doc/vigpics/normalnet-007.pdf | 1255 +
inst/doc/vigpics/oprobit-FirstDifferencesPlot.eps | 7294 +-
inst/doc/vigpics/oprobit-FirstDifferencesPlot.pdf | 8317 +-
inst/doc/vigpics/poisson-ExamplePlot.eps | 885 +-
inst/doc/vigpics/poisson-ExamplePlot.pdf | 1110 +-
inst/doc/vigpics/poisson_gee-ExamplePlot.eps | 717 +
inst/doc/vigpics/poisson_gee-ExamplePlot.pdf | 691 +
.../{netpoisson-007.eps => poissonnet-007.eps} | 849 +-
inst/doc/vigpics/poissonnet-007.pdf | 755 +
inst/doc/vigpics/probit_gee-ExamplePlot.eps | 702 +
inst/doc/vigpics/probit_gee-ExamplePlot.pdf | 685 +
...Plot.eps => probit_gee-FirstDifferencePlot.eps} | 2291 +-
.../doc/vigpics/probit_gee-FirstDifferencePlot.pdf | 1832 +
.../{netprobit-009.eps => probitnet-007.eps} | 742 +-
inst/doc/vigpics/probitnet-007.pdf | 732 +
.../{netprobit-007.eps => probitnet-009.eps} | 742 +-
inst/doc/vigpics/probitnet-009.pdf | 732 +
inst/doc/vigpics/relogit-Example1Plot.eps | 684 +-
inst/doc/vigpics/relogit-Example1Plot.pdf | 1043 +-
inst/doc/vigpics/relogit-Example3Plot.eps | 759 +-
inst/doc/vigpics/relogit-Example3Plot.pdf | 1074 +-
inst/doc/vigpics/threesls-Examplesthreesls.eps | 3034 +-
inst/doc/vigpics/threesls-Examplesthreesls.pdf | 4094 +-
inst/doc/vigpics/twosls-Examplestwosls.eps | 4668 +-
inst/doc/vigpics/twosls-Examplestwosls.pdf | 6038 +-
inst/doc/vigpics/weibull-ExamplePlot.eps | 2362 +-
inst/doc/vigpics/weibull-ExamplePlot.pdf | 3111 +-
inst/doc/weibull.pdf | Bin 137089 -> 137633 bytes
inst/doc/zelig.html | 34 +-
inst/doc/zelig.pdf | 156009 +++---
inst/doc/zelig.tex | 39 +-
inst/doc/zeligmodels.html | 34 +-
inst/zideal/zideal.RData | Bin 1337 -> 1656 bytes
man/Zelig-package.Rd | 74 +-
man/bivariate.Rd | 24 +
man/coalition2.Rd | 32 +
man/free1.Rd | 99 +
man/free2.Rd | 99 +
man/homerun.Rd | 25 +
man/tobin.Rd | 5 +-
man/voteincome.Rd | 27 +
522 files changed, 441379 insertions(+), 364550 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index d702e13..117d776 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: Zelig
-Version: 3.0-6
-Date: 2007-10-03
+Version: 3.1-0
+Date: 2007-11-30
Title: Everyone's Statistical Software
Author: Kosuke Imai <kimai at Princeton.Edu>,
Gary King <king at harvard.edu>,
@@ -20,9 +20,9 @@ Description: Zelig is an easy-to-use program that can estimate, and
model, and mimicking the program Clarify (for Stata) that
takes the raw output of existing statistical procedures and
translates them into quantities of direct interest.
-License: GPL version 2 or newer
+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: Wed Oct 3 16:22:15 2007; king
+ systemfit, mgcv, lme4, anchors (>= 2.0)
+Packaged: Fri Nov 30 17:03:48 2007; king
diff --git a/INDEX b/INDEX
index b1b4b50..4e05d7a 100644
--- a/INDEX
+++ b/INDEX
@@ -1,10 +1,15 @@
approval U.S. Presidential Approval Data
+bivariate Sample data for bivariate probit regression
coalition Coalition Dissolution in Parliamentary
Democracies
+coalition2 Coalition Dissolution in Parliamentary
+ Democracies, Modified Version
current.packages Find all packages in a dependency chain
dims Return Dimensions of Vectors, Arrays, and Data
Frames
eidat Simulation Data for Ecological Inference
+free1 Freedom of Speech Data
+free2 Freedom of Speech Data
friendship Simulated Example of Schoolchildren Friendship
Network
grunfeld Simulation Data for model Seemingly Unrelated
@@ -13,6 +18,8 @@ grunfeld Simulation Data for model Seemingly Unrelated
gsource Read Data As a Space-Delimited Table
help.zelig HTML Help for Zelig Commands and Models
hoff Social Security Expenditure Data
+homerun Sample Data on Home Runs Hit By Mark McGwire
+ and Sammy Sosa in 1998.
immigration Individual Preferences Over Immigration Policy
klein Simulation Data for model Two-Stage Least
Square (twosls) that corresponds to method 2SLS
@@ -59,6 +66,8 @@ tobin Tobin's Tobit Data
turnout Turnout Data Set from the National Election
Survey
user.prompt Pause in demo files
+voteincome Sample Turnout and Demographic Data from the
+ 2000 Current Population Survey
Weimar 1932 Weimar election data
zelig Estimating a Statistical Model
zeligDepStatus Zelig Dependencies Packages Client Status
diff --git a/NAMESPACE b/NAMESPACE
index 8d6ddfa..c62db3b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -48,6 +48,7 @@ export( current.packages,
#S3method("$", summary.ZeligS4vglm)
#S3method("$<-", ZeligS4vglm)
#S3method("$<-", summary.ZeligS4vglm)
+S3method(coef, chopit)
S3method(formula, vglm)
S3method(model.matrix, multiple)
S3method(model.frame, multiple)
@@ -59,42 +60,33 @@ S3method(names, zelig)
S3method(names, zelig.relogit)
S3method(plot, zelig)
S3method(plot, zelig.arima)
+S3method(plot, zeliglist)
S3method(plot.zelig, blogit)
S3method(plot.zelig, bprobit)
-S3method(plot.zelig, exp)
-S3method(plot.zelig, gamma)
-S3method(plot.zelig, gam.logit)
-S3method(plot.zelig, gam.normal)
-S3method(plot.zelig, gam.poisson)
-S3method(plot.zelig, gam.probit)
+S3method(plot.zelig, coxph)
+S3method(plot.zelig, logit.gam)
+S3method(plot.zelig, poisson.gam)
+S3method(plot.zelig, probit.gam)
S3method(plot.zelig, logit)
S3method(plot.zelig, logit.gee)
-S3method(plot.zelig, lognorm)
-S3method(plot.zelig, ls)
+S3method(plot.zelig, gamma.gee)
+S3method(plot.zelig, poisson.gee)
+S3method(plot.zelig, probit.gee)
+S3method(plot.zelig, normal.gee)
S3method(plot.zelig, mlogit)
S3method(plot.zelig, negbin)
-S3method(plot.zelig, netls)
-
-S3method(plot.zelig, netcloglog)
-S3method(plot.zelig, netgamma)
-S3method(plot.zelig, netlogit)
-S3method(plot.zelig, netnormal)
-S3method(plot.zelig, netpoisson)
-S3method(plot.zelig, netprobit)
-
-S3method(plot.zelig, normal)
+S3method(plot.zelig, cloglog.net)
+S3method(plot.zelig, logit.net)
+S3method(plot.zelig, poisson.net)
+S3method(plot.zelig, probit.net)
S3method(plot.zelig, ologit)
S3method(plot.zelig, oprobit)
S3method(plot.zelig, probit)
S3method(plot.zelig, poisson)
S3method(plot.zelig, relogit)
-S3method(plot.zelig, sur)
-S3method(plot.zelig, twosls)
-S3method(plot.zelig, threesls)
-S3method(plot.zelig, w2sls)
-S3method(plot.zelig, weibull)
S3method(print, arimaSummary)
S3method(print, BetaReg)
+S3method(print, coxhazard)
S3method(print, names.relogit)
S3method(print, names.zelig)
S3method(print, relogit)
@@ -110,16 +102,21 @@ S3method(print, summary.MCMCZelig)
S3method(print, summary.zelig)
S3method(print, eiRxC)
S3method(print, zelig)
+S3method(qi, chopit)
S3method(repl, default)
S3method(repl, zelig)
S3method(sim, setxArima)
S3method(sim, default)
S3method(sim, cond)
+S3method(sim, coxph)
S3method(sim, eiRxC)
S3method(sim, counter)
S3method(sim, setx.MI)
S3method(sim, setx.strata)
+S3method(sim, zaovlist)
+S3method(sim, eiRxC)
S3method(setx, Arima)
+S3method(setx, coxph)
S3method(setx, default)
S3method(setx, eiRxC)
S3method(setx, gam)
@@ -128,11 +125,15 @@ S3method(setx, MI)
S3method(setx, netglm)
S3method(setx, strata)
S3method(setx, relogit2)
+S3method(setx, zaovlist)
S3method(summarize, array)
S3method(summarize, ei)
+S3method(summarize, coxhazard)
S3method(summary, Arima)
S3method(summary, zelig.arima)
S3method(summary, BetaReg)
+S3method(summary, coxph.naive)
+S3method(summary, coxph.robust)
S3method(summary, MI)
S3method(summary, netglm)
S3method(summary, strata)
@@ -147,12 +148,18 @@ S3method(summary, vglm)
S3method(summary, lm.robust)
S3method(summary, glm.robust)
S3method(summary, eiRxC)
+S3method(summary, zaovlist)
+S3method(summary, zmaov)
S3method(terms, vglm)
+S3method(summary, zmlm)
S3method(terms, multiple)
S3method(vcov, BetaReg)
S3method(vcov, eiRxC)
S3method(vcov, relogit)
S3method(vcov, lm.robust)
S3method(vcov, glm.robust)
+S3method(vcov, zmlm)
+S3method(vcov, chopit)
S3method(zelig, default)
+
diff --git a/R/MIsimulation.R b/R/MIsimulation.R
index 429d4dd..c74b6bd 100644
--- a/R/MIsimulation.R
+++ b/R/MIsimulation.R
@@ -1,24 +1,48 @@
MIsimulation <- function (object, num = c(1000, 100), prev = NULL,
- bootstrap = FALSE, bootfn = NULL, ...) {
+ bootstrap = FALSE, bootfn = NULL, x = NULL, x1 = NULL, ...) {
M <- length(object)
simpar <- simqi <- res <- list()
+ fn <- paste("zelig4", getzelig(object[[1]]), sep = "")
if (is.null(prev)) {
numM <- round(num/M)
if (!bootstrap) {
- for (i in 1:M)
+ for (i in 1:M){
simpar[[i]] <- param(object[[i]], num = numM, bootstrap =
bootstrap)
+ if(exists(fn)){
+ if(any(class(x)=="setx.cond")){
+ xvar <- as.matrix(cbind(1,x[[1]][,2:ncol(x[[1]])]))
+ for (h in 2:M)
+ xvar <- as.matrix(rbind(xvar, as.matrix(cbind(1,x[[h]][,2:ncol(x[[1]])]))))
+
+ simpar[[i]] <- do.call(fn, list(object=object[[i]], simpar=simpar[[i]], x=xvar, x1=NULL, bootstrap=bootstrap, bootfn=bootfn))
+ }
+ else
+ simpar[[i]] <- do.call(fn, list(object=object[[i]], simpar=simpar[[i]], x=x, x1=x1, bootstrap=bootstrap, bootfn=bootfn))
+ }
+ }
}
else {
tt <- terms(object[[1]])
- if (missing(bootfn))
+ if (is.null(bootfn))
bootfn <- bootfn.default
for (i in 1:M) {
dta <- eval(object[[i]]$data, sys.parent())
- res <- boot(dta, bootfn, R = num, object = object[[i]], ...)
+ res <- boot(dta, bootfn, R = num, object = object[[i]],...)
colnames(res$t) <- names(res$t0)
simpar[[i]] <- res$t
- }
+ if(exists(fn)){
+ if(any(class(x)=="setx.cond")){
+ xvar <- as.matrix(cbind(1,x[[1]][,2:ncol(x[[1]])]))
+ for (h in 2:M)
+ xvar <- as.matrix(rbind(xvar, as.matrix(cbind(1,x[[h]][,2:ncol(x[[1]])]))))
+
+ simpar[[i]] <- do.call(fn, list(object=object[[i]], simpar=simpar[[i]], x=xvar, x1=NULL, bootstrap=bootstrap, bootfn=bootfn, dta=dta))
+ }
+ else
+ simpar[[i]] <- do.call(fn, list(object=object[[i]], simpar=simpar[[i]], x=x, x1=x1, bootstrap=bootstrap, bootfn=bootfn, dta=dta))
+ }
+ }
}
params <- as.matrix(simpar[[1]])
for (j in 2:M)
diff --git a/R/MLutils.R b/R/MLutils.R
index f8a76f9..e245c91 100644
--- a/R/MLutils.R
+++ b/R/MLutils.R
@@ -245,3 +245,54 @@ return (c(src %w/o% src[index],dest))
return(v)
}
+##
+# Reaction ~ Days + tag(1 + Days | subject) ==>
+# list (fixed = ~ Days,
+# random = ~ 1 + Days)
+#
+
+.getRandAndFixedTerms <- function (fml){
+ f <- function(x){
+ as.formula(paste("~",paste(x, collapse = "+")))
+ }
+ res <- list()
+ if(length(fml)!=3)
+ stop("the main formula in the extended form should be of length 3 !")
+ lhs <- fml[[2]]
+ rhs <- fml[[3]]
+ TT <- terms(fml,specials="tag")
+ TT.labels <- attr(TT,"term.labels")
+ TT.vars <- attr(TT,"variables")
+ tagattr<-attr(TT,"specials")$tag
+
+ hastag<-!(is.null(tagattr))
+
+ if (hastag){
+ ## fixed
+ F.labels <- TT.labels[-(tagattr-1)]
+ if (!length(F.labels))
+ F.labels <- 1
+ res$fixed <- as.formula(paste("~",paste(F.labels,collapse="+")))
+
+ ## random
+ random <- list()
+ idx = 1
+ for (j in tagattr){
+ vind <- j + 1
+ tmp <- .deparseTag(TT.vars[[vind]])
+ idx <- idx + 1
+ ## if tags have the same id, merge them together
+ if (tmp$id %in% names(random)){
+ random[[tmp$id]] <- c(random[[tmp$id]], tmp$var)
+ } else {
+ random[[tmp$id]] <- tmp$var
+ }
+ }
+ res$random <- lapply(random,f)
+
+ } else {
+ res$fixed <- fml
+ }
+ return(res)
+}
+
diff --git a/R/callnetlogit.R b/R/callnetlogit.R
index da85489..5cfaf88 100644
--- a/R/callnetlogit.R
+++ b/R/callnetlogit.R
@@ -1,4 +1,4 @@
-callnetlogit <- function(formula, data, family = binomial(link=logit), ...)
+calllogit.net <- function(formula, data, family = binomial(link=logit), ...)
{
Terms <- terms(formula)
intercept.value <- attr(Terms, "intercept")
@@ -24,10 +24,10 @@ callnetlogit <- function(formula, data, family = binomial(link=logit), ...)
for(i in 2:length(D)) {
x[i - 1,,] <- D[[i]] }
if (intercept == TRUE){
- fit <- netlogit.zelig(y, x, intercept=TRUE,...)
+ fit <- logit.net.zelig(y, x, intercept=TRUE,...)
}
if (intercept == FALSE){
- fit <- netlogit.zelig(y, x, intercept=FALSE, ...)
+ fit <- logit.net.zelig(y, x, intercept=FALSE, ...)
}
fit$names <- names(mf[2:stackcount(mf)]) # paste("x", 1:(nx - intercept), sep = "")
if (intercept)
@@ -47,6 +47,6 @@ callnetlogit <- function(formula, data, family = binomial(link=logit), ...)
fit$mod.coefficients <- so$coefficients
fit$cov.unscaled <- so$cov.unscaled
fit$cov.scaled <- so$cov.scaled
- class(fit) <- "netlogit"
+ class(fit) <- "logit.net"
return(fit)
}
diff --git a/R/chopitcode.R b/R/chopitcode.R
new file mode 100644
index 0000000..2ce19dd
--- /dev/null
+++ b/R/chopitcode.R
@@ -0,0 +1,245 @@
+## Need to register the following in the NAMESPACE
+## S3method(coef, chopit)
+## S3method(vcov, chopit)
+## S3method(qi, chopit)
+
+coef.chopit <- function(object, ...) {
+ object$chopit.optim$par
+}
+
+vcov.chopit <- function(object, ...) {
+ solve(object$chopit.hess)
+}
+
+print.chopit <- function(x, ...) {
+ cat("Use summary() for more information.\n")
+}
+
+zelig2chopit <- function(formula, model, data, M, ...) {
+ require(anchors)
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- NULL
+ mf$M <- mf$robust <- NULL
+ mf[[1]] <- prechop
+ as.call(mf)
+}
+
+prechop <- function(formula, data, ...) {
+ "%w/o%" <- function(x,y) x[!x %in% y]
+ ## Setting up complete formula
+ if (is.list(formula)) {
+ fs <- sapply(formula$self, deparse, width.cutoff = 500)
+ fv <- sapply(formula$vign, deparse, width.cutoff = 500)
+ ft <- sapply(formula$tau, deparse, width.cutoff = 500)
+ } else { stop("formula must be a list of formula objects with names `self', `vign', and `tau'.") }
+ s2 <- unlist(strsplit(fs[2], "cbind(", fixed = TRUE))
+ s2 <- unlist(strsplit(s2, ",", fixed = TRUE))
+ s2 <- unlist(strsplit(s2, ")", fixed = TRUE))
+ v2 <- unlist(strsplit(fv[2], "cbind(", fixed = TRUE))
+ v2 <- unlist(strsplit(v2, ",", fixed = TRUE))
+ v2 <- unlist(strsplit(v2, ")", fixed = TRUE))
+ xs <- all.vars(formula$self) %w/o% s2
+ xt <- all.vars(formula$tau)
+ xx <- unique(c(xt, xs))
+ f1 <- as.formula(paste("~", paste(s2, collapse = "+"), "+",
+ paste(xs, collapse = "+")))
+ f2a <- as.formula(paste("~", paste(xt, collapse = "+")))
+ f2b <- as.formula(paste("~", paste(v2, collapse = "+")))
+ f2c <- as.formula(paste("~", paste(v2, collapse = "+"), "+",
+ paste(xt, collapse = "+")))
+
+ ## Setting up complete data frame
+ if (is.list(data) & !is.data.frame(data)) {
+ if (all( c("self", "vign") %in% names(data))) {
+ DD <- model.frame(formula$self, data = data$self)
+ D1 <- model.frame(f1, data = data$self)
+ D2a <- rownames(model.frame(f2a, data = data$vign))
+ D2b <- model.frame(f2b, data = data$vign, na.action = NULL)
+ } else if (length(data) == 2){
+ DD <- model.frame(as.formula(formula[[1]]), data = data[[1]])
+ D1 <- model.frame(f1, data = data[[1]])
+ D2a <- rownames(model.frame(f2a, data = data[[2]]))
+ D2b <- model.frame(f2b, data = data[[2]], na.action = NULL)
+ } else if (length(data) > 2) { stop("data cannot have more than two data frames.") }
+ ridx <- names(which(apply(!apply(D2b, 1, is.na), 2, all)))
+ tmp <- data$vign[ridx %in% c(rownames(data$vign), D2a), c(names(D2b), xx)]
+ D2 <- model.frame(f2c, data = tmp)
+ remove(tmp)
+ idx1 <- names(D1)
+ idx2 <- names(D2)
+ miss1 <- idx1[!(idx1 %in% idx2)]
+ miss2 <- idx2[!(idx2 %in% idx1)]
+ for (i in miss1) { D2[[i]] <- rep(NA, length = nrow(D2)) }
+ for (i in miss2) { D1[[i]] <- rep(NA, length = nrow(D1)) }
+ data <- rbind(D1, D2)
+ } else if (is.data.frame(data)) {
+ data <- data
+ }
+ else { stop("data must be either a data frame or list of data frames") }
+ out <- chopit(formula, data = data, ...)
+ out$par <- out$chopit.optim$par
+ out$value <- out$chopit.optim$value
+ out$counts <- out$chopit.optim$counts
+ out$convergence <- out$chopit.optim$convergence
+ out$message <- out$chopit.optim$message
+ out$formula <- list()
+ out$formula$self <- as.formula(formula$self)
+ out$formula$tau <- as.formula(formula$tau)
+ out$formula$vign <- as.formula(formula$vign)
+ out$data <- D1
+ tt <- attr(D1, "terms")
+ tt[[3]] <- all.vars(tt)[1]
+ attr(tt, "factors") <- attr(tt, "factors")[,-1]
+ attr(tt, "response") <- 1
+ attr(out, "terms") <- out$terms <- attr(out$data, "terms") <- tt
+# ttt <- attr(tt,"dataClasses")
+# ttt[ttt == "factor"] <- "character"
+# attr(tt, "dataClasses") <- ttt
+
+ out
+}
+
+qi.chopit <- function(object, simpar, x, x1 = NULL, y = NULL) {
+
+ getcut <- function(x) x[1]
+ getpars <- function(x) x[2]
+
+ getsimpars <- function(simpar, k, name) {
+ idx <- match(name, names(k))
+ if (idx == 1) { k1 <- 1
+ } else k1 <- sum(k[1:(idx - 1)]) + 1
+ k2 <- sum(k[1:idx])
+ if ((k2 - k1) > 0) {
+ return(simpar[, k1:k2])
+ } else return(NULL)
+ }
+
+# keeptau <- function(xx, yy, mu, sigma) {
+# out <- NULL
+# out[one] <- pnorm(xx[yy[one]], mean = mu[one], sd = sigma)
+# out[last] <- 1 - pnorm(xx[(yy[last]-1)], mean = mu[last], sd = sigma)
+# out[inter] <- pnorm(xx[yy[inter]], mean = mu[inter], sd = sigma) -
+# pnorm(xx[(yy[inter]-1)], mean = mu[inter], sd = sigma)
+# out
+# }
+
+ makepr <- function(mu, xx, nt, y, control) {
+ tau <- xx[1:nt]
+ sigma <- xx[nt+1]
+ switch(as.character(control), "1" = pnorm((tau[1] - mu) / sigma),
+ "2" = pnorm((tau[y] - mu) / sigma) - pnorm((tau[y-1] - mu) / sigma),
+ "3" = 1 - pnorm((tau[nt] - mu) / sigma))
+ }
+
+ fn <- function(mu, xxx, nt, control, y, psd) {
+ pm <- xxx[(nt + 2)]
+ p1 <- makepr(mu = mu, xx = xxx[1:(nt+1)], nt = nt, control = control,
+ y = y)
+ mu * p1 * dnorm((mu - pm) / psd)
+ }
+
+ fint <- function(xxx, nt, control, psd, y) {
+ integrate(fn, lower = -Inf, upper = Inf,
+ xxx = xxx, nt = nt, control = control,
+ psd = psd, y = y)$value
+ }
+
+ num <- nrow(simpar)
+ labs <- object$chopit.parm$labels
+ ests <- object$chopit.parm$estimated
+ for (i in names(labs)) labs[[i]] <- labs[[i]][ests[[i]]]
+ k <- sapply(labs, length)
+ colnames(simpar) <- unlist(labs)
+
+ gpars <- getsimpars(simpar, k, name = "gamma")
+ lnse.re <- getsimpars(simpar, k, name = "lnse.re")
+ if (is.null(lnse.re)) { omega <- 0 } else { omega <- exp(lnse.re) }
+ lnse.self <- getsimpars(simpar, k, name = "lnse.self")
+ if (is.null(lnse.self)) { sigma <- 1 } else { sigma <- exp(lnse.self)}
+ ## Only need lnse.vign = sigmaj for distn in footnote 8, p.209 (APSR)
+ ## lnse.vign <- getsimpars(simpar, k, name = "lnse.vign")
+ ## if (is.na(match("lnse.vign.vign1", colnames(lnse.vign)))) {
+ ## lnse.vign <- cbind(lnse.vign1 = rep(0, num), lnse.vign, rep(1, num))
+ ## colnames(lnse.vign)[ncol(lnse.vign)] <-
+ ## paste("lnse.vign.vign", ncol(lnse.vign), sep = "")
+ ## }
+ ## sigmaj <- exp(lnse.vign)
+ ## theta1 <- getsimpars(simpar, k, name = "theta1")
+ beta <- getsimpars(simpar, k, name = "beta")
+
+ fmls <- object$formula
+ if (length(names(fmls)) == 3) {
+ ft <- fmls$tau
+ fs <- fmls$self
+ } else {
+ ft <- fmls[[3]]
+ fs <- fmls[[1]]
+ }
+
+ vid <- sort(ncol(vcov(object)) - (0:(length(labs$beta) - 1)))
+ Vbeta <- vcov(object)[vid,vid]
+ X <- as.matrix(x)[,-1, drop = FALSE]
+ ev <- X %*% t(beta)
+ qi <- list(ev = t(ev))
+ qi.name <- list(ev = "Expected value: E(mu|x)")
+ if (!is.null(x1)) {
+ X1 <- as.matrix(x1)[,-1, drop = FALSE]
+ ev1 <- X1 %*% t(beta)
+ qi$fd <- t(ev1 - ev)
+ qi.name$fd <- "First Differences in Expected Value: E(mu|x1) - E(mu|x)"
+ }
+ if (!is.null(y)) {
+ pmean <- X %*% t(beta)
+ pvar <- diag(X %*% Vbeta %*% t(X) + omega^2)
+ tmp <- strsplit(unlist(strsplit(labs$gamma, "gamma1.", fixed = TRUE)),
+ ".", fixed = TRUE)
+ cuts <- na.omit(unique(sapply(tmp, getcut)))
+ pars <- na.omit(unique(sapply(tmp, getpars)))
+ gamma <- array(NA, dim = c(num, length(pars), length(cuts)),
+ dimnames = list(NULL, pars, cuts))
+ for (i in cuts) {
+ tmp <- paste("gamma1", i, pars, sep = ".")
+ idx <- match(tmp, labs$gamma)
+ gamma[,,i] <- simpar[,idx]
+ }
+
+ check <- identical(deparse(ft[[length(ft)]], width.cutoff = 500),
+ deparse(fs[[3]], width.cutoff = 500))
+ checkInt <- "(Intercept)" %in% pars
+ if (check) {
+ V <- X
+ } else {
+ xt <- all.vars(ft)
+ xidx <- NULL
+ for (ii in 1:length(xt)) xidx <- c(xidx, grep(xt[ii], colnames(X)))
+ V <- X[, xidx, drop = FALSE]
+ }
+ if (checkInt) V <- cbind("(Intercept)" = rep(1, nrow(V)), V)
+
+ tau <- array(NA, c(num, length(cuts), nrow(x)),
+ dimnames = list(NULL, cuts, rownames(x)))
+ for (i in 1:num) tau[i,,] <- V %*% drop(gamma[i,,])
+ tau[,2:ncol(tau),] <- exp(tau[,2:ncol(tau),])
+ tau1 <- aperm(apply(tau, c(1,3), cumsum), c(2, 1, 3))
+
+ control <- rep(2, length(y))
+ control[which(y == 1)] <- 1
+ control[which(y == (ncol(tau) + 1))] <- 3
+ if (length(sigma) == 1) sigma <- rep(sigma, num)
+
+ cev <- matrix(NA, nrow = num, ncol = nrow(X),
+ dimnames = list(NULL, rownames(X)))
+
+ for (i in 1:length(y)) {
+ if ((i %% 10) == 0)
+ cat("Calculating E(mu|X,Y) for observation", i, "of", length(y), "\n")
+ tmp <- cbind(tau[,,i], sigma, pmean[i,])
+ cev[,i] <- apply(tmp, 1, fint, psd = sqrt(pvar[i]),
+ control = as.character(control[i]), nt = dim(tau)[2],
+ y = y[i])
+ }
+ qi$cev <- cev
+ qi.name$cev <- "Conditional Expected Value: E(mu|X, Y)"
+ }
+ list(qi = qi, qi.name = qi.name)
+}
diff --git a/R/describe.aov.R b/R/describe.aov.R
new file mode 100644
index 0000000..6cd69d5
--- /dev/null
+++ b/R/describe.aov.R
@@ -0,0 +1,17 @@
+describe.aov<-function(){
+category <- "continuous"
+description <- "Fit an Analysis of Variance Model"
+package <-list( name ="stats",
+ version ="2.5.0"
+ )
+parameters<-list(mu="mu")
+parameters$mu<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ ##specialFunction="cbind",
+ ##varInSpecialFunction= c(2,Inf)
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.arima.R b/R/describe.arima.R
index 6dedf69..e5dcb28 100644
--- a/R/describe.arima.R
+++ b/R/describe.arima.R
@@ -1,19 +1,19 @@
-describe.arima<- function(){
-category<- "continuous"
-description <- "Arima models for Time Series Data"
-package <-list( name ="stats",
- version ="0.1"
- )
-mu<- list(equations=c(1,1),
- tagsAllowed=FALSE,
- depVar=TRUE,
- expVar=TRUE,
- specialFunction="Diff",
- varInSpecialFunction= 4)
-sigma2<- list(equations=c(1,1),
- tagsAllowed=FALSE,
- depVar=FALSE,
- expVar=FALSE)
-pars<- list(mu=mu, sigma2=sigma2)
-model<- list(category=category,description=description,package=package, parameters=pars)
-}
+describe.arima<- function(){
+category<- "continuous"
+description <- "Arima models for Time Series Data"
+package <-list( name ="stats",
+ version ="0.1"
+ )
+mu<- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE,
+ specialFunction="Diff",
+ varInSpecialFunction= 4)
+sigma2<- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=FALSE,
+ expVar=FALSE)
+pars<- list(mu=mu, sigma2=sigma2)
+model<- list(category=category,description=description,package=package, parameters=pars)
+}
diff --git a/R/describe.chopit.R b/R/describe.chopit.R
new file mode 100644
index 0000000..c5ba39b
--- /dev/null
+++ b/R/describe.chopit.R
@@ -0,0 +1,28 @@
+describe.chopit <- function(){
+ category <- "ordinal"
+ description <- "Compound Hierarchical Ordinal Probit Regression for Survey Vignettes"
+ package <-list(name = "anchors",
+ version = "2.0",
+ CRAN = "http://wand.stanford.edu/R/CRAN")
+
+ parameters <- list()
+ parameters$self <- list(equations = c(1,1),
+ tagsAllowed = FALSE,
+ depVar = TRUE,
+ expVar = TRUE)
+
+ parameters$vign <- list(equations = c(1,1),
+ tagsAllowed = FALSE,
+ depVar = TRUE,
+ expVar = FALSE)
+
+ parameters$tau <- list(equations = c(1,1),
+ tagsAllowed = FALSE,
+ depVar = FALSE,
+ expVar = TRUE)
+
+ list(category = category,
+ description = description,
+ package = package,
+ parameters = parameters)
+}
diff --git a/R/describe.coxph.R b/R/describe.coxph.R
new file mode 100644
index 0000000..a9e31cb
--- /dev/null
+++ b/R/describe.coxph.R
@@ -0,0 +1,17 @@
+describe.coxph<-function(){
+ category <- "bounded"
+ description <- "Cox Proportional Hazard Regression for Duration Dependent Variables"
+ package <- list(name ="survival",
+ version ="2.34"
+ )
+ parameters <- list()
+ parameters$mu<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE,
+ specialFunction="Surv",
+ varInSpecialFunction=c(2,2)
+ )
+
+ list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.gam.logit.R b/R/describe.gam.logit.R
index b6f4e4c..bda22f6 100644
--- a/R/describe.gam.logit.R
+++ b/R/describe.gam.logit.R
@@ -1,4 +1,4 @@
-describe.gam.logit<-function(){
+describe.logit.gam<-function(){
category <- "dichotomous"
description <- "Generalized Additive Model for Dichotomous Dependent Variables"
package <-list( name ="mgcv",
diff --git a/R/describe.gam.normal.R b/R/describe.gam.normal.R
index e42cc1f..99e5973 100644
--- a/R/describe.gam.normal.R
+++ b/R/describe.gam.normal.R
@@ -1,4 +1,4 @@
-describe.gam.normal<-function(){
+describe.normal.gam<-function(){
category <- "continuous"
description <- "Generalized Additive Model for Continuous Dependent Variables"
package <-list( name ="mgcv",
diff --git a/R/describe.gam.poisson.R b/R/describe.gam.poisson.R
index 55b4efb..9f1d104 100644
--- a/R/describe.gam.poisson.R
+++ b/R/describe.gam.poisson.R
@@ -1,4 +1,4 @@
-describe.gam.poisson<-function(){
+describe.poisson.gam<-function(){
category <- "count"
description <- "Generalized Additive Model for Event Count Dependent Variables"
package <-list( name ="mgcv",
diff --git a/R/describe.gam.probit.R b/R/describe.gam.probit.R
index 18a0339..e3368ea 100644
--- a/R/describe.gam.probit.R
+++ b/R/describe.gam.probit.R
@@ -1,4 +1,4 @@
-describe.gam.probit<-function(){
+describe.probit.gam<-function(){
category <- "dichotomous"
description <- "Generalized Additive Model for Dichotomous Dependent Variables"
package <-list( name ="mgcv",
diff --git a/R/describe.gamma.gee.R b/R/describe.gamma.gee.R
new file mode 100644
index 0000000..8293a25
--- /dev/null
+++ b/R/describe.gamma.gee.R
@@ -0,0 +1,15 @@
+describe.gamma.gee<-function(){
+ category <- "bounded"
+ description <- "General Estimating Equation for Gamma Regression"
+ package <- list(name ="gee",
+ version ="4.13-12"
+ )
+ lambda <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+ list(category=category,description=description,package=package,parameters=list(lambda=lambda))
+}
+
diff --git a/R/describe.gamma.mixed.R b/R/describe.gamma.mixed.R
new file mode 100644
index 0000000..b063e89
--- /dev/null
+++ b/R/describe.gamma.mixed.R
@@ -0,0 +1,29 @@
+describe.gamma.mixed <- function(){
+ category <- "bounded"
+ description <- "Mixed effects gamma model"
+ parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
+
+ parameters$mu <- list(equations=c(1,1),
+ tagsAllowed=TRUE,
+ depVar=TRUE,
+ expVar=TRUE)
+
+ parameters$delta <- list(equations=c(1,2),
+ tagsAllowed=TRUE,
+ depVar=FALSE,
+ expVar=TRUE)
+
+ parameters$sigma2 <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=FALSE,
+ expVar=FALSE)
+
+ # Does Zelig need all dependencies here?
+ # e.g., lme4 depends on Matrix and lattice
+ package <- list(name="lme4", version="0.99875-9")
+
+
+ list(category=category, description = description, package=package, parameters=parameters)
+}
+
+
diff --git a/R/describe.lm.mixed.R b/R/describe.lm.mixed.R
new file mode 100644
index 0000000..f17539c
--- /dev/null
+++ b/R/describe.lm.mixed.R
@@ -0,0 +1,29 @@
+describe.ls.mixed <- function(){
+ category <- "continuous"
+ description <- "Mixed effects linear model"
+ parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
+
+ parameters$mu <- list(equations=c(1,1),
+ tagsAllowed=TRUE,
+ depVar=TRUE,
+ expVar=TRUE)
+
+ parameters$delta <- list(equations=c(1,2),
+ tagsAllowed=TRUE,
+ depVar=FALSE,
+ expVar=TRUE)
+
+ parameters$sigma2 <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=FALSE,
+ expVar=FALSE)
+
+ # Does Zelig need all dependencies here?
+ # e.g., lme4 depends on Matrix and lattice
+ package <- list(name="lme4", version="0.99875-9")
+
+
+ list(category=category, package=package, description = description, parameters=parameters)
+}
+
+
diff --git a/R/describe.logit.gee.R b/R/describe.logit.gee.R
index 0f4fb8c..7222bf2 100644
--- a/R/describe.logit.gee.R
+++ b/R/describe.logit.gee.R
@@ -1,15 +1,15 @@
-describe.logit.gee<-function(){
- category <- "dichotomous"
- description <- "General Estimating Equation for Logistic Regression"
- package <- list(name ="gee",
- version ="4.13-12"
- )
- pi <- list(equations=c(1,1),
- tagsAllowed=FALSE,
- depVar=TRUE,
- expVar=TRUE
- )
-
- list(category=category,description=description,package=package,parameters=list(pi=pi))
-}
-
+describe.logit.gee<-function(){
+ category <- "dichotomous"
+ description <- "General Estimating Equation for Logistic Regression"
+ package <- list(name ="gee",
+ version ="4.13-12"
+ )
+ pi <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+ list(category=category,description=description,package=package,parameters=list(pi=pi))
+}
+
diff --git a/R/describe.logit.mixed.R b/R/describe.logit.mixed.R
new file mode 100644
index 0000000..e7341e9
--- /dev/null
+++ b/R/describe.logit.mixed.R
@@ -0,0 +1,29 @@
+describe.logit.mixed <- function(){
+ category <- "dichotomous"
+ description <- "Mixed effects logistic model"
+ parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
+
+ parameters$mu <- list(equations=c(1,1),
+ tagsAllowed=TRUE,
+ depVar=TRUE,
+ expVar=TRUE)
+
+ parameters$delta <- list(equations=c(1,2),
+ tagsAllowed=TRUE,
+ depVar=FALSE,
+ expVar=TRUE)
+
+ parameters$sigma2 <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=FALSE,
+ expVar=FALSE)
+
+ # Does Zelig need all dependencies here?
+ # e.g., lme4 depends on Matrix and lattice
+ package <- list(name="lme4", version="0.99875-9")
+
+
+ list(category=category, package=package,description = description, parameters=parameters)
+}
+
+
diff --git a/R/describe.netcloglog.R b/R/describe.netcloglog.R
index 71dee74..12ec288 100644
--- a/R/describe.netcloglog.R
+++ b/R/describe.netcloglog.R
@@ -1,8 +1,8 @@
-describe.netcloglog<-function(){
+describe.cloglog.net<-function(){
category <- "dichotomous"
description <- "Social Network Complementary Log Log Regression for Dichotomous Dependent Variables"
-package <-list( name ="stats",
- version ="0.1"
+package <-list( name ="sna",
+ version ="1.4"
)
parameters<-list(pi="pi")
parameters$pi<-list(equations=c(1,1),
diff --git a/R/describe.netgamma.R b/R/describe.netgamma.R
index 75d7294..18ceb31 100644
--- a/R/describe.netgamma.R
+++ b/R/describe.netgamma.R
@@ -1,8 +1,8 @@
-describe.netgamma<-function(){
+describe.gamma.net<-function(){
category <- "bounded"
description <- "Social Network Gamma Regression for Continuous, Positive Dependent Variables"
-package <-list( name ="stats",
- version ="0.1"
+package <-list( name ="sna",
+ version ="1.4"
)
parameters<-list(lambda="lambda")
parameters$lambda<-list(equations=c(1,1),
diff --git a/R/describe.netlogit.R b/R/describe.netlogit.R
index 51c4929..c5c55f3 100644
--- a/R/describe.netlogit.R
+++ b/R/describe.netlogit.R
@@ -1,8 +1,8 @@
-describe.netlogit<-function(){
+describe.logit.net<-function(){
category <- "dichotomous"
description <- "Social Network Logistic Regression for Dichotomous Dependent Variables"
-package <-list( name ="stats",
- version ="0.1"
+package <-list( name ="sna",
+ version ="1.4"
)
parameters<-list(pi="pi")
parameters$pi<-list(equations=c(1,1),
diff --git a/R/describe.netls.R b/R/describe.netls.R
index f4c8a19..74c34ca 100644
--- a/R/describe.netls.R
+++ b/R/describe.netls.R
@@ -1,8 +1,8 @@
-describe.netls<-function(){
+describe.ls.net<-function(){
category <- "continuous"
description <- "Social Network Least Squares Regression for Continuous Dependent Variables"
package <-list( name ="sna",
- version ="0.1"
+ version ="1.4"
)
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,1),
diff --git a/R/describe.netnormal.R b/R/describe.netnormal.R
index 1f20aa2..90abad9 100644
--- a/R/describe.netnormal.R
+++ b/R/describe.netnormal.R
@@ -1,8 +1,8 @@
-describe.netnormal<-function(){
+describe.normal.net<-function(){
category <- "continuous"
description <- "Social Network Normal Regression for Continuous Dependent Variables"
-package <-list( name ="stats",
- version ="0.1"
+package <-list( name ="sna",
+ version ="1.4"
)
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,1),
diff --git a/R/describe.netpoisson.R b/R/describe.netpoisson.R
index 82506ff..9a817fc 100644
--- a/R/describe.netpoisson.R
+++ b/R/describe.netpoisson.R
@@ -1,8 +1,8 @@
-describe.netpoisson<-function(){
+describe.poisson.net<-function(){
category <- "count"
description <- "Social Network Poisson Regression for Event Count Dependent Variables"
-package <-list( name ="stats",
- version ="0.1"
+package <-list( name ="sna",
+ version ="1.4"
)
parameters<-list(lambda="lambda")
parameters$lambda<-list(equations=c(1,1),
diff --git a/R/describe.netprobit.R b/R/describe.netprobit.R
index 78fcd9c..47cfb76 100644
--- a/R/describe.netprobit.R
+++ b/R/describe.netprobit.R
@@ -1,4 +1,4 @@
-describe.netprobit<-function(){
+describe.probit.net<-function(){
category <- "dichotomous"
description <- "Social Network Probit Regression for Dichotomous Dependent Variables"
package <-list( name ="stats",
diff --git a/R/describe.normal.gee.R b/R/describe.normal.gee.R
new file mode 100644
index 0000000..ec452ab
--- /dev/null
+++ b/R/describe.normal.gee.R
@@ -0,0 +1,15 @@
+describe.normal.gee<-function(){
+ category <- "continuous"
+ description <- "General Estimating Equation for Normal Regression"
+ package <- list(name ="gee",
+ version ="4.13-12"
+ )
+ mu <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+ list(category=category,description=description,package=package,parameters=list(mu=mu))
+}
+
diff --git a/R/describe.poisson.gee.R b/R/describe.poisson.gee.R
new file mode 100644
index 0000000..6a3fdd5
--- /dev/null
+++ b/R/describe.poisson.gee.R
@@ -0,0 +1,15 @@
+describe.poisson.gee<-function(){
+ category <- "count"
+ description <- "General Estimating Equation for Poisson Regression"
+ package <- list(name ="gee",
+ version ="4.13-12"
+ )
+ lambda <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+ list(category=category,description=description,package=package,parameters=list(lambda=lambda))
+}
+
diff --git a/R/describe.poisson.mixed.R b/R/describe.poisson.mixed.R
new file mode 100644
index 0000000..b1cfbf8
--- /dev/null
+++ b/R/describe.poisson.mixed.R
@@ -0,0 +1,29 @@
+describe.poisson.mixed <- function(){
+ category <- "count"
+ description <- "Mixed effects poisson model"
+ parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
+
+ parameters$mu <- list(equations=c(1,1),
+ tagsAllowed=TRUE,
+ depVar=TRUE,
+ expVar=TRUE)
+
+ parameters$delta <- list(equations=c(1,2),
+ tagsAllowed=TRUE,
+ depVar=FALSE,
+ expVar=TRUE)
+
+ parameters$sigma2 <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=FALSE,
+ expVar=FALSE)
+
+ # Does Zelig need all dependencies here?
+ # e.g., lme4 depends on Matrix and lattice
+ package <- list(name="lme4", version="0.99875-9")
+
+
+ list(category=category, package=package, description = description, parameters=parameters)
+}
+
+
diff --git a/R/describe.logit.gee.R b/R/describe.probit.gee.R
similarity index 69%
copy from R/describe.logit.gee.R
copy to R/describe.probit.gee.R
index 0f4fb8c..183f3cd 100644
--- a/R/describe.logit.gee.R
+++ b/R/describe.probit.gee.R
@@ -1,15 +1,15 @@
-describe.logit.gee<-function(){
- category <- "dichotomous"
- description <- "General Estimating Equation for Logistic Regression"
- package <- list(name ="gee",
- version ="4.13-12"
- )
- pi <- list(equations=c(1,1),
- tagsAllowed=FALSE,
- depVar=TRUE,
- expVar=TRUE
- )
-
- list(category=category,description=description,package=package,parameters=list(pi=pi))
-}
-
+describe.probit.gee<-function(){
+ category <- "dichotomous"
+ description <- "General Estimating Equation for Probit Regression"
+ package <- list(name ="gee",
+ version ="4.13-12"
+ )
+ pi <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+ list(category=category,description=description,package=package,parameters=list(pi=pi))
+}
+
diff --git a/R/describe.probit.mixed.R b/R/describe.probit.mixed.R
new file mode 100644
index 0000000..e8155f8
--- /dev/null
+++ b/R/describe.probit.mixed.R
@@ -0,0 +1,29 @@
+describe.probit.mixed <- function(){
+ category <- "dichotomous"
+ description <- "Mixed effects probit model"
+ parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
+
+ parameters$mu <- list(equations=c(1,1),
+ tagsAllowed=TRUE,
+ depVar=TRUE,
+ expVar=TRUE)
+
+ parameters$delta <- list(equations=c(1,2),
+ tagsAllowed=TRUE,
+ depVar=FALSE,
+ expVar=TRUE)
+
+ parameters$sigma2 <- list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=FALSE,
+ expVar=FALSE)
+
+ # Does Zelig need all dependencies here?
+ # e.g., lme4 depends on Matrix and lattice
+ package <- list(name="lme4", version="0.99875-9")
+
+
+ list(category=category, package=package, description = description, parameters=parameters)
+}
+
+
diff --git a/R/eiRxC.R b/R/eiRxC.R
index c13bdfb..5b63c40 100644
--- a/R/eiRxC.R
+++ b/R/eiRxC.R
@@ -54,6 +54,13 @@ call.difp <- function(par, mx, my, covar, nR, nC, nP, const){
ebeta <- exp(gamma)/(1 + apply(exp(gamma), 1, sum))
diff <- sum((mx %*% ebeta - my[, -nC])^2) + (const * sum(gamma^2))
}
+
+ ## Trap bad values
+ if (is.null(diff))
+ diff <- 9999999;
+ if (!is.finite(diff))
+ diff <- 9999999;
+
diff
}
@@ -192,7 +199,7 @@ calc.fractions <- function(object, simpar) {
cmei <- function(formula, data, covar = NULL, ...){
if (is.null(rownames(data))) {
rownames(data) <- 1:nrow(data)
- assign(data, as.character(mc$data), env = .GlobalEnv)
+ assign(data, as.character(data), env = .GlobalEnv)
}
res<-NULL
myVars<-all.vars(formula[[2]])
diff --git a/R/netbinom.R b/R/netbinom.R
index 4c60019..5b95ea4 100644
--- a/R/netbinom.R
+++ b/R/netbinom.R
@@ -69,7 +69,7 @@ netbinom <- function (formula, data, LF="logit", family=binomial(link=LF), ...,
else for (i in 1:(nx - intercept)) g[[i + 1 + intercept]] <- x[i,
, ]
if (any(sapply(lapply(g, is.na), any)))
- warning("Missing data supplied to netpoisson; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
+ warning("Missing data supplied to poisson.net; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
fit.base <- gfit(g, mode = mode, diag = diag)
fit <- list()
fit$coefficients <- fit.base$coefficients
diff --git a/R/netgamma.R b/R/netgamma.R
index f6ead8a..5af9d1d 100644
--- a/R/netgamma.R
+++ b/R/netgamma.R
@@ -1,4 +1,4 @@
-netgamma <- function (formula, data, LF= "inverse", family=Gamma(link=LF), ..., mode = "digraph", diag = FALSE,
+gamma.net <- function (formula, data, LF= "inverse", family=Gamma(link=LF), ..., mode = "digraph", diag = FALSE,
nullhyp = c("qap", "qapspp", "qapy", "qapx", "qapallx", "cugtie",
"cugden", "cuguman", "classical"), tol = 1e-07, reps = 1000)
{
@@ -69,7 +69,7 @@ netgamma <- function (formula, data, LF= "inverse", family=Gamma(link=LF), ...,
else for (i in 1:(nx - intercept)) g[[i + 1 + intercept]] <- x[i,
, ]
if (any(sapply(lapply(g, is.na), any)))
- warning("Missing data supplied to netpoisson; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
+ warning("Missing data supplied to poisson.net; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
fit.base <- gfit(g, mode = mode, diag = diag)
fit <- list()
fit$coefficients <- fit.base$coefficients
diff --git a/R/netlogit.zelig.R b/R/netlogit.zelig.R
index beed6a8..94edde9 100644
--- a/R/netlogit.zelig.R
+++ b/R/netlogit.zelig.R
@@ -1,4 +1,4 @@
-netlogit.zelig <- function (y, x, intercept = TRUE, mode = "digraph", diag = FALSE,
+logit.net.zelig <- function (y, x, intercept = TRUE, mode = "digraph", diag = FALSE,
nullhyp = c("qap", "qapspp", "qapy", "qapx", "qapallx", "cugtie",
"cugden", "cuguman", "classical"), tol = 1e-07, reps = 1000)
{
@@ -27,11 +27,11 @@ netlogit.zelig <- function (y, x, intercept = TRUE, mode = "digraph", diag = FAL
y <- as.sociomatrix.sna(y)
x <- as.sociomatrix.sna(x)
if (is.list(y) || ((length(dim(y)) > 2) && (dim(y)[1] > 1)))
- stop("y must be a single graph in netlogit.")
+ stop("y must be a single graph in logit.net.")
if (length(dim(y)) > 2)
y <- y[1, , ]
if (is.list(x) || (dim(x)[2] != dim(y)[2]))
- stop("Homogeneous graph orders required in netlogit.")
+ stop("Homogeneous graph orders required in logit.net.")
nx <- stackcount(x) + intercept
n <- dim(y)[2]
g <- list(y)
@@ -42,7 +42,7 @@ netlogit.zelig <- function (y, x, intercept = TRUE, mode = "digraph", diag = FAL
else for (i in 1:(nx - intercept)) g[[i + 1 + intercept]] <- x[i,
, ]
if (any(sapply(lapply(g, is.na), any)))
- warning("Missing data supplied to netlogit; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
+ warning("Missing data supplied to logit.net; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
fit.base <- gfit(g, mode = mode, diag = diag)
fit <- list()
fit$coefficients <- fit.base$coefficients
@@ -195,6 +195,6 @@ netlogit.zelig <- function (y, x, intercept = TRUE, mode = "digraph", diag = FAL
if (intercept)
fit$names <- c("(intercept)", fit$names)
fit$intercept <- intercept
- class(fit) <- "netlogit"
+ class(fit) <- "logit.net"
fit
}
diff --git a/R/netnormal.R b/R/netnormal.R
index 72dee2b..7675e7c 100644
--- a/R/netnormal.R
+++ b/R/netnormal.R
@@ -1,4 +1,4 @@
-netnormal <- function (formula, data, LF= "identity", family=gaussian(link=LF), ..., mode = "digraph", diag = FALSE,
+normal.net <- function (formula, data, LF= "identity", family=gaussian(link=LF), ..., mode = "digraph", diag = FALSE,
nullhyp = c("qap", "qapspp", "qapy", "qapx", "qapallx", "cugtie",
"cugden", "cuguman", "classical"), tol = 1e-07, reps = 1000)
{
@@ -69,7 +69,7 @@ netnormal <- function (formula, data, LF= "identity", family=gaussian(link=LF),
else for (i in 1:(nx - intercept)) g[[i + 1 + intercept]] <- x[i,
, ]
if (any(sapply(lapply(g, is.na), any)))
- warning("Missing data supplied to netpoisson; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
+ warning("Missing data supplied to poisson.net; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
fit.base <- gfit(g, mode = mode, diag = diag)
fit <- list()
fit$coefficients <- fit.base$coefficients
diff --git a/R/netpoisson.R b/R/netpoisson.R
index 8ca1b09..31f51ee 100644
--- a/R/netpoisson.R
+++ b/R/netpoisson.R
@@ -1,4 +1,4 @@
-netpoisson <- function (formula, data, LF="log", family=poisson(link=LF), ..., mode = "digraph", diag = FALSE,
+poisson.net <- function (formula, data, LF="log", family=poisson(link=LF), ..., mode = "digraph", diag = FALSE,
nullhyp = c("qap", "qapspp", "qapy", "qapx", "qapallx", "cugtie",
"cugden", "cuguman", "classical"), tol = 1e-07, reps = 1000)
{
@@ -69,7 +69,7 @@ netpoisson <- function (formula, data, LF="log", family=poisson(link=LF), ..., m
else for (i in 1:(nx - intercept)) g[[i + 1 + intercept]] <- x[i,
, ]
if (any(sapply(lapply(g, is.na), any)))
- warning("Missing data supplied to netpoisson; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
+ warning("Missing data supplied to poisson.net; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
fit.base <- gfit(g, mode = mode, diag = diag)
fit <- list()
fit$coefficients <- fit.base$coefficients
diff --git a/R/param.gam.R b/R/param.gam.R
index ba3d473..a9d38c8 100644
--- a/R/param.gam.R
+++ b/R/param.gam.R
@@ -1,7 +1,7 @@
param.gam <- function(object, num = NULL, bootstrap = FALSE) {
if (!bootstrap) {
coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
- if (getzelig(object) == "gam.normal") {
+ if (getzelig(object) == "normal.gam") {
df <- object$df.residual
sig2 <- summary(object)$dispersion
alpha <- sqrt(df*sig2/rchisq(num, df=df))
diff --git a/R/param.gee.R b/R/param.gee.R
new file mode 100644
index 0000000..7ea4675
--- /dev/null
+++ b/R/param.gee.R
@@ -0,0 +1,12 @@
+param.gee <- function(object, num = NULL, bootstrap = FALSE){
+ model <- getzelig(object)
+ if (!bootstrap) {
+ res <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
+ }
+ else {
+ res <- coef(object)
+ }
+ res
+}
+
+
diff --git a/R/param.glm.R b/R/param.glm.R
index 4b190cf..4ca72da 100644
--- a/R/param.glm.R
+++ b/R/param.glm.R
@@ -9,8 +9,8 @@ param.glm <- function(object, num = NULL, bootstrap = FALSE){
res <- cbind(coef, alpha)
}
else if (model == "gamma") {
- rate <- gamma.shape(object)
- alpha <- rnorm(num, mean = rate$alpha, sd = rate$SE)
+ shape <- gamma.shape(object)
+ alpha <- rnorm(num, mean = shape$alpha, sd = shape$SE)
res <- cbind(coef, alpha)
}
else if (model == "negbin") {
@@ -23,11 +23,11 @@ param.glm <- function(object, num = NULL, bootstrap = FALSE){
else {
coef <- coef(object)
if (object$family$family == "gaussian") {
- alpha <- sum(object$residuals^2)/length(object$residuals)
+ alpha <- sum(object$residuals^2)/object$df.residual
res <- c(coef, alpha)
}
else if (object$family$family == "Gamma") {
- alpha <- gamma.dispersion(object)
+ alpha <- gamma.shape(object)$alpha
res <- c(coef, alpha)
}
else if (object$family$family == "neg.bin") {
@@ -43,3 +43,4 @@ param.glm <- function(object, num = NULL, bootstrap = FALSE){
+
diff --git a/R/param.lm.R b/R/param.lm.R
index 838e0a0..4e9286b 100644
--- a/R/param.lm.R
+++ b/R/param.lm.R
@@ -1,15 +1,16 @@
param.lm <-function(object, num, bootstrap = FALSE) {
# if (num < 1) num <- 1
+
if (!bootstrap) {
coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
df <- object$df.residual
- sig2 <- summary(object)$sigma^2
+ sig2 <- summary.lm(object)$sigma^2
alpha <- sqrt(df*sig2/rchisq(num, df=df))
res <- cbind(coef, alpha)
}
else {
coef <- coef(object)
- alpha <- summary(object)$sigma
+ alpha <- summary.lm(object)$sigma
res <- c(coef, alpha)
}
res
diff --git a/R/param.mixed.R b/R/param.mixed.R
new file mode 100644
index 0000000..933bcc8
--- /dev/null
+++ b/R/param.mixed.R
@@ -0,0 +1,45 @@
+param.lmer <- param.glmer <- function(object, num, bootstrap = FALSE) {
+
+ if (!bootstrap) {
+ object <- selectMethod("summary", "lmer") (object)
+ #print(class(object))
+ fixed <- fixef(object)
+ vcov.fix <- vcov(object)
+ betasF <- NULL
+ if (length(fixed) > 0){
+ betasF <- mvrnorm(num, fixed, vcov.fix)
+ }
+ scale <- attr(VarCorr(object), "sc")
+ vars <- object at bVar
+ gammas <- NULL
+ n.G <- length(vars)
+
+ for (m in 1:n.G){
+ V.beta <- VarCorr(object)[[m]]
+ vars.m <- vars[[m]]
+ J <- dim(vars.m)[1]
+ gammas[[m]] <- mvrnorm(num, rep(0, J), V.beta)
+ }
+ names(gammas) <- names(vars)
+ betas <- betasF
+ scale <- object at sigma
+
+ } else {
+ object <- summary(object)
+ fixed <- fixef(object)
+ scale <- attr (VarCorr(object), "sc")
+ betasF <- fixed
+ vars <- object at bVar
+ gammas <- NULL
+ n.G <- length(vars)
+ for (m in 1:n.G){
+ V.beta <- VarCorr(object)[[m]]
+ gammas[[m]] <- mvrnorm(1, 0, V.beta)
+ }
+ names(gammas)<- names(vars)
+ betas <- betasF
+ scale <- object at sigma
+ }
+ res <- list(betas=betas, gammas=gammas, scale=scale)
+ res
+}
diff --git a/R/param.netglm.R b/R/param.netglm.R
index b1be0f9..d119de6 100644
--- a/R/param.netglm.R
+++ b/R/param.netglm.R
@@ -1,13 +1,13 @@
-param.netglm <- function(object, num = NULL, bootstrap = FALSE) {
+param.netglm <- function(object, num = NULL, bootstrap = FALSE, x = NULL) {
if (!bootstrap) {
coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
- if (getzelig(object) == "netnormal") {
+ if (getzelig(object) == "normal.net") {
df <- object$df.residual
sig2 <- summary(object)$dispersion
alpha <- sqrt(df*sig2/rchisq(num, df=df))
res <- cbind(coef, alpha)
}
- else if (getzelig(object) == "netgamma") {
+ else if (getzelig(object) == "gamma.net") {
class(object) <- c("glm","lm")
rate <- gamma.shape(object)
class(object) <- "netglm"
@@ -23,11 +23,11 @@ param.netglm <- function(object, num = NULL, bootstrap = FALSE) {
}
else {
coef <- coef(object)
- if (object$family$family == "netnormal") {
+ if (object$family$family == "normal.net") {
alpha <- sum(object$residuals^2)/length(object$residuals)
res <- c(coef, alpha)
}
- else if (object$family$family == "netgamma") {
+ else if (object$family$family == "gamma.net") {
alpha <- gamma.dispersion(object)
res <- c(coef, alpha)
}
diff --git a/R/param.relogit.R b/R/param.relogit.R
index eaa465c..ef8af98 100644
--- a/R/param.relogit.R
+++ b/R/param.relogit.R
@@ -1,4 +1,4 @@
-param.relogit <- function(object, num, x, bootstrap = FALSE) {
+param.relogit <- function(object, num, x, bootstrap = FALSE, bootfn = NULL) {
if ("relogit2" %in% class(object)) {
pping <- function(tmp0, tmp1, num, bootstrap, x) {
par0 <- param.relogit(tmp0, num=num, x=x, bootstrap=bootstrap)
diff --git a/R/param.zaov.R b/R/param.zaov.R
new file mode 100644
index 0000000..a7ef225
--- /dev/null
+++ b/R/param.zaov.R
@@ -0,0 +1,46 @@
+param.mlm <-function(object, num, bootstrap = FALSE) {
+ mu <- coef(object)
+ depVars <- colnames(mu)
+ sigma <- vcov(object)
+ mu <- matrix(mu, ncol=1)
+
+
+ if (!bootstrap) {
+ coef <- mvrnorm(num, mu=mu, Sigma=sigma)
+ res <- append.sigma(object, coef,num,bootstrap)
+
+ } else {
+ coef <- coef(object)
+ res <- append.sigma(object, coef,num,bootstrap)
+
+ }
+
+ res
+}
+
+append.sigma <- function(object, coef,num, bootstrap){
+ smobj <- summary.zmlm(object)
+ df <- object$df.residual
+ mat <- as.matrix(coef)
+ for(n in 1:length(smobj)){
+ resp <- smobj[[n]]
+ nm <- names(smobj)[n]
+
+ if(!bootstrap){
+ sig2 <- resp$sigma^2
+ alpha <- sqrt(df*sig2/rchisq(num, df=df))
+ mat <- cbind(mat, alpha)
+ nc <- ncol(mat)
+ del <- sub("([[:alpha:]+])[[:space:]+](.*)","\\1", nm)
+ cnm <- sub(paste(del, "[[:space:]+](.*)", sep=""),"\\1", nm)
+ cnm <- paste(cnm,":alpha",sep="")
+ colnames(mat)[nc] <- cnm
+
+ }else{
+ alpha <- resp$sigma
+ mat <- cbind(mat, alpha)
+ }
+
+ }
+ return(mat)
+}
diff --git a/R/plot.zelig.2sls.R b/R/plot.zelig.2sls.R
deleted file mode 100644
index a32a4f7..0000000
--- a/R/plot.zelig.2sls.R
+++ /dev/null
@@ -1,15 +0,0 @@
-plot.zelig.w2sls <- plot.zelig.2sls <- function(x, xlab = "",
- user.par = FALSE, ...) {
- k <- length(x$qi)
- op <- par(no.readonly = TRUE)
- if (!user.par)
- par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, dims(x$qi[[1]])[2]))
- for (i in 1:k) {
- for (j in 1:dims(x$qi[[i]])[2]){
- qi <- as.vector((x$qi[[i]])[,j])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
diff --git a/R/plot.zelig.R b/R/plot.zelig.R
index dc72f32..4c39aa2 100644
--- a/R/plot.zelig.R
+++ b/R/plot.zelig.R
@@ -5,7 +5,11 @@ plot.zelig <- function(x, xlab = "", user.par = FALSE, ...) {
class(x) <- x$zelig.call$model
if (exists(paste("plot.zelig", x$zelig.call$model, sep = ".")))
UseMethod("plot.zelig", x)
- else
- stop(paste(x$zelig.call$model, "does not have an applicable plot method for sim() output."))
+ else{
+ res <- try(plot.zelig.default(x, xlab = xlab, user.par = user.par, ...), silent=F)
+ if(class(res) =="try-error")
+ message("No plot generated for model ", class(x)[1])
+ }
+
}
}
diff --git a/R/plot.zelig.coxph.R b/R/plot.zelig.coxph.R
new file mode 100644
index 0000000..1b2bf26
--- /dev/null
+++ b/R/plot.zelig.coxph.R
@@ -0,0 +1,30 @@
+plot.zelig.coxph <- function(x, xlab = "", user.par = FALSE, alt.col = "red", alt.lty = "dashed", CI = 95, ...) {
+ s <- summary(x, CI=CI)
+ k <- length(x$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))
+ if(!is.null(x$qi$hr)){
+ hr <- x$qi$hr
+ plot(density(hr), main = x$qi.name$hr, xlab = xlab, ...)
+ s$qi.stats$hr <- x$qi.name$hr <- NULL
+ j <- k-1
+ }
+ else{
+ j <- k
+ }
+ for (i in 1:(j-1)) {
+ qi <- as.vector(s$qi.stats[[i]][,1])
+ time <- as.numeric(rownames(s$qi.stats[[i]]))
+ ci.lower <- as.vector(s$qi.stats[[i]][,3])
+ ci.upper <- as.vector(s$qi.stats[[i]][,4])
+ plot(y = qi, x = time, main = x$qi.name[[i]], xlab = "time", ylab = names(x$qi.name)[i], type = "s", ...)
+ lines(y = ci.lower, x = time, type = "s", lty = alt.lty, col = alt.col)
+ lines(y = ci.upper, x = time, type = "s", lty = alt.lty, col = alt.col)
+ }
+ haz <- as.vector(s$qi.stats[[j]])
+ time <- as.numeric(rownames(s$qi.stats[[j-1]]))
+ plot(y = haz, x = time, main = x$qi.name[[j]], xlab = "time", ylab = names(x$qi.name)[j], type = "s")
+ par(op)
+}
diff --git a/R/plot.zelig.3sls.R b/R/plot.zelig.default.R
similarity index 83%
rename from R/plot.zelig.3sls.R
rename to R/plot.zelig.default.R
index aeb81b9..5019d7e 100644
--- a/R/plot.zelig.3sls.R
+++ b/R/plot.zelig.default.R
@@ -1,4 +1,4 @@
-plot.zelig.3sls <- function(x, xlab = "", user.par = FALSE, ...) {
+plot.zelig.default <- function(x, xlab = "", user.par = FALSE, ...) {
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.exp.R b/R/plot.zelig.exp.R
deleted file mode 100644
index e12de93..0000000
--- a/R/plot.zelig.exp.R
+++ /dev/null
@@ -1,12 +0,0 @@
-plot.zelig.exp <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$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))
- for (i in 1:k) {
- qi <- as.vector(x$qi[[i]])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- par(op)
-}
diff --git a/R/plot.zelig.gam.logit.R b/R/plot.zelig.gam.logit.R
index 19d2c5d..03239bb 100644
--- a/R/plot.zelig.gam.logit.R
+++ b/R/plot.zelig.gam.logit.R
@@ -1,4 +1,4 @@
-plot.zelig.gam.logit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+plot.zelig.logit.gam <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.gam.poisson.R b/R/plot.zelig.gam.poisson.R
index 9e68534..8399f33 100644
--- a/R/plot.zelig.gam.poisson.R
+++ b/R/plot.zelig.gam.poisson.R
@@ -1,4 +1,4 @@
-plot.zelig.gam.poisson <- function(x, xlab = "", user.par = FALSE, ...){
+plot.zelig.poisson.gam <- function(x, xlab = "", user.par = FALSE, ...){
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.gam.probit.R b/R/plot.zelig.gam.probit.R
index 5379b51..8c74603 100644
--- a/R/plot.zelig.gam.probit.R
+++ b/R/plot.zelig.gam.probit.R
@@ -1,4 +1,4 @@
-plot.zelig.gam.probit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+plot.zelig.probit.gam <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.gamma.R b/R/plot.zelig.gamma.R
deleted file mode 100644
index 8f8627e..0000000
--- a/R/plot.zelig.gamma.R
+++ /dev/null
@@ -1,12 +0,0 @@
-plot.zelig.gamma <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$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))
- for (i in 1:k) {
- qi <- as.vector(x$qi[[i]])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- par(op)
-}
diff --git a/R/plot.zelig.netnormal.R b/R/plot.zelig.gamma.gee.R
similarity index 81%
rename from R/plot.zelig.netnormal.R
rename to R/plot.zelig.gamma.gee.R
index e57bcfe..a407567 100644
--- a/R/plot.zelig.netnormal.R
+++ b/R/plot.zelig.gamma.gee.R
@@ -1,4 +1,4 @@
-plot.zelig.netnormal <- function(x, xlab = "", user.par = FALSE, ...) {
+plot.zelig.gamma.gee <- function(x, xlab = "", user.par = FALSE, ...) {
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
@@ -10,3 +10,5 @@ plot.zelig.netnormal <- function(x, xlab = "", user.par = FALSE, ...) {
}
par(op)
}
+
+
diff --git a/R/plot.zelig.logit.gee.R b/R/plot.zelig.logit.gee.R
index 3bc3136..0aef3c4 100644
--- a/R/plot.zelig.logit.gee.R
+++ b/R/plot.zelig.logit.gee.R
@@ -1,20 +1,17 @@
-plot.zelig.logit.gee <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
- k <- length(x$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)
-}
+plot.zelig.logit.gee <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ if (k > 1){
+ for (i in 1:k) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ }
+ else{
+ plot(density(x$qi$ev), main = x$qi.name$ev, xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.lognorm.R b/R/plot.zelig.lognorm.R
deleted file mode 100644
index cb9c5a2..0000000
--- a/R/plot.zelig.lognorm.R
+++ /dev/null
@@ -1,12 +0,0 @@
-plot.zelig.lognorm <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$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))
- for (i in 1:k) {
- qi <- as.vector(x$qi[[i]])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- par(op)
-}
diff --git a/R/plot.zelig.ls.R b/R/plot.zelig.ls.R
deleted file mode 100644
index bdfb6a3..0000000
--- a/R/plot.zelig.ls.R
+++ /dev/null
@@ -1,12 +0,0 @@
-plot.zelig.ls <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$qi)
- op <- par(no.readonly = TRUE)
- if (!user.par)
- par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, 1))
- for (i in 1:k) {
- qi <- as.vector(x$qi[[i]])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- par(op)
-}
diff --git a/R/plot.zelig.netcloglog.R b/R/plot.zelig.netcloglog.R
index 0a15b3b..0315d39 100644
--- a/R/plot.zelig.netcloglog.R
+++ b/R/plot.zelig.netcloglog.R
@@ -1,4 +1,4 @@
-plot.zelig.netcloglog <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+plot.zelig.cloglog.net <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.netgamma.R b/R/plot.zelig.netgamma.R
deleted file mode 100644
index 8ce87f8..0000000
--- a/R/plot.zelig.netgamma.R
+++ /dev/null
@@ -1,12 +0,0 @@
-plot.zelig.netgamma <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$qi)
- op <- par(no.readonly = TRUE)
- if (!user.par)
- par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, 1))
- for (i in 1:k) {
- qi <- as.vector(x$qi[[i]])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- par(op)
-}
diff --git a/R/plot.zelig.netlogit.R b/R/plot.zelig.netlogit.R
index e219b11..000d3e2 100644
--- a/R/plot.zelig.netlogit.R
+++ b/R/plot.zelig.netlogit.R
@@ -1,4 +1,4 @@
-plot.zelig.netlogit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+plot.zelig.logit.net <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.netls.R b/R/plot.zelig.netls.R
deleted file mode 100755
index b1f67f9..0000000
--- a/R/plot.zelig.netls.R
+++ /dev/null
@@ -1,12 +0,0 @@
-plot.zelig.netls <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$qi)
- op <- par(no.readonly = TRUE)
- if (!user.par)
- par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, 1))
- for (i in 1:k) {
- qi <- as.vector(x$qi[[i]])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- par(op)
-}
diff --git a/R/plot.zelig.netpoisson.R b/R/plot.zelig.netpoisson.R
index fdcc314..163c484 100644
--- a/R/plot.zelig.netpoisson.R
+++ b/R/plot.zelig.netpoisson.R
@@ -1,4 +1,4 @@
-plot.zelig.netpoisson <- function(x, xlab = "", user.par = FALSE, ...){
+plot.zelig.poisson.net <- function(x, xlab = "", user.par = FALSE, ...){
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.netprobit.R b/R/plot.zelig.netprobit.R
index 9324f18..78c0ef0 100644
--- a/R/plot.zelig.netprobit.R
+++ b/R/plot.zelig.netprobit.R
@@ -1,4 +1,4 @@
-plot.zelig.netprobit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+plot.zelig.probit.net <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.normal.R b/R/plot.zelig.normal.R
deleted file mode 100644
index c172af6..0000000
--- a/R/plot.zelig.normal.R
+++ /dev/null
@@ -1,12 +0,0 @@
-plot.zelig.normal <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$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))
- for (i in 1:k) {
- qi <- as.vector(x$qi[[i]])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- par(op)
-}
diff --git a/R/plot.zelig.gam.normal.R b/R/plot.zelig.normal.gee.R
similarity index 81%
rename from R/plot.zelig.gam.normal.R
rename to R/plot.zelig.normal.gee.R
index 8c5553b..a3f7d5a 100644
--- a/R/plot.zelig.gam.normal.R
+++ b/R/plot.zelig.normal.gee.R
@@ -1,4 +1,4 @@
-plot.zelig.gam.normal <- function(x, xlab = "", user.par = FALSE, ...) {
+plot.zelig.normal.gee <- function(x, xlab = "", user.par = FALSE, ...) {
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.netpoisson.R b/R/plot.zelig.poisson.gee.R
similarity index 66%
copy from R/plot.zelig.netpoisson.R
copy to R/plot.zelig.poisson.gee.R
index fdcc314..06c03b1 100644
--- a/R/plot.zelig.netpoisson.R
+++ b/R/plot.zelig.poisson.gee.R
@@ -1,11 +1,9 @@
-plot.zelig.netpoisson <- function(x, xlab = "", user.par = FALSE, ...){
+plot.zelig.poisson.gee <- function(x, xlab = "", user.par = FALSE, ...) {
k <- length(x$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)
@@ -14,3 +12,4 @@ plot.zelig.netpoisson <- function(x, xlab = "", user.par = FALSE, ...){
}
par(op)
}
+
diff --git a/R/plot.zelig.probit.gee.R b/R/plot.zelig.probit.gee.R
new file mode 100644
index 0000000..3f77ce9
--- /dev/null
+++ b/R/plot.zelig.probit.gee.R
@@ -0,0 +1,20 @@
+plot.zelig.probit.gee <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ if (k > 1){
+ for (i in 1:k) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ }
+ else{
+ plot(density(x$qi$ev), main = x$qi.name$ev, xlab = xlab, ...)
+ }
+ par(op)
+}
+
+
+
diff --git a/R/plot.zelig.sur.R b/R/plot.zelig.sur.R
deleted file mode 100644
index 691e8e1..0000000
--- a/R/plot.zelig.sur.R
+++ /dev/null
@@ -1,14 +0,0 @@
-plot.zelig.sur <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$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, dims(x$qi[[1]])[2]))
- for (i in 1:k) {
- for (j in 1:dims(x$qi[[i]])[2]){
- qi <- as.vector((x$qi[[i]])[,j])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
diff --git a/R/plot.zelig.threesls.R b/R/plot.zelig.threesls.R
deleted file mode 100644
index 0c549b2..0000000
--- a/R/plot.zelig.threesls.R
+++ /dev/null
@@ -1,15 +0,0 @@
-plot.zelig.threesls <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$qi)
- op <- par(no.readonly = TRUE)
- if (!user.par)
- par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, dims(x$qi[[1]])[2]))
- for (i in 1:k) {
-
- for (j in 1:dims(x$qi[[i]])[2]){
- qi <- as.vector((x$qi[[i]])[,j])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
diff --git a/R/plot.zelig.twosls.R b/R/plot.zelig.twosls.R
deleted file mode 100644
index 08f1312..0000000
--- a/R/plot.zelig.twosls.R
+++ /dev/null
@@ -1,15 +0,0 @@
-plot.zelig.w2sls <- plot.zelig.twosls <- function(x, xlab = "",
- user.par = FALSE, ...) {
- k <- length(x$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, dims(x$qi[[1]])[2]))
- for (i in 1:k) {
- for (j in 1:dims(x$qi[[i]])[2]){
- qi <- as.vector((x$qi[[i]])[,j])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
diff --git a/R/plot.zelig.weibull.R b/R/plot.zelig.weibull.R
deleted file mode 100644
index 3d09774..0000000
--- a/R/plot.zelig.weibull.R
+++ /dev/null
@@ -1,12 +0,0 @@
-plot.zelig.weibull <- function(x, xlab = "", user.par = FALSE, ...) {
- k <- length(x$qi)
- op <- par(no.readonly = TRUE)
- if (!user.par)
- par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, 1))
- for (i in 1:k) {
- qi <- as.vector(x$qi[[i]])
- plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
- }
- par(op)
-}
diff --git a/R/plot.zeliglist.R b/R/plot.zeliglist.R
new file mode 100644
index 0000000..6cf5efe
--- /dev/null
+++ b/R/plot.zeliglist.R
@@ -0,0 +1,21 @@
+plot.zeliglist <- function(x, xlab = "", user.par = FALSE, ...) {
+
+ kk <- length(x)
+ j <- dims(x[[1]]$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(kk, j))
+ k <- length(x[[1]]$qi)
+ for(i in 1:k){
+ for(n in 1:kk){
+ xx <- x[[n]]
+ class(xx) <- xx$zelig.call$model
+ qi <- as.vector(xx$qi[[i]])
+ plot(density(qi), main=xx$qi.name[[i]], xlab = xlab, ...)
+
+
+ }
+ }
+
+}
diff --git a/R/print.coxhazard.R b/R/print.coxhazard.R
new file mode 100644
index 0000000..6005943
--- /dev/null
+++ b/R/print.coxhazard.R
@@ -0,0 +1,8 @@
+print.coxhazard <- function(x,...){
+ haz <- matrix(x, ncol=1)
+ colnames(haz) <- "hazard"
+ rownames(haz) <- rownames(x)
+ print(haz)
+}
+
+
diff --git a/R/print.zaovlist.R b/R/print.zaovlist.R
new file mode 100644
index 0000000..fe4ea42
--- /dev/null
+++ b/R/print.zaovlist.R
@@ -0,0 +1,12 @@
+print.zaovlist <- function (x, ...)
+{
+ cl <- attr(x, "call")
+ if (!is.null(cl)) {
+ attr(x,"call") <- NULL
+
+ }
+
+
+ stats:::print.aovlist(x,...)
+}
+
diff --git a/R/qi.BetaReg.R b/R/qi.BetaReg.R
index 340982b..3f1c7c2 100644
--- a/R/qi.BetaReg.R
+++ b/R/qi.BetaReg.R
@@ -21,8 +21,10 @@ qi.BetaReg <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
if (!is.null(y)) {
yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ #tmp.ev <- qi$tt.ev <- yvar - qi$ev
tmp.ev <- yvar - qi$ev
qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ #qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
}
list(qi=qi, qi.name=qi.name)
diff --git a/R/qi.aov.R b/R/qi.aov.R
new file mode 100644
index 0000000..8f9f54b
--- /dev/null
+++ b/R/qi.aov.R
@@ -0,0 +1,10 @@
+qi.aov <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ ### Calls coef.aov that deletes all entries with NA.
+ ### qi.lm calls coef.default that do not delete entries with
+ ### NA yielding error messages
+
+ cc <- coef(object)
+ object$coef <- cc
+ qi.lm(object, simpar, x, x1, y)
+}
+
diff --git a/R/qi.coxph.R b/R/qi.coxph.R
new file mode 100644
index 0000000..413f5d4
--- /dev/null
+++ b/R/qi.coxph.R
@@ -0,0 +1,241 @@
+qi.coxph <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ #model <- getzelig(object)
+ num <- nrow(simpar)
+ if(!any(class(object) == "MI"))
+ k <- length(getcoef(object))
+ else
+ k <- length(getcoef(object[[1]]))
+ #xnames <- colnames(x)[1:k]
+
+## Strata
+ clean.strata <-function(aStr){ #fix strata string
+ aStr <- gsub('[[:space:]]', '', aStr)
+ ge <- unlist(strsplit(aStr, split=">="))
+ le <- unlist(strsplit(aStr, split="<="))
+ g <- unlist(strsplit(aStr, split=">"))
+ l <- unlist(strsplit(aStr, split="<"))
+ e <- unlist(strsplit(aStr, split="="))
+ split <- equal <- NULL
+ if(length(ge)>1)
+ split <- ">="
+ if(length(le)>1)
+ split <- "<="
+ if(length(g)>1 & length(ge)==1)
+ split <- ">"
+ if(length(l)>1 & length(le)==1)
+ split <- "<"
+ if(is.null(split)){
+ split <- "="
+ equal <- 1
+ }
+ res <- unlist(strsplit(aStr, split="=|>|<|>=|<="))
+ if(!is.null(equal)){
+ string <- paste(res[[1]],split,res[[2]], sep="")
+ }
+ else{
+ string <- paste(res[[1]], " ", split, " ", res[[2]], "=",
+ res[[3]], sep="")
+ }
+ return(string)
+ }
+
+ if(length(x)>k){
+ strata <- as.matrix(x[k+1])
+ strata <- clean.strata(strata)
+ x <- x[1:k]
+ }
+ else
+ strata <- NULL
+ if(!is.null(x1) & length(x1)>k){
+ strata1 <- as.matrix(x1[k+1])
+ strata1 <- clean.strata(strata1)
+ x1 <- x1[1:k]
+ }
+ else
+ strata1 <- NULL
+ if(!is.null(x1) & !identical(strata, strata1))
+ stop("Strata must be identical for x and x1")
+
+ #if(!is.null(x)){
+ # x <- matrix(as.numeric(x), nrow=1)
+ # colnames(x) <- xnames
+ #}
+ #if(!is.null(x1)){
+ # x1 <- matrix(as.numeric(x1), nrow=1)
+ # colnames(x1) <- xnames
+ #}
+
+ if (!is.null(x))
+ x <- as.matrix(x)
+ if (!is.null(x1))
+ x1 <- as.matrix(x1)
+
+
+ coef <- as.matrix(simpar[,1:k])
+ eta <- coef%*%t(x)
+ risk <- exp(eta)
+ qi <- qi.name <- list()
+
+
+## Rate Ratio
+ if(!is.null(x1)){
+ eta1 <- coef%*%t(x1)
+ risk1 <- exp(eta1)
+ qi$hr <- risk1/risk
+ qi.name$hr <- "Hazard Ratios: h(t|X1)/h(t|X)"
+ }
+
+## Survival Function
+## Not MI data
+ if(!any(class(object) == "MI")){
+ surv.fit <- survfit(object, newdata=x)
+
+ if(!is.null(strata)){
+ index <- which(match(summary(surv.fit)$strata, strata) == 1)
+ surv <- surv.fit$surv[index]
+ time <- surv.fit$time[index]
+ surv.se <- summary(surv.fit)$std.err[index]
+ log.surv <- log(surv)
+ log.surv.se <- surv.fit$std.err[index]
+ }
+ else{
+ surv <- surv.fit$surv
+ time <- surv.fit$time
+ surv.se <- summary(surv.fit)$std.err
+ log.surv <- log(surv)
+ log.surv.se <- surv.fit$std.err
+ }
+ }
+
+## MI data
+
+ else{
+ m <- length(object)
+
+ surv.fit <- survfit(object[[1]], newdata=x)
+
+ if(!is.null(strata)){
+ index <- which(match(summary(surv.fit)$strata, strata) == 1)
+ surv <- surv.fit$surv[index]
+ time <- surv.fit$time[index]
+ }
+ else{
+ surv <- surv.fit$surv
+ time <- surv.fit$time
+ }
+ means <- cbind(time, "1"=surv)
+
+ #Merge data by survival time
+ for(i in 2:m){
+ surv.fit <- survfit(object[[i]], newdata=x)
+
+ if(!is.null(strata)){
+ index <- which(match(summary(surv.fit)$strata, strata) == 1)
+ surv <- surv.fit$surv[index]
+ time <- surv.fit$time[index]
+ }
+ else{
+ surv <- surv.fit$surv
+ time <- surv.fit$time
+ }
+
+ new.means <- cbind(time,surv)
+ colnames(new.means) <- c("time",paste(i))
+ means <- merge(means, new.means, by="time", all = T)
+ }
+ means <- means[,-1]
+ surv <- rowMeans(means, na.rm = T) #survival means
+ na <- apply(means, 2, is.na)
+ na <- 1 - apply(na, 2, as.numeric)
+ n <- apply(na, 1, sum) #number of non-NA per survival time
+ if(any(n==1)){
+ warning("Some imputed survival times appear in only one
+dataset. Suggest increasing number of imputed datasets and/or specify
+duration variable as ordinal")
+ n[which(n==1)] <- n[which(n==1)]+1
+ }
+
+ surv.fit <- survfit(object[[1]], newdata=x)
+
+ if(!is.null(strata)){
+ index <- which(match(summary(surv.fit)$strata, strata) == 1)
+ surv.se <- summary(surv.fit)$std.err[index]
+ time <- surv.fit$time[index]
+ }
+ else{
+ surv.se <- summary(surv.fit)$std.err
+ time <- surv.fit$time
+ }
+ se <- cbind(time, "1"=surv.se)
+ for(i in 2:m){
+ surv.fit <- survfit(object[[i]], newdata=x)
+
+ if(!is.null(strata)){
+ index <- which(match(summary(surv.fit)$strata, strata) == 1)
+ surv.se <- summary(surv.fit)$std.err[index]
+ time <- surv.fit$time[index]
+ }
+ else{
+ surv.se <- summary(surv.fit)$std.err
+ time <- surv.fit$time
+ }
+ new.se <- cbind(time,surv.se)
+ colnames(new.se) <- c("time",paste(i))
+ se <- merge(se, new.se, by="time", all = T)
+ }
+
+ time <- se[,1]
+ t <- length(t)
+ se <- se[,-1]
+ var <- se^2
+ surv.se <- c()
+ mean.var <- rowMeans(var, na.rm = T)
+
+ #Rubin's rule
+
+ B <- (means-surv)^2
+ B[is.na(B)] <- 0
+ B <- apply(B,1,sum)/(n-1)
+ surv.se <- sqrt(mean.var + B*(1+1/n))
+
+ log.surv <- log(surv)
+ log.surv.se <- sqrt(1/(surv^2) * surv.se^2) #delta method
+
+ }
+
+ surv.sims <- matrix(NA, nrow=num, ncol=length(surv))
+ for (i in 1:length(surv)){
+ surv.sims[,i] <- exp(rnorm(num, mean=log.surv[i], sd=log.surv.se[i]))
+ }
+ colnames(surv.sims) <- time
+
+ qi$survival <- surv.sims
+ qi.name$survival <- "Estimated Survival Function Over Time: S(t|X)"
+
+## Cumulative Hazard
+ qi$cumhaz <- cumhaz <- -log(surv.sims)
+ qi.name$cumhaz <- "Estimated Cumulative Hazard Over Time: H(t|X)"
+
+## Hazard
+ cumhaz.means <- colMeans(cumhaz)
+ hazard <- matrix(NA, ncol=ncol(cumhaz), nrow=1)
+ colnames(hazard) <- colnames(cumhaz)
+ hazard[,1] <- cumhaz.means[1]
+ for (i in 2:length(time)){
+ hazard[,i] <- cumhaz.means[i] - cumhaz.means[i-1]
+ }
+ qi$hazard <- hazard
+ qi.name$hazard <- "Estimated Hazard Rate Over Time: h(t|X)"
+ class(qi$hazard) <- "coxhazard"
+
+
+ list(qi=qi, qi.name=qi.name)
+}
+
+
+
+
+
+
+
+
diff --git a/R/qi.eiRxC.R b/R/qi.eiRxC.R
index 3b184c4..77793ab 100644
--- a/R/qi.eiRxC.R
+++ b/R/qi.eiRxC.R
@@ -1,15 +1,15 @@
qi.eiRxC <- function(object, simpar, x=NULL, x1=NULL, y=NULL,
user.fn=NULL) {
- if (!is.null(x1))
- warning ("no first difference are available for EI models")
- if (!is.null(x))
- object$covar <- x
- ev <- calc.fractions(object, simpar)
- # print(ev)
- ev <- aperm(ev, perm = c(3,1,2))
- class(ev) <- c("ei", "array")
- qi <- list(ev=ev)
- qi.name <- list(ev = "Expected Values: E(Y|X)")
- list(qi=qi, qi.name=qi.name)
+ if (!is.null(x1))
+ warning ("no first difference are available for EI models")
+ if (!is.null(x))
+ object$covar <- x
+ ev <- calc.fractions(object, simpar)
+
+ ev <- aperm(ev, perm = c(3,1,2))
+ class(ev) <- c("ei", "array")
+ qi <- list(ev=ev)
+ qi.name <- list(ev = "Expected Values: E(Y|X)")
+ list(qi=qi, qi.name=qi.name)
}
diff --git a/R/qi.gam.R b/R/qi.gam.R
index d6b650a..fc789a1 100644
--- a/R/qi.gam.R
+++ b/R/qi.gam.R
@@ -9,7 +9,7 @@ qi.gam <- function(object, simpar, x, x1 = NULL, y = NULL) {
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("gam.logit", "gam.probit")) {
+ if (model %in% c("logit.gam", "probit.gam")) {
check <- TRUE
ev <- theta
for (i in 1:ncol(theta))
@@ -19,7 +19,7 @@ qi.gam <- function(object, simpar, x, x1 = NULL, y = NULL) {
y <- y[,1]
}
}
- else if (model == "gam.normal") {
+ else if (model == "normal.gam") {
ev <- theta
for (i in 1:nrow(ev))
pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
@@ -29,7 +29,7 @@ qi.gam <- function(object, simpar, x, x1 = NULL, y = NULL) {
for (i in 1:nrow(ev))
pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
}
- else if (model == "gam.poisson") {
+ else if (model == "poisson.gam") {
ev <- theta
for (i in 1:ncol(ev))
pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
@@ -51,13 +51,20 @@ qi.gam <- function(object, simpar, x, x1 = NULL, y = NULL) {
ev1 <- theta1
qi$fd <- ev1-ev
qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
- if (model %in% c("gam.logit", "gam.probit")) {
+ if (model %in% c("logit.gam", "probit.gam")) {
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 <- qi$tt.ev <- yvar - qi$ev
+# qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
+# if (check)
+# tmp.pr <- qi$tt.pr <- yvar - as.integer(qi$pr)
+# else
+# tmp.pr <- qi$tt.pr <- yvar - qi$pr
+# qi.name$tt.pr <- "Unit Treatment Effect for the Treated: Y - PR"
tmp.ev <- yvar - qi$ev
if (check)
tmp.pr <- yvar - as.integer(qi$pr)
diff --git a/R/qi.gee.R b/R/qi.gee.R
index 1dc128e..8681355 100644
--- a/R/qi.gee.R
+++ b/R/qi.gee.R
@@ -1,63 +1,27 @@
-qi.gee <- function(object, simpar, x, x1 = NULL, y = NULL) {
- check <- FALSE
- model <- getzelig(object)
- k <- length(object$coef)
- coef <- simpar[,1:k]
- if (k < ncol(simpar))
- alpha <- simpar[,(k+1):ncol(simpar)]
- eta <- coef %*% t(x)
- theta <- matrix(object$family$linkinv(eta), nrow = nrow(coef))
- pr <- ev <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta))
- dimnames(pr) <- dimnames(ev) <- dimnames(theta)
- if (model =="logit.gee") {
- check <- TRUE
- ev <- theta
- for (i in 1:ncol(theta))
- pr[,i] <- as.character(rbinom(length(ev[,i]), 1, ev[,i]))
- }
- else if (model == "normal.gee") {
- ev <- theta
- for (i in 1:nrow(ev))
- pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
- }
- else if (model == "gamma.gee") {
- ev <- theta * 1/alpha
- for (i in 1:nrow(ev))
- pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
- }
- else if (model == "poisson.gee") {
- ev <- theta
- for (i in 1:ncol(ev))
- pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
- }
- qi <- list(ev = ev, pr = pr)
- qi.name <- list(ev = "Expected Values: E(Y|X)",
- pr = "Predicted Values: Y|X")
- if (!is.null(x1)){
- theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
- nrow = nrow(coef))
- if (model == "gamma.gee")
- ev1 <- theta1 * 1/alpha
- else
- ev1 <- theta1
- qi$fd <- ev1-ev
- qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
- if (model %in% c("logit.gee", "probit.gee")) {
- qi$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
- qi$ate.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
- qi$ate.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(simpar))
- qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
- qi.name$ate.pr <- "Average Treatment Effect: Y - PR"
- }
- list(qi=qi, qi.name=qi.name)
-}
+qi.gee <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ model <- getzelig(object)
+ coef <- simpar
+ eta <- coef %*% t(x)
+ ev <- theta <- matrix(object$family$linkinv(eta), nrow = nrow(coef))
+ qi <- list(ev = ev)
+ qi.name <- list(ev = "Expected Values: E(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.gee", "probit.gee")) {
+ 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$ev
+# tmp.ev <- qi$tt.ev <- yvar - qi$ev
+# qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
+ qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
diff --git a/R/qi.glm.R b/R/qi.glm.R
index 9555a55..7b61fe2 100644
--- a/R/qi.glm.R
+++ b/R/qi.glm.R
@@ -25,9 +25,9 @@ qi.glm <- function(object, simpar, x, x1 = NULL, y = NULL) {
pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
}
else if (model == "gamma") {
- ev <- theta * 1/alpha
+ ev <- theta
for (i in 1:nrow(ev))
- pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
+ pr[i,] <- rgamma(length(ev[i,]), shape = alpha[i], scale = theta[i,]/alpha[i])
}
else if (model == "poisson") {
ev <- theta
@@ -43,12 +43,8 @@ qi.glm <- function(object, simpar, x, x1 = NULL, y = NULL) {
qi.name <- list(ev = "Expected Values: E(Y|X)",
pr = "Predicted Values: Y|X")
if (!is.null(x1)){
- theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
+ ev1 <- theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
nrow = nrow(coef))
- if (model == "gamma")
- ev1 <- theta1 * 1/alpha
- else
- ev1 <- theta1
qi$fd <- ev1-ev
qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
if (model %in% c("logit", "probit", "relogit")) {
@@ -63,6 +59,13 @@ qi.glm <- function(object, simpar, x, x1 = NULL, y = NULL) {
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$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
@@ -76,3 +79,4 @@ qi.glm <- function(object, simpar, x, x1 = NULL, y = NULL) {
+
diff --git a/R/qi.lm.R b/R/qi.lm.R
index 87ac268..fa6be00 100644
--- a/R/qi.lm.R
+++ b/R/qi.lm.R
@@ -18,6 +18,8 @@ qi.lm <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
if (!is.null(y)) {
yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+# tmp.ev <- qi$tt.ev <- yvar - qi$ev
+# qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
tmp.ev <- yvar - qi$ev
qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
diff --git a/R/qi.mixed.R b/R/qi.mixed.R
new file mode 100644
index 0000000..a9a5d8f
--- /dev/null
+++ b/R/qi.mixed.R
@@ -0,0 +1,137 @@
+## dbailey at wustl.edu
+## modeified by Ferdi 10/30/07
+################################
+
+qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
+
+ x <- as.data.frame(x)
+ if (!is.null(x1))
+ x1 <- as.data.frame(x1)
+
+
+ ## original dataset
+ D <- eval(getcall(object)$data, envir = parent.frame())
+ fml <- eval(object at call$formula)
+ parsefml <- .getRandAndFixedTerms(fml)
+
+ betas <- simpar[["betas"]]
+ gammas <- simpar[["gammas"]]
+ alpha <- simpar[["scale"]]
+
+
+ fTermsNames <- colnames(model.matrix(parsefml$fixed, data = D))
+
+ fTerms <- x[,fTermsNames]
+ rTerms <- list()
+ for (i in 1:length(parsefml$random)){
+ ## for now, intercept is always present
+ tt <- terms(parsefml$random[[i]])
+ attr(tt,"intercept") <- 1
+ rTermsNames <- colnames(model.matrix(tt,data=D))
+ rTerms[[i]] <- x[, rTermsNames]
+ }
+ names(rTerms) <- names(parsefml$random)
+ if (!is.null(x1)){
+ fTermsNames <- colnames(model.matrix(parsefml$fixed, data = D))
+ fTerms.x1 <- x1[,fTermsNames]
+ rTerms.x1 <- list()
+ for (i in 1:length(parsefml$random)){
+ tt <- terms(parsefml$random[[i]])
+ attr(tt,"intercept") <- 1
+ rTermsNames <- colnames(model.matrix(tt,data=D))
+ rTerms.x1[[i]] <- x1[, rTermsNames]
+ ##rTermsNames <- colnames(model.matrix(parsefml$random[[i]],data=D))
+ ##rTerms.x1[[i]] <- x1[, rTermsNames]
+ }
+ names(rTerms.x1) <- names(parsefml$random)
+ }
+
+ ## Expected Values and Predicted Values
+ if (class(object) == "glmer"){
+ family <- object at family[[1]] ## is a list, is family name always the first element ? -Ferdi
+ eta <- betas %*% t(as.matrix(fTerms))
+ theta <- matrix(object at family$linkinv(eta), nrow=nrow(betas))
+ mu <- eta
+ ## For predicted values, add in random effects draws
+ for (i in 1:length(rTerms)){
+ mu <- mu + gammas[[names(rTerms[i])]] %*% t(as.matrix(rTerms[[i]]))
+ }
+ mut <- matrix(object at family$linkinv(mu), nrow=nrow(betas))
+ ev <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta))
+ pr <- matrix(NA, nrow=nrow(mut), ncol=ncol(mut))
+ dimnames(ev) <- dimnames(pr) <- dimnames(theta)
+ if (family == "binomial"){
+ ev <- theta
+ for (i in 1:ncol(mut)){
+ pr[,i] <- as.character(rbinom(length(mut[,i]), 1, mut[,i]))
+ }
+ if (!is.null(y)) {
+ if (NCOL(y) > 1) {
+ y <- y[,1]
+ }
+ }
+ }
+ else if (family == "Gamma"){
+ ev <- theta * 1/alpha
+ for (i in 1:nrow(mut)){
+ pr[i,] <- rgamma(length(mut[i,]), shape = mut[i,], scale= 1/alpha)
+ }
+ }
+ else if (family == "poisson"){
+ ev <- theta
+ for (i in 1:ncol(mut)){
+ pr[,i] <- rpois(length(mut[,i]), lambda = mut[,i])
+ }
+ }
+ }
+ else if (class(object) == "lmer"){
+ ev <- betas %*% t(as.matrix(fTerms))
+ mu <- ev
+ ## For predicted values, add in random effects draws
+ for (i in 1:length(rTerms)){
+ mu <- mu + gammas[[names(rTerms[i])]] %*% t(as.matrix(rTerms[[i]]))
+ }
+ pr <- mu
+ }
+
+ qi <- list(ev=ev, pr = pr)
+ qi.name <- list(ev="Expected Values: E(Y|X)", pr="Predicted Values: Y|X")
+ if (!is.null(x1)){
+ if (class(object) == "glmer"){
+ theta1 <- matrix(object at family$linkinv(betas %*% t(as.matrix(fTerms.x1))),
+ nrow = nrow(betas))
+ if (family == "Gamma")
+ ev1 <- theta1 * 1/alpha
+ else
+ ev1 <- theta1
+ qi$fd <- ev1-ev
+ qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ if (family == "binomial") {
+ qi$rr <- ev1/ev
+ qi.name$rr <- "Risk Ratios: P(Y=1|X1)/P(Y=1|X)"
+ }
+ }
+ else if (class(object) == "lmer"){
+ ev1 <- betas %*% t(as.matrix(fTerms.x1))
+ qi$fd <- ev1-ev
+ qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ }
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ tmp.ev <- yvar - qi$ev
+ if (family == "binomial")
+ tmp.pr <- yvar - as.integer(qi$pr)
+ else
+ tmp.pr <- yvar - qi$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/qi.netglm.R b/R/qi.netglm.R
index 12412e5..12e4c80 100644
--- a/R/qi.netglm.R
+++ b/R/qi.netglm.R
@@ -9,7 +9,7 @@ qi.netglm <- function(object, simpar, x, x1 = NULL, y = NULL) {
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("netlogit", "netprobit", "netcloglog", "netbinom")) {
+ if (model %in% c("logit.net", "probit.net", "cloglog.net", "netbinom")) {
check <- TRUE
ev <- theta
for (i in 1:ncol(theta))
@@ -19,17 +19,17 @@ qi.netglm <- function(object, simpar, x, x1 = NULL, y = NULL) {
y <- y[,1]
}
}
- else if (model == "netnormal") {
+ else if (model == "normal.net") {
ev <- theta
for (i in 1:nrow(ev))
pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
}
- else if (model == "netgamma") {
+ else if (model == "gamma.net") {
ev <- theta * 1/alpha
for (i in 1:nrow(ev))
pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
}
- else if (model == "netpoisson") {
+ else if (model == "poisson.net") {
ev <- theta
for (i in 1:ncol(ev))
pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
@@ -41,7 +41,7 @@ qi.netglm <- function(object, simpar, x, x1 = NULL, y = NULL) {
if (!is.null(x1)){
theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
nrow = nrow(coef))
- if (model == "netgamma")
+ if (model == "gamma.net")
ev1 <- theta1 * 1/alpha
else
ev1 <- theta1
@@ -53,7 +53,14 @@ qi.netglm <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
}
if (!is.null(y)) {
- yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+# 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"
tmp.ev <- yvar - qi$ev
if (check)
tmp.pr <- yvar - as.integer(qi$pr)
diff --git a/R/qi.netlm.R b/R/qi.netlm.R
index 51219a7..412181c 100644
--- a/R/qi.netlm.R
+++ b/R/qi.netlm.R
@@ -13,6 +13,8 @@ qi.netlm <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
if (!is.null(y)) {
yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ #tmp.ev <- qi$tt.ev <- yvar - qi$ev
+ #qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
tmp.ev <- yvar - qi$ev
qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
diff --git a/R/qi.netlogit.R b/R/qi.netlogit.R
index 2b1ad95..2b636a1 100644
--- a/R/qi.netlogit.R
+++ b/R/qi.netlogit.R
@@ -1,4 +1,4 @@
-qi.netlogit <- function(object, simpar, x, x1 = NULL, y = NULL) {
+qi.logit.net <- function(object, simpar, x, x1 = NULL, y = NULL) {
check <- FALSE
model <- getzelig(object)
k <- length(getcoef(object))
@@ -9,7 +9,7 @@ qi.netlogit <- function(object, simpar, x, x1 = NULL, y = NULL) {
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 == "netlogit") {
+ if (model == "logit.net") {
check <- TRUE
ev <- theta
for (i in 1:ncol(theta))
@@ -25,7 +25,7 @@ qi.netlogit <- function(object, simpar, x, x1 = NULL, y = NULL) {
if (!is.null(x1)){
theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
nrow = nrow(coef))
- if (model == "netlogit")
+ if (model == "logit.net")
ev1 <- theta1
qi$fd <- ev1-ev
qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
@@ -36,6 +36,12 @@ qi.netlogit <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
if (!is.null(y)) {
yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+# tmp.ev <- qi$tt.ev <- yvar - qi$ev
+# qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
+# if (check)
+# tmp.pr <- qi$tt.pr <- yvar - as.integer(qi$pr)
+# else
+# tmp.pr <- qi$tt.pr <- yvar - qi$pr
tmp.ev <- yvar - qi$ev
if (check)
tmp.pr <- yvar - as.integer(qi$pr)
diff --git a/R/qi.polr.R b/R/qi.polr.R
index 64cc1a8..6308c7b 100644
--- a/R/qi.polr.R
+++ b/R/qi.polr.R
@@ -23,7 +23,7 @@ qi.polr <- function(object, simpar, x, x1 = NULL, y = NULL) {
pr[,n] <- 1 + rowSums(Ipr[,,n])
pr <- matrix(factor(pr, labels = lev[1:length(lev) %in% sort(unique(pr))],
ordered = TRUE),
- nrow = nrow(sim.coef), ncol = nrow(x))
+ nrow = num, ncol = nrow(x))
colnames(pr) <- rownames(x)
qi <- list(ev = ev, pr = pr)
qi.name <- list(ev = "Expected Values: P(Y=j|X)",
diff --git a/R/qi.relogit.R b/R/qi.relogit.R
index 922f72b..b373b34 100644
--- a/R/qi.relogit.R
+++ b/R/qi.relogit.R
@@ -17,8 +17,8 @@ qi.relogit <- function(object, simpar, x, x1 = NULL, y = NULL) {
FD <- RR <- array(NA, dim = c(num, 2, nrow(x)),
dimnames = list(NULL, d2 = c("Lower Bound", "Upper Bound"),
rownames(x)))
- sim01 <- qi.glm(tmp0, par0, x = x1, x1 = NULL)
- sim11 <- qi.glm(tmp1, par1, x = x1, x1 = NULL)
+ sim01 <- qi.glm(tmp0, simpar$par0, x = x1, x1 = NULL)
+ sim11 <- qi.glm(tmp1, simpar$par1, x = x1, x1 = NULL)
tau0 <- object$lower.estimate$tau
tau1 <- object$upper.estimate$tau
P01 <- as.matrix(sim01$qi$ev)
@@ -61,6 +61,10 @@ qi.relogit <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
if (!is.null(y)) {
yvar <- matrix(rep(y, num), nrow = num, byrow = TRUE)
+# tmp.ev <- qi$tt.ev <- yvar - qi$ev
+# tmp.pr <- qi$tt.pr <- yvar - as.integer(qi$pr)
+# qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
+# qi.name$tt.pr <- "Unit Treatment Effect for the Treated: Y - PR"
tmp.ev <- yvar - qi$ev
tmp.pr <- yvar - as.integer(qi$pr)
qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = num)
diff --git a/R/qi.survreg.R b/R/qi.survreg.R
index 3e4628c..4d0b535 100644
--- a/R/qi.survreg.R
+++ b/R/qi.survreg.R
@@ -97,6 +97,10 @@ qi.survreg <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
else
yvar <- matrix(y, ncol = length(y), nrow = nrow(qi$ev), byrow = TRUE)
+# tmp.ev <- qi$tt.ev <- yvar - qi$ev
+# tmp.pr <- qi$tt.pr <- yvar - qi$pr
+# qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
+# qi.name$tt.pr <- "Unit Treatment Effect for the Treated: Y - PR"
tmp.ev <- yvar - qi$ev
tmp.pr <- yvar - qi$pr
qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
@@ -106,18 +110,3 @@ qi.survreg <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
list(qi=qi, qi.name=qi.name)
}
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/R/qi.zmlm.R b/R/qi.zmlm.R
new file mode 100644
index 0000000..cad3fdc
--- /dev/null
+++ b/R/qi.zmlm.R
@@ -0,0 +1,71 @@
+qi.zmlm <- function(object, simpar, x, x1 = NULL, y = NULL) {
+
+ k <- length(getcoef(object))
+ coef <- simpar[,1:k]
+ alpha <- simpar[,(k+1):ncol(simpar)]
+ if(length(dim(coef(object))))
+ rw <- rownames(coef(object))
+ else
+ rw <- names(coef(object))
+
+
+ if (k < ncol(x))
+ x <- as.data.frame(x[,rw,drop = FALSE])
+ dx <- dim(x)[[2]]
+ ##return a list to obtain expected values for each element
+ ##that stands for each depVar in mlm or maov
+
+ coeflist <- split.simpar(coef, dx)
+ ev <- matrix(,nrow(simpar), length(coeflist))
+ colnames(ev) <- names(coeflist)
+ for(n in 1:ncol(ev))
+ ev[,n] <- coeflist[[n]] %*% t(x)
+ qi <- list(ev=ev)
+ qi.name <- list(ev="Expected Values: E(Y|X)")
+ if(!is.null(x1)){
+ if (k < ncol(x1))
+ x1 <- as.data.frame(x1[,rw,drop=FALSE])
+ dx1 <- dim(x1)[[2]]
+ coeflist <- split.simpar(coef, dx1)
+ ev1 <- matrix(,nrow(simpar), length(coeflist))
+ colnames(ev1) <- names(coeflist)
+ for(n in 1:ncol(ev1))
+ ev1[,n] <- coeflist[[n]] %*% t(x1)
+
+ qi$fd <- ev1-ev
+ qi.name$fd <-
+ "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ #tmp.ev <- qi$tt.ev <- apply(as.matrix(qi$ev), 2, function(m)yvar - m)
+ tmp.ev <- apply(as.matrix(qi$ev), 2, function(m)yvar - m)
+ qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ #qi.name$tt.ev <- "Unit Treatment Effect for the Treated: Y - EV"
+ qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
+
+
+
+split.simpar <- function(coef, dx){
+ ix <- grep("Intercept", colnames(coef))
+ nmall <- colnames(coef)[ix]
+ vecnm <- NULL
+ for(nm in nmall)
+ vecnm <- c(vecnm, sub("(.*):(.*)", "\\1",nm))
+
+ dc <- dim(coef)[[2]]
+ nt <- dc/dx
+ lst <- list()
+ ptf <- 0
+ for(n in 1:nt){
+ pti <- ptf + 1
+ ptf <- ifelse(n<= 1, dx, dx +ptf)
+ pc <- coef[,pti:ptf]
+ lst <- c(lst, list(pc))
+ }
+ names(lst) <- vecnm
+ return(lst)
+}
diff --git a/R/repl.zelig.R b/R/repl.zelig.R
index 0213538..d8a5536 100644
--- a/R/repl.zelig.R
+++ b/R/repl.zelig.R
@@ -12,6 +12,7 @@ repl.zelig <- function(object, data=NULL, prev = NULL, x=NULL,
object$call$x1
if (!is.null(prev))
object$call$prev <- prev
+ if (!is.null(object$seed)) set.seed(object$seed)
eval(object$call, sys.parent())
}
diff --git a/R/setx.MI.R b/R/setx.MI.R
index 0fccf3f..96cd65f 100644
--- a/R/setx.MI.R
+++ b/R/setx.MI.R
@@ -3,8 +3,8 @@ setx.MI <- function(object, fn = list(numeric = mean, ordered =
cond = FALSE, counter = NULL, ...) {
M <- length(object)
dta <- NULL
+ obj <- object[[1]]
if (!cond) {# unconditional predition
- obj <- object[[1]]
for (i in 1:M) {
if(is.null(data))
tmp <- as.data.frame(eval(getcall(obj)$data,
@@ -13,20 +13,22 @@ setx.MI <- function(object, fn = list(numeric = mean, ordered =
tmp <- data[[i]]
dta <- rbind(dta, tmp)
}
- X <- NextMethod("setx", object = object[[1]], fn = fn, data = dta, cond = FALSE,
- counter = NULL, ...)
- #X <- setx(object[[1]], fn = fn, data = dta, cond = FALSE,
- # counter = NULL, dots)
+ #X <- NextMethod("setx", object = object[[1]], fn = fn, data = dta, cond = FALSE,
+ # counter = NULL, ...)
+ X <- setx(object[[1]], fn = fn, data = dta, cond = FALSE,
+ counter = NULL, ...)
class(X) <- c("setx.MI", "setx", "data.frame")
}
else { # conditional prediction
X <- list()
if (is.null(data))
data <- eval(getcall(obj)$data, sys.parent())
- for (i in 1:M)
+ for (i in 1:M){
X[[i]] <- setx(object[[i]], fn = NULL, data = data[[i]], cond = TRUE,
counter = counter, ...)
- class(X) <- c("setx.MI", "cond", "data.frame")
+ class(X[[i]]) <- c("cond", "data.frame")
+ }
+ class(X) <- c("setx.MI", "setx.cond", "cond")
}
return(X)
}
diff --git a/R/setx.default.R b/R/setx.coxph.R
similarity index 56%
copy from R/setx.default.R
copy to R/setx.coxph.R
index 129520f..bdb6aa2 100644
--- a/R/setx.default.R
+++ b/R/setx.coxph.R
@@ -1,10 +1,9 @@
-setx.default <- function(object, fn = list(numeric = mean, ordered =
+setx.coxph <- function(object, fn = list(numeric = mean, ordered =
median, other = mode), data = NULL,
cond = FALSE, counter = NULL, ...){
mc <- match.call()
if (class(object)[1]=="MI")
object <- object[[1]]
-
mode <- function(x){
tb <- tapply(x, x, length)
if(is.factor(x))
@@ -22,7 +21,6 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
}
return(value)
}
-
median.default <- median
median <- function(x) {
if(is.numeric(x))
@@ -34,8 +32,6 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
stop("median cannot be calculated for this data type")
return(value)
}
-
-
max.default <- max
max <- function(x, na.rm=FALSE) {
if(is.numeric(x))
@@ -46,7 +42,6 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
stop("max cannot be calculated for this data type")
return(value)
}
-
min.default <- min
min <- function(x, na.rm=FALSE) {
if(is.numeric(x))
@@ -57,12 +52,10 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
stop("min cannot be calculated for this data type")
return(value)
}
-
+
# Testing From Here
- if(length(fn))
- fn <- updatefn(fn, operVec=c("mode", "median","min", "max"),
- ev=environment(), global=parent.frame())
+
tt <- terms(object)
tt.attr <- attributes(tt)
@@ -78,6 +71,29 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
}
else
dta <- as.data.frame(data)
+
+################################ create new terms without strata
+ if(!is.null(tt.attr$specials$strata)){
+ no.strata <- rownames(tt.attr$factors)[-tt.attr$specials$strata]
+ ind.var <- paste(no.strata[-tt.attr$response], collapse="+")
+ nf <- as.formula(paste(paste(no.strata[tt.attr$response]), paste("~"), ind.var)) #new formula for terms
+#####extract strata
+ mf1 <- model.frame(tt, data = dta, na.action = na.pass)
+ stratas <- mf1[complete.cases(mf1),tt.attr$specials$strata]
+ st <- na.omit(pmatch(names(mc), as.character("strata")))
+ if (length(st>0))
+ strata <- mc[["strata"]]
+ else
+ strata <- mode(stratas)
+
+ tt <- terms(nf)
+ tt.attr <- attributes(tt)
+ }
+ else
+ strata <- NULL
+ #################################################
+
+
## extract variables we need
mf <- model.frame(tt, data = dta, na.action = na.pass)
if(any(class(tt)=="multiple"))
@@ -89,44 +105,8 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
else
resvars <- NULL
data <- dta[complete.cases(mf), names(dta)%in%vars, drop=FALSE]
- if (!is.null(counter)) {
- if (!any(counter == vars))
- stop("the variable specified for counter is not used in the model")
- treat <- data[, names(data)==counter]
- if(is.numeric(treat)) {
- data[treat==1, names(data)==counter] <- 0
- data[treat==0, names(data)==counter] <- 1
- }
- else if(is.factor(treat)) {
- lev <- levels(treat)
- if(length(lev)==2) {
- treat <- as.numeric(treat) - 1
- data[treat==1, names(data)==counter] <- lev[1]
- data[treat==0, names(data)==counter] <- lev[2]
- }
- else
- stop("counter only takes a binary variable")
- }
- else if(is.logical(treat)) {
- treat <- as.numeric(treat)
- data[treat==1, names(data)==counter] <- FALSE
- data[treat==0, names(data)==counter] <- TRUE
- }
- else
- stop("not supported variable type for counter")
- if(!cond)
- stop("if counter is specified, cond must be TRUE")
- }
if (cond) {
- if (is.null(data))
- stop("if cond = TRUE, you must specify the data frame.")
- if (is.null(mc$fn))
- fn <- NULL
- if (!is.null(fn)) {
- warning("when cond = TRUE, fn is coerced to NULL")
- fn <- NULL
- }
- maxl <- nrow(data)
+ stop("conditional prediction not supported for coxph models")
}
else if (!is.null(fn)) {
if (is.null(fn$numeric) || !is.function(fn$numeric)) {
@@ -186,58 +166,14 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
}
data <- data[1:maxl,,drop = FALSE]
- if (cond) {
- X <- model.frame(tt, data = dta)
- if (!is.null(counter)) {
- X <- list(treat=X[treat==1,,drop=FALSE],
- control=X[treat==0,,drop=FALSE])
- class(X$treat) <- class(X$control) <- c("data.frame", "cond")
- class(X) <- "setx.counter"
- }
- else
- class(X) <- c("data.frame", "cond")
- }
- else {
- X <- as.data.frame(model.matrix(tt, data = data))
- }
+ X <- as.data.frame(model.matrix(tt, data = data))[-1]
+ k <- length(coef(object))
+ if(length(X)>k) #delete cluster variable
+ X <- X[-(k+1)]
+ if(!is.null(strata))
+ X <- as.data.frame(cbind(X, strata))
+ class(X) <- c("data.frame", "coxph")
+
return(X)
}
-### DESCRIPTION: Takes the operations in vector operVec and updates list fn
-### so that list elements "numeric", "ordered", and "other" in fn
-### are as defined in setx rather
-### than those taken from .GlobalEnv or namespace:base
-###
-### INPUTS: fn a list with default operations for numeric, ordered, other
-### operVec a vector of chars with operations, e.g max, min, median, mode
-### ev, parent environment; global, granparent environment
-###
-updatefn <- function(fn, operVec=c("mode", "median","min", "max"), ev=parent.frame(), global=.GlobalEnv)
- {
- mode <- get("mode", env=ev)
- median <- get("median", env=ev)
- max <- get("max", env=ev)
- min <- get("min", env=ev)
-
- modeG <- get("mode", env=global)
- medianG <- get("median", env=global)
- minG <- get("min", env=global)
- maxG <- get("max", env=global)
- if(!identical(sort(c("max", "median", "min", "mode")), sort(operVec)))
- stop("updatefn missing some operations from setx")
-
- for(oper in operVec){
- operGlob <- switch(EXPR=oper,"mode"=, "mode.default"=modeG,"median"=, "median.default"=medianG,
- "min"=,"min.default"= minG,"max"=, "max.default"=maxG)
- operSetx <- switch(EXPR=oper,"mode"=, "mode.default"=mode,"median"=, "median.default"=median,
- "min"=,"min.default"= min,"max"=, "max.default"=max)
- if(identical(fn$other, operGlob))
- fn$other <- operSetx
-
- if(identical(fn$numeric, operGlob))
- fn$numeric <- operSetx
-
- if(identical(fn$ordered, operGlob))
- fn$ordered <- operSetx
- }
- fn
- }
+
diff --git a/R/setx.default.R b/R/setx.default.R
index 129520f..1310d27 100644
--- a/R/setx.default.R
+++ b/R/setx.default.R
@@ -88,6 +88,7 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
resvars <- all.vars(tt.attr$variables[[1+tt.attr$response]])
else
resvars <- NULL
+ opt <- vars[na.omit(pmatch(names(mc), vars))]
data <- dta[complete.cases(mf), names(dta)%in%vars, drop=FALSE]
if (!is.null(counter)) {
if (!any(counter == vars))
@@ -127,8 +128,7 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
fn <- NULL
}
maxl <- nrow(data)
- }
- else if (!is.null(fn)) {
+ } else if (!is.null(fn)) {
if (is.null(fn$numeric) || !is.function(fn$numeric)) {
warning("fn$numeric coerced to mean().")
fn$numeric <- mean
@@ -149,21 +149,22 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
fn$other <- mode
}
for (i in 1:ncol(data)) {
- if (!(colnames(data)[i] %in% resvars)) {
- if (is.numeric(data[,i]))
- value <- lapply(list(data[,i]), fn$numeric)[[1]]
- else if (is.ordered(data[,i]))
- value <- lapply(list(data[,i]), fn$ordered)[[1]]
- else
- value <- lapply(list(data[,i]), fn$other)[[1]]
- data[,i] <- value
+ if (!(colnames(data)[i] %in% opt)) {
+ if (!(colnames(data)[i] %in% resvars)) {
+ if (is.numeric(data[,i]))
+ value <- lapply(list(data[,i]), fn$numeric)[[1]]
+ else if (is.ordered(data[,i]))
+ value <- lapply(list(data[,i]), fn$ordered)[[1]]
+ else
+ value <- lapply(list(data[,i]), fn$other)[[1]]
+ data[,i] <- value
+ }
}
}
maxl <- 1
} else {
maxl <- nrow(data)
}
- opt <- vars[na.omit(pmatch(names(mc), vars))]
if (length(opt) > 0)
for (i in 1:length(opt)) {
value <- eval(mc[[opt[i]]], envir = env)
@@ -219,7 +220,7 @@ updatefn <- function(fn, operVec=c("mode", "median","min", "max"), ev=parent.fra
min <- get("min", env=ev)
modeG <- get("mode", env=global)
- medianG <- get("median", env=global)
+ medianG <- get("median.default", env=global)
minG <- get("min", env=global)
maxG <- get("max", env=global)
if(!identical(sort(c("max", "median", "min", "mode")), sort(operVec)))
diff --git a/R/setx.zaovlist.R b/R/setx.zaovlist.R
new file mode 100644
index 0000000..c5bb72e
--- /dev/null
+++ b/R/setx.zaovlist.R
@@ -0,0 +1,9 @@
+setx.zaovlist <- function(object,fn = list(numeric = mean, ordered =
+ median, other = mode),
+ data = NULL,
+ cond = FALSE, counter = NULL, ...) {
+
+ x <- setx.default(object, fn=fn,data=data,cond=cond,counter=counter,...)
+ class(x) <- c("zaovlist", class(x))
+ x
+}
diff --git a/R/sim.cond.R b/R/sim.cond.R
index 5aabefb..06b8eb5 100644
--- a/R/sim.cond.R
+++ b/R/sim.cond.R
@@ -32,13 +32,19 @@ sim.cond <- function(object, x, x1=NULL, num=c(1000, 100),
colnames(res$t) <- names(res$t0)
simpar <- res$t
}
- }
- else {
+ } else {
if (bootstrap)
stop("Error: Choosing 'bootstrap = TRUE' generates new parameters. \nIf you wish to use previously generated parameters, \nplease specify only 'prev'.")
else
simpar <- prev
}
+ fn <- paste("zelig4", getzelig(object), sep = "")
+ if(exists(fn)){
+ if(!bootstrap)
+ simpar <- do.call(fn, list(object=object, simpar=simpar, x=xvar, x1=x1, bootstrap=bootstrap, bootfn=bootfn))
+ else
+ simpar <- do.call(fn, list(object=object, simpar=simpar, x=xvar, x1=x1, bootstrap=bootstrap, bootfn=bootfn, dta=dta))
+ }
simqi <- qi(object, simpar = simpar, x = xvar, x1 = x1, y = yvar)
class(xvar) <- c("matrix", "cond")
ca <- match.call()
diff --git a/R/sim.counter.R b/R/sim.counter.R
index 934b896..8ce35c7 100644
--- a/R/sim.counter.R
+++ b/R/sim.counter.R
@@ -1,4 +1,4 @@
-sim.counter <- function(object, x, x1=NULL, num = c(100, 1000), ...) {
+sim.counter <- function(object, x, x1=NULL, bootstrap = FALSE, num = c(100, 1000), ...) {
if (any(class(object) == "MCMCZelig"))
num <- nrow(object$coefficients)
else if (length(num) == 2) {
diff --git a/R/sim.default.R b/R/sim.coxph.R
similarity index 73%
copy from R/sim.default.R
copy to R/sim.coxph.R
index 71ff81e..637d9d9 100644
--- a/R/sim.default.R
+++ b/R/sim.coxph.R
@@ -1,22 +1,15 @@
-sim.default <- function(object, x=NULL, x1=NULL, num=c(1000, 100),
+sim.coxph <- function(object, x=NULL, x1=NULL, num=c(1000, 100),
prev = NULL, bootstrap = FALSE, bootfn=NULL,
cond.data = NULL, ...) {
- if (!is.null(x))
- x <- as.matrix(x)
- if (!is.null(x1))
- x1 <- as.matrix(x1)
- if (any(class(object) == "MCMCZelig"))
- num <- nrow(object$coefficients)
- else if (length(num) == 2) {
+
+ if (length(num) == 2) {
if (!bootstrap)
num <- num[1]
else
num <- num[2]
}
if (is.null(prev)) {
- if (any(class(object) == "relogit"))
- simpar <- param.relogit(object, num=num, x=x, bootstrap=bootstrap)
- else if (!bootstrap)
+ if (!bootstrap)
simpar <- param(object, num=num, bootstrap=bootstrap)
else {
tt <- terms(object)
@@ -35,6 +28,7 @@ sim.default <- function(object, x=NULL, x1=NULL, num=c(1000, 100),
else
simpar <- prev
}
+
simqi <- qi(object, simpar = simpar, x = x, x1 = x1, y = NULL)
c <- match.call()
c[[1]] <- as.name("sim")
@@ -59,4 +53,3 @@ sim.default <- function(object, x=NULL, x1=NULL, num=c(1000, 100),
-
diff --git a/R/sim.default.R b/R/sim.default.R
index 71ff81e..bc3362b 100644
--- a/R/sim.default.R
+++ b/R/sim.default.R
@@ -35,6 +35,14 @@ sim.default <- function(object, x=NULL, x1=NULL, num=c(1000, 100),
else
simpar <- prev
}
+ fn <- paste("zelig4", getzelig(object), sep = "")
+ if(exists(fn)){
+ if(!bootstrap)
+ simpar <- do.call(fn, list(object=object, simpar=simpar, x=x, x1=x1, bootstrap=bootstrap, bootfn=bootfn))
+ else
+ simpar <- do.call(fn, list(object=object, simpar=simpar, x=x, x1=x1, bootstrap=bootstrap, bootfn=bootfn, dta=dta))
+ }
+
simqi <- qi(object, simpar = simpar, x = x, x1 = x1, y = NULL)
c <- match.call()
c[[1]] <- as.name("sim")
diff --git a/R/sim.ei.RxC.R b/R/sim.eiRxC.R
similarity index 96%
rename from R/sim.ei.RxC.R
rename to R/sim.eiRxC.R
index 771b440..37fd95f 100644
--- a/R/sim.ei.RxC.R
+++ b/R/sim.eiRxC.R
@@ -1,59 +1,59 @@
-###
-## sim function for eiRxC model.
-## NOTES:
-## - this model accepts only bootstrap = FALSE argument.
-## - the default bootfn will be "paramsei.estim"
-##
-
-sim.eiRxC <- function(object, x=NULL, x1=NULL, num= 100,
- prev = NULL, bootstrap = TRUE, bootfn=NULL,
- cond.data = NULL, ...) {
- if (!is.null(x))
- x <- as.matrix(x)
-
- if (!is.null(x1))
- warning ("no first difference are available for EI models")
-
- if (!is.null (prev))
- stop("Error: prev option is not supported for this model")
-
- if (!bootstrap)
- stop ("Error: please use bootstrapping to simulate parameters for this model")
-
- ## get the data to pass to boot function
- tt <- terms(object)
- dta <- eval(getcall(object)$data, sys.parent())
- dta <- dta[complete.cases(model.frame(tt, dta)),]
- if (is.null(bootfn))
- bootfn <- bootfn.default
- res <- boot(dta, bootfn, R = num, object = object, ...)
- #res <- boot(data= dta, statistic = bootfn, R = num, nR = object$dims[[1]], nC = object$dims[[2]], ...)
- colnames(res$t) <- names(res$t0)
- simpar <- res$t
-
-
- simqi <- qi(object, simpar = simpar, x = x, x1 = x1, y = NULL)
- c <- match.call()
- c[[1]] <- as.name("sim")
- c$num <- num
- res <- list(x=x, x1=x1, call = c, zelig.call = getcall(object),
- par = simpar, qi=simqi$qi, qi.name=simqi$qi.name)
- class(res) <- "zelig"
- res
-}
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+###
+## sim function for eiRxC model.
+## NOTES:
+## - this model accepts only bootstrap = FALSE argument.
+## - the default bootfn will be "paramsei.estim"
+##
+
+sim.eiRxC <- function(object, x=NULL, x1=NULL, num= 100,
+ prev = NULL, bootstrap = TRUE, bootfn=NULL,
+ cond.data = NULL, ...) {
+ if (!is.null(x))
+ x <- as.matrix(x)
+
+ if (!is.null(x1))
+ warning ("no first difference are available for EI models")
+
+ if (!is.null (prev))
+ stop("Error: prev option is not supported for this model")
+
+ if (!bootstrap)
+ stop ("Error: please use bootstrapping to simulate parameters for this model")
+
+ ## get the data to pass to boot function
+ tt <- terms(object)
+ dta <- eval(getcall(object)$data, sys.parent())
+ dta <- dta[complete.cases(model.frame(tt, dta)),]
+ if (is.null(bootfn))
+ bootfn <- bootfn.default
+ res <- boot(dta, bootfn, R = num, object = object, ...)
+ #res <- boot(data= dta, statistic = bootfn, R = num, nR = object$dims[[1]], nC = object$dims[[2]], ...)
+ colnames(res$t) <- names(res$t0)
+ simpar <- res$t
+
+
+ simqi <- qi(object, simpar = simpar, x = x, x1 = x1, y = NULL)
+ c <- match.call()
+ c[[1]] <- as.name("sim")
+ c$num <- num
+ res <- list(x=x, x1=x1, call = c, zelig.call = getcall(object),
+ par = simpar, qi=simqi$qi, qi.name=simqi$qi.name)
+ class(res) <- "zelig"
+ res
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/sim.netglm.R b/R/sim.netglm.R
index 9a867ca..b957866 100644
--- a/R/sim.netglm.R
+++ b/R/sim.netglm.R
@@ -1,4 +1,4 @@
-sim.netlogit <- function(object, x=NULL, x1=NULL, num=c(1000, 100),
+sim.logit.net <- function(object, x=NULL, x1=NULL, num=c(1000, 100),
prev = NULL, bootstrap = FALSE, bootfn=NULL,
cond.data = NULL, ...) {
if (!is.null(x))
diff --git a/R/sim.setx.MI.R b/R/sim.setx.MI.R
index bf3d4d1..de93c67 100644
--- a/R/sim.setx.MI.R
+++ b/R/sim.setx.MI.R
@@ -10,15 +10,18 @@ sim.setx.MI <- function(object, x, x1 = NULL, num = c(1000, 100), prev = NULL,
}
ca <- match.call()
if (!any(class(x) == "cond")) {
- simpar <- MIsimulation(object, num, prev, bootstrap, bootfn, ...)
- simqi <- qi(object[[1]], simpar = simpar, x = as.matrix(x),
+ simpar <- MIsimulation(object, num, prev, bootstrap, bootfn=bootfn, x=x, x1=x1, ...)
+ if(any(class(object[[1]]) == "coxph"))
+ simqi <- qi.coxph(object, simpar = simpar, x = x, x1 = x1)
+ else
+ simqi <- qi(object[[1]], simpar = simpar, x = as.matrix(x),
x1 = if (!is.null(x1)) as.matrix(x1))
ca$num <- num
res <- list(x = x, x1 = x1, call = ca, zelig.call = getcall(object[[1]]),
par = simpar, qi = simqi$qi, qi.name = simqi$qi.name)
}
else {
- simpar <- MIsimulation(object, num, prev, bootstrap, bootfn, ...)
+ simpar <- MIsimulation(object, num, prev, bootstrap, bootfn=bootfn, x=x, x1=NULL, ...)
tmp.qi <- list()
for (i in 1:length(x)) {
if (!is.null(x1)) {
@@ -28,10 +31,12 @@ sim.setx.MI <- function(object, x, x1 = NULL, num = c(1000, 100), prev = NULL,
if (object[[i]]$call$model %in% c("bprobit", "blogit")) {
yvar <- x[[i]][,1:2]
x[[i]] <- x[[i]][,3:ncol(x[[i]])]
+ x[[i]] <- cbind(1,x[[i]])
}
else {
yvar <- x[[i]][,1]
x[[i]] <- x[[i]][,2:ncol(x[[i]])]
+ x[[i]] <- cbind(1,x[[i]])
}
tmp.qi[[i]] <- qi(object[[1]], simpar = simpar, x = x[[i]], x1 =
x1[[i]], y = yvar)
diff --git a/R/sim.setx.strata.R b/R/sim.setx.strata.R
index c48528e..ded8966 100644
--- a/R/sim.setx.strata.R
+++ b/R/sim.setx.strata.R
@@ -1,5 +1,5 @@
sim.setx.strata <- function(object, x, x1 = NULL, num = c(1000, 100),
- bootstrap = FALSE, ...){
+ bootstrap = FALSE, ...){
if (any(class(object) == "MCMCZelig"))
num <- nrow(getcoef(object))
if (length(num) == 2) {
diff --git a/R/sim.zaovlist.R b/R/sim.zaovlist.R
new file mode 100644
index 0000000..aa0a249
--- /dev/null
+++ b/R/sim.zaovlist.R
@@ -0,0 +1,119 @@
+### DESCRIPTION: Simulation of quantities of interest for model=aovlist
+### object is of class = c(zaovlist,aovlist,listof)
+### It contains list elements for multiple strata,
+### which individual class is c(aov,lm).
+### A formula with an Error term describes the strata
+### for the "Accross" and "Within" aov-models.
+### We extract those aov-models from object with relevant
+### information and apply sim.default to each of
+### them separately.
+###
+### OUTPUT a list after applying sim.default to each aov-model
+### of multiple-strata object separately.
+### The result is of class zeliglist because contains a list
+### of zelig simulation models.
+###
+### Elena Villalon (evillalon at iq.harvard.edu)
+
+sim.zaovlist <- function(object, x=NULL, x1=NULL, num=c(1000, 100),
+ prev = NULL, bootstrap = FALSE,
+ bootfn=NULL, cond.data=NULL,...) {
+
+ tt <- attributes(object)$terms
+ tt.attr <- attributes(tt)
+ vars <- tt.attr$term.labels
+ z.out <- object
+ ## only those lists elements (or strata) that are relevant to sim()
+ ind <- unlist(find.ind(object))
+ if(length(ind))
+ object <- object[ind]
+
+ nm <- names(object)
+
+ sout <- list()
+ zcall <- getcall(z.out)
+
+ for(n in 1:length(object)){
+ xn <- x
+ x1n <- x1
+ obj <- object[[n]]
+ objcoef <- coefficients(obj)
+ if(length(x))
+ xn <- pick.coef(objcoef,x)
+ if(length(x1))
+ x1n <- pick.coef(objcoef,x1)
+
+
+ if(bootstrap){
+ stop("Option bootstrap=TRUE with Error term is not yet supported")
+ }
+
+ ss <- sim.default(obj,x=xn, x1=x1n, num=num,
+ prev = prev, bootstrap = bootstrap,
+ bootfn=bootfn,cond.data=cond.data, ...)
+
+ ix <- grep("^zelig\\.call$", names(ss)$default)
+
+ if(length(ix))
+ ss[[ix]] <- zcall
+ else
+ ss <- c(ss,list(zelig.call=zcall))
+ sout <- c(sout, list(ss))
+ }
+ names(sout) <- nm
+ class(sout) <- c("zeliglist", class(sout))
+ sout
+}
+
+###DESCRIPTION: object is a list of elements, each is class c("aov", "lm")
+### Find those elemnts of the list with relevant information
+###
+###INPUT: object, a list of class c("aovlist", "listof")
+###OUTPUT: index to relevant list elements
+###
+### AUTHOR: Elena Villalon (evillalon at iq.harvard.edu)
+###
+find.ind <- function(object){
+ nmobj <- names(object)
+
+ ixa <- sapply(1:length(object), function(n){
+ obj <- object[[n]]
+ nm <- nmobj[n]
+ ixd <- c(grep("Intercept", nm),grep("^[Cc]all", nm))
+ if(length(ixd))
+ return(NULL)
+ resterms <- ifelse(!length(grep("terms", attributes(obj)$names)), NA, n)
+
+ rescoef <- ifelse(!length(coefficients(obj)), NA, n)
+ res <- NULL
+ if(!is.na(resterms) && !is.na(rescoef))
+ res <- n
+ return(res)
+ })
+ return(ixa)
+}
+
+
+###DESCRIPTION: objectcoef is vector with the names and values of
+### explanatory variables. Find the values of x applicable to
+### objcoef, i.e. that are also values of objcoef
+### There is no need to use this function in sim.zaovlist
+###
+###INPUT: objectcoef, the coefficients for obj of class c("aov", "lm")
+### x the result of setx, with values for explanatory variables
+###
+###OUTPUT: x but with only explanatory variables in objcoef
+###
+### AUTHOR: Elena Villalon (evillalon at iq.harvard.edu)
+###
+pick.coef <- function(objcoef,x){
+
+ objnmcoef <- names(objcoef)
+ objnmcoef <- sapply(objnmcoef,function(m) paste("^", m,"$", sep=""))
+ if(length(x)) xnmcoef <- names(x)
+ ix <- sapply(objnmcoef, grep, xnmcoef)
+ ix <- unlist(ix)
+ if (length(ix))
+ x <- x[ix]
+ return(x)
+}
diff --git a/R/summarize.coxhazard.R b/R/summarize.coxhazard.R
new file mode 100644
index 0000000..c5e9a36
--- /dev/null
+++ b/R/summarize.coxhazard.R
@@ -0,0 +1,3 @@
+summarize.coxhazard <- function(x, rows, cip, stats, subset = NULL) {
+ x
+}
diff --git a/R/summary.coxph.naive.R b/R/summary.coxph.naive.R
new file mode 100644
index 0000000..311085e
--- /dev/null
+++ b/R/summary.coxph.naive.R
@@ -0,0 +1,12 @@
+summary.coxph.naive <- function(object, ...){
+ class(object) <- c("coxph")
+ res <- summary(object,...)
+ naivese <- res$coef[,3]
+ expcoef <- res$coef[,2]
+ res$coef[,2] <- naivese
+ res$coef[,3] <- expcoef
+ colnames(res$coef)[2] <- "se(coef)"
+ colnames(res$coef)[3] <- "exp(coef)"
+ class(res) <- c("summary.coxph.naive", "summary.coxph")
+ return(res)
+}
diff --git a/R/summary.coxph.robust.R b/R/summary.coxph.robust.R
new file mode 100644
index 0000000..c120113
--- /dev/null
+++ b/R/summary.coxph.robust.R
@@ -0,0 +1,15 @@
+summary.coxph.robust <- function(object, ...){
+ class(object) <- c("coxph")
+ res <- summary(object,...)
+ robustse <- res$coef[,4]
+ expcoef <- res$coef[,2]
+ res$coef[,2] <- robustse
+ res$coef[,4] <- expcoef
+ colnames(res$coef)[2] <- "robust se"
+ colnames(res$coef)[4] <- "exp(coef)"
+ class(res) <- c("summary.coxph.robust", "summary.coxph")
+ return(res)
+}
+
+
+
diff --git a/R/summary.gee.naive.R b/R/summary.gee.naive.R
index f80b3eb..7300683 100644
--- a/R/summary.gee.naive.R
+++ b/R/summary.gee.naive.R
@@ -1,11 +1,12 @@
-summary.gee.naive <- function(object, ...){
- class(object) <- c("gee", "glm")
- res <- summary.gee(object,...)
- res$coefficients <- res$coefficients[,-5]
- res$coefficients[,2] <- s.err <- sqrt(diag(object$naive.variance))
- res$coefficients[,3] <- zvalue <- coef(object)/s.err
- res$coefficients[,4] <- 2 * pnorm(-abs(zvalue))
- colnames(res$coefficients) <- c("Estimate", "Naive SE", "z value", "Pr(>|z|)")
- class(res) <- c("summary.gee.naive", "summary.gee")
- return(res)
-}
+summary.gee.naive <- function(object, ...){
+ class(object) <- c("gee", "glm")
+ ##res <- summary.gee(object,...)
+ res <- summary(object,...)
+ res$coefficients <- res$coefficients[,-5]
+ res$coefficients[,2] <- s.err <- sqrt(diag(object$naive.variance))
+ res$coefficients[,3] <- zvalue <- coef(object)/s.err
+ res$coefficients[,4] <- 2 * pnorm(-abs(zvalue))
+ colnames(res$coefficients) <- c("Estimate", "Naive SE", "z value", "Pr(>|z|)")
+ class(res) <- c("summary.gee.naive", "summary.gee")
+ return(res)
+}
diff --git a/R/summary.gee.robust.R b/R/summary.gee.robust.R
index 15447b6..e27c8df 100644
--- a/R/summary.gee.robust.R
+++ b/R/summary.gee.robust.R
@@ -1,11 +1,12 @@
-summary.gee.robust <- function(object, ...){
- class(object) <- c("gee", "glm")
- res <- summary.gee(object,...)
- res$coefficients <- res$coefficients[,-5]
- res$coefficients[,2] <- s.err <- sqrt(diag(object$robust.variance))
- res$coefficients[,3] <- zvalue <- coef(object)/s.err
- res$coefficients[,4] <- 2 * pnorm(-abs(zvalue))
- colnames(res$coefficients) <- c("Estimate", "Robust SE", "z value", "Pr(>|z|)")
- class(res) <- c("summary.gee.robust", "summary.gee")
- return(res)
-}
+summary.gee.robust <- function(object, ...){
+ class(object) <- c("gee", "glm")
+ ##res <- summary.gee(object,...)
+ res <- summary(object,...)
+ res$coefficients <- res$coefficients[,-5]
+ res$coefficients[,2] <- s.err <- sqrt(diag(object$robust.variance))
+ res$coefficients[,3] <- zvalue <- coef(object)/s.err
+ res$coefficients[,4] <- 2 * pnorm(-abs(zvalue))
+ colnames(res$coefficients) <- c("Estimate", "Robust SE", "z value", "Pr(>|z|)")
+ class(res) <- c("summary.gee.robust", "summary.gee")
+ return(res)
+}
diff --git a/R/summary.setx.cond.R b/R/summary.setx.cond.R
index 95c0bf0..e585b1c 100644
--- a/R/summary.setx.cond.R
+++ b/R/summary.setx.cond.R
@@ -1,8 +1,23 @@
summary.setx.cond <- function(object, ...) {
- res <- matrix(NA, nrow(object), ncol(object))
- for (i in 1:ncol(object))
- res[,i] <- object[,i]
- colnames(res) <- colnames(object)
- res <- as.data.frame(res)
- summary(res, ...)
-}
+ if(any(class(object)=="setx.MI")){
+ res <- list()
+ for (j in 1:length(object)){
+ res[[j]] <- matrix(NA, nrow(object[[j]]), ncol(object[[j]]))
+ for (i in 1:ncol(object[[j]])){
+ res[[j]][,i] <- object[[j]][,i]
+ }
+ colnames(res[[j]]) <- colnames(object[[j]])
+ res[[j]] <- as.data.frame(res[[j]])
+ res[[j]] <- summary(res[[j]],...)
+ }
+ }
+ else{
+ res <- matrix(NA, nrow(object), ncol(object))
+ for (i in 1:ncol(object))
+ res[,i] <- object[,i]
+ colnames(res) <- colnames(object)
+ res <- as.data.frame(res)
+ res <- summary(res, ...)
+ }
+ return(res)
+}
\ No newline at end of file
diff --git a/R/summary.zaov.R b/R/summary.zaov.R
new file mode 100644
index 0000000..86c697a
--- /dev/null
+++ b/R/summary.zaov.R
@@ -0,0 +1,26 @@
+summary.zaovlist <- function (object, ...) {
+ object$call <- NULL
+ stats:::summary.aovlist(object,...)
+}
+
+
+summary.zmaov <- function(object, ...){
+ summary.aov(object)
+}
+
+
+summary.zmlm <- function (object, ...) {
+ frm <- object$call$formula
+ frm <- eval(frm)
+ ## solving a bug in the stats::summary.mlm, it does not work if
+ ## you pass the formula as a variable
+ object$call$formula <- frm
+ ## coef.aov gives the wrong formatting and I want
+ ## to use coef.default instead for class aov
+ if("aov" %in% class(object)){
+ ix <- grep("aov", class(object))
+ class(object) <- class(object)[-ix]
+ }
+
+ summary.mlm(object,...)
+}
diff --git a/R/summary.zelig.R b/R/summary.zelig.R
index c81ed9c..8f24649 100644
--- a/R/summary.zelig.R
+++ b/R/summary.zelig.R
@@ -30,6 +30,8 @@ summary.zelig<-function(object, subset = NULL, CI=95,
X1 <- X1[subset,]
}
rows <- rownames(X)
+ #object$qi$tt.pr <- object$qi$tt.ev <-
+ # object$qi.name$tt.pr <- object$qi.name$tt.ev <- NULL
for (i in 1:length(object$qi)) {
qi.stats[[i]] <- summarize(object$qi[[i]], rows = rows,
stats = stats, cip = cip,
diff --git a/R/summary.zelig.strata.R b/R/summary.zelig.strata.R
index 9252a21..756cd59 100644
--- a/R/summary.zelig.strata.R
+++ b/R/summary.zelig.strata.R
@@ -14,26 +14,25 @@ summary.zelig.strata <-function(object, subset = NULL, CI=95,
if(length(dim(qi1i))==3){
tmp <- array(NA, dim=c(dim(qi1i)[1:2], sum(w)))
tmp[,1:dim(qi1i)[2],1:dim(qi1i)[3]] <- qi1i
- }
- else
+ } else {
tmp <- as.matrix(qi1i)
- }
- else { # unconditional prediction
+ }
+ } else { # unconditional prediction
if(length(dim(qi1i))==3){
tmp <- array(NA, dim=c(dim(qi1i)[1:2], sum(w)))
tmp[,,1:w[1]] <- qi1i[,,1:w[1]]
- }
- else
+ } else {
tmp <- as.matrix(qi1i)
+ }
}
for (j in 2:m) {
qiji <- object[[j]]$qi[[i]]
- if(any(class(object[[j]]$x)=="cond")) # conditional prediction
+ if(any(class(object[[j]]$x)=="cond")) {# conditional prediction
if(length(dim(qi1i))==3)
tmp[,,(sum(w[1:(m-1)])+1):sum(w[1:m])] <- qiji
else
tmp <- cbind(tmp, qiji)
- else{ # unconditional prediction
+ } else{ # unconditional prediction
if(length(dim(qi1i))==3)
tmp[(sum(w[1:(j-1)])+1):sum(w[1:j]),,] <- qiji[1:w[j],,]
else
@@ -48,8 +47,7 @@ summary.zelig.strata <-function(object, subset = NULL, CI=95,
x=object[[1]]$x, x1=NULL, call=c,
zelig.call=object[[1]]$zelig.call)
return(summary.zelig(res, ...))
- }
- else { # summary for each strata
+ } else { # summary for each strata
res <- list()
if(is.function(subset)){
m <- length(object)
diff --git a/R/summary.zeliglist.R b/R/summary.zeliglist.R
new file mode 100644
index 0000000..fde97d8
--- /dev/null
+++ b/R/summary.zeliglist.R
@@ -0,0 +1,33 @@
+summary.zeliglist<-function(object, subset = NULL, CI=95,
+ stats=c("mean", "sd"), ...){
+ nm <- names(object)
+ lst <- list()
+ for(obj in object){
+ class(obj) <- c("zelig", class(obj))
+ res <- summary.zelig(obj, subset = subset, CI=CI,stats=stats, ...)
+
+ lst <- c(lst, list(res))
+ }
+ names(lst) <- nm
+ return(lst)
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/R/terms.zaov.R b/R/terms.zaov.R
new file mode 100644
index 0000000..1808fb9
--- /dev/null
+++ b/R/terms.zaov.R
@@ -0,0 +1,13 @@
+terms.zaovlist <- function(object,...){
+ Terms<- attr(object,"terms")
+ indError <- attr(Terms, "specials")$Error
+ if (length(indError)){
+ errorterm <- attr(Terms, "variables")[[1 + indError]]
+ formula <- update.formula(Terms, paste(". ~ .-", deparse(errorterm,
+ width = 500, backtick = TRUE), "+", deparse(errorterm[[2]],
+ width = 500, backtick = TRUE)))
+ terms.formula(formula)
+ } else {
+ terms.formula(object)
+ }
+}
diff --git a/R/vcov.gee.naive.R b/R/vcov.gee.naive.R
index 7a52dd5..ba42664 100644
--- a/R/vcov.gee.naive.R
+++ b/R/vcov.gee.naive.R
@@ -1,6 +1,6 @@
-vcov.gee.naive <- function(object, ...){
- class(object) <- c("gee", "glm")
- return(object$naive.variance)
-}
-
+vcov.gee.naive <- function(object, ...){
+ class(object) <- c("gee", "glm")
+ return(object$naive.variance)
+}
+
\ No newline at end of file
diff --git a/R/vcov.gee.robust.R b/R/vcov.gee.robust.R
index d4ba892..2665feb 100644
--- a/R/vcov.gee.robust.R
+++ b/R/vcov.gee.robust.R
@@ -1,4 +1,4 @@
-vcov.gee.robust <- function(object, ...){
- class(object) <- c("gee", "glm")
- return(object$robust.variance)
-}
+vcov.gee.robust <- function(object, ...){
+ class(object) <- c("gee", "glm")
+ return(object$robust.variance)
+}
diff --git a/R/vcov.netlogit.R b/R/vcov.netlogit.R
index 71bab1c..9d1ae29 100644
--- a/R/vcov.netlogit.R
+++ b/R/vcov.netlogit.R
@@ -1,4 +1,4 @@
-vcov.netlogit <- function(object, ...)
+vcov.logit.net <- function(object, ...)
{
so <- summary.glm(object, corr=FALSE, ...)
so$dispersion * so$cov.unscaled
diff --git a/R/vcov.zmlm.R b/R/vcov.zmlm.R
new file mode 100644
index 0000000..b9d924b
--- /dev/null
+++ b/R/vcov.zmlm.R
@@ -0,0 +1,4 @@
+vcov.zmlm <- function(object,...){
+ so <- summary.zmlm(object, corr = FALSE)[[1]]
+ kronecker(estVar(object), so$cov.unscaled, make.dimnames = TRUE)
+ }
diff --git a/R/vdc.R b/R/vdc.R
index 92fe42a..c27e0be 100644
--- a/R/vdc.R
+++ b/R/vdc.R
@@ -232,7 +232,9 @@ modeling.types <-function(){
bounded=c("continuous","discrete","nominal","ordinal","binary"),
multinomial=c("continuous","discrete","nominal","ordinal","binary"),
count=c("continuous","discrete","nominal","ordinal","binary"),
- mixed=c("continuous","discrete","nominal","ordinal","binary")),
+ mixed=c("continuous","discrete","nominal","ordinal","binary"),
+ ei=c("continuous","discrete","nominal","ordinal","binary")
+ ),
depVar=list(
continuous="continuous",
dichotomous="binary",
@@ -240,7 +242,10 @@ modeling.types <-function(){
bounded="continuous",
multinomial=c("nominal","ordinal"),
count="discrete",
- mixed=c("continuous","discrete","nominal","ordinal","binary")))
+ mixed=c("continuous","discrete","nominal","ordinal","binary"),
+ ei=c("continuous")
+ )
+ )
res
}
diff --git a/R/zelig.default.R b/R/zelig.default.R
index d36856e..7251988 100644
--- a/R/zelig.default.R
+++ b/R/zelig.default.R
@@ -1,14 +1,16 @@
zelig.default <- function(formula, model, data, by = NULL, save.data =
FALSE, ...) {
-# fn0 <- paste("describe", model, sep = ".")
+
fn1 <- paste("zelig2", model, sep = "")
fn2 <- paste("zelig3", model, sep = "")
-# if (exists(fn0))
-# formula <- do.call("parse.formula", list(formula = formula,
-# model = model, data = data))
+
if (!exists(fn1))
stop(model,
" not supported. Type help.zelig(\"models\") to list supported models.")
+
+ ## load model dependeny pakcages
+ loadModelDeps(model)
+
mf <- zelig.call <- match.call(expand.dots = TRUE)
zelig.call[[1]] <- as.name("zelig")
if (missing(by))
@@ -22,69 +24,108 @@ zelig.default <- function(formula, model, data, by = NULL, save.data =
else
dat <- data
if (!is.null(by)) {
- if (any(as.character(by) %in% c(formula[[2]], formula[[3]])))
- stop("the variable selected for subsetting cannot be called in the formula.")
- idx <- dat[,by]
- mf$by <- NULL
- lev <- sort(unique(idx))
- N <- length(lev)
+ if (any(as.character(by) %in% c(formula[[2]], formula[[3]])))
+ stop("the variable selected for subsetting cannot be called in the formula.")
+ idx <- dat[,by]
+ mf$by <- NULL
+ lev <- sort(unique(idx))
+ N <- length(lev)
}
+
+ ## call zelig2* function
mf <- do.call(fn1, list(formula, model, dat, N, ...))
+
for (i in 1:N) {
- if (N > 1) {
- dat <- list()
- if (M > 1) {
- for (j in 1:M)
- dat[[j]] <- data[[j]][idx == lev[i],]
- }
- else
- dat <- data[idx == lev[i],]
- }
- else
- dat <- data
- obj <- list()
- for (j in 1:M) {
- if (M > 1)
- d <- dat[[j]]
- else
- d <- dat
- if (is.data.frame(d)) {
- d <- d[complete.cases(model.frame(mf$formula, data=d,
- na.action = na.pass)),]
- mf$data <- d
-# res <- create.ZeligS4(eval(as.call(mf)))
- res <- eval(as.call(mf))
- if (exists(fn2))
- res <- do.call(fn2, list(res = res, fcall = mf,
- zcall = as.list(zelig.call)))
- check <- length(slotNames(res)) > 0
- if (check) {
- res at call <- as.call(zelig.call)
- res at model <- as.data.frame(d)
+ if (N > 1) {
+ dat <- list()
+ if (M > 1) {
+ for (j in 1:M)
+ dat[[j]] <- data[[j]][idx == lev[i],]
+ } else {
+ dat <- data[idx == lev[i],]
+ }
+ } else {
+ dat <- data
+ }
+ obj <- list()
+ for (j in 1:M) {
+ if (M > 1)
+ d <- dat[[j]]
+ else
+ d <- dat
+ if (is.data.frame(d)) {
+ ## List-wise deletion on d performed on the
+ ## rows which have NAs only for the variables which appear in
+ ## formula.
+ d <- d[complete.cases(d[,all.vars(as.expression(formula))]),]
+ mf$data <- d
+ }
+ ## evaluate the call returned by zelig2* function
+ ## (which is basically a call to the foreign model
+ res <- eval(as.call(mf))
+
+ ## if zelig3* exists (i.e. we want to manipulate the output coming
+ ## from the foreign model) then call it
+ if (exists(fn2))
+ res <- do.call(fn2, list(res = res, fcall = mf,
+ zcall = as.list(zelig.call)))
+
+ ## check the class of the object (S3/S4)
+ check <- length(slotNames(res)) > 0
+ if (check) { #S4
+ if ("call" %in% slotNames(res))
+ res at call <- as.call(zelig.call)
+ else
+ stop("no slot \"call\" in the result")
+
+ ## if is S4 and "model" slot found, save data in that slot
+ ## otherwise, we cannot save the data
+ if (save.data){
+ if ("model" %in% slotNames(res))
+ res at model <- as.data.frame(d)
+ else
+ stop("\"save.data\" option is not supported for this model")
+ }
+ } else { #S3
+ res$call <- as.call(zelig.call)
+ if (save.data) res$zelig.data <- as.data.frame(d)
+ }
+ ## res$zelig <- model
+ if (M > 1)
+ obj[[j]] <- res
+ else
+ obj <- res
+ }
+ if (M > 1)
+ class(obj) <- "MI"
+ if (N > 1)
+ object[[i]] <- obj
+ else
+ object <- obj
}
- else{
- res$call <- as.call(zelig.call)
- if (save.data) res$zelig.data <- as.data.frame(d)
- }
-# res$call <- as.call(zelig.call)
-# if (save.data) res$zelig.data <- d
-# res$zelig <- model
- if (M > 1)
- obj[[j]] <- res
- else
- obj <- res
- }
- }
- if (M > 1)
- class(obj) <- "MI"
- if (N > 1)
- object[[i]] <- obj
- else
- object <- obj
- }
if (N > 1) {
class(object) <- "strata"
names(object) <- lev
}
return(object)
}
+
+###
+## loadModelDeps
+##
+## For a given model, tries to load the package it depends on
+## model : model name
+##
+
+loadModelDeps <- function(model){
+ zdpd <- Zelig:::zeligModelDependency(model)
+
+ ## e.g. describe does not exist
+ if(!is.null(zdpd)){
+ pkg <- zdpd[1,1] ## what if more then 1 dependency?!
+ if (!is.na(pkg)) {
+ if (!suppressWarnings(require(pkg,quietly=TRUE,character.only=TRUE)))
+ stop("This model depends on package \"",pkg,"\". Please install this package and try again")
+ }
+ }
+}
diff --git a/R/zelig2MCMC.R b/R/zelig2MCMC.R
index 880be0b..6ea0212 100644
--- a/R/zelig2MCMC.R
+++ b/R/zelig2MCMC.R
@@ -1,362 +1,392 @@
zelig2ei.hier <- function(formula, model, data, M, ...) {
packageConflicts("VGAM")
- require(MCMCpack)
+
mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 50000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 5000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
+ if (is.null(mf$verbose) || !mf$verbose){
+ mf$verbose <- 0
+ } else {
+ if (is.null(mf$mcmc)) {
+ mcmc <- 50000
+ } else {
+ mcmc <- mf$mcmc
+ }
+ if (is.null(mf$burnin)){
+ burnin <- 5000
+ } else {
+ burnin <- mf$burnin
+ }
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
mf$model <- mf$M <- NULL
temp <- mcmcei(formula=formula, data=data)
-
+
if ((any(temp<0)) || ((any(temp<1) && !any(temp==0) ) && any(temp>1)))
stop("data need to be either counts or proportions.\n")
- if (is.null(mf$N))
- {
- if (all(temp>=0)) #N is not needed
- {
- mf$r0 <- temp$r0
- mf$r1 <- temp$r1
- mf$c0 <- temp$c0
- mf$c1 <- temp$c1
- }
- else stop("Needs total counts for inputs as porportion.\n")
- }
- else if (((length(mf$N)!= nrow(data)) && (length(mf$N)!=1)) || (any(mf$N<1)))
- stop("N needs to have same length as the observations and be postive numbers\n.")
- else if ((all(temp<1)) && (all(mf$N>1)))
- {
- mf$r0 <- round(temp$r0*mf$N)
- mf$r1 <- mf$N-mf$r0
- mf$c0 <- round(temp$c0*mf$N)
- mf$c1 <- mf$N-mf$c0
- }
+ if (is.null(mf$N)) {
+ if (all(temp>=0)){ #N is not needed
+ mf$r0 <- temp$r0
+ mf$r1 <- temp$r1
+ mf$c0 <- temp$c0
+ mf$c1 <- temp$c1
+ } else {
+ stop("Needs total counts for inputs as porportion.\n")
+ }
+ } else if (((length(mf$N)!= nrow(data)) && (length(mf$N)!=1)) || (any(mf$N<1)))
+ stop("N needs to have same length as the observations and be postive numbers\n.")
+ else if ((all(temp<1)) && (all(mf$N>1))){
+ mf$r0 <- round(temp$r0*mf$N)
+ mf$r1 <- mf$N-mf$r0
+ mf$c0 <- round(temp$c0*mf$N)
+ mf$c1 <- mf$N-mf$c0
+
+ }
mf[[1]] <- MCMCpack::MCMChierEI
as.call(mf)
}
zelig2ei.dynamic <- function(formula, model, data, M, ...) {
- packageConflicts("VGAM")
- require(MCMCpack)
- mf <- match.call(expand.dots = TRUE)
-
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 50000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 5000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
+ packageConflicts("VGAM")
+
+ mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- temp <- mcmcei(formula=formula, data=data)
-
- if ((any(temp<0)) || ((any(temp<1) && !any(temp==0) ) && any(temp>1)))
- stop("data need to be either counts or proportions.\n")
- if (is.null(mf$N))
- {
- if (all(temp>=0)) #N is not needed
- {
- mf$r0 <- temp$r0
- mf$r1 <- temp$r1
- mf$c0 <- temp$c0
- mf$c1 <- temp$c1
+ if (is.null(mf$verbose) || !mf$verbose) {
+ mf$verbose <- 0
+ } else {
+ if (is.null(mf$mcmc))
+ mcmc <- 50000
+ else
+ mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 5000
+ else
+ burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
}
- else stop("Needs total counts for inputs as porportion.\n")
- }
- else if (((length(mf$N)!= nrow(data)) && (length(mf$N)!=1)) || (any(mf$N<1)))
- stop("N needs to have same length as the observations and be postive numbers\n.")
- else if ((all(temp<1)) && (all(mf$N>1)))
- {
- mf$r0 <- round(temp$r0*mf$N)
- mf$r1 <- mf$N-mf$r0
- mf$c0 <- round(temp$c0*mf$N)
- mf$c1 <- mf$N-mf$c0
-
- }
- mf[[1]] <- MCMCpack::MCMCdynamicEI
- as.call(mf)
+ mf$model <- mf$M <- NULL
+ temp <- mcmcei(formula=formula, data=data)
+
+ if ((any(temp<0)) || ((any(temp<1) && !any(temp==0) ) && any(temp>1)))
+ stop("data need to be either counts or proportions.\n")
+ if (is.null(mf$N)) {
+ if (all(temp>=0)){ #N is not needed
+ mf$r0 <- temp$r0
+ mf$r1 <- temp$r1
+ mf$c0 <- temp$c0
+ mf$c1 <- temp$c1
+ }
+ else stop("Needs total counts for inputs as porportion.\n")
+ }
+ else if (((length(mf$N)!= nrow(data)) && (length(mf$N)!=1)) || (any(mf$N<1)))
+ stop("N needs to have same length as the observations and be postive numbers\n.")
+ else if ((all(temp<1)) && (all(mf$N>1))){
+ mf$r0 <- round(temp$r0*mf$N)
+ mf$r1 <- mf$N-mf$r0
+ mf$c0 <- round(temp$c0*mf$N)
+ mf$c1 <- mf$N-mf$c0
+ }
+ mf[[1]] <- MCMCpack::MCMCdynamicEI
+ as.call(mf)
}
-
-
-
zelig2logit.bayes <- function(formula, model, data, M, ...) {
packageConflicts("VGAM")
- require(MCMCpack)
+
mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
-
- mf$model <- mf$M <- NULL
-
- mf[[1]] <- MCMCpack::MCMClogit
- as.call(mf)
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 10000
+ else
+ mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
+ else
+ burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+
+ mf$model <- mf$M <- NULL
+
+ mf[[1]] <- MCMCpack::MCMClogit
+ as.call(mf)
}
zelig2probit.bayes <- function(formula, model, data, M, ...) {
packageConflicts("VGAM")
- require(MCMCpack)
- mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
+ mf <- match.call(expand.dots = TRUE)
+
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 10000
else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
+ if (is.null(mf$burnin))
+ burnin <- 1000
else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
-
- mf$model <- mf$M <- NULL
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
- mf[[1]] <- MCMCpack::MCMCprobit
- as.call(mf)
+ mf$model <- mf$M <- NULL
+
+ mf[[1]] <- MCMCpack::MCMCprobit
+ as.call(mf)
}
zelig2normal.bayes <- function(formula, model, data, M, ...) {
packageConflicts("VGAM")
- require(MCMCpack)
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
+
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 10000
+ else
+ mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
+ else
+ burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
}
- mf[[1]] <- MCMCpack::MCMCregress
- as.call(mf)
+ mf[[1]] <- MCMCpack::MCMCregress
+ as.call(mf)
}
zelig2poisson.bayes <- function(formula, model, data, M, ...) {
packageConflicts("VGAM")
- require(MCMCpack)
+
mf <- match.call(expand.dots = TRUE)
mf$model <- mf$M <- NULL
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
- mf[[1]] <- MCMCpack::MCMCpoisson
- as.call(mf)
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 10000
+ else
+ mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
+ else
+ burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+ mf[[1]] <- MCMCpack::MCMCpoisson
+ as.call(mf)
}
zelig2tobit.bayes <- function(formula, model, data, M, ...) {
- packageConflicts("VGAM")
- require(MCMCpack)
- mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
-
- mf$model <- mf$M <- NULL
- mf[[1]] <- MCMCpack::MCMCtobit
- as.call(mf)
+ packageConflicts("VGAM")
+
+ mf <- match.call(expand.dots = TRUE)
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 10000
+ else mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
+ else burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- MCMCpack::MCMCtobit
+ as.call(mf)
}
zelig2mlogit.bayes <- function(formula, model, data, M, ...) {
- packageConflicts("VGAM")
- require(MCMCpack)
- require(stats)
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
-
- mf[[1]] <- MCMCpack::MCMCmnl
- as.call(mf)
+ packageConflicts("VGAM")
+
+ require(stats)
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 10000
+ else mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
+ else
+ burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+
+ mf[[1]] <- MCMCpack::MCMCmnl
+ as.call(mf)
}
zelig2oprobit.bayes <- function(formula, model, data, M, ...) {
- packageConflicts("VGAM")
- require(MCMCpack)
- require(stats)
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
-
- mf[[1]] <- MCMCpack::MCMCoprobit
- as.call(mf)
+ packageConflicts("VGAM")
+
+ require(stats)
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 10000
+ else mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
+ else
+ burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+
+ mf[[1]] <- MCMCpack::MCMCoprobit
+ as.call(mf)
}
-
-
-
-
-
zelig2factor.bayes <- function(formula, model, data, M, ...) {
packageConflicts("VGAM")
- require(MCMCpack)
+
mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 20000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 20000
+ else
+ mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
-
- if (is.null(mf$factors)) mf$factors<-2
- else if (mf$factors<2) stop("number of factors needs to be at
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+
+ if (is.null(mf$factors))
+ mf$factors<-2
+ else if (mf$factors<2) stop("number of factors needs to be at
least 2")
- mf$model <- mf$M <- NULL
- mf$x <- as.matrix(model.response(model.frame(formula, data=data, na.action=NULL)))
- mf[[1]] <- MCMCpack::MCMCfactanal
- as.call(mf)
+ mf$model <- mf$M <- NULL
+ mf$x <- as.matrix(model.response(model.frame(formula, data=data, na.action=NULL)))
+ mf[[1]] <- MCMCpack::MCMCfactanal
+ as.call(mf)
}
zelig2factor.ord <- function(formula, model, data, M, ...) {
- packageConflicts("VGAM")
- require(MCMCpack)
- mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 20000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
- if (is.null(mf$factors)) mf$factors<-2
- else if (mf$factors<1) stop("number of factors needs to be at
+ packageConflicts("VGAM")
+
+ mf <- match.call(expand.dots = TRUE)
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 20000
+ else
+ mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
+ else
+ burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+ if (is.null(mf$factors)) mf$factors<-2
+ else if (mf$factors<1) stop("number of factors needs to be at
least 1")
- mf$model <- mf$M <- NULL
- mf$x <- as.matrix(model.response(model.frame(formula, data=data, na.action=NULL)))
-
- mf[[1]] <- MCMCpack::MCMCordfactanal
- as.call(mf)
+ mf$model <- mf$M <- NULL
+ mf$x <- as.matrix(model.response(model.frame(formula, data=data, na.action=NULL)))
+
+ mf[[1]] <- MCMCpack::MCMCordfactanal
+ as.call(mf)
}
zelig2factor.mix <- function(formula, model, data, M, ...) {
- packageConflicts("VGAM")
- require(MCMCpack)
- mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
-
- if (is.null(mf$factors)) mf$factors<-2
- else if (mf$factors<1) stop("number of factors needs to be at
+ packageConflicts("VGAM")
+
+ mf <- match.call(expand.dots = TRUE)
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc))
+ mcmc <- 10000
+ else mcmc <- mf$mcmc
+ if (is.null(mf$burnin))
+ burnin <- 1000
+ else
+ burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+
+ if (is.null(mf$factors))
+ mf$factors<-2
+ else if (mf$factors<1)
+ stop("number of factors needs to be at
least 1")
- mf$model <- mf$M <- NULL
-
- var <- model.response(model.frame(formula, data=data,
- na.action=NULL))
- varnames <- colnames(var)
- mf$x <- as.formula(paste("~", paste(varnames, collapse="+")))
- mf[[1]] <- MCMCpack::MCMCmixfactanal
-
- as.call(mf)
+ mf$model <- mf$M <- NULL
+
+ var <- model.response(model.frame(formula, data=data,
+ na.action=NULL))
+ varnames <- colnames(var)
+ mf$x <- as.formula(paste("~", paste(varnames, collapse="+")))
+ mf[[1]] <- MCMCpack::MCMCmixfactanal
+
+ as.call(mf)
}
zelig2irt1d <- function(formula, model, data, M, ...) {
- packageConflicts("VGAM")
- require(MCMCpack)
- mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 20000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
-
- mf$model <- mf$M <- NULL
- mf$datamatrix <- as.matrix(model.response(model.frame(formula, data=data,
- na.action=NULL)))
- mf$datamatrix <- t(mf$datamatrix)
- mf[[1]] <- MCMCpack::MCMCirt1d
-
- as.call(mf)
+ packageConflicts("VGAM")
+
+ mf <- match.call(expand.dots = TRUE)
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc)) mcmc <- 20000
+ else mcmc <- mf$mcmc
+ if (is.null(mf$burnin)) burnin <- 1000
+ else burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+
+ mf$model <- mf$M <- NULL
+ mf$datamatrix <- as.matrix(model.response(model.frame(formula, data=data,
+ na.action=NULL)))
+ mf$datamatrix <- t(mf$datamatrix)
+ mf[[1]] <- MCMCpack::MCMCirt1d
+
+ as.call(mf)
}
zelig2irtkd <- function(formula, model, data, M, ...) {
- packageConflicts("VGAM")
- require(MCMCpack)
- mf <- match.call(expand.dots = TRUE)
- if (is.null(mf$verbose) || !mf$verbose) mf$verbose <- 0
- else
- {
- if (is.null(mf$mcmc)) mcmc <- 10000
- else mcmc <- mf$mcmc
- if (is.null(mf$burnin)) burnin <- 1000
- else burnin <- mf$burnin
- mf$verbose <- round((mcmc+burnin)/10)
- }
- if (is.null(mf$dimensions)) mf$dimensions <- 1
-
- mf$model <- mf$M <- NULL
- mf$datamatrix <- as.matrix(model.response(model.frame(formula, data=data,
- na.action=NULL)))
- mf$datamatrix <- t(mf$datamatrix)
- mf[[1]] <- MCMCpack::MCMCirtKd
-
- as.call(mf)
+ packageConflicts("VGAM")
+
+ mf <- match.call(expand.dots = TRUE)
+ if (is.null(mf$verbose) || !mf$verbose)
+ mf$verbose <- 0
+ else {
+ if (is.null(mf$mcmc)) mcmc <- 10000
+ else mcmc <- mf$mcmc
+ if (is.null(mf$burnin)) burnin <- 1000
+ else burnin <- mf$burnin
+ mf$verbose <- round((mcmc+burnin)/10)
+ }
+ if (is.null(mf$dimensions)) mf$dimensions <- 1
+
+ mf$model <- mf$M <- NULL
+ mf$datamatrix <- as.matrix(model.response(model.frame(formula, data=data,
+ na.action=NULL)))
+ mf$datamatrix <- t(mf$datamatrix)
+ mf[[1]] <- MCMCpack::MCMCirtKd
+
+ as.call(mf)
}
diff --git a/R/zelig2aov.R b/R/zelig2aov.R
new file mode 100644
index 0000000..6d83822
--- /dev/null
+++ b/R/zelig2aov.R
@@ -0,0 +1,11 @@
+zelig2aov <- function(formula, model, data, M, ...) {
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- FALSE
+ mf$M <- mf$robust <- NULL
+ mf[[1]] <- aov
+# ix <- grep("Error", as.character(formula))
+# if(length(ix)) class(formula) <- c(class(formula),"aoverrorF")
+ mf$formula <- formula
+ as.call(mf)
+}
+
diff --git a/R/zelig2blogit.R b/R/zelig2blogit.R
index e31d325..e28fa72 100644
--- a/R/zelig2blogit.R
+++ b/R/zelig2blogit.R
@@ -1,9 +1,4 @@
zelig2blogit <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "VGAM"))
- require(VGAM)
- else
- stop("Please install VGAM using \n install.packages(\"VGAM\", repos = \"http://www.stat.auckland.ac.nz/~yee\")")
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- VGAM::vglm
mf$family <- as.name("blogit")
@@ -12,8 +7,10 @@ zelig2blogit <- function(formula, model, data, M, ...) {
tmp <- cmvglm(formula, model, 3)
mf$formula <- tmp$formula
mf$constraints <- tmp$constraints
- blogit <<- function() binom2.or(zero=NULL)
+
mf$model <- mf$constant <- mf$M <- NULL
mf$robust <- NULL
as.call(mf)
}
+
+ blogit <- function() binom2.or(zero=NULL)
diff --git a/R/zelig2bprobit.R b/R/zelig2bprobit.R
index adc38ac..042662a 100644
--- a/R/zelig2bprobit.R
+++ b/R/zelig2bprobit.R
@@ -1,9 +1,4 @@
zelig2bprobit <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "VGAM"))
- require(VGAM)
- else
- stop("Please install VGAM using \n install.packages(\"VGAM\", repos = \"http://www.stat.auckland.ac.nz/~yee\")")
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- VGAM::vglm
mf$family <- as.name("bprobit")
@@ -11,8 +6,11 @@ zelig2bprobit <- function(formula, model, data, M, ...) {
tmp <- cmvglm(formula, model,3)
mf$formula <- tmp$formula
mf$constraints <- tmp$constraints
- bprobit <<- function() binom2.rho(zero=NULL)
+
mf$model <- mf$constant <- mf$M <- NULL
as.call(mf)
}
+
+ bprobit <- function() binom2.rho(zero=NULL)
+
diff --git a/R/zelig2coxph.R b/R/zelig2coxph.R
new file mode 100644
index 0000000..0bb5b42
--- /dev/null
+++ b/R/zelig2coxph.R
@@ -0,0 +1,24 @@
+zelig2coxph <- function(formula, model, data, M, ...) {
+ require(survival)
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- survival::coxph
+ if (is.null(mf$robust))
+ mf$robust <- FALSE
+ if (!is.null(mf$cluster) & !mf$robust)
+ stop("\nIf cluster is specified, robust must be TRUE.")
+ if (!is.null(mf$cluster)) {
+ mf$formula <- update(mf$formula, paste(". ~ . + ",
+ paste("cluster(",mf$cluster,")")))
+ mf$cluster <- NULL
+ }
+ #mf$formula <- as.formula(paste(paste(deparse(formula[[2]])),
+ # paste(deparse(formula[[1]])),
+ # paste(deparse(formula[[3]], width.cutoff=500)),
+ # paste("+", " cluster(",
+ # mf$cluster, ")")))
+ #mf$cluster <- NULL
+ #}
+ as.call(mf)
+}
+
diff --git a/R/zelig2exp.R b/R/zelig2exp.R
index a902e52..a3a2ad6 100644
--- a/R/zelig2exp.R
+++ b/R/zelig2exp.R
@@ -1,19 +1,16 @@
zelig2exp <- function(formula, model, data, M, ...) {
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- require(survival)
- mf[[1]] <- survival::survreg
- mf$dist <- "exponential"
- if (is.null(mf$robust))
- mf$robust <- FALSE
- if (!is.null(mf$cluster) & !mf$robust)
- stop("\nIf cluster is specified, robust must be TRUE.")
- if (!is.null(mf$cluster)) {
- mf$formula <- as.formula(call("~", formula[[2]], call("+",formula[[3]],call("cluster",mf$cluster))))
- mf$cluster <- NULL
- }
- else if (mf$robust)
- mf$formula <- as.formula(call("~", formula[[2]], call("+",formula[[3]],call("cluster",call(":",1,call("nrow",formula[[2]]))))))
-
- as.call(mf)
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- survival::survreg
+ mf$dist <- "exponential"
+ if (is.null(mf$robust))
+ mf$robust <- FALSE
+ if (!is.null(mf$cluster) & !mf$robust)
+ stop("\nIf cluster is specified, robust must be TRUE.")
+ if (!is.null(mf$cluster)) {
+ mf$formula <- update(mf$formula, paste(". ~ . + ", paste("cluster(",mf$cluster,")")))
+ mf$cluster <- NULL
+ } else if (mf$robust)
+ mf$formula <- update(formula, paste(". ~ . + ", paste("cluster(1:nrow(",deparse(formula[[2]]),"))")))
+ as.call(mf)
}
diff --git a/R/zelig2gam.logit.R b/R/zelig2gam.logit.R
index 6cf39e7..491e4eb 100644
--- a/R/zelig2gam.logit.R
+++ b/R/zelig2gam.logit.R
@@ -1,13 +1,9 @@
-zelig2gam.logit <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "mgcv"))
- require(mgcv)
- else
- stop("Please install mgcv using \n install.packages(\"mgcv\")")
+zelig2logit.gam <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- as.name("gam")
mf$M <- mf$model <- NULL
class(formula) <- "gamF"
mf$formula <- formula
mf$family <- binomial()
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2gam.normal.R b/R/zelig2gam.normal.R
index 08fa0ed..ab1b5b6 100644
--- a/R/zelig2gam.normal.R
+++ b/R/zelig2gam.normal.R
@@ -1,12 +1,8 @@
-zelig2gam.normal <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "mgcv"))
- require(mgcv)
- else
- stop("Please install mgcv using \n install.packages(\"mgcv\")")
+zelig2normal.gam <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- as.name("gam")
mf$M <- mf$model <- NULL
class(formula) <- "gamF"
mf$formula <- formula
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2gam.poisson.R b/R/zelig2gam.poisson.R
index e93a483..78439fa 100644
--- a/R/zelig2gam.poisson.R
+++ b/R/zelig2gam.poisson.R
@@ -1,13 +1,9 @@
-zelig2gam.poisson <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "mgcv"))
- require(mgcv)
- else
- stop("Please install mgcv using \n install.packages(\"mgcv\")")
+zelig2poisson.gam <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- as.name("gam")
mf$M <- mf$model <- NULL
class(formula) <- "gamF"
mf$formula <- formula
mf$family <- poisson()
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2gam.probit.R b/R/zelig2gam.probit.R
index 38c1507..559cc63 100644
--- a/R/zelig2gam.probit.R
+++ b/R/zelig2gam.probit.R
@@ -1,13 +1,9 @@
-zelig2gam.probit <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "mgcv"))
- require(mgcv)
- else
- stop("Please install mgcv using \n install.packages(\"mgcv\")")
+zelig2probit.gam <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- as.name("gam")
mf$M <- mf$model <- NULL
class(formula) <- "gamF"
mf$formula <- formula
mf$family <- binomial(link="probit")
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2logit.gee.R b/R/zelig2gamma.gee.R
similarity index 63%
copy from R/zelig2logit.gee.R
copy to R/zelig2gamma.gee.R
index 629a99a..1e202f9 100644
--- a/R/zelig2logit.gee.R
+++ b/R/zelig2gamma.gee.R
@@ -1,13 +1,15 @@
-zelig2logit.gee <- function(formula, model, data, M, ...) {
- require(gee)
- mf <- match.call(expand.dots = TRUE)
- if(mf$corstr=="fixed" & is.null (mf$R))
- stop("R must be defined.")
- mf$model <- mf$M <- NULL
- mf[[1]] <- gee::gee
- if (is.character(mf$id))
- mf$id <- as.name(mf$id)
- mf$family <- binomial(link="logit")
- mf$robust <- NULL
- as.call(mf)
+zelig2gamma.gee <- function(formula, model, data, M, ...) {
+ require(gee)
+ mf <- match.call(expand.dots = TRUE)
+ if(is.null(mf$corstr))
+ mf$corstr <- as.character("independence")
+ if(mf$corstr=="fixed" & is.null (mf$R))
+ stop("R must be defined.")
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- gee::gee
+ if (is.character(mf$id))
+ mf$id <- as.name(mf$id)
+ mf$family <- Gamma
+ mf$robust <- NULL
+ as.call(mf)
}
\ No newline at end of file
diff --git a/R/zelig2gamma.mixed.R b/R/zelig2gamma.mixed.R
new file mode 100644
index 0000000..e122e85
--- /dev/null
+++ b/R/zelig2gamma.mixed.R
@@ -0,0 +1,17 @@
+zelig2gamma.mixed <- function(formula, model, data, M, ...){
+ mf <- match.call(expand.dots=TRUE)
+ mf[[1]]<- as.name("lmer")
+
+ mf$formula <- tolmerFormat(reduceMI(formula))
+
+ mf$model <- mf$M <- NULL
+
+ if (is.null(mf$family)){
+ mf$family <- Gamma
+ }
+ return(as.call(mf))
+}
+
+model.frame.lmer <- function(obj){
+ obj at frame
+}
diff --git a/R/zelig2lm.mixed.R b/R/zelig2lm.mixed.R
new file mode 100644
index 0000000..f9596a5
--- /dev/null
+++ b/R/zelig2lm.mixed.R
@@ -0,0 +1,15 @@
+zelig2ls.mixed <- function(formula, model, data, M, ...){
+ mf <- match.call(expand.dots=TRUE)
+
+ mf[[1]]<- as.name("lmer")
+
+ mf$formula <- tolmerFormat(reduceMI(formula))
+
+ mf$model <- mf$M <- NULL
+
+ return (as.call(mf))
+}
+
+model.frame.lmer<-function(obj){
+obj at frame
+}
diff --git a/R/zelig2logit.gee.R b/R/zelig2logit.gee.R
index 629a99a..cca362d 100644
--- a/R/zelig2logit.gee.R
+++ b/R/zelig2logit.gee.R
@@ -1,13 +1,15 @@
-zelig2logit.gee <- function(formula, model, data, M, ...) {
- require(gee)
- mf <- match.call(expand.dots = TRUE)
- if(mf$corstr=="fixed" & is.null (mf$R))
- stop("R must be defined.")
- mf$model <- mf$M <- NULL
- mf[[1]] <- gee::gee
- if (is.character(mf$id))
- mf$id <- as.name(mf$id)
- mf$family <- binomial(link="logit")
- mf$robust <- NULL
- as.call(mf)
+zelig2logit.gee <- function(formula, model, data, M, ...) {
+ require(gee)
+ mf <- match.call(expand.dots = TRUE)
+ if(is.null(mf$corstr))
+ mf$corstr <- as.character("independence")
+ if(mf$corstr=="fixed" & is.null (mf$R))
+ stop("R must be defined.")
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- gee::gee
+ if (is.character(mf$id))
+ mf$id <- as.name(mf$id)
+ mf$family <- binomial(link="logit")
+ mf$robust <- NULL
+ as.call(mf)
}
\ No newline at end of file
diff --git a/R/zelig2logit.mixed.R b/R/zelig2logit.mixed.R
new file mode 100644
index 0000000..180f5ba
--- /dev/null
+++ b/R/zelig2logit.mixed.R
@@ -0,0 +1,17 @@
+zelig2logit.mixed <- function(formula, model, data, M, ...){
+ mf <- match.call(expand.dots=TRUE)
+ mf[[1]]<- as.name("lmer")
+
+ mf$formula <- tolmerFormat(reduceMI(formula))
+
+ mf$model <- mf$M <- NULL
+
+ if (is.null(mf$family)){
+ mf$family <- binomial(link="logit")
+ }
+ return(as.call(mf))
+}
+
+model.frame.lmer <- function(obj){
+ obj at frame
+}
diff --git a/R/zelig2lognorm.R b/R/zelig2lognorm.R
index 001a375..bf00fa3 100644
--- a/R/zelig2lognorm.R
+++ b/R/zelig2lognorm.R
@@ -1,20 +1,19 @@
zelig2lognorm <- function(formula, model, data, M, ...) {
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- require(survival)
- mf[[1]] <- survival::survreg
- mf$dist <- "lognormal"
- if (is.null(mf$robust))
- mf$robust <- FALSE
- if (!is.null(mf$cluster) & !mf$robust)
- stop("\nIf cluster is specified, robust must be TRUE.")
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+
+ mf[[1]] <- survival::survreg
+ mf$dist <- "lognormal"
+ if (is.null(mf$robust))
+ mf$robust <- FALSE
+ if (!is.null(mf$cluster) & !mf$robust)
+ stop("\nIf cluster is specified, robust must be TRUE.")
- if (!is.null(mf$cluster)) {
- mf$formula <- as.formula(call("~", formula[[2]], call("+",formula[[3]],call("cluster",mf$cluster))))
- mf$cluster <- NULL
- }
- else if (mf$robust)
- mf$formula <- as.formula(call("~", formula[[2]], call("+",formula[[3]],call("cluster",call(":",1,call("nrow",formula[[2]]))))))
+ if (!is.null(mf$cluster)) {
+ mf$formula <- update(mf$formula, paste(". ~ . + ", paste("cluster(",mf$cluster,")")))
+ mf$cluster <- NULL
+ } else if (mf$robust)
+ mf$formula <- update(formula, paste(". ~ . + ", paste("cluster(1:nrow(",deparse(formula[[2]]),"))")))
- as.call(mf)
+ as.call(mf)
}
diff --git a/R/zelig2mlogit.R b/R/zelig2mlogit.R
index 99ee889..cfc15f0 100644
--- a/R/zelig2mlogit.R
+++ b/R/zelig2mlogit.R
@@ -1,19 +1,14 @@
zelig2mlogit <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "VGAM"))
- require(VGAM)
- else
- stop("Please install VGAM using \n install.packages(\"VGAM\", repos = \"http://www.stat.auckland.ac.nz/~yee\")")
- mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- VGAM::vglm
- mf$family <- VGAM::multinomial
- formula<-parse.formula(formula,model,data)
- tt<-terms(formula)
- fact<-attr(tt,"depFactors")$depFactorVar
- ndim<-length(attr(tt,"depFactors")$depLevels)
- tmp <- cmvglm(formula, mf$model, ndim,data,fact)
- mf$formula <- tmp$formula
- mf$constraints <- tmp$constraints
- mf$model <- mf$M <- NULL
- as.call(mf)
+ mf <- match.call(expand.dots = TRUE)
+ mf[[1]] <- VGAM::vglm
+ mf$family <- VGAM::multinomial
+ formula<-parse.formula(formula,model,data)
+ tt<-terms(formula)
+ fact<-attr(tt,"depFactors")$depFactorVar
+ ndim<-length(attr(tt,"depFactors")$depLevels)
+ tmp <- cmvglm(formula, mf$model, ndim,data,fact)
+ mf$formula <- tmp$formula
+ mf$constraints <- tmp$constraints
+ mf$model <- mf$M <- NULL
+ as.call(mf)
}
diff --git a/R/zelig2netcloglog.R b/R/zelig2netcloglog.R
index b7bc8af..d4d615f 100644
--- a/R/zelig2netcloglog.R
+++ b/R/zelig2netcloglog.R
@@ -1,12 +1,8 @@
-zelig2netcloglog <- function(formula, model, data, M, LF="cloglog", ...) {
- check <- library()
- if(any(check$results[,"Package"] == "sna"))
- require(sna)
- else
- stop("Please install sna using \n install.packages(\"sna\")")
+zelig2cloglog.net <- function(formula, model, data, M, LF="cloglog", ...) {
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- as.name("netbinom")
mf$M <- mf$model <- NULL
mf$formula <- formula
mf$LF <- "cloglog"
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2netgamma.R b/R/zelig2netgamma.R
index d21cfab..437d96d 100644
--- a/R/zelig2netgamma.R
+++ b/R/zelig2netgamma.R
@@ -1,11 +1,8 @@
-zelig2netgamma <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "sna"))
- require(sna)
- else
- stop("Please install sna using \n install.packages(\"sna\")")
+zelig2gamma.net <- function(formula, model, data, M, ...) {
+
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("netgamma")
+ mf[[1]] <- as.name("gamma.net")
mf$M <- mf$model <- NULL
mf$formula <- formula
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2netlogit.R b/R/zelig2netlogit.R
index 69b5e31..e312ad9 100644
--- a/R/zelig2netlogit.R
+++ b/R/zelig2netlogit.R
@@ -1,12 +1,8 @@
-zelig2netlogit <- function(formula, model, data, M, LF="logit", ...) {
- check <- library()
- if(any(check$results[,"Package"] == "sna"))
- require(sna)
- else
- stop("Please install sna using \n install.packages(\"sna\")")
+zelig2logit.net <- function(formula, model, data, M, LF="logit", ...) {
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- as.name("netbinom")
mf$M <- mf$model <- NULL
mf$formula <- formula
mf$LF <- "logit"
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2netls.R b/R/zelig2netls.R
index 5fe8862..51376de 100644
--- a/R/zelig2netls.R
+++ b/R/zelig2netls.R
@@ -1,11 +1,7 @@
-zelig2netls <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "sna"))
- require(sna)
- else
- stop("Please install sna using \n install.packages(\"sna\")")
+zelig2ls.net <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
mf[[1]] <- as.name("callnetlm")
mf$M <- mf$model <- NULL
mf$formula <- formula
- as.call(mf) }
+ as.call(mf)
+}
diff --git a/R/zelig2netnormal.R b/R/zelig2netnormal.R
index 51d32a6..404d935 100644
--- a/R/zelig2netnormal.R
+++ b/R/zelig2netnormal.R
@@ -1,11 +1,7 @@
-zelig2netnormal <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "sna"))
- require(sna)
- else
- stop("Please install sna using \n install.packages(\"sna\")")
+zelig2normal.net <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("netnormal")
+ mf[[1]] <- as.name("normal.net")
mf$M <- mf$model <- NULL
mf$formula <- formula
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2netpoisson.R b/R/zelig2netpoisson.R
index 748ea83..e17d3f4 100644
--- a/R/zelig2netpoisson.R
+++ b/R/zelig2netpoisson.R
@@ -1,11 +1,7 @@
-zelig2netpoisson <- function(formula, model, data, M, ...) {
- check <- library()
- if(any(check$results[,"Package"] == "sna"))
- require(sna)
- else
- stop("Please install sna using \n install.packages(\"sna\")")
+zelig2poisson.net <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("netpoisson")
+ mf[[1]] <- as.name("poisson.net")
mf$M <- mf$model <- NULL
mf$formula <- formula
- as.call(mf) }
\ No newline at end of file
+ as.call(mf)
+}
diff --git a/R/zelig2netprobit.R b/R/zelig2netprobit.R
index b1d1d80..0fc7c2d 100644
--- a/R/zelig2netprobit.R
+++ b/R/zelig2netprobit.R
@@ -1,4 +1,4 @@
-zelig2netprobit <- function(formula, model, data, M, ...) {
+zelig2probit.net <- function(formula, model, data, M, ...) {
check <- library()
if(any(check$results[,"Package"] == "sna"))
require(sna)
diff --git a/R/zelig2logit.gee.R b/R/zelig2normal.gee.R
similarity index 62%
copy from R/zelig2logit.gee.R
copy to R/zelig2normal.gee.R
index 629a99a..f48f308 100644
--- a/R/zelig2logit.gee.R
+++ b/R/zelig2normal.gee.R
@@ -1,13 +1,15 @@
-zelig2logit.gee <- function(formula, model, data, M, ...) {
- require(gee)
- mf <- match.call(expand.dots = TRUE)
- if(mf$corstr=="fixed" & is.null (mf$R))
- stop("R must be defined.")
- mf$model <- mf$M <- NULL
- mf[[1]] <- gee::gee
- if (is.character(mf$id))
- mf$id <- as.name(mf$id)
- mf$family <- binomial(link="logit")
- mf$robust <- NULL
- as.call(mf)
+zelig2normal.gee <- function(formula, model, data, M, ...) {
+ require(gee)
+ mf <- match.call(expand.dots = TRUE)
+ if(is.null(mf$corstr))
+ mf$corstr <- as.character("independence")
+ if(mf$corstr=="fixed" & is.null (mf$R))
+ stop("R must be defined.")
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- gee::gee
+ if (is.character(mf$id))
+ mf$id <- as.name(mf$id)
+ mf$family <- gaussian
+ mf$robust <- NULL
+ as.call(mf)
}
\ No newline at end of file
diff --git a/R/zelig2logit.gee.R b/R/zelig2poisson.gee.R
similarity index 62%
copy from R/zelig2logit.gee.R
copy to R/zelig2poisson.gee.R
index 629a99a..590838b 100644
--- a/R/zelig2logit.gee.R
+++ b/R/zelig2poisson.gee.R
@@ -1,13 +1,15 @@
-zelig2logit.gee <- function(formula, model, data, M, ...) {
- require(gee)
- mf <- match.call(expand.dots = TRUE)
- if(mf$corstr=="fixed" & is.null (mf$R))
- stop("R must be defined.")
- mf$model <- mf$M <- NULL
- mf[[1]] <- gee::gee
- if (is.character(mf$id))
- mf$id <- as.name(mf$id)
- mf$family <- binomial(link="logit")
- mf$robust <- NULL
- as.call(mf)
+zelig2poisson.gee <- function(formula, model, data, M, ...) {
+ require(gee)
+ mf <- match.call(expand.dots = TRUE)
+ if(is.null(mf$corstr))
+ mf$corstr <- as.character("independence")
+ if(mf$corstr=="fixed" & is.null (mf$R))
+ stop("R must be defined.")
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- gee::gee
+ if (is.character(mf$id))
+ mf$id <- as.name(mf$id)
+ mf$family <- poisson
+ mf$robust <- NULL
+ as.call(mf)
}
\ No newline at end of file
diff --git a/R/zelig2poisson.mixed.R b/R/zelig2poisson.mixed.R
new file mode 100644
index 0000000..774fec8
--- /dev/null
+++ b/R/zelig2poisson.mixed.R
@@ -0,0 +1,17 @@
+zelig2poisson.mixed <- function(formula, model, data, M, ...){
+ mf <- match.call(expand.dots=TRUE)
+ mf[[1]]<- as.name("lmer")
+
+ mf$formula <- tolmerFormat(reduceMI(formula))
+
+ mf$model <- mf$M <- NULL
+
+ if (is.null(mf$family)){
+ mf$family <- poisson
+ }
+ return(as.call(mf))
+}
+
+model.frame.lmer <- function(obj){
+ obj at frame
+}
diff --git a/R/zelig2logit.gee.R b/R/zelig2probit.gee.R
similarity index 60%
copy from R/zelig2logit.gee.R
copy to R/zelig2probit.gee.R
index 629a99a..e8fb809 100644
--- a/R/zelig2logit.gee.R
+++ b/R/zelig2probit.gee.R
@@ -1,13 +1,15 @@
-zelig2logit.gee <- function(formula, model, data, M, ...) {
- require(gee)
- mf <- match.call(expand.dots = TRUE)
- if(mf$corstr=="fixed" & is.null (mf$R))
- stop("R must be defined.")
- mf$model <- mf$M <- NULL
- mf[[1]] <- gee::gee
- if (is.character(mf$id))
- mf$id <- as.name(mf$id)
- mf$family <- binomial(link="logit")
- mf$robust <- NULL
- as.call(mf)
+zelig2probit.gee <- function(formula, model, data, M, ...) {
+ require(gee)
+ mf <- match.call(expand.dots = TRUE)
+ if(is.null(mf$corstr))
+ mf$corstr <- as.character("independence")
+ if(mf$corstr=="fixed" & is.null (mf$R))
+ stop("R must be defined.")
+ mf$model <- mf$M <- NULL
+ mf[[1]] <- gee::gee
+ if (is.character(mf$id))
+ mf$id <- as.name(mf$id)
+ mf$family <- binomial(link="probit")
+ mf$robust <- NULL
+ as.call(mf)
}
\ No newline at end of file
diff --git a/R/zelig2probit.mixed.R b/R/zelig2probit.mixed.R
new file mode 100644
index 0000000..e8eb356
--- /dev/null
+++ b/R/zelig2probit.mixed.R
@@ -0,0 +1,19 @@
+zelig2probit.mixed <- function(formula, model, data, M, ...){
+ mf <- match.call(expand.dots=TRUE)
+
+ mf[[1]]<- as.name("lmer")
+
+ mf$formula <- tolmerFormat(reduceMI(formula))
+
+ mf$model <- mf$M <- NULL
+
+ if (is.null(mf$family)){
+ mf$family <- binomial(link="probit")
+ }
+
+ return (as.call(mf))
+}
+
+model.frame.lmer<-function(obj){
+obj at frame
+}
diff --git a/R/zelig2sur.R b/R/zelig2sur.R
index e786cc2..61431dd 100644
--- a/R/zelig2sur.R
+++ b/R/zelig2sur.R
@@ -1,16 +1,10 @@
zelig2sur <- function(formula, model, data, M,...) {
- check <- library()
- if(any(check$results[,"Package"] == "systemfit"))
- require(systemfit)
- else
- stop("Please install systemfit using \n install.packages(\"systemfit\")")
- mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callsystemfit")
- formula<-parse.formula(formula,model)
- print(formula)
- tt<-terms(formula)
- mf$method<-"SUR"
- mf$model<- mf$M<-NULL
- mf$formula<-formula
- as.call(mf)
+ mf <- match.call(expand.dots = TRUE)
+ mf[[1]] <- as.name("callsystemfit")
+ formula<-parse.formula(formula,model)
+ tt<-terms(formula)
+ mf$method<-"SUR"
+ mf$model<- mf$M<-NULL
+ mf$formula<-formula
+ as.call(mf)
}
diff --git a/R/zelig2threesls.R b/R/zelig2threesls.R
index 615feeb..dbf39f2 100644
--- a/R/zelig2threesls.R
+++ b/R/zelig2threesls.R
@@ -1,26 +1,20 @@
zelig2threesls <- function(formula, model, data, M,...) {
- check <- library()
- if(any(check$results[,"Package"] == "systemfit"))
- require(systemfit)
- else
- stop("Please install systemfit using \n install.packages(\"systemfit\")")
- "%w/o%" <- function(x,y) x[!x %in% y]
- mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callsystemfit")
- formula<-parse.formula(formula,model)
- tt<-terms(formula)
- ins<-names(tt) %w/o% names(attr(tt,"depVars"))
- if(length(ins)!=0)
- if(length(ins)==1)
- inst<-formula[[ins]]
- else inst<-formula[ins]
- else
- stop("threesls model requires instrument!!\n")
- mf$method<-"3SLS"
- mf$inst<-inst
- mf$model<- mf$M<-NULL
- mf$formula<-formula[names(attr(tt,"depVars"))]
- class(mf$formula)<-c("multiple","list")
- as.call(mf)
-
+ "%w/o%" <- function(x,y) x[!x %in% y]
+ mf <- match.call(expand.dots = TRUE)
+ mf[[1]] <- as.name("callsystemfit")
+ formula<-parse.formula(formula,model)
+ tt<-terms(formula)
+ ins<-names(tt) %w/o% names(attr(tt,"depVars"))
+ if(length(ins)!=0)
+ if(length(ins)==1)
+ inst<-formula[[ins]]
+ else inst<-formula[ins]
+ else
+ stop("threesls model requires instrument!!\n")
+ mf$method<-"3SLS"
+ mf$inst<-inst
+ mf$model<- mf$M<-NULL
+ mf$formula<-formula[names(attr(tt,"depVars"))]
+ class(mf$formula)<-c("multiple","list")
+ as.call(mf)
}
diff --git a/R/zelig2tobit.R b/R/zelig2tobit.R
index 1f0496e..7a54e1b 100644
--- a/R/zelig2tobit.R
+++ b/R/zelig2tobit.R
@@ -1,52 +1,52 @@
zelig2tobit <- function(formula, model, data, M, ...) {
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- NULL
- require(survival)
- mf[[1]] <- survival::survreg
- mf$dist <- "gaussian"
- if (is.null(mf$above))
- above <- Inf
- else {
- above <- mf$above
- mf$above <- NULL
- }
- if (is.null(mf$below))
- below <- 0
- else {
- below <- mf$below
- mf$below <- NULL
- }
-
- ## Fixing the call for robust SEs
- if (is.null(mf$robust))
- mf$robust <- FALSE
- if (!is.null(mf$cluster) & !mf$robust)
- stop("\nIf cluster is specified, robust must be TRUE.")
+ mf <- match.call(expand.dots = TRUE)
+ mf$model <- mf$M <- NULL
+
+ mf[[1]] <- survival::survreg
+ mf$dist <- "gaussian"
+ if (is.null(mf$above))
+ above <- Inf
+ else {
+ above <- mf$above
+ mf$above <- NULL
+ }
+ if (is.null(mf$below))
+ below <- 0
+ else {
+ below <- mf$below
+ mf$below <- NULL
+ }
+
+ ## Fixing the call for robust SEs
+ if (is.null(mf$robust))
+ mf$robust <- FALSE
+ if (!is.null(mf$cluster) & !mf$robust)
+ stop("\nIf cluster is specified, robust must be TRUE.")
- if (!is.null(mf$cluster)) {
- mf$formula <- as.formula(call("~", formula[[2]], call("+",formula[[3]],call("cluster",mf$cluster))))
- mf$cluster <- NULL
- }
- else if (mf$robust)
- mf$formula <- as.formula(call("~", formula[[2]], call("+",formula[[3]],call("cluster",call(":",1,call("nrow",formula[[2]]))))))
- ## Fixing the call for left censoring
- if (length(grep("Surv", as.character(formula[[2]]))) == 0) {
- if (above == Inf & is.numeric(below) & below != -Inf) {
- tt <- "left"
- }
- else if (below == -Inf & above == Inf) {
- stop("No censoring, use zelig(..., model = \"normal\") instead!")
- }
- else if (below == -Inf & is.numeric(above) & above != Inf) {
- stop("Right censored data not supported in a tobit model.")
- }
- else if (is.numeric(above) & is.numeric(below) & above != Inf & below != -Inf) {
- stop("Interval censored data not suppored in a tobit model.")
- }
- mf$formula[[2]] <- call("Surv", formula[[2]],
- call("<", below, formula[[2]]),
- type = "left")
- }
- as.call(mf)
+ if (!is.null(mf$cluster)) {
+ mf$formula <- update(mf$formula, paste(". ~ . + ", paste("cluster(",mf$cluster,")")))
+ mf$cluster <- NULL
+ } else if (mf$robust)
+ mf$formula <- update(formula, paste(". ~ . + ", paste("cluster(1:nrow(",deparse(formula[[2]]),"))")))
+
+ ## Fixing the call for left censoring
+ if (length(grep("Surv", as.character(formula[[2]]))) == 0) {
+ if (above == Inf & is.numeric(below) & below != -Inf) {
+ tt <- "left"
+ }
+ else if (below == -Inf & above == Inf) {
+ stop("No censoring, use zelig(..., model = \"normal\") instead!")
+ }
+ else if (below == -Inf & is.numeric(above) & above != Inf) {
+ stop("Right censored data not supported in a tobit model.")
+ }
+ else if (is.numeric(above) & is.numeric(below) & above != Inf & below != -Inf) {
+ stop("Interval censored data not suppored in a tobit model.")
+ }
+ mf$formula[[2]] <- call("Surv", formula[[2]],
+ call("<", below, formula[[2]]),
+ type = "left")
+ }
+ as.call(mf)
}
diff --git a/R/zelig2twosls.R b/R/zelig2twosls.R
index 674b134..4abd046 100644
--- a/R/zelig2twosls.R
+++ b/R/zelig2twosls.R
@@ -1,29 +1,24 @@
zelig2twosls <- function(formula, model, data, M,...) {
- check <- library()
- if(any(check$results[,"Package"] == "systemfit"))
- require(systemfit)
- else
- stop("Please install systemfit using \n install.packages(\"systemfit\")")
- "%w/o%" <- function(x,y) x[!x %in% y]
- mf <- match.call(expand.dots = TRUE)
-
- mf[[1]] <- as.name("callsystemfit")
-
- formula<-parse.formula(formula,model)
- tt<-terms(formula)
-
- ins<-names(tt) %w/o% names(attr(tt,"depVars"))
- if(length(ins)!=0)
- if(length(ins)==1)
- inst<-formula[[ins]]
- else inst<-formula[ins]
- else
- stop("twosls model requires instrument!!\n")
- mf$method<-"2SLS"
- mf$inst<-inst
- mf$model<- mf$M<-NULL
- mf$formula<-formula[names(attr(tt,"depVars"))]
- class(mf$formula)<-c("multiple","list")
-
- as.call(mf)
+ "%w/o%" <- function(x,y) x[!x %in% y]
+ mf <- match.call(expand.dots = TRUE)
+
+ mf[[1]] <- as.name("callsystemfit")
+
+ formula<-parse.formula(formula,model)
+ tt<-terms(formula)
+
+ ins<-names(tt) %w/o% names(attr(tt,"depVars"))
+ if(length(ins)!=0)
+ if(length(ins)==1)
+ inst<-formula[[ins]]
+ else inst<-formula[ins]
+ else
+ stop("twosls model requires instrument!!\n")
+ mf$method<-"2SLS"
+ mf$inst<-inst
+ mf$model<- mf$M<-NULL
+ mf$formula<-formula[names(attr(tt,"depVars"))]
+ class(mf$formula)<-c("multiple","list")
+
+ as.call(mf)
}
diff --git a/R/zelig2weibull.R b/R/zelig2weibull.R
index 4b01594..f56eac9 100644
--- a/R/zelig2weibull.R
+++ b/R/zelig2weibull.R
@@ -1,20 +1,18 @@
zelig2weibull <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
mf$model <- mf$M <- NULL
- require(survival)
+
mf[[1]] <- survival::survreg
mf$dist <- "weibull"
if (is.null(mf$robust))
mf$robust <- FALSE
if (!is.null(mf$cluster) & !mf$robust)
stop("\nIf cluster is specified, robust must be TRUE.")
-
+
if (!is.null(mf$cluster)) {
- mf$formula <- as.formula(call("~", formula[[2]], call("+",formula[[3]],call("cluster",mf$cluster))))
+ mf$formula <- update(mf$formula, paste(". ~ . + ", paste("cluster(",mf$cluster,")")))
mf$cluster <- NULL
- }
- else if (mf$robust)
- mf$formula <- as.formula(call("~", formula[[2]], call("+",formula[[3]],call("cluster",call(":",1,call("nrow",formula[[2]]))))))
-
+ } else if (mf$robust)
+ mf$formula <- update(formula, paste(". ~ . + ", paste("cluster(1:nrow(",deparse(formula[[2]]),"))")))
as.call(mf)
}
diff --git a/R/zelig3aov.R b/R/zelig3aov.R
new file mode 100644
index 0000000..d3b7da6
--- /dev/null
+++ b/R/zelig3aov.R
@@ -0,0 +1,9 @@
+zelig3aov <- function(res, fcall = NULL, zcall = NULL) {
+
+ if(length(attr(attributes(res)$terms, "specials")$Error))
+ class(res) <- c("zaovlist", class(res))
+ if("maov" %in% class(res))
+ class(res) <- c("zmaov", "zmlm", class(res))
+ return(res)
+}
+
diff --git a/R/zelig3gee.R b/R/zelig3coxph.R
old mode 100755
new mode 100644
similarity index 50%
copy from R/zelig3gee.R
copy to R/zelig3coxph.R
index 7e962ca..907128a
--- a/R/zelig3gee.R
+++ b/R/zelig3coxph.R
@@ -1,15 +1,15 @@
-zelig3logit.gee <- function(res, fcall=NULL, zcall=NULL){
- rob <- eval(zcall$robust)
- if (!is.null(rob)){
- if (!is.logical(rob))
- stop("invalid input for robust. Choose either TRUE or FALSE.")
- else if(!rob)
- class(res) <- c("gee.naive", class(res))
- else if (rob)
- class(res) <- c("gee.robust", class(res))
- }
- else {
- class(res) <- c("gee.robust", class(res))
- }
- return(res)
-}
\ No newline at end of file
+zelig3coxph <- function(res, fcall=NULL, zcall=NULL){
+ rob <- eval(zcall$robust)
+ if (!is.null(rob)){
+ if (!is.logical(rob))
+ stop("invalid input for robust. Choose either TRUE or FALSE.")
+ else if(!rob)
+ class(res) <- c("coxph.naive", class(res))
+ else if (rob)
+ class(res) <- c("coxph.robust", class(res))
+ }
+ else {
+ class(res) <- c("coxph.naive", class(res))
+ }
+ return(res)
+}
diff --git a/R/zelig3gee.R b/R/zelig3gee.R
index 7e962ca..c865bcd 100755
--- a/R/zelig3gee.R
+++ b/R/zelig3gee.R
@@ -1,15 +1,16 @@
-zelig3logit.gee <- function(res, fcall=NULL, zcall=NULL){
- rob <- eval(zcall$robust)
- if (!is.null(rob)){
- if (!is.logical(rob))
- stop("invalid input for robust. Choose either TRUE or FALSE.")
- else if(!rob)
- class(res) <- c("gee.naive", class(res))
- else if (rob)
- class(res) <- c("gee.robust", class(res))
- }
- else {
- class(res) <- c("gee.robust", class(res))
- }
- return(res)
+zelig3logit.gee <- zelig3probit.gee <- zelig3normal.gee <-
+zelig3poisson.gee <- zelig3gamma.gee <- function(res, fcall=NULL, zcall=NULL){
+ rob <- eval(zcall$robust)
+ if (!is.null(rob)){
+ if (!is.logical(rob))
+ stop("invalid input for robust. Choose either TRUE or FALSE.")
+ else if(!rob)
+ class(res) <- c("gee.naive", class(res))
+ else if (rob)
+ class(res) <- c("gee.robust", class(res))
+ }
+ else {
+ class(res) <- c("gee.robust", class(res))
+ }
+ return(res)
}
\ No newline at end of file
diff --git a/R/zelig3ls.R b/R/zelig3ls.R
index db5662b..6210a75 100644
--- a/R/zelig3ls.R
+++ b/R/zelig3ls.R
@@ -14,6 +14,9 @@ zelig3ls <- function(res, fcall = NULL, zcall = NULL) {
else if (rob)
class(res) <- c("lm.robust", class(res))
}
+ rc <- class(res)
+ if("mlm" %in% class(res))
+ class(res) <- c("zmlm", rc)
return(res)
}
diff --git a/R/zelig3mixed.R b/R/zelig3mixed.R
new file mode 100644
index 0000000..3ef7013
--- /dev/null
+++ b/R/zelig3mixed.R
@@ -0,0 +1,6 @@
+zelig3ls.mixed <- zelig3gamma.mixed <- zelig3poisson.mixed <- zelig3probit.mixed <- zelig3logit.mixed <- function (res, fcall = NULL,zcall=NULL) {
+ tt <- attr(res at frame, "terms")
+ attr(tt, "intercept") <- 1
+ res at terms <- tt
+ return (res)
+}
diff --git a/R/zelig4gee.R b/R/zelig4gee.R
new file mode 100644
index 0000000..3cd0901
--- /dev/null
+++ b/R/zelig4gee.R
@@ -0,0 +1,46 @@
+zelig4gamma.gee <- function(object, simpar, x, x1=NULL, bootstrap = FALSE, bootfn = NULL, dta = NULL){
+ num <- nrow(simpar)
+ coef <- simpar
+ eta <- coef %*% t(x)
+ if(!is.null(x1))
+ eta1 <- coef %*% t(x1)
+ else
+ eta1 <- NULL
+
+ good.params <- function(par, x, x1=NULL){
+ eta <- par %*% t(x)
+ if(!is.null(x1)){
+ eta1 <- par %*% t(x1)
+ pos <- which(eta>0 & eta1>0)
+ }
+ else{
+ pos <- which(apply(eta > 0,1,all))
+ }
+ params <- matrix(par[pos,], nrow=length(pos), ncol=ncol(par))
+ return(params)
+ }
+
+ if(length(which(apply(eta<=0,1,any)))>0 | (!is.null(eta1) & any(eta1<=0))){
+ warning(paste("Negative expected values in simulations. Rejection sampling method used."))
+ sum.neg <- length(which(apply(eta<=0,1,any)))
+ coef <- good.params(par=coef, x=x, x1=x1)
+ counter <- 1
+ while(sum.neg > 0){
+ if(!bootstrap)
+ new.coef <- matrix(mvrnorm(sum.neg, mu = coef(object), Sigma = vcov(object)), nrow=sum.neg)
+ else
+ new.coef <- matrix(boot(dta, bootfn, R = sum.neg, object = object)$t, nrow=sum.neg)
+
+ new.coef <- good.params(par=new.coef, x=x, x1=x1)
+ coef <- rbind(coef, new.coef)
+ sum.neg <- num - nrow(coef)
+ counter <- counter + 1
+ if(counter==200)
+ warning(paste("Suitable parameters not found after 200 iterations of rejection sampling. Iterations will continue, but choosing another x is suggested for non-conditional prediction models."))
+ if(counter==2000)
+ stop("Rejection sampling stopped after 2000 iterations. Please choose another x value.")
+ }
+ }
+ res <- coef
+ return(res)
+}
\ No newline at end of file
diff --git a/R/zelig4glm.R b/R/zelig4glm.R
new file mode 100644
index 0000000..5913ced
--- /dev/null
+++ b/R/zelig4glm.R
@@ -0,0 +1,75 @@
+zelig4gamma <- function(object, simpar, x, x1=NULL, bootstrap = FALSE, bootfn = NULL, dta = NULL){
+ k <- length(getcoef(object))
+ num <- nrow(simpar)
+ coef <- simpar[,1:k]
+ alpha <- simpar[,k+1]
+ eta <- coef %*% t(x)
+ if(!is.null(x1))
+ eta1 <- coef %*% t(x1)
+ else
+ eta1 <- NULL
+
+ good.params <- function(par, x, x1=NULL){
+ eta <- par[,1:k] %*% t(x)
+ if(!is.null(x1)){
+ eta1 <- par[,1:k] %*% t(x1)
+ pos <- which(eta>0 & eta1>0)
+ }
+ else{
+ pos <- which(apply(eta > 0,1,all))
+ }
+ params <- matrix(par[pos,], nrow=length(pos), ncol=ncol(par))
+ return(params)
+ }
+
+
+ if(length(which(apply(eta<=0,1,any)))>0 | (!is.null(eta1) & any(eta1<=0))){
+ warning(paste("Negative expected values in simulations. Rejection sampling method used."))
+ if(!is.null(eta1))
+ sum.neg <- length(unique(c(which(apply(eta<=0,1,any)), which(apply(eta1<=0,1,any)))))
+ else
+ sum.neg <- length(which(apply(eta<=0,1,any)))
+ if(!bootstrap)
+ coef <- good.params(par=coef, x=x, x1=x1)
+ else
+ simpar <- good.params(par=simpar, x=x, x1=x1)
+ counter <- 1
+ while(sum.neg > 0){
+ if(!bootstrap){
+ new.coef <- matrix(mvrnorm(sum.neg, mu = coef(object), Sigma = vcov(object)), nrow=sum.neg)
+ new.coef <- good.params(par=new.coef, x=x, x1=x1)
+ coef <- rbind(coef, new.coef)
+ sum.neg <- num - nrow(coef)
+ }
+ else{
+ new.simpar <- matrix(boot(dta, bootfn, R = sum.neg, object = object)$t, nrow=sum.neg)
+ new.simpar <- good.params(par=new.simpar, x=x, x1=x1)
+ simpar <- rbind(simpar, new.simpar)
+ sum.neg <- num - nrow(simpar)
+ }
+ counter <- counter + 1
+ if(counter==200)
+ warning(paste("Suitable parameters not found after 200 iterations of rejection sampling. Iterations will continue, but choosing another x is suggested for non-conditional prediction models."))
+ if(counter==2000)
+ stop("Rejection sampling stopped after 2000 iterations. Please choose another x value.")
+ }
+ }
+ if(!bootstrap & any(alpha<=0)){
+ pos <- which(alpha > 0)
+ alpha <- alpha[pos]
+ sum.neg <- num-length(pos)
+ while(sum.neg > 0){
+ new.alpha <- rnorm(sum.neg, mean = gamma.shape(object)$alpha, sd = gamma.shape(object)$SE)
+ pos <- which(new.alpha > 0)
+ alpha <- c(alpha, new.alpha[pos])
+ sum.neg <- num - length(pos)
+ }
+ }
+ if(!bootstrap){
+ res <- cbind(coef, alpha)
+ }
+ else{
+ res <- simpar
+ }
+ return(res)
+}
\ No newline at end of file
diff --git a/R/zvcClient.R b/R/zvcClient.R
index 224fa6d..c22818d 100644
--- a/R/zvcClient.R
+++ b/R/zvcClient.R
@@ -403,7 +403,7 @@ zelig.new.packages <- function(lib.loc=NULL,zmat=matrixDependencies(),matin=NULL
lrw <- unlist(suppressWarnings(sapply(lrw, FUN="trim.blanks")))
lrwuq <- unique.default(lrw)
dec <- 0
- if(length(grep("^R",lrwuq[length(lrwuq)], extended=T)) > 0) dec <- 1
+ if(length(grep("^R",lrwuq[length(lrwuq)], extended=TRUE)) > 0) dec <- 1
### print(zrwuq)
ind <- sapply(lrwuq, match, zrwuq)
@@ -441,7 +441,7 @@ zelig.new.packages <- function(lib.loc=NULL,zmat=matrixDependencies(),matin=NULL
newpkgmat <- NULL
pkgmod <- sapply(pkgsnoinst,function(m) paste("^",m,"$",sep=""))
- ind <- unlist(sapply(pkgmod,grep,zrw,extended=T))
+ ind <- unlist(sapply(pkgmod,grep,zrw,extended=TRUE))
if(length(ind) > 0){
ind <- unlist(ind)
@@ -466,7 +466,9 @@ zelig.new.packages <- function(lib.loc=NULL,zmat=matrixDependencies(),matin=NULL
if(length(ind) <= 0){
message("No new packages ")
- return(Rvers)}
+ ##return(Rvers) ## this var does not exists, r cmd check complains ## ferdi
+ return (NULL)
+ }
###for display
nrw <- nrow(newpkgmat)
if(length(nrw) <= 0){
@@ -610,7 +612,7 @@ selectLevels <- function(mat, depth){
nmcol <- colnames(mat)
ix <- unique.default(c(grep("depth", nmvec),grep("depth", nmcol)))
mat.depth <- mat[[ix]]
- if(length(grep("^1[z-Z]$",mat.depth),extended=T)<=0){
+ if(length(grep("^1[z-Z]$",mat.depth,extended=TRUE))<=0){
bool1 <- as.numeric(mat.depth) <= depth
}else{
bool1 <- mat.depth <= "1z" || mat.depth <= "1Z"
diff --git a/data/bivariate.tab b/data/bivariate.tab
new file mode 100644
index 0000000..80ed648
--- /dev/null
+++ b/data/bivariate.tab
@@ -0,0 +1,80 @@
+"x3" "x2" "x1" "y2" "y1" "x4"
+"1" 1 4 3 1 1 4
+"2" 0 2 3 0 1 3
+"3" 0 1 3 1 0 2
+"4" 1 1 3 1 1 2
+"5" 0 1 3 1 1 2
+"6" 0 1 3 0 1 2
+"7" 1 2 2 0 1 2
+"8" 0 1 3 0 0 2
+"9" 0 2 1 0 0 1
+"10" 1 2 3 1 1 2
+"11" 1 1 2 0 0 1
+"12" 0 1 2 1 1 2
+"13" 0 3 1 1 1 2
+"14" 0 3 3 1 1 4
+"15" 0 3 2 1 1 3
+"16" 0 1 2 0 0 1
+"17" 0 4 2 1 1 2
+"18" 0 3 3 0 0 2
+"19" 0 1 1 0 0 1
+"20" 0 3 3 1 0 3
+"21" 0 4 3 0 0 2
+"22" 0 1 2 0 0 1
+"23" 0 1 2 1 1 4
+"24" 0 1 2 0 0 1
+"25" 0 1 1 0 0 1
+"26" 0 3 2 1 1 2
+"27" 0 1 2 1 0 2
+"28" 0 1 2 1 0 2
+"29" 0 1 2 0 0 1
+"30" 0 4 2 1 1 3
+"31" 0 1 2 0 0 1
+"32" 0 1 2 0 0 1
+"33" 0 1 1 0 0 1
+"34" 0 1 2 0 1 1
+"35" 0 2 1 1 1 2
+"36" 0 3 3 0 1 1
+"37" 0 1 2 0 0 1
+"38" 0 1 1 0 0 1
+"39" 0 1 2 0 0 1
+"40" 0 2 3 0 1 2
+"41" 0 2 2 0 1 2
+"42" 0 2 3 0 0 2
+"43" 0 1 3 1 0 2
+"44" 0 2 3 0 1 2
+"45" 0 1 1 1 1 1
+"46" 0 1 2 0 1 1
+"47" 0 1 3 0 1 2
+"48" 0 2 1 1 0 1
+"49" 0 1 3 0 0 1
+"50" 0 1 2 0 0 1
+"51" 0 1 2 0 1 2
+"52" 0 1 3 0 1 2
+"53" 0 1 1 0 1 1
+"54" 0 1 1 0 0 1
+"55" 0 1 2 0 0 1
+"56" 0 1 2 0 1 2
+"57" 0 2 2 0 1 2
+"58" 0 2 3 0 1 2
+"59" 0 2 3 0 1 2
+"60" 0 3 2 1 1 3
+"61" 1 3 2 0 0 1
+"62" 0 1 3 1 1 3
+"63" 0 3 1 1 1 3
+"64" 0 2 2 0 0 1
+"65" 0 3 3 1 1 2
+"66" 0 2 1 0 0 1
+"67" 0 3 3 0 1 3
+"68" 0 1 2 0 1 2
+"69" 0 1 2 1 0 2
+"70" 0 3 1 1 1 2
+"71" 0 2 3 0 1 3
+"72" 0 2 2 0 0 1
+"73" 1 3 1 1 1 2
+"74" 0 2 1 0 0 1
+"75" 0 1 3 0 1 2
+"76" 0 4 3 1 0 2
+"77" 0 1 2 0 0 1
+"78" 1 3 1 1 1 2
+
diff --git a/data/coalition2.txt b/data/coalition2.txt
new file mode 100644
index 0000000..310ceb2
--- /dev/null
+++ b/data/coalition2.txt
@@ -0,0 +1,315 @@
+"duration" "ciep12" "invest" "fract" "polar" "numst2" "crisis" "country"
+"1" 0.5 1 1 656 11 0 24 "belgium"
+"2" 3 1 1 656 11 1 10 "belgium"
+"3" 7 1 1 656 11 1 24 "belgium"
+"4" 20 1 1 656 11 1 7 "belgium"
+"5" 6 1 1 656 11 1 7 "belgium"
+"6" 7 1 1 634 6 1 45 "belgium"
+"7" 2 1 1 599 3 1 51 "belgium"
+"8" 17 1 1 599 3 1 4 "belgium"
+"9" 27 1 1 599 3 1 6 "belgium"
+"10" 49 0 1 620 2 1 10 "belgium"
+"11" 4 1 1 592 1 0 23 "belgium"
+"12" 29 1 1 592 1 1 2 "belgium"
+"13" 49 0 1 628 5 1 29 "belgium"
+"14" 6 1 1 719 11 1 65 "belgium"
+"15" 23 1 1 719 11 1 38 "belgium"
+"16" 41 0 1 757 18 1 132 "belgium"
+"17" 10 1 1 775 24 1 73 "belgium"
+"18" 12 1 1 775 24 1 61 "belgium"
+"19" 2 1 1 762 24 0 65 "belgium"
+"20" 33 1 1 762 24 1 0 "belgium"
+"21" 1 1 1 762 24 0 0 "belgium"
+"22" 16 1 1 735 17 1 46 "belgium"
+"23" 2 1 1 753 17 1 9 "belgium"
+"24" 9 1 1 850 17 1 106 "belgium"
+"25" 3 1 1 850 17 1 7 "belgium"
+"26" 5 1 1 850 17 1 39 "belgium"
+"27" 5 1 1 850 17 1 18 "belgium"
+"28" 6 1 1 850 17 1 6 "belgium"
+"29" 45 0 1 868 15 1 87 "belgium"
+"30" 23 1 1 857 9 1 46 "belgium"
+"31" 41 1 0 648 1 0 0 "canada"
+"32" 7 1 0 648 1 1 0 "canada"
+"33" 49 0 0 428 0 1 0 "canada"
+"34" 46 1 0 536 0 1 0 "canada"
+"35" 9 1 0 650 0 0 4 "canada"
+"36" 51 0 0 349 0 1 0 "canada"
+"37" 10 1 0 648 0 0 0 "canada"
+"38" 32 1 0 622 0 0 5 "canada"
+"39" 28 1 0 614 0 0 0 "canada"
+"40" 3 1 0 614 0 0 0 "canada"
+"41" 53 0 0 571 0 1 1 "canada"
+"42" 17 1 0 648 0 0 0 "canada"
+"43" 59 0 0 583 0 1 62 "canada"
+"44" 9 1 0 602 0 0 0 "canada"
+"45" 52 0 0 582 0 1 0 "canada"
+"46" 3 1 0 582 0 1 0 "canada"
+"47" 23 1 0 777 12 0 14 "denmark"
+"48" 33 1 0 719 6 0 40 "denmark"
+"49" 1 1 0 749 5 0 36 "denmark"
+"50" 30 1 0 749 5 0 1 "denmark"
+"51" 5 1 0 740 5 0 0 "denmark"
+"52" 16 1 0 725 5 0 3 "denmark"
+"53" 27 1 0 725 5 0 3 "denmark"
+"54" 33 1 0 735 3 1 12 "denmark"
+"55" 9 1 0 735 3 1 2 "denmark"
+"56" 22 1 0 722 6 0 2 "denmark"
+"57" 25 1 0 722 6 0 0 "denmark"
+"58" 25 1 0 715 6 0 3 "denmark"
+"59" 14 1 0 748 11 0 26 "denmark"
+"60" 44 0 0 764 9 1 8 "denmark"
+"61" 12 1 0 746 10 0 14 "denmark"
+"62" 14 1 0 746 10 0 2 "denmark"
+"63" 13 1 0 855 26 0 14 "denmark"
+"64" 24 1 0 815 25 0 15 "denmark"
+"65" 18 1 0 811 26 0 0 "denmark"
+"66" 13 1 0 811 26 0 0 "denmark"
+"67" 29 1 0 791 21 0 28 "denmark"
+"68" 8 1 0 817 21 0 48 "denmark"
+"69" 16 1 0 817 24 0 7 "denmark"
+"70" 43 0 0 802 18 0 14 "denmark"
+"71" 28 1 0 791 25 1 20 "finland"
+"72" 19 1 0 780 19 0 7 "finland"
+"73" 10 1 0 780 19 0 16 "finland"
+"74" 2 1 0 780 19 1 0 "finland"
+"75" 21 1 0 791 22 1 183 "finland"
+"76" 4 1 0 791 22 0 12 "finland"
+"77" 5 1 0 788 22 1 148 "finland"
+"78" 16 1 0 788 22 1 6 "finland"
+"79" 15 1 0 788 22 1 14 "finland"
+"80" 1 1 0 788 22 0 5 "finland"
+"81" 2 1 0 788 22 0 0 "finland"
+"82" 2 1 0 788 22 0 0 "finland"
+"83" 3 1 0 795 25 1 0 "finland"
+"84" 30 1 0 795 25 0 40 "finland"
+"85" 8 1 0 795 25 0 16 "finland"
+"86" 20 1 0 803 24 1 44 "finland"
+"87" 19 1 0 803 24 1 1 "finland"
+"88" 21 1 0 799 21 1 43 "finland"
+"89" 24 1 0 799 21 1 21 "finland"
+"90" 8 1 0 821 27 1 0 "finland"
+"91" 7 1 0 821 27 1 9 "finland"
+"92" 5 1 0 818 28 0 0 "finland"
+"93" 33 1 0 818 28 1 47 "finland"
+"94" 10 1 0 812 21 1 0 "finland"
+"95" 7 1 0 812 21 0 12 "finland"
+"96" 9 1 0 812 21 1 4 "finland"
+"97" 13 1 0 812 21 1 13 "finland"
+"98" 32 1 0 808 21 1 69 "finland"
+"99" 11 1 0 808 21 1 16 "finland"
+"100" 3 1 0 808 21 0 1 "finland"
+"101" 47 0 0 805 22 1 25 "finland"
+"102" 5 1 1 788 28 1 11 "france"
+"103" 1 1 1 788 31 0 18 "france"
+"104" 3 1 1 788 31 1 2 "france"
+"105" 5 1 1 788 31 1 5 "france"
+"106" 1 1 1 788 31 1 0 "france"
+"107" 8 1 1 788 31 1 5 "france"
+"108" 1 1 1 788 31 1 7 "france"
+"109" 0.5 1 1 788 31 1 9 "france"
+"110" 13 1 1 788 31 1 4 "france"
+"111" 3 1 1 788 31 1 23 "france"
+"112" 5 1 1 788 31 0 3 "france"
+"113" 0.5 1 1 788 31 0 8 "france"
+"114" 7 1 1 788 31 1 8 "france"
+"115" 4 1 1 788 31 1 10 "france"
+"116" 5 1 1 842 27 0 41 "france"
+"117" 1 1 1 842 27 0 13 "france"
+"118" 10 1 1 842 27 0 8 "france"
+"119" 4 1 1 842 27 0 16 "france"
+"120" 11 1 1 842 27 1 38 "france"
+"121" 7 1 1 842 27 1 7 "france"
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diff --git a/data/free1.tab b/data/free1.tab
new file mode 100644
index 0000000..c8b54dd
--- /dev/null
+++ b/data/free1.tab
@@ -0,0 +1,451 @@
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diff --git a/data/free2.tab b/data/free2.tab
new file mode 100644
index 0000000..c8b54dd
--- /dev/null
+++ b/data/free2.tab
@@ -0,0 +1,451 @@
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diff --git a/data/homerun.txt b/data/homerun.txt
new file mode 100644
index 0000000..882876a
--- /dev/null
+++ b/data/homerun.txt
@@ -0,0 +1,315 @@
+"gameno" "month" "homeruns" "playerstatus" "player"
+"1" 1 "March" 1 0 "McGwire"
+"2" 2 "April" 1 0 "McGwire"
+"3" 3 "April" 1 0 "McGwire"
+"4" 4 "April" 1 0 "McGwire"
+"5" 5 "April" 0 0 "McGwire"
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+"7" 7 "April" 0 0 "McGwire"
+"8" 8 "April" 0 0 "McGwire"
+"9" 9 "April" 0 0 "McGwire"
+"10" 10 "April" 0 0 "McGwire"
+"11" 11 "April" 0 0 "McGwire"
+"12" 12 "April" 0 0 "McGwire"
+"13" 13 "April" 3 0 "McGwire"
+"14" 14 "April" 0 0 "McGwire"
+"16" 16 "April" 1 0 "McGwire"
+"17" 17 "April" 0 0 "McGwire"
+"18" 18 "April" 0 0 "McGwire"
+"19" 19 "April" 1 0 "McGwire"
+"20" 20 "April" 0 0 "McGwire"
+"22" 22 "April" 0 0 "McGwire"
+"23" 23 "April" 1 0 "McGwire"
+"24" 24 "April" 0 0 "McGwire"
+"25" 25 "April" 0 0 "McGwire"
+"26" 26 "April" 0 0 "McGwire"
+"27" 27 "April" 1 0 "McGwire"
+"28" 28 "May" 1 0 "McGwire"
+"29" 29 "May" 0 0 "McGwire"
+"30" 30 "May" 0 0 "McGwire"
+"31" 31 "May" 0 0 "McGwire"
+"32" 32 "May" 0 0 "McGwire"
+"33" 33 "May" 0 0 "McGwire"
+"34" 34 "May" 1 0 "McGwire"
+"35" 35 "May" 0 0 "McGwire"
+"36" 36 "May" 1 0 "McGwire"
+"37" 37 "May" 0 0 "McGwire"
+"38" 38 "May" 1 0 "McGwire"
+"39" 39 "May" 0 0 "McGwire"
+"40" 40 "May" 1 0 "McGwire"
+"41" 41 "May" 0 0 "McGwire"
+"42" 42 "May" 1 0 "McGwire"
+"43" 43 "May" 3 0 "McGwire"
+"45" 45 "May" 0 0 "McGwire"
+"46" 46 "May" 1 0 "McGwire"
+"47" 47 "May" 2 0 "McGwire"
+"48" 48 "May" 1 0 "McGwire"
+"49" 49 "May" 1 0 "McGwire"
+"50" 50 "May" 0 0 "McGwire"
+"51" 51 "May" 0 0 "McGwire"
+"52" 52 "May" 1 0 "McGwire"
+"53" 53 "May" 1 0 "McGwire"
+"54" 54 "May" 0 0 "McGwire"
+"55" 55 "June" 0 0 "McGwire"
+"59" 59 "June" 1 0 "McGwire"
+"60" 60 "June" 0 0 "McGwire"
+"61" 61 "June" 0 0 "McGwire"
+"62" 62 "June" 1 0 "McGwire"
+"63" 63 "June" 0 0 "McGwire"
+"64" 64 "June" 1 0 "McGwire"
+"65" 65 "June" 1 0 "McGwire"
+"66" 66 "June" 0 0 "McGwire"
+"67" 67 "June" 0 0 "McGwire"
+"68" 68 "June" 0 0 "McGwire"
+"69" 69 "June" 1 0 "McGwire"
+"70" 70 "June" 1 0 "McGwire"
+"71" 71 "June" 0 0 "McGwire"
+"72" 72 "June" 0 0 "McGwire"
+"73" 73 "June" 0 0 "McGwire"
+"74" 74 "June" 0 0 "McGwire"
+"75" 75 "June" 0 0 "McGwire"
+"76" 76 "June" 1 0 "McGwire"
+"77" 77 "June" 1 0 "McGwire"
+"78" 78 "June" 0 0 "McGwire"
+"79" 79 "June" 1 0 "McGwire"
+"80" 80 "June" 0 0 "McGwire"
+"81" 81 "June" 1 0 "McGwire"
+"82" 82 "July" 0 0 "McGwire"
+"83" 83 "July" 0 0 "McGwire"
+"84" 84 "July" 0 0 "McGwire"
+"85" 85 "July" 0 0 "McGwire"
+"86" 86 "July" 0 0 "McGwire"
+"87" 87 "July" 0 0 "McGwire"
+"88" 88 "July" 0 0 "McGwire"
+"89" 89 "July" 1 0 "McGwire"
+"90" 90 "July" 2 0 "McGwire"
+"91" 91 "July" 0 0 "McGwire"
+"92" 92 "July" 0 0 "McGwire"
+"93" 93 "July" 0 0 "McGwire"
+"94" 94 "July" 0 0 "McGwire"
+"95" 95 "July" 2 0 "McGwire"
+"96" 96 "July" 0 0 "McGwire"
+"97" 97 "July" 0 0 "McGwire"
+"98" 98 "July" 1 0 "McGwire"
+"99" 99 "July" 0 0 "McGwire"
+"100" 100 "July" 0 0 "McGwire"
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+"102" 102 "July" 0 0 "McGwire"
+"103" 103 "July" 0 0 "McGwire"
+"104" 104 "July" 1 0 "McGwire"
+"105" 105 "July" 1 0 "McGwire"
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+"108" 108 "July" 0 0 "McGwire"
+"109" 109 "August" 0 0 "McGwire"
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+"111" 111 "August" 0 0 "McGwire"
+"112" 112 "August" 0 0 "McGwire"
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+"118" 118 "August" 1 0 "McGwire"
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+"121" 121 "August" 0 0 "McGwire"
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+"124" 124 "August" 2 0 "McGwire"
+"125" 125 "August" 1 0 "McGwire"
+"126" 126 "August" 1 0 "McGwire"
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+"128" 128 "August" 0 0 "McGwire"
+"129" 129 "August" 1 0 "McGwire"
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+"133" 133 "August" 1 0 "McGwire"
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+"135" 135 "August" 0 0 "McGwire"
+"136" 136 "August" 0 0 "McGwire"
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diff --git a/data/voteincome.txt b/data/voteincome.txt
new file mode 100644
index 0000000..b247d5b
--- /dev/null
+++ b/data/voteincome.txt
@@ -0,0 +1,1501 @@
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diff --git a/demo/00Index b/demo/00Index
index 9cb5c39..231b1b9 100644
--- a/demo/00Index
+++ b/demo/00Index
@@ -1,14 +1,16 @@
+aov Analysis of Variance Model
arima Arima models
blogit Bivariate Logit regression and simulation
bivariate.probit New model example: Bivariate probit MLE
bprobit Bivariate Probit regression and simulation
+chopit Compound Hierarchical Ordered Probit regression
conditional Conditional prediction
exp Exponential regression and simulation
gamma Gamma regression and simulation
-gam.logit Generalized Additive Model for Dichotomous Dependent Variables
-gam.normal Generalized Additive Model for Continuous Dependent Variables
-gam.poisson Generalized Additive Model for Count Dependent Variables
-gam.probit Generalized Additive Model for Dichotomous Dependent Variable
+logit.gam Generalized Additive Model for Dichotomous Dependent Variables
+normal.gam Generalized Additive Model for Continuous Dependent Variables
+poisson.gam Generalized Additive Model for Count Dependent Variables
+probit.gam Generalized Additive Model for Dichotomous Dependent Variable
logit Logit regression and simulation
lognorm Lognormal regression and simulation
ls Least Squares regression and simulation
@@ -16,13 +18,13 @@ match Regression and simulation on a matched data set
mi Regression and simulation on multiply imputed data sets
mlogit Multinomial Logit regression and simulation
negbin Negative Binomial regression and simulation
-netls Network analysis least squares
-netlogit Network analysis logit
-netcloglog Social Network Complementary Log Log Regression for Dichotomous Dependent Variables
-netgamma Social Network Gamma Regression for Continuous, Positive Dependent Variables
-netnormal Social Network Normal Regression for Continuous Dependent Variables
-netpoisson Social Network Poisson Regression for Event Count Dependent Variables
-netprobit Social Network Probit Regression for Dichotomous Dependent Variables
+ls.net Network analysis least squares
+logit.net Network analysis logit
+cloglog.net Social Network Complementary Log Log Regression for Dichotomous Dependent Variables
+gamma.net Social Network Gamma Regression for Continuous, Positive Dependent Variables
+normal.net Social Network Normal Regression for Continuous Dependent Variables
+poisson.net Social Network Poisson Regression for Event Count Dependent Variables
+probit.net Social Network Probit Regression for Dichotomous Dependent Variables
normal Normal (Gaussian) regression and simulation
normal.regression New model example for normal regression
ologit Ordinal Logit regression and simulation
@@ -33,6 +35,7 @@ relogit Rare events logit regression and simulation
robust Robust estimation and simulation
strata Regression and simulation in a stratified model
tobit Regression for classical tobit model
+repl Replication of model fitting and simulation procedures
roc ROC plot
vertci Vertical confidence interval plot
weibull Weibull regression and simulation
@@ -55,6 +58,17 @@ sur Seemingly Unrelated Regression
twosls Two Stage Least Squares
threesls Three Stage Least Squares
logit.gee GEE logistic regression
+gamma.gee GEE gamma regression
+normal.gee GEE normal regression
+poisson.gee GEE poisson regression
+probit.gee GEE probit regression
+gamma.negvalues Rejection sampling with gamma regression
+ls.mixed Mixed effects linear regression
+logit.mixed Mixed effects logistic regression
+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
diff --git a/demo/aov.R b/demo/aov.R
new file mode 100644
index 0000000..f2017c5
--- /dev/null
+++ b/demo/aov.R
@@ -0,0 +1,47 @@
+ ## From Venables and Ripley (2002) p.165.
+### Attach data and set contrasts
+data(npk, package="MASS")
+ op <- options(contrasts=c("contr.helmert", "contr.poly"))
+user.prompt()
+z.out1 <- zelig(formula=yield ~ block + N*P*K, model="aov", data=npk)
+user.prompt()
+summary(z.out1)
+user.prompt()
+###Set explanatory variables
+ x <- setx(z.out1)
+###Simulate model at explanatory variables
+user.prompt()
+s.out1 <- sim(z.out1, x = x)
+user.prompt()
+plot(s.out1)
+user.prompt()
+###Example with Error term
+z.out2 <- zelig(yield ~ N*P*K + Error(block), model="aov",data=npk)
+user.prompt()
+summary(z.out2)
+user.prompt()
+ x <- setx(z.out2)
+user.prompt()
+s.out2 <- sim(z.out2, x=x)
+user.prompt()
+plot(s.out2)
+###Reset previous contrasts
+options(op)
+### Use data set oats from MASS
+ z.out3 <- zelig(Y ~ N*V + Error(B/V), model="aov", data=oats)
+user.prompt()
+summary(z.out3)
+user.prompt()
+ x.out <- setx(z.out3, N="0.0cwt", V="Golden.rain")
+x.out1 <- setx(z.out3, N="0.0cwt", V="Victory")
+user.prompt()
+s.out3 <- sim(z.out3, x = x.out,x1=x.out1)
+summary(s.out3)
+user.prompt()
+ plot(s.out3)
+
+
+
+
+
+
diff --git a/demo/bivariate.probit.R b/demo/bivariate.probit.R
index 218159d..b89f9d9 100644
--- a/demo/bivariate.probit.R
+++ b/demo/bivariate.probit.R
@@ -15,19 +15,24 @@ describe.bivariate.probit <- function() {
}
zelig2bivariate.probit <- function(formula, model, data, M, ...) {
+ Zelig:::packageConflicts("Matrix")
require(mvtnorm)
mf <- match.call(expand.dots = TRUE)
- mf$formula <- parse.formula(formula, model)
mf$model <- mf$M <- NULL
mf[[1]] <- as.name("bivariate.probit")
as.call(mf)
}
bivariate.probit <- function(formula, data, start.val = NULL, ...) {
-
- fml <- formula
- D <- model.frame(fml, data = data)
- X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2")) # [1]
+
+ # fml <- parse.formula(formula, req=c("mu1","mu2"), opt="rho") # [1]
+ fml <- parse.formula(formula, model = "bivariate.probit") # [1]
+ D <- model.frame(fml, data = data)
+ X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2")) # [2a]
+ # X <- model.matrix(fml, data = D, shape = "stacked", # [2b]
+ # eqn = c("mu1", "mu2"))
+ # X <- model.matrix(fml, data = D, shape = "array", # [2c]
+ # eqn = c("mu1", "mu2"))
Xrho <- model.matrix(fml, data = D, eqn = "rho")
Y <- model.response(D)
@@ -36,14 +41,18 @@ bivariate.probit <- function(formula, data, start.val = NULL, ...) {
start.val <- put.start(start.val, 1, terms, eqn = "rho")
log.lik <- function(par, X, Y, terms) {
- Beta <- parse.par(par, terms, eqn = c("mu1", "mu2")) # [2]
- gamma <- parse.par(par, terms, eqn = "rho")
- rho <- (exp(Xrho %*% gamma) - 1) / (1 + exp(Xrho %*% gamma))
+ Beta <- parse.par(par, terms, eqn = c("mu1", "mu2")) # [3a]
+ # Beta <- parse.par(par, terms, shape = "vector", # [3b] & [3c]
+ # eqn = c("mu1", "mu2"))
+ gamm <- parse.par(par, terms, eqn = "rho")
+ rho <- (exp(Xrho %*% gamm) - 1) / (1 + exp(Xrho %*% gamm))
- mu <- X %*% Beta # [3]
+ mu <- X %*% Beta # [4a]
+ # mu <- X %*% Beta; mu <- matrix(mu, ncol = 2) # [4b]
+ # mu <- apply(X, 3, '%*%', Beta) # [4c]
llik <- 0
- for (i in 1:nrow(mu)){
- Sigma <- matrix(c(1, rho[i,], rho[i,], 1), 2, 2)
+ for (i in 1:length(rho)){
+ Sigma <- matrix(c(1, rho[i], rho[i], 1), 2, 2)
if (Y[i,1]==1)
if (Y[i,2]==1)
llik <- llik + log(pmvnorm(lower = c(0, 0), upper = c(Inf, Inf),
@@ -66,16 +75,38 @@ bivariate.probit <- function(formula, data, start.val = NULL, ...) {
hessian = TRUE, control = list(fnscale = -1),
X = X, Y = Y, terms = terms, ...)
- fit <- model.end(res, D)
+ fit <- model.end(res, D)
class(fit) <- "bivariate.probit"
fit
}
-data(sanction)
+data(bivariate)
+
user.prompt()
-z.out1 <- zelig(cbind(import, export) ~ coop + cost + target, model = "bivariate.probit", data = sanction)
+
+## Shorthand notation for list(mu1 = y1 ~ x1, mu2 = y2 ~ x1)
+z.out1 <- zelig(cbind(y1, y2) ~ x1, model = "bivariate.probit",
+ data = bivariate)
+z.out1$coef
+user.prompt()
+
+## Different explanatory variables for each response
+z.out2 <- zelig(list(mu1 = y1 ~ x1, mu2 = y2 ~ x2),
+ model = "bivariate.probit", data = bivariate)
+z.out2$coef
user.prompt()
-z.out2 <- zelig(list(mu1 = import ~ coop, mu2 = export ~ cost + target),
- model = "bivariate.probit", data = sanction)
+## Using tag() to constrain x1 to "gamma" in both formulae;
+## while mu1:x3 and mu2:x3 are estimated separately
+z.out3 <- zelig(list(mu1 = y1 ~ tag(x1, "theta") + x3,
+ mu2 = y2 ~ tag(x1, "theta") + x3),
+ model = "bivariate.probit", data = bivariate)
+z.out3$coef
user.prompt()
+## Using tag() to constrain mu1:x1 = mu2:x2 = "gamma",
+## and specifying an explanatory variable for rho.
+z.out4 <- zelig(list(mu1 = y1 ~ tag(x1, "theta") + x2,
+ mu2 = y2 ~ tag(x2, "theta") + x3,
+ rho = ~ x4),
+ model = "bivariate.probit", data = bivariate)
+z.out4$coef
diff --git a/demo/chopit.R b/demo/chopit.R
new file mode 100644
index 0000000..8395fff
--- /dev/null
+++ b/demo/chopit.R
@@ -0,0 +1,36 @@
+data(free1, free2)
+
+## Setting up the formula as a list for the self-response,
+## vignettes, and the cut points (drawn from both the self-response
+## and vignette data sets).
+formulas <- list(self = y ~ sex + age + factor(country),
+ vign = cbind(v1, v2, v3, v4, v5) ~ 1,
+ tau = ~ sex + age + factor(country))
+
+## Setting up the data as a list, one data set corresponding to the
+## self-response, and one to the vignette responses. Note that the
+## tau variables must be in both data sets.
+data <- list(self = free1, vign = free2)
+z.out <- zelig(formulas, data = data, model = "chopit")
+user.prompt()
+
+## Using defaults
+x.out1 <- setx(z.out)
+s.out1 <- sim(z.out, x = x.out1)
+summary(s.out1)
+user.prompt()
+
+## Calculating first differences
+x.out2 <- setx(z.out, age = 25)
+s.out2 <- sim(z.out, x = x.out1, x1 = x.out2)
+summary(s.out2)
+user.prompt()
+
+## Conditional predication in this case calculates E(mu|X,Y).
+## This procedure involves numeric integration, which takes
+## approximately 1 second per observation on 64-bit R.
+x.out3 <- setx(z.out, cond = TRUE)
+s.out3 <- sim(z.out, x = x.out3)
+user.prompt()
+summary(s.out3)
+
diff --git a/demo/netcloglog.R b/demo/cloglog.net.R
similarity index 85%
rename from demo/netcloglog.R
rename to demo/cloglog.net.R
index 5cd1edb..6ef29a7 100644
--- a/demo/netcloglog.R
+++ b/demo/cloglog.net.R
@@ -4,7 +4,7 @@
## Estimate the model
## Summarize the results
data(friendship)
-z.out <- zelig(friends ~ advice + prestige + perpower, model="netcloglog", data=friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model="cloglog.net", data=friendship)
summary(z.out)
user.prompt()
diff --git a/demo/coxph.R b/demo/coxph.R
new file mode 100644
index 0000000..44b4236
--- /dev/null
+++ b/demo/coxph.R
@@ -0,0 +1,58 @@
+#### Example 1: Basic Example ####
+
+data(coalition)
+
+# Running the basic coxph model with robust clustered standard errors.
+user.prompt()
+z.out1 <- zelig(Surv(duration, ciep12) ~ invest + numst2 + crisis,
+ robust = TRUE, cluster = "polar", model = "coxph",
+ data = coalition)
+user.prompt()
+summary(z.out1)
+
+# Setting the explanatory variables at their default values
+# (mode for factor variables and mean for non-factor variables),
+# with numst2 set to the vector 0 = no crisis, 1 = crisis.
+user.prompt()
+x.low1<- setx(z.out1, numst2 = 0)
+x.high1 <- setx(z.out1, numst2 = 1)
+
+# Simulating draws using the default method.
+user.prompt()
+s.out1 <- sim(z.out1, x = x.low1, x1 = x.high1)
+user.prompt()
+
+# Viewing the simulated quantities of interest.
+summary(s.out1)
+user.prompt()
+plot(s.out1)
+
+#### Example 2: Example with Stratified Cox Model ####
+
+# Running the stratified coxph model with strata defined by polar variable.
+user.prompt()
+z.out2 <- zelig(Surv(duration, ciep12) ~ invest + numst2 + crisis + strata(polar),
+ model = "coxph", data = coalition)
+user.prompt()
+summary(z.out2)
+
+# Setting the explanatory variables at their default values
+# with numst2 set to the vector 0 = no crisis, 1 = crisis and
+# strata set to polar=3.
+user.prompt()
+x.low2<- setx(z.out2, numst2 = 0, strata = "polar=3")
+x.high2 <- setx(z.out2, numst2 = 1, strata = "polar=3")
+
+# Simulating draws using the default method.
+user.prompt()
+s.out2 <- sim(z.out2, x = x.low2, x1 = x.high2)
+user.prompt()
+
+# Viewing the simulated quantities of interest.
+summary(s.out2)
+user.prompt()
+plot(s.out2)
+
+
+
+
diff --git a/demo/ei.RxC.R b/demo/ei.RxC.R
index e765066..da8df6f 100644
--- a/demo/ei.RxC.R
+++ b/demo/ei.RxC.R
@@ -11,7 +11,7 @@ summary(z.out1)
user.prompt()
## In-sample simulations from the posterior distribution:
-s.out1 <- sim(z.out1)
+s.out1 <- sim(z.out1, num =10)
## Summarizing in-sample simulations at aggregate level
## weighted by the count in each unit:
@@ -42,7 +42,7 @@ x.out2 <- setx(z.out2)
user.prompt()
## In-sample simulations from the posterior distribution:
-s.out2 <- sim(z.out2)
+s.out2 <- sim(z.out2, num = 10)
## Summarizing in-sample simulations at aggregate level
## weighted by the count in each unit:
diff --git a/demo/gamma.gee.R b/demo/gamma.gee.R
new file mode 100644
index 0000000..cb7e5b9
--- /dev/null
+++ b/demo/gamma.gee.R
@@ -0,0 +1,43 @@
+##### Example 1: Basic Example #####
+
+# Attach sample data and variable names:
+data(coalition)
+
+# Variable identifying clusters
+coalition$cluster <- c(rep(c(1:62),5),rep(c(63),4))
+
+# Sorting by cluster
+sorted.coalition <- coalition[order(coalition$cluster),]
+
+# Estimate model and present a summary:
+user.prompt()
+z.out <- zelig(duration ~ fract + numst2, model = "gamma.gee", id = "cluster", data = sorted.coalition, robust=TRUE, corstr="exchangeable")
+user.prompt()
+summary(z.out)
+
+# Setting the explanatory variables at their default values
+# (mode for factor variables and mean for non-factor variables),
+# with numst2 set to the vector 0 = no crisis, 1 = crisis.
+user.prompt()
+x.low <- setx(z.out, numst2 = 0)
+x.high <- setx(z.out, numst2 = 1)
+
+
+# Simulate quantities of interest
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+user.prompt()
+summary(s.out)
+
+# Generate a plot of quantities of interest:
+user.prompt()
+plot(s.out)
+
+
+
+
+
+
+
+
+
diff --git a/demo/gamma.mixed.R b/demo/gamma.mixed.R
new file mode 100644
index 0000000..502abf7
--- /dev/null
+++ b/demo/gamma.mixed.R
@@ -0,0 +1,26 @@
+data(coalition2)
+user.prompt()
+z.out <- zelig(duration ~ invest + fract + polar + numst2 + crisis +
+ tag(1 | country),
+ data=coalition2, model="gamma.mixed", method="PQL",
+ family=Gamma(link="log"))
+user.prompt()
+summary(z.out)
+
+## Setting the explanatory variables at their default values
+## (mode for factor variables and mean for non-factor variables),
+## comparing the ruling coalition in the minority to the ruling
+## coalition in the majority.
+user.prompt()
+x.high <- setx(z.out, numst2 = 1)
+x.low <- setx(z.out, numst2 = 0)
+
+## Simulating draws using the default bootstrap method.
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1=x.high)
+user.prompt()
+
+## Viewing the simulated quantities of interest, for every
+## observation:
+summary(s.out)
+user.prompt()
diff --git a/demo/gamma.negvalues.R b/demo/gamma.negvalues.R
new file mode 100644
index 0000000..40b238a
--- /dev/null
+++ b/demo/gamma.negvalues.R
@@ -0,0 +1,35 @@
+data(coalition)
+user.prompt()
+z.out <- zelig(duration ~ fract + numst2 + crisis, model = "gamma", data = coalition)
+user.prompt()
+summary(z.out)
+
+## Setting the explanatory variables.
+user.prompt()
+x.low <- setx(z.out, fract=300, numst2 = 0, crisis=200)
+x.high <- setx(z.out, fract=300, numst2 = 1, crisis=200)
+
+## Simulating draws using the default method.
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+user.prompt()
+
+## Simulating draws using bootstrap method.
+user.prompt()
+s.out1 <- sim(z.out, x=x.low, x1=x.high, bootstrap=T, num=10)
+user.prompt()
+
+## Viewing the simulated quantities of interest, for every
+## observation:
+summary(s.out)
+user.prompt()
+plot(s.out)
+
+user.prompt()
+summary(s.out1)
+user.prompt()
+plot(s.out1)
+
+
+
+
diff --git a/demo/netgamma.R b/demo/gamma.net.R
similarity index 84%
rename from demo/netgamma.R
rename to demo/gamma.net.R
index 7005633..aed44c5 100644
--- a/demo/netgamma.R
+++ b/demo/gamma.net.R
@@ -4,7 +4,7 @@
## Estimate the model
## Summarize the results
data(friendship)
-z.out <- zelig(per ~ perpower, LF="inverse", model="netgamma", data=friendship)
+z.out <- zelig(per ~ perpower, LF="inverse", model="gamma.net", data=friendship)
summary(z.out)
## Estimating the risk difference (and risk ratio) between low personal power
diff --git a/demo/logit.R b/demo/logit.R
index cdddde8..449e929 100644
--- a/demo/logit.R
+++ b/demo/logit.R
@@ -5,7 +5,7 @@ data(turnout)
## Generating empirical estimates:
user.prompt()
-z.out1 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+z.out1 <- zelig(vote ~ age + race, model = "logit", data = turnout)
user.prompt()
## Viewing the regression output:
summary(z.out1)
@@ -13,7 +13,7 @@ summary(z.out1)
## Using setx to generate baseline and alternative velus for the
## explanatory variables.
user.prompt()
-x.out1 <- setx(z.out1)
+x.out1 <- setx(z.out1, age = 36, race = "white")
## Simulating quantities of interest (predicted probabilites, risk
## ratios, and risk differences):
@@ -30,11 +30,12 @@ plot(s.out1)
## Example 2: First Differences
user.prompt()
-x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
-x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
+z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+x.high <- setx(z.out2, educate = quantile(turnout$educate, prob = 0.75))
+x.low <- setx(z.out2, educate = quantile(turnout$educate, prob = 0.25))
user.prompt()
-s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
+s.out2 <- sim(z.out2, x = x.high, x1 = x.low)
user.prompt()
summary(s.out2)
user.prompt()
diff --git a/demo/gam.logit.R b/demo/logit.gam.R
similarity index 89%
rename from demo/gam.logit.R
rename to demo/logit.gam.R
index d75827b..0a9ccb3 100644
--- a/demo/gam.logit.R
+++ b/demo/logit.gam.R
@@ -20,7 +20,7 @@ my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
# Estimate model, present a summary and a plot of the results:
user.prompt()
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.logit", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="logit.gam", data=my.data)
user.prompt()
summary(z.out)
user.prompt()
@@ -57,6 +57,6 @@ anova(z.out)
## We can pull out and compare AICs too...
user.prompt()
-z1.out <- zelig(y~s(x0)+s(x1)+s(x2), model="gam.logit", data=my.data)
-z2.out <- zelig(y~s(x1)+s(x2), model="gam.logit", data=my.data)
+z1.out <- zelig(y~s(x0)+s(x1)+s(x2), model="logit.gam", data=my.data)
+z2.out <- zelig(y~s(x1)+s(x2), model="logit.gam", data=my.data)
AIC(z.out,z1.out,z2.out)
diff --git a/demo/logit.gee.R b/demo/logit.gee.R
index e4b8d40..26c6770 100755
--- a/demo/logit.gee.R
+++ b/demo/logit.gee.R
@@ -22,8 +22,7 @@ summary(z.out1)
user.prompt()
x.out1 <- setx(z.out1)
-## Simulating quantities of interest (predicted probabilites, risk
-## ratios, and risk differences):
+## Simulating quantities of interest:
user.prompt()
s.out1 <- sim(z.out1, x = x.out1)
user.prompt()
diff --git a/demo/logit.mixed.R b/demo/logit.mixed.R
new file mode 100644
index 0000000..a7ad563
--- /dev/null
+++ b/demo/logit.mixed.R
@@ -0,0 +1,23 @@
+data(voteincome)
+user.prompt()
+z.out <- zelig(vote ~ education + age + female + tag(1 | state),
+ data=voteincome, model="logit.mixed")
+user.prompt()
+summary(z.out)
+
+## Setting the explanatory variables at their default values
+## (mode for factor variables and mean for non-factor variables),
+## with education set to 80th and 20th percentiles.
+user.prompt()
+x.low <- setx(z.out, education=quantile(voteincome$education, 0.8))
+x.high <- setx(z.out, education=quantile(voteincome$education, 0.2))
+
+## Simulating draws using the default bootstrap method.
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+user.prompt()
+
+## Viewing the simulated quantities of interest, for every
+## observation:
+summary(s.out)
+user.prompt()
diff --git a/demo/netlogit.R b/demo/logit.net.R
similarity index 85%
rename from demo/netlogit.R
rename to demo/logit.net.R
index e6dc3f1..0787057 100644
--- a/demo/netlogit.R
+++ b/demo/logit.net.R
@@ -4,7 +4,7 @@
## Estimate the model
## Summarize the results
data(friendship)
-z.out <- zelig(friends ~ advice + prestige + perpower, model="netlogit", data=friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model="logit.net", data=friendship)
summary(z.out)
user.prompt()
diff --git a/demo/ls.mixed.R b/demo/ls.mixed.R
new file mode 100644
index 0000000..1cad42b
--- /dev/null
+++ b/demo/ls.mixed.R
@@ -0,0 +1,23 @@
+data(voteincome)
+user.prompt()
+z.out <- zelig(income ~ education + age + female + tag(1 | state),
+ data=voteincome, model="ls.mixed")
+user.prompt()
+summary(z.out)
+
+## Setting the explanatory variables at their default values
+## (mode for factor variables and mean for non-factor variables),
+## with education set to 80th and 20th percentiles.
+user.prompt()
+x.low <- setx(z.out, education=quantile(voteincome$education, 0.8))
+x.high <- setx(z.out, education=quantile(voteincome$education, 0.2))
+
+## Simulating draws using the default bootstrap method.
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+user.prompt()
+
+## Viewing the simulated quantities of interest, for every
+## observation:
+summary(s.out)
+user.prompt()
diff --git a/demo/netls.R b/demo/ls.net.R
similarity index 91%
rename from demo/netls.R
rename to demo/ls.net.R
index 2a0d7bd..c98dac4 100644
--- a/demo/netls.R
+++ b/demo/ls.net.R
@@ -5,7 +5,7 @@ data(sna.ex)
## Generating empirical estimates:
user.prompt()
-z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "netls", data=sna.ex)
+z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "ls.net", data=sna.ex)
user.prompt()
## Viewing the regression output:
summary(z.out)
diff --git a/demo/mi.R b/demo/mi.R
index 19a4ff2..89b0495 100644
--- a/demo/mi.R
+++ b/demo/mi.R
@@ -2,7 +2,7 @@ data(immi1, immi2, immi3, immi4, immi5)
user.prompt()
z.out <- zelig(as.factor(ipip) ~ wage1992 + prtyid + ideol, model = "ologit",
- data = mi(immi1, immi2, immi3, immi4, immi5))
+ data = mi(immi1, immi2, immi3, immi4, immi5), by = "gender")
user.prompt()
summary(z.out)
user.prompt()
diff --git a/demo/gam.normal.R b/demo/normal.gam.R
similarity index 91%
rename from demo/gam.normal.R
rename to demo/normal.gam.R
index 2ad0ffd..f86eb5e 100644
--- a/demo/gam.normal.R
+++ b/demo/normal.gam.R
@@ -20,7 +20,7 @@ my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
# Estimate model, present a summary and a plot of the results:
user.prompt()
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.normal", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="normal.gam", data=my.data)
user.prompt()
summary(z.out)
user.prompt()
@@ -45,7 +45,7 @@ plot(s.out)
##### Example 2: An extra ridge penalty (useful with convergence problems) #####
user.prompt()
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37), model="gam.normal", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37), model="normal.gam", data=my.data)
user.prompt()
# Set values for the explanatory variables, using the default mean/mode values
user.prompt()
@@ -61,16 +61,16 @@ plot(s.out)
##### Example 3: Set the smoothing parameter for the first term, estimate the rest #####
user.prompt()
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1), model="gam.normal", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1), model="normal.gam", data=my.data)
plot(z.out,pages=1)
##### Example 4: Set lower bounds on smoothing parameters #####
user.prompt()
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10), model="gam.normal", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10), model="normal.gam", data=my.data)
print(z.out)
##### Example 5: A GAM with 3df regression spline term & 2 penalized terms #####
user.prompt()
-z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15), model="gam.normal", data=my.data)
+z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15), model="normal.gam", data=my.data)
plot(z.out,pages=1)
diff --git a/demo/normal.gee.R b/demo/normal.gee.R
new file mode 100644
index 0000000..79befa1
--- /dev/null
+++ b/demo/normal.gee.R
@@ -0,0 +1,36 @@
+##### Example 1: Basic Example with First Differences #####
+
+# Attach sample data and variable names:
+data(macro)
+
+# Estimate model and present a summary:
+user.prompt()
+z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.gee", id = "country", data = macro, robust=TRUE, corstr="AR-M", Mv=1)
+user.prompt()
+summary(z.out)
+
+# Set explanatory variables to their default (mean/mode) values, with
+# high (80th percentile) and low (20th percentile) values:
+user.prompt()
+x.high <- setx(z.out, trade = quantile(macro$trade, 0.8))
+x.low <- setx(z.out, trade = quantile(macro$trade, 0.2))
+
+# Generate first differences for the effect of high versus low trade on
+# GDP:
+user.prompt()
+s.out <- sim(z.out, x = x.high, x1 = x.low)
+user.prompt()
+summary(s.out)
+
+# Generate a plot of quantities of interest:
+user.prompt()
+plot(s.out)
+
+
+
+
+
+
+
+
+
diff --git a/demo/netnormal.R b/demo/normal.net.R
similarity index 91%
rename from demo/netnormal.R
rename to demo/normal.net.R
index d35b9c1..e8c114f 100644
--- a/demo/netnormal.R
+++ b/demo/normal.net.R
@@ -4,7 +4,7 @@
## Estimate the model
## Summarize the results
data(friendship)
-z.out <- zelig(perpower ~ friends + advice + prestige, LF="identity", model="netnormal", data=friendship)
+z.out <- zelig(perpower ~ friends + advice + prestige, LF="identity", model="normal.net", data=friendship)
summary(z.out)
diff --git a/demo/normal.regression.R b/demo/normal.regression.R
index b35ddd6..39c4ac8 100644
--- a/demo/normal.regression.R
+++ b/demo/normal.regression.R
@@ -20,32 +20,29 @@ zelig2normal.regression <- function(formula, model, data, M, ...) {
}
normal.regression <- function(formula, data, start.val = NULL, ...) {
-
- fml <- parse.formula(formula, model = "normal.regression") # [1]
+ # fml <- parse.formula(formula, req = "mu", ancil = "sigma2") # [1a]
+ fml <- parse.formula(formula, model = "normal.regression") # [1b]
D <- model.frame(fml, data = data)
X <- model.matrix(fml, data = D)
Y <- model.response(D)
terms <- attr(D, "terms")
- n <- nrow(X)
-
- start.val <- set.start(start.val, terms)
- start.val <- put.start(start.val, 1, terms, eqn = "sigma2") # [2]
+
+ start.val <- set.start(start.val, terms) # [2]
- ll.normal <- function(par, X, Y, n, terms) { # [3]
- beta <- parse.par(par, terms, eqn = "mu")
- sigma2 <- exp(parse.par(par, terms, eqn = "sigma2"))
- -n / 2 * log(2 * pi * sigma2) -
- 1/(2 * sigma2) * sum((Y - X %*% beta)^2)
+ ll.normal <- function(par, X, Y, n, terms) { # [3]
+ beta <- parse.par(par, terms, eqn = "mu") # [3a]
+ gamma <- parse.par(par, terms, eqn = "sigma2") # [3b]
+ sigma2 <- exp(gamma)
+ -0.5 * (n * log(sigma2) + sum((Y - X %*% beta)^2 / sigma2))
}
- res <- optim(start.val, ll.normal, method = "BFGS",
+ res <- optim(start.val, ll.normal, method = "BFGS", # [4]
hessian = TRUE, control = list(fnscale = -1),
- X = X, Y = Y, n = n, terms = terms, ...) # [4]
+ X = X, Y = Y, n = nrow(X), terms = terms, ...)
- fit <- model.end(res, D) # [5]
- fit$n <- n
- class(fit) <- "normal" # [6]
- fit # [7]
+ fit <- model.end(res, D) # [5]
+ class(fit) <- "normal"
+ fit
}
param.normal <- function(object, num = NULL, bootstrap = FALSE,
diff --git a/demo/gam.poisson.R b/demo/poisson.gam.R
similarity index 90%
rename from demo/gam.poisson.R
rename to demo/poisson.gam.R
index 48140a1..8aa100a 100644
--- a/demo/gam.poisson.R
+++ b/demo/poisson.gam.R
@@ -20,7 +20,7 @@ my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
# Estimate model, present a summary and a plot of the results:
user.prompt()
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.poisson", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="poisson.gam", data=my.data)
user.prompt()
summary(z.out)
user.prompt()
@@ -46,7 +46,7 @@ plot(s.out)
user.prompt()
gm <- gam.method(gam="perf.magic")
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), method=gm, model="gam.poisson", data=my.data )
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), method=gm, model="poisson.gam", data=my.data )
plot(z.out,pages=1)
# Set values for the explanatory variables, using the default mean/mode values
diff --git a/demo/poisson.gee.R b/demo/poisson.gee.R
new file mode 100644
index 0000000..daf687c
--- /dev/null
+++ b/demo/poisson.gee.R
@@ -0,0 +1,39 @@
+##### Example 1: Basic Example #####
+
+# Attach sample data and variable names:
+data(sanction)
+
+# Variable identifying clusters
+sanction$cluster <- c(rep(c(1:15),5),rep(c(16),3))
+
+# Sorting by cluster
+sorted.sanction <- sanction[order(sanction$cluster),]
+
+# Estimate model and present a summary:
+user.prompt()
+z.out <- zelig(num ~ target + coop, model = "poisson.gee", id = "cluster", data = sorted.sanction, robust=TRUE, corstr="exchangeable")
+user.prompt()
+summary(z.out)
+
+# Set explanatory variables to their default values:
+user.prompt()
+x.out <- setx(z.out)
+
+# Simulate quantities of interest
+user.prompt()
+s.out <- sim(z.out, x = x.out)
+user.prompt()
+summary(s.out)
+
+# Generate a plot of quantities of interest:
+user.prompt()
+plot(s.out)
+
+
+
+
+
+
+
+
+
diff --git a/demo/poisson.mixed.R b/demo/poisson.mixed.R
new file mode 100644
index 0000000..5bc2b00
--- /dev/null
+++ b/demo/poisson.mixed.R
@@ -0,0 +1,21 @@
+data(homerun)
+user.prompt()
+z.out <- zelig(homeruns ~ player + tag(player - 1 | month),
+ data=homerun, model="poisson.mixed")
+user.prompt()
+summary(z.out)
+
+## Setting the explanatory variables at their default values
+## (mode for factor variables and mean for non-factor variables),
+user.prompt()
+x.out <- setx(z.out)
+
+## Simulating draws using the default bootstrap method.
+user.prompt()
+s.out <- sim(z.out, x = x.out)
+user.prompt()
+
+## Viewing the simulated quantities of interest, for every
+## observation:
+summary(s.out)
+user.prompt()
diff --git a/demo/netpoisson.R b/demo/poisson.net.R
similarity index 85%
rename from demo/netpoisson.R
rename to demo/poisson.net.R
index aa3b78f..1ec822b 100644
--- a/demo/netpoisson.R
+++ b/demo/poisson.net.R
@@ -4,7 +4,7 @@
## Estimate the model
## Summarize the results
data(friendship)
-z.out <- zelig(count ~ advice + prestige + perpower, model="netpoisson", data=friendship)
+z.out <- zelig(count ~ advice + prestige + perpower, model="poisson.net", data=friendship)
summary(z.out)
user.prompt()
diff --git a/demo/gam.probit.R b/demo/probit.gam.R
similarity index 89%
rename from demo/gam.probit.R
rename to demo/probit.gam.R
index e76379e..3a65425 100644
--- a/demo/gam.probit.R
+++ b/demo/probit.gam.R
@@ -20,7 +20,7 @@ my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
# Estimate model, present a summary and a plot of the results:
user.prompt()
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.probit", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="probit.gam", data=my.data)
user.prompt()
summary(z.out)
user.prompt()
@@ -57,6 +57,6 @@ anova(z.out)
## We can pull out and compare AICs too...
user.prompt()
-z1.out <- zelig(y~s(x0)+s(x1)+s(x2), model="gam.probit", data=my.data)
-z2.out <- zelig(y~s(x1)+s(x2), model="gam.probit", data=my.data)
+z1.out <- zelig(y~s(x0)+s(x1)+s(x2), model="probit.gam", data=my.data)
+z2.out <- zelig(y~s(x1)+s(x2), model="probit.gam", data=my.data)
AIC(z.out,z1.out,z2.out)
diff --git a/demo/logit.gee.R b/demo/probit.gee.R
old mode 100755
new mode 100644
similarity index 80%
copy from demo/logit.gee.R
copy to demo/probit.gee.R
index e4b8d40..3a13cf9
--- a/demo/logit.gee.R
+++ b/demo/probit.gee.R
@@ -11,7 +11,7 @@ sorted.turnout <- turnout[order(turnout$cluster),]
## Generating empirical estimates:
user.prompt()
-z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster",
+z.out1 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster",
data = sorted.turnout, robust = T, corstr = "stat_M_dep", Mv=3)
user.prompt()
## Viewing the regression output:
@@ -22,8 +22,7 @@ summary(z.out1)
user.prompt()
x.out1 <- setx(z.out1)
-## Simulating quantities of interest (predicted probabilites, risk
-## ratios, and risk differences):
+## Simulating quantities of interest:
user.prompt()
s.out1 <- sim(z.out1, x = x.out1)
user.prompt()
@@ -56,7 +55,7 @@ diag(corr.mat) <- 1
## Generating empirical estimates:
user.prompt()
-z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster",
+z.out2 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster",
data = sorted.turnout, robust = T, corstr = "fixed", R=corr.mat)
user.prompt()
## Viewing the regression output:
diff --git a/demo/probit.mixed.R b/demo/probit.mixed.R
new file mode 100644
index 0000000..8c627f8
--- /dev/null
+++ b/demo/probit.mixed.R
@@ -0,0 +1,23 @@
+data(voteincome)
+user.prompt()
+z.out <- zelig(vote ~ education + age + female + tag(1 | state),
+ data=voteincome, model="probit.mixed")
+user.prompt()
+summary(z.out)
+
+## Setting the explanatory variables at their default values
+## (mode for factor variables and mean for non-factor variables),
+## with education set to 80th and 20th percentiles.
+user.prompt()
+x.low <- setx(z.out, education=quantile(voteincome$education, 0.8))
+x.high <- setx(z.out, education=quantile(voteincome$education, 0.2))
+
+## Simulating draws using the default bootstrap method.
+user.prompt()
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+user.prompt()
+
+## Viewing the simulated quantities of interest, for every
+## observation:
+summary(s.out)
+user.prompt()
diff --git a/demo/netprobit.R b/demo/probit.net.R
similarity index 85%
rename from demo/netprobit.R
rename to demo/probit.net.R
index 5b3c49e..b32abe9 100644
--- a/demo/netprobit.R
+++ b/demo/probit.net.R
@@ -4,7 +4,7 @@
## Estimate the model
## Summarize the results
data(friendship)
-z.out <- zelig(friends ~ advice + prestige + perpower, model="netprobit", data=friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model="probit.net", data=friendship)
summary(z.out)
user.prompt()
diff --git a/demo/repl.R b/demo/repl.R
new file mode 100644
index 0000000..650f114
--- /dev/null
+++ b/demo/repl.R
@@ -0,0 +1,39 @@
+## Setting up output to be replicated
+data(turnout)
+z.out <- zelig(vote ~ race + age, model = "logit", data = turnout)
+x.out <- setx(z.out)
+set.seed(12345)
+s.out <- sim(z.out, x = x.out)
+s.out$seed <- 12345
+user.prompt()
+
+## Saving replication files
+save(turnout, z.out, s.out, file = "demo_replication.RData")
+user.prompt()
+
+## Replicating simulations assuming that the seed was saved
+load("demo_replication.RData")
+s.rep <- repl(s.out)
+identical(s.out$qi, s.rep$qi)
+user.prompt()
+
+## Replicating simulations, with previously generated parameters
+s.rep2 <- repl(s.out, prev = s.out$par)
+identical(s.rep2$qi$ev, s.out$qi$ev)
+user.prompt()
+
+## Replicating analyses on original data, assumes that the
+## data frame is in the workspace with the original name
+z.rep <- repl(z.out)
+identical(coef(z.rep), coef(z.out))
+user.prompt()
+
+## Replicating analyses on new data
+z.alt <- repl(z.out, data = turnout[1:100,])
+
+## Saving replication files
+save(turnout, z.out, s.out, file = "demo_replication.RData")
+user.prompt()
+
+## Cleaning up the directory
+unlink("demo_replication.RData")
diff --git a/inst/doc/Rplots.ps b/inst/doc/Rplots.ps
index fd0e669..43a2d65 100644
--- a/inst/doc/Rplots.ps
+++ b/inst/doc/Rplots.ps
@@ -80,7 +80,7 @@ dup length dict begin
%%EndProlog
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%%Trailer
-%%Pages: 88
+%%Pages: 101
%%EOF
diff --git a/inst/doc/aov.Rnw b/inst/doc/aov.Rnw
new file mode 100644
index 0000000..0ff8ef3
--- /dev/null
+++ b/inst/doc/aov.Rnw
@@ -0,0 +1,256 @@
+\SweaveOpts{results=hide,prefix.string=vigpics/aov}
+\include{zinput}
+%\VignetteIndexEntry{Fit an Analysis of Variance Model}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,continuous,analysis variance, error, multiple responses,sum squares, residual}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt aov}: Analysis of Variance for Continuous Dependent Variables}
+\label{aov}
+
+Model ``aov'' uses least squares regression to estimate the residual
+sum of squares and degrees of freedom for each explanatory variable
+around the best linear predictor for the specified dependent
+variables. Model ``aov'' is particularly useful for the analysis of
+randomized experiments with more than one strata or group (e.g.,
+balanced incomplete block design). For the model with only one
+strata, ``aov'' reduces to ``ls''.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2 + Error(Z), model = "aov", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+where the {\tt Error()} term is optional and takes strata formula.
+
+\subsubsection{Examples}\begin{enumerate}
+
+\item Basic Example of aov.
+
+Attach sample data and set orthogonal contrasts:
+<<Example1.data>>=
+ data(npk, package="MASS")
+ op <- options(contrasts=c("contr.helmert", "contr.poly"))
+@
+Estimate the model \citep[p.165]{VenRip02}:
+<<Example1.zelig>>=
+ z.out1 <- zelig(yield ~ block + N * P + K, model="aov", data=npk)
+@
+Summarize the fitted model:
+<<Example1.summary>>=
+ summary(z.out1)
+@
+Set explanatory variables to their default (mean/mode) values
+<<Example1.setx>>=
+ x <- setx(z.out1)
+@
+Simulate model at values explanatory variables as in x
+<<Example1.sim>>=
+ s.out1 <- sim(z.out1, x = x)
+@
+<<Example1.summary.sim>>=
+summary(s.out1)
+@
+\begin{center}
+<<label=ExamplesPlot1,fig=true,echo=true, width=5,height=2.5>>=
+ plot(s.out1)
+@
+\end{center}
+
+\item Example with {\tt Error()} term allowing for more than one
+ source of random variation in an experiment (multistratum model).
+
+Estimate the model:
+<<Example2.zelig>>=
+ z.out2 <- zelig(yield ~ N*P*K + Error(block), model="aov",data=npk)
+@
+Summarize regression coefficients:
+<<Example2.summary>>=
+ summary(z.out2)
+@
+Set explanatory variables to their default (mean/mode) values
+<<Example2.setx>>=
+ x <- setx(z.out2)
+@
+Simulate model at values explanatory variables as in x
+<<Example2.sim>>=
+ s.out2 <- sim(z.out2, x = x)
+@
+<<Example2.summary.sim>>=
+ summary(s.out2)
+@
+\begin{center}
+<<label=ExamplesPlot2,fig=true,echo=true, width=5, height=4>>=
+ plot(s.out2)
+@
+\end{center}
+
+\item Example with {\it Error()} term (multistratum model) and first
+ differences.
+
+Reset to previous contrasts
+<<Options>>=
+options(op)
+@
+Estimate model \citep[p.283]{VenRip02}:
+<<Example3.zelig>>=
+ z.out3 <- zelig(Y ~ N*V + Error(B/V), model="aov", data=oats)
+@
+Summarize regression coefficients:
+<<Example3.summary>>=
+ summary(z.out3)
+@
+Set explanatory variables using mode
+<<Example3.setx>>=
+ x.out <- setx(z.out3, N="0.0cwt", V="Golden.rain")
+ x.out1 <- setx(z.out3, N="0.0cwt", V="Victory")
+@
+Simulate model at values explanatory variables as in x
+<<Example3.sim>>=
+ s.out3 <- sim(z.out3, x = x.out,x1=x.out1)
+@
+<<Example3.summary.sim>>=
+summary(s.out3)
+@
+\begin{center}
+<<label=ExamplesPlot3,fig=true,echo=true, width=8, height=6>>=
+ plot(s.out3)
+@
+\end{center}
+
+\end{enumerate}
+
+\subsubsection{Model}
+\begin{itemize}
+\item The \emph{stochastic component} is described by a normal density
+ with mean $\mu_i$ and the common variance $\sigma^2$
+ \begin{equation*}
+ Y_i \; \sim \; f(y_i \mid \mu_i, \sigma^2).
+ \end{equation*}
+\item The \emph{systematic component} models the conditional mean as
+ \begin{equation*}
+ \mu_i = x_i \beta
+ \end{equation*}
+ where $x_i$ is the vector of covariates, and $\beta$ is the vector
+ of coefficients.
+
+ The least squares estimator is the best linear predictor of a
+ dependent variable given $x_i$, and minimizes the sum of squared
+ residuals, $\sum_{i=1}^n (Y_i-x_i \beta)^2$. The output of {\tt aov}
+ model is the sum of squared residuals. Note that {\tt aov} is the
+ same model as {\tt ls} but the output values of function call
+ {\tt zelig} are different. You may check that {\tt name(z.out)}
+ returns the same arguments for the two models.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+ from the stochastic component,
+\begin{equation*}
+E(Y) = x_i \beta,\end{equation*}
+given a draw of $\beta$ from its sampling distribution.
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+ where $t_i$ is a binary explanatory variable defining the treatment
+ ($t_i=1$) and control ($t_i=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+ the counterfactual expected value of $Y_i$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "aov", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+ \item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the IWLS fit.
+ \item {\tt fitted.values}: fitted values.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the residuals sum of squares estimated
+ with their associated degree of freedom, their mean squares,
+ $F$-values, and $F$-statistics for all explanatory variables.
+
+ \item {\tt residuals}: the sum of square, mean, degree of freedom,
+ $F$-values, and $F$-statistics for the vector of residuals or
+ standard errors that check the adequecy of the fit for the
+ dependent variable versus the true values or data points.
+
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the specified
+ values of {\tt x}.
+ \item {\tt qi\$fd}: the simulated first differences (or
+ differences in expected values) for the specified values of {\tt
+ x} and {\tt x1}.
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
+\end{itemize}
+
+
+\subsection* {How to Cite}
+To cite the \emph{aov} Zelig model:
+\begin{verse}
+Kosuke Imai, Gary King, and Olivia Lau. 2007. ``aov: Analysis of Variance for Continuous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Elena Villalon implemented the software.
+\end{verse}
+\input{citeZelig}
+
+\subsection* {See also}
+The analysis of variance model is part of the stats package by William
+N. Venables and Brian D. Ripley \citep{VenRip02}. In addition,
+advanced users may wish to refer to \texttt{help(aov)} and
+\texttt{help(lm)}.
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
diff --git a/inst/doc/aov.pdf b/inst/doc/aov.pdf
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diff --git a/inst/doc/arima.pdf b/inst/doc/arima.pdf
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diff --git a/inst/doc/blogit.pdf b/inst/doc/blogit.pdf
index d2f5d37..292f486 100644
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diff --git a/inst/doc/bprobit.pdf b/inst/doc/bprobit.pdf
index eec1be6..95747f7 100644
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diff --git a/inst/doc/chopit.Rnw b/inst/doc/chopit.Rnw
new file mode 100644
index 0000000..ac82cec
--- /dev/null
+++ b/inst/doc/chopit.Rnw
@@ -0,0 +1,391 @@
+\SweaveOpts{eval=false, results=hide, prefix.string=vigpics/chopit}
+\include{zinput}
+%\VignetteIndexEntry{Compound Hierarchical Ordered Probit for Survey Vignettes}
+%\VignetteDepends{Zelig, stats, anchors}
+%\VignetteKeyWords{model,chopit,anchors,regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt chopit}: Compound Hierarchical Ordered Probit for Survey
+Vignettes}
+\label{chopit}
+
+The Compound Hierarchical Ordered Probit ({\sc chopit}) model corrects
+for ``differential item functioning'' or ``interpersonal
+comparability'' in ordinal survey responses. Given a self-assessment
+question (such as, ``How healthy are you? Excellent, good, fair, or
+poor.''), different respondents may interpret the response categories
+in different ways, such that excellent health to one individual may be
+fair health to a hypochondriac. For each ordinal self-assessment to be
+corrected, the {\sc chopit} model requires one or more vignette
+question (such as a description of a hypothetical person's health,
+followed by the same response categories as the self-assessment), and
+a set of associated explanatory variables for the respondent. The key
+assumption of the approach is that the thresholds (which determine how
+respondents translate their views into the response categories) have
+the same effect for different questions asked of the same respondent,
+but may differ across respondents; the model uses
+a parametric specification to predict the thresholds associated with an
+individual. The self-assessment and vignette questions may be taken
+from different surveys, so long as both surveys include the same
+explanatory variable questions to predict the thresholds. For ordinal
+data (without vignettes), see \Sref{oprobit}, \Sref{ologit}, and
+\Sref{oprobit.bayes}.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> fml <- list(self = Y ~ X1 + X2,
+ vign = cbind(Z1, Z2, Z3) ~ 1,
+ tau = ~ X1 + X2)
+> z.out <- zelig(fml, data = list(self = data1, vign = data2),
+ model = "chopit")
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+In this hierarchical model, the {\tt formula} and {\tt data} inputs to
+{\tt zelig()} are lists with the following structure:
+\begin{itemize}
+\item The {\tt formula} is a list with three {\tt formula} objects
+corresponding to:
+\begin{itemize}
+\item {\tt self}: Specifies the self-response question ({\tt Y}) as a function
+of a set of explanatory variables.
+\item {\tt vign}: Specifies the vignette questions on the left-hand
+side as a matrix in the form {\tt cbind(Z1, Z2, Z3)}.
+\item {\tt tau}: Specifies explanatory variables that constrain the cut points
+across both the vignette and self-response questions. These
+explanatory variables do not necessarily need to overlap with the set
+of explanatory variables specified in the {\tt self} formula, but must
+be observed in both the {\tt vign} and {\tt self} data frames,
+described below.
+\end{itemize}
+\item The {\tt data} argument is a list of two data frames with
+\begin{itemize}
+\item {\tt self}: A data frame containing the self-response
+question(s) specified in the {\tt self} formula and associated
+explanatory variables listed in the {\tt self} and {\tt tau} formulas.
+\item {\tt vign}: A data frame containing the vignette questions
+specified in the {\tt vign} formula and associated explanatory
+variables listed in the {\tt tau} formula.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes many
+additional options for compound hierarchical ordered probit
+regression, see {\tt help(chopit)} and \cite{WanKinLau07} for details.
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item {Basic Example}
+
+Setting up the formula as a list for the self-response, vignettes, and
+the cut points (drawn from both the self-response and vignette data sets).
+<<<Example.formula>>=
+formula <- list(self = y ~ sex + age + educ + factor(country),
+ vign = cbind(v1, v2, v3, v4, v5) ~ 1,
+ tau = ~ sex + age + educ + factor(country))
+@
+Attaching the sample data sets. The {\tt free1} data correspond to
+the self-response data, and the {\tt free2} data correspond to the
+vignette subset. Note that the variables specified in the {\tt tau}
+formula must be in both data sets.
+<<<Examples.library, echo=false>>=
+ if(is.na(match("anchors",.packages()))){
+ message("Loading package anchors...")
+ library(anchors)
+ }
+@
+<<Example.sample>>=
+data(free1, free2)
+@
+<<Example.data>>=
+data <- list(self = free1, vign = free2)
+@
+Estimating parameter values for the {\sc chopit} regression:
+<<Example.zelig>>=
+ z.out <- zelig(formula, data = data, model = "chopit")
+@
+Setting values for the explanatory variables to their default values:
+<<Example.setx>>=
+ x.out1 <- setx(z.out)
+@
+Simulating quantities of interest from the sampling distribution.
+<<Example.sim>>=
+ s.out1 <- sim(z.out, x = x.out1)
+@
+<<Example.summary>>=
+ summary(s.out1)
+@
+
+\item {Simulating First Differences}
+
+Estimate the first difference in expected values between the average
+age (about 40 years old) and a 25 year old individual, with the other
+explanatory variables held at their default values:
+<<FirstDifferences.setx>>=
+ x.out2 <- setx(z.out, age = 25)
+@
+<<FirstDifferences.sim>>=
+ s.out2 <- sim(z.out, x = x.out1, x1 = x.out2)
+@
+<<FirstDifferences.summary>>=
+ summary(s.out2)
+@
+
+\item {Conditional prediction}
+
+Conditional prediction generates expected values that are conditional
+on the observed self-response.
+<<Conditional.setx>>=
+x.out3 <- setx(z.out, cond = TRUE)
+@
+Since conditional prediction involves numeric integration, the
+procedure takes approximately one second per observation in {\tt
+x.out3} on 64-bit R.
+<<Conditional.sim>>=
+s.out3 <- sim(z.out, x = x.out3)
+@
+<<Conditional.summary>>=
+summary(s.out3)
+@
+\end{enumerate}
+
+\subsubsection{Model}
+
+This model has two sets of response variables, one for the
+self-assessment and one for the vignettes. Let $Y_i$ be the observed
+ordinal self-assessment for respondents $i = 1, \dots, n$, and
+$Z_{lj}$ be the ordinal vignette responses for individuals $l = 1,
+\dots, L$ in the vignette subset for $j = 1, \dots, J$ vignette
+questions, such that both $\{Y_i, Z_{lj} \}$ take integer values $k
+= 1, \dots, K$ corresponding to the same ordinal assessment response categories.
+
+\begin{itemize}
+\item The \emph{stochastic components} are described by unobserved continuous
+ variables, $Y_i^*$ and $Z_{lj}^*$, which follows normal
+ distributions with mean $\mu_i$ and variance $\sigma^2$ in
+ the case of $Y_i^*$, and mean $\theta_j$ and variance
+ $\sigma_j^2$ in the case of each $Z_{lj}^*$. Using the default
+identification mechanism, the variance $\sigma^2$ for the
+self-assessment is fixed to 1. Thus,
+\begin{eqnarray*}
+ Y_i^* & \sim & N(\mu_i, 1)\\
+ Z_{lj}^* & \sim& N(\theta_j, \sigma_j^2)
+\end{eqnarray*}
+such that each vignette response $j$ has a scalar mean $\theta_j$ and
+variance $\sigma_j^2$ that does not vary over observations
+$l$. In cases where more than one self-response was administered to the same subject, an additional random effect may be included in
+the distribution of the latent $Y_i^*$ in the form
+\begin{eqnarray*}
+ Y_i^* & \sim & N(\mu_i, 1 + \omega^2)\\
+\end{eqnarray*}
+where the variance term is obtained via the proof described in
+Appendix A of \cite{KinMurSal04}.
+
+The observation mechanisms that divide the continuous $\{ Y_i^*,
+Z_{lj}^* \}$ into the discrete $\{ Y_i, Z_{lj} \}$ are
+\begin{eqnarray*}
+ Y_i & = & k \quad {\rm if} \quad \tau_{i}^{k-1} \le Y_i^* \le \tau_{i}^{k}
+ \; {\rm for} \; k = 1,\dots, K \\
+ Z_{lj} & = & k \quad {\rm if} \quad \tau_{l}^{k-1} \le Z_{lj}^* \le \tau_{l}^{k}
+ \; {\rm for} \; k = 1,\dots, K
+\end{eqnarray*}
+where the threshold parameters $\tau$ vary over individuals $\{i, l
+\}$, but are subject to the following constraints within each
+individual: $\tau^p < \tau^q$ for all $p < q$ and $\tau_0=-\infty$ and
+$\tau_K=\infty$.
+
+\item There are three \emph{systematic components} in the model.
+\begin{itemize}
+
+\item For the self-assessment component, let
+\begin{eqnarray*}
+\mu_i &=& x_i \beta
+\end{eqnarray*}
+where $x_i$ is the vector of $q$ explanatory variables for observation $i$,
+and $\beta$ is the associated vector of coefficients.
+
+\item In addition, the threshold parameters also vary over individuals
+in the self-assessment component as follows
+\begin{eqnarray*}
+\tau_{i}^{1} &=& v_i \gamma^1 \\
+\tau_{i}^{k} &=& \tau_{i}^{k-1} + \exp(v_i \gamma^k) \; {\rm for} \;
+k = 2, \dots, K
+\end{eqnarray*}
+where $v_i$ is the vector of $p$ explanatory variables for observation
+$i$, and $\gamma^k$ for $k = 1, \dots, K$ are the vectors of
+coefficients associated with each categorical response. Thus, the
+threshold parameters vary over individuals since $v_i$ vary, and over
+response categories since the $\gamma^k$ vary over the threshold
+parameters.
+
+\item Similarly, the threshold parameters vary over individuals in the
+vignette component as follows
+\begin{eqnarray*}
+\tau_{l}^{1} &=& v_l \gamma^1 \\
+\tau_{l}^{k} &=& \tau_{l}^{k-1} + \exp(v_l \gamma^k) \; {\rm for} \;
+k = 2, \dots, K
+\end{eqnarray*}
+where $v_l$ is a vector of $p$ explanatory variables for observation
+$l$ in the vignette subset, and $\gamma^k$ are restricted to be the
+same $\gamma^k$ used to parameterize the threshold parameters for the
+self-assessment component.
+\end{itemize}
+As \cite{KinMurSal04} note, the interpersonal comparability of
+responses (or response consistency) is achieved by constraining
+$\gamma^k$ to be the same in both the self-assessment and vignette
+components of the model. Note that the variables included in $v_i$ and
+$v_l$ are the same, but the observed values of those variables differ across
+the vignette and self-response samples.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) for the {\sc chopit} model
+is the expected value of the posterior density for the systematic
+component $\mu_i$,
+\begin{equation*}
+\textrm{EV} \; = \; E(\mu_i \mid x_i) \; = \; x_i \beta
+\end{equation*}
+given draws of $\beta$ from its sampling distribution.
+
+\item The first difference is the difference in the expected value of
+the posterior density for the systematic component $\mu_i$ given $x_1$
+and $x_0$:
+\begin{equation*}
+\textrm{FD} = E(\mu_i \mid x_1) - E(\mu_i \mid x_0).
+\end{equation*}
+
+\item In conditional prediction models, the conditional expected
+values ({\tt qi\$cev}) are the expected value of the distribution of
+$\mu_i$ conditional on the observed self-assessment response $Y_i$,
+%%This quantity is calculated in two steps. First, using draws of
+%%$\gamma^k$ for $k = 1, \dots, K$ from their posterior distributions,
+%%we calculate simulations of the threshold parameters
+%%\begin{eqnarray*}
+%%\tau_i^1 &=& v_i \gamma^1 \\
+%%\tau_i^k &=& \tau_i^{k-1} + \exp(v_i \gamma^k) \; {\rm for} \; k = 2, \dots, K.
+%%\end{eqnarray*}
+%%Next, we use adaptive integration to calculate the expected value of
+%%the posterior distribution conditional on $Y_i$:
+%%\begin{eqnarray*}
+%%E(\mu_i \mid x_i, Y_i) &=& \int \mu_i P(\mu_i \mid \tau_i, \beta, x_i,
+%%Y_i) d \mu_i
+%%\end{eqnarray*}
+where
+\begin{eqnarray*}
+P(\mu_i \mid \tau_i, \beta, x_i, Y_i) &=& \prod_{k=1}^K [ \Phi(\tau_i^k -
+\mu_i) - \Phi(\tau_i^{k-1} - \mu_i) ] \times N(x_i \beta, x_i
+\widehat{V}(\widehat{\beta}) x_i^{\prime} + \widehat{\omega}^2)
+\end{eqnarray*}
+given the simulations of the threshold parameters calculated above,
+draws of $\beta$ from its sampling distribution, and the
+estimated variance-covariance matrix for $\widehat{\beta}$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(\dots,
+ model = "chopit")}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the estimated parameters by using {\tt z.out\$par}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt par}: the maximum likelihood parameter estimates for
+$\widehat{\gamma}^k$ for $k = 1, \dots, K$ response categories,
+log($\widehat{\omega}$) (if estimated), log($\widehat{\sigma}$) (if
+estimated), log($\widehat{\sigma_j}$) for $j = 1, \dots, J$ vignette
+questions, $\widehat{\theta_j}$, and $\widehat{\beta}$.
+\item {\tt chopit.hessian}: the estimated Hessian matrix, with rows
+and columns corresponding to the elements in {\tt par}.
+\item {\tt value}: the value of the log-likelihood at its maximum
+\item {\tt counts}: the number of function and gradient calls to
+reach the maximum.
+\item {\tt formula}: the formula for {\tt self}, {\tt vign}, and {\tt
+tau} selected by the user.
+\item {\tt call}: the call to {\tt zelig()}.
+\item {\tt \dots}: additional outputs described in {\tt
+help(chopit)}.
+ \end{itemize}
+
+\item Typing {\tt summary(z.out)} provides a useful summary of the
+output from {\tt zelig()}, but no items can be extracted.
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected values for the
+ specified values of {\tt x}.
+ \item {\tt qi\$fd}: the simulated first difference in the expected
+ values for the values specified in {\tt x} and {\tt x1}.
+ \item {\tt qi\$cev}: the simulated conditional expected value given
+{\tt x}.
+ \end{itemize}
+\end{itemize}
+
+\subsection* {How to Cite}
+
+To cite the \emph{chopit} Zelig model:
+\begin{verse}
+Kosuke Imai, Gary King, and Olivia Lau. 2007. ``chopit: Compound Hierarchical
+Ordered Probit for Survey Vignettes'' 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}
+The {\sc chopit} model is part of the anchors package by Jonathan
+Wand, Gary King, and Olivia Lau \citep{WanKinLau07}. Advanced users
+may wish to refer to \texttt{help(chopit)}, as well as
+\cite{KinMurSal04} and \cite{KinWan07}.
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+ \end{document}
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/chopit.pdf b/inst/doc/chopit.pdf
new file mode 100644
index 0000000..dc41a66
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diff --git a/inst/doc/netcloglog.Rnw b/inst/doc/cloglog.net.Rnw
similarity index 75%
rename from inst/doc/netcloglog.Rnw
rename to inst/doc/cloglog.net.Rnw
index 81c64bd..df10c6a 100644
--- a/inst/doc/netcloglog.Rnw
+++ b/inst/doc/cloglog.net.Rnw
@@ -1,8 +1,8 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/netcloglog}
+\SweaveOpts{results=hide, prefix.string=vigpics/cloglognet}
\include{zinput}
%\VignetteIndexEntry{Social Network Complementary Log Log Regression for Dichotomous Dependent Variables }
%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,netcloglog}
+%\VignetteKeyWords{model,cloglog.net}
%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
@@ -13,13 +13,13 @@
library(Zelig)
@
-\section{{\tt netcloglog}: Network Complementary Log Log Regression for Dichotomous Proximity Matrix Dependent Variables}
+\section{{\tt cloglog.net}: Network Complementary Log Log Regression for Dichotomous Proximity Matrix Dependent Variables}
Use network complementary log log regression analysis for a dependent variable that is a binary valued proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netcloglog", data = mydata)
+> z.out <- zelig(y ~ x1 + x2, model = "cloglog.net", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -39,7 +39,7 @@ data(friendship)
Estimate model:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(friends ~ advice + prestige + perpower, model = "netcloglog", data = friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model = "cloglog.net", data = friendship)
summary(z.out)
@
@@ -93,7 +93,7 @@ plot(s.out)
\subsubsection{Model}
-The {\tt netcloglog} model performs a complementary log log regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard complementary log log regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which pro [...]
+The {\tt cloglog.net} model performs a complementary log log regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard complementary log log regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which pr [...]
Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
\begin{itemize}
@@ -113,7 +113,7 @@ where $x_i$ the vector of $k$ explanatory variables for observation $i$ and $\be
\subsubsection{Quantities of Interest}
The quantities of interest for the network complementary log log regression are the same as those for the standard complementary log log regression.
\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the {\tt netcloglogt} model are simulations of the predicted probability of a success:
+\item The expected values ({\tt qi\$ev}) for the {\tt cloglog.nett} model are simulations of the predicted probability of a success:
\begin{equation*}
E(Y) = \pi_{i} = 1 - \exp[\exp (-x_i \beta)],
\end{equation*}
@@ -130,7 +130,7 @@ FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "netcloglog", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "cloglog.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -162,9 +162,9 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{netcloglog} Zelig model:
+To cite the \emph{cloglog.net} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``netcloglog: Social Network Complementary Log Log Regression for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``cloglog.net: Social Network Complementary Log Log Regression for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
\input{citeZelig}
diff --git a/inst/doc/cloglog.net.pdf b/inst/doc/cloglog.net.pdf
new file mode 100644
index 0000000..3f8aea0
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diff --git a/inst/doc/commands/model.end.aux b/inst/doc/commands/model.end.aux
index daba10f..cfdd695 100644
--- a/inst/doc/commands/model.end.aux
+++ b/inst/doc/commands/model.end.aux
@@ -1,14 +1,14 @@
\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.2}{\tt model.end}: Cleaning up after optimization}{413}{section.13.2}}
-\newlabel{model.end}{{13.2}{413}{{\tt model.end}: Cleaning up after optimization\relax }{section.13.2}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{413}{section*.535}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{413}{section*.536}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{413}{section*.537}}
-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{413}{section*.538}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{413}{section*.539}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{413}{section*.540}}
+\@writefile{toc}{\contentsline {section}{\numberline {13.2}{\tt model.end}: Cleaning up after optimization}{490}{section.13.2}}
+\newlabel{model.end}{{13.2}{490}{{\tt model.end}: Cleaning up after optimization\relax }{section.13.2}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{490}{section*.637}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{490}{section*.638}}
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index eec2b7d..10d90dc 100644
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index 7662095..8d40dd9 100644
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index b8c186d..820e09a 100644
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index 5ac9454..2156af7 100644
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index dc4cb2d..884112e 100644
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diff --git a/inst/doc/commandsRd/MatchIt.url.pdf b/inst/doc/commandsRd/MatchIt.url.pdf
index 4933400..5df2404 100644
Binary files a/inst/doc/commandsRd/MatchIt.url.pdf and b/inst/doc/commandsRd/MatchIt.url.pdf differ
diff --git a/inst/doc/commandsRd/PErisk.pdf b/inst/doc/commandsRd/PErisk.pdf
index d68ab0f..976c3e4 100644
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diff --git a/inst/doc/commandsRd/SupremeCourt.pdf b/inst/doc/commandsRd/SupremeCourt.pdf
index 18f0bb3..6069ccb 100644
Binary files a/inst/doc/commandsRd/SupremeCourt.pdf and b/inst/doc/commandsRd/SupremeCourt.pdf differ
diff --git a/inst/doc/commandsRd/Weimar.pdf b/inst/doc/commandsRd/Weimar.pdf
index 816f6f0..8961bdb 100644
Binary files a/inst/doc/commandsRd/Weimar.pdf and b/inst/doc/commandsRd/Weimar.pdf differ
diff --git a/inst/doc/commandsRd/Zelig-package.pdf b/inst/doc/commandsRd/Zelig-package.pdf
index 2371f6d..40d968b 100644
Binary files a/inst/doc/commandsRd/Zelig-package.pdf and b/inst/doc/commandsRd/Zelig-package.pdf differ
diff --git a/inst/doc/commandsRd/Zelig-package.tex b/inst/doc/commandsRd/Zelig-package.tex
index 6a81241..6f046b7 100644
--- a/inst/doc/commandsRd/Zelig-package.tex
+++ b/inst/doc/commandsRd/Zelig-package.tex
@@ -1,4 +1,4 @@
-\include{zinput} \begin{document}\section{{\tt Zelig-package}: Zelig: Everyone's Statistical Software}\label{ss:Zelig.Rdash.package}
+\include{zinput} \begin{document}\section{{\tt Zelig-package}: Everyone's Statistical Software}\label{ss:Zelig.Rdash.package}
\aliasA{Zelig}{Zelig-package}{Zelig}
\keyword{package}{Zelig-package}
\begin{Description}\relax
@@ -19,31 +19,49 @@ quantities of direct interest.
\begin{Details}\relax
\Tabular{ll}{
Package: & Zelig\\
-Version: & 2.8-5\\
+Version: & 3.1-0\\
Date: & 2007-06-12\\
-Depends: & R (>= 2.4.0), MASS, boot\\
+Depends: & R (>= 2.5.0), MASS, boot\\
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\\
+systemfit, mgcv, lme4, anchors (>= 2.0)\\
License: & GPL version 2 or newer\\
URL: & http://gking.harvard.edu/zelig\\
+Packaged: & Wed May 30 16:14:15 2007; falimadh\\
+Built: & R 2.6.0; ; 2007-11-30 13:37:37; unix\\
}
Index:
\begin{alltt}
approval U.S. Presidential Approval Data
+bivariate Sample data for bivariate probit regression
coalition Coalition Dissolution in Parliamentary
Democracies
+coalition2 Coalition Dissolution in Parliamentary
+ Democracies, Modified Version
current.packages Find all packages in a dependency chain
dims Return Dimensions of Vectors, Arrays, and Data
Frames
eidat Simulation Data for Ecological Inference
+free1 Freedom of Speech Data
+free2 Freedom of Speech Data
friendship Simulated Example of Schoolchildren Friendship
Network
+grunfeld Simulation Data for model Seemingly Unrelated
+ Regression (sur) that corresponds to method SUR
+ of systemfit
gsource Read Data As a Space-Delimited Table
help.zelig HTML Help for Zelig Commands and Models
hoff Social Security Expenditure Data
+homerun Sample Data on Home Runs Hit By Mark McGwire
+ and Sammy Sosa in 1998.
immigration Individual Preferences Over Immigration Policy
+klein Simulation Data for model Two-Stage Least
+ Square (twosls) that corresponds to method 2SLS
+ of systemfit
+kmenta Simulation Data for model Three-Stage Least
+ Square (threesls) that corresponds to method
+ 3SLS of systemfit
macro Macroeconomic Data
match.data Output matched data sets
MatchIt.url Table of links for Zelig
@@ -83,67 +101,79 @@ tobin Tobin's Tobit Data
turnout Turnout Data Set from the National Election
Survey
user.prompt Pause in demo files
+voteincome Sample Turnout and Demographic Data from the
+ 2000 Current Population Survey
Weimar 1932 Weimar election data
zelig Estimating a Statistical Model
zeligDepStatus Zelig Dependencies Packages Client Status
zeligDepUpdate Download Zelig Dependencies Packages
zeligDescribeModelXML Zelig interface functions
-Zelig-package Everyone's Statistical Software
+Zelig-package Zelig: Everyone's Statistical Software
Zelig.url Table of links for Zelig
-zideal Zelig Matrix of Dependencies
\end{alltt}
Further information is available in the following vignettes:
-
\Tabular{ll}{
+\code{aov} & Fit an Analysis of Variance Model (source)\\
\code{arima} & ARIMA Models for Time Series Data (source)\\
\code{blogit} & Bivariate Logistic Regression for Two Dichotomous Dependent Variables (source)\\
\code{bprobit} & Bivariate Probit Regression for Dichotomous Dependent Variables (source)\\
+\code{chopit} & Compound Hierarchical Ordered Probit for Survey Vignettes (source)\\
+\code{cloglog.net} & Social Network Complementary Log Log Regression for Dichotomous Dependent Variables (source)\\
+\code{coxph} & Cox Proportional Hazards Regression for Duration Dependent Variables (source)\\
\code{ei.RxC} & Hierarchical Multinomial-Dirichlet Ecological Inference Model (source)\\
\code{ei.dynamic} & Quinn's Dynamic Ecological Inference (source)\\
-\code{ei.hier} & Hierarchical Ecological Inference Model (source)\\
+\code{ei.hier} & Hierarchical Ecological Inference Model for 2x2 tables (source)\\
\code{exp} & Exponential Regression for Duration Dependent Variables (source)\\
\code{factor.bayes} & Bayesian Factor Analysis (source)\\
\code{factor.mix} & Mixed Data Factor Analysis (source)\\
\code{factor.ord} & Ordinal Data Factor Analysis (source)\\
-\code{gam.logit} & gam.logit: Generalized Additive Model for Dichotomous Dependent Variables (source)\\
-\code{gam.normal} & Generalized Additive Model for Continuous Dependent Variables (source)\\
-\code{gam.poisson} & Generalized Additive Model for Count Dependent Variables (source)\\
-\code{gam.probit} & Generalized Additive Model for Dichotomous Dependent Variables (source)\\
\code{gamma} & Gamma Regression for Continuous, Positive Dependent Variables (source)\\
-\code{irt1d} & One Dimensional Item Response Mode (source)\\
+\code{gamma.gee} & Generalized Estimating Equation for Gamma Regression (source)\\
+\code{gamma.mixed} & Gamma mixed effects linear regression (source)\\
+\code{gamma.net} & Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables (source)\\
+\code{irt1d} & One Dimensional Item Response Mode (source)\\
\code{irtkd} & K-Dimensional Item Response Model (source)\\
\code{logit} & Logistic Regression for Dichotomous Dependent Variables (source)\\
\code{logit.bayes} & Bayesian Logistic Regression for Dichotomous Dependent Variables (source)\\
+\code{logit.gam} & Generalized Additive Model for Dichotomous Dependent Variables (source)\\
\code{logit.gee} & Generalized Estimating Equation for Logistic Regression (source)\\
+\code{logit.mixed} & Mixed effects linear regression (source)\\
+\code{logit.net} & Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables (source)\\
\code{lognorm} & Log-Normal Regression for Duration Dependent Variables (source)\\
\code{ls} & Least Squares Regression for Continuous Dependent Variables (source)\\
+\code{ls.mixed} & Mixed effects linear regression (source)\\
+\code{ls.net} & Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables (source)\\
\code{mlogit} & Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values (source)\\
\code{mlogit.bayes} & Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values (source)\\
\code{mloglm} & Multinomial Log-Linear Regression for Contingency Table Models (source)\\
\code{negbin} & Negative Binomial Regression for Event Count Dependent Variables (source)\\
-\code{netcloglog} & Least Squares Regression for Continuous Dependent Variables (source)\\
-\code{netgamma} & Least Squares Regression for Continuous Dependent Variables (source)\\
-\code{netlogit} & Least Squares Regression for Continuous Dependent Variables (source)\\
-\code{netls} & Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables (source)\\
-\code{netnormal} & Least Squares Regression for Continuous Dependent Variables (source)\\
-\code{netpoisson} & Least Squares Regression for Continuous Dependent Variables (source)\\
-\code{netprobit} & Least Squares Regression for Continuous Dependent Variables (source)\\
\code{normal} & Normal Regression for Continuous Dependent Variables (source)\\
\code{normal.bayes} & Bayesian Normal Linear Regression (source)\\
+\code{normal.gam} & Generalized Additive Model for Continuous Dependent Variables (source)\\
+\code{normal.gee} & Generalized Estimating Equation for Normal Regression (source)\\
+\code{normal.net} & Network Normal Regression for Continuous Proximity Matrix Dependent Variables (source)\\
\code{ologit} & Ordinal Logistic Regression for Ordered Categorical Dependent Variables (source)\\
\code{oprobit} & Ordinal Probit Regression for Ordered Categorical Dependent Variables (source)\\
\code{oprobit.bayes} & Bayesian Ordered Probit Regression (source)\\
\code{poisson} & Poisson Regression for Event Count Dependent Variables (source)\\
\code{poisson.bayes} & Bayesian Poisson Regression (source)\\
+\code{poisson.gam} & Generalized Additive Model for Count Dependent Variables (source)\\
+\code{poisson.gee} & Generalized Estimating Equation for Poisson Regression (source)\\
+\code{poisson.mixed} & Mixed effects poisson regression (source)\\
+\code{poisson.net} & Network Poisson Regression for Event Count Proximity Matrix Dependent Variables (source)\\
\code{probit} & Probit Regression for Dichotomous Dependent Variables (source)\\
\code{probit.bayes} & Bayesian Probit Regression for Dichotomous Dependent Variable (source)\\
+\code{probit.gam} & Generalized Additive Model for Dichotomous Dependent Variables (source)\\
+\code{probit.gee} & Generalized Estimating Equation for Probit Regression (source)\\
+\code{probit.mixed} & Mixed effects probit regression (source)\\
+\code{probit.net} & Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables (source)\\
\code{relogit} & Rare Events Logistic Regression for Dichotomous Dependent Variables (source)\\
\code{sur} & Seemingly Unrelated Regression (source)\\
-\code{threesls} & Three Stage Least Squares (source)\\
+\code{threesls} & Three Stage Least Squares (source)\\
\code{tobit} & Linear regression for Left-Censored Dependet Variable (source)\\
\code{tobit.bayes} & Bayesian Linear Regression for a Censored Dependent Variable (source)\\
-\code{twosls} & Two Stage Least Squares (source)\\
+\code{twosls} & Two Stage Least Squares (source)\\
\code{weibull} & Weibull Regression for Duration Dependent Variables (source)\\
}
\end{Details}
diff --git a/inst/doc/commandsRd/Zelig.pdf b/inst/doc/commandsRd/Zelig.pdf
index d25a6b9..ce1129c 100644
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diff --git a/inst/doc/commandsRd/Zelig.url.pdf b/inst/doc/commandsRd/Zelig.url.pdf
index 5b0deff..c0d0a1b 100644
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diff --git a/inst/doc/commandsRd/approval.pdf b/inst/doc/commandsRd/approval.pdf
index 1ef8934..d4ad781 100644
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diff --git a/inst/doc/commandsRd/bivariate.pdf b/inst/doc/commandsRd/bivariate.pdf
new file mode 100644
index 0000000..ed78926
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diff --git a/inst/doc/commandsRd/bivariate.tex b/inst/doc/commandsRd/bivariate.tex
new file mode 100644
index 0000000..a5b0180
--- /dev/null
+++ b/inst/doc/commandsRd/bivariate.tex
@@ -0,0 +1,22 @@
+\include{zinput} \begin{document}\section{{\tt bivariate}: Sample data for bivariate probit regression}\label{ss:bivariate}
+\keyword{datasets}{bivariate}
+\begin{Description}\relax
+Sample data for the bivariate probit regression.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(bivariate)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A table containing 6 variables ("y1", "y2", "x1",
+"x2", "x3", and "x4") and 78 observations.
+\end{Format}
+\begin{Source}\relax
+This is a cleaned and relabelled version of the sanction data
+set, available in Zelig.
+\end{Source}
+\begin{References}\relax
+Martin, Lisa (1992). \emph{Coercive Cooperation: Explaining Multilateral
+Economic Sanctions}, Princeton: Princeton University Press.
+\end{References}
+
+\end{document}
diff --git a/inst/doc/commandsRd/coalition.pdf b/inst/doc/commandsRd/coalition.pdf
index e04381f..0713647 100644
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diff --git a/inst/doc/commandsRd/coalition2.pdf b/inst/doc/commandsRd/coalition2.pdf
new file mode 100644
index 0000000..b2c6b1a
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diff --git a/inst/doc/commandsRd/coalition2.tex b/inst/doc/commandsRd/coalition2.tex
new file mode 100644
index 0000000..b3d208d
--- /dev/null
+++ b/inst/doc/commandsRd/coalition2.tex
@@ -0,0 +1,31 @@
+\include{zinput} \begin{document}\section{{\tt coalition2}: Coalition Dissolution in Parliamentary Democracies, Modified Version}\label{ss:coalition2}
+\keyword{datasets}{coalition2}
+\begin{Description}\relax
+This data set contains survival data on government coalitions in
+parliamentary democracies (Belgium, Canada, Denmark, Finland, France,
+Iceland, Ireland, Israel, Italy, Netherlands, Norway, Portugal, Spain,
+Sweden, and the United Kingdom) for the period 1945-1987. Country indicator variables are included in the sample data.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(coalition2)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A data frame containing 8 variables ("duration", "ciep12", "invest",
+"fract", "polar", "numst2", "crisis", "country") and 314 observations. For
+variable descriptions, please refer to King, Alt, Burns and Laver
+(1990).
+\end{Format}
+\begin{Source}\relax
+ICPSR
+\end{Source}
+\begin{References}\relax
+King, Gary, James E. Alt, Nancy Elizabeth Burns and Michael Laver (1990).
+``A Unified Model of Cabinet Dissolution in Parliamentary
+Democracies,'' \emph{American Journal of Political Science}, vol. 34,
+no. 3, pp. 846-870.
+
+Gary King, James E. Alt, Nancy Burns, and Michael Laver. ICPSR
+Publication Related Archive, 1115.
+\end{References}
+
+\end{document}
diff --git a/inst/doc/commandsRd/current.packages.pdf b/inst/doc/commandsRd/current.packages.pdf
index 2b39e08..8c60821 100644
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diff --git a/inst/doc/commandsRd/dims.pdf b/inst/doc/commandsRd/dims.pdf
index 5f1affa..9f94b4e 100644
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diff --git a/inst/doc/commandsRd/eidat.pdf b/inst/doc/commandsRd/eidat.pdf
index a21c7ed..d796739 100644
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diff --git a/inst/doc/commandsRd/free1.pdf b/inst/doc/commandsRd/free1.pdf
new file mode 100644
index 0000000..4c7c96d
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diff --git a/inst/doc/commandsRd/free1.tex b/inst/doc/commandsRd/free1.tex
new file mode 100644
index 0000000..df55e16
--- /dev/null
+++ b/inst/doc/commandsRd/free1.tex
@@ -0,0 +1,96 @@
+\include{zinput} \begin{document}\section{{\tt free1}: Freedom of Speech Data}\label{ss:free1}
+\keyword{datasets}{free1}
+\begin{Description}\relax
+Selection of individual-level survey data for freedom of speech.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(free1)\end{verbatim}
+\end{Usage}
+\begin{Details}\relax
+A table with 150 observations and 12 variables. \Itemize{
+\item[sex] 1 for men and 0 for women
+\item[age] Age of respondent in years
+\item[educ] Levels of education, coded as a numeric variable with
+\Itemize{
+\item[1] No formal education
+\item[2] Less than primary school education
+\item[3] Completed primary school
+\item[4] Completed secondary school
+\item[5] Completed high school
+\item[6] Completed college
+\item[7] Completed post-graduate degree
+}
+
+\item[country] Character strings consisting of "Oceana",
+"Eurasia", and "Eastasia", after Orwell's \emph{1984}.
+\item[y] Self assessment (see below).
+\item[v1-v6] Response to vignettes (see below).
+}
+Survey respondents were asked in almost the same language for a
+self-assessment and for an assessment of several hypothetical persons
+described by written vignettes. The self assessment (\code{self}, in
+the data set), "How free do you think [name/you] [is/are] to express
+[him-her/your]self without fear of government reprisal?" was first
+asked of the survey respondent with respect to him or herself, and
+then after each of vignette. The possible response categories are: \Itemize{
+\item[1] Completely free
+\item[2] Very free
+\item[3] Moderately free
+\item[4] Slightly free
+\item[5] Not free at all
+}
+The vignettes, ordered from most free to least free, are:
+\Itemize{
+\item[vign1] [Kay] does not like many of the government's
+policies. She frequently publishes her opinion in newspapers,
+criticizing decisions by officials and calling for change. She sees
+little reason these actions could lead to government reprisal.
+
+\item[vign2] [Michael] disagrees with many of the government's
+policies. Though he knows criticism is frowned upon, he doesn't
+believe the government would punish someone for expressing critical
+views. He makes his opinion known on most issues without regard to
+who is listening.
+
+\item[vign3] [Bob] has political views at odds with the
+government. He has heard of people occasionally being arrested for
+speaking out against the government, and government leaders
+sometimes make political speeches condemning those who criticize. He
+sometimes writes letters to newspapers about politics, but he is
+careful not to use his real name.
+
+\item[vign4] [Connie] does not like the government's stance on many
+issues. She has a friend who was arrested for being too openly
+critical of governmental leaders, and so she avoids voicing her
+opinions in public places.
+
+\item[vign5] [Vito] disagrees with many of the government's
+policies, and is very careful about whom he says this to, reserving
+his real opinions for family and close friends only. He knows
+several men who have been taken away by government officials for
+saying negative things in public.
+
+\item[vign6] [Sonny] lives in fear of being harassed for his
+political views. Everyone he knows who has spoken out against the
+government has been arrested or taken away. He never says a word
+about anything the government does, not even when he is at home
+alone with his family.
+}
+\end{Details}
+\begin{References}\relax
+\emph{WHO's World Health Survey}
+by Lydia Bendib, Somnath Chatterji, Alena Petrakova, Ritu Sadana,
+Joshua A. Salomon, Margie Schneider, Bedirhan Ustun, Maria
+Villanueva
+
+Jonathan Wand, Gary King and Olivia Lau. (2007) ``Anchors: Software for
+Anchoring Vignettes''. \emph{Journal of Statistical Software}. Forthcoming.
+copy at http://wand.stanford.edu/research/anchors-jss.pdf
+
+Gary King and Jonathan Wand. "Comparing Incomparable Survey
+Responses: New Tools for Anchoring Vignettes," Political Analysis, 15,
+1 (Winter, 2007): Pp. 46-66,
+copy at http://gking.harvard.edu/files/abs/c-abs.shtml.
+\end{References}
+
+\end{document}
diff --git a/inst/doc/commandsRd/free2.pdf b/inst/doc/commandsRd/free2.pdf
new file mode 100644
index 0000000..b74d748
Binary files /dev/null and b/inst/doc/commandsRd/free2.pdf differ
diff --git a/inst/doc/commandsRd/free2.tex b/inst/doc/commandsRd/free2.tex
new file mode 100644
index 0000000..78ab75b
--- /dev/null
+++ b/inst/doc/commandsRd/free2.tex
@@ -0,0 +1,96 @@
+\include{zinput} \begin{document}\section{{\tt free2}: Freedom of Speech Data}\label{ss:free2}
+\keyword{datasets}{free2}
+\begin{Description}\relax
+Selection of individual-level survey data for freedom of speech.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(free2)\end{verbatim}
+\end{Usage}
+\begin{Details}\relax
+A table with 150 observations and 12 variables. \Itemize{
+\item[sex] 1 for men and 0 for women
+\item[age] Age of respondent in years
+\item[educ] Levels of education, coded as a numeric variable with
+\Itemize{
+\item[1] No formal education
+\item[2] Less than primary school education
+\item[3] Completed primary school
+\item[4] Completed secondary school
+\item[5] Completed high school
+\item[6] Completed college
+\item[7] Completed post-graduate degree
+}
+
+\item[country] Character strings consisting of "Oceana",
+"Eurasia", and "Eastasia", after Orwell's \emph{1984}.
+\item[y] Self assessment (see below).
+\item[v1-v6] Response to vignettes (see below).
+}
+Survey respondents were asked in almost the same language for a
+self-assessment and for an assessment of several hypothetical persons
+described by written vignettes. The self assessment (\code{self}, in
+the data set), "How free do you think [name/you] [is/are] to express
+[him-her/your]self without fear of government reprisal?" was first
+asked of the survey respondent with respect to him or herself, and
+then after each of vignette. The possible response categories are: \Itemize{
+\item[1] Completely free
+\item[2] Very free
+\item[3] Moderately free
+\item[4] Slightly free
+\item[5] Not free at all
+}
+The vignettes, ordered from most free to least free, are:
+\Itemize{
+\item[vign1] [Kay] does not like many of the government's
+policies. She frequently publishes her opinion in newspapers,
+criticizing decisions by officials and calling for change. She sees
+little reason these actions could lead to government reprisal.
+
+\item[vign2] [Michael] disagrees with many of the government's
+policies. Though he knows criticism is frowned upon, he doesn't
+believe the government would punish someone for expressing critical
+views. He makes his opinion known on most issues without regard to
+who is listening.
+
+\item[vign3] [Bob] has political views at odds with the
+government. He has heard of people occasionally being arrested for
+speaking out against the government, and government leaders
+sometimes make political speeches condemning those who criticize. He
+sometimes writes letters to newspapers about politics, but he is
+careful not to use his real name.
+
+\item[vign4] [Connie] does not like the government's stance on many
+issues. She has a friend who was arrested for being too openly
+critical of governmental leaders, and so she avoids voicing her
+opinions in public places.
+
+\item[vign5] [Vito] disagrees with many of the government's
+policies, and is very careful about whom he says this to, reserving
+his real opinions for family and close friends only. He knows
+several men who have been taken away by government officials for
+saying negative things in public.
+
+\item[vign6] [Sonny] lives in fear of being harassed for his
+political views. Everyone he knows who has spoken out against the
+government has been arrested or taken away. He never says a word
+about anything the government does, not even when he is at home
+alone with his family.
+}
+\end{Details}
+\begin{References}\relax
+\emph{WHO's World Health Survey}
+by Lydia Bendib, Somnath Chatterji, Alena Petrakova, Ritu Sadana,
+Joshua A. Salomon, Margie Schneider, Bedirhan Ustun, Maria
+Villanueva
+
+Jonathan Wand, Gary King and Olivia Lau. (2007) ``Anchors: Software for
+Anchoring Vignettes''. \emph{Journal of Statistical Software}. Forthcoming.
+copy at http://wand.stanford.edu/research/anchors-jss.pdf
+
+Gary King and Jonathan Wand. "Comparing Incomparable Survey
+Responses: New Tools for Anchoring Vignettes," Political Analysis, 15,
+1 (Winter, 2007): Pp. 46-66,
+copy at http://gking.harvard.edu/files/abs/c-abs.shtml.
+\end{References}
+
+\end{document}
diff --git a/inst/doc/commandsRd/friendship.pdf b/inst/doc/commandsRd/friendship.pdf
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diff --git a/inst/doc/commandsRd/homerun.tex b/inst/doc/commandsRd/homerun.tex
new file mode 100644
index 0000000..404b876
--- /dev/null
+++ b/inst/doc/commandsRd/homerun.tex
@@ -0,0 +1,26 @@
+\include{zinput} \begin{document}\section{{\tt homerun}: Sample Data on Home Runs Hit By Mark McGwire and Sammy Sosa in 1998.}\label{ss:homerun}
+\keyword{datasets}{homerun}
+\begin{Description}\relax
+Game-by-game information for the 1998 season for Mark McGwire and Sammy Sosa. Data are a subset of the dataset provided in Simonoff (1998).
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(homerun)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A data frame containing 5 variables ("gameno", "month", "homeruns", "playerstatus", "player") and 326 observations.
+\describe{
+\item[\code{gameno}] an integer variable denoting the game number
+\item[\code{month}] a factor variable taking with levels "March" through "September" denoting the month of the game
+\item[\code{homeruns}] an integer vector denoting the number of homeruns hit in that game for that player
+\item[\code{playerstatus}] an integer vector equal to "0" if the player played in the game, and "1" if they did not.
+\item[\code{player}] an integer vector equal to "0" (McGwire) or "1" (Sosa)
+}
+\end{Format}
+\begin{Source}\relax
+\url{http://www.amstat.org}
+\end{Source}
+\begin{References}\relax
+Simonoff, Jeffrey S. 1998. ``Move Over, Roger Maris: Breaking Baseball's Most Famous Record.'' {\it Journal of Statistics Education} 6(3). Data used are a subset of the data in the article. \bsl{}
+\end{References}
+
+\end{document}
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diff --git a/inst/doc/commandsRd/voteincome.pdf b/inst/doc/commandsRd/voteincome.pdf
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diff --git a/inst/doc/commandsRd/voteincome.tex b/inst/doc/commandsRd/voteincome.tex
new file mode 100644
index 0000000..160b0db
--- /dev/null
+++ b/inst/doc/commandsRd/voteincome.tex
@@ -0,0 +1,28 @@
+\include{zinput} \begin{document}\section{{\tt voteincome}: Sample Turnout and Demographic Data from the 2000 Current Population Survey}\label{ss:voteincome}
+\keyword{datasets}{voteincome}
+\begin{Description}\relax
+This data set contains turnout and demographic data from a sample of respondents to the 2000 Current Population Survey (CPS). The states represented are South Carolina and Arkansas. The data represent only a sample and results from this example should not be used in publication.
+\end{Description}
+\begin{Usage}
+\begin{verbatim}data(voteincome)\end{verbatim}
+\end{Usage}
+\begin{Format}\relax
+A data frame containing 7 variables ("state", "year", "vote", "income", "education", "age", "female") and 1500 observations.
+\describe{
+\item[\code{state}] a factor variable with levels equal to "AR" (Arkansas) and "SC" (South Carolina)
+\item[\code{year}] an integer vector
+\item[\code{vote}] an integer vector taking on values "1" (Voted) and "0" (Did Not Vote)
+\item[\code{income}] an integer vector ranging from "4" (Less than \$5000) to "17" (Greater than \$75000) denoting family income. See the CPS codebook for more information on variable coding
+\item[\code{education}] an integer vector ranging from "1" (Less than High School Education) to "4" (More than a College Education). See the CPS codebook for more information on variable coding
+\item[\code{age}] an integer vector ranging from "18" to "85"
+\item[\code{female}] an integer vector taking on values "1" (Female) and "0" (Male)
+}
+\end{Format}
+\begin{Source}\relax
+Census Bureau Current Population Survey
+\end{Source}
+\begin{References}\relax
+\url{http://www.census.gov/cps}
+\end{References}
+
+\end{document}
diff --git a/inst/doc/commandsRd/zeligDepStatus.pdf b/inst/doc/commandsRd/zeligDepStatus.pdf
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diff --git a/inst/doc/commandsRd/zeligDepUpdate.tex b/inst/doc/commandsRd/zeligDepUpdate.tex
index 51b09db..b0047dc 100644
--- a/inst/doc/commandsRd/zeligDepUpdate.tex
+++ b/inst/doc/commandsRd/zeligDepUpdate.tex
@@ -9,7 +9,7 @@ installed locally.
Downloads packages that are locally installed with lower versions and
those from the Zelig-matrix that are not installed.
The download repository is taken from either the default
-\code{repos} argument or from the \sQuote{URL} column of
+\code{\var{repos}} argument or from the \sQuote{URL} column of
the Zelig-matrix of dependencies.
\end{Description}
\begin{Usage}
@@ -38,7 +38,7 @@ No return value.
\end{Value}
\begin{Note}\relax
Installs first level dependencies packages of \pkg{Zelig} using R function
-\code{install.packages} with the variable \code{dependencies} set
+\code{install.packages} with the variable \code{\var{dependencies}} set
equal to TRUE. If the installed packages have versions higher than the
corresponding entry in Zelig-matrix, they are reported with a
message. If the R version in the local environment is different from
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diff --git a/inst/doc/coxph.Rnw b/inst/doc/coxph.Rnw
new file mode 100644
index 0000000..8c34d3c
--- /dev/null
+++ b/inst/doc/coxph.Rnw
@@ -0,0 +1,327 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/coxph}
+\include{zinput}
+%\VignetteIndexEntry{Cox Proportional Hazards Regression for Duration Dependent Variables}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,cox proportional hazards}
+%\VignettePackage{Zelig}
+\begin{document}
+
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+
+\section{\texttt{coxph}: Cox Proportional Hazards Regression for Duration Dependent Variables}
+\label{coxph}
+
+Choose the Cox proportional hazards regression model if the values in your dependent
+variable are duration observations. The advantage of the semi-parametric Cox proportional hazards model over fully parametric models such as the exponential or Weibull models is that it makes no assumptions about the shape of the baseline hazard. The model only requires the proportional hazards assumption that the baseline hazard does not vary across observations. The baseline hazard can be estimated from the model via post-hoc analysis.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X1 + X2, model = "coxph", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Cox proportional hazards models require that the dependent variable be in the form {\tt
+ Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example), and the associated $c_i$ is a binary
+variable such that $c_i = 1$ if the duration is not censored ({\it
+ e.g.}, the subject dies during the study) or $c_i = 0$ if the
+duration is censored ({\it e.g.}, the subject is still alive at the
+end of the study). If $c_i$ is omitted, all Y are assumed to be
+completed; that is, $c_i$ defaults to 1 for all observations.
+
+\subsubsection{Stratified Cox Model}
+
+In addition, {\tt zelig()} also supports the stratified Cox model, where the baseline hazards are assumed to be different across different strata but the coefficients are restricted to be the same across strata. Let {\tt id} be a variable that consists of either discrete numeric values, character strings, or factors that define the strata. Then the stratified Cox model can be estimated using {\tt strata()} in the formula. The user can then find quantities of interest for a specific st [...]
+
+\begin{itemize}
+\item If strata were defined by a variable ({\tt strata(id)}), then strata should be defined as {\tt strata = "id=5"}.
+\item If strata were defined by a mathematical expression ({\tt strata(id>10)}), then strata should be defined as {\tt strata = "id>10=TRUE"} or {\tt strata = "id>10=FALSE"}.
+\end{itemize}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y,C) ~ X1 + X2 + strata(id), model = "coxph",
+ data = mydata)
+> x.out <- setx(z.out, strata = "id=5")
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following additional options for Cox proportional hazards regression:
+
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE, zelig()} computes robust standard errors based on sandwich estimators (see \citet{Huber81} and \citet{White80}) based on the options in {\tt cluster}.
+
+\item {\tt cluster}: if {\tt robust = TRUE}, you may select a variable to define groups of correlated observations. Let {\tt X3} be a variable that consists of either discrete numeric values, character strings, or factors that define the clusters. Then
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y,C) ~ X1 + X2, robust = TRUE, cluster = "X3",
+ model = "coxph", data = mydata)
+\end{verbatim}
+
+means that the observations can be correlated within the clusters defined by the variable {\tt X3}, and that robust standard errors should be calculated according to those clusters. If {\tt robust = TRUE} but {\tt cluster} is not specified, {\tt zelig()} assumes that each observation falls into its own cluster.
+
+\item {\tt method}: defaults to {\tt "efron"}. Use this argument to specify how to handle ties within event times. The model assumes that no two event times should theoretically ever be the same, and any ties that occur are simply because the observation mechanism is not precise enough. In practice, ties often exist in the data so the model commonly uses one of three methods to deal with ties.
+
+\begin{itemize}
+
+\item \textbf{Breslow method} ({\tt method = "breslow"}): This method is the simplest computationally but also the least precise, especially as the number of tied events increases.
+
+%Because it is impossible to tell which of the tied events occurred first, the Breslow method simply treats the risk set as the same for all tied events in the risk set. Suppose observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. Theoretically, if the event occurred in 1 before in 3, then the risk set for observation 3 would have dropped observation 1. However, since we cannot tell which event occurred first, in the partial likelihood, the risk set for observat [...]
+%\begin{equation*}
+%L(\beta | y) = \prod_{j=1}^J \frac{\prod_{i \in D_j}\exp(x_i \beta)}{\left[ \sum_{k \in R_j} \exp(x_k \beta)\right] ^{d_j}}
+%\end{equation*}
+%where $j$ indexes the $J$ number of risk sets, $k$ denotes the observations in the risk sets, and $i$ denotes the observations for which the events occur .
+
+\item \textbf{Efron method} ({\tt method = "efron"}): This is the default method and is more intensive computationally but also more precise than the Breslow method.
+
+%The Efron method is more precise because it tries to account for how the risk set changes depending on the sequence of tied events. For an intuition behind the Efron approximation, suppose as in the previous example that observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. If the event occurred in 1 before 3, then the risk set for the second event would consist of observations $\{2, 3, 4\}$. On the other hand, if the event occurred in 3 before 1, then the risk [...]
+%\begin{equation*}
+%L(\beta | y) = \prod_{j=1}^J \left(\frac{\prod_{i \in D_j}\exp(x_i \beta)}{\prod_{r=1}^{d_j} \left[\sum_{k \in R_j} \exp(x_k \beta) - \frac{r-1}{d_j} \sum_{k \in D_j} \exp(x_k \beta)\right] } \right)
+%\end{equation*}
+%where $r$ indexes $D_j$, which is the set of $d_j$ tied events for the $j$th risk set, and all other subscripts are the same as above. In the example above, $D_j = \{1, 3\}$ with size $d_j = 2$ and observation 3 is the $r=2$nd observation in $D_j$.
+
+\item \textbf{Exact discrete method} ({\tt method = "exact"}): This is the preferred method if the number of distinct events is rather small due to a large number of ties. Although it can be very computationally intensive, the exact discrete method, which computes the exact partial likelihood, is the most precise method when there are many ties.
+
+%Unlike the Breslow and Efron methods, which assume a continuous time process, the exact discrete method assumes a discrete time process where the tied events actually do occur at exactly the same time. The method begins by assuming that the data are grouped into risk sets $R_j$. In each risk set, denote a binary dependent variable for each observation that takes on the value of 1 for each observation that experiences the event and 0 for each observation that does not experience the ev [...]
+%\begin{eqnarray*}
+%\mathrm{Pr}(D_j | d_j) = \frac{\prod_{i \in D_j} \exp(x_i \beta)}{\sum_{m=1}^M \left[\prod_{k \in A_{jm}}\exp(x_k \beta)\right] }
+%\end{eqnarray*}
+%where $A_{jm}$ is a set of observations that represents one combination of $d_j$ number of 1s in $R_j$. There are $M$ possible combinations for each risk set. For example, in the case where there are two events among a risk set of four observations $\{1,2,3,4\}$, there are 6 combinations of choosing 2 events out of 4 observations. Therefore, $M = 6$ and $A_{j1} = \{1,2\}, A_{j2} = \{1,3\}, A_{j3} = \{1,4\} ,A_{j4} = \{2,3\}, A_{j5} = \{2,4\}, A_{j6} = \{3,4\}$. The partial likelihoo [...]
+\end{itemize}
+\end{itemize}
+
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item {Example 1: Basic Example}
+
+Attaching the sample dataset:
+<<Example.data>>=
+data(coalition)
+@
+Estimating parameter values for the coxph regression:
+<<Example.zelig>>=
+z.out1 <- zelig(Surv(duration, ciep12) ~ invest + numst2 + crisis, robust = TRUE, cluster = "polar", model = "coxph", data = coalition)
+@
+Setting values for the explanatory variables:
+<<Example.setx>>=
+x.low1<- setx(z.out1, numst2 = 0)
+x.high1 <- setx(z.out1, numst2 = 1)
+@
+Simulating quantities of interest:
+<<Example.sim>>=
+s.out1 <- sim(z.out1, x = x.low1, x1 = x.high1)
+@
+<<Example.summary.sim>>=
+summary(s.out1)
+@
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=true>>=
+plot(s.out1)
+@
+\end{center}
+
+\item {Example 2: Example with Stratified Cox Model}
+
+Estimating parameter values for the stratified coxph regression:
+<<Example2.zelig>>=
+z.out2 <- zelig(Surv(duration, ciep12) ~ invest + strata(polar) + numst2 + crisis, model = "coxph", data = coalition)
+@
+Setting values for the explanatory variables:
+<<Example2.setx>>=
+x.low2 <- setx(z.out2, numst2 = 0, strata = "polar=3")
+x.high2 <- setx(z.out2, numst2 = 1, strata = "polar=3")
+@
+Simulating quantities of interest:
+<<Example2.sim>>=
+s.out2 <- sim(z.out2, x = x.low2, x1 = x.high2)
+@
+<<Example2.summary.sim>>=
+summary(s.out2)
+@
+\begin{center}
+<<label=ExamplePlot2,fig=true,echo=true>>=
+plot(s.out2)
+@
+\end{center}
+\end{enumerate}
+
+
+\subsubsection{The Model}
+Let $Y_i^*$ be the survival time for observation $i$. This variable
+might be censored for some observations at a fixed time $y_c$ such
+that the fully observed dependent variable, $Y_i$, is defined as
+\begin{equation*}
+ Y_i = \left\{ \begin{array}{ll}
+ Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+ y_c & \textrm{if }Y_i^* > y_c
+ \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \textit{stochastic component} is described by the distribution of the partially observed variable $Y^*$:
+\begin{equation*}
+Y_i^* \sim f(y_i^* | \mu_i, \alpha)
+\end{equation*}
+where $f$ is an unspecified distribution with some mean $\mu_i$ and shape $\alpha$. In the Cox proportional hazards model, the distributional form of the duration times is unknown and left unparameterized. Instead it uses the proportional hazards assumption to model the set of (ordered) event times on particular covariates.
+
+An important component of all survival models is the hazard function $h(t)$, which measures the probability of an observation not surviving past time $t$ given survival up to $t$. The hazard function is given by
+\begin{equation*}
+h_i(t) = \lambda(t) \times \lambda_i
+\end{equation*}
+where $\lambda(t)$ is the baseline hazard (when all covariates equal 0), which varies over $t$ but not over $i$, and $\lambda_i$ is the parameterized part of the hazard function, which varies over $i$ but not over $t$ (the proportional hazards assumption).
+
+The model estimates the parameters without a distributional assumption on the duration times by focusing on the occurrence of events and ignoring the time between events. The data are reconceptualized from duration times to $J$ discrete event times such that each $y_i^*$ corresponds to exactly one event time $t_i$. The model assumes that no two $y_i^*$ have the same event times.
+
+For each event time, denote $R_j$ as the set of all observations $k$ that are at risk at $t_i$. Given that an event occurred at $t_i$, we are interested in the conditional probability that the event occurred in observation $i$. The conditional probability is given by
+\begin{eqnarray*}
+\mathrm{Pr}(y_i^* = t_i \mid \mathrm{an \: event \: at \:}t_i) &=& \frac{h_i(t_i)}{\sum_{k \in R_j} h_k(t_i)}\\
+&=& \frac{\lambda(t_i) \, \lambda_i}{\sum_{k \in R_j} \lambda(t_i) \, \lambda_k}\\
+&=& \frac{\lambda_i}{\sum_{k \in R_j} \lambda_k}
+\end{eqnarray*}
+where the numerator denotes the probability of observation $i$ experiencing the event at $t_i$ and the denominator denotes the probability that an event occurred at $t_i$.
+%
+%The model estimates the parameters without a distributional assumption on the duration times by focusing on the occurrence of events and ignoring the time between events. The data are first sorted by $n$ discrete event times (indexed by $j$) such that $t_1 < t_2 < ... < t_n$, where $n$ corresponds to both the number of events and the number of uncensored observations. The model assumes that there are no ties among the $n$ discrete event times. See Additional Inputs for methods to dea [...]
+%
+%Each $y_i^*$ corresponds to one event time $t_j$ at which the event occurs for $i$ (for example, if the 5th observation experienced the shortest duration of 2, then $y_5^* = t_1 = 2$). For each $j$ event time, denote $R_j$ as the set of all observations $k$ that are at risk at time $t_j$. Then for each $t_j$, we are interested in the conditional probability of the event occurring in the \textbf{corresponding} observation $i$ given that an event occurred at $t_j$. Note that for each e [...]
+%\begin{eqnarray*}
+%\mathrm{Pr}(y_i^* = t_j \mid \mathrm{1 \: event \: at \:}t_j) &=& \frac{h_i(t_j)}{\sum_{k \in R_j} h_k(t_j)}\\
+%&=& \frac{\lambda(t_j) \, \lambda_i}{\sum_{k \in R_j} \lambda(t_j) \, \lambda_k}\\
+%&=& \frac{\lambda_i}{\sum_{k \in R_j} \lambda_k}
+%\end{eqnarray*}
+%where the numerator denotes the probability of observation $i$ experiencing the event at $t_j$ and the denominator denotes the probability that an event occurred at $t_j$. Observations that are censored contribute to the denominator for event times prior to its censoring (as part of the risk set) but not the numerator since censored observations do not have corresponding event times. Each $j$ event time contributes one conditional probability to the partial likelihood function, which [...]
+
+\item The \textit{systematic component} $\lambda_i$ is modeled as
+\begin{equation*}
+\lambda_i = \exp(x_i \beta)
+\end{equation*}
+where $x_i$ is the vector of explanatory variables, and $\beta$ is the vector of coefficients.
+
+\item Each risk set (and thus each event time) contributes one conditional probability to the partial likelihood function, given by
+\begin{equation*}
+L(\beta | y) = \prod_{j=1}^J \frac{\exp(x_i \beta)}{\sum_{k \in R_j} \exp(x_k \beta)}
+\end{equation*}
+where $J$ corresponds to the number of risk sets. Note that censored observations are considered part of the risk sets $R_j$ for all event times prior to their censoring, but otherwise do not contribute to the partial likelihood since they do not have corresponding event times. For an example, see \citet[53]{BoxJon04}.
+
+\item Although the model assumes that there are no tied event times, in practice, data often have tied event times due to imprecise measurement. There are three commonly used methods to deal with tied event times.
+\begin{itemize}
+\item \textbf{Breslow method}: The Breslow method simply treats the risk set as the same for all tied events in the risk set. Suppose observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. Theoretically, if the event occurred in 1 before in 3, then the risk set for observation 3 would have dropped observation 1. However, since we cannot tell which event occurred first, in the partial likelihood, the risk set for observation 1 and observation 3 are the same, consis [...]
+\begin{equation*}
+L(\beta | y) = \prod_{j=1}^J \frac{\prod_{i \in D_j}\exp(x_i \beta)}{\left[ \sum_{k \in R_j} \exp(x_k \beta)\right] ^{d_j}}
+\end{equation*}
+where $j$ indexes the $J$ number of risk sets, $k$ denotes the observations in the risk sets, and $i$ denotes the observations for which the events occur .
+
+\item \textbf{Efron method}: The Efron method is more precise because it tries to account for how the risk set changes depending on the sequence of tied events. For an intuition behind the Efron approximation, suppose as in the previous example that observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. If the event occurred in 1 before 3, then the risk set for the second event would consist of observations $\{2, 3, 4\}$. On the other hand, if the event occurred i [...]
+\begin{equation*}
+L(\beta | y) = \prod_{j=1}^J \left(\frac{\prod_{i \in D_j}\exp(x_i \beta)}{\prod_{r=1}^{d_j} \left[\sum_{k \in R_j} \exp(x_k \beta) - \frac{r-1}{d_j} \sum_{k \in D_j} \exp(x_k \beta)\right] } \right)
+\end{equation*}
+where $r$ indexes $D_j$, which is the set of $d_j$ tied events for the $j$th risk set.
+
+\item \textbf{Exact discrete method}: Unlike the Breslow and Efron methods, which assume a continuous time process, the exact discrete method assumes a discrete time process where the tied events actually do occur at exactly the same time. The method begins by assuming that the data are grouped into risk sets $R_j$. In each risk set and for each observation, denote a binary dependent variable which takes on the value of 1 for each observation that experiences the event and 0 for each o [...]
+\begin{eqnarray*}
+\mathrm{Pr}(D_j | d_j) = \frac{\prod_{i \in D_j} \exp(x_i \beta)}{\sum_{m=1}^M \left[\prod_{k \in A_{jm}}\exp(x_k \beta)\right] }
+\end{eqnarray*}
+where $A_{jm}$ is a set of observations that represents one combination of $d_j$ number of 1s in $R_j$. There are $M$ possible combinations for each risk set. The partial likelihood then takes the conditional probability over each $j$ risk set. Note that the exact discrete approximation method is equivalent to a conditional logit model.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The hazard ratio ({\tt qi\$hr}) is defined as
+\begin{equation*}
+\textrm{HR} = \frac{h(t \mid x_1)}{h(t \mid x)} = \frac{\lambda(t) \exp(x_1 \beta)}{\lambda(t) \exp(x \beta)} = \frac{\exp(x_1 \beta)}{\exp(x \beta)}
+\end{equation*}
+given draws of $\beta$ from its sampling distribution, where $x$ and $x_1$ are values of the independent variables chosen by the user. Typically, $x$ and $x_1$ should only differ over one independent variable to interpret the effect of that variable on the hazard rate. In a stratified Cox model, the strata should be the same in both $x$ and $x_1$.
+
+\item The survival function ({\tt qi\$survival}) is defined as the fraction of observations surviving past time $t$. It is derived from the cumulative hazard function ({\tt exp(-cumhaz)}). The confidence interval of the survival function is drawn on the {\tt log(survival)} scale.
+
+\item The cumulative hazard function ({\tt qi\$cumhaz}) is defined as {\tt -log(survival)}. Although there is no direct interpretation, the cumulative hazard function is estimated from the data and then other quantities of interest are derived from the cumulative hazard function.
+
+\item The hazard function ({\tt qi\$hazard}) is defined as the probability of an observation not surviving past time $t$ given survival up to $t$. It is derived directly from the cumulative hazard function.
+
+\item For MI data, if survival times are multiply imputed, we suggest having a larger number of imputed datasets. Because the quantities of interest are derived semi-parametrically, there may be instances in which survival times appear only in one or a small fraction of the multiply imputed datasets, which may bias the results.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(Surv(y,c) \~\, x,
+ model = "coxph", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt var}: the variance-covariance matrix.
+ \item {\tt residuals}: the working residuals of the fit.
+ \item {\tt loglik}: the log-likelihood for the baseline and full models
+ \item {\tt linear.predictors}: a mean-adjusted linear predictor $x_i \beta$, where $x_i = x_i - \mathrm{mean}(x)$.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coef}: the parameter estimates with their
+ associated standard errors, $p$-values, and $z$-statistics.
+ \item {\tt conf.int}: $\exp(\beta)$ and their associated confidence intervals.
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$hr}: the simulated hazard ratios for the
+ specified values of {\tt x} and {\tt x1}.
+ \item {\tt qi\$survival}: the estimated survival function for the values specified in {\tt x}.
+ \item {\tt qi\$cumhaz}: the estimated cumulative hazard function for the values specified in {\tt x}.
+ \item {\tt qi\$hazard}: the estimated hazard function for the values specified in {\tt x}.
+ \end{itemize}
+\end{itemize}
+
+\subsection*{How To Cite}
+
+To cite the \emph{coxph} Zelig model:
+\begin{verse}
+Patrick Lam. 2007. ``coxph: Cox Proportional Hazards Regression for Duration Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+
+\end{verse}
+\input{citeZelig}
+
+\subsection*{See also}
+
+The Cox proportional hazards model is part of the survival library by Terry Therneau \citep{TheGra00}, ported to R by Thomas Lumley. Advanced users may wish to refer to {\tt help(coxph)} and {\tt help(survfit)} in the survival library. Sample data are from \citet{KinAltBurLav90}
+
+\bibliographystyle{asa}
+\bibliography{gk}
+
+<<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+
+
+
+\end{document}
diff --git a/inst/doc/coxph.pdf b/inst/doc/coxph.pdf
new file mode 100644
index 0000000..05d03d2
Binary files /dev/null and b/inst/doc/coxph.pdf differ
diff --git a/inst/doc/ei.RxC.Rnw b/inst/doc/ei.RxC.Rnw
index 841188b..2f98993 100644
--- a/inst/doc/ei.RxC.Rnw
+++ b/inst/doc/ei.RxC.Rnw
@@ -67,7 +67,7 @@ Estimating the model:
Estimate fractions of different social groups that support political parties:
<<ExampleNoCov.sim>>=
- s.out <- sim(z.out)
+ s.out <- sim(z.out, num = 10)
@
Summarizing fractions of different social groups that support political parties:
@@ -94,14 +94,16 @@ Set the covariate to its default (mean/median) value
@
Estimate fractions of different social groups that support political parties:
-<<ExampleCov.sim>>=
- s.out <- sim(z.out)
-@
+
+\begin{verbatim}
+> s.out <- sim(z.out, num = 100)
+\end{verbatim}
Summarizing fractions of different social groups that support political parties:
-<<ExampleCov.summary>>=
- summary(s.out)
-@
+\begin{verbatim}
+> s.out <- summary(s.out)
+\end{verbatim}
+
\end{enumerate}
\clearpage
@@ -208,7 +210,7 @@ view. For example, if you run
\subsection*{How to Cite}
To cite the \emph{ei.RxC} Zelig model use:
\begin{verse}
-Jason Wittenberg, Ferdinand Alimadhi, and Olivia Lau. 2007. ``ei.RxC:Hierarchical Multinomial-Dirichlet Ecological Inference Model,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Jason Wittenberg, Ferdinand Alimadhi, Badri Narayan Bhaskar, and Olivia Lau. 2007. ``ei.RxC:Hierarchical Multinomial-Dirichlet Ecological Inference Model,'' 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}
diff --git a/inst/doc/ei.RxC.pdf b/inst/doc/ei.RxC.pdf
index 050677e..69c4039 100644
Binary files a/inst/doc/ei.RxC.pdf and b/inst/doc/ei.RxC.pdf differ
diff --git a/inst/doc/ei.dynamic.pdf b/inst/doc/ei.dynamic.pdf
index 143c637..469805d 100644
Binary files a/inst/doc/ei.dynamic.pdf and b/inst/doc/ei.dynamic.pdf differ
diff --git a/inst/doc/ei.hier.pdf b/inst/doc/ei.hier.pdf
index 0b5808a..24eb12e 100644
Binary files a/inst/doc/ei.hier.pdf and b/inst/doc/ei.hier.pdf differ
diff --git a/inst/doc/exp.pdf b/inst/doc/exp.pdf
index 9dafabe..356e788 100644
Binary files a/inst/doc/exp.pdf and b/inst/doc/exp.pdf differ
diff --git a/inst/doc/factor.bayes.pdf b/inst/doc/factor.bayes.pdf
index 9ade6a6..f4bfe2f 100644
Binary files a/inst/doc/factor.bayes.pdf and b/inst/doc/factor.bayes.pdf differ
diff --git a/inst/doc/factor.mix.pdf b/inst/doc/factor.mix.pdf
index 8012c3f..38d7c13 100644
Binary files a/inst/doc/factor.mix.pdf and b/inst/doc/factor.mix.pdf differ
diff --git a/inst/doc/factor.ord.pdf b/inst/doc/factor.ord.pdf
index 710db62..1cfa72b 100644
Binary files a/inst/doc/factor.ord.pdf and b/inst/doc/factor.ord.pdf differ
diff --git a/inst/doc/gamma.Rnw b/inst/doc/gamma.Rnw
index ef9d2f2..67b736c 100644
--- a/inst/doc/gamma.Rnw
+++ b/inst/doc/gamma.Rnw
@@ -222,8 +222,13 @@ Kosuke Imai, Gary King, and Olivia Lau. 2007. ``gamma: Gamma Regression for Cont
\end{verse}
\input{citeZelig}
+
\subsection* {See also}
-The gamma model is part of the base packages by William N. Venable and Brian D. Ripley \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are from \cite{KinAltBur90}.
+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}. Robust standard
+errors are implemented via the sandwich package by \citet{Zeileis04}.
+Sample data are from \cite{KinTomWit00}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/logit.gee.Rnw b/inst/doc/gamma.gee.Rnw
similarity index 57%
copy from inst/doc/logit.gee.Rnw
copy to inst/doc/gamma.gee.Rnw
index 84c1798..a367f58 100644
--- a/inst/doc/logit.gee.Rnw
+++ b/inst/doc/gamma.gee.Rnw
@@ -1,37 +1,36 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/logit_gee}
+\SweaveOpts{results=hide, prefix.string=vigpics/gamma_gee}
\include{zinput}
-%\VignetteIndexEntry{logit.gee: Generalized Estimating Equation for Logistic Regression}
+%\VignetteIndexEntry{Generalized Estimating Equation for Gamma Regression}
%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, logistic regression}
+%\VignetteKeyWords{model,generalized estimating equation, gamma regression}
%\VignettePackage{Zelig}
\begin{document}
+
\nobibliography*
<<beforepkgs, echo=FALSE>>=
before=search()
@
-
<<loadLibrary, echo=F,results=hide>>=
library(Zelig)
@
-\section{\texttt{logit.gee}: Generalized Estimating Equation for Logistic Regression}
-\label{logit.gee}
+\section{\texttt{gamma.gee}: Generalized Estimating Equation for Gamma Regression}
+\label{gamma.gee}
-The GEE logit estimates the same model as the standard logistic
-regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables). Unlike in logistic
-regression, GEE logit allows for dependence within clusters, such as
+The GEE gamma is similar to standard gamma
+regression (appropriate when you have an uncensored, positive-valued, continuous dependent variable such as the time until a parliamentary cabinet falls). Unlike in gamma
+regression, GEE gamma allows for dependence within clusters, such as
in longitudinal data, although its use is not limited to just
-panel data. The user must first specify a ``working''
+panel data. GEE models make no distributional assumptions but require three specifications: a mean function, a variance function, and a ``working''
correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
the standard errors can be obtained using a robust ``sandwich''
estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters.
+specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit.gee",
+> z.out <- zelig(Y ~ X1 + X2, model = "gamma.gee",
id = "X3", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
@@ -50,7 +49,7 @@ Use the following arguments to specify the structure of the ``working'' correlat
\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
\begin{itemize}
\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard logistic regression. The ``working'' correlation matrix is the identity matrix.
+ cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard gamma regression. The ``working'' correlation matrix is the identity matrix.
\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
@@ -130,125 +129,77 @@ $\left( \begin{array}{ccccc}
\subsubsection{Examples}
\begin{enumerate}
-\item {Example with Stationary 3 Dependence}
+\item {Example with Exchangeable Dependence}
Attaching the sample turnout dataset:
<<Example.data>>=
-data(turnout)
+data(coalition)
@
-Variable identifying clusters
+Sorted variable identifying clusters
<<Example.cluster>>=
-turnout$cluster <- rep(c(1:200),10)
-@
-Sorting by cluster
-<<Example.sort>>=
-sorted.turnout <- turnout[order(turnout$cluster),]
+coalition$cluster <- c(rep(c(1:62),5),rep(c(63),4))
+sorted.coalition <- coalition[order(coalition$cluster),]
@
-Estimating parameter values for the logistic regression:
+Estimating model and presenting summary:
<<Example.zelig>>=
-z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3)
+z.out <- zelig(duration ~ fract + numst2, model = "gamma.gee", id = "cluster", data = sorted.coalition, robust=TRUE, corstr="exchangeable")
+summary(z.out)
@
-Setting values for the explanatory variables to their default values:
+Setting the explanatory variables at their default values
+(mode for factor variables and mean for non-factor variables),
+with numst2 set to the vector 0 = no crisis, 1 = crisis.
<<Example.setx>>=
-x.out1 <- setx(z.out1)
+x.low <- setx(z.out, numst2 = 0)
+x.high <- setx(z.out, numst2 = 1)
@
-Simulating quantities of interest from the posterior distribution.
+Simulate quantities of interest
<<Example.sim>>=
-s.out1 <- sim(z.out1, x = x.out1)
-@
-<<Example.summary.sim>>=
-summary(s.out1)
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+summary(s.out)
@
+Generate a plot of quantities of interest:
\begin{center}
<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out1)
+plot(s.out)
@
\end{center}
-
-\item {Simulating First Differences}
-
-Estimating the risk difference (and risk ratio) between low education
-(25th percentile) and high education (75th percentile) while all the
-other variables held at their default values.
-
-<<FirstDifference.setx>>=
-x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
-x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
-@
-
-<<FirstDifference.sim>>=
-s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
-@
-<<FirstDifference.summary.sim>>=
-summary(s.out2)
-@
-\begin{center}
-<<label=FirstDifferencePlot,fig=true,echo=true>>=
-plot(s.out2)
-@
-\end{center}
-
-\item Example with Fixed Correlation Structure
-
-User-defined correlation structure
-<<Example2.corr>>=
-corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
-diag(corr.mat) <- 1
-@
-Generating empirical estimates:
-<<Example2.zelig>>=
-z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat)
-@
-Viewing the regression output:
-<<Example2.summary>>=
-summary(z.out2)
-@
\end{enumerate}
\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the binary
-dependent variable for unit $i$ at time $t$ which takes the value of
-either 0 or 1. $Y_{i}$ is a vector or cluster of correlated data
+Suppose we have a panel dataset, with $Y_{it}$ denoting the positive-valued, continuous dependent variable for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. In the GEE model, we must specify a mean function, a
-variance function, and the structure of the correlation matrix of the within-cluster
-observations. Observations are assumed to be correlated within each
-cluster but not across clusters.
-
+$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
\begin{itemize}
-\item The \emph{stochastic component} is given by the joint distribution
-
+\item The \emph{stochastic component} is given by the joint and marginal distributions
\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \pi_{i})
+Y_{i} &\sim& f(y_{i} \mid \lambda_{i})\\
+Y_{it} &\sim& g(y_{it} \mid \lambda_{it})
\end{eqnarray*}
-where $f$ is an unspecified multivariate distribution. The marginal distributions of $f$, which characterize each nonindependent unit-time observation $Y_{it}$, is given by
-\begin{eqnarray*}
-Y_{it} &\sim& \mathrm{Bernoulli}(y_{it} \mid \pi_{it}) \\
- &=& \pi_{it}^{y_{it}} (1-\pi_{it})^{1-y_{it}}
-\end{eqnarray*}
-where $\pi_{it}=\Pr(Y_{it}=1)$ for $t=1,...,T$. The correlations
-within each unit $i$ are modeled by defining the structure of a $T \times T$
-``working'' correlation matrix, which is specified by the user
-\textit{a priori}. Note that the model assumes correlations within $i$ but independence across $i$. The ``working'' correlation matrix then enters
-the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
+where $f$ and $g$ are unspecified distributions with means $\lambda_{i}$ and $\lambda_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-\item The \emph{systematic component} is given by:
+\item The \emph{systematic component} is the \textit{mean function}, given by:
\begin{equation*}
-\pi_{it} \; = \; \frac{1}{1 + \exp(-x_{it} \beta)}.
+\lambda_{it} = \frac{1}{x_{it} \beta}
\end{equation*}
where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
and $\beta$ is the vector of coefficients.
+\item The \textit{variance function} is given by:
+\begin{equation*}
+V_{it} = \lambda_{it}^2 = \frac{1}{(x_{it} \beta)^2}
+\end{equation*}
+
+\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
+\begin{equation*}
+V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
+\end{equation*}
+where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = \lambda_{it}^2$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-\item GEE models require three specifications: a mean function (the systematic component above), a variance function (given by the variance of the Bernoulli stochastic component: $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$), and a correlation structure (the ``working'' correlation matrix above). If the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as [...]
+\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{fitlaiwar04} for more details.
+\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
\end{itemize}
@@ -257,25 +208,17 @@ and $\beta$ is the vector of coefficients.
\subsubsection{Quantities of Interest}
\begin{itemize}
\item All quantities of interest are for marginal means rather than joint means.
-\item The expected values ({\tt qi\$ev}) for the logit model are
- simulations of the predicted probability of a success: $$E(Y) =
- \pi_{c}= \frac{1}{1 + \exp(-x_{c} \beta)},$$ given draws of $\beta$ from
+\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
+\item The expected values ({\tt qi\$ev}) for the GEE gamma model is the mean: $$E(Y) =
+ \lambda_{c} = \frac{1}{x_c \beta},$$ given draws of $\beta$ from
its sampling distribution, where $x_{c}$ is a vector of values, one for
each independent variable, chosen by the user.
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
- distribution with mean equal to the simulated expected value $\pi_{c}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\item The first difference ({\tt qi\$fd}) for the GEE gamma model is defined as
\begin{equation*}
\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
\end{equation*}
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
-\end{equation*}
-
\item In conditional prediction models, the average expected treatment
effect ({\tt att.ev}) for the treatment group is
\begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
@@ -288,25 +231,13 @@ each independent variable, chosen by the user.
treatment group, under the assumption that everything stays the
same except that the treatment indicator is switched to $tr_{it}=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 \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- \widehat{Y_{it}(tr_{it}=0)}\right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the
- treatment ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_{it}(tr_{it}=0)}$, the counterfactual predicted value of
- $Y_{it}$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $tr_{it}=0$.
\end{itemize}
\subsubsection{Output Values}
The output of each Zelig command contains useful information which you
may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "logit.gee", id, data, corstr)}, then you may examine the available
+ model = "gamma.gee", id, data)}, then you may examine the available
information in \texttt{z.out} by using \texttt{names(z.out)},
see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
a default summary of information through \texttt{summary(z.out)}.
@@ -322,7 +253,7 @@ below.
\item {\tt residuals}: the working residuals in the final iteration
of the fit.
\item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_{it}$.
+ systemic component.
\item {\tt linear.predictors}: the vector of $x_{it}\beta$
\item {\tt max.id}: the size of the largest cluster.
\end{itemize}
@@ -340,39 +271,35 @@ below.
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
+ \item {\tt qi\$ev}: the simulated expected values for the
specified values of {\tt x}.
\item {\tt qi\$fd}: the simulated first difference in the expected
probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$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}
-\subsection* {How to Cite}
+\subsection*{How To Cite}
-To cite the \emph{logit.gee} Zelig model:
+To cite the \emph{gamma.gee} Zelig model:
\begin{verse}
-Patrick Lam. 2007. ``logit.gee: Generalized Estimating Equation for Logistic Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Patrick Lam. 2007. ``gamma.gee: Generalized Estimating Equation for Gamma Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
-\input{citeZelig}
+%\input{citeZelig}
-\subsection* {See also}
-The logit function is part of the base packages by William N. Venable \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are a selection of $2,000$ observations from \cite{KinTomWit00}
+\subsection*{See also}
+The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinAltBur90}.
\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
+\bibliography{gk}
+
<<afterpkgs, echo=FALSE>>=
after<-search()
torm<-setdiff(after,before)
for (pkg in torm)
detach(pos=match(pkg,search()))
@
+
\end{document}
diff --git a/inst/doc/gamma.gee.pdf b/inst/doc/gamma.gee.pdf
new file mode 100644
index 0000000..3b11b98
Binary files /dev/null and b/inst/doc/gamma.gee.pdf differ
diff --git a/inst/doc/gamma.mixed.Rnw b/inst/doc/gamma.mixed.Rnw
new file mode 100644
index 0000000..cfcd599
--- /dev/null
+++ b/inst/doc/gamma.mixed.Rnw
@@ -0,0 +1,188 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/gammamixed}
+\include{zinput}
+%\VignetteIndexEntry{Gamma mixed effects linear regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,linear, linear regression, gamma}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt gamma.mixed}: Mixed effects gamma regression}
+\label{gamma.mixed}
+
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. Gamma regression models a continuous, positive dependent variable.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="gamma.mixed")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, delta | g),
+ delta= ~ tag(w1 + w2 | g)), data=mydata, model="gamma.mixed")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, delta | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
+\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
+\end{itemize}
+Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(coalition2)
+@
+
+Estimate model using optional arguments to specify approximation method for the log-likelihood, and the log link function for the Gamma family:
+<<Examples.zelig>>=
+z.out1 <- zelig(duration ~ invest + fract + polar + numst2 + crisis + tag(1 | country), data=coalition2, model="gamma.mixed", method="PQL",family=Gamma(link=log))
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+
+Set the baseline values (with the ruling coalition in the minority) and the alternative values (with the ruling coalition in the majority) for X:\\
+<<Examples.setx>>=
+x.high <- setx(z.out1, numst2 = 1)
+x.low <- setx(z.out1, numst2 = 0)
+@
+
+Simulate expected values ({\tt qi\$ev}) and first differences({\tt qi\$fd}): \\
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+@
+
+\end{enumerate}
+
+\subsubsection{Mixed effects gamma regression Model}
+
+Let $Y_{ij}$ be the continuous, positive dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a Gamma model with scale parameter $\alpha$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Gamma}(y_{ij} | \lambda_{ij}, \alpha)
+\end{equation*}
+where
+\begin{equation*}
+Gamma(y_{ij} | \lambda_{ij}, \alpha) = \frac{1}{\alpha^{\lambda_{ij}} \Gamma \lambda_{ij}} y_{ij}^{\lambda_{ij} - 1} \exp (- \{ \frac{y_{ij}}{\alpha} \})
+\end{equation*}
+for $\alpha, \; \lambda_{ij}, \; y_{ij} \; > 0$.
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\lambda_{ij} \equiv \frac{1}{X_{ij} \beta + Z_{ij} b_i}
+\end{equation*}
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The predicted values ({\tt qi\$pr}) are draws from the gamma distribution for each given set of parameters $(\alpha, \lambda_{ij})$, for
+\begin{equation*}
+\lambda_{ij} = \frac{1}{X_{ij} \beta + Z_{ij} b_i}
+\end{equation*}
+given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+
+\item The expected values ({\tt qi\$ev}) are simulations of the mean of the stochastic component given draws of $\alpha$, $\beta$ from their posteriors:
+\begin{equation*}
+E(Y_{ij} | X_{ij}) = \alpha \lambda_{ij} = \frac{\alpha}{X_{ij} \beta}.
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in expected values, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = E(Y_{ij} | X_{ij}) - E(Y_{ij} | X_{ij}^\prime)
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+\subsection* {How to Cite}
+
+To cite the \emph{gamma.mixed} Zelig model:
+\begin{verse}
+Delia Bailey, Ferdinand Alimadhi. 2007. ``gamma.mixed: Mixed effects gamma regression'' 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}
+Mixed effects gamma regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+<<afterpkgs, echo=FALSE>>=
+ after <- search()
+ torm <- setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+\end{document}
+
+
+
diff --git a/inst/doc/gamma.mixed.pdf b/inst/doc/gamma.mixed.pdf
new file mode 100644
index 0000000..27c29ba
Binary files /dev/null and b/inst/doc/gamma.mixed.pdf differ
diff --git a/inst/doc/netgamma.Rnw b/inst/doc/gamma.net.Rnw
similarity index 76%
rename from inst/doc/netgamma.Rnw
rename to inst/doc/gamma.net.Rnw
index 94d1d15..420c463 100644
--- a/inst/doc/netgamma.Rnw
+++ b/inst/doc/gamma.net.Rnw
@@ -1,4 +1,4 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/netgamma}
+\SweaveOpts{results=hide, prefix.string=vigpics/gammanet}
\include{zinput}
%\VignetteIndexEntry{Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables}
%\VignetteDepends{Zelig, stats}
@@ -13,13 +13,13 @@
library(Zelig)
@
-\section{{\tt netgamma}: Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables}
+\section{{\tt gamma.net}: Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables}
Use the network gamma regression model if you have a positive-valued dependent variable that is a binary valued proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs). The gamma distribution assumes that all waiting times are complete by the end of the study (censoring is not allowed).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netgamma", data = mydata)
+> z.out <- zelig(y ~ x1 + x2, model = "gamma.net", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -47,7 +47,7 @@ data(friendship)
Estimate model:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(per ~ perpower, LF="inverse", model="netgamma", data=friendship)
+z.out <- zelig(per ~ perpower, LF="inverse", model="gamma.net", data=friendship)
summary(z.out)
@
@@ -98,7 +98,7 @@ plot(s.out)
\subsubsection{Model}
-The {\tt netgamma} model performs a gamma regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard gamma regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the $\ma [...]
+The {\tt gamma.net} model performs a gamma regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard gamma regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the $\m [...]
Let $Y_{i}$ be the dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$.
\begin{itemize}
@@ -135,7 +135,7 @@ FD = \text{Pr}(Y | x_{1}) - \text{Pr}(Y | x)
\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "netgamma", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "gamma.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -166,14 +166,14 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{netgamma} Zelig model:
+To cite the \emph{gamma.net} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``netgamma: Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``gamma.net: Network Gamma Regression for Continuous, Positive Proximity Matrix 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}
-The network gamma regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netgamma)}. Sample data are fictional.
+The network gamma regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(gamma.net)}. Sample data are fictional.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/gamma.net.pdf b/inst/doc/gamma.net.pdf
new file mode 100644
index 0000000..67b73c5
Binary files /dev/null and b/inst/doc/gamma.net.pdf differ
diff --git a/inst/doc/gamma.pdf b/inst/doc/gamma.pdf
index 1cd1b1b..9db310b 100644
Binary files a/inst/doc/gamma.pdf and b/inst/doc/gamma.pdf differ
diff --git a/inst/doc/index.shtml b/inst/doc/index.shtml
index e6f53bb..c7c832a 100644
--- a/inst/doc/index.shtml
+++ b/inst/doc/index.shtml
@@ -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:3.0-6</b>
+<b>Version:3.1-0</b>
<table border='0' cellpadding='0' cellspacing='8'><tr><td valign='top' width='200'>
<p !style="float:left; width: 200; margin: 1.5em;"> <a
diff --git a/inst/doc/install.tex b/inst/doc/install.tex
index f812ccf..1204dfc 100644
--- a/inst/doc/install.tex
+++ b/inst/doc/install.tex
@@ -392,7 +392,7 @@ This will load Zelig every time you start R.
% directory path. If you don't specify a path, the default value is
% {\tt \~\,/.R/library}.
\item Download the latest bundles for Unix from the \hlink{Zelig
- website}{http://gking.harvard.edu/R/CRAN/src/contrib/}, and (for
+ website}{http://gking.harvard.edu/src/contrib/}, and (for
the VGAM, MCMCpack, coda, sandwich, and zoo packages) from the
\hlink{CRAN}{http://cran.r-project.org/src/contrib/PACKAGES.html}
website.
diff --git a/inst/doc/irt1d.pdf b/inst/doc/irt1d.pdf
index 75e55b2..b072528 100644
Binary files a/inst/doc/irt1d.pdf and b/inst/doc/irt1d.pdf differ
diff --git a/inst/doc/irtkd.pdf b/inst/doc/irtkd.pdf
index 39a1ed0..5c1c6ea 100644
Binary files a/inst/doc/irtkd.pdf and b/inst/doc/irtkd.pdf differ
diff --git a/inst/doc/logit.Rnw b/inst/doc/logit.Rnw
index 2afef82..e5951e1 100644
--- a/inst/doc/logit.Rnw
+++ b/inst/doc/logit.Rnw
@@ -73,11 +73,11 @@ Attaching the sample turnout dataset:
@
Estimating parameter values for the logistic regression:
<<Example.zelig>>=
- z.out1 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+ z.out1 <- zelig(vote ~ age + race, model = "logit", data = turnout)
@
-Setting values for the explanatory variables to their default values:
+Setting values for the explanatory variables:
<<Example.setx>>=
- x.out1 <- setx(z.out1)
+ x.out1 <- setx(z.out1, age = 36, race = "white")
@
Simulating quantities of interest from the posterior distribution.
<<Example.sim>>=
@@ -98,12 +98,13 @@ Estimating the risk difference (and risk ratio) between low education
(25th percentile) and high education (75th percentile) while all the
other variables held at their default values.
<<FirstDifferences.setx>>=
- x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
- x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
+ z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+ x.high <- setx(z.out2, educate = quantile(turnout$educate, prob = 0.75))
+ x.low <- setx(z.out2, educate = quantile(turnout$educate, prob = 0.25))
@
<<FirstDifferences.sim>>=
- s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
+ s.out2 <- sim(z.out2, x = x.high, x1 = x.low)
@
<<FirstDifferences.summary>>=
summary(s.out2)
@@ -268,8 +269,14 @@ Kosuke Imai, Gary King, and Olivia Lau. 2007. ``logit: Logistic Regression for D
\end{verse}
\input{citeZelig}
-\subsection* {See also}
-The gamma model is part of the base packages by William N. Venable and Brian D. Ripley \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are from \cite{KinTomWit00}.
+
+\subsection*{See also}
+
+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}. Robust standard
+errors are implemented via the sandwich package by \citet{Zeileis04}.
+Sample data are from \cite{KinTomWit00}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/logit.bayes.pdf b/inst/doc/logit.bayes.pdf
index bed9467..a88d20f 100644
Binary files a/inst/doc/logit.bayes.pdf and b/inst/doc/logit.bayes.pdf differ
diff --git a/inst/doc/gam.logit.Rnw b/inst/doc/logit.gam.Rnw
similarity index 92%
rename from inst/doc/gam.logit.Rnw
rename to inst/doc/logit.gam.Rnw
index f1a76d3..c359de5 100644
--- a/inst/doc/gam.logit.Rnw
+++ b/inst/doc/logit.gam.Rnw
@@ -2,7 +2,7 @@
\include{zinput}
%\VignetteIndexEntry{Generalized Additive Model for Dichotomous Dependent Variables}
%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, gam.logit, gam}
+%\VignetteKeyWords{model, logit.gam, gam}
%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
@@ -14,13 +14,13 @@ library(Zelig)
@
-\section{{\tt gam.logit}: Generalized Additive Model for Dichotomous Dependent Variables}\label{gam.logit}
+\section{{\tt logit.gam}: Generalized Additive Model for Dichotomous Dependent Variables}\label{gam.logit}
This function runs a nonparametric Generalized Additive Model (GAM) for dichotomous dependent variables.
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + s(x2), model = "gam.logit", data = mydata)
+> z.out <- zelig(y ~ x1 + s(x2), model = "logit.gam", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -34,7 +34,7 @@ In addition to the standard inputs, {\tt zelig()} takes the following additional
\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
\item {\tt sp}: A vector of smoothing parameters for each term.
-\item {\tt \ldots}: additional options passed to the {\tt gam.logit} model. See the {\tt mgcv} library for details.
+\item {\tt \ldots}: additional options passed to the {\tt logit.gam} model. See the {\tt mgcv} library for details.
\end{itemize}
@@ -80,7 +80,7 @@ my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
Estimate the model, summarize the results, and plot nonlinearities:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.logit", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="logit.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1,residuals=TRUE)
@
@@ -138,7 +138,7 @@ plot(s.out)
An extra ridge penalty (useful with convergence problems):
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
- model="gam.logit", data=my.data)
+ model="logit.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1,residuals=TRUE)
@@ -146,7 +146,7 @@ plot(z.out,pages=1,residuals=TRUE)
Set the smoothing parameter for the first term, estimate the rest:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
- model="gam.logit", data=my.data)
+ model="logit.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1)
@@ -154,7 +154,7 @@ plot(z.out,pages=1)
Set lower bounds on smoothing parameters:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
- model="gam.logit", data=my.data)
+ model="logit.gam", data=my.data)
summary(z.out)
plot(z.out, pages=1)
@@ -162,7 +162,7 @@ plot(z.out, pages=1)
A GAM with 3df regression spline term \& 2 penalized terms:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
- model="gam.logit", data=my.data)
+ model="logit.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1)
@@ -174,7 +174,7 @@ plot(z.out,pages=1)
\subsubsection{Model}
-GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt gam.logit} a logistic link function is used. Specifically, let $Y_{i}$ be the binary dependent variable for observation $i$ which takes the value of either 0 or 1.
+GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt logit.gam} a logistic link function is used. Specifically, let $Y_{i}$ be the binary dependent variable for observation $i$ which takes the value of either 0 or 1.
\begin{itemize}
\item The logistic distribution has \emph{stochastic component}
\begin{eqnarray*}
@@ -209,9 +209,9 @@ Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
\subsubsection{Quantities of Interest}
-The quantities of interest for the {\tt gam.logit} model are the same as those for the standard logistic regression.
+The quantities of interest for the {\tt logit.gam} model are the same as those for the standard logistic regression.
\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt gam.logit} model is the mean of simulations from the stochastic component,
+\item The expected value ({\tt qi\$ev}) for the {\tt logit.gam} model is the mean of simulations from the stochastic component,
\begin{equation*}
\pi_{i} = \frac{1}{1 + \exp \left(-x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right)},
\end{equation*}
@@ -219,7 +219,7 @@ The quantities of interest for the {\tt gam.logit} model are the same as those f
\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 GAM logit model is defined as
+\item The first difference ({\tt qi\$fd}) for the logit.gam model is defined as
\begin{equation*}
FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
\end{equation*}
@@ -232,7 +232,7 @@ for $w=\{X, Z\}$.
-The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "gam.logit", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "logit.gam", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -272,14 +272,14 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{gam.logit} Zelig model:
+To cite the \emph{logit.gam} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``gam.logit: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``logit.gam: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
\input{citeZelig}
\subsection* {See also}
-The {\tt gam.logit} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
+The {\tt logit.gam} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/gam.logit.pdf b/inst/doc/logit.gam.pdf
similarity index 83%
rename from inst/doc/gam.logit.pdf
rename to inst/doc/logit.gam.pdf
index d816943..8dfffa7 100644
Binary files a/inst/doc/gam.logit.pdf and b/inst/doc/logit.gam.pdf differ
diff --git a/inst/doc/logit.gee.Rnw b/inst/doc/logit.gee.Rnw
index 84c1798..e160a99 100644
--- a/inst/doc/logit.gee.Rnw
+++ b/inst/doc/logit.gee.Rnw
@@ -1,15 +1,15 @@
\SweaveOpts{results=hide, prefix.string=vigpics/logit_gee}
\include{zinput}
-%\VignetteIndexEntry{logit.gee: Generalized Estimating Equation for Logistic Regression}
+%\VignetteIndexEntry{Generalized Estimating Equation for Logistic Regression}
%\VignetteDepends{Zelig, gee}
%\VignetteKeyWords{model,generalized estimating equation, logistic regression}
%\VignettePackage{Zelig}
\begin{document}
+
\nobibliography*
<<beforepkgs, echo=FALSE>>=
before=search()
@
-
<<loadLibrary, echo=F,results=hide>>=
library(Zelig)
@
@@ -26,7 +26,7 @@ panel data. The user must first specify a ``working''
correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
the standard errors can be obtained using a robust ``sandwich''
estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters.
+specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
\subsubsection{Syntax}
@@ -152,7 +152,7 @@ Setting values for the explanatory variables to their default values:
<<Example.setx>>=
x.out1 <- setx(z.out1)
@
-Simulating quantities of interest from the posterior distribution.
+Simulating quantities of interest:
<<Example.sim>>=
s.out1 <- sim(z.out1, x = x.out1)
@
@@ -206,67 +206,54 @@ summary(z.out2)
\end{enumerate}
\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the binary
-dependent variable for unit $i$ at time $t$ which takes the value of
-either 0 or 1. $Y_{i}$ is a vector or cluster of correlated data
+Suppose we have a panel dataset, with $Y_{it}$ denoting the binary dependent variable for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. In the GEE model, we must specify a mean function, a
-variance function, and the structure of the correlation matrix of the within-cluster
-observations. Observations are assumed to be correlated within each
-cluster but not across clusters.
-
+$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
\begin{itemize}
-\item The \emph{stochastic component} is given by the joint distribution
-
-\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \pi_{i})
-\end{eqnarray*}
-where $f$ is an unspecified multivariate distribution. The marginal distributions of $f$, which characterize each nonindependent unit-time observation $Y_{it}$, is given by
+\item The \emph{stochastic component} is given by the joint and marginal distributions
\begin{eqnarray*}
-Y_{it} &\sim& \mathrm{Bernoulli}(y_{it} \mid \pi_{it}) \\
- &=& \pi_{it}^{y_{it}} (1-\pi_{it})^{1-y_{it}}
+Y_{i} &\sim& f(y_{i} \mid \pi_{i})\\
+Y_{it} &\sim& g(y_{it} \mid \pi_{it})
\end{eqnarray*}
-where $\pi_{it}=\Pr(Y_{it}=1)$ for $t=1,...,T$. The correlations
-within each unit $i$ are modeled by defining the structure of a $T \times T$
-``working'' correlation matrix, which is specified by the user
-\textit{a priori}. Note that the model assumes correlations within $i$ but independence across $i$. The ``working'' correlation matrix then enters
-the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
+where $f$ and $g$ are unspecified distributions with means $\pi_{i}$ and $\pi_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-\item The \emph{systematic component} is given by:
+\item The \emph{systematic component} is the \textit{mean function}, given by:
\begin{equation*}
-\pi_{it} \; = \; \frac{1}{1 + \exp(-x_{it} \beta)}.
+\pi_{it} = \frac{1}{1 + \exp(-x_{it} \beta)}
\end{equation*}
-where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
+where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
and $\beta$ is the vector of coefficients.
+\item The \textit{variance function} is given by:
+\begin{equation*}
+V_{it} = \pi_{it} (1-\pi_{it})
+\end{equation*}
-\item GEE models require three specifications: a mean function (the systematic component above), a variance function (given by the variance of the Bernoulli stochastic component: $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$), and a correlation structure (the ``working'' correlation matrix above). If the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as [...]
-
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{fitlaiwar04} for more details.
+\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
+\begin{equation*}
+V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
+\end{equation*}
+where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-\end{itemize}
+\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
+\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
+\end{itemize}
\subsubsection{Quantities of Interest}
\begin{itemize}
\item All quantities of interest are for marginal means rather than joint means.
-\item The expected values ({\tt qi\$ev}) for the logit model are
+\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
+\item The expected values ({\tt qi\$ev}) for the GEE logit model are
simulations of the predicted probability of a success: $$E(Y) =
\pi_{c}= \frac{1}{1 + \exp(-x_{c} \beta)},$$ given draws of $\beta$ from
its sampling distribution, where $x_{c}$ is a vector of values, one for
each independent variable, chosen by the user.
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
- distribution with mean equal to the simulated expected value $\pi_{c}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\item The first difference ({\tt qi\$fd}) for the GEE logit model is defined as
\begin{equation*}
\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
\end{equation*}
@@ -288,25 +275,13 @@ each independent variable, chosen by the user.
treatment group, under the assumption that everything stays the
same except that the treatment indicator is switched to $tr_{it}=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 \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- \widehat{Y_{it}(tr_{it}=0)}\right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the
- treatment ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_{it}(tr_{it}=0)}$, the counterfactual predicted value of
- $Y_{it}$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $tr_{it}=0$.
\end{itemize}
\subsubsection{Output Values}
The output of each Zelig command contains useful information which you
may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "logit.gee", id, data, corstr)}, then you may examine the available
+ model = "logit.gee", id, data)}, then you may examine the available
information in \texttt{z.out} by using \texttt{names(z.out)},
see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
a default summary of information through \texttt{summary(z.out)}.
@@ -342,37 +317,36 @@ below.
\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}
-\subsection* {How to Cite}
+\subsection*{How To Cite}
To cite the \emph{logit.gee} Zelig model:
\begin{verse}
-Patrick Lam. 2007. ``logit.gee: Generalized Estimating Equation for Logistic Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Patrick Lam. 2007. ``logit.gee: Generalized Estimating Equation for Logit Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
-\input{citeZelig}
+%\input{citeZelig}
-\subsection* {See also}
-The logit function is part of the base packages by William N. Venable \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are a selection of $2,000$ observations from \cite{KinTomWit00}
+\subsection*{See also}
+The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
+\bibliography{gk}
+
<<afterpkgs, echo=FALSE>>=
after<-search()
torm<-setdiff(after,before)
for (pkg in torm)
detach(pos=match(pkg,search()))
@
+
+
\end{document}
diff --git a/inst/doc/logit.gee.pdf b/inst/doc/logit.gee.pdf
index a372f42..13ba0b4 100644
Binary files a/inst/doc/logit.gee.pdf and b/inst/doc/logit.gee.pdf differ
diff --git a/inst/doc/logit.mixed.Rnw b/inst/doc/logit.mixed.Rnw
new file mode 100644
index 0000000..75b73ef
--- /dev/null
+++ b/inst/doc/logit.mixed.Rnw
@@ -0,0 +1,191 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/logitmixed}
+\include{zinput}
+%\VignetteIndexEntry{Mixed effects linear regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,logit regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt logit.mixed}: Mixed effects logistic Regression}
+\label{mixed}
+
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The logit model is appropriate when the dependent variable is dichotomous.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="logit.mixed")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+ gamma= ~ tag(w1 + w2 | g)), data=mydata, model="logit.mixed")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
+\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
+\end{itemize}
+Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(voteincome)
+@
+Estimate model:
+<<Examples.zelig>>=
+z.out1 <- zelig(vote ~ education + age + female + tag(1 | state), data=voteincome, model="logit.mixed")
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
+
+<<Examples.setx>>=
+x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
+x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
+@
+Generate first differences for the effect of high versus low education on voting: \\
+
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+@
+
+\end{enumerate}
+
+
+\subsubsection{Mixed effects Logistic Regression Model}
+
+Let $Y_{ij}$ be the binary dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$ which takes the value of either 0 or 1, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a Bernoulli distribution with mean vector $\pi_{ij}$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Bernoulli}(y_{ij} | \pi_{ij}) = \pi_{ij}^{y_{ij}} (1 - \pi_{ij})^{1 - y_{ij}}
+\end{equation*}
+where
+\begin{equation*}
+\pi_{ij} = \mathrm{Pr}(Y_{ij} = 1)
+\end{equation*}
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\pi_{ij} \equiv \frac{1}{1 + \exp(-(X_{ij} \beta + Z_{ij} b_i))}
+\end{equation*}
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value, $\pi_{ij}$ for
+\begin{equation*}
+\pi_{ij} = \frac{1}{1 + \exp(-(X_{ij} \beta + Z_{ij} b_i))}
+\end{equation*}
+given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+
+\item The expected values ({\tt qi\$ev}) are simulations of the predicted probability of a success given draws of $\beta$ from its posterior:
+\begin{equation*}
+E(Y_{ij} | X_{ij}) = \pi_{ij} = \frac{1}{1 + exp(- X_{ij} \beta)}.
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in predicted probabilities, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = Pr(Y_{ij} = 1 | X_{ij}) - Pr(Y_{ij} = 1 | X_{ij}^\prime)
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+RR(Y_{ij} | X_{ij}, X_{ij}^{\prime}) = \frac{Pr(Y_{ij} = 1 | X_{ij})}{Pr(Y_{ij} = 1 | X_{ij}^{\prime})}
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+\subsection* {How to Cite}
+
+To cite the \emph{logit.mixed} Zelig model:
+\begin{verse}
+Delia Bailey, Ferdinand Alimadhi. 2007. ``logit.mixed: Mixed effects logistic regression'' 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}
+Mixed effects logistic regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\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/logit.mixed.pdf b/inst/doc/logit.mixed.pdf
new file mode 100644
index 0000000..8f8bb30
Binary files /dev/null and b/inst/doc/logit.mixed.pdf differ
diff --git a/inst/doc/netlogit.Rnw b/inst/doc/logit.net.Rnw
similarity index 76%
rename from inst/doc/netlogit.Rnw
rename to inst/doc/logit.net.Rnw
index 8a85b2b..f795e64 100644
--- a/inst/doc/netlogit.Rnw
+++ b/inst/doc/logit.net.Rnw
@@ -1,4 +1,4 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/netlogit}
+\SweaveOpts{results=hide, prefix.string=vigpics/logitnet}
\include{zinput}
%\VignetteIndexEntry{Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
%\VignetteDepends{Zelig, stats}
@@ -13,13 +13,13 @@
library(Zelig)
@
-\section{{\tt netlogit}: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
+\section{{\tt logit.net}: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
Use network logistic regression analysis for a dependent variable that is a binary valued proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netlogit", data = mydata)
+> z.out <- zelig(y ~ x1 + x2, model = "logit.net", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -41,7 +41,7 @@ data(friendship)
Estimate model:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(friends ~ advice + prestige + perpower, model = "netlogit", data = friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model = "logit.net", data = friendship)
summary(z.out)
@
@@ -97,7 +97,7 @@ plot(s.out)
\subsubsection{Model}
-The {\tt netlogit} model performs a logistic regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard logistic regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from th [...]
+The {\tt logit.net} model performs a logistic regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard logistic regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from t [...]
Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
\begin{itemize}
@@ -118,7 +118,7 @@ where $x_{i}$ is the vector of $k$ covariates for observation $i$ and $\beta$ is
\subsubsection{Quantities of Interest}
The quantities of interest for the network logistic regression are the same as those for the standard logistic regression.
\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the {\tt netlogit} model are simulations of the predicted probability of a success:
+\item The expected values ({\tt qi\$ev}) for the {\tt logit.net} model are simulations of the predicted probability of a success:
\begin{equation*}
E(Y) = \pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)},
\end{equation*}
@@ -135,7 +135,7 @@ FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "netlogit", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "logit.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -167,9 +167,9 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{netlogit} Zelig model:
+To cite the \emph{logit.net} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``netlogit: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``logit.net: Network Logistic Regression for Dichotomous Proximity Matrix 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}
diff --git a/inst/doc/logit.net.pdf b/inst/doc/logit.net.pdf
new file mode 100644
index 0000000..e6db1bd
Binary files /dev/null and b/inst/doc/logit.net.pdf differ
diff --git a/inst/doc/logit.pdf b/inst/doc/logit.pdf
index 55d5b53..b269bdd 100644
Binary files a/inst/doc/logit.pdf and b/inst/doc/logit.pdf differ
diff --git a/inst/doc/lognorm.pdf b/inst/doc/lognorm.pdf
index 9b8b60f..963910d 100644
Binary files a/inst/doc/lognorm.pdf and b/inst/doc/lognorm.pdf differ
diff --git a/inst/doc/ls.Rnw b/inst/doc/ls.Rnw
index 103e293..1973dc8 100644
--- a/inst/doc/ls.Rnw
+++ b/inst/doc/ls.Rnw
@@ -95,7 +95,7 @@ GDP:
summary(s.out1)
@
\begin{center}
-<<label=ExamplesPlot,fig=true,echo=true>>=
+<<label=ExamplesPlot,fig=true,echo=true,width=5.5,height=4>>=
plot(s.out1)
@
\end{center}
@@ -121,11 +121,41 @@ Simulate quantities of interest:
s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
@
\begin{center}
-<<label=DummyPlot,fig=true,echo=true>>=
+<<label=DummyPlot,fig=true,echo=true, width=5.5, height=4>>=
plot(s.out2)
@
\end{center}
+\item Multiple responses (least squares regression will be fitted
+ separately to each dependent variable)
+
+Two responses for data set macro:
+<<Multiple.zelig>>=
+ z.out3 <- zelig(cbind(unem, gdp) ~ capmob + trade,model = "ls", data = macro)
+@
+<<Multiple.zelig.summary>>=
+summary(z.out3)
+@
+Set values for the explanatory variables, using the default mean/mode
+values, with country set to the United States and Japan, respectively:
+<<Multiple.setx>>=
+ x.US <- setx(z.out3, country = "United States")
+ x.Japan <- setx(z.out3, country = "Japan")
+@
+Simulate quantities of interest:
+<<Multiple.sim>>=
+ s.out3 <- sim(z.out3, x = x.US, x1 = x.Japan)
+@
+Summary
+<<Example4.sim.summary>>=
+summary(s.out3)
+@
+\begin{center}
+<<label=Example4Plot,fig=true,echo=true, width=7.5, height=6>>=
+ plot(s.out3)
+@
+\end{center}
+
\end{enumerate}
\subsubsection{Model}
diff --git a/inst/doc/ls.mixed.Rnw b/inst/doc/ls.mixed.Rnw
new file mode 100644
index 0000000..0158ae1
--- /dev/null
+++ b/inst/doc/ls.mixed.Rnw
@@ -0,0 +1,190 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/lmmixed}
+\include{zinput}
+%\VignetteIndexEntry{Mixed effects linear regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,linear, linear regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt ls.mixed}: Mixed effects Linear Regression}
+\label{mixed}
+
+Use multi-level linear regression if you have covariates that are grouped according to one or more classification factors and a continuous dependent variable.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="lm.multi")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+ gamma= ~ tag(w1 + w2 | g)), data=mydata, model="lm.multi")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt multi}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt method:} a character string. For a linear mixed model the default is {\tt "REML"} indicating that the model should be fit by maximizing the restricted log-likelihood. The alternative is {\tt "ML"} indicating that the log-likelihood should be maximized.
+\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
+\end{itemize}
+Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(voteincome)
+@
+
+Estimate model:
+
+<<Examples.zelig>>=
+z.out1 <- zelig(income ~ education + age + female + tag(1 | state), data=voteincome, model="ls.mixed")
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
+<<Examples.setx>>=
+x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
+x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
+@
+
+Generate first differences for the effect of high versus low education on income: \\
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+@
+\begin{center}
+<<label=ExamplesPlot, fig=true, echo=true>>=
+plot(s.out1)
+@
+\end{center}
+
+\end{enumerate}
+
+\subsubsection{Mixed effects linear regression model}
+
+Let $Y_{ij}$ be the continuous dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a univariate normal model with a vector of means $\mu_{ij}$ and scalar variance $\sigma^2$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Normal}(y_{ij} | \mu_{ij}, \sigma^2)
+\end{equation*}
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\mu_{ij} \equiv X_{ij} \beta + Z_{ij} b_i
+\end{equation*}
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The predicted values ({\tt qi\$pr}) are calculated as
+\begin{equation*}
+Y_{ij} = X_{ij} \beta + Z_{ij} b_i
+\end{equation*}
+given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+
+\item The expected values ({\tt qi\$ev}) are averaged over the stochastic components and are given by
+\begin{equation*}
+E(Y_{ij} | X_{ij}) = X_{ij} \beta.
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in expected values, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = E(Y_{ij} | X_{ij}) - E(Y_{ij} | X_{ij}^\prime)
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+
+
+\subsection* {How to Cite}
+
+To cite the \emph{ls.mixed} Zelig model:
+\begin{verse}
+Delia Bailey, Ferdinand Alimadhi. 2007. ``ls.mixed: Mixed effects linear regression'' 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}
+Mixed effects linear regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\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/ls.mixed.pdf b/inst/doc/ls.mixed.pdf
new file mode 100644
index 0000000..13f7364
Binary files /dev/null and b/inst/doc/ls.mixed.pdf differ
diff --git a/inst/doc/netls.Rnw b/inst/doc/ls.net.Rnw
similarity index 89%
rename from inst/doc/netls.Rnw
rename to inst/doc/ls.net.Rnw
index 246bcaa..9a87c70 100644
--- a/inst/doc/netls.Rnw
+++ b/inst/doc/ls.net.Rnw
@@ -1,4 +1,4 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/netls, eval=true}
+\SweaveOpts{results=hide, prefix.string=vigpics/lsnet, eval=true}
\include{zinput}
%\VignetteIndexEntry{Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}
%\VignetteDepends{Zelig}
@@ -17,7 +17,8 @@ if(!length(grep("package:Zelig",pkg)))
library(Zelig)
@
-\section{{\tt netls}: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}\label{netls}
+\section{{\tt ls.net}: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}
+\label{ls.net}
Use network least squares regression analysis to estimate the
best linear predictor when the dependent variable is a
@@ -26,7 +27,7 @@ matrices, or matrix representations of directed graphs).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netls", data = mydata)
+> z.out <- zelig(y ~ x1 + x2, model = "ls.net", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -48,7 +49,7 @@ library(sna)
@
Estimate model:
<<Examples.zelig>>=
- z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "netls", data = sna.ex)
+ z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "ls.net", data = sna.ex)
@
@@ -78,7 +79,7 @@ Var3 on the outcome variable.
\end{enumerate}
\subsubsection{Model}
-The {\tt netls} model performs a least squares regression of the
+The {\tt ls.net} model performs a least squares regression of the
sociomatrix $\mathbf{Y}$, a $m \times m$ matrix representing network
ties, on a set of sociomatrices $\mathbf{X}$. This network regression
model is a directly analogue to standard least squares regression
@@ -129,7 +130,7 @@ FD = E(Y | x_{1}) - E(Y | x)
The output of each Zelig command contains useful information which you
may view. For example, you run {\tt z.out <- zelig(y ~ x,
-model="netls", data)}, then you may examine the available information
+model="ls.net", data)}, then you may examine the available information
in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by
using {\tt z.out\$coefficients}, and a default summary of information
through {\tt summary(z.out)}. Other elements available through the
@@ -175,9 +176,9 @@ expected values) for the specified values of {\tt x} and {\tt x1}.
\subsection* {How to Cite}
-To cite the \emph{netls} Zelig model:
+To cite the \emph{ls.net} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``netls: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``ls.net: Network Least Squares Regression for Continuous Proximity Matrix 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}
diff --git a/inst/doc/ls.net.pdf b/inst/doc/ls.net.pdf
new file mode 100644
index 0000000..6ecd40e
Binary files /dev/null and b/inst/doc/ls.net.pdf differ
diff --git a/inst/doc/ls.pdf b/inst/doc/ls.pdf
index 3296280..488603b 100644
Binary files a/inst/doc/ls.pdf and b/inst/doc/ls.pdf differ
diff --git a/inst/doc/makeDocs b/inst/doc/makeDocs
index f4a3785..cdfb8c6 100755
--- a/inst/doc/makeDocs
+++ b/inst/doc/makeDocs
@@ -44,6 +44,7 @@ for f in `cat models.txt`
## remove bibstyle and bib database at the end
perl -i -p -e 's#\\bibliographystyle{asa}# #i' $f.tex
perl -i -p -e 's#\\bibliography{gk,gkpubs}# #i' $f.tex
+ perl -i -p -e 's#\\bibliography{gk}# #i' $f.tex
done
## create pdf file for a007.Rnw, just to pass the local R CMD check
diff --git a/inst/doc/mlogit.bayes.pdf b/inst/doc/mlogit.bayes.pdf
index 0632123..334355d 100644
Binary files a/inst/doc/mlogit.bayes.pdf and b/inst/doc/mlogit.bayes.pdf differ
diff --git a/inst/doc/mlogit.pdf b/inst/doc/mlogit.pdf
index f99c9c2..4ad027c 100644
Binary files a/inst/doc/mlogit.pdf and b/inst/doc/mlogit.pdf differ
diff --git a/inst/doc/mloglm.pdf b/inst/doc/mloglm.pdf
index 85ae1cd..1f05d5d 100644
Binary files a/inst/doc/mloglm.pdf and b/inst/doc/mloglm.pdf differ
diff --git a/inst/doc/negbin.Rnw b/inst/doc/negbin.Rnw
index b07d440..fc8a7b3 100644
--- a/inst/doc/negbin.Rnw
+++ b/inst/doc/negbin.Rnw
@@ -89,7 +89,11 @@ Simulate fitted values:
<<Example.summary.sim>>=
summary(s.out)
@
-
+\begin{center}
+<<label=Example1Plot,fig=true>>=
+ plot(s.out)
+@
+\end{center}
\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 value.
diff --git a/inst/doc/negbin.pdf b/inst/doc/negbin.pdf
index fcf578e..1870e4d 100644
Binary files a/inst/doc/negbin.pdf and b/inst/doc/negbin.pdf differ
diff --git a/inst/doc/netcloglog.pdf b/inst/doc/netcloglog.pdf
deleted file mode 100644
index 4cfc128..0000000
Binary files a/inst/doc/netcloglog.pdf and /dev/null differ
diff --git a/inst/doc/netgamma.pdf b/inst/doc/netgamma.pdf
deleted file mode 100644
index 2f6ee52..0000000
Binary files a/inst/doc/netgamma.pdf and /dev/null differ
diff --git a/inst/doc/netlogit.pdf b/inst/doc/netlogit.pdf
deleted file mode 100644
index fb51559..0000000
Binary files a/inst/doc/netlogit.pdf and /dev/null differ
diff --git a/inst/doc/netls.pdf b/inst/doc/netls.pdf
deleted file mode 100644
index 91c8882..0000000
Binary files a/inst/doc/netls.pdf and /dev/null differ
diff --git a/inst/doc/netnormal.pdf b/inst/doc/netnormal.pdf
deleted file mode 100644
index 4b13ea4..0000000
Binary files a/inst/doc/netnormal.pdf and /dev/null differ
diff --git a/inst/doc/netpoisson.pdf b/inst/doc/netpoisson.pdf
deleted file mode 100644
index 0fc69c5..0000000
Binary files a/inst/doc/netpoisson.pdf and /dev/null differ
diff --git a/inst/doc/normal.Rnw b/inst/doc/normal.Rnw
index 09640a5..5e2c85e 100644
--- a/inst/doc/normal.Rnw
+++ b/inst/doc/normal.Rnw
@@ -267,7 +267,12 @@ Kosuke Imai, Gary King, and Olivia Lau. 2007. ``normal: Normal Regression for C
\input{citeZelig}
\subsection* {See also}
-The normal model is part of the base packages by William N. Venable and Brian D. Ripley \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are from \cite{KinTomWit00}.
+
+The normal model is part of the stats package by \citet{VenRip02}.
+Advanced users may wish to refer to \texttt{help(glm)} and
+\texttt{help(family)}, as well as \cite{McCNel89}. Robust standard
+errors are implemented via the sandwich package by \citet{Zeileis04}.
+Sample data are from \cite{KinTomWit00}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/normal.bayes.pdf b/inst/doc/normal.bayes.pdf
index b8432c2..f8d5a55 100644
Binary files a/inst/doc/normal.bayes.pdf and b/inst/doc/normal.bayes.pdf differ
diff --git a/inst/doc/gam.normal.Rnw b/inst/doc/normal.gam.Rnw
similarity index 92%
rename from inst/doc/gam.normal.Rnw
rename to inst/doc/normal.gam.Rnw
index f4970ba..9173642 100644
--- a/inst/doc/gam.normal.Rnw
+++ b/inst/doc/normal.gam.Rnw
@@ -2,7 +2,7 @@
\include{zinput}
%\VignetteIndexEntry{Generalized Additive Model for Continuous Dependent Variables}
%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, gam, gam.normal}
+%\VignetteKeyWords{model, gam, normal.gam}
%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
@@ -13,13 +13,13 @@
library(Zelig)
@
-\section{{\tt gam.normal}: Generalized Additive Model for Continuous Dependent Variables}\label{gam.normal}
+\section{{\tt normal.gam}: Generalized Additive Model for Continuous Dependent Variables}\label{gam.normal}
This function runs a nonparametric Generalized Additive Model (GAM) for continuous dependent variables.
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + s(x2), model = "gam.normal", data = mydata)
+> z.out <- zelig(y ~ x1 + s(x2), model = "normal.gam", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -33,7 +33,7 @@ In addition to the standard inputs, {\tt zelig()} takes the following additional
\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
\item {\tt sp}: A vector of smoothing parameters for each term.
-\item {\tt \ldots}: additional options passed to the {\tt gam.normal} model. See the {\tt mgcv} library for details.
+\item {\tt \ldots}: additional options passed to the {\tt normal.gam} model. See the {\tt mgcv} library for details.
\end{itemize}
@@ -78,7 +78,7 @@ my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
Estimate the model, summarize the results, and plot nonlinearities:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.normal", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="normal.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1,residuals=TRUE)
@
@@ -136,7 +136,7 @@ plot(s.out)
An extra ridge penalty (useful with convergence problems):
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
- model="gam.normal", data=my.data)
+ model="normal.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1,residuals=TRUE)
@@ -144,7 +144,7 @@ plot(z.out,pages=1,residuals=TRUE)
Set the smoothing parameter for the first term, estimate the rest:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
- model="gam.normal", data=my.data)
+ model="normal.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1)
@@ -152,7 +152,7 @@ plot(z.out,pages=1)
Set lower bounds on smoothing parameters:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
- model="gam.normal", data=my.data)
+ model="normal.gam", data=my.data)
summary(z.out)
plot(z.out, pages=1)
@@ -160,7 +160,7 @@ plot(z.out, pages=1)
A GAM with 3df regression spline term \& 2 penalized terms:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
- model="gam.normal", data=my.data)
+ model="normal.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1)
@@ -172,7 +172,7 @@ plot(z.out,pages=1)
\subsubsection{Model}
-GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt gam.normal} a normal link function is used. Specifically, let $Y_{i}$ be the continuous dependent variable for observation $i$.
+GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt normal.gam} a normal link function is used. Specifically, let $Y_{i}$ be the continuous dependent variable for observation $i$.
\begin{itemize}
\item The \emph{stochastic component} is described by a univariate normal model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
\begin{equation*}
@@ -206,14 +206,14 @@ Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
\subsubsection{Quantities of Interest}
-The quantities of interest for the {\tt gam.normal} model are the same as those for the standard Normal regression.
+The quantities of interest for the {\tt normal.gam} model are the same as those for the standard Normal regression.
\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt gam.normal} model is the mean of simulations from the stochastic component,
+\item The expected value ({\tt qi\$ev}) for the {\tt normal.gam} model is the mean of simulations from the stochastic component,
\begin{equation*}
E(Y) = \mu_{i} = x_{i}\beta + \sum_{j=1}^{J} f_j (Z_j).
\end{equation*}
\item The predicted value ({\tt qi\$pr}) is a draw from the Normal distribution defined by the set of parameters $(\mu_i, \sigma^2)$.
-\item The first difference ({\tt qi\$fd}) for the GAM Normal model is defined as
+\item The first difference ({\tt qi\$fd}) for the normal.gam model is defined as
\begin{equation*}
FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
\end{equation*}
@@ -226,7 +226,7 @@ for $w=\{X, Z\}$.
-The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "gam.normal", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "normal.gam", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -266,9 +266,9 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{gam.normal} Zelig model:
+To cite the \emph{normal.gam} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``gam.normal: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``normal.gam: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
\input{citeZelig}
diff --git a/inst/doc/gam.normal.pdf b/inst/doc/normal.gam.pdf
similarity index 77%
rename from inst/doc/gam.normal.pdf
rename to inst/doc/normal.gam.pdf
index 8ce10b0..f00a6fe 100644
Binary files a/inst/doc/gam.normal.pdf and b/inst/doc/normal.gam.pdf differ
diff --git a/inst/doc/logit.gee.Rnw b/inst/doc/normal.gee.Rnw
similarity index 57%
copy from inst/doc/logit.gee.Rnw
copy to inst/doc/normal.gee.Rnw
index 84c1798..884c013 100644
--- a/inst/doc/logit.gee.Rnw
+++ b/inst/doc/normal.gee.Rnw
@@ -1,37 +1,36 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/logit_gee}
+\SweaveOpts{results=hide, prefix.string=vigpics/normal_gee}
\include{zinput}
-%\VignetteIndexEntry{logit.gee: Generalized Estimating Equation for Logistic Regression}
+%\VignetteIndexEntry{Generalized Estimating Equation for Normal Regression}
%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, logistic regression}
+%\VignetteKeyWords{model,generalized estimating equation, normal regression}
%\VignettePackage{Zelig}
\begin{document}
+
\nobibliography*
<<beforepkgs, echo=FALSE>>=
before=search()
@
-
<<loadLibrary, echo=F,results=hide>>=
library(Zelig)
@
-\section{\texttt{logit.gee}: Generalized Estimating Equation for Logistic Regression}
-\label{logit.gee}
+\section{\texttt{normal.gee}: Generalized Estimating Equation for Normal Regression}
+\label{normal.gee}
-The GEE logit estimates the same model as the standard logistic
-regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables). Unlike in logistic
-regression, GEE logit allows for dependence within clusters, such as
+The GEE normal estimates the same model as the standard normal
+regression. Unlike in normal
+regression, GEE normal allows for dependence within clusters, such as
in longitudinal data, although its use is not limited to just
panel data. The user must first specify a ``working''
correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
the standard errors can be obtained using a robust ``sandwich''
estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters.
+specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit.gee",
+> z.out <- zelig(Y ~ X1 + X2, model = "normal.gee",
id = "X3", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
@@ -50,7 +49,7 @@ Use the following arguments to specify the structure of the ``working'' correlat
\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
\begin{itemize}
\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard logistic regression. The ``working'' correlation matrix is the identity matrix.
+ cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard normal regression. The ``working'' correlation matrix is the identity matrix.
\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
@@ -130,152 +129,89 @@ $\left( \begin{array}{ccccc}
\subsubsection{Examples}
\begin{enumerate}
-\item {Example with Stationary 3 Dependence}
+\item {Example with AR-1 Dependence}
Attaching the sample turnout dataset:
<<Example.data>>=
-data(turnout)
-@
-Variable identifying clusters
-<<Example.cluster>>=
-turnout$cluster <- rep(c(1:200),10)
+data(macro)
@
-Sorting by cluster
-<<Example.sort>>=
-sorted.turnout <- turnout[order(turnout$cluster),]
-@
-Estimating parameter values for the logistic regression:
+Estimating model and presenting summary:
<<Example.zelig>>=
-z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3)
+z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.gee", id = "country", data = macro, robust=TRUE, corstr="AR-M", Mv=1)
+summary(z.out)
@
-Setting values for the explanatory variables to their default values:
+Set explanatory variables to their default (mean/mode) values, with high (80th percentile) and low (20th percentile) values:
<<Example.setx>>=
-x.out1 <- setx(z.out1)
+x.high <- setx(z.out, trade = quantile(macro$trade, 0.8))
+x.low <- setx(z.out, trade = quantile(macro$trade, 0.2))
@
-Simulating quantities of interest from the posterior distribution.
+Generate first differences for the effect of high versus low trade on GDP:
<<Example.sim>>=
-s.out1 <- sim(z.out1, x = x.out1)
-@
+s.out <- sim(z.out, x = x.high, x1 = x.low)
<<Example.summary.sim>>=
-summary(s.out1)
+summary(s.out)
@
+Generate a plot of quantities of interest:
\begin{center}
<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out1)
+plot(s.out)
@
\end{center}
-
-\item {Simulating First Differences}
-
-Estimating the risk difference (and risk ratio) between low education
-(25th percentile) and high education (75th percentile) while all the
-other variables held at their default values.
-
-<<FirstDifference.setx>>=
-x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
-x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
-@
-
-<<FirstDifference.sim>>=
-s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
-@
-<<FirstDifference.summary.sim>>=
-summary(s.out2)
-@
-\begin{center}
-<<label=FirstDifferencePlot,fig=true,echo=true>>=
-plot(s.out2)
-@
-\end{center}
-
-\item Example with Fixed Correlation Structure
-
-User-defined correlation structure
-<<Example2.corr>>=
-corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
-diag(corr.mat) <- 1
-@
-Generating empirical estimates:
-<<Example2.zelig>>=
-z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat)
-@
-Viewing the regression output:
-<<Example2.summary>>=
-summary(z.out2)
-@
\end{enumerate}
\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the binary
-dependent variable for unit $i$ at time $t$ which takes the value of
-either 0 or 1. $Y_{i}$ is a vector or cluster of correlated data
+Suppose we have a panel dataset, with $Y_{it}$ denoting the continuous dependent variable for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. In the GEE model, we must specify a mean function, a
-variance function, and the structure of the correlation matrix of the within-cluster
-observations. Observations are assumed to be correlated within each
-cluster but not across clusters.
-
+$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
\begin{itemize}
-\item The \emph{stochastic component} is given by the joint distribution
-
+\item The \emph{stochastic component} is given by the joint and marginal distributions
\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \pi_{i})
+Y_{i} &\sim& f(y_{i} \mid \mu_{i})\\
+Y_{it} &\sim& g(y_{it} \mid \mu_{it})
\end{eqnarray*}
-where $f$ is an unspecified multivariate distribution. The marginal distributions of $f$, which characterize each nonindependent unit-time observation $Y_{it}$, is given by
-\begin{eqnarray*}
-Y_{it} &\sim& \mathrm{Bernoulli}(y_{it} \mid \pi_{it}) \\
- &=& \pi_{it}^{y_{it}} (1-\pi_{it})^{1-y_{it}}
-\end{eqnarray*}
-where $\pi_{it}=\Pr(Y_{it}=1)$ for $t=1,...,T$. The correlations
-within each unit $i$ are modeled by defining the structure of a $T \times T$
-``working'' correlation matrix, which is specified by the user
-\textit{a priori}. Note that the model assumes correlations within $i$ but independence across $i$. The ``working'' correlation matrix then enters
-the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
+where $f$ and $g$ are unspecified distributions with means $\mu_{i}$ and $\mu_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-\item The \emph{systematic component} is given by:
+\item The \emph{systematic component} is the \textit{mean function}, given by:
\begin{equation*}
-\pi_{it} \; = \; \frac{1}{1 + \exp(-x_{it} \beta)}.
+\mu_{it} = x_{it} \beta
\end{equation*}
where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
and $\beta$ is the vector of coefficients.
+\item The \textit{variance function} is given by:
+\begin{equation*}
+V_{it} = 1
+\end{equation*}
-\item GEE models require three specifications: a mean function (the systematic component above), a variance function (given by the variance of the Bernoulli stochastic component: $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$), and a correlation structure (the ``working'' correlation matrix above). If the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as [...]
+\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
+\begin{equation*}
+V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
+\end{equation*}
+where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = 1$ as the $t$th diagonal element (in the case of GEE normal, $A_{i}$ is the identity matrix), $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{fitlaiwar04} for more details.
+\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-\end{itemize}
+\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
+\end{itemize}
\subsubsection{Quantities of Interest}
\begin{itemize}
\item All quantities of interest are for marginal means rather than joint means.
-\item The expected values ({\tt qi\$ev}) for the logit model are
- simulations of the predicted probability of a success: $$E(Y) =
- \pi_{c}= \frac{1}{1 + \exp(-x_{c} \beta)},$$ given draws of $\beta$ from
+\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
+\item The expected values ({\tt qi\$ev}) for the GEE normal model is the mean of simulations from the stochastic component: $$E(Y) =
+ \mu_{c}= x_{c} \beta,$$ given draws of $\beta$ from
its sampling distribution, where $x_{c}$ is a vector of values, one for
each independent variable, chosen by the user.
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
- distribution with mean equal to the simulated expected value $\pi_{c}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\item The first difference ({\tt qi\$fd}) for the GEE normal model is defined as
\begin{equation*}
\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
\end{equation*}
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
-\end{equation*}
-
\item In conditional prediction models, the average expected treatment
effect ({\tt att.ev}) for the treatment group is
\begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
@@ -288,25 +224,13 @@ each independent variable, chosen by the user.
treatment group, under the assumption that everything stays the
same except that the treatment indicator is switched to $tr_{it}=0$.
-\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 \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- \widehat{Y_{it}(tr_{it}=0)}\right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the
- treatment ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_{it}(tr_{it}=0)}$, the counterfactual predicted value of
- $Y_{it}$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $tr_{it}=0$.
\end{itemize}
\subsubsection{Output Values}
The output of each Zelig command contains useful information which you
may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "logit.gee", id, data, corstr)}, then you may examine the available
+ model = "normal.gee", id, data)}, then you may examine the available
information in \texttt{z.out} by using \texttt{names(z.out)},
see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
a default summary of information through \texttt{summary(z.out)}.
@@ -322,7 +246,7 @@ below.
\item {\tt residuals}: the working residuals in the final iteration
of the fit.
\item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_{it}$.
+ systemic component, $\mu_{it}$.
\item {\tt linear.predictors}: the vector of $x_{it}\beta$
\item {\tt max.id}: the size of the largest cluster.
\end{itemize}
@@ -340,39 +264,37 @@ below.
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
+ \item {\tt qi\$ev}: the simulated expected values for the
specified values of {\tt x}.
\item {\tt qi\$fd}: the simulated first difference in the expected
probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$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}
-\subsection* {How to Cite}
+\subsection*{How To Cite}
-To cite the \emph{logit.gee} Zelig model:
+To cite the \emph{normal.gee} Zelig model:
\begin{verse}
-Patrick Lam. 2007. ``logit.gee: Generalized Estimating Equation for Logistic Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Patrick Lam. 2007. ``normal.gee: Generalized Estimating Equation for Normal Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
-\input{citeZelig}
+%\input{citeZelig}
-\subsection* {See also}
-The logit function is part of the base packages by William N. Venable \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are a selection of $2,000$ observations from \cite{KinTomWit00}
+\subsection*{See also}
+The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
+\bibliography{gk}
+
<<afterpkgs, echo=FALSE>>=
after<-search()
torm<-setdiff(after,before)
for (pkg in torm)
detach(pos=match(pkg,search()))
@
+
+
+
\end{document}
diff --git a/inst/doc/normal.gee.pdf b/inst/doc/normal.gee.pdf
new file mode 100644
index 0000000..8f2638e
Binary files /dev/null and b/inst/doc/normal.gee.pdf differ
diff --git a/inst/doc/netnormal.Rnw b/inst/doc/normal.net.Rnw
similarity index 74%
rename from inst/doc/netnormal.Rnw
rename to inst/doc/normal.net.Rnw
index 2385558..086b3ef 100644
--- a/inst/doc/netnormal.Rnw
+++ b/inst/doc/normal.net.Rnw
@@ -1,4 +1,4 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/netnormal}
+\SweaveOpts{results=hide, prefix.string=vigpics/normalnet}
\include{zinput}
%\VignetteIndexEntry{Network Normal Regression for Continuous Proximity Matrix Dependent Variables}
%\VignetteDepends{Zelig, stats}
@@ -13,13 +13,13 @@
library(Zelig)
@
-\section{{\tt netnormal}: Network Normal Regression for Continuous Proximity Matrix Dependent Variables}
+\section{{\tt normal.net}: Network Normal Regression for Continuous Proximity Matrix Dependent Variables}
The Network Normal regression model is a close variant of the more standard least squares regression model (see {\tt netlm}). Both models specify a continuous proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs) dependent variable as a linear function of a set of explanatory variables. The network Normal model reports maximum likelihood (rather than least squares) estimates. The two models differ only in their estimate for the stochas [...]
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netnormal", data = mydata)
+> z.out <- zelig(y ~ x1 + x2, model = "normal.net", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -47,7 +47,7 @@ data(friendship)
Estimate model:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(perpower ~ friends + advice + prestige, model = "netnormal", data = friendship)
+z.out <- zelig(perpower ~ friends + advice + prestige, model = "normal.net", data = friendship)
summary(z.out)
@
@@ -78,7 +78,7 @@ plot(s.out)
\subsubsection{Model}
-The {\tt netnormal} model performs a Normal regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard Normal regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the $ [...]
+The {\tt normal.net} model performs a Normal regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard Normal regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the [...]
Let $Y_{i}$ be the continuous dependent variable, produced by vectorizing a continuous proximity matrix, for observation $i$.
\begin{itemize}
@@ -97,7 +97,7 @@ where $ x_i$ is the vector of $k$ explanatory variables and $\beta$ is the vecto
\subsubsection{Quantities of Interest}
The quantities of interest for the network Normal regression are the same as those for the standard Normal regression.
\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt netnormal} model is the mean of simulations from the stochastic component,
+\item The expected value ({\tt qi\$ev}) for the {\tt normal.net} model is the mean of simulations from the stochastic component,
\begin{equation*}
E(Y) = \mu_{i} = x_{i}\beta,
\end{equation*}
@@ -114,7 +114,7 @@ FD = \text{Pr}(Y| x_{1}) - \text{Pr}(Y| x)
\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "netnormal", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "normal.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -146,14 +146,14 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{netnormal} Zelig model:
+To cite the \emph{normal.net} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``netnormal: Network Normal Regression for Continuous Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``normal.net: Network Normal Regression for Continuous Proximity Matrix 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}
-The network normal regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netnormal)}. Sample data are fictional.
+The network normal regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(normal.net)}. Sample data are fictional.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/normal.net.pdf b/inst/doc/normal.net.pdf
new file mode 100644
index 0000000..b8e029e
Binary files /dev/null and b/inst/doc/normal.net.pdf differ
diff --git a/inst/doc/normal.pdf b/inst/doc/normal.pdf
index d0fc620..a4240bd 100644
Binary files a/inst/doc/normal.pdf and b/inst/doc/normal.pdf differ
diff --git a/inst/doc/ologit.pdf b/inst/doc/ologit.pdf
index 2477d52..06e6db4 100644
Binary files a/inst/doc/ologit.pdf and b/inst/doc/ologit.pdf differ
diff --git a/inst/doc/oprobit.bayes.pdf b/inst/doc/oprobit.bayes.pdf
index 44ae6a8..7b9946a 100644
Binary files a/inst/doc/oprobit.bayes.pdf and b/inst/doc/oprobit.bayes.pdf differ
diff --git a/inst/doc/oprobit.pdf b/inst/doc/oprobit.pdf
index 86a9020..1669f8f 100644
Binary files a/inst/doc/oprobit.pdf and b/inst/doc/oprobit.pdf differ
diff --git a/inst/doc/poisson.Rnw b/inst/doc/poisson.Rnw
index 74cadb1..b617582 100644
--- a/inst/doc/poisson.Rnw
+++ b/inst/doc/poisson.Rnw
@@ -222,7 +222,11 @@ Kosuke Imai, Gary King, and Olivia Lau. 2007. ``poisson: Poisson Regression for
\input{citeZelig}
\subsection* {See also}
-The poisson model is part of the base packages by William N. Venable and Brian D. Ripley \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are from \cite{Martin92}.
+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}. Robust standard
+errors are implemented via the sandwich package by \citet{Zeileis04}.
+Sample data are from \cite{Martin92}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/poisson.bayes.pdf b/inst/doc/poisson.bayes.pdf
index adc2654..563b9da 100644
Binary files a/inst/doc/poisson.bayes.pdf and b/inst/doc/poisson.bayes.pdf differ
diff --git a/inst/doc/gam.poisson.Rnw b/inst/doc/poisson.gam.Rnw
similarity index 92%
rename from inst/doc/gam.poisson.Rnw
rename to inst/doc/poisson.gam.Rnw
index cb36d05..dd7087b 100644
--- a/inst/doc/gam.poisson.Rnw
+++ b/inst/doc/poisson.gam.Rnw
@@ -2,7 +2,7 @@
\include{zinput}
%\VignetteIndexEntry{Generalized Additive Model for Count Dependent Variables}
%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, gam, gam.poisson}
+%\VignetteKeyWords{model, gam, poisson.gam}
%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
@@ -15,13 +15,13 @@
library(Zelig)
@
-\section{{\tt gam.poisson}: Generalized Additive Model for Count Dependent Variables}\label{gam.poisson}
+\section{{\tt poisson.gam}: Generalized Additive Model for Count Dependent Variables}\label{gam.poisson}
This function runs a nonparametric Generalized Additive Model (GAM) for count dependent variables.
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + s(x2), model = "gam.poisson", data = mydata)
+> z.out <- zelig(y ~ x1 + s(x2), model = "poisson.gam", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -35,7 +35,7 @@ In addition to the standard inputs, {\tt zelig()} takes the following additional
\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
\item {\tt sp}: A vector of smoothing parameters for each term.
-\item {\tt \ldots}: additional options passed to the {\tt gam.poisson} model. See the {\tt mgcv} library for details.
+\item {\tt \ldots}: additional options passed to the {\tt poisson.gam} model. See the {\tt mgcv} library for details.
\end{itemize}
@@ -79,7 +79,7 @@ my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
Estimate the model, summarize the results, and plot nonlinearities:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.poisson", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="poisson.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1,residuals=TRUE)
@
@@ -137,7 +137,7 @@ plot(s.out)
An extra ridge penalty (useful with convergence problems):
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
- model="gam.poisson", data=my.data)
+ model="poisson.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1,residuals=TRUE)
@@ -145,7 +145,7 @@ plot(z.out,pages=1,residuals=TRUE)
Set the smoothing parameter for the first term, estimate the rest:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
- model="gam.poisson", data=my.data)
+ model="poisson.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1)
@@ -153,7 +153,7 @@ plot(z.out,pages=1)
Set lower bounds on smoothing parameters:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
- model="gam.poisson", data=my.data)
+ model="poisson.gam", data=my.data)
summary(z.out)
plot(z.out, pages=1)
@@ -161,7 +161,7 @@ plot(z.out, pages=1)
A GAM with 3df regression spline term \& 2 penalized terms:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
- model="gam.poisson", data=my.data)
+ model="poisson.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1)
@@ -173,7 +173,7 @@ plot(z.out,pages=1)
\subsubsection{Model}
-GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt gam.poisson} a Poisson link function is used. Specifically, let $Y_{i}$ be the dependent variable for observation $i$. $Y_i$ is thus the number of independent events that occur during a fixed time period. This variable can take any non-negative integer.
+GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt poisson.gam} a Poisson link function is used. Specifically, let $Y_{i}$ be the dependent variable for observation $i$. $Y_i$ is thus the number of independent events that occur during a fixed time period. This variable can take any non-negative integer.
\begin{itemize}
\item The Poisson distribution has \emph{stochastic component}
\begin{equation*}
@@ -207,9 +207,9 @@ Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
\subsubsection{Quantities of Interest}
-The quantities of interest for the {\tt gam.poisson} model are the same as those for the standard Poisson regression.
+The quantities of interest for the {\tt poisson.gam} model are the same as those for the standard Poisson regression.
\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt gam.poisson} model is the mean of simulations from the stochastic component,
+\item The expected value ({\tt qi\$ev}) for the {\tt poisson.gam} model is the mean of simulations from the stochastic component,
\begin{equation*}
E(Y) = \lambda_{i} = \exp \left(x_{i}\beta \sum_{j=1}^{J} f_j(Z_j) \right).
\end{equation*}
@@ -217,7 +217,7 @@ E(Y) = \lambda_{i} = \exp \left(x_{i}\beta \sum_{j=1}^{J} f_j(Z_j) \right).
\item The predicted value ({\tt qi\$pr}) is a random draw from the Poisson distribution defined by mean $\lambda_i$.
-\item The first difference ({\tt qi\$fd}) for the GAM Poisson model is defined as
+\item The first difference ({\tt qi\$fd}) for the poisson.gam model is defined as
\begin{equation*}
FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
\end{equation*}
@@ -230,7 +230,7 @@ for $w=\{X, Z\}$.
-The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "gam.poisson", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "poisson.gam", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -270,9 +270,9 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{gam.poisson} Zelig model:
+To cite the \emph{poisson.gam} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``gam.poisson: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``poisson.gam: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
\input{citeZelig}
diff --git a/inst/doc/gam.poisson.pdf b/inst/doc/poisson.gam.pdf
similarity index 80%
rename from inst/doc/gam.poisson.pdf
rename to inst/doc/poisson.gam.pdf
index 7a3804e..7ee5f60 100644
Binary files a/inst/doc/gam.poisson.pdf and b/inst/doc/poisson.gam.pdf differ
diff --git a/inst/doc/logit.gee.Rnw b/inst/doc/poisson.gee.Rnw
similarity index 58%
copy from inst/doc/logit.gee.Rnw
copy to inst/doc/poisson.gee.Rnw
index 84c1798..74bfff7 100644
--- a/inst/doc/logit.gee.Rnw
+++ b/inst/doc/poisson.gee.Rnw
@@ -1,10 +1,11 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/logit_gee}
+\SweaveOpts{results=hide, prefix.string=vigpics/poisson_gee}
\include{zinput}
-%\VignetteIndexEntry{logit.gee: Generalized Estimating Equation for Logistic Regression}
+%\VignetteIndexEntry{Generalized Estimating Equation for Poisson Regression}
%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, logistic regression}
+%\VignetteKeyWords{model,generalized estimating equation, poisson regression}
%\VignettePackage{Zelig}
\begin{document}
+
\nobibliography*
<<beforepkgs, echo=FALSE>>=
before=search()
@@ -14,24 +15,24 @@
library(Zelig)
@
-\section{\texttt{logit.gee}: Generalized Estimating Equation for Logistic Regression}
-\label{logit.gee}
+\section{\texttt{poisson.gee}: Generalized Estimating Equation for Poisson Regression}
+\label{poisson.gee}
-The GEE logit estimates the same model as the standard logistic
-regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables). Unlike in logistic
-regression, GEE logit allows for dependence within clusters, such as
+The GEE poisson estimates the same model as the standard poisson
+regression (appropriate when your dependent variable represents
+the number of independent events that occur during a fixed period of time). Unlike in poisson
+regression, GEE poisson allows for dependence within clusters, such as
in longitudinal data, although its use is not limited to just
panel data. The user must first specify a ``working''
correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
the standard errors can be obtained using a robust ``sandwich''
estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters.
+specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit.gee",
+> z.out <- zelig(Y ~ X1 + X2, model = "poisson.gee",
id = "X3", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
@@ -50,7 +51,7 @@ Use the following arguments to specify the structure of the ``working'' correlat
\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
\begin{itemize}
\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard logistic regression. The ``working'' correlation matrix is the identity matrix.
+ cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard poisson regression. The ``working'' correlation matrix is the identity matrix.
\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
@@ -130,125 +131,77 @@ $\left( \begin{array}{ccccc}
\subsubsection{Examples}
\begin{enumerate}
-\item {Example with Stationary 3 Dependence}
+\item {Example with Exchangeable Dependence}
Attaching the sample turnout dataset:
<<Example.data>>=
-data(turnout)
+data(sanction)
@
Variable identifying clusters
<<Example.cluster>>=
-turnout$cluster <- rep(c(1:200),10)
+sanction$cluster <- c(rep(c(1:15),5),rep(c(16),3))
@
Sorting by cluster
<<Example.sort>>=
-sorted.turnout <- turnout[order(turnout$cluster),]
+sorted.sanction <- sanction[order(sanction$cluster),]
@
-Estimating parameter values for the logistic regression:
+Estimating model and presenting summary:
<<Example.zelig>>=
-z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3)
+z.out <- zelig(num ~ target + coop, model = "poisson.gee", id = "cluster", data = sorted.sanction, robust=TRUE, corstr="exchangeable")
+summary(z.out)
@
-Setting values for the explanatory variables to their default values:
+Set explanatory variables to their default values:
<<Example.setx>>=
-x.out1 <- setx(z.out1)
+x.out <- setx(z.out)
@
-Simulating quantities of interest from the posterior distribution.
+Simulate quantities of interest
<<Example.sim>>=
-s.out1 <- sim(z.out1, x = x.out1)
-@
-<<Example.summary.sim>>=
-summary(s.out1)
+s.out <- sim(z.out, x = x.out)
+summary(s.out)
@
+Generate a plot of quantities of interest:
\begin{center}
<<label=ExamplePlot,fig=true,echo=true>>=
-plot(s.out1)
-@
-\end{center}
-
-\item {Simulating First Differences}
-
-Estimating the risk difference (and risk ratio) between low education
-(25th percentile) and high education (75th percentile) while all the
-other variables held at their default values.
-
-<<FirstDifference.setx>>=
-x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
-x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
-@
-
-<<FirstDifference.sim>>=
-s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
-@
-<<FirstDifference.summary.sim>>=
-summary(s.out2)
-@
-\begin{center}
-<<label=FirstDifferencePlot,fig=true,echo=true>>=
-plot(s.out2)
+plot(s.out)
@
\end{center}
-
-\item Example with Fixed Correlation Structure
-
-User-defined correlation structure
-<<Example2.corr>>=
-corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
-diag(corr.mat) <- 1
-@
-Generating empirical estimates:
-<<Example2.zelig>>=
-z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat)
-@
-Viewing the regression output:
-<<Example2.summary>>=
-summary(z.out2)
-@
\end{enumerate}
\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the binary
-dependent variable for unit $i$ at time $t$ which takes the value of
-either 0 or 1. $Y_{i}$ is a vector or cluster of correlated data
+Suppose we have a panel dataset, with $Y_{it}$ denoting the dependent variable of the number of independent events for a fixed period of time for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. In the GEE model, we must specify a mean function, a
-variance function, and the structure of the correlation matrix of the within-cluster
-observations. Observations are assumed to be correlated within each
-cluster but not across clusters.
-
+$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
\begin{itemize}
-\item The \emph{stochastic component} is given by the joint distribution
-
-\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \pi_{i})
-\end{eqnarray*}
-where $f$ is an unspecified multivariate distribution. The marginal distributions of $f$, which characterize each nonindependent unit-time observation $Y_{it}$, is given by
+\item The \emph{stochastic component} is given by the joint and marginal distributions
\begin{eqnarray*}
-Y_{it} &\sim& \mathrm{Bernoulli}(y_{it} \mid \pi_{it}) \\
- &=& \pi_{it}^{y_{it}} (1-\pi_{it})^{1-y_{it}}
+Y_{i} &\sim& f(y_{i} \mid \lambda_{i})\\
+Y_{it} &\sim& g(y_{it} \mid \lambda_{it})
\end{eqnarray*}
-where $\pi_{it}=\Pr(Y_{it}=1)$ for $t=1,...,T$. The correlations
-within each unit $i$ are modeled by defining the structure of a $T \times T$
-``working'' correlation matrix, which is specified by the user
-\textit{a priori}. Note that the model assumes correlations within $i$ but independence across $i$. The ``working'' correlation matrix then enters
-the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
+where $f$ and $g$ are unspecified distributions with means $\lambda_{i}$ and $\lambda_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-\item The \emph{systematic component} is given by:
+\item The \emph{systematic component} is the \textit{mean function}, given by:
\begin{equation*}
-\pi_{it} \; = \; \frac{1}{1 + \exp(-x_{it} \beta)}.
+\lambda_{it} = \mathrm{exp}(x_{it} \beta)
\end{equation*}
where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
and $\beta$ is the vector of coefficients.
+\item The \textit{variance function} is given by:
+\begin{equation*}
+V_{it} = \lambda_{it}
+\end{equation*}
+
+\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
+\begin{equation*}
+V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
+\end{equation*}
+where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = \lambda_{it}$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-\item GEE models require three specifications: a mean function (the systematic component above), a variance function (given by the variance of the Bernoulli stochastic component: $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$), and a correlation structure (the ``working'' correlation matrix above). If the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as [...]
+\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{fitlaiwar04} for more details.
+\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
\end{itemize}
@@ -257,25 +210,17 @@ and $\beta$ is the vector of coefficients.
\subsubsection{Quantities of Interest}
\begin{itemize}
\item All quantities of interest are for marginal means rather than joint means.
-\item The expected values ({\tt qi\$ev}) for the logit model are
- simulations of the predicted probability of a success: $$E(Y) =
- \pi_{c}= \frac{1}{1 + \exp(-x_{c} \beta)},$$ given draws of $\beta$ from
+\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
+\item The expected values ({\tt qi\$ev}) for the GEE poisson model is the mean of simulations from the stochastic component: $$E(Y) =
+ \lambda_{c} = \mathrm{exp}(x_{c} \beta),$$ given draws of $\beta$ from
its sampling distribution, where $x_{c}$ is a vector of values, one for
each independent variable, chosen by the user.
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
- distribution with mean equal to the simulated expected value $\pi_{c}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\item The first difference ({\tt qi\$fd}) for the GEE poisson model is defined as
\begin{equation*}
\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
\end{equation*}
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
-\end{equation*}
-
\item In conditional prediction models, the average expected treatment
effect ({\tt att.ev}) for the treatment group is
\begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
@@ -287,26 +232,13 @@ each independent variable, chosen by the user.
the counterfactual expected value of $Y_{it}$ for observations in the
treatment group, under the assumption that everything stays the
same except that the treatment indicator is switched to $tr_{it}=0$.
-
-\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 \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- \widehat{Y_{it}(tr_{it}=0)}\right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the
- treatment ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_{it}(tr_{it}=0)}$, the counterfactual predicted value of
- $Y_{it}$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $tr_{it}=0$.
\end{itemize}
\subsubsection{Output Values}
The output of each Zelig command contains useful information which you
may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "logit.gee", id, data, corstr)}, then you may examine the available
+ model = "poisson.gee", id, data)}, then you may examine the available
information in \texttt{z.out} by using \texttt{names(z.out)},
see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
a default summary of information through \texttt{summary(z.out)}.
@@ -322,7 +254,7 @@ below.
\item {\tt residuals}: the working residuals in the final iteration
of the fit.
\item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_{it}$.
+ systemic component, $\lambda_{it}$.
\item {\tt linear.predictors}: the vector of $x_{it}\beta$
\item {\tt max.id}: the size of the largest cluster.
\end{itemize}
@@ -340,39 +272,37 @@ below.
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
+ \item {\tt qi\$ev}: the simulated expected values for the
specified values of {\tt x}.
\item {\tt qi\$fd}: the simulated first difference in the expected
probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$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}
-\subsection* {How to Cite}
+\subsection*{How To Cite}
-To cite the \emph{logit.gee} Zelig model:
+To cite the \emph{poisson.gee} Zelig model:
\begin{verse}
-Patrick Lam. 2007. ``logit.gee: Generalized Estimating Equation for Logistic Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Patrick Lam. 2007. ``poisson.gee: Generalized Estimating Equation for Poisson Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
-\input{citeZelig}
+%\input{citeZelig}
-\subsection* {See also}
-The logit function is part of the base packages by William N. Venable \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are a selection of $2,000$ observations from \cite{KinTomWit00}
+\subsection*{See also}
+The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{Martin92}. Please inquire with Lisa Martin before publishing
+results from these data, as this dataset includes errors that have since
+been corrected.
\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
+\bibliography{gk}
+
<<afterpkgs, echo=FALSE>>=
after<-search()
torm<-setdiff(after,before)
for (pkg in torm)
detach(pos=match(pkg,search()))
@
+
\end{document}
diff --git a/inst/doc/poisson.gee.pdf b/inst/doc/poisson.gee.pdf
new file mode 100644
index 0000000..729fe84
Binary files /dev/null and b/inst/doc/poisson.gee.pdf differ
diff --git a/inst/doc/poisson.mixed.Rnw b/inst/doc/poisson.mixed.Rnw
new file mode 100644
index 0000000..03277c2
--- /dev/null
+++ b/inst/doc/poisson.mixed.Rnw
@@ -0,0 +1,186 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/poissonmixed}
+\include{zinput}
+%\VignetteIndexEntry{Mixed effects poisson regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,poisson regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt poisson.mixed}: Mixed effects poisson Regression}
+\label{mixed}
+
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. Poisson regression applies to dependent variables that represent the number of independent events that occur during a fixed period of time.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="poisson.mixed")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+ gamma= ~ tag(w1 + w2 | g)), data=mydata, model="poisson.mixed")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
+\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
+\end{itemize}
+Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(homerun)
+@
+Estimate model:
+<<Examples.zelig>>=
+z.out1 <- zelig(homeruns ~ player + tag(player - 1 | month), data=homerun, model="poisson.mixed")
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+Set explanatory variables to their default values:\\
+<<Examples.setx>>=
+x.out <- setx(z.out1)
+@
+Simulate draws using the default bootstrap method and view simulated quantities of interest: \\
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.out)
+summary(s.out1)
+@
+
+\end{enumerate}
+
+
+\subsubsection{Mixed effects Poisson Regression Model}
+
+Let $Y_{ij}$ be the number of independent events that occur during a fixed time period, realized for observation $j$ in group $i$ as $y_{ij}$, which takes any non-negative integer as its value, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a Poisson distribution with mean and variance parameter $\lambda_{ij}$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Poisson}(y_{ij} | \lambda_{ij}) = \frac{\exp(-\lambda_{ij}) \lambda_{ij}^{y_{ij}}}{y_{ij}!}
+\end{equation*}
+where
+\begin{equation*}
+y_{ij} = 0, 1, \ldots
+\end{equation*}
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\lambda_{ij} \equiv \exp(X_{ij} \beta + Z_{ij} b_i)
+\end{equation*}
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The predicted values ({\tt qi\$pr}) are draws from the poisson distribution defined by mean $ \lambda_{ij} $, for
+\begin{equation*}
+\lambda_{ij} = \exp(X_{ij} \beta + Z_{ij} b_i)
+\end{equation*}
+given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+
+\item The expected values ({\tt qi\$ev}) is the mean of simulations of the stochastic component given draws of $\beta$ from its posterior:
+\begin{equation*}
+E(Y_{ij} | X_{ij}) = \lambda_{ij} = \exp(X_{ij} \beta).
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in expected values, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = E(Y_{ij} | X_{ij}) - E(Y_{ij} | X_{ij}^\prime)
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+
+\subsection* {How to Cite}
+
+To cite the \emph{poisson.mixed} Zelig model:
+\begin{verse}
+Delia Bailey, Ferdinand Alimadhi. 2007. ``poisson.mixed: Mixed effects poisson regression'' 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}
+Mixed effects poisson regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\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/poisson.mixed.pdf b/inst/doc/poisson.mixed.pdf
new file mode 100644
index 0000000..58adc9f
Binary files /dev/null and b/inst/doc/poisson.mixed.pdf differ
diff --git a/inst/doc/netpoisson.Rnw b/inst/doc/poisson.net.Rnw
similarity index 73%
rename from inst/doc/netpoisson.Rnw
rename to inst/doc/poisson.net.Rnw
index cb5c9f8..d3d18bf 100644
--- a/inst/doc/netpoisson.Rnw
+++ b/inst/doc/poisson.net.Rnw
@@ -1,4 +1,4 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/netpoisson}
+\SweaveOpts{results=hide, prefix.string=vigpics/poissonnet}
\include{zinput}
%\VignetteIndexEntry{Network Poisson Regression for Event Count Proximity Matrix Dependent Variables}
%\VignetteDepends{Zelig, stats}
@@ -13,13 +13,13 @@
library(Zelig)
@
-\section{{\tt netpoisson}: Network Poisson Regression for Event Count Proximity Matrix Dependent Variables}
+\section{{\tt poisson.net}: Network Poisson Regression for Event Count Proximity Matrix Dependent Variables}
Use network Poisson regression analysis for a dependent variable that represents the number of events that occur during a fixed period of time as a proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netpoisson", data = mydata)
+> z.out <- zelig(y ~ x1 + x2, model = "poisson.net", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -47,7 +47,7 @@ data(friendship)
Estimate model:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(count ~ advice + prestige + perpower, model = "netpoisson", data = friendship)
+z.out <- zelig(count ~ advice + prestige + perpower, model = "poisson.net", data = friendship)
summary(z.out)
@
@@ -79,7 +79,7 @@ plot(s.out)
\subsubsection{Model}
-The {\tt netpoisson} model performs a Poisson regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard Poisson regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from th [...]
+The {\tt poisson.net} model performs a Poisson regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard Poisson regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from t [...]
Let $Y_{i}$ be the dependent variable, produced by vectorizing an event count proximity matrix, for observation $i$. $Y_i$ is thus the number of independent events that occur during a fixed time period. This variable can take any non-negative integer.
\begin{itemize}
@@ -99,7 +99,7 @@ where $ x_i$ is the vector of explanatory variables and $\beta$ is the vector of
\subsubsection{Quantities of Interest}
The quantities of interest for the network Poisson regression are the same as those for the standard Poisson regression.
\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt netpoisson} model is the mean of simulations from the stochastic component,
+\item The expected value ({\tt qi\$ev}) for the {\tt poisson.net} model is the mean of simulations from the stochastic component,
\begin{equation*}
E(Y) = \lambda_{i} = \exp(x_{i}\beta),
\end{equation*}
@@ -116,7 +116,7 @@ FD = \text{Pr}(Y| x_{1}) - \text{Pr}(Y| x)
\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "netpoisson", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "poisson.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -147,14 +147,14 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{netpoisson} Zelig model:
+To cite the \emph{poisson.net} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``netpoisson: Network Poisson Regression for Event Count Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``poisson.net: Network Poisson Regression for Event Count Proximity Matrix 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}
-The network normal regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netpoisson)}. Sample data are fictional.
+The network normal regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(poisson.net)}. Sample data are fictional.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/poisson.net.pdf b/inst/doc/poisson.net.pdf
new file mode 100644
index 0000000..52df532
Binary files /dev/null and b/inst/doc/poisson.net.pdf differ
diff --git a/inst/doc/poisson.pdf b/inst/doc/poisson.pdf
index 25a0768..dfc8487 100644
Binary files a/inst/doc/poisson.pdf and b/inst/doc/poisson.pdf differ
diff --git a/inst/doc/probit.Rnw b/inst/doc/probit.Rnw
index d9c41d5..a6fe696 100644
--- a/inst/doc/probit.Rnw
+++ b/inst/doc/probit.Rnw
@@ -222,7 +222,11 @@ Kosuke Imai, Gary King, and Olivia Lau. 2007. ``probit: Probit Regression for Di
\input{citeZelig}
\subsection* {See also}
-The gamma model is part of the base packages by William N. Venable and Brian D. Ripley \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are from \cite{KinTomWit00}.
+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}. Robust standard
+errors are implemented via the sandwich package by \citet{Zeileis04}.
+Sample data are from \cite{KinTomWit00}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/probit.bayes.pdf b/inst/doc/probit.bayes.pdf
index ec8d6f1..6d62054 100644
Binary files a/inst/doc/probit.bayes.pdf and b/inst/doc/probit.bayes.pdf differ
diff --git a/inst/doc/gam.probit.Rnw b/inst/doc/probit.gam.Rnw
similarity index 92%
rename from inst/doc/gam.probit.Rnw
rename to inst/doc/probit.gam.Rnw
index 7e1427d..8a0b7a3 100644
--- a/inst/doc/gam.probit.Rnw
+++ b/inst/doc/probit.gam.Rnw
@@ -2,7 +2,7 @@
\include{zinput}
%\VignetteIndexEntry{Generalized Additive Model for Dichotomous Dependent Variables}
%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, gam, gam.probit}
+%\VignetteKeyWords{model, gam, probit.gam}
%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
@@ -13,13 +13,13 @@
library(Zelig)
@
-\section{{\tt gam.probit}: Generalized Additive Model for Dichotomous Dependent Variables}
+\section{{\tt probit.gam}: Generalized Additive Model for Dichotomous Dependent Variables}
This function runs a nonparametric Generalized Additive Model (GAM) for dichotomous dependent variables.
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + s(x2), model = "gam.probit", data = mydata)
+> z.out <- zelig(y ~ x1 + s(x2), model = "probit.gam", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -33,7 +33,7 @@ In addition to the standard inputs, {\tt zelig()} takes the following additional
\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
\item {\tt sp}: A vector of smoothing parameters for each term.
-\item {\tt \ldots}: additional options passed to the {\tt gam.probit} model. See the {\tt mgcv} library for details.
+\item {\tt \ldots}: additional options passed to the {\tt probit.gam} model. See the {\tt mgcv} library for details.
\end{itemize}
@@ -75,7 +75,7 @@ my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
Estimate the model, summarize the results, and plot nonlinearities:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.probit", data=my.data)
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="probit.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1,residuals=TRUE)
@
@@ -133,7 +133,7 @@ plot(s.out)
An extra ridge penalty (useful with convergence problems):
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
- model="gam.probit", data=my.data)
+ model="probit.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1,residuals=TRUE)
@@ -141,7 +141,7 @@ plot(z.out,pages=1,residuals=TRUE)
Set the smoothing parameter for the first term, estimate the rest:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
- model="gam.probit", data=my.data)
+ model="probit.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1)
@@ -149,7 +149,7 @@ plot(z.out,pages=1)
Set lower bounds on smoothing parameters:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
- model="gam.probit", data=my.data)
+ model="probit.gam", data=my.data)
summary(z.out)
plot(z.out, pages=1)
@@ -157,7 +157,7 @@ plot(z.out, pages=1)
A GAM with 3df regression spline term \& 2 penalized terms:
<<echo=TRUE, results=hide, fig=FALSE>>=
z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
- model="gam.probit", data=my.data)
+ model="probit.gam", data=my.data)
summary(z.out)
plot(z.out,pages=1)
@@ -169,7 +169,7 @@ plot(z.out,pages=1)
\subsubsection{Model}
-GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt gam.probit} a normal link function is used. Specifically, let $Y_{i}$ be the binary dependent variable for observation $i$ which takes the value of either 0 or 1.
+GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt probit.gam} a normal link function is used. Specifically, let $Y_{i}$ be the binary dependent variable for observation $i$ which takes the value of either 0 or 1.
\begin{itemize}
\item The normal distribution has \emph{stochastic component}
\begin{equation*}
@@ -203,9 +203,9 @@ Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
\subsubsection{Quantities of Interest}
-The quantities of interest for the {\tt gam.probit} model are the same as those for the standard normal regression.
+The quantities of interest for the {\tt probit.gam} model are the same as those for the standard normal regression.
\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt gam.probit} model is the mean of simulations from the stochastic component,
+\item The expected value ({\tt qi\$ev}) for the {\tt probit.gam} model is the mean of simulations from the stochastic component,
\begin{equation*}
\pi_{i} = \pmb{\Phi} \left( x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right).
\end{equation*}
@@ -213,7 +213,7 @@ The quantities of interest for the {\tt gam.probit} model are the same as those
\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 GAM probit model is defined as
+\item The first difference ({\tt qi\$fd}) for the probit.gam model is defined as
\begin{equation*}
FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
\end{equation*}
@@ -226,7 +226,7 @@ for $w=\{X, Z\}$.
-The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "gam.probit", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "probit.gam", data)}, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt coefficients(z.out)}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -266,9 +266,9 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{gam.probit} Zelig model:
+To cite the \emph{probit.gam} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``gam.probit: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``probit.gam: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
\input{citeZelig}
diff --git a/inst/doc/gam.probit.pdf b/inst/doc/probit.gam.pdf
similarity index 80%
rename from inst/doc/gam.probit.pdf
rename to inst/doc/probit.gam.pdf
index dbb6cea..09e1f8b 100644
Binary files a/inst/doc/gam.probit.pdf and b/inst/doc/probit.gam.pdf differ
diff --git a/inst/doc/logit.gee.Rnw b/inst/doc/probit.gee.Rnw
similarity index 67%
copy from inst/doc/logit.gee.Rnw
copy to inst/doc/probit.gee.Rnw
index 84c1798..a0b1769 100644
--- a/inst/doc/logit.gee.Rnw
+++ b/inst/doc/probit.gee.Rnw
@@ -1,37 +1,37 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/logit_gee}
+\SweaveOpts{results=hide, prefix.string=vigpics/probit_gee}
\include{zinput}
-%\VignetteIndexEntry{logit.gee: Generalized Estimating Equation for Logistic Regression}
+%\VignetteIndexEntry{Generalized Estimating Equation for Probit Regression}
%\VignetteDepends{Zelig, gee}
-%\VignetteKeyWords{model,generalized estimating equation, logistic regression}
+%\VignetteKeyWords{model,generalized estimating equation, probit regression}
%\VignettePackage{Zelig}
\begin{document}
+
\nobibliography*
<<beforepkgs, echo=FALSE>>=
before=search()
@
-
<<loadLibrary, echo=F,results=hide>>=
library(Zelig)
@
-\section{\texttt{logit.gee}: Generalized Estimating Equation for Logistic Regression}
-\label{logit.gee}
+\section{\texttt{probit.gee}: Generalized Estimating Equation for Probit Regression}
+\label{probit.gee}
-The GEE logit estimates the same model as the standard logistic
+The GEE probit estimates the same model as the standard probit
regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables). Unlike in logistic
-regression, GEE logit allows for dependence within clusters, such as
+variable and a set of explanatory variables). Unlike in probit
+regression, GEE probit allows for dependence within clusters, such as
in longitudinal data, although its use is not limited to just
panel data. The user must first specify a ``working''
correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
the standard errors can be obtained using a robust ``sandwich''
estimator even if the ``working'' correlation matrix is incorrectly
-specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters.
+specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters. GEE models measure population-averaged effects as opposed to cluster-specific effects (See \citet{Zorn01}).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit.gee",
+> z.out <- zelig(Y ~ X1 + X2, model = "probit.gee",
id = "X3", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
@@ -50,7 +50,7 @@ Use the following arguments to specify the structure of the ``working'' correlat
\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
\begin{itemize}
\item Independence (\texttt{corstr = "independence"}): ${\rm
- cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard logistic regression. The ``working'' correlation matrix is the identity matrix.
+ cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard probit regression. The ``working'' correlation matrix is the identity matrix.
\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
@@ -144,15 +144,15 @@ Sorting by cluster
<<Example.sort>>=
sorted.turnout <- turnout[order(turnout$cluster),]
@
-Estimating parameter values for the logistic regression:
+Estimating parameter values:
<<Example.zelig>>=
-z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3)
+z.out1 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3)
@
Setting values for the explanatory variables to their default values:
<<Example.setx>>=
x.out1 <- setx(z.out1)
@
-Simulating quantities of interest from the posterior distribution.
+Simulating quantities of interest:
<<Example.sim>>=
s.out1 <- sim(z.out1, x = x.out1)
@
@@ -165,6 +165,10 @@ plot(s.out1)
@
\end{center}
+<<echo=F, results=hide>>=
+options(width=80)
+@
+
\item {Simulating First Differences}
Estimating the risk difference (and risk ratio) between low education
@@ -188,6 +192,10 @@ plot(s.out2)
@
\end{center}
+<<echo=F, results=hide>>=
+options(width=75)
+@
+
\item Example with Fixed Correlation Structure
User-defined correlation structure
@@ -197,7 +205,7 @@ diag(corr.mat) <- 1
@
Generating empirical estimates:
<<Example2.zelig>>=
-z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat)
+z.out2 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat)
@
Viewing the regression output:
<<Example2.summary>>=
@@ -206,67 +214,55 @@ summary(z.out2)
\end{enumerate}
\subsubsection{The Model}
-Suppose we have a panel dataset, with $Y_{it}$ denoting the binary
-dependent variable for unit $i$ at time $t$ which takes the value of
-either 0 or 1. $Y_{i}$ is a vector or cluster of correlated data
+Suppose we have a panel dataset, with $Y_{it}$ denoting the binary dependent variable for unit $i$ at time $t$. $Y_{i}$ is a vector or cluster of correlated data
where $y_{it}$ is correlated with $y_{it^\prime}$ for some or all
-$t, t^\prime$. In the GEE model, we must specify a mean function, a
-variance function, and the structure of the correlation matrix of the within-cluster
-observations. Observations are assumed to be correlated within each
-cluster but not across clusters.
-
+$t, t^\prime$. Note that the model assumes correlations within $i$ but independence across $i$.
\begin{itemize}
-\item The \emph{stochastic component} is given by the joint distribution
-
-\begin{eqnarray*}
-Y_{i} &\sim& f(y_{i} \mid \pi_{i})
-\end{eqnarray*}
-where $f$ is an unspecified multivariate distribution. The marginal distributions of $f$, which characterize each nonindependent unit-time observation $Y_{it}$, is given by
+\item The \emph{stochastic component} is given by the joint and marginal distributions
\begin{eqnarray*}
-Y_{it} &\sim& \mathrm{Bernoulli}(y_{it} \mid \pi_{it}) \\
- &=& \pi_{it}^{y_{it}} (1-\pi_{it})^{1-y_{it}}
+Y_{i} &\sim& f(y_{i} \mid \pi_{i})\\
+Y_{it} &\sim& g(y_{it} \mid \pi_{it})
\end{eqnarray*}
-where $\pi_{it}=\Pr(Y_{it}=1)$ for $t=1,...,T$. The correlations
-within each unit $i$ are modeled by defining the structure of a $T \times T$
-``working'' correlation matrix, which is specified by the user
-\textit{a priori}. Note that the model assumes correlations within $i$ but independence across $i$. The ``working'' correlation matrix then enters
-the variance term for each $i$, given by:
-\begin{equation*}
-V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
-\end{equation*}
-where $A_{i}$ is a $T \times T$ diagonal matrix with $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
+where $f$ and $g$ are unspecified distributions with means $\pi_{i}$ and $\pi_{it}$. GEE models make no distributional assumptions and only require three specifications: a mean function, a variance function, and a correlation structure.
-\item The \emph{systematic component} is given by:
+\item The \emph{systematic component} is the \textit{mean function}, given by:
\begin{equation*}
-\pi_{it} \; = \; \frac{1}{1 + \exp(-x_{it} \beta)}.
+\pi_{it} = \Phi(x_{it} \beta)
\end{equation*}
-where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
+where $\Phi(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance, $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
and $\beta$ is the vector of coefficients.
+\item The \textit{variance function} is given by:
+\begin{equation*}
+V_{it} = \pi_{it} (1-\pi_{it})
+\end{equation*}
-\item GEE models require three specifications: a mean function (the systematic component above), a variance function (given by the variance of the Bernoulli stochastic component: $var(\pi_{it}) = \pi_{it} (1-\pi_{it})$), and a correlation structure (the ``working'' correlation matrix above). If the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as [...]
+\item The \textit{correlation structure} is defined by a $T \times T$ ``working'' correlation matrix, where $T$ is the size of the largest cluster. Users must specify the structure of the ``working'' correlation matrix \textit{a priori}. The ``working'' correlation matrix then enters the variance term for each $i$, given by:
+\begin{equation*}
+V_{i} = \phi \, A_{i}^{\frac{1}{2}} R_{i}(\alpha) A_{i}^{\frac{1}{2}}
+\end{equation*}
+where $A_{i}$ is a $T \times T$ diagonal matrix with the variance function $V_{it} = \pi_{it} (1-\pi_{it})$ as the $t$th diagonal element, $R_{i}(\alpha)$ is the ``working'' correlation matrix, and $\phi$ is a scale parameter. The parameters are then estimated via a quasi-likelihood approach.
-\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{fitlaiwar04} for more details.
+\item In GEE models, if the mean is correctly specified, but the variance and correlation structure are incorrectly specified, then GEE models provide consistent estimates of the parameters and thus the mean function as well, while consistent estimates of the standard errors can be obtained via a robust ``sandwich'' estimator. Similarly, if the mean and variance are correctly specified but the correlation structure is incorrectly specified, the parameters can be estimated consistently a [...]
-\end{itemize}
+\item The robust ``sandwich'' estimator gives consistent estimates of the standard errors when the correlations are specified incorrectly only if the number of units $i$ is relatively large and the number of repeated periods $t$ is relatively small. Otherwise, one should use the ``na\"{i}ve'' model-based standard errors, which assume that the specified correlations are close approximations to the true underlying correlations. See \citet{FitLaiWar04} for more details.
+\end{itemize}
\subsubsection{Quantities of Interest}
\begin{itemize}
\item All quantities of interest are for marginal means rather than joint means.
-\item The expected values ({\tt qi\$ev}) for the logit model are
+\item The method of bootstrapping generally should not be used in GEE models. If you must bootstrap, bootstrapping should be done within clusters, which is not currently supported in Zelig. For conditional prediction models, data should be matched within clusters.
+\item The expected values ({\tt qi\$ev}) for the GEE probit model are
simulations of the predicted probability of a success: $$E(Y) =
- \pi_{c}= \frac{1}{1 + \exp(-x_{c} \beta)},$$ given draws of $\beta$ from
+ \pi_{c}= \Phi(x_{c} \beta),$$ given draws of $\beta$ from
its sampling distribution, where $x_{c}$ is a vector of values, one for
each independent variable, chosen by the user.
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
- distribution with mean equal to the simulated expected value $\pi_{c}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\item The first difference ({\tt qi\$fd}) for the GEE probit model is defined as
\begin{equation*}
\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
\end{equation*}
@@ -288,25 +284,13 @@ each independent variable, chosen by the user.
treatment group, under the assumption that everything stays the
same except that the treatment indicator is switched to $tr_{it}=0$.
-\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 \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
- \widehat{Y_{it}(tr_{it}=0)}\right\},
- \end{equation*}
- where $tr_{it}$ is a binary explanatory variable defining the
- treatment ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_{it}(tr_{it}=0)}$, the counterfactual predicted value of
- $Y_{it}$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $tr_{it}=0$.
\end{itemize}
\subsubsection{Output Values}
The output of each Zelig command contains useful information which you
may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "logit.gee", id, data, corstr)}, then you may examine the available
+ model = "probit.gee", id, data)}, then you may examine the available
information in \texttt{z.out} by using \texttt{names(z.out)},
see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
a default summary of information through \texttt{summary(z.out)}.
@@ -342,37 +326,36 @@ below.
\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}
-\subsection* {How to Cite}
+\subsection*{How To Cite}
-To cite the \emph{logit.gee} Zelig model:
+To cite the \emph{probit.gee} Zelig model:
\begin{verse}
-Patrick Lam. 2007. ``logit.gee: Generalized Estimating Equation for Logistic Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Patrick Lam. 2007. ``probit.gee: Generalized Estimating Equation for Probit Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
-\input{citeZelig}
+%\input{citeZelig}
-\subsection* {See also}
-The logit function is part of the base packages by William N. Venable \citep{VenRip02}. Advanced users may wish to refer to \texttt{help(glm)} and \texttt{help(family)}, as well as \cite{McCNel89}. Robust standard errors are implemented via sandwich package by Achim Zeileis \citep{Zeileis04}.Sample data are a selection of $2,000$ observations from \cite{KinTomWit00}
+\subsection*{See also}
+The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
+\bibliography{gk}
+
<<afterpkgs, echo=FALSE>>=
after<-search()
torm<-setdiff(after,before)
for (pkg in torm)
detach(pos=match(pkg,search()))
@
+
+
\end{document}
diff --git a/inst/doc/probit.gee.pdf b/inst/doc/probit.gee.pdf
new file mode 100644
index 0000000..5a8f9d7
Binary files /dev/null and b/inst/doc/probit.gee.pdf differ
diff --git a/inst/doc/probit.mixed.Rnw b/inst/doc/probit.mixed.Rnw
new file mode 100644
index 0000000..f7be4c3
--- /dev/null
+++ b/inst/doc/probit.mixed.Rnw
@@ -0,0 +1,192 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/probitmixed}
+\include{zinput}
+%\VignetteIndexEntry{Mixed effects probit regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,probit regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt probit.mixed}: Mixed effects probit Regression}
+\label{mixed}
+
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The probit model is appropriate when the dependent variable is dichotomous.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="probit.mixed")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+ gamma= ~ tag(w1 + w2 | g)), data=mydata, model="probit.mixed")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
+\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
+\end{itemize}
+Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(voteincome)
+@
+Estimate model:
+<<Examples.zelig>>=
+z.out1 <- zelig(vote ~ education + age + female + tag(1 | state), data=voteincome, model="probit.mixed")
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
+<<Examples.setx>>=
+x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
+x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
+@
+Generate first differences for the effect of high versus low education on voting: \\
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+@
+
+\end{enumerate}
+
+\subsubsection{Mixed effects probit Regression Model}
+
+Let $Y_{ij}$ be the binary dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$ which takes the value of either 0 or 1, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a Bernoulli distribution with mean vector $\pi_{ij}$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Bernoulli}(y_{ij} | \pi_{ij}) = \pi_{ij}^{y_{ij}} (1 - \pi_{ij})^{1 - y_{ij}}
+\end{equation*}
+where
+\begin{equation*}
+\pi_{ij} = \mathrm{Pr}(Y_{ij} = 1)
+\end{equation*}
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\pi_{ij} \equiv \Phi(X_{ij} \beta + Z_{ij} b_i)
+\end{equation*}
+where $\Phi(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance, and \\
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value, $\pi_{ij}$ for
+\begin{equation*}
+\pi_{ij} = \Phi(X_{ij} \beta + Z_{ij} b_i)
+\end{equation*}
+given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+
+\item The expected values ({\tt qi\$ev}) are simulations of the predicted probability of a success given draws of $\beta$ from its posterior:
+\begin{equation*}
+E(Y_{ij} | X_{ij}) = \pi_{ij} = \Phi(X_{ij} \beta).
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in predicted probabilities, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = Pr(Y_{ij} = 1 | X_{ij}) - Pr(Y_{ij} = 1 | X_{ij}^\prime)
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+RR(Y_{ij} | X_{ij}, X_{ij}^{\prime}) = \frac{Pr(Y_{ij} = 1 | X_{ij})}{Pr(Y_{ij} = 1 | X_{ij}^{\prime})}
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+
+\subsection* {How to Cite}
+
+To cite the \emph{probit.mixed} Zelig model:
+\begin{verse}
+Delia Bailey, Ferdinand Alimadhi. 2007. ``probit.mixed: Mixed effects probit regression'' 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}
+Mixed effects probit regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\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.mixed.pdf b/inst/doc/probit.mixed.pdf
new file mode 100644
index 0000000..78aeffb
Binary files /dev/null and b/inst/doc/probit.mixed.pdf differ
diff --git a/inst/doc/netprobit.Rnw b/inst/doc/probit.net.Rnw
similarity index 75%
rename from inst/doc/netprobit.Rnw
rename to inst/doc/probit.net.Rnw
index 48f43c0..b95a3f8 100644
--- a/inst/doc/netprobit.Rnw
+++ b/inst/doc/probit.net.Rnw
@@ -1,4 +1,4 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/netprobit}
+\SweaveOpts{results=hide, prefix.string=vigpics/probitnet}
\include{zinput}
%\VignetteIndexEntry{Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables}
%\VignetteDepends{Zelig, stats}
@@ -13,13 +13,13 @@
library(Zelig)
@
-\section{{\tt netprobit}: Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables}
+\section{{\tt probit.net}: Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables}
Use network probit regression analysis for a dependent variable that is a binary valued proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs).
\subsubsection{Syntax}
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netprobit", data = mydata)
+> z.out <- zelig(y ~ x1 + x2, model = "probit.net", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
@@ -40,7 +40,7 @@ data(friendship)
Estimate model:
<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(friends ~ advice + prestige + perpower, model = "netprobit", data = friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model = "probit.net", data = friendship)
summary(z.out)
@
@@ -94,7 +94,7 @@ plot(s.out)
\subsubsection{Model}
-The {\tt netprobit} model performs a probit regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard probit regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the $ [...]
+The {\tt probit.net} model performs a probit regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard probit regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from the [...]
Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
\begin{itemize}
@@ -114,7 +114,7 @@ where $ \pmb{\Phi}(\mu)$ is the cumulative distribution function of the Normal d
\subsubsection{Quantities of Interest}
The quantities of interest for the network probit regression are the same as those for the standard probit regression.
\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the {\tt netprobit} model are simulations of the predicted probability of a success:
+\item The expected values ({\tt qi\$ev}) for the {\tt probit.net} model are simulations of the predicted probability of a success:
\begin{equation*}
E(Y) = \pi_{i} = \pmb{\Phi}(x_{i}\beta),
\end{equation*}
@@ -131,7 +131,7 @@ FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "netprobit", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "probit.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
\begin{itemize}
\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
\begin{itemize}
@@ -163,9 +163,9 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{netprobit} Zelig model:
+To cite the \emph{probit.net} Zelig model:
\begin{verse}
-Skyler J. Cranmer. 2007. ``netprobit: Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+Skyler J. Cranmer. 2007. ``probit.net: Network Probit Regression for Dichotomous Proximity Matrix 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}
diff --git a/inst/doc/netprobit.pdf b/inst/doc/probit.net.pdf
similarity index 50%
rename from inst/doc/netprobit.pdf
rename to inst/doc/probit.net.pdf
index 613f851..351f485 100644
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diff --git a/inst/doc/probit.pdf b/inst/doc/probit.pdf
index 0b9f4b4..01f7d90 100644
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diff --git a/inst/doc/refman.tex b/inst/doc/refman.tex
index 7135c9f..34c1a14 100644
--- a/inst/doc/refman.tex
+++ b/inst/doc/refman.tex
@@ -79,9 +79,13 @@ the distribution of the expected values tends toward a constant; the
distribution of the predicted values does not collapse as the number
of simulations increases.
+\include{aov}
\include{arima}
\include{blogit}
\include{bprobit}
+\include{chopit}
+\include{cloglog.net}
+\include{coxph}
\include{ei.dynamic}
\include{ei.hier}
\include{ei.RxC}
@@ -90,38 +94,45 @@ of simulations increases.
\include{factor.mix}
\include{factor.ord}
\include{gamma}
-\include{gee.logit}
-\include{gam.logit}
-\include{gam.normal}
-\include{gam.poisson}
-\include{gam.probit}
+\include{gamma.gee}
+\include{gamma.mixed}
+\include{gamma.net}
\include{irt1d}
\include{irtkd}
\include{logit}
-\include{logit.gee}
\include{logit.bayes}
+\include{logit.gam}
+\include{logit.gee}
+\include{logit.mixed}
+\include{logit.net}
\include{lognorm}
\include{ls}
+\include{ls.mixed}
+\include{ls.net}
\include{mlogit}
\include{mlogit.bayes}
\include{mloglm}
\include{negbin}
-\include{netls}
-\include{netlogit}
-\include{netcloglog}
-\include{netgamma}
-\include{netnormal}
-\include{netpoisson}
-\include{netprobit}
\include{normal}
\include{normal.bayes}
+\include{normal.gam}
+\include{normal.gee}
+\include{normal.net}
\include{ologit}
\include{oprobit}
\include{oprobit.bayes}
\include{poisson}
\include{poisson.bayes}
+\include{poisson.gam}
+\include{poisson.gee}
+\include{poisson.mixed}
+\include{poisson.net}
\include{probit}
\include{probit.bayes}
+\include{probit.gam}
+\include{probit.gee}
+\include{probit.mixed}
+\include{probit.net}
\include{relogit}
\include{sur}
\include{threesls}
@@ -130,6 +141,7 @@ of simulations increases.
\include{twosls}
\include{weibull}
+
\chapter{Commands for Programmers and Contributors}
\input{commands/describe.mymodel}
diff --git a/inst/doc/relogit.pdf b/inst/doc/relogit.pdf
index ab55f00..17a74d9 100644
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diff --git a/inst/doc/static/Zcommands.pdf b/inst/doc/static/Zcommands.pdf
index bffc5a9..fdb18cf 100644
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diff --git a/inst/doc/static/acknowledgments.pdf b/inst/doc/static/acknowledgments.pdf
index 4710bed..c173047 100644
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diff --git a/inst/doc/static/addingModels.pdf b/inst/doc/static/addingModels.pdf
index e13d79f..3192944 100644
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diff --git a/inst/doc/static/advancedR.pdf b/inst/doc/static/advancedR.pdf
index 57bd338..63375c7 100644
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diff --git a/inst/doc/static/faq.pdf b/inst/doc/static/faq.pdf
index b154486..6ac52e1 100644
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diff --git a/inst/doc/static/graphs.pdf b/inst/doc/static/graphs.pdf
index b397dfd..79b7478 100644
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diff --git a/inst/doc/static/install.pdf b/inst/doc/static/install.pdf
index a42d381..f3de3da 100644
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diff --git a/inst/doc/static/intro.pdf b/inst/doc/static/intro.pdf
index 3d0ab71..b4fe7ad 100644
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diff --git a/inst/doc/static/newModels.pdf b/inst/doc/static/newModels.pdf
index f5a8c46..51813f6 100644
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diff --git a/inst/doc/static/syntax.pdf b/inst/doc/static/syntax.pdf
index 8ef3df0..c4b6163 100644
Binary files a/inst/doc/static/syntax.pdf and b/inst/doc/static/syntax.pdf differ
diff --git a/inst/doc/sur.Rnw b/inst/doc/sur.Rnw
index 111c406..7576745 100644
--- a/inst/doc/sur.Rnw
+++ b/inst/doc/sur.Rnw
@@ -22,7 +22,7 @@ be viewed as a special case of generalized least squares.
\subsubsection{Syntax}
\begin{verbatim}
> fml <- list ("mu1" = Y1 ~ X1,
- "mu2" = Y2 ~ X2,
+ "mu2" = Y2 ~ X2,
"mu3" = Y3 ~ X3)
> z.out<-zelig(formula = fml, model = "2sls", data = mydata)
> x.out <- setx(z.out)
@@ -37,7 +37,7 @@ the $M$ equations and their respective dependent and explanatory variables.
For example, when there are no constraints on the coefficients:
<<Inputs.list1>>=
fml <- list ("mu1" = Y1 ~ X1,
- "mu2" = Y2 ~ X2,
+ "mu2" = Y2 ~ X2,
"mu3" = Y3 ~ X3)
@
\texttt{"mu1"} is the label for the first equation with Y1 as the dependent variable
@@ -50,7 +50,7 @@ parameter is label for the constrained coefficient. Each label uniquely identifi
the constrained coefficient. For example:
<<Inputs.list2>>=
fml <- list ("mu1" = Y1 ~ tag(Xc,"constrain1")+ X1,
- "mu2" = Y2 ~ tag(Xc,"constrain1")+ X2,
+ "mu2" = Y2 ~ tag(Xc,"constrain1")+ X2,
"mu3" = Y3 ~ X3)
@
\end{itemize}
@@ -203,8 +203,8 @@ Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. ``sur: Seemingly Unrelate
\input{citeZelig}
\subsection* {See also}
-The sur function is adapted from the \texttt{systemfit} library by Jeff Hamann
-and Arne Henningsen \citep{HamaHenn05}.
+The sur function is adapted from the \texttt{systemfit} library
+\citep{HamHen05}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/sur.pdf b/inst/doc/sur.pdf
index 3552935..11fab9d 100644
Binary files a/inst/doc/sur.pdf and b/inst/doc/sur.pdf differ
diff --git a/inst/doc/threesls.Rnw b/inst/doc/threesls.Rnw
index 458a6e8..9ad3af2 100644
--- a/inst/doc/threesls.Rnw
+++ b/inst/doc/threesls.Rnw
@@ -200,8 +200,8 @@ Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. ``threesls: Three Stage L
\input{citeZelig}
\subsection* {See also}
-The threesls function is adapted from the \texttt{systemfit} library by Jeff Hamann
-and Arne Henningsen \citep{HamaHenn05}.
+The threesls function is adapted from the \texttt{systemfit} library
+\citep{HamHen05}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/threesls.pdf b/inst/doc/threesls.pdf
index d997cdd..02cf178 100644
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diff --git a/inst/doc/tobit.bayes.pdf b/inst/doc/tobit.bayes.pdf
index 87d1ae9..d1c1871 100644
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diff --git a/inst/doc/tobit.pdf b/inst/doc/tobit.pdf
index bce06c7..f3e5079 100644
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diff --git a/inst/doc/twosls.Rnw b/inst/doc/twosls.Rnw
index a993400..dca3580 100644
--- a/inst/doc/twosls.Rnw
+++ b/inst/doc/twosls.Rnw
@@ -16,20 +16,21 @@ library(Zelig)
\section{{\tt twosls}: Two Stage Least Squares}
\label{twosls}
-\texttt{twosls} provides consistent estimates for linear regression models with
-some explanatory variable (the instrumental variable) correlated with the error term.
-In this situation, ordinary least squares fails to provide consistent
-estimates. The name two-stage least squares stems from the two regressions
-in the estimation procedure. In stage one, an ordinary least squares
-prediction of the instrumental variable is obtained from regressing it on
-the instrument variables. In stage two, the coefficients of interest are
-estimated using ordinary least square after substituting the instrumental
-variable by its predictions from stage one.
+\texttt{twosls} provides consistent estimates for linear regression
+models with some explanatory variable correlated with the error term
+using instrumental variables. In this situation, ordinary least
+squares fails to provide consistent estimates. The name two-stage
+least squares stems from the two regressions in the estimation
+procedure. In stage one, an ordinary least squares prediction of the
+instrumental variable is obtained from regressing it on the instrument
+variables. In stage two, the coefficients of interest are estimated
+using ordinary least square after substituting the instrumental
+variable by its predictions from stage one.
\subsubsection{Syntax}
\begin{verbatim}
-> fml <- list ("mu" = Y ~ X + Z,
- "inst" = Z ~ W + X)
+> fml <- list("mu1" = Y ~ X + W, "mu2" = W ~ X + Z,
+ "inst" = ~ X + Z)
> z.out <- zelig(formula = fml, model = "twosls", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
@@ -37,19 +38,22 @@ variable by its predictions from stage one.
\subsubsection{Inputs}
\texttt{twosls} regression take the following inputs:
\begin{itemize}
-\item \texttt{formula}:a list of the main equation and instrumental variable
-equation. The first object in the list \texttt{mu} corresponds to the
-regression model needs to be estimated. The second list object \texttt{inst}
-specifies the regression model for the instrumental variable \texttt{Z}.
+\item \texttt{formula}: A list of the formula for the main equation,
+the formula for the endogenous variable, and the (one-sided) formula
+for instrumental variables (including covariates). The first object in
+the list \texttt{mu} corresponds to the main regression model needs to
+be estimated. Alternatively, a system of simultaneous equations can be
+used. See the help file of {\tt systemfit} for more information.
For example:
<<Inputs.list>>=
- fml <- list ("mu" = Y ~ X + Z,
- "inst" = Z ~ W + X)
+ fml <- list("mu1" = Y ~ X + W, "mu2" = W ~ X + Z,
+ "inst" = ~ X + Z)
@
\begin{itemize}
\item \texttt{Y}: the dependent variable of interest.
-\item \texttt{Z}: the instrumental variable.
-\item \texttt{W}: exogenous instrument variables.
+\item \texttt{X}: the covariate.
+\item \texttt{W}: the endogenous variable.
+\item \texttt{Z}: the exogenous instrumental variable.
\end{itemize}
\end{itemize}
\subsubsection{Additional Inputs}
@@ -222,8 +226,8 @@ Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. ``twosls: Two Stage Least
\input{citeZelig}
\subsection* {See also}
-The twosls function is adapted from the \texttt{systemfit} library by Jeff Hamann
-and Arne Henningsen \citep{HamaHenn05}.
+The twosls function is adapted from the \texttt{systemfit} library
+\citep{HamHen05}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/twosls.pdf b/inst/doc/twosls.pdf
index 839bc81..2aa1fbf 100644
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diff --git a/inst/doc/vigpics/aov-ExamplesPlot1.eps b/inst/doc/vigpics/aov-ExamplesPlot1.eps
new file mode 100644
index 0000000..f0f13cc
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+++ b/inst/doc/vigpics/aov-ExamplesPlot1.eps
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%%Trailer
diff --git a/inst/doc/vigpics/bprobit-JointEstimationPlot.pdf b/inst/doc/vigpics/bprobit-JointEstimationPlot.pdf
index a13e81e..f63cbe2 100644
--- a/inst/doc/vigpics/bprobit-JointEstimationPlot.pdf
+++ b/inst/doc/vigpics/bprobit-JointEstimationPlot.pdf
@@ -2,10 +2,10 @@
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copy to inst/doc/vigpics/probit_gee-FirstDifferencePlot.eps
index 8ed1541..80145c5 100644
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diff --git a/inst/doc/weibull.pdf b/inst/doc/weibull.pdf
index 354b080..22f29d1 100644
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diff --git a/inst/doc/zelig.html b/inst/doc/zelig.html
index 82a0cce..95044a4 100644
--- a/inst/doc/zelig.html
+++ b/inst/doc/zelig.html
@@ -234,14 +234,17 @@ left: 100px;
<ul id="models">
<li class="mainindx"><a href="">Models for Continous Dependent Variables</a>
<ul>
+ <li><a href="aov.pdf">aov : Fit an Analysis of Variance Model</a></li>
<li><a href="arima.pdf">arima : Arima models for Time Series Data</a></li>
<li><a href="factor.bayes.pdf">factor.bayes : Bayesian Factor Analysis</a></li>
- <li><a href="gam.normal.pdf">gam.normal : Generalized Additive Model for Continuous Dependent Variables</a></li>
<li><a href="ls.pdf">ls : Least Squares Regression for Continuous Dependent Variables</a></li>
- <li><a href="netls.pdf">netls : Social Network Least Squares Regression for Continuous Dependent Variables</a></li>
- <li><a href="netnormal.pdf">netnormal : Social Network Normal Regression for Continuous Dependent Variables</a></li>
+ <li><a href="ls.mixed.pdf">ls.mixed : Mixed effects linear model</a></li>
+ <li><a href="ls.net.pdf">ls.net : Social Network Least Squares Regression for Continuous Dependent Variables</a></li>
<li><a href="normal.pdf">normal : Normal Regression for Continuous Dependent Variables</a></li>
<li><a href="normal.bayes.pdf">normal.bayes : Bayesian Normal Linear Regression</a></li>
+ <li><a href="normal.gam.pdf">normal.gam : Generalized Additive Model for Continuous Dependent Variables</a></li>
+ <li><a href="normal.gee.pdf">normal.gee : General Estimating Equation for Normal Regression</a></li>
+ <li><a href="normal.net.pdf">normal.net : Social Network Normal Regression for Continuous Dependent Variables</a></li>
<li><a href="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>
@@ -253,23 +256,27 @@ left: 100px;
<ul>
<li><a href="blogit.pdf">blogit : Bivariate Logistic Regression for Dichotomous Dependent Variables</a></li>
<li><a href="bprobit.pdf">bprobit : Bivariate Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="gam.logit.pdf">gam.logit : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
- <li><a href="gam.probit.pdf">gam.probit : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
+ <li><a href="cloglog.net.pdf">cloglog.net : Social Network Complementary Log Log Regression for Dichotomous Dependent Variables</a></li>
<li><a href="irt1d.pdf">irt1d : One Dimensional Item Response Model</a></li>
<li><a href="irtkd.pdf">irtkd : K-Dimensional Item Response Model</a></li>
<li><a href="logit.pdf">logit : Logistic Regression for Dichotomous Dependent Variables</a></li>
<li><a href="logit.bayes.pdf">logit.bayes : Bayesian Logistic Regression for Dichotomous Dependent Variables</a></li>
+ <li><a href="logit.gam.pdf">logit.gam : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
<li><a href="logit.gee.pdf">logit.gee : General Estimating Equation for Logistic Regression</a></li>
- <li><a href="netcloglog.pdf">netcloglog : Social Network Complementary Log Log Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="netlogit.pdf">netlogit : Social Network Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="netprobit.pdf">netprobit : Social Network Probit Regression for Dichotomous Dependent Variables</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="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="relogit.pdf">relogit : Rare Events Logistic Regression for Dichotomous Dependent Variables</a></li>
</ul>
</li>
<li class="mainindx"><a href="">Models for Ordinal Dependent Variables</a>
<ul>
+ <li><a href="chopit.pdf">chopit : Compound Hierarchical Ordinal Probit Regression for Survey Vignettes</a></li>
<li><a href="factor.ord.pdf">factor.ord : Ordinal Data Factor Analysis</a></li>
<li><a href="ologit.pdf">ologit : Ordinal Logistic Regression for Ordered Categorical Dependent Variables</a></li>
<li><a href="oprobit.pdf">oprobit : Ordinal Probit Regression for Ordered Categorical Dependent Variables</a></li>
@@ -278,10 +285,13 @@ left: 100px;
</li>
<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
<ul>
+ <li><a href="coxph.pdf">coxph : Cox Proportional Hazard Regression for Duration Dependent Variables</a></li>
<li><a href="exp.pdf">exp : Exponential Regression for Duration Dependent Variables</a></li>
<li><a href="gamma.pdf">gamma : Gamma Regression for Continuous, Positive Dependent Variables</a></li>
+ <li><a href="gamma.gee.pdf">gamma.gee : General Estimating Equation for Gamma Regression</a></li>
+ <li><a href="gamma.mixed.pdf">gamma.mixed : Mixed effects gamma model</a></li>
+ <li><a href="gamma.net.pdf">gamma.net : Social Network Gamma Regression for Continuous, Positive Dependent Variables</a></li>
<li><a href="lognorm.pdf">lognorm : Log-Normal Regression for Duration Dependent Variables</a></li>
- <li><a href="netgamma.pdf">netgamma : Social Network Gamma Regression for Continuous, Positive Dependent Variables</a></li>
<li><a href="weibull.pdf">weibull : Weibull Regression for Duration Dependent Variables</a></li>
</ul>
</li>
@@ -293,11 +303,13 @@ left: 100px;
</li>
<li class="mainindx"><a href="">Event Count Models</a>
<ul>
- <li><a href="gam.poisson.pdf">gam.poisson : Generalized Additive Model for Event Count Dependent Variables</a></li>
<li><a href="negbin.pdf">negbin : Negative Binomial Regression for Event Count Dependent Variables</a></li>
- <li><a href="netpoisson.pdf">netpoisson : Social Network Poisson Regression for Event Count Dependent Variables</a></li>
<li><a href="poisson.pdf">poisson : Poisson Regression for Event Count Dependent Variables</a></li>
<li><a href="poisson.bayes.pdf">poisson.bayes : Bayesian Poisson Regression</a></li>
+ <li><a href="poisson.gam.pdf">poisson.gam : Generalized Additive Model for Event Count Dependent Variables</a></li>
+ <li><a href="poisson.gee.pdf">poisson.gee : General Estimating Equation for Poisson Regression</a></li>
+ <li><a href="poisson.mixed.pdf">poisson.mixed : Mixed effects poisson model</a></li>
+ <li><a href="poisson.net.pdf">poisson.net : Social Network Poisson Regression for Event Count Dependent Variables</a></li>
</ul>
</li>
<li class="mainindx"><a href="">Models for Mixed Dependent Variables</a>
diff --git a/inst/doc/zelig.pdf b/inst/doc/zelig.pdf
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diff --git a/inst/doc/zelig.tex b/inst/doc/zelig.tex
index 5fd3a0f..7ce8716 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 3.0-6\\ \today}
+\date{Version 3.1-0\\ \today}
\begin{document}
\maketitle
@@ -74,17 +74,46 @@ may contain bugs, but are usually replaced by stable releases within a
few days.
\begin{itemize}
+
+\item {\bf 3.1} (November 30, 2007): Stable release for R 2.6.0-2.6.1.
+ Adding many new models such as {\tt aov}, {\tt chopit}, {\tt coxph}, generalized linear mixed-effects models, and gee models. Also, several bugs are
+ fixed. First version dependencies are as follows:\newline
+\begin{tabular}{ll}\label{table.compat}
+MASS & 7.2-34 \\
+nlme & 3.1-86 \\
+survival & 2.34 \\
+boot & 1.2-30 \\
+nnet & 7.2-34 \\
+zoo & 1.4-0 \\
+sandwich & 2.0-2 \\
+sna & 1.4 \\
+lme4 & 0.99875-9 \\
+coda & 0.12-1 \\
+systemfit & 0.8-5 \\
+VGAM & 0.7-4 \\
+MCMCpack & 0.8-2 \\
+mvtnorm & 0.8-1 \\
+gee & 4.13-13 \\
+mgcv & 1.3-29 \\
+anchors & 2.0
+\end{tabular}
-\item {\bf 3.0} (July 20, 2007): Stable release for R 2.5.0-2.5.1.
-Introducing vignettes for each model. Improving documentation in the
-Zelig web site, improving citation style, improving {\tt help.zelig()} function, adding gam models, social network methods, logit gee model, adding support for cross-validation procedures and diagnostics tools, etc.
+\item {\bf 3.0-1} -- {\bf 3.0-6}: Minor bug fixes. Stable release for
+ R 2.5.0-2.5.1.
+
+\item {\bf 3.0} (July 20, 2007): Stable release for R 2.5.0-2.5.1.
+ Introducing vignettes for each model. Improving documentation in the
+ Zelig web site, improving citation style, improving {\tt
+ help.zelig()} function, adding gam models, social network methods,
+ logit gee model, adding support for cross-validation procedures and
+ diagnostics tools, etc.
\item {\bf 2.8-3} (May 29, 2007): Stable release for R 2.4.0-2.5.0.
Fixed bugs in {\tt help.zelig()}, and summary for multinomial logit,
bivariate probit, and bivariate logit with multiple imputation. (Thanks
to Brant Inman and Javier Marquez.) First version dependencies are as
follows:\newline
-\begin{tabular}{ll}\label{table.compat}
+\begin{tabular}{ll}
MASS & 7.2-34 \\
boot & 1.2-27 \\
VGAM & 0.7-1 \\
diff --git a/inst/doc/zeligmodels.html b/inst/doc/zeligmodels.html
index bbe384e..f1386d9 100644
--- a/inst/doc/zeligmodels.html
+++ b/inst/doc/zeligmodels.html
@@ -1,14 +1,17 @@
<ul id="models">
<li class="mainindx"><a href="">Models for Continous Dependent Variables</a>
<ul>
+ <li><a href="aov.pdf">aov : Fit an Analysis of Variance Model</a></li>
<li><a href="arima.pdf">arima : Arima models for Time Series Data</a></li>
<li><a href="factor.bayes.pdf">factor.bayes : Bayesian Factor Analysis</a></li>
- <li><a href="gam.normal.pdf">gam.normal : Generalized Additive Model for Continuous Dependent Variables</a></li>
<li><a href="ls.pdf">ls : Least Squares Regression for Continuous Dependent Variables</a></li>
- <li><a href="netls.pdf">netls : Social Network Least Squares Regression for Continuous Dependent Variables</a></li>
- <li><a href="netnormal.pdf">netnormal : Social Network Normal Regression for Continuous Dependent Variables</a></li>
+ <li><a href="ls.mixed.pdf">ls.mixed : Mixed effects linear model</a></li>
+ <li><a href="ls.net.pdf">ls.net : Social Network Least Squares Regression for Continuous Dependent Variables</a></li>
<li><a href="normal.pdf">normal : Normal Regression for Continuous Dependent Variables</a></li>
<li><a href="normal.bayes.pdf">normal.bayes : Bayesian Normal Linear Regression</a></li>
+ <li><a href="normal.gam.pdf">normal.gam : Generalized Additive Model for Continuous Dependent Variables</a></li>
+ <li><a href="normal.gee.pdf">normal.gee : General Estimating Equation for Normal Regression</a></li>
+ <li><a href="normal.net.pdf">normal.net : Social Network Normal Regression for Continuous Dependent Variables</a></li>
<li><a href="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>
@@ -20,23 +23,27 @@
<ul>
<li><a href="blogit.pdf">blogit : Bivariate Logistic Regression for Dichotomous Dependent Variables</a></li>
<li><a href="bprobit.pdf">bprobit : Bivariate Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="gam.logit.pdf">gam.logit : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
- <li><a href="gam.probit.pdf">gam.probit : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
+ <li><a href="cloglog.net.pdf">cloglog.net : Social Network Complementary Log Log Regression for Dichotomous Dependent Variables</a></li>
<li><a href="irt1d.pdf">irt1d : One Dimensional Item Response Model</a></li>
<li><a href="irtkd.pdf">irtkd : K-Dimensional Item Response Model</a></li>
<li><a href="logit.pdf">logit : Logistic Regression for Dichotomous Dependent Variables</a></li>
<li><a href="logit.bayes.pdf">logit.bayes : Bayesian Logistic Regression for Dichotomous Dependent Variables</a></li>
+ <li><a href="logit.gam.pdf">logit.gam : Generalized Additive Model for Dichotomous Dependent Variables</a></li>
<li><a href="logit.gee.pdf">logit.gee : General Estimating Equation for Logistic Regression</a></li>
- <li><a href="netcloglog.pdf">netcloglog : Social Network Complementary Log Log Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="netlogit.pdf">netlogit : Social Network Logistic Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="netprobit.pdf">netprobit : Social Network Probit Regression for Dichotomous Dependent Variables</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="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="relogit.pdf">relogit : Rare Events Logistic Regression for Dichotomous Dependent Variables</a></li>
</ul>
</li>
<li class="mainindx"><a href="">Models for Ordinal Dependent Variables</a>
<ul>
+ <li><a href="chopit.pdf">chopit : Compound Hierarchical Ordinal Probit Regression for Survey Vignettes</a></li>
<li><a href="factor.ord.pdf">factor.ord : Ordinal Data Factor Analysis</a></li>
<li><a href="ologit.pdf">ologit : Ordinal Logistic Regression for Ordered Categorical Dependent Variables</a></li>
<li><a href="oprobit.pdf">oprobit : Ordinal Probit Regression for Ordered Categorical Dependent Variables</a></li>
@@ -45,10 +52,13 @@
</li>
<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
<ul>
+ <li><a href="coxph.pdf">coxph : Cox Proportional Hazard Regression for Duration Dependent Variables</a></li>
<li><a href="exp.pdf">exp : Exponential Regression for Duration Dependent Variables</a></li>
<li><a href="gamma.pdf">gamma : Gamma Regression for Continuous, Positive Dependent Variables</a></li>
+ <li><a href="gamma.gee.pdf">gamma.gee : General Estimating Equation for Gamma Regression</a></li>
+ <li><a href="gamma.mixed.pdf">gamma.mixed : Mixed effects gamma model</a></li>
+ <li><a href="gamma.net.pdf">gamma.net : Social Network Gamma Regression for Continuous, Positive Dependent Variables</a></li>
<li><a href="lognorm.pdf">lognorm : Log-Normal Regression for Duration Dependent Variables</a></li>
- <li><a href="netgamma.pdf">netgamma : Social Network Gamma Regression for Continuous, Positive Dependent Variables</a></li>
<li><a href="weibull.pdf">weibull : Weibull Regression for Duration Dependent Variables</a></li>
</ul>
</li>
@@ -60,11 +70,13 @@
</li>
<li class="mainindx"><a href="">Event Count Models</a>
<ul>
- <li><a href="gam.poisson.pdf">gam.poisson : Generalized Additive Model for Event Count Dependent Variables</a></li>
<li><a href="negbin.pdf">negbin : Negative Binomial Regression for Event Count Dependent Variables</a></li>
- <li><a href="netpoisson.pdf">netpoisson : Social Network Poisson Regression for Event Count Dependent Variables</a></li>
<li><a href="poisson.pdf">poisson : Poisson Regression for Event Count Dependent Variables</a></li>
<li><a href="poisson.bayes.pdf">poisson.bayes : Bayesian Poisson Regression</a></li>
+ <li><a href="poisson.gam.pdf">poisson.gam : Generalized Additive Model for Event Count Dependent Variables</a></li>
+ <li><a href="poisson.gee.pdf">poisson.gee : General Estimating Equation for Poisson Regression</a></li>
+ <li><a href="poisson.mixed.pdf">poisson.mixed : Mixed effects poisson model</a></li>
+ <li><a href="poisson.net.pdf">poisson.net : Social Network Poisson Regression for Event Count Dependent Variables</a></li>
</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
index 4645ca6..4b4ecc6 100644
Binary files a/inst/zideal/zideal.RData and b/inst/zideal/zideal.RData differ
diff --git a/man/Zelig-package.Rd b/man/Zelig-package.Rd
index 67b6382..aa1a5c4 100644
--- a/man/Zelig-package.Rd
+++ b/man/Zelig-package.Rd
@@ -3,7 +3,7 @@
\alias{Zelig}
\docType{package}
\title{
-Zelig: Everyone's Statistical Software
+Everyone's Statistical Software
}
\description{
Zelig is an easy-to-use program that can estimate, and
@@ -23,31 +23,49 @@ quantities of direct interest.
\details{
\tabular{ll}{
Package: \tab Zelig\cr
-Version: \tab 2.8-5\cr
+Version: \tab 3.1-0\cr
Date: \tab 2007-06-12\cr
-Depends: \tab R (>= 2.4.0), MASS, boot\cr
+Depends: \tab R (>= 2.5.0), MASS, boot\cr
Suggests: \tab VGAM (>= 0.7-1), MCMCpack (>= 0.7-4), mvtnorm, survival,
sandwich (>= 2.0-0), zoo (>= 1.2-1), coda, nnet, sna, gee,
-systemfit, mgcv\cr
+systemfit, mgcv, lme4, anchors (>= 2.0)\cr
License: \tab GPL version 2 or newer\cr
URL: \tab http://gking.harvard.edu/zelig\cr
+Packaged: \tab Wed May 30 16:14:15 2007; falimadh\cr
+Built: \tab R 2.6.0; ; 2007-11-30 13:37:37; unix\cr
}
Index:
\preformatted{
approval U.S. Presidential Approval Data
+bivariate Sample data for bivariate probit regression
coalition Coalition Dissolution in Parliamentary
Democracies
+coalition2 Coalition Dissolution in Parliamentary
+ Democracies, Modified Version
current.packages Find all packages in a dependency chain
dims Return Dimensions of Vectors, Arrays, and Data
Frames
eidat Simulation Data for Ecological Inference
+free1 Freedom of Speech Data
+free2 Freedom of Speech Data
friendship Simulated Example of Schoolchildren Friendship
Network
+grunfeld Simulation Data for model Seemingly Unrelated
+ Regression (sur) that corresponds to method SUR
+ of systemfit
gsource Read Data As a Space-Delimited Table
help.zelig HTML Help for Zelig Commands and Models
hoff Social Security Expenditure Data
+homerun Sample Data on Home Runs Hit By Mark McGwire
+ and Sammy Sosa in 1998.
immigration Individual Preferences Over Immigration Policy
+klein Simulation Data for model Two-Stage Least
+ Square (twosls) that corresponds to method 2SLS
+ of systemfit
+kmenta Simulation Data for model Three-Stage Least
+ Square (threesls) that corresponds to method
+ 3SLS of systemfit
macro Macroeconomic Data
match.data Output matched data sets
MatchIt.url Table of links for Zelig
@@ -87,67 +105,79 @@ tobin Tobin's Tobit Data
turnout Turnout Data Set from the National Election
Survey
user.prompt Pause in demo files
+voteincome Sample Turnout and Demographic Data from the
+ 2000 Current Population Survey
Weimar 1932 Weimar election data
zelig Estimating a Statistical Model
zeligDepStatus Zelig Dependencies Packages Client Status
zeligDepUpdate Download Zelig Dependencies Packages
zeligDescribeModelXML Zelig interface functions
-Zelig-package Everyone's Statistical Software
+Zelig-package Zelig: Everyone's Statistical Software
Zelig.url Table of links for Zelig
-zideal Zelig Matrix of Dependencies
}
Further information is available in the following vignettes:
-
\tabular{ll}{
+\code{aov} \tab Fit an Analysis of Variance Model (source)\cr
\code{arima} \tab ARIMA Models for Time Series Data (source)\cr
\code{blogit} \tab Bivariate Logistic Regression for Two Dichotomous Dependent Variables (source)\cr
\code{bprobit} \tab Bivariate Probit Regression for Dichotomous Dependent Variables (source)\cr
+\code{chopit} \tab Compound Hierarchical Ordered Probit for Survey Vignettes (source)\cr
+\code{cloglog.net} \tab Social Network Complementary Log Log Regression for Dichotomous Dependent Variables (source)\cr
+\code{coxph} \tab Cox Proportional Hazards Regression for Duration Dependent Variables (source)\cr
\code{ei.RxC} \tab Hierarchical Multinomial-Dirichlet Ecological Inference Model (source)\cr
\code{ei.dynamic} \tab Quinn's Dynamic Ecological Inference (source)\cr
-\code{ei.hier} \tab Hierarchical Ecological Inference Model (source)\cr
+\code{ei.hier} \tab Hierarchical Ecological Inference Model for 2x2 tables (source)\cr
\code{exp} \tab Exponential Regression for Duration Dependent Variables (source)\cr
\code{factor.bayes} \tab Bayesian Factor Analysis (source)\cr
\code{factor.mix} \tab Mixed Data Factor Analysis (source)\cr
\code{factor.ord} \tab Ordinal Data Factor Analysis (source)\cr
-\code{gam.logit} \tab gam.logit: Generalized Additive Model for Dichotomous Dependent Variables (source)\cr
-\code{gam.normal} \tab Generalized Additive Model for Continuous Dependent Variables (source)\cr
-\code{gam.poisson} \tab Generalized Additive Model for Count Dependent Variables (source)\cr
-\code{gam.probit} \tab Generalized Additive Model for Dichotomous Dependent Variables (source)\cr
\code{gamma} \tab Gamma Regression for Continuous, Positive Dependent Variables (source)\cr
-\code{irt1d} \tab One Dimensional Item Response Mode (source)\cr
+\code{gamma.gee} \tab Generalized Estimating Equation for Gamma Regression (source)\cr
+\code{gamma.mixed} \tab Gamma mixed effects linear regression (source)\cr
+\code{gamma.net} \tab Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables (source)\cr
+\code{irt1d} \tab One Dimensional Item Response Mode (source)\cr
\code{irtkd} \tab K-Dimensional Item Response Model (source)\cr
\code{logit} \tab Logistic Regression for Dichotomous Dependent Variables (source)\cr
\code{logit.bayes} \tab Bayesian Logistic Regression for Dichotomous Dependent Variables (source)\cr
+\code{logit.gam} \tab Generalized Additive Model for Dichotomous Dependent Variables (source)\cr
\code{logit.gee} \tab Generalized Estimating Equation for Logistic Regression (source)\cr
+\code{logit.mixed} \tab Mixed effects linear regression (source)\cr
+\code{logit.net} \tab Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables (source)\cr
\code{lognorm} \tab Log-Normal Regression for Duration Dependent Variables (source)\cr
\code{ls} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
+\code{ls.mixed} \tab Mixed effects linear regression (source)\cr
+\code{ls.net} \tab Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables (source)\cr
\code{mlogit} \tab Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values (source)\cr
\code{mlogit.bayes} \tab Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values (source)\cr
\code{mloglm} \tab Multinomial Log-Linear Regression for Contingency Table Models (source)\cr
\code{negbin} \tab Negative Binomial Regression for Event Count Dependent Variables (source)\cr
-\code{netcloglog} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
-\code{netgamma} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
-\code{netlogit} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
-\code{netls} \tab Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables (source)\cr
-\code{netnormal} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
-\code{netpoisson} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
-\code{netprobit} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
\code{normal} \tab Normal Regression for Continuous Dependent Variables (source)\cr
\code{normal.bayes} \tab Bayesian Normal Linear Regression (source)\cr
+\code{normal.gam} \tab Generalized Additive Model for Continuous Dependent Variables (source)\cr
+\code{normal.gee} \tab Generalized Estimating Equation for Normal Regression (source)\cr
+\code{normal.net} \tab Network Normal Regression for Continuous Proximity Matrix Dependent Variables (source)\cr
\code{ologit} \tab Ordinal Logistic Regression for Ordered Categorical Dependent Variables (source)\cr
\code{oprobit} \tab Ordinal Probit Regression for Ordered Categorical Dependent Variables (source)\cr
\code{oprobit.bayes} \tab Bayesian Ordered Probit Regression (source)\cr
\code{poisson} \tab Poisson Regression for Event Count Dependent Variables (source)\cr
\code{poisson.bayes} \tab Bayesian Poisson Regression (source)\cr
+\code{poisson.gam} \tab Generalized Additive Model for Count Dependent Variables (source)\cr
+\code{poisson.gee} \tab Generalized Estimating Equation for Poisson Regression (source)\cr
+\code{poisson.mixed} \tab Mixed effects poisson regression (source)\cr
+\code{poisson.net} \tab Network Poisson Regression for Event Count Proximity Matrix Dependent Variables (source)\cr
\code{probit} \tab Probit Regression for Dichotomous Dependent Variables (source)\cr
\code{probit.bayes} \tab Bayesian Probit Regression for Dichotomous Dependent Variable (source)\cr
+\code{probit.gam} \tab Generalized Additive Model for Dichotomous Dependent Variables (source)\cr
+\code{probit.gee} \tab Generalized Estimating Equation for Probit Regression (source)\cr
+\code{probit.mixed} \tab Mixed effects probit regression (source)\cr
+\code{probit.net} \tab Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables (source)\cr
\code{relogit} \tab Rare Events Logistic Regression for Dichotomous Dependent Variables (source)\cr
\code{sur} \tab Seemingly Unrelated Regression (source)\cr
-\code{threesls} \tab Three Stage Least Squares (source)\cr
+\code{threesls} \tab Three Stage Least Squares (source)\cr
\code{tobit} \tab Linear regression for Left-Censored Dependet Variable (source)\cr
\code{tobit.bayes} \tab Bayesian Linear Regression for a Censored Dependent Variable (source)\cr
-\code{twosls} \tab Two Stage Least Squares (source)\cr
+\code{twosls} \tab Two Stage Least Squares (source)\cr
\code{weibull} \tab Weibull Regression for Duration Dependent Variables (source)\cr
}
}
diff --git a/man/bivariate.Rd b/man/bivariate.Rd
new file mode 100644
index 0000000..db1ef7a
--- /dev/null
+++ b/man/bivariate.Rd
@@ -0,0 +1,24 @@
+\name{bivariate}
+
+\alias{bivariate}
+
+\title{Sample data for bivariate probit regression}
+
+\description{
+ Sample data for the bivariate probit regression.
+}
+
+\usage{data(bivariate)}
+
+\format{A table containing 6 variables ("y1", "y2", "x1",
+"x2", "x3", and "x4") and 78 observations.}
+
+\source{This is a cleaned and relabelled version of the sanction data
+ set, available in Zelig.}
+
+\references{
+ Martin, Lisa (1992). \emph{Coercive Cooperation: Explaining Multilateral
+ Economic Sanctions}, Princeton: Princeton University Press.
+}
+
+\keyword{datasets}
diff --git a/man/coalition2.Rd b/man/coalition2.Rd
new file mode 100644
index 0000000..e0c6be3
--- /dev/null
+++ b/man/coalition2.Rd
@@ -0,0 +1,32 @@
+\name{coalition2}
+\alias{coalition2}
+\docType{data}
+
+\title{Coalition Dissolution in Parliamentary Democracies, Modified Version}
+\description{
+ This data set contains survival data on government coalitions in
+ parliamentary democracies (Belgium, Canada, Denmark, Finland, France,
+ Iceland, Ireland, Israel, Italy, Netherlands, Norway, Portugal, Spain,
+ Sweden, and the United Kingdom) for the period 1945-1987. Country indicator variables are included in the sample data.
+}
+\usage{data(coalition2)}
+\format{
+ A data frame containing 8 variables ("duration", "ciep12", "invest",
+ "fract", "polar", "numst2", "crisis", "country") and 314 observations. For
+ variable descriptions, please refer to King, Alt, Burns and Laver
+ (1990).
+}
+
+\source{ICPSR}
+
+\references{
+ King, Gary, James E. Alt, Nancy Elizabeth Burns and Michael Laver (1990).
+ ``A Unified Model of Cabinet Dissolution in Parliamentary
+ Democracies,'' \emph{American Journal of Political Science}, vol. 34,
+ no. 3, pp. 846-870.
+
+ Gary King, James E. Alt, Nancy Burns, and Michael Laver. ICPSR
+ Publication Related Archive, 1115.
+}
+
+\keyword{datasets}
\ No newline at end of file
diff --git a/man/free1.Rd b/man/free1.Rd
new file mode 100644
index 0000000..220dfaa
--- /dev/null
+++ b/man/free1.Rd
@@ -0,0 +1,99 @@
+\name{free1}
+\alias{free1}
+\title{Freedom of Speech Data}
+\usage{data(free1)}
+\description{
+
+ Selection of individual-level survey data for freedom of speech.
+
+ }
+
+\details{
+
+ A table with 150 observations and 12 variables. \itemize{
+ \item{sex}{1 for men and 0 for women}
+ \item{age}{Age of respondent in years}
+ \item{educ}{Levels of education, coded as a numeric variable with
+ \itemize{
+ \item{1}{No formal education}
+ \item{2}{Less than primary school education}
+ \item{3}{Completed primary school}
+ \item{4}{Completed secondary school}
+ \item{5}{Completed high school}
+ \item{6}{Completed college}
+ \item{7}{Completed post-graduate degree}
+ }
+ }
+ \item{country}{Character strings consisting of "Oceana",
+ "Eurasia", and "Eastasia", after Orwell's \emph{1984}.}
+ \item{y}{Self assessment (see below).}
+ \item{v1-v6}{Response to vignettes (see below).}
+ }
+ Survey respondents were asked in almost the same language for a
+ self-assessment and for an assessment of several hypothetical persons
+ described by written vignettes. The self assessment (\code{self}, in
+ the data set), "How free do you think [name/you] [is/are] to express
+ [him-her/your]self without fear of government reprisal?" was first
+ asked of the survey respondent with respect to him or herself, and
+ then after each of vignette. The possible response categories are: \itemize{
+ \item{1}{Completely free}
+ \item{2}{Very free}
+ \item{3}{Moderately free}
+ \item{4}{Slightly free}
+ \item{5}{Not free at all}
+ }
+ The vignettes, ordered from most free to least free, are:
+ \itemize{
+ \item{vign1}{[Kay] does not like many of the government's
+ policies. She frequently publishes her opinion in newspapers,
+ criticizing decisions by officials and calling for change. She sees
+ little reason these actions could lead to government reprisal.}
+
+ \item{vign2}{[Michael] disagrees with many of the government's
+ policies. Though he knows criticism is frowned upon, he doesn't
+ believe the government would punish someone for expressing critical
+ views. He makes his opinion known on most issues without regard to
+ who is listening.}
+
+ \item{vign3}{[Bob] has political views at odds with the
+ government. He has heard of people occasionally being arrested for
+ speaking out against the government, and government leaders
+ sometimes make political speeches condemning those who criticize. He
+ sometimes writes letters to newspapers about politics, but he is
+ careful not to use his real name.}
+
+ \item{vign4}{[Connie] does not like the government's stance on many
+ issues. She has a friend who was arrested for being too openly
+ critical of governmental leaders, and so she avoids voicing her
+ opinions in public places.}
+
+ \item{vign5}{[Vito] disagrees with many of the government's
+ policies, and is very careful about whom he says this to, reserving
+ his real opinions for family and close friends only. He knows
+ several men who have been taken away by government officials for
+ saying negative things in public.}
+
+ \item{vign6}{[Sonny] lives in fear of being harassed for his
+ political views. Everyone he knows who has spoken out against the
+ government has been arrested or taken away. He never says a word
+ about anything the government does, not even when he is at home
+ alone with his family. }
+ }
+}
+
+\references{
+ \emph{WHO's World Health Survey}
+ by Lydia Bendib, Somnath Chatterji, Alena Petrakova, Ritu Sadana,
+ Joshua A. Salomon, Margie Schneider, Bedirhan Ustun, Maria
+ Villanueva
+
+ Jonathan Wand, Gary King and Olivia Lau. (2007) ``Anchors: Software for
+ Anchoring Vignettes''. \emph{Journal of Statistical Software}. Forthcoming.
+ copy at http://wand.stanford.edu/research/anchors-jss.pdf
+
+ Gary King and Jonathan Wand. "Comparing Incomparable Survey
+ Responses: New Tools for Anchoring Vignettes," Political Analysis, 15,
+ 1 (Winter, 2007): Pp. 46-66,
+ copy at http://gking.harvard.edu/files/abs/c-abs.shtml.
+}
+\keyword{datasets}
diff --git a/man/free2.Rd b/man/free2.Rd
new file mode 100644
index 0000000..c42d03f
--- /dev/null
+++ b/man/free2.Rd
@@ -0,0 +1,99 @@
+\name{free2}
+\alias{free2}
+\title{Freedom of Speech Data}
+\usage{data(free2)}
+\description{
+
+ Selection of individual-level survey data for freedom of speech.
+
+ }
+
+\details{
+
+ A table with 150 observations and 12 variables. \itemize{
+ \item{sex}{1 for men and 0 for women}
+ \item{age}{Age of respondent in years}
+ \item{educ}{Levels of education, coded as a numeric variable with
+ \itemize{
+ \item{1}{No formal education}
+ \item{2}{Less than primary school education}
+ \item{3}{Completed primary school}
+ \item{4}{Completed secondary school}
+ \item{5}{Completed high school}
+ \item{6}{Completed college}
+ \item{7}{Completed post-graduate degree}
+ }
+ }
+ \item{country}{Character strings consisting of "Oceana",
+ "Eurasia", and "Eastasia", after Orwell's \emph{1984}.}
+ \item{y}{Self assessment (see below).}
+ \item{v1-v6}{Response to vignettes (see below).}
+ }
+ Survey respondents were asked in almost the same language for a
+ self-assessment and for an assessment of several hypothetical persons
+ described by written vignettes. The self assessment (\code{self}, in
+ the data set), "How free do you think [name/you] [is/are] to express
+ [him-her/your]self without fear of government reprisal?" was first
+ asked of the survey respondent with respect to him or herself, and
+ then after each of vignette. The possible response categories are: \itemize{
+ \item{1}{Completely free}
+ \item{2}{Very free}
+ \item{3}{Moderately free}
+ \item{4}{Slightly free}
+ \item{5}{Not free at all}
+ }
+ The vignettes, ordered from most free to least free, are:
+ \itemize{
+ \item{vign1}{[Kay] does not like many of the government's
+ policies. She frequently publishes her opinion in newspapers,
+ criticizing decisions by officials and calling for change. She sees
+ little reason these actions could lead to government reprisal.}
+
+ \item{vign2}{[Michael] disagrees with many of the government's
+ policies. Though he knows criticism is frowned upon, he doesn't
+ believe the government would punish someone for expressing critical
+ views. He makes his opinion known on most issues without regard to
+ who is listening.}
+
+ \item{vign3}{[Bob] has political views at odds with the
+ government. He has heard of people occasionally being arrested for
+ speaking out against the government, and government leaders
+ sometimes make political speeches condemning those who criticize. He
+ sometimes writes letters to newspapers about politics, but he is
+ careful not to use his real name.}
+
+ \item{vign4}{[Connie] does not like the government's stance on many
+ issues. She has a friend who was arrested for being too openly
+ critical of governmental leaders, and so she avoids voicing her
+ opinions in public places.}
+
+ \item{vign5}{[Vito] disagrees with many of the government's
+ policies, and is very careful about whom he says this to, reserving
+ his real opinions for family and close friends only. He knows
+ several men who have been taken away by government officials for
+ saying negative things in public.}
+
+ \item{vign6}{[Sonny] lives in fear of being harassed for his
+ political views. Everyone he knows who has spoken out against the
+ government has been arrested or taken away. He never says a word
+ about anything the government does, not even when he is at home
+ alone with his family. }
+ }
+}
+
+\references{
+ \emph{WHO's World Health Survey}
+ by Lydia Bendib, Somnath Chatterji, Alena Petrakova, Ritu Sadana,
+ Joshua A. Salomon, Margie Schneider, Bedirhan Ustun, Maria
+ Villanueva
+
+ Jonathan Wand, Gary King and Olivia Lau. (2007) ``Anchors: Software for
+ Anchoring Vignettes''. \emph{Journal of Statistical Software}. Forthcoming.
+ copy at http://wand.stanford.edu/research/anchors-jss.pdf
+
+ Gary King and Jonathan Wand. "Comparing Incomparable Survey
+ Responses: New Tools for Anchoring Vignettes," Political Analysis, 15,
+ 1 (Winter, 2007): Pp. 46-66,
+ copy at http://gking.harvard.edu/files/abs/c-abs.shtml.
+}
+\keyword{datasets}
diff --git a/man/homerun.Rd b/man/homerun.Rd
new file mode 100644
index 0000000..08059d6
--- /dev/null
+++ b/man/homerun.Rd
@@ -0,0 +1,25 @@
+\name{homerun}
+\alias{homerun}
+\docType{data}
+
+\title{Sample Data on Home Runs Hit By Mark McGwire and Sammy Sosa in 1998.}
+\description{
+ Game-by-game information for the 1998 season for Mark McGwire and Sammy Sosa. Data are a subset of the dataset provided in Simonoff (1998).
+}
+\usage{data(homerun)}
+\format{
+ A data frame containing 5 variables ("gameno", "month", "homeruns", "playerstatus", "player") and 326 observations.
+ \describe{
+ \item{\code{gameno}}{an integer variable denoting the game number}
+ \item{\code{month}}{a factor variable taking with levels "March" through "September" denoting the month of the game}
+ \item{\code{homeruns}}{an integer vector denoting the number of homeruns hit in that game for that player}
+ \item{\code{playerstatus}}{an integer vector equal to "0" if the player played in the game, and "1" if they did not.}
+ \item{\code{player}}{an integer vector equal to "0" (McGwire) or "1" (Sosa)}
+}
+}
+
+\source{\url{http://www.amstat.org}}
+
+\references{Simonoff, Jeffrey S. 1998. ``Move Over, Roger Maris: Breaking Baseball's Most Famous Record.'' {\it Journal of Statistics Education} 6(3). Data used are a subset of the data in the article. \\}
+
+\keyword{datasets}
\ No newline at end of file
diff --git a/man/tobin.Rd b/man/tobin.Rd
index 3f40806..606e263 100644
--- a/man/tobin.Rd
+++ b/man/tobin.Rd
@@ -24,9 +24,6 @@
\source{
J. Tobin, Estimation of relationships for limited dependent
variables, Econometrica, v26, 24-36, 1958.
- }
-
-}
-
+ }
\keyword{datasets}
diff --git a/man/voteincome.Rd b/man/voteincome.Rd
new file mode 100644
index 0000000..bf98cfd
--- /dev/null
+++ b/man/voteincome.Rd
@@ -0,0 +1,27 @@
+\name{voteincome}
+\alias{voteincome}
+\docType{data}
+
+\title{Sample Turnout and Demographic Data from the 2000 Current Population Survey}
+\description{
+ This data set contains turnout and demographic data from a sample of respondents to the 2000 Current Population Survey (CPS). The states represented are South Carolina and Arkansas. The data represent only a sample and results from this example should not be used in publication.
+}
+\usage{data(voteincome)}
+\format{
+ A data frame containing 7 variables ("state", "year", "vote", "income", "education", "age", "female") and 1500 observations.
+ \describe{
+ \item{\code{state}}{a factor variable with levels equal to "AR" (Arkansas) and "SC" (South Carolina)}
+ \item{\code{year}}{an integer vector}
+ \item{\code{vote}}{an integer vector taking on values "1" (Voted) and "0" (Did Not Vote)}
+ \item{\code{income}}{an integer vector ranging from "4" (Less than \$5000) to "17" (Greater than \$75000) denoting family income. See the CPS codebook for more information on variable coding}
+ \item{\code{education}}{an integer vector ranging from "1" (Less than High School Education) to "4" (More than a College Education). See the CPS codebook for more information on variable coding}
+ \item{\code{age}}{an integer vector ranging from "18" to "85"}
+ \item{\code{female}}{an integer vector taking on values "1" (Female) and "0" (Male)}
+}
+}
+
+\source{Census Bureau Current Population Survey}
+
+\references{\url{http://www.census.gov/cps}}
+
+\keyword{datasets}
\ No newline at end of file
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