[r-cran-zelig] 48/102: Import Upstream version 2.8-5
Andreas Tille
tille at debian.org
Sun Jan 8 16:58:25 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 86afa58186692bf8dd6abe5090d1b1add1c602d1
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:39:18 2017 +0100
Import Upstream version 2.8-5
---
DESCRIPTION | 10 +-
INDEX | 77 +-
NAMESPACE | 26 +-
R/beta.regression.R | 22 -
R/callsystemfit.R | 9 +
R/categories.R | 3 +-
R/describe.arima.R | 19 +
R/describe.ei.RxC.R | 17 +
R/describe.ei.dynamic.R | 18 +
R/describe.ei.hier.R | 18 +
R/describe.gam.logit.R | 15 +
R/{describe.tobit.R => describe.gam.normal.R} | 8 +-
R/describe.gam.poisson.R | 15 +
R/{describe.tobit.R => describe.gam.probit.R} | 10 +-
R/describe.logit.gee.R | 15 +
R/describe.netcloglog.R | 15 +
R/describe.netgamma.R | 15 +
R/describe.netlogit.R | 15 +
R/{describe.tobit.R => describe.netls.R} | 8 +-
R/{describe.tobit.R => describe.netnormal.R} | 8 +-
R/describe.netpoisson.R | 15 +
R/{describe.tobit.R => describe.netprobit.R} | 10 +-
R/describe.threesls.R | 18 +
R/describe.tobit.R | 2 +-
R/describe.twosls.R | 18 +
R/help.zelig.R | 28 +-
R/model.frame.gamF.R | 5 +
R/netbinom.R | 241 +
R/netgamma.R | 241 +
R/netnormal.R | 241 +
R/netpoisson.R | 241 +
R/param.betareg.R | 11 -
R/param.gam.R | 27 +
R/param.lm.R | 2 +-
R/param.netglm.R | 42 +
R/plot.zelig.gam.logit.R | 20 +
R/{plot.zelig.beta.R => plot.zelig.gam.normal.R} | 2 +-
R/plot.zelig.gam.poisson.R | 16 +
R/plot.zelig.gam.probit.R | 20 +
R/plot.zelig.logit.gee.R | 20 +
R/plot.zelig.netcloglog.R | 20 +
R/{plot.zelig.beta.R => plot.zelig.netgamma.R} | 2 +-
R/{plot.zelig.beta.R => plot.zelig.netnormal.R} | 2 +-
R/plot.zelig.netpoisson.R | 16 +
R/plot.zelig.netprobit.R | 20 +
R/plot.zelig.sur.R | 28 +-
R/{plot.zelig.sur.R => plot.zelig.threesls.R} | 3 +-
R/{plot.zelig.sur.R => plot.zelig.twosls.R} | 3 +-
R/qi.gam.R | 78 +
R/qi.gee.R | 63 +
R/qi.lm.R | 5 +
R/qi.netglm.R | 74 +
R/setx.gam.R | 236 +
R/setx.netglm.R | 196 +
R/sim.netglm.R | 46 +
R/summary.gee.naive.R | 11 +
R/summary.gee.robust.R | 11 +
R/summary.netglm.R | 74 +
R/vcov.gee.naive.R | 6 +
R/vcov.gee.robust.R | 4 +
R/vcov.lm.R | 4 -
R/vcov.netglm.R | 5 +
R/vdc.R | 10 +-
R/zelig.default.R | 2 +-
R/zelig2beta.R | 6 -
R/zelig2gam.logit.R | 13 +
R/zelig2gam.normal.R | 12 +
R/zelig2gam.poisson.R | 13 +
R/zelig2gam.probit.R | 13 +
R/zelig2logit.gee.R | 13 +
R/{zelig2netlogit.R => zelig2netcloglog.R} | 5 +-
R/{zelig2netlogit.R => zelig2netgamma.R} | 4 +-
R/zelig2netlogit.R | 5 +-
R/{zelig2netlogit.R => zelig2netnormal.R} | 4 +-
R/{zelig2netlogit.R => zelig2netpoisson.R} | 4 +-
R/{zelig2netlogit.R => zelig2netprobit.R} | 5 +-
R/zelig2sur.R | 16 +
R/zelig2threesls.R | 26 +
R/zelig2twosls.R | 29 +
R/zelig3gee.R | 15 +
cleanup | 18 -
data/friendship.RData | Bin 2328 -> 2782 bytes
data/grunfeld.txt | 21 +
data/klein.txt | 22 +
data/kmenta.txt | 21 +
demo/00Index | 17 +-
demo/gam.logit.R | 62 +
demo/gam.normal.R | 76 +
demo/gam.poisson.R | 62 +
demo/gam.probit.R | 62 +
demo/logit.gee.R | 112 +
demo/netcloglog.R | 24 +
demo/netgamma.R | 23 +
demo/netlogit.R | 71 +-
demo/netnormal.R | 24 +
demo/netpoisson.R | 24 +
demo/netprobit.R | 23 +
demo/sur.R | 19 +
demo/threesls.R | 53 +
demo/twosls.R | 59 +
inst/doc/.log | 32 +
inst/doc/Makefile | 7 +-
inst/doc/Rplots.ps | 314880 ++++++++++++++++++--
inst/doc/Zcommands.tex | 25 +-
inst/doc/acknowledgments.tex | 3 +-
inst/doc/arima.pdf | Bin 111103 -> 111099 bytes
inst/doc/arima.tex | 349 -
inst/doc/blogit.pdf | Bin 137905 -> 151403 bytes
inst/doc/blogit.tex | 375 -
inst/doc/bprobit.pdf | Bin 152500 -> 153010 bytes
inst/doc/bprobit.tex | 410 -
inst/doc/commands/describe.mymodel.tex | 206 -
inst/doc/commands/matchit.tex | 29 -
inst/doc/commands/matchitref.tex | 390 -
inst/doc/commands/mi.tex | 36 -
inst/doc/commands/model.end.aux | 44 -
inst/doc/commands/model.end.tex | 49 -
inst/doc/commands/model.frame.multiple.aux | 45 -
inst/doc/commands/model.frame.multiple.tex | 80 -
inst/doc/commands/model.matrix.multiple.aux | 45 -
inst/doc/commands/model.matrix.multiple.tex | 111 -
inst/doc/commands/names.tex | 57 -
inst/doc/commands/network.tex | 49 -
inst/doc/commands/parse.formula.aux | 47 -
inst/doc/commands/parse.formula.tex | 113 -
inst/doc/commands/parse.par.aux | 45 -
inst/doc/commands/parse.par.tex | 95 -
inst/doc/commands/plot.ci.tex | 86 -
inst/doc/commands/plot.zelig.tex | 68 -
inst/doc/commands/print.aux | 44 -
inst/doc/commands/print.tex | 51 -
inst/doc/commands/put.start.aux | 44 -
inst/doc/commands/put.start.tex | 44 -
inst/doc/commands/replicate.tex | 82 -
inst/doc/commands/rocplot.tex | 91 -
inst/doc/commands/set.start.aux | 45 -
inst/doc/commands/set.start.tex | 53 -
inst/doc/commands/setx.tex | 149 -
inst/doc/commands/sim.tex | 152 -
inst/doc/commands/summary.tex | 125 -
inst/doc/commands/tag.aux | 45 -
inst/doc/commands/tag.tex | 54 -
inst/doc/commands/ternaryplot.tex | 81 -
inst/doc/commands/ternarypoints.tex | 63 -
inst/doc/commands/zelig.tex | 193 -
inst/doc/commandsRd/CVS.tex | 2 -
inst/doc/commandsRd/MatchIt.url.tex | 6 -
inst/doc/commandsRd/PErisk.tex | 67 -
inst/doc/commandsRd/SupremeCourt.tex | 26 -
inst/doc/commandsRd/Weimar.tex | 26 -
inst/doc/commandsRd/Zelig.tex | 62 -
inst/doc/commandsRd/Zelig.url.tex | 6 -
inst/doc/commandsRd/approval.tex | 20 -
inst/doc/commandsRd/coalition.tex | 31 -
inst/doc/commandsRd/current.packages.tex | 35 -
inst/doc/commandsRd/dims.tex | 37 -
inst/doc/commandsRd/eidat.tex | 14 -
inst/doc/commandsRd/friendship.tex | 15 -
inst/doc/commandsRd/gsource.tex | 49 -
inst/doc/commandsRd/help.zelig.tex | 29 -
inst/doc/commandsRd/hoff.tex | 24 -
inst/doc/commandsRd/immigration.tex | 28 -
inst/doc/commandsRd/macro.tex | 28 -
inst/doc/commandsRd/match.data.tex | 43 -
inst/doc/commandsRd/mexico.tex | 24 -
inst/doc/commandsRd/mi.aux | 38 -
inst/doc/commandsRd/mi.tex | 39 -
inst/doc/commandsRd/mid.tex | 28 -
inst/doc/commandsRd/model.end.tex | 44 -
inst/doc/commandsRd/model.frame.multiple.tex | 56 -
inst/doc/commandsRd/model.matrix.multiple.tex | 83 -
inst/doc/commandsRd/network.aux | 38 -
inst/doc/commandsRd/network.tex | 42 -
inst/doc/commandsRd/newpainters.tex | 32 -
inst/doc/commandsRd/parse.formula.tex | 77 -
inst/doc/commandsRd/parse.par.tex | 82 -
inst/doc/commandsRd/plot.ci.aux | 38 -
inst/doc/commandsRd/plot.ci.tex | 68 -
inst/doc/commandsRd/plot.zelig.aux | 38 -
inst/doc/commandsRd/plot.zelig.tex | 48 -
inst/doc/commandsRd/put.start.tex | 33 -
inst/doc/commandsRd/repl.aux | 38 -
inst/doc/commandsRd/repl.tex | 89 -
inst/doc/commandsRd/rocplot.aux | 38 -
inst/doc/commandsRd/rocplot.tex | 87 -
inst/doc/commandsRd/sanction.tex | 24 -
inst/doc/commandsRd/set.start.tex | 42 -
inst/doc/commandsRd/setx.aux | 38 -
inst/doc/commandsRd/setx.tex | 113 -
inst/doc/commandsRd/sim.aux | 38 -
inst/doc/commandsRd/sim.tex | 132 -
inst/doc/commandsRd/sna.ex.tex | 15 -
inst/doc/commandsRd/summary.aux | 36 -
inst/doc/commandsRd/summary.zelig.tex | 51 -
inst/doc/commandsRd/swiss.tex | 40 -
inst/doc/commandsRd/ternaryplot.aux | 38 -
inst/doc/commandsRd/ternaryplot.tex | 77 -
inst/doc/commandsRd/ternarypoints.aux | 38 -
inst/doc/commandsRd/ternarypoints.tex | 39 -
inst/doc/commandsRd/tobin.tex | 23 -
inst/doc/commandsRd/turnout.tex | 25 -
inst/doc/commandsRd/user.prompt.tex | 25 -
inst/doc/commandsRd/zeligVDC.tex | 62 -
inst/doc/{models => }/contributors_systemfit.tex | 0
inst/doc/ei.RxC.Rnw | 7 +-
inst/doc/ei.RxC.pdf | Bin 86751 -> 86748 bytes
inst/doc/ei.RxC.tex | 238 -
inst/doc/ei.dynamic.Rnw | 6 +-
inst/doc/ei.dynamic.pdf | Bin 95055 -> 95051 bytes
inst/doc/ei.dynamic.tex | 306 -
inst/doc/ei.hier.Rnw | 6 +-
inst/doc/ei.hier.pdf | Bin 92998 -> 93005 bytes
inst/doc/ei.hier.tex | 309 -
inst/doc/exp.pdf | Bin 123848 -> 123720 bytes
inst/doc/exp.tex | 304 -
inst/doc/factor.bayes.pdf | Bin 87105 -> 87112 bytes
inst/doc/factor.bayes.tex | 283 -
inst/doc/factor.mix.pdf | Bin 91656 -> 91660 bytes
inst/doc/factor.mix.tex | 295 -
inst/doc/factor.ord.pdf | Bin 90074 -> 90082 bytes
inst/doc/factor.ord.tex | 274 -
inst/doc/forMake.sh | 93 -
inst/doc/gam.logit.Rnw | 294 +
inst/doc/gam.logit.pdf | Bin 0 -> 267158 bytes
inst/doc/gam.normal.Rnw | 286 +
inst/doc/gam.normal.pdf | Bin 0 -> 262560 bytes
inst/doc/gam.poisson.Rnw | 290 +
inst/doc/gam.poisson.pdf | Bin 0 -> 263022 bytes
inst/doc/gam.probit.Rnw | 286 +
inst/doc/gam.probit.pdf | Bin 0 -> 262898 bytes
inst/doc/gamma.Rnw | 4 +-
inst/doc/gamma.pdf | Bin 118924 -> 118931 bytes
inst/doc/gamma.tex | 266 -
inst/doc/index.shtml | 4 +-
inst/doc/intro.tex | 16 +
inst/doc/irt1d.pdf | Bin 90626 -> 90633 bytes
inst/doc/irt1d.tex | 274 -
inst/doc/irtkd.pdf | Bin 93380 -> 93365 bytes
inst/doc/irtkd.tex | 279 -
inst/doc/logit.bayes.pdf | Bin 91888 -> 91896 bytes
inst/doc/logit.bayes.tex | 288 -
inst/doc/logit.gee.Rnw | 394 +
inst/doc/logit.gee.pdf | Bin 0 -> 155269 bytes
inst/doc/logit.pdf | Bin 140338 -> 140404 bytes
inst/doc/logit.tex | 323 -
inst/doc/lognorm.pdf | Bin 123488 -> 123318 bytes
inst/doc/lognorm.tex | 293 -
inst/doc/ls.pdf | Bin 130081 -> 129935 bytes
inst/doc/ls.tex | 285 -
inst/doc/makeDocs | 195 +
inst/doc/mlogit.bayes.pdf | Bin 94347 -> 94353 bytes
inst/doc/mlogit.bayes.tex | 316 -
inst/doc/mlogit.pdf | Bin 144664 -> 144664 bytes
inst/doc/mlogit.tex | 296 -
inst/doc/mloglm.pdf | Bin 43976 -> 43980 bytes
inst/doc/mloglm.tex | 116 -
inst/doc/models/arima.tex | 290 -
inst/doc/models/blogit.tex | 326 -
inst/doc/models/bprobit.tex | 357 -
inst/doc/models/coda_diag.tex | 32 -
inst/doc/models/contributors.tex | 15 -
inst/doc/models/contributors2.tex | 11 -
inst/doc/models/ei.dynamic.tex | 283 -
inst/doc/models/ei.hier.tex | 287 -
inst/doc/models/eiRxC.tex | 212 -
inst/doc/models/exp.tex | 275 -
inst/doc/models/factor.bayes.tex | 260 -
inst/doc/models/factor.mix.tex | 272 -
inst/doc/models/factor.ord.tex | 244 -
inst/doc/models/gamma.tex | 235 -
inst/doc/models/irt1d.tex | 248 -
inst/doc/models/irtkd.tex | 250 -
inst/doc/models/logit.bayes.tex | 238 -
inst/doc/models/logit.tex | 270 -
inst/doc/models/lognormal.tex | 262 -
inst/doc/models/ls.tex | 241 -
inst/doc/models/mlogit.bayes.tex | 274 -
inst/doc/models/mlogit.tex | 249 -
inst/doc/models/mloglm.tex | 105 -
inst/doc/models/negbin.tex | 237 -
inst/doc/models/netlogit.tex | 165 -
inst/doc/models/netls.tex | 154 -
inst/doc/models/normal.bayes.tex | 230 -
inst/doc/models/normal.tex | 273 -
inst/doc/models/ologit.tex | 244 -
inst/doc/models/oprobit.bayes.tex | 273 -
inst/doc/models/oprobit.tex | 252 -
inst/doc/models/poisson.bayes.tex | 230 -
inst/doc/models/poisson.tex | 219 -
inst/doc/models/probit.bayes.tex | 253 -
inst/doc/models/probit.tex | 220 -
inst/doc/models/relogit.tex | 398 -
inst/doc/models/tobit.bayes.tex | 262 -
inst/doc/models/tobit.tex | 208 -
inst/doc/models/weibull.tex | 267 -
inst/doc/negbin.pdf | Bin 87261 -> 87261 bytes
inst/doc/negbin.tex | 264 -
inst/doc/netcloglog.Rnw | 166 +
inst/doc/netcloglog.pdf | Bin 0 -> 101230 bytes
inst/doc/netgamma.Rnw | 173 +
inst/doc/netgamma.pdf | Bin 0 -> 126901 bytes
inst/doc/netlogit.Rnw | 370 +-
inst/doc/netlogit.pdf | Bin 115142 -> 106232 bytes
inst/doc/netlogit.tex | 206 -
inst/doc/netls.pdf | Bin 100596 -> 100408 bytes
inst/doc/netls.tex | 182 -
inst/doc/netnormal.Rnw | 151 +
inst/doc/netnormal.pdf | Bin 0 -> 96960 bytes
inst/doc/netpoisson.Rnw | 153 +
inst/doc/netpoisson.pdf | Bin 0 -> 88397 bytes
inst/doc/netprobit.Rnw | 168 +
inst/doc/netprobit.pdf | Bin 0 -> 102502 bytes
inst/doc/normal.bayes.pdf | Bin 95205 -> 95214 bytes
inst/doc/normal.bayes.tex | 277 -
inst/doc/normal.pdf | Bin 118002 -> 118100 bytes
inst/doc/normal.tex | 299 -
inst/doc/ologit.pdf | Bin 89133 -> 89134 bytes
inst/doc/ologit.tex | 273 -
inst/doc/oprobit.bayes.pdf | Bin 97739 -> 97749 bytes
inst/doc/oprobit.bayes.tex | 309 -
inst/doc/oprobit.pdf | Bin 159009 -> 158969 bytes
inst/doc/oprobit.tex | 291 -
inst/doc/poisson.bayes.pdf | Bin 90909 -> 90919 bytes
inst/doc/poisson.bayes.tex | 273 -
inst/doc/poisson.pdf | Bin 95705 -> 95762 bytes
inst/doc/poisson.tex | 253 -
inst/doc/probit.bayes.pdf | Bin 92535 -> 92552 bytes
inst/doc/probit.bayes.tex | 300 -
inst/doc/probit.pdf | Bin 83930 -> 83929 bytes
inst/doc/probit.tex | 247 -
inst/doc/refman.tex | 22 +-
inst/doc/relogit.pdf | Bin 132238 -> 132245 bytes
inst/doc/relogit.tex | 453 -
inst/doc/static/Zcommands.pdf | Bin 0 -> 159934 bytes
inst/doc/static/acknowledgments.pdf | Bin 0 -> 29859 bytes
inst/doc/static/addingModels.pdf | Bin 0 -> 87603 bytes
inst/doc/static/advancedR.pdf | Bin 0 -> 96393 bytes
inst/doc/static/faq.pdf | Bin 0 -> 104985 bytes
inst/doc/static/figs/icon_minus.gif | Bin 0 -> 959 bytes
inst/doc/static/figs/icon_plus.gif | Bin 0 -> 587 bytes
inst/doc/static/figs/increase.eps | 12290 +
inst/doc/static/figs/increase.pdf | Bin 0 -> 30671 bytes
inst/doc/static/figs/roc.eps | 396 +
inst/doc/static/figs/roc.pdf | 401 +
inst/doc/static/figs/sample1.eps | 914 +
inst/doc/static/figs/sample1.pdf | 568 +
inst/doc/static/figs/sample2.eps | 695 +
inst/doc/static/figs/sample2.pdf | 709 +
inst/doc/static/figs/ternary.eps | 2182 +
inst/doc/static/figs/ternary.pdf | 2220 +
inst/doc/static/figs/vertci.eps | 962 +
inst/doc/static/figs/vertci.pdf | 820 +
inst/doc/static/graphs.pdf | Bin 0 -> 173302 bytes
inst/doc/static/install.pdf | Bin 0 -> 97400 bytes
inst/doc/static/intro.pdf | Bin 0 -> 69847 bytes
inst/doc/static/newModels.pdf | Bin 0 -> 135793 bytes
inst/doc/static/syntax.pdf | Bin 0 -> 93890 bytes
inst/doc/static/upquote.sty | 76 +
inst/doc/{models/sur.tex => sur.Rnw} | 81 +-
inst/doc/sur.pdf | Bin 0 -> 91177 bytes
inst/doc/{models => }/systemfit_output.tex | 0
inst/doc/{models/3sls.tex => threesls.Rnw} | 107 +-
inst/doc/threesls.pdf | Bin 0 -> 123094 bytes
inst/doc/tobit.bayes.pdf | Bin 95686 -> 95694 bytes
inst/doc/tobit.bayes.tex | 306 -
inst/doc/tobit.pdf | Bin 81296 -> 81292 bytes
inst/doc/tobit.tex | 235 -
inst/doc/{models/2sls.tex => twosls.Rnw} | 97 +-
inst/doc/twosls.pdf | Bin 0 -> 152065 bytes
inst/doc/weibull.pdf | Bin 126082 -> 125671 bytes
inst/doc/weibull.tex | 298 -
inst/doc/zelig.html | 344 +
inst/doc/zelig.pdf | 110832 ++++---
inst/doc/zelig.tex | 45 +-
inst/doc/zeligfooter.html | 33 +
inst/doc/zeligheader.html | 229 +
inst/doc/zeligmodels.html | 82 +
inst/doc/zinput.tex | 3 +-
inst/unitTests/report.html | 24 +-
inst/unitTests/report.txt | 6 +-
inst/unitTests/runit.multiple.R | 10 +-
inst/zideal/zideal.RData | Bin 1263 -> 527 bytes
inst/zideal/zvcServer.R | 4 -
man/Zelig-package.Rd | 160 +
man/Zelig.Rd | 1 -
man/friendship.Rd | 47 +-
man/grunfeld.Rd | 23 +
man/klein.Rd | 25 +
man/kmenta.Rd | 24 +
man/zeligDepStatus.Rd | 57 +
man/zeligDepUpdate.Rd | 72 +
tests/check.describe.R | 5 +-
tests/test.parse.formula.RData | Bin 23578 -> 23256 bytes
tests/test.terms.multiple.RData | Bin 10475 -> 10083 bytes
394 files changed, 381928 insertions(+), 98946 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 8a84358..2c070a3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,12 +1,12 @@
Package: Zelig
-Version: 2.8-4
-Date: 2007-06-01
+Version: 2.8-5
+Date: 2007-07-06
Title: Everyone's Statistical Software
Author: Kosuke Imai <kimai at Princeton.Edu>,
Gary King <king at harvard.edu>,
Olivia Lau <olau at fas.harvard.edu>
-Maintainer: Olivia Lau <olau at fas.harvard.edu>
-Depends: R (>= 2.4.0), MASS, boot
+Maintainer: Kosuke Imai <kimai at Princeton.Edu>
+Depends: R (>= 2.5.0), MASS, boot
Description: Zelig is an easy-to-use program that can estimate, and
help interpret the results of, an enormous range of
statistical models. It literally is ``everyone's statistical
@@ -22,4 +22,4 @@ Description: Zelig is an easy-to-use program that can estimate, and
translates them into quantities of direct interest.
License: GPL version 2 or newer
URL: http://gking.harvard.edu/zelig
-Packaged: Fri Jun 1 03:05:56 2007; king
+Packaged: Fri Jul 6 16:00:59 2007; king
diff --git a/INDEX b/INDEX
index 77fba43..00dce25 100644
--- a/INDEX
+++ b/INDEX
@@ -1,9 +1,68 @@
-Zelig "Zelig: Everyone's Statistical Software" package.
- Refer to http://gking.harvard.edu/zelig for full
- documentation.
-zelig Estimate statistitcal models
-setx Set values of explanatory variables
-sim Simulate quantities of interest
-help.zelig Get html help for functions and models in the Zelig
- package.
-network Create a network data object
+approval U.S. Presidential Approval Data
+coalition Coalition Dissolution in Parliamentary
+ Democracies
+current.packages Find all packages in a dependency chain
+dims Return Dimensions of Vectors, Arrays, and Data
+ Frames
+eidat Simulation Data for Ecological Inference
+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
+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
+mexico Voting Data from the 1988 Mexican Presidental
+ Election
+mi Bundle multiply imputed data sets as a list
+mid Militarized Interstate Disputes
+model.end Cleaning up after optimization
+model.frame.multiple Extracting the "environment" of a model formula
+model.matrix.multiple Design matrix for multivariate models
+network Format matricies into a data frame for social
+ network analysis
+newpainters The Discretized Painter's Data of de Piles
+parse.formula Parsing user-input formulas into multiple
+ syntax
+parse.par Select and reshape parameter vectors
+PErisk Political Economic Risk Data from 62 Countries
+ in 1987
+plot.ci Plotting Vertical confidence Intervals
+plot.zelig Graphing Quantities of Interest
+put.start Set specific starting values for certain
+ parameters
+repl Replicating Analyses
+rocplot Receiver Operator Characteristic Plots
+sanction Multilateral Economic Sanctions
+set.start Set starting values for all parameters
+setx Setting Explanatory Variable Values
+sim Simulating Quantities of Interest
+sna.ex Simulated Example of Social Network Data
+summary.zelig Summary of Simulated Quantities of Interest
+SupremeCourt U.S. Supreme Court Vote Matrix
+swiss Swiss Fertility and Socioeconomic Indicators
+ (1888) Data
+ternaryplot Ternary diagram
+ternarypoints Adding Points to Ternary Diagrams
+tobin Tobin's Tobit Data
+turnout Turnout Data Set from the National Election
+ Survey
+user.prompt Pause in demo files
+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.url Table of links for Zelig
diff --git a/NAMESPACE b/NAMESPACE
index 07304cf..824f647 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -11,6 +11,7 @@ importFrom(stats, quasi)
importFrom(stats, quasibinomial)
importFrom(stats, quasipoisson)
+
export( current.packages,
dims,
gsource,
@@ -34,12 +35,14 @@ export( current.packages,
ternarypoints,
user.prompt,
zelig,
+ zeligDepStatus,
+ zeligDepUpdate,
zeligListModels,
zeligInstalledModels,
zeligModelDependency,
zeligGetSpecial,
zeligDescribeModelXML
-)
+ )
#S3method("$", ZeligS4vglm)
#S3method("$", summary.ZeligS4vglm)
@@ -56,20 +59,29 @@ S3method(names, zelig)
S3method(names, zelig.relogit)
S3method(plot, zelig)
S3method(plot, zelig.arima)
-S3method(plot.zelig, "2sls")
-S3method(plot.zelig, "3sls")
-S3method(plot.zelig, beta)
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, logit)
+S3method(plot.zelig, logit.gee)
S3method(plot.zelig, lognorm)
S3method(plot.zelig, ls)
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, ologit)
S3method(plot.zelig, oprobit)
@@ -77,6 +89,8 @@ 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)
@@ -107,8 +121,10 @@ S3method(sim, setx.strata)
S3method(setx, Arima)
S3method(setx, default)
S3method(setx, eiRxC)
+S3method(setx, gam)
S3method(setx, noX)
S3method(setx, MI)
+S3method(setx, netglm)
S3method(setx, strata)
S3method(setx, relogit2)
S3method(summarize, array)
@@ -117,6 +133,7 @@ S3method(summary, Arima)
S3method(summary, zelig.arima)
S3method(summary, BetaReg)
S3method(summary, MI)
+S3method(summary, netglm)
S3method(summary, strata)
S3method(summary, relogit)
S3method(summary, relogit2)
@@ -133,7 +150,6 @@ S3method(terms, vglm)
S3method(terms, multiple)
S3method(vcov, BetaReg)
S3method(vcov, eiRxC)
-S3method(vcov, lm)
S3method(vcov, relogit)
S3method(vcov, lm.robust)
S3method(vcov, glm.robust)
diff --git a/R/beta.regression.R b/R/beta.regression.R
deleted file mode 100644
index 3118cf2..0000000
--- a/R/beta.regression.R
+++ /dev/null
@@ -1,22 +0,0 @@
-beta.regression <- function(formula, data, start = NULL, ...) {
- kall <- match.call()
- Terms <- define.data(formula, data)
- if (is.null(start))
- start <- c(lm(y ~ x - 1)$coef, 1)
- start.val <- define.par(Terms, coef.name = "beta", ancillary = "phi",
- start = start)
- llik.beta <- function(par, X, Y, Terms) {
- parse.par(par, X, Terms)
- Xbeta <- X %*% beta
- mu <- exp(Xbeta) / (1 + exp(Xbeta))
- sum(lgamma(phi) - lgamma(mu * phi) - lgamma((1 - mu) * phi) +
- (mu * phi - 1) * log(Y) + ((1 - mu) * phi - 1) * log(1 - y))
- }
- res <- optim(start.val, llik.beta, method = "BFGS",
- control = list(fnscale = -1),
- hessian = TRUE, X = x, Y = y,
- Terms = Terms, ...)
- fit <- model.end(Terms, res)
- class(fit) <- "BetaReg"
- fit
-}
diff --git a/R/callsystemfit.R b/R/callsystemfit.R
new file mode 100644
index 0000000..e90bc2d
--- /dev/null
+++ b/R/callsystemfit.R
@@ -0,0 +1,9 @@
+callsystemfit<-function(formula,data,method,inst=NULL,...){
+### t<-terms.multiple(formula)
+ t<-terms(formula)
+
+ out<-systemfit(data=data,eqns=formula,method=method,inst=inst,...)
+ attr(out,"terms")<-t
+ class(out)<-c(class(out),"multiple")
+ return (out)
+}
diff --git a/R/categories.R b/R/categories.R
index 6f76b3b..f4f53ff 100644
--- a/R/categories.R
+++ b/R/categories.R
@@ -5,5 +5,6 @@ list(continuous="Models for Continous Dependent Variables",
bounded="Models for Continous Bounded Dependent Variables",
multinomial="Multinomial Choice Models",
count="Event Count Models",
- mixed="Models for Mixed Dependent Variables")
+ mixed="Models for Mixed Dependent Variables",
+ ei="Ecological inverence models")
}
diff --git a/R/describe.arima.R b/R/describe.arima.R
new file mode 100644
index 0000000..6dedf69
--- /dev/null
+++ b/R/describe.arima.R
@@ -0,0 +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)
+}
diff --git a/R/describe.ei.RxC.R b/R/describe.ei.RxC.R
new file mode 100644
index 0000000..ef2074a
--- /dev/null
+++ b/R/describe.ei.RxC.R
@@ -0,0 +1,17 @@
+describe.ei.RxC <- function(){
+category <- "ei"
+description <- "Hierarchical Multinomial-Dirichlet Ecological Inference Model for R x C Tables"
+package <-list( name ="stats",
+ version ="0.1"
+ )
+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.ei.dynamic.R b/R/describe.ei.dynamic.R
new file mode 100644
index 0000000..cef56d9
--- /dev/null
+++ b/R/describe.ei.dynamic.R
@@ -0,0 +1,18 @@
+describe.ei.dynamic <- function(){
+category <- "ei"
+description <- "Quinn's Dynamic Ecological Inference Model"
+package <- list (
+ name="MCMCpack",
+ version="0.8-2"
+ )
+parameters<-list(mu="mu")
+parameters$mu<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE,
+ specialFunction="cbind",
+ varInSpecialFunction=c(2,Inf)
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.ei.hier.R b/R/describe.ei.hier.R
new file mode 100644
index 0000000..adf69c6
--- /dev/null
+++ b/R/describe.ei.hier.R
@@ -0,0 +1,18 @@
+describe.ei.hier <- function(){
+category <- "ei"
+description <- "Hierarchical Ecological Inference Model for 2 x 2 Tables"
+package <- list (
+ name="MCMCpack",
+ version="0.8-2"
+ )
+parameters<-list(mu="mu")
+parameters$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.gam.logit.R b/R/describe.gam.logit.R
new file mode 100644
index 0000000..b6f4e4c
--- /dev/null
+++ b/R/describe.gam.logit.R
@@ -0,0 +1,15 @@
+describe.gam.logit<-function(){
+category <- "dichotomous"
+description <- "Generalized Additive Model for Dichotomous Dependent Variables"
+package <-list( name ="mgcv",
+ version ="0.1"
+ )
+parameters<-list(pi="pi")
+parameters$pi<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.tobit.R b/R/describe.gam.normal.R
similarity index 60%
copy from R/describe.tobit.R
copy to R/describe.gam.normal.R
index cce9cdf..e42cc1f 100644
--- a/R/describe.tobit.R
+++ b/R/describe.gam.normal.R
@@ -1,8 +1,8 @@
-describe.tobit<-function(){
+describe.gam.normal<-function(){
category <- "continuous"
-description <- "Linear regression for Left-Censored Dependet Variable"
-package <-list( name ="survival",
- version ="2.2"
+description <- "Generalized Additive Model for Continuous Dependent Variables"
+package <-list( name ="mgcv",
+ version ="0.1"
)
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,1),
diff --git a/R/describe.gam.poisson.R b/R/describe.gam.poisson.R
new file mode 100644
index 0000000..55b4efb
--- /dev/null
+++ b/R/describe.gam.poisson.R
@@ -0,0 +1,15 @@
+describe.gam.poisson<-function(){
+category <- "count"
+description <- "Generalized Additive Model for Event Count Dependent Variables"
+package <-list( name ="mgcv",
+ version ="0.1"
+ )
+parameters<-list(lambda="lambda")
+parameters$lambda<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.tobit.R b/R/describe.gam.probit.R
similarity index 53%
copy from R/describe.tobit.R
copy to R/describe.gam.probit.R
index cce9cdf..18a0339 100644
--- a/R/describe.tobit.R
+++ b/R/describe.gam.probit.R
@@ -1,8 +1,8 @@
-describe.tobit<-function(){
-category <- "continuous"
-description <- "Linear regression for Left-Censored Dependet Variable"
-package <-list( name ="survival",
- version ="2.2"
+describe.gam.probit<-function(){
+category <- "dichotomous"
+description <- "Generalized Additive Model for Dichotomous Dependent Variables"
+package <-list( name ="mgcv",
+ version ="0.1"
)
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,1),
diff --git a/R/describe.logit.gee.R b/R/describe.logit.gee.R
new file mode 100644
index 0000000..0f4fb8c
--- /dev/null
+++ b/R/describe.logit.gee.R
@@ -0,0 +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))
+}
+
diff --git a/R/describe.netcloglog.R b/R/describe.netcloglog.R
new file mode 100644
index 0000000..71dee74
--- /dev/null
+++ b/R/describe.netcloglog.R
@@ -0,0 +1,15 @@
+describe.netcloglog<-function(){
+category <- "dichotomous"
+description <- "Social Network Complementary Log Log Regression for Dichotomous Dependent Variables"
+package <-list( name ="stats",
+ version ="0.1"
+ )
+parameters<-list(pi="pi")
+parameters$pi<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.netgamma.R b/R/describe.netgamma.R
new file mode 100644
index 0000000..75d7294
--- /dev/null
+++ b/R/describe.netgamma.R
@@ -0,0 +1,15 @@
+describe.netgamma<-function(){
+category <- "bounded"
+description <- "Social Network Gamma Regression for Continuous, Positive Dependent Variables"
+package <-list( name ="stats",
+ version ="0.1"
+ )
+parameters<-list(lambda="lambda")
+parameters$lambda<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.netlogit.R b/R/describe.netlogit.R
new file mode 100644
index 0000000..51c4929
--- /dev/null
+++ b/R/describe.netlogit.R
@@ -0,0 +1,15 @@
+describe.netlogit<-function(){
+category <- "dichotomous"
+description <- "Social Network Logistic Regression for Dichotomous Dependent Variables"
+package <-list( name ="stats",
+ version ="0.1"
+ )
+parameters<-list(pi="pi")
+parameters$pi<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.tobit.R b/R/describe.netls.R
similarity index 59%
copy from R/describe.tobit.R
copy to R/describe.netls.R
index cce9cdf..f4c8a19 100644
--- a/R/describe.tobit.R
+++ b/R/describe.netls.R
@@ -1,8 +1,8 @@
-describe.tobit<-function(){
+describe.netls<-function(){
category <- "continuous"
-description <- "Linear regression for Left-Censored Dependet Variable"
-package <-list( name ="survival",
- version ="2.2"
+description <- "Social Network Least Squares Regression for Continuous Dependent Variables"
+package <-list( name ="sna",
+ version ="0.1"
)
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,1),
diff --git a/R/describe.tobit.R b/R/describe.netnormal.R
similarity index 59%
copy from R/describe.tobit.R
copy to R/describe.netnormal.R
index cce9cdf..1f20aa2 100644
--- a/R/describe.tobit.R
+++ b/R/describe.netnormal.R
@@ -1,8 +1,8 @@
-describe.tobit<-function(){
+describe.netnormal<-function(){
category <- "continuous"
-description <- "Linear regression for Left-Censored Dependet Variable"
-package <-list( name ="survival",
- version ="2.2"
+description <- "Social Network Normal Regression for Continuous Dependent Variables"
+package <-list( name ="stats",
+ version ="0.1"
)
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,1),
diff --git a/R/describe.netpoisson.R b/R/describe.netpoisson.R
new file mode 100644
index 0000000..82506ff
--- /dev/null
+++ b/R/describe.netpoisson.R
@@ -0,0 +1,15 @@
+describe.netpoisson<-function(){
+category <- "count"
+description <- "Social Network Poisson Regression for Event Count Dependent Variables"
+package <-list( name ="stats",
+ version ="0.1"
+ )
+parameters<-list(lambda="lambda")
+parameters$lambda<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.tobit.R b/R/describe.netprobit.R
similarity index 52%
copy from R/describe.tobit.R
copy to R/describe.netprobit.R
index cce9cdf..78fcd9c 100644
--- a/R/describe.tobit.R
+++ b/R/describe.netprobit.R
@@ -1,8 +1,8 @@
-describe.tobit<-function(){
-category <- "continuous"
-description <- "Linear regression for Left-Censored Dependet Variable"
-package <-list( name ="survival",
- version ="2.2"
+describe.netprobit<-function(){
+category <- "dichotomous"
+description <- "Social Network Probit Regression for Dichotomous Dependent Variables"
+package <-list( name ="stats",
+ version ="0.1"
)
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,1),
diff --git a/R/describe.threesls.R b/R/describe.threesls.R
new file mode 100644
index 0000000..bac6261
--- /dev/null
+++ b/R/describe.threesls.R
@@ -0,0 +1,18 @@
+describe.threesls<-function(){
+category <- "continuous"
+description <- "Three Stage Least Squares"
+package <-list( name ="systemfit",
+ version ="0.8"
+ )
+parameters<-list(mu="mu", inst="inst")
+parameters$mu<-list(equations=c(2,Inf),
+ tagsAllowed=TRUE,
+ depVar=TRUE,
+ expVar=TRUE)
+
+parameters$inst<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=FALSE,
+ expVar=TRUE)
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.tobit.R b/R/describe.tobit.R
index cce9cdf..68046c1 100644
--- a/R/describe.tobit.R
+++ b/R/describe.tobit.R
@@ -1,6 +1,6 @@
describe.tobit<-function(){
category <- "continuous"
-description <- "Linear regression for Left-Censored Dependet Variable"
+description <- "Linear regression for Left-Censored Dependent Variable"
package <-list( name ="survival",
version ="2.2"
)
diff --git a/R/describe.twosls.R b/R/describe.twosls.R
new file mode 100644
index 0000000..b2a6d29
--- /dev/null
+++ b/R/describe.twosls.R
@@ -0,0 +1,18 @@
+describe.twosls<-function(){
+category <- "continuous"
+description <- "Two Stage Least Squares"
+package <-list( name ="systemfit",
+ version ="0.8"
+ )
+parameters<-list(mu="mu", inst="inst")
+parameters$mu<-list(equations=c(2,Inf),
+ tagsAllowed=TRUE,
+ depVar=TRUE,
+ expVar=TRUE)
+
+parameters$inst<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=FALSE,
+ expVar=TRUE)
+list(category=category,description=description,package=package,parameters=parameters)
+}
diff --git a/R/help.zelig.R b/R/help.zelig.R
index 296ed2a..00012d6 100644
--- a/R/help.zelig.R
+++ b/R/help.zelig.R
@@ -1,15 +1,19 @@
help.zelig <- function (...) {
-
-
-### system(paste(getOption("pdfviewer"), system.file("doc/models/", "graphs.pdf",package="Zelig", lib.loc="~/.R/library")))
-### oprsys <- .Platform$OS.type
-### showpdf <- getOption("pdfviewer") unix
-### showpdf <- shell.exec() windows
+
driver <- match.call()
driver <- as.character(driver)
name <- NULL
- if(length(driver) > 1) name <- driver[2]
-
+ if(length(driver) > 1){
+ name <- driver[2]
+ } else {
+ print(do.call("help",list(package="Zelig"), envir=parent.frame()))
+ return(invisible(NULL))
+ }
+ if (name == "models"){
+ print(do.call("vignette", list(package="Zelig")))
+ return(invisible(NULL))
+ }
+
filesPDf <- NULL
helpfile <- try(system.file("Meta", "vignette.rds", package="Zelig"))
@@ -26,7 +30,7 @@ help.zelig <- function (...) {
if(length(ix)){
file <- filesPDF[ix]
print(do.call("vignette", c(list(topic=name),list(package="Zelig"))))
- return(list())
+ return(invisible(list()))
}
}
}
@@ -47,12 +51,12 @@ help.zelig <- function (...) {
if(length(ix)){
print(do.call("help", c(list(as.name(name)), list(package="Zelig")), envir=parent.frame()))
- return(NULL)
+ return(invisible(NULL))
}
}
}
- message("Not valid input...Showing package description")
- do.call("help", c(list("zelig"), list(package="Zelig")), envir=parent.frame())
+ ##message("Not valid input...Showing package description")
+ do.call("help", c(list("Zelig"), list(package="Zelig")), envir=parent.frame())
}
diff --git a/R/model.frame.gamF.R b/R/model.frame.gamF.R
new file mode 100644
index 0000000..975d567
--- /dev/null
+++ b/R/model.frame.gamF.R
@@ -0,0 +1,5 @@
+model.frame.gamF <- function(formula, data, ...){
+ gp <- interpret.gam(formula)
+ ff<- gp$fake.formula
+ return(model.frame.default(ff, data))
+ }
\ No newline at end of file
diff --git a/R/netbinom.R b/R/netbinom.R
new file mode 100644
index 0000000..4c60019
--- /dev/null
+++ b/R/netbinom.R
@@ -0,0 +1,241 @@
+netbinom <- function (formula, data, LF="logit", family=binomial(link=LF), ..., mode = "digraph", diag = FALSE,
+ nullhyp = c("qap", "qapspp", "qapy", "qapx", "qapallx", "cugtie",
+ "cugden", "cuguman", "classical"), tol = 1e-07, reps = 1000)
+{
+ gfit <- function(glist, mode, diag) {
+ y <- gvectorize(glist[[1]], mode = mode, diag = diag,
+ censor.as.na = TRUE)
+ x <- vector()
+ for (i in 2:length(glist)) x <- cbind(x, gvectorize(glist[[i]],
+ mode = mode, diag = diag, censor.as.na = TRUE))
+ if (!is.matrix(x))
+ x <- matrix(x, nc = 1)
+ mis <- is.na(y) | apply(is.na(x), 1, any)
+ glm.fit(x[!mis, ], y[!mis], family = binomial(link=LF), intercept = FALSE)
+ }
+ gfitlm <- function(glist, mode, diag, tol) {
+ y <- gvectorize(glist[[1]], mode = mode, diag = diag,
+ censor.as.na = TRUE)
+ x <- vector()
+ for (i in 2:length(glist)) x <- cbind(x, gvectorize(glist[[i]],
+ mode = mode, diag = diag, censor.as.na = TRUE))
+ if (!is.matrix(x))
+ x <- matrix(x, nc = 1)
+ mis <- is.na(y) | apply(is.na(x), 1, any)
+ list(qr(x[!mis, ], tol = tol), y[!mis])
+ }
+ call <- match.call()
+ Terms <- terms(formula)
+ intercept.value <- attr(Terms, "intercept")
+ if (intercept.value > 0){
+ intercept = TRUE
+ }
+ if (intercept.value == 0){
+ intercept = FALSE
+ }
+ if (missing(data))
+ data <- environment(formula)
+ mf <- match.call(expand.dots = FALSE)
+ m <- match(c("formula", "data", "weights"), names(mf), 0)
+ mf <- mf[c(1, m)]
+ mf$drop.unused.levels <- TRUE
+ mf[[1]] <- as.name("model.frame")
+ mf <- eval(mf, parent.frame())
+ mt <- attr(mf, "terms")
+ D <- model.frame(formula, data = data)
+ y <- D[[1]]
+ #x.array.names <- as.list(for(i in 2:length(D)) {names(D[[i]])})
+ x <- array(dim=c((length(D) - 1), nrow(y), ncol(y)))
+ for(i in 2:length(D)) {
+ x[i - 1,,] <- D[[i]] }
+
+
+
+ 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.")
+ if (length(dim(y)) > 2)
+ y <- y[1, , ]
+ if (is.list(x) || (dim(x)[2] != dim(y)[2]))
+ stop("Homogeneous graph orders required.")
+ nx <- stackcount(x) + intercept
+ n <- dim(y)[2]
+ g <- list(y)
+ if (intercept)
+ g[[2]] <- matrix(1, n, n)
+ if (nx - intercept == 1)
+ g[[2 + intercept]] <- x
+ 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....")
+ fit.base <- gfit(g, mode = mode, diag = diag)
+ fit <- list()
+ fit$coefficients <- fit.base$coefficients
+ fit$fitted.values <- fit.base$fitted.values
+ fit$residuals <- fit.base$residuals
+ fit$linear.predictors <- fit.base$linear.predictors
+ fit$n <- length(fit.base$y)
+ fit$df.model <- fit.base$rank
+ fit$df.residual <- fit.base$df.residual
+ fit$deviance <- fit.base$deviance
+ fit$null.deviance <- fit.base$null.deviance
+ fit$df.null <- fit.base$df.null
+ ######
+ fit$weights <- fit.base$weights
+ fit$boundary <- fit.base$boundary
+ fit$converged <- fit.base$converged
+ fit$contrasts <- fit.base$contrasts
+ fit$control <- fit.base$control
+ fit$effects <- fit.base$effects
+ fit$iter <- fit.base$iter
+ fit$model <- fit.base$model
+ fit$offset <- fit.base$offset
+ fit$prior.weights <- fit.base$prior.weights
+ fit$R <- fit.base$R
+ fit$y <- fit.base$y
+ #fit$summary <- summary(fit.base)
+ ######
+ fit$aic <- fit.base$aic
+ fit$bic <- fit$deviance + fit$df.model * log(fit$n)
+ fit$qr <- fit.base$qr
+ #fit$ctable <- table(as.numeric(fit$fitted.values >= 0.5),
+ # fit.base$y, dnn = c("Predicted", "Actual"))
+ #if (NROW(fit$ctable) == 1) {
+ # if (rownames(fit$ctable) == "0")
+ # fit$ctable <- rbind(fit$ctable, c(0, 0))
+ # else fit$ctable <- rbind(c(0, 0), fit$ctable)
+ # rownames(fit$ctable) <- c("0", "1")
+ #}
+ nullhyp <- match.arg(nullhyp)
+ if ((nullhyp %in% c("qap", "qapspp")) && (nx == 1))
+ nullhyp <- "qapy"
+ if (nullhyp == "classical") {
+ cvm <- chol2inv(fit$qr$qr)
+ se <- sqrt(diag(cvm))
+ tval <- fit$coefficients/se
+ fit$dist <- NULL
+ fit$pleeq <- pt(tval, fit$df.residual)
+ fit$pgreq <- pt(tval, fit$df.residual, lower.tail = FALSE)
+ fit$pgreqabs <- 2 * pt(abs(tval), fit$df.residual, lower.tail = FALSE)
+ }
+ else if (nullhyp %in% c("cugtie", "cugden", "cuguman")) {
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ gr <- g
+ for (j in 1:reps) {
+ gr[[i + 1]] <- switch(nullhyp, cugtie <- rgraph(n,
+ mode = mode, diag = diag, replace = FALSE,
+ tielist = g[[i + 1]]), cugden <- rgraph(n,
+ tp = gden(g[[i + 1]], mode = mode, diag = diag),
+ mode = mode, diag = diag), cuguman <- (function(dc,
+ n) {
+ rguman(1, n, mut = x[1], asym = x[2], null = x[3],
+ method = "exact")
+ })(dyad.census(g[[i + 1]]), n))
+ repdist[j, i] <- gfit(gr, mode = mode, diag = diag)$coef[i]
+ }
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapy") {
+ repdist <- matrix(0, reps, nx)
+ gr <- g
+ for (i in 1:reps) {
+ gr[[1]] <- rmperm(g[[1]])
+ repdist[i, ] <- gfit(gr, mode = mode, diag = diag)$coef
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapx") {
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ gr <- g
+ for (j in 1:reps) {
+ gr[[i + 1]] <- rmperm(gr[[i + 1]])
+ repdist[j, i] <- gfit(gr, mode = mode, diag = diag)$coef[i]
+ }
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapallx") {
+ repdist <- matrix(0, reps, nx)
+ gr <- g
+ for (i in 1:reps) {
+ for (j in 1:nx) gr[[1 + j]] <- rmperm(g[[1 + j]])
+ repdist[i, ] <- gfit(gr, mode = mode, diag = diag)$coef
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if ((nullhyp == "qap") || (nullhyp == "qapspp")) {
+ xsel <- matrix(TRUE, n, n)
+ if (!diag)
+ diag(xsel) <- FALSE
+ if (mode == "graph")
+ xsel[upper.tri(xsel)] <- FALSE
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ xfit <- gfitlm(g[1 + c(i, (1:nx)[-i])], mode = mode,
+ diag = diag, tol = tol)
+ xres <- g[[1 + i]]
+ xres[xsel] <- qr.resid(xfit[[1]], xfit[[2]])
+ if (mode == "graph")
+ xres[upper.tri(xres)] <- t(xres)[upper.tri(xres)]
+ for (j in 1:reps) repdist[j, i] <- gfit(c(g[-(1 +
+ i)], list(rmperm(xres))), mode = mode, diag = diag)$coef[nx]
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ fit$nullhyp <- nullhyp
+ fit$names <- names(mf[2:stackcount(mf)]) # paste("x", 1:(nx - intercept), sep = "")
+ if (intercept)
+ fit$names <- c("(intercept)", fit$names)
+ fit$intercept <- intercept
+ fit$xlevels <- .getXlevels(mt, mf)
+ fit <- c(fit, list(call = call, formula = formula, terms = mt,
+ data = data, xlevels = .getXlevels(mt, mf)))
+ new.data <- as.data.frame(as.vector(data[,1]))
+ for(i in 2:ncol(data)){
+ new.data <- cbind(new.data, as.vector(data[,i])) }
+ names(new.data) <- names(data)
+ fit$data <- new.data
+ fit$family <- family
+ fit$rank <- fit$df.model
+ so <- summary.glm(fit)
+ fit$mod.coefficients <- so$coefficients
+ fit$cov.unscaled <- so$cov.unscaled
+ fit$cov.scaled <- so$cov.scaled
+ class(fit) <- c("netglm")
+ return(fit)
+}
diff --git a/R/netgamma.R b/R/netgamma.R
new file mode 100644
index 0000000..f6ead8a
--- /dev/null
+++ b/R/netgamma.R
@@ -0,0 +1,241 @@
+netgamma <- 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)
+{
+ gfit <- function(glist, mode, diag) {
+ y <- gvectorize(glist[[1]], mode = mode, diag = diag,
+ censor.as.na = TRUE)
+ x <- vector()
+ for (i in 2:length(glist)) x <- cbind(x, gvectorize(glist[[i]],
+ mode = mode, diag = diag, censor.as.na = TRUE))
+ if (!is.matrix(x))
+ x <- matrix(x, nc = 1)
+ mis <- is.na(y) | apply(is.na(x), 1, any)
+ glm.fit(x[!mis, ], y[!mis], family = Gamma(link=LF), intercept = FALSE)
+ }
+ gfitlm <- function(glist, mode, diag, tol) {
+ y <- gvectorize(glist[[1]], mode = mode, diag = diag,
+ censor.as.na = TRUE)
+ x <- vector()
+ for (i in 2:length(glist)) x <- cbind(x, gvectorize(glist[[i]],
+ mode = mode, diag = diag, censor.as.na = TRUE))
+ if (!is.matrix(x))
+ x <- matrix(x, nc = 1)
+ mis <- is.na(y) | apply(is.na(x), 1, any)
+ list(qr(x[!mis, ], tol = tol), y[!mis])
+ }
+ call <- match.call()
+ Terms <- terms(formula)
+ intercept.value <- attr(Terms, "intercept")
+ if (intercept.value > 0){
+ intercept = TRUE
+ }
+ if (intercept.value == 0){
+ intercept = FALSE
+ }
+ if (missing(data))
+ data <- environment(formula)
+ mf <- match.call(expand.dots = FALSE)
+ m <- match(c("formula", "data", "weights"), names(mf), 0)
+ mf <- mf[c(1, m)]
+ mf$drop.unused.levels <- TRUE
+ mf[[1]] <- as.name("model.frame")
+ mf <- eval(mf, parent.frame())
+ mt <- attr(mf, "terms")
+ D <- model.frame(formula, data = data)
+ y <- D[[1]]
+ #x.array.names <- as.list(for(i in 2:length(D)) {names(D[[i]])})
+ x <- array(dim=c((length(D) - 1), nrow(y), ncol(y)))
+ for(i in 2:length(D)) {
+ x[i - 1,,] <- D[[i]] }
+
+
+
+ y <- as.sociomatrix.sna(y)
+ x <- as.sociomatrix.sna(x)
+ if (is.list(y) || ((length(dim(y)) > 2) && (dim(y)[1] > 1)))
+ stop("y must be a single graph.")
+ if (length(dim(y)) > 2)
+ y <- y[1, , ]
+ if (is.list(x) || (dim(x)[2] != dim(y)[2]))
+ stop("Homogeneous graph orders required.")
+ nx <- stackcount(x) + intercept
+ n <- dim(y)[2]
+ g <- list(y)
+ if (intercept)
+ g[[2]] <- matrix(1, n, n)
+ if (nx - intercept == 1)
+ g[[2 + intercept]] <- x
+ else for (i in 1:(nx - intercept)) g[[i + 1 + intercept]] <- x[i,
+ , ]
+ if (any(sapply(lapply(g, is.na), any)))
+ warning("Missing data supplied to netpoisson; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
+ fit.base <- gfit(g, mode = mode, diag = diag)
+ fit <- list()
+ fit$coefficients <- fit.base$coefficients
+ fit$fitted.values <- fit.base$fitted.values
+ fit$residuals <- fit.base$residuals
+ fit$linear.predictors <- fit.base$linear.predictors
+ fit$n <- length(fit.base$y)
+ fit$df.model <- fit.base$rank
+ fit$df.residual <- fit.base$df.residual
+ fit$deviance <- fit.base$deviance
+ fit$null.deviance <- fit.base$null.deviance
+ fit$df.null <- fit.base$df.null
+ ######
+ fit$weights <- fit.base$weights
+ fit$boundary <- fit.base$boundary
+ fit$converged <- fit.base$converged
+ fit$contrasts <- fit.base$contrasts
+ fit$control <- fit.base$control
+ fit$effects <- fit.base$effects
+ fit$iter <- fit.base$iter
+ fit$model <- fit.base$model
+ fit$offset <- fit.base$offset
+ fit$prior.weights <- fit.base$prior.weights
+ fit$R <- fit.base$R
+ fit$y <- fit.base$y
+ #fit$summary <- summary(fit.base)
+ ######
+ fit$aic <- fit.base$aic
+ fit$bic <- fit$deviance + fit$df.model * log(fit$n)
+ fit$qr <- fit.base$qr
+ #fit$ctable <- table(as.numeric(fit$fitted.values >= 0.5),
+ # fit.base$y, dnn = c("Predicted", "Actual"))
+ #if (NROW(fit$ctable) == 1) {
+ # if (rownames(fit$ctable) == "0")
+ # fit$ctable <- rbind(fit$ctable, c(0, 0))
+ # else fit$ctable <- rbind(c(0, 0), fit$ctable)
+ # rownames(fit$ctable) <- c("0", "1")
+ #}
+ nullhyp <- match.arg(nullhyp)
+ if ((nullhyp %in% c("qap", "qapspp")) && (nx == 1))
+ nullhyp <- "qapy"
+ if (nullhyp == "classical") {
+ cvm <- chol2inv(fit$qr$qr)
+ se <- sqrt(diag(cvm))
+ tval <- fit$coefficients/se
+ fit$dist <- NULL
+ fit$pleeq <- pt(tval, fit$df.residual)
+ fit$pgreq <- pt(tval, fit$df.residual, lower.tail = FALSE)
+ fit$pgreqabs <- 2 * pt(abs(tval), fit$df.residual, lower.tail = FALSE)
+ }
+ else if (nullhyp %in% c("cugtie", "cugden", "cuguman")) {
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ gr <- g
+ for (j in 1:reps) {
+ gr[[i + 1]] <- switch(nullhyp, cugtie <- rgraph(n,
+ mode = mode, diag = diag, replace = FALSE,
+ tielist = g[[i + 1]]), cugden <- rgraph(n,
+ tp = gden(g[[i + 1]], mode = mode, diag = diag),
+ mode = mode, diag = diag), cuguman <- (function(dc,
+ n) {
+ rguman(1, n, mut = x[1], asym = x[2], null = x[3],
+ method = "exact")
+ })(dyad.census(g[[i + 1]]), n))
+ repdist[j, i] <- gfit(gr, mode = mode, diag = diag)$coef[i]
+ }
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapy") {
+ repdist <- matrix(0, reps, nx)
+ gr <- g
+ for (i in 1:reps) {
+ gr[[1]] <- rmperm(g[[1]])
+ repdist[i, ] <- gfit(gr, mode = mode, diag = diag)$coef
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapx") {
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ gr <- g
+ for (j in 1:reps) {
+ gr[[i + 1]] <- rmperm(gr[[i + 1]])
+ repdist[j, i] <- gfit(gr, mode = mode, diag = diag)$coef[i]
+ }
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapallx") {
+ repdist <- matrix(0, reps, nx)
+ gr <- g
+ for (i in 1:reps) {
+ for (j in 1:nx) gr[[1 + j]] <- rmperm(g[[1 + j]])
+ repdist[i, ] <- gfit(gr, mode = mode, diag = diag)$coef
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if ((nullhyp == "qap") || (nullhyp == "qapspp")) {
+ xsel <- matrix(TRUE, n, n)
+ if (!diag)
+ diag(xsel) <- FALSE
+ if (mode == "graph")
+ xsel[upper.tri(xsel)] <- FALSE
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ xfit <- gfitlm(g[1 + c(i, (1:nx)[-i])], mode = mode,
+ diag = diag, tol = tol)
+ xres <- g[[1 + i]]
+ xres[xsel] <- qr.resid(xfit[[1]], xfit[[2]])
+ if (mode == "graph")
+ xres[upper.tri(xres)] <- t(xres)[upper.tri(xres)]
+ for (j in 1:reps) repdist[j, i] <- gfit(c(g[-(1 +
+ i)], list(rmperm(xres))), mode = mode, diag = diag)$coef[nx]
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ fit$nullhyp <- nullhyp
+ fit$names <- names(mf[2:stackcount(mf)]) # paste("x", 1:(nx - intercept), sep = "")
+ if (intercept)
+ fit$names <- c("(intercept)", fit$names)
+ fit$intercept <- intercept
+ fit$xlevels <- .getXlevels(mt, mf)
+ fit <- c(fit, list(call = call, formula = formula, terms = mt,
+ data = data, xlevels = .getXlevels(mt, mf)))
+ new.data <- as.data.frame(as.vector(data[,1]))
+ for(i in 2:ncol(data)){
+ new.data <- cbind(new.data, as.vector(data[,i])) }
+ names(new.data) <- names(data)
+ fit$data <- new.data
+ fit$family <- family
+ fit$rank <- fit$df.model
+ so <- summary.glm(fit)
+ fit$mod.coefficients <- so$coefficients
+ fit$cov.unscaled <- so$cov.unscaled
+ fit$cov.scaled <- so$cov.scaled
+ class(fit) <- c("netglm")
+ return(fit)
+}
diff --git a/R/netnormal.R b/R/netnormal.R
new file mode 100644
index 0000000..72dee2b
--- /dev/null
+++ b/R/netnormal.R
@@ -0,0 +1,241 @@
+netnormal <- function (formula, data, LF= "identity", family=gaussian(link=LF), ..., mode = "digraph", diag = FALSE,
+ nullhyp = c("qap", "qapspp", "qapy", "qapx", "qapallx", "cugtie",
+ "cugden", "cuguman", "classical"), tol = 1e-07, reps = 1000)
+{
+ gfit <- function(glist, mode, diag) {
+ y <- gvectorize(glist[[1]], mode = mode, diag = diag,
+ censor.as.na = TRUE)
+ x <- vector()
+ for (i in 2:length(glist)) x <- cbind(x, gvectorize(glist[[i]],
+ mode = mode, diag = diag, censor.as.na = TRUE))
+ if (!is.matrix(x))
+ x <- matrix(x, nc = 1)
+ mis <- is.na(y) | apply(is.na(x), 1, any)
+ glm.fit(x[!mis, ], y[!mis], family = gaussian(link=LF), intercept = FALSE)
+ }
+ gfitlm <- function(glist, mode, diag, tol) {
+ y <- gvectorize(glist[[1]], mode = mode, diag = diag,
+ censor.as.na = TRUE)
+ x <- vector()
+ for (i in 2:length(glist)) x <- cbind(x, gvectorize(glist[[i]],
+ mode = mode, diag = diag, censor.as.na = TRUE))
+ if (!is.matrix(x))
+ x <- matrix(x, nc = 1)
+ mis <- is.na(y) | apply(is.na(x), 1, any)
+ list(qr(x[!mis, ], tol = tol), y[!mis])
+ }
+ call <- match.call()
+ Terms <- terms(formula)
+ intercept.value <- attr(Terms, "intercept")
+ if (intercept.value > 0){
+ intercept = TRUE
+ }
+ if (intercept.value == 0){
+ intercept = FALSE
+ }
+ if (missing(data))
+ data <- environment(formula)
+ mf <- match.call(expand.dots = FALSE)
+ m <- match(c("formula", "data", "weights"), names(mf), 0)
+ mf <- mf[c(1, m)]
+ mf$drop.unused.levels <- TRUE
+ mf[[1]] <- as.name("model.frame")
+ mf <- eval(mf, parent.frame())
+ mt <- attr(mf, "terms")
+ D <- model.frame(formula, data = data)
+ y <- D[[1]]
+ #x.array.names <- as.list(for(i in 2:length(D)) {names(D[[i]])})
+ x <- array(dim=c((length(D) - 1), nrow(y), ncol(y)))
+ for(i in 2:length(D)) {
+ x[i - 1,,] <- D[[i]] }
+
+
+
+ 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.")
+ if (length(dim(y)) > 2)
+ y <- y[1, , ]
+ if (is.list(x) || (dim(x)[2] != dim(y)[2]))
+ stop("Homogeneous graph orders required.")
+ nx <- stackcount(x) + intercept
+ n <- dim(y)[2]
+ g <- list(y)
+ if (intercept)
+ g[[2]] <- matrix(1, n, n)
+ if (nx - intercept == 1)
+ g[[2 + intercept]] <- x
+ 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....")
+ fit.base <- gfit(g, mode = mode, diag = diag)
+ fit <- list()
+ fit$coefficients <- fit.base$coefficients
+ fit$fitted.values <- fit.base$fitted.values
+ fit$residuals <- fit.base$residuals
+ fit$linear.predictors <- fit.base$linear.predictors
+ fit$n <- length(fit.base$y)
+ fit$df.model <- fit.base$rank
+ fit$df.residual <- fit.base$df.residual
+ fit$deviance <- fit.base$deviance
+ fit$null.deviance <- fit.base$null.deviance
+ fit$df.null <- fit.base$df.null
+ ######
+ fit$weights <- fit.base$weights
+ fit$boundary <- fit.base$boundary
+ fit$converged <- fit.base$converged
+ fit$contrasts <- fit.base$contrasts
+ fit$control <- fit.base$control
+ fit$effects <- fit.base$effects
+ fit$iter <- fit.base$iter
+ fit$model <- fit.base$model
+ fit$offset <- fit.base$offset
+ fit$prior.weights <- fit.base$prior.weights
+ fit$R <- fit.base$R
+ fit$y <- fit.base$y
+ #fit$summary <- summary(fit.base)
+ ######
+ fit$aic <- fit.base$aic
+ fit$bic <- fit$deviance + fit$df.model * log(fit$n)
+ fit$qr <- fit.base$qr
+ #fit$ctable <- table(as.numeric(fit$fitted.values >= 0.5),
+ # fit.base$y, dnn = c("Predicted", "Actual"))
+ #if (NROW(fit$ctable) == 1) {
+ # if (rownames(fit$ctable) == "0")
+ # fit$ctable <- rbind(fit$ctable, c(0, 0))
+ # else fit$ctable <- rbind(c(0, 0), fit$ctable)
+ # rownames(fit$ctable) <- c("0", "1")
+ #}
+ nullhyp <- match.arg(nullhyp)
+ if ((nullhyp %in% c("qap", "qapspp")) && (nx == 1))
+ nullhyp <- "qapy"
+ if (nullhyp == "classical") {
+ cvm <- chol2inv(fit$qr$qr)
+ se <- sqrt(diag(cvm))
+ tval <- fit$coefficients/se
+ fit$dist <- NULL
+ fit$pleeq <- pt(tval, fit$df.residual)
+ fit$pgreq <- pt(tval, fit$df.residual, lower.tail = FALSE)
+ fit$pgreqabs <- 2 * pt(abs(tval), fit$df.residual, lower.tail = FALSE)
+ }
+ else if (nullhyp %in% c("cugtie", "cugden", "cuguman")) {
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ gr <- g
+ for (j in 1:reps) {
+ gr[[i + 1]] <- switch(nullhyp, cugtie <- rgraph(n,
+ mode = mode, diag = diag, replace = FALSE,
+ tielist = g[[i + 1]]), cugden <- rgraph(n,
+ tp = gden(g[[i + 1]], mode = mode, diag = diag),
+ mode = mode, diag = diag), cuguman <- (function(dc,
+ n) {
+ rguman(1, n, mut = x[1], asym = x[2], null = x[3],
+ method = "exact")
+ })(dyad.census(g[[i + 1]]), n))
+ repdist[j, i] <- gfit(gr, mode = mode, diag = diag)$coef[i]
+ }
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapy") {
+ repdist <- matrix(0, reps, nx)
+ gr <- g
+ for (i in 1:reps) {
+ gr[[1]] <- rmperm(g[[1]])
+ repdist[i, ] <- gfit(gr, mode = mode, diag = diag)$coef
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapx") {
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ gr <- g
+ for (j in 1:reps) {
+ gr[[i + 1]] <- rmperm(gr[[i + 1]])
+ repdist[j, i] <- gfit(gr, mode = mode, diag = diag)$coef[i]
+ }
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapallx") {
+ repdist <- matrix(0, reps, nx)
+ gr <- g
+ for (i in 1:reps) {
+ for (j in 1:nx) gr[[1 + j]] <- rmperm(g[[1 + j]])
+ repdist[i, ] <- gfit(gr, mode = mode, diag = diag)$coef
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if ((nullhyp == "qap") || (nullhyp == "qapspp")) {
+ xsel <- matrix(TRUE, n, n)
+ if (!diag)
+ diag(xsel) <- FALSE
+ if (mode == "graph")
+ xsel[upper.tri(xsel)] <- FALSE
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ xfit <- gfitlm(g[1 + c(i, (1:nx)[-i])], mode = mode,
+ diag = diag, tol = tol)
+ xres <- g[[1 + i]]
+ xres[xsel] <- qr.resid(xfit[[1]], xfit[[2]])
+ if (mode == "graph")
+ xres[upper.tri(xres)] <- t(xres)[upper.tri(xres)]
+ for (j in 1:reps) repdist[j, i] <- gfit(c(g[-(1 +
+ i)], list(rmperm(xres))), mode = mode, diag = diag)$coef[nx]
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ fit$nullhyp <- nullhyp
+ fit$names <- names(mf[2:stackcount(mf)]) # paste("x", 1:(nx - intercept), sep = "")
+ if (intercept)
+ fit$names <- c("(intercept)", fit$names)
+ fit$intercept <- intercept
+ fit$xlevels <- .getXlevels(mt, mf)
+ fit <- c(fit, list(call = call, formula = formula, terms = mt,
+ data = data, xlevels = .getXlevels(mt, mf)))
+ new.data <- as.data.frame(as.vector(data[,1]))
+ for(i in 2:ncol(data)){
+ new.data <- cbind(new.data, as.vector(data[,i])) }
+ names(new.data) <- names(data)
+ fit$data <- new.data
+ fit$family <- family
+ fit$rank <- fit$df.model
+ so <- summary.glm(fit)
+ fit$mod.coefficients <- so$coefficients
+ fit$cov.unscaled <- so$cov.unscaled
+ fit$cov.scaled <- so$cov.scaled
+ class(fit) <- c("netglm")
+ return(fit)
+}
diff --git a/R/netpoisson.R b/R/netpoisson.R
new file mode 100644
index 0000000..8ca1b09
--- /dev/null
+++ b/R/netpoisson.R
@@ -0,0 +1,241 @@
+netpoisson <- 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)
+{
+ gfit <- function(glist, mode, diag) {
+ y <- gvectorize(glist[[1]], mode = mode, diag = diag,
+ censor.as.na = TRUE)
+ x <- vector()
+ for (i in 2:length(glist)) x <- cbind(x, gvectorize(glist[[i]],
+ mode = mode, diag = diag, censor.as.na = TRUE))
+ if (!is.matrix(x))
+ x <- matrix(x, nc = 1)
+ mis <- is.na(y) | apply(is.na(x), 1, any)
+ glm.fit(x[!mis, ], y[!mis], family = poisson(link=LF), intercept = FALSE)
+ }
+ gfitlm <- function(glist, mode, diag, tol) {
+ y <- gvectorize(glist[[1]], mode = mode, diag = diag,
+ censor.as.na = TRUE)
+ x <- vector()
+ for (i in 2:length(glist)) x <- cbind(x, gvectorize(glist[[i]],
+ mode = mode, diag = diag, censor.as.na = TRUE))
+ if (!is.matrix(x))
+ x <- matrix(x, nc = 1)
+ mis <- is.na(y) | apply(is.na(x), 1, any)
+ list(qr(x[!mis, ], tol = tol), y[!mis])
+ }
+ call <- match.call()
+ Terms <- terms(formula)
+ intercept.value <- attr(Terms, "intercept")
+ if (intercept.value > 0){
+ intercept = TRUE
+ }
+ if (intercept.value == 0){
+ intercept = FALSE
+ }
+ if (missing(data))
+ data <- environment(formula)
+ mf <- match.call(expand.dots = FALSE)
+ m <- match(c("formula", "data", "weights"), names(mf), 0)
+ mf <- mf[c(1, m)]
+ mf$drop.unused.levels <- TRUE
+ mf[[1]] <- as.name("model.frame")
+ mf <- eval(mf, parent.frame())
+ mt <- attr(mf, "terms")
+ D <- model.frame(formula, data = data)
+ y <- D[[1]]
+ #x.array.names <- as.list(for(i in 2:length(D)) {names(D[[i]])})
+ x <- array(dim=c((length(D) - 1), nrow(y), ncol(y)))
+ for(i in 2:length(D)) {
+ x[i - 1,,] <- D[[i]] }
+
+
+
+ y <- as.sociomatrix.sna(y)
+ x <- as.sociomatrix.sna(x)
+ if (is.list(y) || ((length(dim(y)) > 2) && (dim(y)[1] > 1)))
+ stop("y must be a single graph.")
+ if (length(dim(y)) > 2)
+ y <- y[1, , ]
+ if (is.list(x) || (dim(x)[2] != dim(y)[2]))
+ stop("Homogeneous graph orders required.")
+ nx <- stackcount(x) + intercept
+ n <- dim(y)[2]
+ g <- list(y)
+ if (intercept)
+ g[[2]] <- matrix(1, n, n)
+ if (nx - intercept == 1)
+ g[[2 + intercept]] <- x
+ else for (i in 1:(nx - intercept)) g[[i + 1 + intercept]] <- x[i,
+ , ]
+ if (any(sapply(lapply(g, is.na), any)))
+ warning("Missing data supplied to netpoisson; this may pose problems for certain null hypotheses. Hope you know what you're doing....")
+ fit.base <- gfit(g, mode = mode, diag = diag)
+ fit <- list()
+ fit$coefficients <- fit.base$coefficients
+ fit$fitted.values <- fit.base$fitted.values
+ fit$residuals <- fit.base$residuals
+ fit$linear.predictors <- fit.base$linear.predictors
+ fit$n <- length(fit.base$y)
+ fit$df.model <- fit.base$rank
+ fit$df.residual <- fit.base$df.residual
+ fit$deviance <- fit.base$deviance
+ fit$null.deviance <- fit.base$null.deviance
+ fit$df.null <- fit.base$df.null
+ ######
+ fit$weights <- fit.base$weights
+ fit$boundary <- fit.base$boundary
+ fit$converged <- fit.base$converged
+ fit$contrasts <- fit.base$contrasts
+ fit$control <- fit.base$control
+ fit$effects <- fit.base$effects
+ fit$iter <- fit.base$iter
+ fit$model <- fit.base$model
+ fit$offset <- fit.base$offset
+ fit$prior.weights <- fit.base$prior.weights
+ fit$R <- fit.base$R
+ fit$y <- fit.base$y
+ #fit$summary <- summary(fit.base)
+ ######
+ fit$aic <- fit.base$aic
+ fit$bic <- fit$deviance + fit$df.model * log(fit$n)
+ fit$qr <- fit.base$qr
+ #fit$ctable <- table(as.numeric(fit$fitted.values >= 0.5),
+ # fit.base$y, dnn = c("Predicted", "Actual"))
+ #if (NROW(fit$ctable) == 1) {
+ # if (rownames(fit$ctable) == "0")
+ # fit$ctable <- rbind(fit$ctable, c(0, 0))
+ # else fit$ctable <- rbind(c(0, 0), fit$ctable)
+ # rownames(fit$ctable) <- c("0", "1")
+ #}
+ nullhyp <- match.arg(nullhyp)
+ if ((nullhyp %in% c("qap", "qapspp")) && (nx == 1))
+ nullhyp <- "qapy"
+ if (nullhyp == "classical") {
+ cvm <- chol2inv(fit$qr$qr)
+ se <- sqrt(diag(cvm))
+ tval <- fit$coefficients/se
+ fit$dist <- NULL
+ fit$pleeq <- pt(tval, fit$df.residual)
+ fit$pgreq <- pt(tval, fit$df.residual, lower.tail = FALSE)
+ fit$pgreqabs <- 2 * pt(abs(tval), fit$df.residual, lower.tail = FALSE)
+ }
+ else if (nullhyp %in% c("cugtie", "cugden", "cuguman")) {
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ gr <- g
+ for (j in 1:reps) {
+ gr[[i + 1]] <- switch(nullhyp, cugtie <- rgraph(n,
+ mode = mode, diag = diag, replace = FALSE,
+ tielist = g[[i + 1]]), cugden <- rgraph(n,
+ tp = gden(g[[i + 1]], mode = mode, diag = diag),
+ mode = mode, diag = diag), cuguman <- (function(dc,
+ n) {
+ rguman(1, n, mut = x[1], asym = x[2], null = x[3],
+ method = "exact")
+ })(dyad.census(g[[i + 1]]), n))
+ repdist[j, i] <- gfit(gr, mode = mode, diag = diag)$coef[i]
+ }
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapy") {
+ repdist <- matrix(0, reps, nx)
+ gr <- g
+ for (i in 1:reps) {
+ gr[[1]] <- rmperm(g[[1]])
+ repdist[i, ] <- gfit(gr, mode = mode, diag = diag)$coef
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapx") {
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ gr <- g
+ for (j in 1:reps) {
+ gr[[i + 1]] <- rmperm(gr[[i + 1]])
+ repdist[j, i] <- gfit(gr, mode = mode, diag = diag)$coef[i]
+ }
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if (nullhyp == "qapallx") {
+ repdist <- matrix(0, reps, nx)
+ gr <- g
+ for (i in 1:reps) {
+ for (j in 1:nx) gr[[1 + j]] <- rmperm(g[[1 + j]])
+ repdist[i, ] <- gfit(gr, mode = mode, diag = diag)$coef
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ else if ((nullhyp == "qap") || (nullhyp == "qapspp")) {
+ xsel <- matrix(TRUE, n, n)
+ if (!diag)
+ diag(xsel) <- FALSE
+ if (mode == "graph")
+ xsel[upper.tri(xsel)] <- FALSE
+ repdist <- matrix(0, reps, nx)
+ for (i in 1:nx) {
+ xfit <- gfitlm(g[1 + c(i, (1:nx)[-i])], mode = mode,
+ diag = diag, tol = tol)
+ xres <- g[[1 + i]]
+ xres[xsel] <- qr.resid(xfit[[1]], xfit[[2]])
+ if (mode == "graph")
+ xres[upper.tri(xres)] <- t(xres)[upper.tri(xres)]
+ for (j in 1:reps) repdist[j, i] <- gfit(c(g[-(1 +
+ i)], list(rmperm(xres))), mode = mode, diag = diag)$coef[nx]
+ }
+ fit$dist <- repdist
+ fit$pleeq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ "<="), 2, mean)
+ fit$pgreq <- apply(sweep(fit$dist, 2, fit$coefficients,
+ ">="), 2, mean)
+ fit$pgreqabs <- apply(sweep(abs(fit$dist), 2, abs(fit$coefficients),
+ ">="), 2, mean)
+ }
+ fit$nullhyp <- nullhyp
+ fit$names <- names(mf[2:stackcount(mf)]) # paste("x", 1:(nx - intercept), sep = "")
+ if (intercept)
+ fit$names <- c("(intercept)", fit$names)
+ fit$intercept <- intercept
+ fit$xlevels <- .getXlevels(mt, mf)
+ fit <- c(fit, list(call = call, formula = formula, terms = mt,
+ data = data, xlevels = .getXlevels(mt, mf)))
+ new.data <- as.data.frame(as.vector(data[,1]))
+ for(i in 2:ncol(data)){
+ new.data <- cbind(new.data, as.vector(data[,i])) }
+ names(new.data) <- names(data)
+ fit$data <- new.data
+ fit$family <- family
+ fit$rank <- fit$df.model
+ so <- summary.glm(fit)
+ fit$mod.coefficients <- so$coefficients
+ fit$cov.unscaled <- so$cov.unscaled
+ fit$cov.scaled <- so$cov.scaled
+ class(fit) <- c("netglm")
+ return(fit)
+}
diff --git a/R/param.betareg.R b/R/param.betareg.R
deleted file mode 100644
index a71dbda..0000000
--- a/R/param.betareg.R
+++ /dev/null
@@ -1,11 +0,0 @@
-param.glm <- function(object, num = NULL, bootstrap = FALSE) {
- if (!bootstrap) {
- coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
- res <- cbind(coef, phi)
- }
- else {
- coef <- coef(object)
- res <- c(coef, phi)
- }
- res
-}
diff --git a/R/param.gam.R b/R/param.gam.R
new file mode 100644
index 0000000..ba3d473
--- /dev/null
+++ b/R/param.gam.R
@@ -0,0 +1,27 @@
+param.gam <- function(object, num = NULL, bootstrap = FALSE) {
+ if (!bootstrap) {
+ coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
+ if (getzelig(object) == "gam.normal") {
+ df <- object$df.residual
+ sig2 <- summary(object)$dispersion
+ alpha <- sqrt(df*sig2/rchisq(num, df=df))
+ res <- cbind(coef, alpha)
+ }
+ else if (getzelig(object) == "gam.gamma") {
+ rate <- gamma.shape(object)
+ alpha <- rnorm(num, mean = rate$alpha, sd = rate$SE)
+ res <- cbind(coef, alpha)
+ }
+ else if (getzelig(object) == "gam.negbin") {
+ alpha <- object$theta
+ res <- cbind(coef, c(alpha))
+ }
+ else
+ res <- coef
+ }
+ res
+}
+
+
+
+
diff --git a/R/param.lm.R b/R/param.lm.R
index d35b785..838e0a0 100644
--- a/R/param.lm.R
+++ b/R/param.lm.R
@@ -1,7 +1,7 @@
param.lm <-function(object, num, bootstrap = FALSE) {
# if (num < 1) num <- 1
if (!bootstrap) {
- coef <- mvrnorm(num, mu=coef(object), Sigma=vcov.lm(object))
+ coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
df <- object$df.residual
sig2 <- summary(object)$sigma^2
alpha <- sqrt(df*sig2/rchisq(num, df=df))
diff --git a/R/param.netglm.R b/R/param.netglm.R
new file mode 100644
index 0000000..b1be0f9
--- /dev/null
+++ b/R/param.netglm.R
@@ -0,0 +1,42 @@
+param.netglm <- function(object, num = NULL, bootstrap = FALSE) {
+ if (!bootstrap) {
+ coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
+ if (getzelig(object) == "netnormal") {
+ 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") {
+ class(object) <- c("glm","lm")
+ rate <- gamma.shape(object)
+ class(object) <- "netglm"
+ alpha <- rnorm(num, mean = rate$alpha, sd = rate$SE)
+ res <- cbind(coef, alpha)
+ }
+ else if (getzelig(object) == "netnegbin") {
+ alpha <- object$theta
+ res <- cbind(coef, c(alpha))
+ }
+ else
+ res <- coef
+ }
+ else {
+ coef <- coef(object)
+ if (object$family$family == "netnormal") {
+ alpha <- sum(object$residuals^2)/length(object$residuals)
+ res <- c(coef, alpha)
+ }
+ else if (object$family$family == "netgamma") {
+ alpha <- gamma.dispersion(object)
+ res <- c(coef, alpha)
+ }
+ else if (object$family$family == "netnegbin") {
+ alpha <- object$theta
+ res <- c(coef, alpha)
+ }
+ else
+ res <- coef
+ }
+ res
+}
diff --git a/R/plot.zelig.gam.logit.R b/R/plot.zelig.gam.logit.R
new file mode 100644
index 0000000..19d2c5d
--- /dev/null
+++ b/R/plot.zelig.gam.logit.R
@@ -0,0 +1,20 @@
+plot.zelig.gam.logit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ pr <- x$qi$pr
+ y0 <- 100 * sum(pr == 0)/length(pr)
+ y1 <- 100 * sum(pr == 1)/length(pr)
+ barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1,
+ names.arg = c("Y = 0", "Y = 1"),
+ xlab = "Percentage of Simulations",
+ main = x$qi.name$pr, xlim = c(0, 100))
+ x$qi$pr <- x$qi.name$pr <- NULL
+ for (i in 1:(k-1)) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.beta.R b/R/plot.zelig.gam.normal.R
similarity index 77%
copy from R/plot.zelig.beta.R
copy to R/plot.zelig.gam.normal.R
index a392bd4..8c5553b 100644
--- a/R/plot.zelig.beta.R
+++ b/R/plot.zelig.gam.normal.R
@@ -1,4 +1,4 @@
-plot.zelig.beta <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+plot.zelig.gam.normal <- function(x, xlab = "", user.par = FALSE, ...) {
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.gam.poisson.R b/R/plot.zelig.gam.poisson.R
new file mode 100644
index 0000000..9e68534
--- /dev/null
+++ b/R/plot.zelig.gam.poisson.R
@@ -0,0 +1,16 @@
+plot.zelig.gam.poisson <- function(x, xlab = "", user.par = FALSE, ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ truehist(x$qi$pr, main = x$qi.name$pr, x0 = -0.25,
+ xlab = xlab, ylab = "Probability", ...)
+ plot(density(x$qi$ev), main = x$qi.name$ev, xlab = xlab, ...)
+ if (k > 2) {
+ for (i in 3:k)
+ plot(density(x$qi[[i]]), main = x$qi.name[[i]],
+ xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.gam.probit.R b/R/plot.zelig.gam.probit.R
new file mode 100644
index 0000000..5379b51
--- /dev/null
+++ b/R/plot.zelig.gam.probit.R
@@ -0,0 +1,20 @@
+plot.zelig.gam.probit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ pr <- x$qi$pr
+ y0 <- 100 * sum(pr == 0)/length(pr)
+ y1 <- 100 * sum(pr == 1)/length(pr)
+ barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1,
+ names.arg = c("Y = 0", "Y = 1"),
+ xlab = "Percentage of Simulations",
+ main = x$qi.name$pr, xlim = c(0, 100))
+ x$qi$pr <- x$qi.name$pr <- NULL
+ for (i in 1:(k-1)) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.logit.gee.R b/R/plot.zelig.logit.gee.R
new file mode 100644
index 0000000..3bc3136
--- /dev/null
+++ b/R/plot.zelig.logit.gee.R
@@ -0,0 +1,20 @@
+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$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.netcloglog.R b/R/plot.zelig.netcloglog.R
new file mode 100644
index 0000000..0a15b3b
--- /dev/null
+++ b/R/plot.zelig.netcloglog.R
@@ -0,0 +1,20 @@
+plot.zelig.netcloglog <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ pr <- x$qi$pr
+ y0 <- 100 * sum(pr == 0)/length(pr)
+ y1 <- 100 * sum(pr == 1)/length(pr)
+ barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1,
+ names.arg = c("Y = 0", "Y = 1"),
+ xlab = "Percentage of Simulations",
+ main = x$qi.name$pr, xlim = c(0, 100))
+ x$qi$pr <- x$qi.name$pr <- NULL
+ for (i in 1:(k-1)) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.beta.R b/R/plot.zelig.netgamma.R
similarity index 77%
copy from R/plot.zelig.beta.R
copy to R/plot.zelig.netgamma.R
index a392bd4..8ce87f8 100644
--- a/R/plot.zelig.beta.R
+++ b/R/plot.zelig.netgamma.R
@@ -1,4 +1,4 @@
-plot.zelig.beta <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+plot.zelig.netgamma <- function(x, xlab = "", user.par = FALSE, ...) {
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.beta.R b/R/plot.zelig.netnormal.R
similarity index 77%
rename from R/plot.zelig.beta.R
rename to R/plot.zelig.netnormal.R
index a392bd4..e57bcfe 100644
--- a/R/plot.zelig.beta.R
+++ b/R/plot.zelig.netnormal.R
@@ -1,4 +1,4 @@
-plot.zelig.beta <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+plot.zelig.netnormal <- function(x, xlab = "", user.par = FALSE, ...) {
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/plot.zelig.netpoisson.R b/R/plot.zelig.netpoisson.R
new file mode 100644
index 0000000..fdcc314
--- /dev/null
+++ b/R/plot.zelig.netpoisson.R
@@ -0,0 +1,16 @@
+plot.zelig.netpoisson <- function(x, xlab = "", user.par = FALSE, ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ truehist(x$qi$pr, main = x$qi.name$pr, x0 = -0.25,
+ xlab = xlab, ylab = "Probability", ...)
+ plot(density(x$qi$ev), main = x$qi.name$ev, xlab = xlab, ...)
+ if (k > 2) {
+ for (i in 3:k)
+ plot(density(x$qi[[i]]), main = x$qi.name[[i]],
+ xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.netprobit.R b/R/plot.zelig.netprobit.R
new file mode 100644
index 0000000..9324f18
--- /dev/null
+++ b/R/plot.zelig.netprobit.R
@@ -0,0 +1,20 @@
+plot.zelig.netprobit <- function(x, xlab = "", user.par = FALSE, alt.col = "red", ...){
+ k <- length(x$qi)
+ op <- par(no.readonly = TRUE)
+ if (!user.par)
+ par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
+ par(mfrow = c(k, 1))
+ pr <- x$qi$pr
+ y0 <- 100 * sum(pr == 0)/length(pr)
+ y1 <- 100 * sum(pr == 1)/length(pr)
+ barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1,
+ names.arg = c("Y = 0", "Y = 1"),
+ xlab = "Percentage of Simulations",
+ main = x$qi.name$pr, xlim = c(0, 100))
+ x$qi$pr <- x$qi.name$pr <- NULL
+ for (i in 1:(k-1)) {
+ qi <- as.vector(x$qi[[i]])
+ plot(density(qi), main = x$qi.name[[i]], xlab = xlab, ...)
+ }
+ par(op)
+}
diff --git a/R/plot.zelig.sur.R b/R/plot.zelig.sur.R
index 8ea83ac..691e8e1 100644
--- a/R/plot.zelig.sur.R
+++ b/R/plot.zelig.sur.R
@@ -1,14 +1,14 @@
-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$qi.name[[i]], xlab = xlab, ...)
- }
- }
- par(op)
-}
+plot.zelig.sur <- function(x, xlab = "", user.par = FALSE, ...) {
+ k <- length(x$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.sur.R b/R/plot.zelig.threesls.R
similarity index 82%
copy from R/plot.zelig.sur.R
copy to R/plot.zelig.threesls.R
index 8ea83ac..0c549b2 100644
--- a/R/plot.zelig.sur.R
+++ b/R/plot.zelig.threesls.R
@@ -1,10 +1,11 @@
-plot.zelig.sur <- function(x, xlab = "", user.par = FALSE, ...) {
+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$qi.name[[i]], xlab = xlab, ...)
diff --git a/R/plot.zelig.sur.R b/R/plot.zelig.twosls.R
similarity index 75%
copy from R/plot.zelig.sur.R
copy to R/plot.zelig.twosls.R
index 8ea83ac..08f1312 100644
--- a/R/plot.zelig.sur.R
+++ b/R/plot.zelig.twosls.R
@@ -1,4 +1,5 @@
-plot.zelig.sur <- function(x, xlab = "", user.par = FALSE, ...) {
+plot.zelig.w2sls <- plot.zelig.twosls <- function(x, xlab = "",
+ user.par = FALSE, ...) {
k <- length(x$qi)
op <- par(no.readonly = TRUE)
if (!user.par)
diff --git a/R/qi.gam.R b/R/qi.gam.R
new file mode 100644
index 0000000..d6b650a
--- /dev/null
+++ b/R/qi.gam.R
@@ -0,0 +1,78 @@
+qi.gam <- function(object, simpar, x, x1 = NULL, y = NULL) {
+ check <- FALSE
+ model <- getzelig(object)
+ k <- length(object$coef)
+ coef <- simpar[,1:k]
+ if (k < ncol(simpar))
+ alpha <- simpar[,(k+1):ncol(simpar)]
+ eta <- coef %*% t(x) ## Here is the nonconformability problem
+ theta <- matrix(object$family$linkinv(eta), nrow = nrow(coef))
+ pr <- ev <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta))
+ dimnames(pr) <- dimnames(ev) <- dimnames(theta)
+ if (model %in% c("gam.logit", "gam.probit")) {
+ check <- TRUE
+ ev <- theta
+ for (i in 1:ncol(theta))
+ pr[,i] <- as.character(rbinom(length(ev[,i]), 1, ev[,i]))
+ if (!is.null(y)) {
+ if (NCOL(y) > 1)
+ y <- y[,1]
+ }
+ }
+ else if (model == "gam.normal") {
+ ev <- theta
+ for (i in 1:nrow(ev))
+ pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
+ }
+ else if (model == "gam.gamma") {
+ ev <- theta * 1/alpha
+ for (i in 1:nrow(ev))
+ pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
+ }
+ else if (model == "gam.poisson") {
+ ev <- theta
+ for (i in 1:ncol(ev))
+ pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
+ }
+ else if (model == "gam.negbin") {
+ ev <- theta
+ for (i in 1:nrow(ev))
+ pr[i,] <- rnegbin(length(ev[i,]), mu = ev[i,], theta = alpha[i])
+ }
+ qi <- list(ev = ev, pr = pr)
+ qi.name <- list(ev = "Expected Values: E(Y|X)",
+ pr = "Predicted Values: Y|X")
+ if (!is.null(x1)){
+ theta1 <- matrix(object$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$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ if (model %in% c("gam.logit", "gam.probit")) {
+ qi$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$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi$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.gee.R b/R/qi.gee.R
new file mode 100644
index 0000000..1dc128e
--- /dev/null
+++ b/R/qi.gee.R
@@ -0,0 +1,63 @@
+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)
+}
diff --git a/R/qi.lm.R b/R/qi.lm.R
index 7643786..87ac268 100644
--- a/R/qi.lm.R
+++ b/R/qi.lm.R
@@ -1,11 +1,16 @@
qi.lm <- function(object, simpar, x, x1 = NULL, y = NULL) {
+
k <- length(getcoef(object))
coef <- simpar[,1:k]
alpha <- simpar[,(k+1):ncol(simpar)]
+ if (k < ncol(x))
+ x <- as.data.frame(x[,names(coef(object)),drop = FALSE])
ev <- coef %*% 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[,names(coef(object)),drop=FALSE])
ev1 <- coef %*% t(x1)
qi$fd <- ev1-ev
qi.name$fd <-
diff --git a/R/qi.netglm.R b/R/qi.netglm.R
new file mode 100644
index 0000000..12412e5
--- /dev/null
+++ b/R/qi.netglm.R
@@ -0,0 +1,74 @@
+qi.netglm <- 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$linkinv(eta), nrow = nrow(coef))
+ pr <- ev <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta))
+ dimnames(pr) <- dimnames(ev) <- dimnames(theta)
+ if (model %in% c("netlogit", "netprobit", "netcloglog", "netbinom")) {
+ check <- TRUE
+ ev <- theta
+ for (i in 1:ncol(theta))
+ pr[,i] <- as.character(rbinom(length(ev[,i]), 1, ev[,i]))
+ if (!is.null(y)) {
+ if (NCOL(y) > 1)
+ y <- y[,1]
+ }
+ }
+ else if (model == "netnormal") {
+ ev <- theta
+ for (i in 1:nrow(ev))
+ pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
+ }
+ else if (model == "netgamma") {
+ ev <- theta * 1/alpha
+ for (i in 1:nrow(ev))
+ pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
+ }
+ else if (model == "netpoisson") {
+ ev <- theta
+ for (i in 1:ncol(ev))
+ pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
+ }
+ ## Still need to add inverse gaussian
+ qi <- list(ev = ev, pr = pr)
+ qi.name <- list(ev = "Expected Values: E(Y|X)",
+ pr = "Predicted Values: Y|X")
+ if (!is.null(x1)){
+ theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
+ nrow = nrow(coef))
+ if (model == "netgamma")
+ ev1 <- theta1 * 1/alpha
+ else
+ ev1 <- theta1
+ qi$fd <- ev1-ev
+ qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ if (model %in% c("netbinom")) {
+ qi$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$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi$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/setx.gam.R b/R/setx.gam.R
new file mode 100644
index 0000000..e12dac1
--- /dev/null
+++ b/R/setx.gam.R
@@ -0,0 +1,236 @@
+setx.gam <- 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))
+ value <- factor(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])),
+ levels=levels(x))
+ else if (is.logical(x))
+ value <- as.logical(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
+ else if (is.character(x))
+ value <- as.character(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
+ else
+ stop(paste(vars[i], "is not a supported variable type."))
+ if (length(value)>1) {
+ warning("There is more than one mode. The first level is selected.")
+ value <- sort(value)[1]
+ }
+ return(value)
+ }
+ median.default <- median
+ median <- function(x) {
+ if(is.numeric(x))
+ value <- median.default(x)
+ else if (is.ordered(x)) {
+ value <- factor(levels(x)[quantile(as.integer(x), type = 1, prob = 0.5)],
+ levels=levels(x))
+ } else
+ stop("median cannot be calculated for this data type")
+ return(value)
+ }
+ max.default <- max
+ max <- function(x, na.rm=FALSE) {
+ if(is.numeric(x))
+ value <- max.default(x, na.rm=na.rm)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[length(levels(x))], levels=levels(x))
+ else
+ stop("max cannot be calculated for this data type")
+ return(value)
+ }
+ min.default <- min
+ min <- function(x, na.rm=FALSE) {
+ if(is.numeric(x))
+ value <- min.default(x, na.rm = na.rm)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[1], levels=levels(x))
+ else
+ stop("min cannot be calculated for this data type")
+ return(value)
+ }
+
+
+ # Testing From Here
+
+
+ tt <- terms(object)
+ tt.attr <- attributes(tt)
+ env <- tt.attr$.Environment
+ if (is.null(env))
+ env <- parent.frame()
+ ## original data
+ if (is.null(data))
+ if (nrow(as.data.frame(object$zelig.data)) > 0)
+ dta <- object$zelig.data
+ else
+ dta <- eval(object$call$data, envir = env)
+ else
+ dta <- as.data.frame(data)
+ ## extract variables we need
+ mf <- model.frame(tt, data = dta, na.action = na.pass)
+ if(any(class(tt)=="multiple"))
+ vars<-unlist(c(attr(tt,"depVars"),attr(tt,"indVars")),use.names=FALSE)
+ else
+ vars <- all.vars(tt)
+ if (!is.null(tt.attr$response) && tt.attr$response)
+ resvars <- all.vars(tt.attr$variables[[1+tt.attr$response]])
+ else
+ resvars <- NULL
+ data <- dta[complete.cases(mf), names(dta)%in%vars, drop=FALSE]
+ if (!is.null(counter)) {
+ if (!any(counter == vars))
+ stop("the variable specified for counter is not used in the model")
+ treat <- data[, names(data)==counter]
+ if(is.numeric(treat)) {
+ data[treat==1, names(data)==counter] <- 0
+ data[treat==0, names(data)==counter] <- 1
+ }
+ else if(is.factor(treat)) {
+ lev <- levels(treat)
+ if(length(lev)==2) {
+ treat <- as.numeric(treat) - 1
+ data[treat==1, names(data)==counter] <- lev[1]
+ data[treat==0, names(data)==counter] <- lev[2]
+ }
+ else
+ stop("counter only takes a binary variable")
+ }
+ else if(is.logical(treat)) {
+ treat <- as.numeric(treat)
+ data[treat==1, names(data)==counter] <- FALSE
+ data[treat==0, names(data)==counter] <- TRUE
+ }
+ else
+ stop("not supported variable type for counter")
+ if(!cond)
+ stop("if counter is specified, cond must be TRUE")
+ }
+ if (cond) {
+ if (is.null(data))
+ stop("if cond = TRUE, you must specify the data frame.")
+ if (is.null(mc$fn))
+ fn <- NULL
+ if (!is.null(fn)) {
+ warning("when cond = TRUE, fn is coerced to NULL")
+ fn <- NULL
+ }
+ maxl <- nrow(data)
+ }
+ else if (!is.null(fn)) {
+ if (is.null(fn$numeric) || !is.function(fn$numeric)) {
+ warning("fn$numeric coerced to mean().")
+ fn$numeric <- mean
+ }
+ if (is.null(fn$ordered) || !is.function(fn$ordered) ||
+ identical(mean, fn$ordered)) {
+ warning("fn$ordered coreced to median().")
+ fn$ordered <- median
+ }
+ else if (identical(min.default, fn$ordered))
+ fn$ordered <- min
+ else if (identical(max.default, fn$ordered))
+ fn$ordered <- max
+ else if (identical(median.default, fn$ordered))
+ fn$ordered <- median
+ if (is.null(fn$other) || !is.function(fn$other)) {
+ warning("the only available fn for other is mode.")
+ fn$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$other)[[1]]
+ data[,i] <- value
+ }
+ }
+ maxl <- 1
+ } else {
+ maxl <- nrow(data)
+ }
+ opt <- vars[na.omit(pmatch(names(mc), vars))]
+ if (length(opt) > 0)
+ for (i in 1:length(opt)) {
+ value <- eval(mc[[opt[i]]], envir = env)
+ lv <- length(value)
+ if (lv>1)
+ if (maxl==1 || maxl==lv) {
+ maxl <- lv
+ data <- data[1:lv,,drop = FALSE]
+ }
+ else
+ stop("vector inputs should have the same length.")
+ if (is.factor(data[,opt[i]]))
+ data[,opt[i]] <- list(factor(value, levels=levels(data[,opt[i]])))
+ else if (is.numeric(data[,opt[i]]))
+ data[,opt[i]] <- list(as.numeric(value))
+ else if (is.logical(data[,opt[i]]))
+ data[,opt[i]] <- list(as.logical(value))
+ else
+ data[,opt[i]] <- list(value)
+ }
+ data <- data[1:maxl,,drop = FALSE]
+
+ if (cond) {
+ X <- model.frame(tt, data = dta)
+ if (!is.null(counter)) {
+ X <- list(treat=X[treat==1,,drop=FALSE],
+ control=X[treat==0,,drop=FALSE])
+ class(X$treat) <- class(X$control) <- c("data.frame", "cond")
+ class(X) <- "setx.counter"
+ }
+ else
+ class(X) <- c("data.frame", "cond")
+ }
+ else {
+ X <- as.data.frame(model.matrix(tt, data = data))
+ }
+
+ ## Now X has the values I want
+ ## Introduce the repetitions
+
+ pterms <- attr(object$pterms, "term.labels")
+ sterms <- list()
+ for(i in 1: length(object$smooth)){
+ sterms[[i]] <- object$smooth[[i]]$term }
+
+
+ df.vec <- vector()
+ for(i in 1: length(object$smooth)){
+ df.vec[[i]] <- object$smooth[[i]]$df}
+
+ if(attr(object$terms, "intercept") > 0){
+ newX <- data.frame(X[[1]])
+ names(newX) <- names(X)[[1]] }
+ if(attr(object$terms, "intercept") == 0){
+ stop("So far, the model must have an intercept")}
+
+
+ for(i in 2:ncol(X)){
+ if( names(X)[[i]] %in% pterms){
+ temp <- as.data.frame(X[[i]])
+ names(temp) <- names(X)[[i]]
+ newX <- cbind(newX, temp) }
+
+ if( names(X)[[i]] %in% sterms){
+ for(j in 1: length(object$smooth)){
+ if(names(X)[[i]] == object$smooth[[j]]$term)
+ repnum <- object$smooth[[j]]$df}
+ temp <- as.data.frame(t(rep(X[[i]], repnum)))
+ names(temp) <- rep(names(X)[[i]] , repnum)
+ newX <- cbind(newX, temp)
+ }
+
+
+ }
+
+
+ return(newX)
+}
diff --git a/R/setx.netglm.R b/R/setx.netglm.R
new file mode 100644
index 0000000..245fa4e
--- /dev/null
+++ b/R/setx.netglm.R
@@ -0,0 +1,196 @@
+setx.netglm <- 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))
+ value <- factor(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])),
+ levels=levels(x))
+ else if (is.logical(x))
+ value <- as.logical(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
+ else if (is.character(x))
+ value <- as.character(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
+ else
+ stop(paste(vars[i], "is not a supported variable type."))
+ if (length(value)>1) {
+ warning("There is more than one mode. The first level is selected.")
+ value <- sort(value)[1]
+ }
+ return(value)
+ }
+ median.default <- median
+ median <- function(x) {
+ if(is.numeric(x))
+ value <- median.default(x)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[median.default(as.integer(x))],
+ levels=levels(x))
+ else
+ stop("median cannot be calculated for this data type")
+ return(value)
+ }
+ max.default <- max
+ max <- function(x, na.rm=FALSE) {
+ if(is.numeric(x))
+ value <- max.default(x, na.rm=na.rm)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[length(levels(x))], levels=levels(x))
+ else
+ stop("max cannot be calculated for this data type")
+ return(value)
+ }
+ min.default <- min
+ min <- function(x, na.rm=FALSE) {
+ if(is.numeric(x))
+ value <- min.default(x, na.rm = na.rm)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[1], levels=levels(x))
+ else
+ stop("min cannot be calculated for this data type")
+ return(value)
+ }
+
+
+ # Testing From Here
+
+
+ tt <- terms(object)
+ tt.attr <- attributes(tt)
+ env <- tt.attr$.Environment
+ if (is.null(env))
+ env <- parent.frame()
+ ## original data
+ if (is.null(data))
+ if (is.data.frame(object$data))
+ dta <- object$data
+ else
+ dta <- eval(object$call$data, envir = env)
+ else
+ dta <- as.data.frame(data)
+ ## extract variables we need
+ mf <- model.frame(tt, data = dta, na.action = na.pass)
+ if(any(class(tt)=="multiple"))
+ vars<-unlist(c(attr(tt,"depVars"),attr(tt,"indVars")),use.names=FALSE)
+ else
+ vars <- all.vars(tt)
+ if (!is.null(tt.attr$response) && tt.attr$response)
+ resvars <- all.vars(tt.attr$variables[[1+tt.attr$response]])
+ 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$fn))
+ fn <- NULL
+ if (!is.null(fn)) {
+ warning("when cond = TRUE, fn is coerced to NULL")
+ fn <- NULL
+ }
+ maxl <- nrow(data)
+ }
+ else if (!is.null(fn)) {
+ if (is.null(fn$numeric) || !is.function(fn$numeric)) {
+ warning("fn$numeric coerced to mean().")
+ fn$numeric <- mean
+ }
+ if (is.null(fn$ordered) || !is.function(fn$ordered) ||
+ identical(mean, fn$ordered)) {
+ warning("fn$ordered coreced to median().")
+ fn$ordered <- median
+ }
+ else if (identical(min.default, fn$ordered))
+ fn$ordered <- min
+ else if (identical(max.default, fn$ordered))
+ fn$ordered <- max
+ else if (identical(median.default, fn$ordered))
+ fn$ordered <- median # "this is what sna.lm ends up with"
+ if (is.null(fn$other) || !is.function(fn$other)) {
+ warning("the only available fn for other is mode.")
+ 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]] # "This is the Problem"
+ 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)
+ lv <- length(value)
+ if (lv>1)
+ if (maxl==1 || maxl==lv) {
+ maxl <- lv
+ data <- data[1:lv,,drop = FALSE]
+ }
+ else
+ stop("vector inputs should have the same length.")
+ if (is.factor(data[,opt[i]]))
+ data[,opt[i]] <- list(factor(value, levels=levels(data[,opt[i]])))
+ else if (is.numeric(data[,opt[i]]))
+ data[,opt[i]] <- list(as.numeric(value))
+ else if (is.logical(data[,opt[i]]))
+ data[,opt[i]] <- list(as.logical(value))
+ else
+ data[,opt[i]] <- list(value)
+ }
+ 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))
+ }
+ return(X)
+}
diff --git a/R/sim.netglm.R b/R/sim.netglm.R
new file mode 100644
index 0000000..9a867ca
--- /dev/null
+++ b/R/sim.netglm.R
@@ -0,0 +1,46 @@
+sim.netlogit <- 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$coefficients)
+ else if (length(num) == 2) {
+ if (!bootstrap)
+ num <- num[1]
+ else
+ num <- num[2]
+ }
+ if (is.null(prev)) {
+ if (any(class(object) == "relogit"))
+ simpar <- param.netglm(object, num=num, x=x, bootstrap=bootstrap)
+ else if (!bootstrap)
+ simpar <- param.netglm(object, num=num, bootstrap=bootstrap)
+ else {
+ tt <- terms(object)
+ dta <- eval(object$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, ...)
+ colnames(res$t) <- names(res$t0)
+ simpar <- res$t
+ }
+ }
+ else {
+ if (bootstrap)
+ stop("Error: Choosing 'bootstrap = TRUE' generates new parameters. \nIf you wish to use previously generated parameters, \nplease specify only 'prev'.")
+ else
+ simpar <- prev
+ }
+ simqi <- qi.netglm(object, simpar = simpar, x = x, x1 = x1, y = NULL)
+ c <- match.call()
+ c[[1]] <- as.name("sim")
+ c$num <- num
+ res <- list(x=x, x1=x1, call = c, zelig.call = object$call,
+ par = simpar, qi=simqi$qi, qi.name=simqi$qi.name)
+ class(res) <- "zelig"
+ res
+}
\ No newline at end of file
diff --git a/R/summary.gee.naive.R b/R/summary.gee.naive.R
new file mode 100644
index 0000000..f80b3eb
--- /dev/null
+++ b/R/summary.gee.naive.R
@@ -0,0 +1,11 @@
+summary.gee.naive <- function(object, ...){
+ class(object) <- c("gee", "glm")
+ res <- summary.gee(object,...)
+ res$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)
+}
diff --git a/R/summary.gee.robust.R b/R/summary.gee.robust.R
new file mode 100644
index 0000000..15447b6
--- /dev/null
+++ b/R/summary.gee.robust.R
@@ -0,0 +1,11 @@
+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$coefficients) <- c("Estimate", "Robust SE", "z value", "Pr(>|z|)")
+ class(res) <- c("summary.gee.robust", "summary.gee")
+ return(res)
+}
diff --git a/R/summary.netglm.R b/R/summary.netglm.R
new file mode 100644
index 0000000..2a1ec54
--- /dev/null
+++ b/R/summary.netglm.R
@@ -0,0 +1,74 @@
+summary.netglm <- function (object, dispersion = NULL, correlation = FALSE, symbolic.cor = FALSE,
+ ...)
+{
+ est.disp <- FALSE
+ df.r <- object$df.residual
+ if (is.null(dispersion))
+ dispersion <- if (object$family$family %in% c("poisson",
+ "binomial"))
+ 1
+ else if (df.r > 0) {
+ est.disp <- TRUE
+ if (any(object$weights == 0))
+ warning("observations with zero weight not used for calculating dispersion")
+ sum((object$weights * object$residuals^2)[object$weights >
+ 0])/df.r
+ }
+ else {
+ est.disp <- TRUE
+ NaN
+ }
+ aliased <- is.na(coef(object))
+ p <- object$rank
+ if (p > 0) {
+ p1 <- 1:p
+ Qr <- object$qr
+ coef.p <- object$coefficients[Qr$pivot[p1]]
+ names(coef.p) <- object$names
+ covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE])
+ dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))
+ covmat <- dispersion * covmat.unscaled
+ var.cf <- diag(covmat)
+ s.err <- sqrt(var.cf)
+ tvalue <- coef.p/s.err
+ dn <- c("Estimate", "Std. Error")
+ if (!est.disp) {
+ pvalue <- 2 * pnorm(-abs(tvalue))
+ coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
+ dimnames(coef.table) <- list(names(coef.p), c(dn,
+ "z value", "Pr(>|z|)"))
+ }
+ else if (df.r > 0) {
+ pvalue <- 2 * pt(-abs(tvalue), df.r)
+ coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
+ dimnames(coef.table) <- list(names(coef.p), c(dn,
+ "t value", "Pr(>|t|)"))
+ }
+ else {
+ coef.table <- cbind(coef.p, NaN, NaN, NaN)
+ dimnames(coef.table) <- list(names(coef.p), c(dn,
+ "t value", "Pr(>|t|)"))
+ }
+ df.f <- NCOL(Qr$qr)
+ }
+ else {
+ coef.table <- matrix(, 0, 4)
+ dimnames(coef.table) <- list(NULL, c("Estimate", "Std. Error",
+ "t value", "Pr(>|t|)"))
+ covmat.unscaled <- covmat <- matrix(, 0, 0)
+ df.f <- length(aliased)
+ }
+ ans <- c(object[c("call", "terms", "family", "deviance",
+ "aic", "contrasts", "df.residual", "null.deviance", "df.null",
+ "iter", "na.action")], list(deviance.resid = residuals(object,
+ type = "deviance"), coefficients = coef.table, aliased = aliased,
+ dispersion = dispersion, df = c(object$rank, df.r, df.f),
+ cov.unscaled = covmat.unscaled, cov.scaled = covmat))
+ if (correlation && p > 0) {
+ dd <- sqrt(diag(covmat.unscaled))
+ ans$correlation <- covmat.unscaled/outer(dd, dd)
+ ans$symbolic.cor <- symbolic.cor
+ }
+ class(ans) <- "summary.glm"
+ return(ans)
+}
diff --git a/R/vcov.gee.naive.R b/R/vcov.gee.naive.R
new file mode 100644
index 0000000..7a52dd5
--- /dev/null
+++ b/R/vcov.gee.naive.R
@@ -0,0 +1,6 @@
+vcov.gee.naive <- function(object, ...){
+ class(object) <- c("gee", "glm")
+ return(object$naive.variance)
+}
+
+
\ No newline at end of file
diff --git a/R/vcov.gee.robust.R b/R/vcov.gee.robust.R
new file mode 100644
index 0000000..d4ba892
--- /dev/null
+++ b/R/vcov.gee.robust.R
@@ -0,0 +1,4 @@
+vcov.gee.robust <- function(object, ...){
+ class(object) <- c("gee", "glm")
+ return(object$robust.variance)
+}
diff --git a/R/vcov.lm.R b/R/vcov.lm.R
deleted file mode 100644
index e313688..0000000
--- a/R/vcov.lm.R
+++ /dev/null
@@ -1,4 +0,0 @@
-vcov.lm <- function(object, ...) {
- so <- summary.lm(object, corr = FALSE, ...)
- so$sigma^2 * so$cov.unscaled
-}
diff --git a/R/vcov.netglm.R b/R/vcov.netglm.R
new file mode 100644
index 0000000..f34e05d
--- /dev/null
+++ b/R/vcov.netglm.R
@@ -0,0 +1,5 @@
+vcov.netglm <- function(object, ...)
+{
+ so <- summary.glm(object, corr=FALSE, ...)
+ so$dispersion * so$cov.unscaled
+}
\ No newline at end of file
diff --git a/R/vdc.R b/R/vdc.R
index c9fa591..92fe42a 100644
--- a/R/vdc.R
+++ b/R/vdc.R
@@ -126,10 +126,12 @@ xmlList<-function(z){
res<-paste(res," specialFunction=",'"',z$parameters[[1]]$specialFunction,'"',sep="")
res<-paste(res,">\n",sep="")
res<-paste(res,"<description>",z$description, "</description>\n",sep="")
- if(z$name=="irtkd")
- res<-paste(res,"<helpLink url=",'"',"http://gking.harvard.edu/zelig/docs/_TT_irtkd_TT__tex2htm.html",'"',sep="")
- else
- res<-paste(res,"<helpLink url=",'"',modelURL(z$name,z$description),'"',sep="")
+ url <- paste("http://gking.harvard.edu/zelig/doc/",z$name,".pdf",sep="")
+ #if(z$name=="irtkd")
+ # res<-paste(res,"<helpLink url=",'"',"http://gking.harvard.edu/zelig/docs/_TT_irtkd_TT__tex2htm.html",'"',sep="")
+ #else
+ #res<-paste(res,"<helpLink url=",'"',modelURL(z$name,z$description),'"',sep="")
+res<-paste(res,"<helpLink url=",'"',url,'"',sep="")
res<-paste(res,"/>\n", sep="")
if(any(!(is.null(z$package)))){
diff --git a/R/zelig.default.R b/R/zelig.default.R
index d36856e..8eb0461 100644
--- a/R/zelig.default.R
+++ b/R/zelig.default.R
@@ -51,7 +51,7 @@ zelig.default <- function(formula, model, data, by = NULL, save.data =
if (is.data.frame(d)) {
d <- d[complete.cases(model.frame(mf$formula, data=d,
na.action = na.pass)),]
- mf$data <- d
+# mf$data <- d
# res <- create.ZeligS4(eval(as.call(mf)))
res <- eval(as.call(mf))
if (exists(fn2))
diff --git a/R/zelig2beta.R b/R/zelig2beta.R
deleted file mode 100644
index 4e4d91e..0000000
--- a/R/zelig2beta.R
+++ /dev/null
@@ -1,6 +0,0 @@
-zelig2beta <- function(formula, model, data, M, ...) {
- mf <- match.call(expand.dots = TRUE)
- mf$model <- mf$M <- mf$robust <- NULL
- mf[[1]] <- as.name("beta.regression")
- as.call(mf)
-}
diff --git a/R/zelig2gam.logit.R b/R/zelig2gam.logit.R
new file mode 100644
index 0000000..6cf39e7
--- /dev/null
+++ b/R/zelig2gam.logit.R
@@ -0,0 +1,13 @@
+zelig2gam.logit <- function(formula, model, data, M, ...) {
+ check <- library()
+ if(any(check$results[,"Package"] == "mgcv"))
+ require(mgcv)
+ else
+ stop("Please install mgcv using \n install.packages(\"mgcv\")")
+ 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
diff --git a/R/zelig2gam.normal.R b/R/zelig2gam.normal.R
new file mode 100644
index 0000000..08fa0ed
--- /dev/null
+++ b/R/zelig2gam.normal.R
@@ -0,0 +1,12 @@
+zelig2gam.normal <- function(formula, model, data, M, ...) {
+ check <- library()
+ if(any(check$results[,"Package"] == "mgcv"))
+ require(mgcv)
+ else
+ stop("Please install mgcv using \n install.packages(\"mgcv\")")
+ mf <- match.call(expand.dots = TRUE)
+ mf[[1]] <- as.name("gam")
+ mf$M <- mf$model <- NULL
+ class(formula) <- "gamF"
+ mf$formula <- formula
+ as.call(mf) }
\ No newline at end of file
diff --git a/R/zelig2gam.poisson.R b/R/zelig2gam.poisson.R
new file mode 100644
index 0000000..e93a483
--- /dev/null
+++ b/R/zelig2gam.poisson.R
@@ -0,0 +1,13 @@
+zelig2gam.poisson <- function(formula, model, data, M, ...) {
+ check <- library()
+ if(any(check$results[,"Package"] == "mgcv"))
+ require(mgcv)
+ else
+ stop("Please install mgcv using \n install.packages(\"mgcv\")")
+ mf <- match.call(expand.dots = TRUE)
+ mf[[1]] <- as.name("gam")
+ mf$M <- mf$model <- NULL
+ class(formula) <- "gamF"
+ mf$formula <- formula
+ mf$family <- poisson()
+ as.call(mf) }
\ No newline at end of file
diff --git a/R/zelig2gam.probit.R b/R/zelig2gam.probit.R
new file mode 100644
index 0000000..38c1507
--- /dev/null
+++ b/R/zelig2gam.probit.R
@@ -0,0 +1,13 @@
+zelig2gam.probit <- function(formula, model, data, M, ...) {
+ check <- library()
+ if(any(check$results[,"Package"] == "mgcv"))
+ require(mgcv)
+ else
+ stop("Please install mgcv using \n install.packages(\"mgcv\")")
+ 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
diff --git a/R/zelig2logit.gee.R b/R/zelig2logit.gee.R
new file mode 100644
index 0000000..629a99a
--- /dev/null
+++ b/R/zelig2logit.gee.R
@@ -0,0 +1,13 @@
+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$robust <- NULL
+ as.call(mf)
+}
\ No newline at end of file
diff --git a/R/zelig2netlogit.R b/R/zelig2netcloglog.R
similarity index 63%
copy from R/zelig2netlogit.R
copy to R/zelig2netcloglog.R
index e85556a..b7bc8af 100644
--- a/R/zelig2netlogit.R
+++ b/R/zelig2netcloglog.R
@@ -1,11 +1,12 @@
-zelig2netlogit <- function(formula, model, data, M, ...) {
+zelig2netcloglog <- function(formula, model, data, M, LF="cloglog", ...) {
check <- library()
if(any(check$results[,"Package"] == "sna"))
require(sna)
else
stop("Please install sna using \n install.packages(\"sna\")")
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callnetlogit")
+ 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
diff --git a/R/zelig2netlogit.R b/R/zelig2netgamma.R
similarity index 69%
copy from R/zelig2netlogit.R
copy to R/zelig2netgamma.R
index e85556a..d21cfab 100644
--- a/R/zelig2netlogit.R
+++ b/R/zelig2netgamma.R
@@ -1,11 +1,11 @@
-zelig2netlogit <- function(formula, model, data, M, ...) {
+zelig2netgamma <- function(formula, model, data, M, ...) {
check <- library()
if(any(check$results[,"Package"] == "sna"))
require(sna)
else
stop("Please install sna using \n install.packages(\"sna\")")
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callnetlogit")
+ mf[[1]] <- as.name("netgamma")
mf$M <- mf$model <- NULL
mf$formula <- formula
as.call(mf) }
\ No newline at end of file
diff --git a/R/zelig2netlogit.R b/R/zelig2netlogit.R
index e85556a..69b5e31 100644
--- a/R/zelig2netlogit.R
+++ b/R/zelig2netlogit.R
@@ -1,11 +1,12 @@
-zelig2netlogit <- function(formula, model, data, M, ...) {
+zelig2netlogit <- function(formula, model, data, M, LF="logit", ...) {
check <- library()
if(any(check$results[,"Package"] == "sna"))
require(sna)
else
stop("Please install sna using \n install.packages(\"sna\")")
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callnetlogit")
+ mf[[1]] <- as.name("netbinom")
mf$M <- mf$model <- NULL
mf$formula <- formula
+ mf$LF <- "logit"
as.call(mf) }
\ No newline at end of file
diff --git a/R/zelig2netlogit.R b/R/zelig2netnormal.R
similarity index 69%
copy from R/zelig2netlogit.R
copy to R/zelig2netnormal.R
index e85556a..51d32a6 100644
--- a/R/zelig2netlogit.R
+++ b/R/zelig2netnormal.R
@@ -1,11 +1,11 @@
-zelig2netlogit <- function(formula, model, data, M, ...) {
+zelig2netnormal <- function(formula, model, data, M, ...) {
check <- library()
if(any(check$results[,"Package"] == "sna"))
require(sna)
else
stop("Please install sna using \n install.packages(\"sna\")")
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callnetlogit")
+ mf[[1]] <- as.name("netnormal")
mf$M <- mf$model <- NULL
mf$formula <- formula
as.call(mf) }
\ No newline at end of file
diff --git a/R/zelig2netlogit.R b/R/zelig2netpoisson.R
similarity index 69%
copy from R/zelig2netlogit.R
copy to R/zelig2netpoisson.R
index e85556a..748ea83 100644
--- a/R/zelig2netlogit.R
+++ b/R/zelig2netpoisson.R
@@ -1,11 +1,11 @@
-zelig2netlogit <- function(formula, model, data, M, ...) {
+zelig2netpoisson <- function(formula, model, data, M, ...) {
check <- library()
if(any(check$results[,"Package"] == "sna"))
require(sna)
else
stop("Please install sna using \n install.packages(\"sna\")")
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callnetlogit")
+ mf[[1]] <- as.name("netpoisson")
mf$M <- mf$model <- NULL
mf$formula <- formula
as.call(mf) }
\ No newline at end of file
diff --git a/R/zelig2netlogit.R b/R/zelig2netprobit.R
similarity index 66%
copy from R/zelig2netlogit.R
copy to R/zelig2netprobit.R
index e85556a..b1d1d80 100644
--- a/R/zelig2netlogit.R
+++ b/R/zelig2netprobit.R
@@ -1,11 +1,12 @@
-zelig2netlogit <- function(formula, model, data, M, ...) {
+zelig2netprobit <- function(formula, model, data, M, ...) {
check <- library()
if(any(check$results[,"Package"] == "sna"))
require(sna)
else
stop("Please install sna using \n install.packages(\"sna\")")
mf <- match.call(expand.dots = TRUE)
- mf[[1]] <- as.name("callnetlogit")
+ mf[[1]] <- as.name("netbinom")
mf$M <- mf$model <- NULL
mf$formula <- formula
+ mf$LF <- "probit"
as.call(mf) }
\ No newline at end of file
diff --git a/R/zelig2sur.R b/R/zelig2sur.R
new file mode 100644
index 0000000..e786cc2
--- /dev/null
+++ b/R/zelig2sur.R
@@ -0,0 +1,16 @@
+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$formula<-formula
+ as.call(mf)
+}
diff --git a/R/zelig2threesls.R b/R/zelig2threesls.R
new file mode 100644
index 0000000..615feeb
--- /dev/null
+++ b/R/zelig2threesls.R
@@ -0,0 +1,26 @@
+zelig2threesls <- function(formula, model, data, M,...) {
+ check <- library()
+ if(any(check$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)
+
+}
diff --git a/R/zelig2twosls.R b/R/zelig2twosls.R
new file mode 100644
index 0000000..674b134
--- /dev/null
+++ b/R/zelig2twosls.R
@@ -0,0 +1,29 @@
+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$formula)<-c("multiple","list")
+
+ as.call(mf)
+}
diff --git a/R/zelig3gee.R b/R/zelig3gee.R
new file mode 100755
index 0000000..7e962ca
--- /dev/null
+++ b/R/zelig3gee.R
@@ -0,0 +1,15 @@
+zelig3logit.gee <- function(res, fcall=NULL, zcall=NULL){
+ rob <- eval(zcall$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
diff --git a/cleanup b/cleanup
deleted file mode 100755
index a3dc9d1..0000000
--- a/cleanup
+++ /dev/null
@@ -1,18 +0,0 @@
-#/bin/sh
-
-#
-# when R CMD build, this will create zideal.RData
-#
-
-# I think cleanup is ALSO run during R CMD INSTALL. We do not want this
-# to run on the user computer.
-# so check if host is part of hmdc.harvard.edu
-#
-
-H=`hostname`
- if echo $H | grep "w[0-9].hmdc.harvard.edu" > /dev/null
-then
- R --vanilla --slave < inst/zideal/zvcServer.R
-fi
-
-
diff --git a/data/friendship.RData b/data/friendship.RData
index 53fa79a..054a142 100644
Binary files a/data/friendship.RData and b/data/friendship.RData differ
diff --git a/data/grunfeld.txt b/data/grunfeld.txt
new file mode 100644
index 0000000..8b8a16a
--- /dev/null
+++ b/data/grunfeld.txt
@@ -0,0 +1,21 @@
+Year Ige Fge Cge Iw Fw Cw
+1935 33.1 1170.6 97.8 12.93 191.5 1.8
+1936 45.0 2015.8 104.4 25.90 516.0 .8
+1937 77.2 2803.3 118.0 35.05 729.0 7.4
+1938 44.6 2039.7 156.2 22.89 560.4 18.1
+1939 48.1 2256.2 172.6 18.84 519.9 23.5
+1940 74.4 2132.2 186.6 28.57 628.5 26.5
+1941 113.0 1834.1 220.9 48.51 537.1 36.2
+1942 91.9 1588.0 287.8 43.34 561.2 60.8
+1943 61.3 1749.4 319.9 37.02 617.2 84.4
+1944 56.8 1687.2 321.3 37.81 626.7 91.2
+1945 93.6 2007.7 319.6 39.27 737.2 92.4
+1946 159.9 2208.3 346.0 53.46 760.5 86.0
+1947 147.2 1656.7 456.4 55.56 581.4 111.1
+1947 146.3 1604.4 543.4 49.56 662.3 130.6
+1949 98.3 1431.8 618.3 32.04 583.8 141.8
+1950 93.5 1610.5 647.4 32.24 635.2 136.7
+1951 135.2 1819.4 671.3 54.38 723.8 129.7
+1952 157.3 2079.7 726.1 71.78 864.1 145.5
+1953 179.5 2371.6 800.3 90.08 1193.5 174.8
+1954 189.6 2759.9 888.9 68.60 1188.9 213.5
diff --git a/data/klein.txt b/data/klein.txt
new file mode 100644
index 0000000..dbab38b
--- /dev/null
+++ b/data/klein.txt
@@ -0,0 +1,22 @@
+year C P P1 Wtot Wp Wg I K1 X G T X1 Tm
+1921 41.9 12.4 12.7 28.2 25.5 2.7 -0.2 182.8 45.6 3.9 7.7 44.9 -10
+1922 45 16.9 12.4 32.2 29.3 2.9 1.9 182.6 50.1 3.2 3.9 45.6 -9
+1923 49.2 18.4 16.9 37 34.1 2.9 5.2 184.5 57.2 2.8 4.7 50.1 -8
+1924 50.6 19.4 18.4 37 33.9 3.1 3 189.7 57.1 3.5 3.8 57.2 -7
+1925 52.6 20.1 19.4 38.6 35.4 3.2 5.1 192.7 61 3.3 5.5 57.1 -6
+1926 55.1 19.6 20.1 40.7 37.4 3.3 5.6 197.8 64 3.3 7 61 -5
+1927 56.2 19.8 19.6 41.5 37.9 3.6 4.2 203.4 64.4 4 6.7 64 -4
+1928 57.3 21.1 19.8 42.9 39.2 3.7 3 207.6 64.5 4.2 4.2 64.4 -3
+1929 57.8 21.7 21.1 45.3 41.3 4 5.1 210.6 67 4.1 4 64.5 -2
+1930 55 15.6 21.7 42.1 37.9 4.2 1 215.7 61.2 5.2 7.7 67 -1
+1931 50.9 11.4 15.6 39.3 34.5 4.8 -3.4 216.7 53.4 5.9 7.5 61.2 0
+1932 45.6 7 11.4 34.3 29 5.3 -6.2 213.3 44.3 4.9 8.3 53.4 1
+1933 46.5 11.2 7 34.1 28.5 5.6 -5.1 207.1 45.1 3.7 5.4 44.3 2
+1934 48.7 12.3 11.2 36.6 30.6 6 -3 202 49.7 4 6.8 45.1 3
+1935 51.3 14 12.3 39.3 33.2 6.1 -1.3 199 54.4 4.4 7.2 49.7 4
+1936 57.7 17.6 14 44.2 36.8 7.4 2.1 197.7 62.7 2.9 8.3 54.4 5
+1937 58.7 17.3 17.6 47.7 41 6.7 2 199.8 65 4.3 6.7 62.7 6
+1938 57.5 15.3 17.3 45.9 38.2 7.7 -1.9 201.8 60.9 5.3 7.4 65 7
+1939 61.6 19 15.3 49.4 41.6 7.8 1.3 199.9 69.5 6.6 8.9 60.9 8
+1940 65 21.1 19 53 45 8 3.3 201.2 75.7 7.4 9.6 69.5 9
+1941 69.7 23.5 21.1 61.8 53.3 8.5 4.9 204.5 88.4 13.8 11.6 75.7 10
diff --git a/data/kmenta.txt b/data/kmenta.txt
new file mode 100644
index 0000000..fcaba51
--- /dev/null
+++ b/data/kmenta.txt
@@ -0,0 +1,21 @@
+q p d f a
+ 98.485 100.323 87.4 98.0 1
+ 99.187 104.264 97.6 99.1 2
+102.163 103.435 96.7 99.1 3
+101.504 104.506 98.2 98.1 4
+104.240 98.001 99.8 110.8 5
+103.243 99.456 100.5 108.2 6
+103.993 101.066 103.2 105.6 7
+ 99.900 104.763 107.8 109.8 8
+100.350 96.446 96.6 108.7 9
+102.820 91.228 88.9 100.6 10
+ 95.435 93.085 75.1 81.0 11
+ 92.424 98.801 76.9 68.6 12
+ 94.535 102.908 84.6 70.9 13
+ 98.757 98.756 90.6 81.4 14
+105.797 95.119 103.1 102.3 15
+100.225 98.451 105.1 105.0 16
+103.522 86.498 96.4 110.5 17
+ 99.929 104.016 104.4 92.5 18
+105.223 105.769 110.7 89.3 19
+106.232 113.490 127.1 93.0 20
diff --git a/demo/00Index b/demo/00Index
index 6eb4baa..9cb5c39 100644
--- a/demo/00Index
+++ b/demo/00Index
@@ -4,7 +4,11 @@ bivariate.probit New model example: Bivariate probit MLE
bprobit Bivariate Probit regression and simulation
conditional Conditional prediction
exp Exponential regression and simulation
-gamma Gamma 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 Logit regression and simulation
lognorm Lognormal regression and simulation
ls Least Squares regression and simulation
@@ -14,6 +18,11 @@ 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
normal Normal (Gaussian) regression and simulation
normal.regression New model example for normal regression
ologit Ordinal Logit regression and simulation
@@ -41,7 +50,11 @@ factor.ord MCMC factor analysis for ordinal data
factor.mix MCMC factor analysis for mixed data
irt1d MCMC one-dimensional item response theory model
irtkd MCMC K-dimensional item response theory model
-ei.RxC RxC ecological inference via penalized least-squares
+ei.RxC RxC ecological inference via penalized least-squares
+sur Seemingly Unrelated Regression
+twosls Two Stage Least Squares
+threesls Three Stage Least Squares
+logit.gee GEE logistic regression
diff --git a/demo/gam.logit.R b/demo/gam.logit.R
new file mode 100644
index 0000000..d75827b
--- /dev/null
+++ b/demo/gam.logit.R
@@ -0,0 +1,62 @@
+##### Example 1: Basic Example with First Differences #####
+
+# Create some sample data:
+set.seed(0)
+n<-400
+sig<-2
+x0 <- runif(n, 0, 1)
+x1 <- runif(n, 0, 1)
+x2 <- runif(n, 0, 1)
+x3 <- runif(n, 0, 1)
+f0 <- function(x) 2 * sin(pi * x)
+f1 <- function(x) exp(2 * x)
+f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
+f3 <- function(x) 0*x
+f <- f0(x0) + f1(x1) + f2(x2)
+g <- (f-5)/3
+g <- binomial()$linkinv(g)
+y <- rbinom(g,1,g)
+my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+
+# 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)
+user.prompt()
+summary(z.out)
+user.prompt()
+plot(z.out, pages=1)
+
+## Plot model components with truth overlaid in red
+op <- par(mfrow=c(2,2))
+for (k in 1:4) {
+ plot(z.out,residuals=TRUE,select=k)
+ xx <- sort(eval(parse(text=paste("x",k-1,sep=""))))
+ ff <- eval(parse(text=paste("f",k-1,"(xx)",sep="")))
+ lines(xx,(ff-mean(ff))/3,col=2) }
+par(op)
+
+# 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, x3= quantile(my.data$x3, 0.8))
+x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
+
+# Generate first differences for the effect of high versus low x3 on y:
+user.prompt()
+s.out <- sim(z.out, x=x.high, x1=x.low)
+user.prompt()
+summary(s.out)
+
+# Generate a second set of fitted values and a plot:
+user.prompt()
+plot(s.out)
+
+## We can also run ANOVA on the model output
+user.prompt()
+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)
+AIC(z.out,z1.out,z2.out)
diff --git a/demo/gam.normal.R b/demo/gam.normal.R
new file mode 100644
index 0000000..2ad0ffd
--- /dev/null
+++ b/demo/gam.normal.R
@@ -0,0 +1,76 @@
+
+##### Example 1: Basic Example with First Differences #####
+
+# Create some sample data:
+set.seed(0)
+n<-400
+sig<-2
+x0 <- runif(n, 0, 1)
+x1 <- runif(n, 0, 1)
+x2 <- runif(n, 0, 1)
+x3 <- runif(n, 0, 1)
+f0 <- function(x) 2 * sin(pi * x)
+f1 <- function(x) exp(2 * x)
+f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
+f3 <- function(x) 0*x
+f <- f0(x0) + f1(x1) + f2(x2)
+e <- rnorm(n, 0, sig)
+y <- f + e
+my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+
+# Estimate 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)
+user.prompt()
+summary(z.out)
+user.prompt()
+plot(z.out,pages=1,residuals=TRUE)
+
+# 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, x3= quantile(my.data$x3, 0.8))
+x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
+
+# Generate first differences for the effect of high versus low x3 on y:
+user.prompt()
+s.out <- sim(z.out, x=x.high, x1=x.low)
+user.prompt()
+summary(s.out)
+
+# Generate a second set of fitted values and a plot:
+user.prompt()
+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)
+user.prompt()
+# Set values for the explanatory variables, using the default mean/mode values
+user.prompt()
+x.out <- setx(z.out)
+
+# Simulate quantities of interest:
+user.prompt()
+s.out <- sim(z.out, x=x.high)
+
+# Plot differences:
+user.prompt()
+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)
+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)
+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)
+plot(z.out,pages=1)
+
diff --git a/demo/gam.poisson.R b/demo/gam.poisson.R
new file mode 100644
index 0000000..48140a1
--- /dev/null
+++ b/demo/gam.poisson.R
@@ -0,0 +1,62 @@
+
+##### Example 1: Basic Example with First Differences #####
+
+# Create some Poisson data:
+set.seed(0)
+n<-400
+sig<-2
+x0 <- runif(n, 0, 1)
+x1 <- runif(n, 0, 1)
+x2 <- runif(n, 0, 1)
+x3 <- runif(n, 0, 1)
+f0 <- function(x) 2 * sin(pi * x)
+f1 <- function(x) exp(2 * x)
+f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
+f3 <- function(x) 0*x
+f <- f0(x0) + f1(x1) + f2(x2)
+g<-exp(f/4)
+y<-rpois(rep(1,n),g)
+my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+
+# 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)
+user.prompt()
+summary(z.out)
+user.prompt()
+plot(z.out,pages=1)
+
+# 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, x3= quantile(my.data$x3, 0.8))
+x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
+
+# Generate first differences for the effect of high versus low x3 on y:
+user.prompt()
+s.out <- sim(z.out, x=x.high, x1=x.low)
+user.prompt()
+summary(s.out)
+
+# Generate a second set of fitted values and a plot:
+user.prompt()
+plot(s.out)
+
+##### Example 2: Repeat fit using performance iteration #####
+
+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 )
+plot(z.out,pages=1)
+
+# Set values for the explanatory variables, using the default mean/mode values
+user.prompt()
+x.out <- setx(z.out)
+
+# Simulate quantities of interest:
+user.prompt()
+s.out <- sim(z.out, x=x.high)
+
+# Plot differences:
+user.prompt()
+plot(s.out)
diff --git a/demo/gam.probit.R b/demo/gam.probit.R
new file mode 100644
index 0000000..e76379e
--- /dev/null
+++ b/demo/gam.probit.R
@@ -0,0 +1,62 @@
+##### Example 1: Basic Example with First Differences #####
+
+# Create some sample data:
+set.seed(0)
+n<-400
+sig<-2
+x0 <- runif(n, 0, 1)
+x1 <- runif(n, 0, 1)
+x2 <- runif(n, 0, 1)
+x3 <- runif(n, 0, 1)
+f0 <- function(x) 2 * sin(pi * x)
+f1 <- function(x) exp(2 * x)
+f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
+f3 <- function(x) 0*x
+f <- f0(x0) + f1(x1) + f2(x2)
+g <- (f-5)/3
+g <- binomial()$linkinv(g)
+y <- rbinom(g,1,g)
+my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+
+# Estimate model, present a summary and a plot of the results:
+user.prompt()
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.probit", data=my.data)
+user.prompt()
+summary(z.out)
+user.prompt()
+plot(z.out, pages=1)
+
+## Plot model components with truth overlaid in red
+op <- par(mfrow=c(2,2))
+for (k in 1:4) {
+ plot(z.out,residuals=TRUE,select=k)
+ xx <- sort(eval(parse(text=paste("x",k-1,sep=""))))
+ ff <- eval(parse(text=paste("f",k-1,"(xx)",sep="")))
+ lines(xx,(ff-mean(ff))/3,col=2) }
+par(op)
+
+# Set explanatory variables to their default (mean/mode) values, with
+# high (80th percentile) and low (20th percentile) values:
+user.prompt()
+x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
+x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
+
+# Generate first differences for the effect of high versus low x3 on y:
+user.prompt()
+s.out <- sim(z.out, x=x.high, x1=x.low)
+user.prompt()
+summary(s.out)
+
+# Generate a second set of fitted values and a plot:
+user.prompt()
+plot(s.out)
+
+## We can also run ANOVA on the model output
+user.prompt()
+anova(z.out)
+
+## We can pull out and compare AICs too...
+user.prompt()
+z1.out <- zelig(y~s(x0)+s(x1)+s(x2), model="gam.probit", data=my.data)
+z2.out <- zelig(y~s(x1)+s(x2), model="gam.probit", data=my.data)
+AIC(z.out,z1.out,z2.out)
diff --git a/demo/logit.gee.R b/demo/logit.gee.R
new file mode 100755
index 0000000..e4b8d40
--- /dev/null
+++ b/demo/logit.gee.R
@@ -0,0 +1,112 @@
+## Attaching the sample turnout dataset:
+data(turnout)
+
+## Variable identifying clusters
+turnout$cluster <- rep(c(1:200),10)
+
+## Sorting by cluster
+sorted.turnout <- turnout[order(turnout$cluster),]
+
+##### Example 1: Simple Example with Stationary 3 Dependence
+
+## Generating empirical estimates:
+user.prompt()
+z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster",
+ data = sorted.turnout, robust = T, corstr = "stat_M_dep", Mv=3)
+user.prompt()
+## Viewing the regression output:
+summary(z.out1)
+
+## Using setx to generate baseline and alternative values for the
+## explanatory variables.
+user.prompt()
+x.out1 <- setx(z.out1)
+
+## Simulating quantities of interest (predicted probabilites, risk
+## ratios, and risk differences):
+user.prompt()
+s.out1 <- sim(z.out1, x = x.out1)
+user.prompt()
+## Summarizing the simulated quantities of interest:
+summary(s.out1)
+
+## Diagnostic plot of the s.out:
+user.prompt()
+plot(s.out1)
+
+## Example 2: First Differences
+
+user.prompt()
+x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
+x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
+
+user.prompt()
+s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
+user.prompt()
+summary(s.out2)
+user.prompt()
+plot(s.out2)
+
+##### Example 3: Example with Fixed Correlation Structure
+
+## User-defined correlation structure
+user.prompt()
+corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
+diag(corr.mat) <- 1
+
+## Generating empirical estimates:
+user.prompt()
+z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster",
+ data = sorted.turnout, robust = T, corstr = "fixed", R=corr.mat)
+user.prompt()
+## Viewing the regression output:
+summary(z.out2)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/demo/netcloglog.R b/demo/netcloglog.R
new file mode 100644
index 0000000..5cd1edb
--- /dev/null
+++ b/demo/netcloglog.R
@@ -0,0 +1,24 @@
+## Example CLogLog Model
+
+## Load sample data
+## Estimate the model
+## Summarize the results
+data(friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model="netcloglog", data=friendship)
+summary(z.out)
+user.prompt()
+
+## Estimating the risk difference (and risk ratio) between low personal power
+## (25th percentile) and high personal power (75th percentile) while all the
+## other variables are held at their default values.
+x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.75))
+x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.25))
+user.prompt()
+
+## Simulate quantities of interest
+## Summarize the results of the simulation
+## Plot those results
+s.out <- sim(z.out, x = x.high, x1 = x.low)
+summary(s.out)
+plot(s.out)
+
diff --git a/demo/netgamma.R b/demo/netgamma.R
new file mode 100644
index 0000000..7005633
--- /dev/null
+++ b/demo/netgamma.R
@@ -0,0 +1,23 @@
+## Example Gamma Model
+
+## Load sample data
+## Estimate the model
+## Summarize the results
+data(friendship)
+z.out <- zelig(per ~ perpower, LF="inverse", model="netgamma", data=friendship)
+summary(z.out)
+
+## Estimating the risk difference (and risk ratio) between low personal power
+## (25th percentile) and high personal power (75th percentile) while all the
+## other variables are held at their default values.
+user.prompt()
+x.low <- setx(z.out, numst2 = 0)
+x.high <- setx(z.out, numst2 = 1)
+
+## Simulate quantities of interest
+## Summarize the results of the simulation
+## Plot those results
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+summary(s.out)
+plot(s.out)
+
diff --git a/demo/netlogit.R b/demo/netlogit.R
index dba4f72..e6dc3f1 100644
--- a/demo/netlogit.R
+++ b/demo/netlogit.R
@@ -1,42 +1,29 @@
-## Attaching the sample friendship data:
-data(friendship)
-
-##### Example 1: Simple Example
-
-## Generating empirical estimates:
-user.prompt()
-z.out <- zelig(friends ~ advice + prestige + perpower, model = "netlogit", data=friendship)
-user.prompt()
-
-## Viewing the regression output:
-summary(z.out)
-
-## Using setx to generate baseline and alternative values for the
-## explanatory variables:
-user.prompt()
-x.out <- setx(z.out)
-
-## Simulating quantities of interest:
-user.prompt()
-s.out <- sim(z.out, x = x.out)
-user.prompt()
-
-## Summmarizing the simulated quantities of interest:
-summary(s.out)
-
-## Diagnostic plot of the s.out:
-user.prompt()
-plot(s.out)
-
-
-## Example 2: First Differences
-user.prompt()
-x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.75))
-x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.25))
-
-user.prompt()
-s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-user.prompt()
-summary(s.out2)
-user.prompt()
-plot(s.out2)
+## Example Logit Model
+
+## Load sample data
+## Estimate the model
+## Summarize the results
+data(friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model="netlogit", data=friendship)
+summary(z.out)
+user.prompt()
+
+## Estimating the risk difference (and risk ratio) between low personal power
+## (25th percentile) and high personal power (75th percentile) while all the
+## other variables are held at their default values.
+x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.75))
+x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.25))
+user.prompt()
+
+## Simulate quantities of interest
+## Summarize the results of the simulation
+## Plot those results
+s.out <- sim(z.out, x = x.high, x1 = x.low)
+summary(s.out)
+plot(s.out)
+
+
+
+
+
+
diff --git a/demo/netnormal.R b/demo/netnormal.R
new file mode 100644
index 0000000..d35b9c1
--- /dev/null
+++ b/demo/netnormal.R
@@ -0,0 +1,24 @@
+## Example Normal Model
+
+## Load sample data
+## Estimate the model
+## Summarize the results
+data(friendship)
+z.out <- zelig(perpower ~ friends + advice + prestige, LF="identity", model="netnormal", data=friendship)
+summary(z.out)
+
+
+## Estimating the risk difference (and risk ratio) between low personal power
+## (25th percentile) and high personal power (75th percentile) while all the
+## other variables are held at their default values.
+x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.75))
+x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.25))
+user.prompt()
+
+## Simulate quantities of interest
+## Summarize the results of the simulation
+## Plot those results
+s.out <- sim(z.out, x = x.high, x1 = x.low)
+summary(s.out)
+plot(s.out)
+
diff --git a/demo/netpoisson.R b/demo/netpoisson.R
new file mode 100644
index 0000000..aa3b78f
--- /dev/null
+++ b/demo/netpoisson.R
@@ -0,0 +1,24 @@
+## Example Poisson Model
+
+## Load sample data
+## Estimate the model
+## Summarize the results
+data(friendship)
+z.out <- zelig(count ~ advice + prestige + perpower, model="netpoisson", data=friendship)
+summary(z.out)
+user.prompt()
+
+## Estimating the risk difference (and risk ratio) between low personal power
+## (25th percentile) and high personal power (75th percentile) while all the
+## other variables are held at their default values.
+x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.75))
+x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.25))
+user.prompt()
+
+## Simulate quantities of interest
+## Summarize the results of the simulation
+## Plot those results
+s.out <- sim(z.out, x = x.high, x1 = x.low)
+summary(s.out)
+plot(s.out)
+
diff --git a/demo/netprobit.R b/demo/netprobit.R
new file mode 100644
index 0000000..5b3c49e
--- /dev/null
+++ b/demo/netprobit.R
@@ -0,0 +1,23 @@
+## Example Probit Model
+
+## Load sample data
+## Estimate the model
+## Summarize the results
+data(friendship)
+z.out <- zelig(friends ~ advice + prestige + perpower, model="netprobit", data=friendship)
+summary(z.out)
+user.prompt()
+
+## Estimating the risk difference (and risk ratio) between low personal power
+## (25th percentile) and high personal power (75th percentile) while all the
+## other variables are held at their default values.
+x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.75))
+x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob=0.25))
+user.prompt()
+
+## Simulate quantities of interest
+## Summarize the results of the simulation
+## Plot those results
+s.out <- sim(z.out, x = x.high, x1 = x.low)
+summary(s.out)
+plot(s.out)
diff --git a/demo/sur.R b/demo/sur.R
new file mode 100644
index 0000000..e0ca802
--- /dev/null
+++ b/demo/sur.R
@@ -0,0 +1,19 @@
+data(grunfeld)
+
+formula<-list(mu1=Ige~Fge+Cge, mu2=Iw~Fw+Cw)
+user.prompt()
+z.out<-zelig(formula=formula,model="sur",data=grunfeld)
+user.prompt()
+summary(z.out)
+user.prompt()
+
+ x.out <- setx(z.out)
+user.prompt()
+
+ s.out <- sim(z.out,x=x.out)
+user.prompt()
+
+summary(s.out)
+user.prompt()
+
+plot(s.out)
diff --git a/demo/threesls.R b/demo/threesls.R
new file mode 100644
index 0000000..ade9a9a
--- /dev/null
+++ b/demo/threesls.R
@@ -0,0 +1,53 @@
+# Example 1
+
+
+# Attach sample data and variable names (Kmenta's simple supply/demand model).
+# q food consumption per capita.
+# p ratio of food prices to general consumer prices.
+# d disposable income in constant dollars.
+# f ratio of preceding year's prices received by farmers to general consumer prices.
+# a time in years.
+
+
+data(kmenta)
+
+# Suppose that we want to estimate the following list of equations
+
+# q ~ p + d
+# q ~ p + f + a
+
+
+# with the following instrumental variable
+# inst <- ~ d + f + a
+
+
+# Write the formula according to Zelig syntax
+
+formula<-list( mu1= q ~ p + d,
+ mu2=q ~ p + f + a,
+ inst =~ d + f + a)
+
+# estimate model and present a summary
+user.prompt()
+z.out<-zelig(formula=formula, model ="threesls",data=kmenta)
+user.prompt()
+summary(z.out)
+
+# Set explanatory variables to their default (mean/mode) values
+user.prompt()
+x.out<-setx(z.out)
+
+# Simulate the quantities of interest and present a summary
+
+user.prompt()
+s.out<-sim(z.out,x=x.out)
+user.prompt()
+summary(s.out)
+
+# plot the quantities of interest for each equation
+
+user.prompt()
+plot(s.out)
+
+
+
diff --git a/demo/twosls.R b/demo/twosls.R
new file mode 100644
index 0000000..940070c
--- /dev/null
+++ b/demo/twosls.R
@@ -0,0 +1,59 @@
+# Example 1
+
+# Attach sample data and variable names.
+# year year
+# C consumption
+# P corporate profits
+# P1 previous year corporate profits
+# Wtot total wage
+# Wp private wage bill
+# Wg goverment wage bill
+# I investment
+# K1 previous year capital stock
+# X GNP
+# G govermment spending
+# T Taxes
+# X1 previous year GNP
+# Tm Year-1931
+
+data(klein)
+
+# Suppose that we want to estimate the following list of equations
+
+# C~Wtot + P + P1
+# I~P + P1 + K1
+# Wp~ X + X1 + Tm
+
+# with the following instrumental variable
+# ~ P1 + K1 + X1 + Tm + Wg + G
+
+# Write the formula conform Zelig syntax and the instrumental variable (required for "2sls"):
+formula <- list(mu1=C~Wtot + P + P1,
+ mu2=I~P + P1 + K1,
+ mu3=Wp~ X + X1 + Tm,
+ inst= ~ P1 + K1 + X1 + Tm + Wg + G)
+
+# Estimate model and present a summary:
+user.prompt()
+z.out<-zelig(formula=formula, model="twosls",data=klein)
+user.prompt()
+summary(z.out)
+
+# Set explanatory variables to their default (mean/mode) values
+
+user.prompt()
+x.out <-setx(z.out,x=x.out)
+
+# Simulate quantities of interests and present a summary
+
+user.prompt()
+s.out <-sim(z.out,x=x.out)
+user.prompt()
+summary(s.out)
+
+# Plot
+
+user.prompt()
+plot(s.out)
+
+
diff --git a/inst/doc/.log b/inst/doc/.log
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## then if a Makefile is found, run that Makefile
##
-all: zelig.pdf
+all:
+ /bin/sh ./makeDocs rbuild
-%.pdf:
- /bin/sh ./forMake.sh
+zelig.pdf:
+ /bin/sh ./makeDocs rbuild
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77.04 91.44 793.65 238.60 cl
ep
%%Trailer
-%%Pages: 26
+%%Pages: 88
%%EOF
diff --git a/inst/doc/Zcommands.tex b/inst/doc/Zcommands.tex
index 9ee85c4..84234b4 100644
--- a/inst/doc/Zcommands.tex
+++ b/inst/doc/Zcommands.tex
@@ -15,7 +15,7 @@ three commands.
\SelectTips{eu}{12} \UseTips
\xymatrix@!C{\quad {\rm Imputation}
\ar@{-->}[dr] & & {\rm Matching} \ar@{-->}[dl] \\
- & *+[F-:<10pt>]{{\tt zelig()}} \ar@{->}[d] \ar@{-->}[r] &
+{\rm Validation } \ar@{<--}[r] & *+[F-:<10pt>]{{\tt zelig()}} \ar@{->}[d] \ar@{-->}[r] &
{\tt summary()}\\
& *+[F-:<10pt>]{{\tt setx()}} \ar@{-->}[r] \ar@{->}[d] & {\tt whatif()}\\
& *+[F-:<10pt>]{{\tt sim()}} \ar@{-->}[dr] \ar@{-->}[dl] & \\
@@ -31,12 +31,15 @@ likelihood models, for example, this step estimates the coefficients,
other model parameters, and a variance-covariance matrix. In
addition, you may choose from a variety of options:
\begin{itemize}
-\item Pre-processing: Prior to calling {\tt zelig()}, you may
+\item Pre-process data: Prior to calling {\tt zelig()}, you may
choose from a variety of data pre-processing commands (matching or
multiple imputation, for example) to make your statistical
inferences more accurate.
\item Summarize model: After calling {\tt zelig()}, you may summarize
-the regression output using {\tt summary()}.
+ the fitted model output using {\tt summary()}.
+\item Validate model: After calling {\tt zelig()}, you may choose to
+ validate the fitted model. This can be done, for example, by using
+ cross-validation procedures and diagnostics tools.
\end{itemize}
\item Use \texttt{setx()} to set each of the explanatory variables to
chosen (actual or counterfactual) values in preparation for
@@ -130,6 +133,17 @@ names.
> summary(s.out)
\end{verbatim}
+\item Validate the fitted model using the leave-one-out cross
+ validation procedure and calculating the average squared prediction
+ error via {\it boot} package. For example:
+\begin{verbatim}
+> library(boot)
+> data(turnout)
+> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+> cv.out <- cv.glm(z.out, data = turnout)
+> print(cv.out$delta)
+\end{verbatim}
+
\end{itemize}
\subsection{Details}
@@ -781,3 +795,8 @@ identical, you may use
\end{verbatim} %$
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: "zelig"
+%%% End:
diff --git a/inst/doc/acknowledgments.tex b/inst/doc/acknowledgments.tex
index 218f2be..5bea3b9 100644
--- a/inst/doc/acknowledgments.tex
+++ b/inst/doc/acknowledgments.tex
@@ -18,7 +18,7 @@ auxiliary statistical procedures and methods available through Zelig:
Plummer, Nicky Best, Kate Cowles, and Karen Vines; {\tt sandwich} by
Achim Zeileis; and {\tt zoo} by Achim Zeileis and Gabor Grothendieck.
-Our appreciation also goes to Ferdinand Almahdi, Skyler J.\ Cranmer,
+Our appreciation also goes to Ferdinand Alimadhi, Skyler J.\ Cranmer,
Ben Goodrich, Justin Grimmer, Yang Lin, and Ying Lu, who have
contributed either code or documentation; Dan Hopkins, Ian Yohai, and
others who have given valuable feedback from the courses in which they
@@ -33,4 +33,3 @@ Social Sciences.
As usual, all errors are our responsibility.
-
diff --git a/inst/doc/arima.pdf b/inst/doc/arima.pdf
index f2437c6..470dda5 100644
Binary files a/inst/doc/arima.pdf and b/inst/doc/arima.pdf differ
diff --git a/inst/doc/arima.tex b/inst/doc/arima.tex
deleted file mode 100644
index e38066f..0000000
--- a/inst/doc/arima.tex
+++ /dev/null
@@ -1,349 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{ARIMA Models for Time Series Data}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,time series}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-\section{{\tt ARIMA}: ARIMA Models for Time Series Data}
-\label{ARIMA}
-
-Use auto-regressive, integrated, moving-average (ARIMA) models for
-time series data. A time series is a set of observations ordered
-according to the time they were observed. Because the value observed
-at time $t$ may depend on values observed at previous time points,
-time series data may violate independence assumptions. An ARIMA($p$,
-$d$, $q$) model can account for temporal dependence in several ways.
-First, the time series is differenced to render it stationary, by
-taking $d$ differences. Second, the time dependence of the stationary
-process is modeled by including $p$ auto-regressive and $q$
-moving-average terms, in addition to any time-varying covariates. For
-a cyclical time series, these steps can be repeated according to the
-period of the cycle, whether quarterly or monthly or another time
-interval. ARIMA models are extremely flexible for continuous data.
-Common formulations include, ARIMA(0, 0, 0) for least squares
-regression (see \Sref{ls}), ARIMA(1, 0, 0), for an AR1 model, and
-ARIMA(0, 0, 1) for an MA1 model. For a more comprehensive review of
-ARIMA models, see \cite{Enders04}.
-
-\subsubsection*{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Diff(Y, d, ds=NULL, per=NULL) ~ lag.y(p, ps=NULL)
- + lag.eps(q, qs=NULL) + X1 + X2,
- model="arima", data=mydata, ...)
-> x.out <- setx(z.out, X1 = list(time, value), cond = FALSE)
-> s.out <- sim(z.out, x=x.out, x1=NULL)
-\end{verbatim}
-
-\subsubsection*{Inputs}
-In addition to independent variables, {\tt zelig()} accepts the
-following arguments to specify the {\tt ARIMA} model:
-\begin{itemize}
-\item {\tt Diff(Y, d, ds, per)} for a dependent variable Y sets the
-number of non-seasonal differences ({\tt d}), the number of seasonal differences ({\tt
-ds}), and the period of the season ({\tt per}).
-\item {\tt lag.y(p, ps)} sets the number of lagged observations of the
-dependent variable for non-seasonal ({\tt p}) and seasonal ({\tt ps})
-components.
-\item {\tt lag.eps(q, qs)} sets the number of lagged innovations, or
-differences between the observed value of the time series and the
-expected value of the time series for non-seasonal ({\tt q}) and
-seasonal ({\tt qs}) components.
-\end{itemize}
-In addition the user can control the estimation of the time
-series with the following terms:
-\begin{itemize}
-\item $\hdots$: Additional inputs. See {\tt help(arima)} in the stats
-library for further information.
-\end{itemize}
-
-\subsubsection*{Stationarity}
-A stationary time series has finite variance, correlations between
-observations that are not time-dependent, and a constant expected
-value for all components of the time series \citep[p.\ 12]{BroDav91}.
-Users should ensure that the time series being analyzed is stationary
-before specifying a model. The following commands provide diagnostics
-to determine if a time series {\tt Y} is stationary.
-\begin{itemize}
-\item {\tt pp.test(Y)}: Tests the null hypothesis that the time series
- is non-stationary.
-\item {\tt kpss.test(Y)}: Tests the null hypothesis that the time
- series model is stationary.
-\end{itemize}
-The following commands provide graphical means of diagnosing whether a
-given time series is stationary.
-\begin{itemize}
-\item {\tt ts.plot(Y)}: Plots the observed time series.
-\item {\tt acf(Y)}: Provides the sample auto-correlation function
-(correlogram) for the time series.
-\item {\tt pacf(Y)}: Provides the sample partial auto-correlation
- function (PACF) for the time series. \\
-\end{itemize}
-These latter two plots are also useful in determining the $p$
-autoregressive terms and the $q$ lagged error terms. See
-\cite{Enders04} for a complete description of how to utilize ACF and
-PACF plots to determine the order of an ARIMA model.
-
-\subsubsection*{Examples}
-
-\begin{enumerate}
-
-\item No covariates\newline
-
-Estimate the ARIMA model, and summarize the results.
-\begin{Schunk}
-\begin{Sinput}
-> data(approval)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(Diff(approve, 1) ~ lag.eps(2) + lag.y(2), data = approval,
-+ model = "arima")
-> summary(z.out1)
-\end{Sinput}
-\end{Schunk}
-Set the number of time periods (ahead) for the prediction to run.
-for which you would like the prediction to run:
-\begin{Schunk}
-\begin{Sinput}
-> x.out1 <- setx(z.out1, pred.ahead = 10)
-\end{Sinput}
-\end{Schunk}
-Simulate the predicted quantities of interest:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out1, x = x.out1)
-\end{Sinput}
-\end{Schunk}
-Summarize and plot the results:
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out1, lty.set = 2)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/arima-Example1Plot}
-\end{center}
-
-\item Calculating a treatment effect\newline
-
-Estimate an ARIMA model with exogenous regressors, in addition to
-lagged errors and lagged values of the dependent variable.
-
-\begin{Schunk}
-\begin{Sinput}
-> z.out2 <- zelig(Diff(approve, 1) ~ iraq.war + sept.oct.2001 +
-+ avg.price + lag.eps(1) + lag.y(2), data = approval, model = "arima")
-\end{Sinput}
-\end{Schunk}
-To calculate a treatment effect, provide one counterfactual value for
-one time period for one of the exogenous regressors (this is the
-counterfactual treatment).
-\begin{Schunk}
-\begin{Sinput}
-> x.out2 <- setx(z.out2, sept.oct.2001 = list(time = 45, value = 0),
-+ cond = T)
-\end{Sinput}
-\end{Schunk}
-Simulate the quantities of interes
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out2, x = x.out2)
-\end{Sinput}
-\end{Schunk}
-Summarizing and plotting the quantities of interest.
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-%the plot does not work
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out2)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/arima-Example2Plot}
-\end{center}
-
-\item Calculating first differences\newline
-
-Continuing the example from above, calculate first differences by
-selecting several counterfactual values for one of the exogenous
-regressors.
-\begin{Schunk}
-\begin{Sinput}
-> x.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value = 0))
-> x1.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value = 1))
-\end{Sinput}
-\end{Schunk}
-Simulating the quantities of interest
-\begin{Schunk}
-\begin{Sinput}
-> s.out3 <- sim(z.out2, x = x.out3, x1 = x1.out3)
-\end{Sinput}
-\end{Schunk}
-Summarizing and plotting the quantities of interest. Choosing {\tt
-pred.se = TRUE} only displays the uncertainty resulting from parameter
-estimation.
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out3)
-\end{Sinput}
-\end{Schunk}
-%the plot does not work
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out3, pred.se = TRUE)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/arima-Example3Plot}
-\end{center}
-\end{enumerate}
-
-\subsubsection*{Model}
-Suppose we observe a time series $Y$, with observations $Y_i$ where
-$i$ denotes the time at which the observation was recorded. The first
-step in the ARIMA procedure is to ensure that this series is
-stationary. If initial diagnostics indicate non-stationarity, then we
-take additional differences until the diagnostics indicate
-stationarity. Formally, define the difference operator, $\nabla^
-{d}$, as follows. When $d = 1$, $\nabla^{1} Y = Y_i - Y_{i-1}$, for
-all observations in the series. When $d=2$, $\nabla^2 Y = (Y_{i} -
-Y_{i-1}) - (Y_{i-1} - Y_{i-2})$. This is analogous to a polynomial
-expansion, $Y_{i} - 2Y_{i-1} + Y_{i-2}$. Higher orders of
-differencing ($d > 2$) following the same function. Let $Y^*$
-represent the stationary time series derived from the initial time
-series by differencing $Y$ $d$ times. In the second step, lagged
-values of $Y^{*}$ and errors $\mu - Y^*_i$ are used to model the time
-series. ARIMA utilizes a state space representation of the ARIMA
-model to assemble the likelihood and then utilizes maximum likelihood
-to estimate the parameters of the model. See \cite{BroDav91} Chapter
-12 for further details.
-\begin{itemize}
-\item A stationary time series $Y_i^*$ that has been differenced $d$
-times has \textit{stochastic component}:
-\begin{equation*}
-Y_i^* \sim \text{Normal} (\mu_i, \sigma^2),
-\end{equation*}
-where $\mu_i$ and $\sigma^2$ are the mean and variance of the Normal
-distribution, respectively.
-\item The \textit{systematic component}, $\mu_i$ is modeled as
-\begin{equation*}
-\mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots + \alpha_p Y_{i-p}^*
-+ \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
-\end{equation*}
-where $x_i$ are the explanatory variables with associated parameter
-vector $\beta$; $Y^*$ the lag-$p$ observations from the stationary time
-series with associated parameter vector $\alpha$; and $\epsilon_i$ the
-lagged errors or innovations of order $q$, with associated parameter vector
-$\gamma$.
-\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*}
-\text{E(}Y_\text{i}) = \mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots + \alpha_p Y_{i-p}^*
-+ \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
-\end{equation*}
-given draws of $\beta$, $\alpha$, and $\gamma$ from their estimated
-distribution.
-\item The first difference ({\tt qi\$fd}) is:
-\begin{equation*}
-\text{FD}_i= E(Y | x_{1i}) - E(Y|x_{i})
-\end{equation*}
-\item The treatment effect ({\tt qi\$t.eff}), obtained with {\tt
-setx(\dots, cond = TRUE)}, represents the difference
-between the observed time series and the expected value of a time
-series with counterfactual values of the external regressors. Formally,
-\begin{equation*}
-\text{t.eff}_\text{i} = Y_i - E[Y_i | x_{i}]
-\end{equation*}
-Zelig will not estimate both first differences and treatment effects.
-\end{itemize}
-
-\subsubsection*{Output Values}
-The output of each Zelig command contains useful information which the
-user may view. For example, if the user runs {\tt z.out <-
-zelig(Diff(Y,1) + lag.y(1) + lag.eps(1) + X1, model = "arima", data)}
-then the user may examine the available information in {\tt z.out} by
-using {\tt names(z.out)}, see the coefficients by using {\tt
-z.out\$coef} and a default summary of information through {\tt
-summary(z.out)}. {\tt tsdiag(z.out)} returns a plot of the residuals,
-the ACF of the residuals, and a plot displaying the $p$-values for the
-Ljung-Box statistic. Other elements, available through the \$
-operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
-\begin{itemize}
-\item {\tt coef}: parameter estimates for the explanatory variables,
-lagged observations of the time series, and lagged innovations.
-\item {\tt sigma2}: maximum likelihood estimate of the variance of
-the stationary time series.
-\item {\tt var.coef}: variance-covariance matrix for the parameters.
-\item {\tt loglik}: maximized log-likelihood.
-\item {\tt aic}: Akaike Information Criterion (AIC) for the maximized
- log-likelihood.
-\item {\tt residuals}: Residuals from the fitted model.
-\item {\tt arma}: A vector with seven elements corresponding to the AR
-and MA, the seasonal AR and MA, the period of the seasonal component,
-and the number of non-seasonal and seasonal differences of the
-dependent variable.
-\item {\tt data}: the name of the input data frame.
-\end{itemize}
-\item From the {\tt sim()} output object {\tt s.out} you may extract
- quantities of interest arranged as matrices, with the rows
- indicating the number of the simulations, and the columns
- representing the simulated value of the dependent variable for the
- counterfactual value at that time period. {\tt summary(s.out)}
- provides a summary of the simulated values, while {\tt plot(s.out)}
- provides a graphical representation of the simulations. Available
- quantities are:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}.
-\item {\tt qi\$fd} : the simulated first difference for the values
- that are specified in {\tt x} and {\tt x1}.
-\item{ \tt qi\$t.eff}: the simulated treatment effect, difference
-between the observed {\tt y} and the expected values given the
-counterfactual values specified in {\tt x}.
-\end{itemize}
-\end{itemize}
-
-\subsection*{See Also}
-
-Additional options for ARIMA models may be found using {\tt
-help(arima)}.
-
-For an accessible introduction to identifying the order of an ARIMA
-model consult:
-\begin{verse}
-\bibentry{Enders04}.
-\end{verse}
-In addition, advanced users may wish to become more familiar with the state-space
-representation of an ARIMA process:
-\begin{verse}
-\bibentry{BroDav91}.
-\end{verse}
-
-\subsubsection*{Contributors}
-The ARIMA function is part of the stats package by William Venables
-and Brian Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-Justin Grimmer added Zelig functionality.
- \end{document}
diff --git a/inst/doc/blogit.pdf b/inst/doc/blogit.pdf
index a1afb41..92ca82d 100644
Binary files a/inst/doc/blogit.pdf and b/inst/doc/blogit.pdf differ
diff --git a/inst/doc/blogit.tex b/inst/doc/blogit.tex
deleted file mode 100644
index 6780481..0000000
--- a/inst/doc/blogit.tex
+++ /dev/null
@@ -1,375 +0,0 @@
-
-\include{zinput}
-
-%\VignetteIndexEntry{Bivariate Logistic Regression for Two Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig, VGAM}
-%\VignetteKeyWords{model,logistic regression, dichotomous}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-
-\section{{\tt blogit}: Bivariate Logistic Regression for Two
-Dichotomous Dependent Variables}\label{blogit}
-
-Use the bivariate logistic regression model if you have two binary
-dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
-function of some explanatory variables. Each pair of dependent
-variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
-Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
-$(Y_{i1}=0, Y_{i2}=0)$. The joint probability for each of these four
-outcomes is modeled with three systematic components: the marginal
-Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the odds ratio $\psi$,
-which describes the dependence of one marginal on the other. Each of
-these systematic components may be modeled as functions of (possibly
-different) sets of explanatory variables.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2 ,
- mu2 = Y2 ~ X1 + X3),
- model = "blogit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Input Values}
-
-In every bivariate logit specification, there are three equations which
-correspond to each dependent variable ($Y_1$, $Y_2$), and $\psi$, the
-odds ratio. You should provide a list of formulas for each equation or,
-you may use {\tt cbind()} if the right hand side is the same for both equations
-\begin{Schunk}
-\begin{Sinput}
-> formulae <- list(cbind(Y1, Y2) ~ X1 + X2)
-\end{Sinput}
-\end{Schunk}
-which means that all the explanatory variables in equations 1 and 2
-(corresponding to $Y_1$ and $Y_2$) are included, but only an intercept
-is estimated (all explanatory variables are omitted) for equation 3
-($\psi$).
-
-You may use the function {\tt tag()} to constrain variables across
-equations:
-\begin{Schunk}
-\begin{Sinput}
-> formulae <- list(mu1 = y1 ~ x1 + tag(x3, "x3"), mu2 = y2 ~ x2 +
-+ tag(x3, "x3"))
-\end{Sinput}
-\end{Schunk}
-where {\tt tag()} is a special function that constrains variables to
-have the same effect across equations. Thus, the coefficient for {\tt
-x3} in equation {\tt mu1} is constrained to be equal to the
-coefficient for {\tt x3} in equation {\tt mu2}.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-
-\item {Basic Example} \label{basic.bl}
-
-Load the data and estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> data(sanction)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
-+ model = "blogit", data = sanction)
-\end{Sinput}
-\end{Schunk}
-By default, {\tt zelig()} estimates two effect parameters
-for each explanatory variable in addition to the odds ratio parameter;
-this formulation is parametrically independent (estimating
-unconstrained effects for each explanatory variable), but
-stochastically dependent because the models share an odds ratio.
-\newline \newline Generate baseline values for the explanatory
-variables (with cost set to 1, net gain to sender) and alternative
-values (with cost set to 4, major loss to sender):
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out1, cost = 1)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out1, cost = 4)
-\end{Sinput}
-\end{Schunk}
-Simulate fitted values and first differences:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out1)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/blogit-BasicExamplePlot}
-\end{center}
-
-\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.logit}
-
-Using sample data \texttt{sanction}, estimate the statistical model,
-with {\tt import} a function of {\tt coop} in the first equation and {\tt export} a
-function of {\tt cost} and {\tt target} in the second equation:
-\begin{Schunk}
-\begin{Sinput}
-> z.out2 <- zelig(list(import ~ coop, export ~ cost + target),
-+ model = "blogit", data = sanction)
-> summary(z.out2)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their means:
-\begin{Schunk}
-\begin{Sinput}
-> x.out2 <- setx(z.out2)
-\end{Sinput}
-\end{Schunk}
-Simulate draws from the posterior distribution:
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out2, x = x.out2)
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out2)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/blogit-JointExamplePlot}
-\end{center}
-
-\item Joint Estimation of a Parametrically and Stochastically
-Dependent Model
-\label{pdep.l}
-
-Using the sample data \texttt{sanction}
-The bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
-some or all explanatory variables, {\it and} the effects of the shared
-explanatory variables are jointly estimated. For example,
-\begin{Schunk}
-\begin{Sinput}
-> z.out3 <- zelig(list(import ~ tag(coop, "coop") + tag(cost, "cost") +
-+ tag(target, "target"), export ~ tag(coop, "coop") + tag(cost,
-+ "cost") + tag(target, "target")), model = "blogit", data = sanction)
-> summary(z.out3)
-\end{Sinput}
-\end{Schunk}
-Note that this model only returns one parameter estimate for each of
-{\tt coop}, {\tt cost}, and {\tt target}. Contrast this to
-Example~\ref{basic.bl} which returns two parameter estimates for each
-of the explanatory variables. \newline \newline Set values for the
-explanatory variables:
-\begin{Schunk}
-\begin{Sinput}
-> x.out3 <- setx(z.out3, cost = 1:4)
-\end{Sinput}
-\end{Schunk}
-Draw simulated expected values:
-\begin{Schunk}
-\begin{Sinput}
-> s.out3 <- sim(z.out3, x = x.out3)
-> summary(s.out3)
-\end{Sinput}
-\end{Schunk}
-
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-For each observation, define two binary dependent variables, $Y_1$ and
-$Y_2$, each of which take the value of either 0 or 1 (in the
-following, we suppress the observation index). We model the joint
-outcome $(Y_1$, $Y_2)$ using a marginal probability for each dependent
-variable, and the odds ratio, which parameterizes the relationship
-between the two dependent variables. Define $Y_{rs}$ such that it is
-equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise, where $r$ and
-$s$ take a value of either 0 or 1. Then, the model is defined as follows,
-
-\begin{itemize}
-
-\item The \emph{stochastic component} is
-\begin{eqnarray*}
- Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
- Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
- Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
-\end{eqnarray*}
-where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
-$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$.
-
-
-\item The \emph{systematic components} model the marginal probabilities,
- $\pi_j=\Pr(Y_j=1)$, as well as the odds ratio. The odds ratio
- is defined as $\psi = \pi_{00} \pi_{01}/\pi_{10}\pi_{11}$ and
- describes the relationship between the two outcomes. Thus, for each
- observation we have
-\begin{eqnarray*}
-\pi_j & = & \frac{1}{1 + \exp(-x_j \beta_j)} \quad \textrm{ for} \quad
-j=1,2, \\
-\psi &= & \exp(x_3 \beta_3).
-\end{eqnarray*}
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the bivariate logit model
- are the predicted joint probabilities. Simulations of $\beta_1$,
- $\beta_2$, and $\beta_3$ (drawn from their sampling distributions)
- are substituted into the systematic components $(\pi_1, \pi_2,
- \psi)$ to find simulations of the predicted joint probabilities:
-\begin{eqnarray*}
-\pi_{11} & = & \left\{ \begin{array}{ll}
- \frac{1}{2}(\psi - 1)^{-1} - {a - \sqrt{a^2 + b}} &
- \textrm{for} \; \psi \ne 1 \\
- \pi_1 \pi_2 & \textrm{for} \; \psi = 1
- \end{array} \right., \\
-\pi_{10} &=& \pi_1 - \pi_{11}, \\
-\pi_{01} &=& \pi_2 - \pi_{11}, \\
-\pi_{00} &=& 1 - \pi_{10} - \pi_{01} - \pi_{11},
-\end{eqnarray*}
-where $a = 1 + (\pi_1 + \pi_2)(\psi - 1)$, $b = -4 \psi(\psi - 1)
-\pi_1 \pi_2$, and the joint probabilities for each observation must sum
-to one. For $n$ simulations, the expected values form an $n \times 4$
-matrix for each observation in {\tt x}.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the
- multinomial distribution given the expected joint probabilities.
-
-\item The first differences ({\tt qi\$fd}) for each
- of the predicted joint probabilities are given by $$\textrm{FD}_{rs}
- = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s \mid x).$$
-
-\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
- probabilities are given by
-\begin{equation*}
-\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
- E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
- \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 $E[Y_{ij}(t_i=0)]$,
- 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 $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
- \widehat{Y_{ij}(t_i=0)} \right\} \textrm{ for } j = 1,2,
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating
- $\widehat{Y_{ij}(t_i=0)}$, 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 $t_i=0$.
-\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 = "blogit", 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
-obtain 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 object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times 3$ matrix of the linear
- predictors $x_j \beta_j$.
- \item {\tt residuals}: an $n \times 3$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays indexed by simulation
- $\times$ quantity $\times$ {\tt x}-observation (for more than one
- {\tt x}-observation; otherwise the quantities are matrices).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected joint probabilities (or expected
- values) for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
- distribution defined by the expected joint probabilities.
- \item {\tt qi\$fd}: the simulated first difference in the
- expected joint probabilities for the values specified in {\tt x} and
- {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio in the predicted
- probabilities for given {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The bivariate logit function is part of the VGAM package by Thomas Yee.
-Please cite the model as:
-
-\begin{verse}
-\bibentry{YeeHas03}.
-\end{verse}
-
-In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM library. Additional
-documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
-
-
-
-
diff --git a/inst/doc/bprobit.pdf b/inst/doc/bprobit.pdf
index 6b6fa01..f81e118 100644
Binary files a/inst/doc/bprobit.pdf and b/inst/doc/bprobit.pdf differ
diff --git a/inst/doc/bprobit.tex b/inst/doc/bprobit.tex
deleted file mode 100644
index bcfd827..0000000
--- a/inst/doc/bprobit.tex
+++ /dev/null
@@ -1,410 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Bivariate Probit Regression for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig, VGAM}
-%\VignetteKeyWords{model,prpbit, logistic regression, dichotomous}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt bprobit}: Bivariate Logistic Regression for Two
-Dichotomous Dependent Variables}\label{bprobit}
-
-Use the bivariate probit regression model if you have two binaryrun
-dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
-function of some explanatory variables. Each pair of dependent
-variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
-Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
-$(Y_{i1}=0, Y_{i2}=0)$. The joint probability for each of these four
-outcomes is modeled with three systematic components: the marginal
-Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the correlation parameter
-$\rho$ for the two marginal distributions. Each of these systematic
-components may be modeled as functions of (possibly different) sets of
-explanatory variables.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2,
- mu2 = Y2 ~ X1 + X3,
- rho = ~ 1),
- model = "bprobit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Input Values}
-
-In every bivariate probit specification, there are three equations
-which correspond to each dependent variable ($Y_1$, $Y_2$), and the
-correlation parameter $\rho$. Since the correlation parameter does
-not correspond to one of the dependent variables, the model estimates
-$\rho$ as a constant by default. Hence, only two formulas (for
-$\mu_1$ and $\mu_2$) are required. If the explanatory variables for
-$\mu_1$ and $\mu_2$ are the same and effects are estimated separately
-for each parameter, you may use the following short hand:
-\begin{Schunk}
-\begin{Sinput}
-> fml <- list(cbind(Y1, Y2) ~ X1 + X2)
-\end{Sinput}
-\end{Schunk}
-which has the same meaning as:
-\begin{Schunk}
-\begin{Sinput}
-> fml <- list(mu1 = Y1 ~ X1 + X2, mu2 = Y2 ~ X1 + X2, rho = ~1)
-\end{Sinput}
-\end{Schunk}
-You may use the function {\tt tag()} to constrain variables across
-equations. The {\tt tag()} function takes a variable and a label for
-the effect parameter. Below, the constrained effect of {\tt
-x3} in both equations is called the {\tt age} parameter:
-\begin{Schunk}
-\begin{Sinput}
-> fml <- list(mu1 = y1 ~ x1 + tag(x3, "age"), mu2 = y2 ~ x2 + tag(x3,
-+ "age"))
-\end{Sinput}
-\end{Schunk}
-You may also constrain different variables across different equations
-to have the same effect.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-
-\item {Basic Example} \label{basic.bp}
-
-Load the data and estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> data(sanction)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
-+ model = "bprobit", data = sanction)
-\end{Sinput}
-\end{Schunk}
-By default, {\tt zelig()} estimates two effect parameters
-for each explanatory variable in addition to the correlation coefficient;
-this formulation is parametrically independent (estimating
-unconstrained effects for each explanatory variable), but
-stochastically dependent because the models share a correlation parameter.
-\newline \newline Generate baseline values for the explanatory
-variables (with cost set to 1, net gain to sender) and alternative
-values (with cost set to 4, major loss to sender):
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out1, cost = 1)
-> x.high <- setx(z.out1, cost = 4)
-\end{Sinput}
-\end{Schunk}
-Simulate fitted values and first differences:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out1)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/bprobit-BasicExamplePlot}
-\end{center}
-
-
-\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.probit}
-
-Using the sample data \texttt{sanction}, estimate the statistical model,
-with {\tt import} a function of {\tt coop} in the first equation and
-{\tt export} a function of {\tt cost} and {\tt target} in the second equation:
-\begin{Schunk}
-\begin{Sinput}
-> fml2 <- list(mu1 = import ~ coop, mu2 = export ~ cost + target)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> z.out2 <- zelig(fml2, model = "bprobit", data = sanction)
-> summary(z.out2)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their means:
-\begin{Schunk}
-\begin{Sinput}
-> x.out2 <- setx(z.out2)
-\end{Sinput}
-\end{Schunk}
-Simulate draws from the posterior distribution:
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out2, x = x.out2)
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out2)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/bprobit-JointEstimationPlot}
-\end{center}
-
-
-\item Joint Estimation of a Parametrically and Stochastically
-Dependent Model
-\label{pdep.p}
-
-Using the sample data \texttt{sanction}.
-The bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
-some or all explanatory variables, {\it and} the effects of the shared
-explanatory variables are jointly estimated. For example,
-\begin{Schunk}
-\begin{Sinput}
-> fml3 <- list(mu1 = import ~ tag(coop, "coop") + tag(cost, "cost") +
-+ tag(target, "target"), mu2 = export ~ tag(coop, "coop") +
-+ tag(cost, "cost") + tag(target, "target"))
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> z.out3 <- zelig(fml3, model = "bprobit", data = sanction)
-> summary(z.out3)
-\end{Sinput}
-\end{Schunk}
-
-Note that this model only returns one parameter estimate for each of
-{\tt coop}, {\tt cost}, and {\tt target}. Contrast this to
-Example~\ref{basic.bp} which returns two parameter estimates for each
-of the explanatory variables. \newline \newline Set values for the
-explanatory variables:
-\begin{Schunk}
-\begin{Sinput}
-> x.out3 <- setx(z.out3, cost = 1:4)
-\end{Sinput}
-\end{Schunk}
-Draw simulated expected values:
-\begin{Schunk}
-\begin{Sinput}
-> s.out3 <- sim(z.out3, x = x.out3)
-> summary(s.out3)
-\end{Sinput}
-\end{Schunk}
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-For each observation, define two binary dependent variables, $Y_1$ and
-$Y_2$, each of which take the value of either 0 or 1 (in the
-following, we suppress the observation index $i$). We model the joint
-outcome $(Y_1$, $Y_2)$ using two marginal probabilities for each
-dependent variable, and the correlation parameter, which describes how
-the two dependent variables are related.
-%Define $Y_{rs}$ such that it
-%is equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise where $r$
-%and $s$ take a value of either 0 or 1. Then, the model is defined as
-%follows,
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by two latent (unobserved)
- continuous variables which follow the bivariate Normal distribution:
-\begin{eqnarray*}
- \left ( \begin{array}{c}
- Y_1^* \\
- Y_2^*
- \end{array}
- \right ) &\sim &
- N_2 \left \{ \left (
- \begin{array}{c}
- \mu_1 \\ \mu_2
- \end{array} \right ), \left( \begin{array}{cc}
- 1 & \rho \\
- \rho & 1
- \end{array} \right) \right\},
-\end{eqnarray*}
-where $\mu_j$ is a mean for $Y_j^*$ and $\rho$ is a scalar correlation
-parameter. The following observation mechanism links the observed
-dependent variables, $Y_j$, with these latent variables
-\begin{eqnarray*}
-Y_j & = & \left \{ \begin{array}{cc}
- 1 & {\rm if} \; Y_j^* \ge 0, \\
- 0 & {\rm otherwise.}
- \end{array}
- \right.
-\end{eqnarray*}
-
-%Alternatively, the stochastic component for the observed dependent
-%variables can be written as
-%\begin{eqnarray*}
-% Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
-% Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
-% Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
-%\end{eqnarray*}
-%where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
-%$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$. Each of these joint
-%probabilities is modeled using the bivariate normal cumulative
-%distribution function.
-
-\item The \emph{systemic components} for each observation are
- \begin{eqnarray*}
- \mu_j & = & x_{j} \beta_j \quad {\rm for} \quad j=1,2, \\
- \rho & = & \frac{\exp(x_3 \beta_3) - 1}{\exp(x_3 \beta_3) + 1}.
-\end{eqnarray*}
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-For $n$ simulations, expected values form an $n \times 4$
-matrix.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the binomial probit model
- are the predicted joint probabilities. Simulations of $\beta_1$,
- $\beta_2$, and $\beta_3$ (drawn form their sampling distributions)
- are substituted into the systematic components, to find simulations
- of the predicted joint probabilities $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$:
-\begin{eqnarray*}
-\pi_{11} &= \Pr(Y_1^* \geq 0 , Y_2^* \geq 0) &= \int_0^{\infty}
-\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^* \\
-\pi_{10} &= \Pr(Y_1^* \geq 0 , Y_2^* < 0) &= \int_0^{\infty}
-\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
-\pi_{01} &= \Pr(Y_1^* < 0 , Y_2^* \geq 0) &= \int_{-\infty}^{0}
-\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
-\pi_{11} &= \Pr(Y_1^* < 0 , Y_2^* < 0) &= \int_{-\infty}^{0}
-\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
-\end{eqnarray*}
-where $r$ and $s$ may take a value of either 0 or 1, $\phi_2$ is the
-bivariate Normal density.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the
- multinomial distribution given the expected joint probabilities.
-
-\item The first difference ({\tt qi\$fd}) in each of the predicted joint
- probabilities are given by
- $$\textrm{FD}_{rs} = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s
- \mid x).$$
-
-\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
- probabilities are given by
-\begin{equation*}
-\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}.
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
- E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
- \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 $E[Y_{ij}(t_i=0)]$,
- 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 $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
- \widehat{Y_{ij}(t_i=0)}\right\} \textrm{ for } j = 1,2,
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating
- $\widehat{Y_{ij}(t_i=0)}$, 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 $t_i=0$.
-
-\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 = "bprobit", 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
-obtain a default summary of information through
-\texttt{summary(z.out)}. Other elements available through the {\tt
- \$} operator are listed below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times 3$ matrix of the linear
- predictors $x_j \beta_j$.
- \item {\tt residuals}: an $n \times 3$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.
-\end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays indexed by simulation
- $\times$ quantity $\times$ {\tt x}-observation (for more than one
- {\tt x}-observation; otherwise the quantities are matrices). Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values (joint predicted
- probabilities) for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
- distribution defined by the joint predicted probabilities.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio in the predicted
- probabilities for given {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The bivariate probit function is part of the VGAM package by Thomas Yee.
-Please cite this model as:
-\begin{verse}
-\bibentry{YeeHas03}.
-\end{verse}
-
-In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM package. Additional documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
-
-
-
-
-
diff --git a/inst/doc/commands/describe.mymodel.tex b/inst/doc/commands/describe.mymodel.tex
deleted file mode 100644
index e2d4705..0000000
--- a/inst/doc/commands/describe.mymodel.tex
+++ /dev/null
@@ -1,206 +0,0 @@
-\section{{\tt describe}: Describe a model's systematic and stochastic
-parameters}
-\label{describe.mymodel}
-
-\subsubsection{Description}
-
-In order to use {\tt parse.formula()}, {\tt parse.par()}, and the {\tt
-model.*.multiple()} commands, you must write a {\tt describe.mymodel()}
-function where {\tt mymodel} is the name of your modeling function.
-(Hence, if your function is called {\tt normal.regression()}, you need
-to write a {\tt describe.normal.regression()} function.) Note that
-{\tt describe()} is \emph{not} a generic function, but is called by
-{\tt parse.formula(\dots, model = "mymodel")} using a combination of
-{\tt paste()} and {\tt exists()}. You will never need to call {\tt
-describe.mymodel()} directly, since it will be called from {\tt
-parse.formula()} as that function checks the user-input formula or
-list of formulas.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-describe.mymodel()
-\end{verbatim}
-
-\subsubsection{Arguments}\label{categories}
-The {\tt describe.mymodel()} function takes no arguments.
-
-\subsubsection{Output Values}
-The {\tt describe.mymodel()} function returns a list with the
-following information:
-\begin{itemize}
-\item {\tt category}: a character string, consisting of one of the
-following:
-\begin{itemize}
-\item {\tt "continuous"}: the dependent variable is continuous, numeric, and
-unbounded (e.g., normal regression), but may be censored with an associated censoring
-indicator (e.g., tobit regression).
-\item {\tt "dichotomous"}: the dependent variable takes two discrete integer
-values, usually 0 and 1 (e.g., logistic regression).
-\item {\tt "ordinal"}: the dependent variable is an ordered factor
-response, taking 3 or more discrete values which are arranged in an
-ascending or descending manner (e.g., ordered logistic regression).
-\item {\tt "multinomial"}: the dependent variable is an unordered
-factor response, taking 3 or more discrete values which are arranged
-in no particular order (e.g., multinomial logistic regression).
-\item {\tt "count"}: the dependent variable takes integer values
-greater than or equal to 0 (e.g., Poisson regression).
-\item {\tt "bounded"}: the dependent variable is a continuous numeric variable, that
-is restricted (bounded within) some range (e.g., $(0, \infty)$). The variable may
-also be censored either on the left and/or right side, with an associated censoring
-indicator (e.g., Weibull regression).
-\item {\tt "mixed"}: the dependent variables are a mix of the above
-categories (usually applies to multiple equation models).
-\end{itemize}
-Selecting the category is particularly important since it sets certain
-interface parameters for the entire GUI.
-
-\item {\tt package}: (optional) a list with the following elements
-
- \begin{itemize}
-
- \item {\tt name}: a characters string with the name of the package
- containing the {\tt mymodel()} function.
-
- \item {\tt version}: the minimum version number that works with
- Zelig.
-
- \item {\tt CRAN}: if the package is not hosted on CRAN mirrors,
- provide the URL here as a character string. You should be able
- to install your package from this URL using {\tt name}, {\tt
-version}, and {\tt CRAN}:
-\begin{verbatim}
-install.packages(name, repos = CRAN, installWithVers = TRUE)
-\end{verbatim}
-By default, {\tt CRAN = "http://cran.us.r-project.org/"}.
-\end{itemize}
-
-\item {\tt parameters}: For each systematic and stochastic parameter
-(or set of parameters) in your model, you should create a list (named
-after the parameters as given in your model's notation, e.g., {\tt
-mu}, {\tt sigma}, {\tt theta}, etc.; not literally {\tt myparameter})
-with the following information:
-\begin{itemize}
-
-\item {\tt equations}: an integer number of equations for the
-parameter. For parameters that can take an undefined number of
-equations (for example in seemingly unrelated regression), use {\tt
-c(2, Inf)} or {\tt c(2, 999)} to indicate that the parameter can take
-a minimum of two equations up to a theoretically infinite number of
-equations.
-
-\item {\tt tagsAllowed}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a given parameter allows constraints. If there is
-only one equation for a parameter (for example, {\tt mu} for the
-normal regression model has {\tt equations = 1}), then {\tt
-tagsAllowed = FALSE} by default. If there are two or more equations
-for the parameter (for example, {\tt mu} for the bivariate probit
-model has {\tt equations = 2}), then {\tt tagsAllowed = TRUE} by
-default.
-
-\item {\tt depVar}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a parameter requires a corresponding dependent
-variable.
-
-\item {\tt expVar}: a logical value ({\tt TRUE}/{\tt FALSE})
-specifying whether a parameter allows explanatory variables. If {\tt
-depVar = TRUE} and {\tt expVar = TRUE}, we call the parameter a
-``systematic component'' and {\tt parse.formula()} will fail if
-formula(s) are not specified for this parameter. If {\tt
-depVar = FALSE} and {\tt expVar = TRUE}, the parameter is estimated as
-a scalar ancillary parameter, with default formula \verb|~ 1|, if the
-user does not specify a formula explicitly. If {\tt depVar = FALSE}
-and {\tt expVar = FALSE}, the parameter can only be estimated as a
-scalar ancillary parameter.
-
-\item {\tt specialFunction}: (optional) a character string giving the
-name of a function that appears on the left-hand side of the formula.
-Options include {\tt "Surv"}, {\tt "cbind"}, and {\tt "as.factor"}.
-
-\item {\tt varInSpecial}: (optional) a scalar or vector giving the
-number of variables taken by the {\tt specialFunction}. For example,
-{\tt Surv()} takes a minimum of 2 arguments, and a maximum of 4
-arguments, which is represented as {\tt c(2, 4)}.
-
-\end{itemize}
-If you have more than one parameter (or set of parameters) in
-your model, you will need to produce a {\tt myparameter} list for each
-one. See examples below for details.
-\end{itemize}
-
-\subsubsection{Examples}
-For a Normal regression model with mean {\tt mu} and scalar variance
-parameter {\tt sigma2}, the minimal {\tt describe.*()} function is as
-follows:
-\begin{verbatim}
-describe.normal.regression <- function() {
- category <- "continuous"
- mu <- list(equations = 1, # Systematic component
- tagsAllowed = FALSE,
- depVar = TRUE,
- expVar = TRUE)
- sigma2 <- list(equations = 1, # Scalar ancillary parameter
- tagsAllowed = FALSE,
- depVar = FALSE,
- expVar = FALSE)
- pars <- list(mu = mu, sigma2 = sigma2)
- model <- list(category = category, parameters = pars)
-}
-\end{verbatim}
-See \Sref{normal.regression} for full code to execute this model from
-scratch in R with Zelig.
-
-Now consider a bivariate probit model with parameter vector {\tt mu} and
-correlation parameter {\tt rho} (which may or may not take explanatory
-variables). Since the bivariate probit function uses the {\tt pmvnorm()}
-function from the mvtnorm library, we list this under {\tt package}.
-\begin{verbatim}
-describe.bivariate.probit <- function() {
- category <- "dichotomous"
- package <- list(name = "mvtnorm",
- version = "0.7")
- mu <- list(equations = 2, # Systematic component
- tagsAllowed = TRUE,
- depVar = TRUE,
- expVar = TRUE)
- rho <- list(equations = 1, # Optional systematic component
- tagsAllowed = FALSE, # Estimated as an ancillary
- depVar = FALSE, # parameter by default
- expVar = TRUE)
- pars <- list(mu = mu, rho = rho)
- list(category = category, package = package, parameters = pars)
-}
-\end{verbatim}
-See \Sref{bivariate.probit} for the full code to write this model from
-scratch in R with Zelig.
-
-For a multinomial logit model, which takes an undefined number of
-equations (corresponding to each level in the response variable):
-\begin{verbatim}
-describe.multinomial.logit <- function() {
- category <- "multinomial"
- mu <- list(equations = c(1, Inf),
- tagsAllowed = TRUE,
- depVAR = TRUE,
- expVar = TRUE,
- specialFunction <- "as.factor",
- varInSpecial <- c(1, 1))
- list(category = category, parameters = list(mu = mu))
-}
-\end{verbatim}
-(This example does not have corresponding executable sample code.)
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{s:new} for an overview of how the {\tt describe.*()}
-function works with {\tt parse.formula()}.
-\item \Sref{parse.formula} for information on {\tt parse.formula()}.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/matchit.tex b/inst/doc/commands/matchit.tex
deleted file mode 100644
index d8d38f2..0000000
--- a/inst/doc/commands/matchit.tex
+++ /dev/null
@@ -1,29 +0,0 @@
-\section{{\tt matchit}: Create matched data}\label{matchit}
-
-\subsubsection{Description}
-
-\hlink{MatchIt}{http://gking.harvard.edu/matchit/} implements the
-suggestions of \cite{HoImaKin05b} for improving parametric statistical
-models by preprocessing data with semi-parametric matching methods. It
-uses a sophisticated array of matching methods to select well-matched
-treated and control units from the original data set, thus reducing
-the dependence of causal inferences on functional form and other
-parametric assumptions. After pre-processing, MatchIt output can be
-used just like any other dataset in Zelig to estimate causal effects.
-In this way, MatchIt improves rather than replaces existing parametric
-models, reducing sensitivity to modeling assumptions. The matching
-methods available in
-\hlink{MatchIt}{http://gking.harvard.edu/matchit/} include exact
-matching on all covariates, nearest neighbor matching,
-subclassification, optimal matching, genetic matching, and full
-matching. An outline of all options are provided below; see the full
-documentation (available at
-\hlink{\url{http://gking.harvard.edu/matchit/}}{http://gking.harvard.edu/matchit/})
-for more details.
-
-\input{commands/matchitref}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "zelig"
-%%% End:
diff --git a/inst/doc/commands/matchitref.tex b/inst/doc/commands/matchitref.tex
deleted file mode 100644
index bed2500..0000000
--- a/inst/doc/commands/matchitref.tex
+++ /dev/null
@@ -1,390 +0,0 @@
-\subsubsection{Syntax}
-\begin{verbatim}
-> m.out <- matchit(formula, data, method = "nearest", verbose = FALSE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\paragraph{Arguments for All Matching Methods}
-
-\begin{itemize}
-
-\item \texttt{formula}: formula used to calculate the distance measure
- for matching. It takes the usual syntax of R formulas, {\tt treat
- \~\ x1 + x2}, where {\tt treat} is a binary treatment indicator,
- and {\tt x1} and {\tt x2} are the pre-treatment covariates. Both the
- treatment indicator and pre-treatment covariates must be contained
- in the same data frame, which is specified as {\tt data} (see
- below). All of the usual R syntax for formulas work here. For
- example, {\tt x1:x2} represents the first order interaction term
- between {\tt x1} and {\tt x2}, and {\tt I(x1 \^\ 2)} represents the
- square term of {\tt x1}. See {\tt help(formula)} for details.
-
-\item \texttt{data}: the data frame containing the variables called in
- {\tt formula}.
-
-\item \texttt{method}: the matching method (default =
- \texttt{"nearest"}, nearest neighbor matching). Currently,
- \texttt{"exact"} (exact matching), \texttt{"full"} (full matching),
- \texttt{"nearest"} (nearest neighbor matching), \texttt{"optimal"}
- (optimal matching), \texttt{"subclass"} (subclassification), and
- \texttt{"genetic"} (genetic matching) are available. Note that
- within each of these matching methods, \MatchIt\ offers a variety of
- options. See below for more details.
-
-\item \texttt{verbose}: a logical value indicating whether to print
- the status of the matching algorithm (default = \texttt{FALSE}).
-\end{itemize}
-
-
-\paragraph{Additional Arguments for Specification of
- Distance Measures}
-\label{subsubsec:inputs-all}
-
-The following arguments specify distance measures that are used for
-matching methods. These arguments apply to all matching methods {\it
- except exact matching}.
-
-\begin{itemize}
-
-\item \texttt{distance}: the method used to estimate the distance
- measure (default = {\tt "logit"}, logistic regression) or a
- numerical vector of user's own distance measure. Before using any
- of these techniques, it is best to understand the theoretical
- groundings of these techniques and to evaluate the results. Most of
- these methods (such as logistic or probit regression) estimate the
- propensity score, defined as the probability of receiving treatment,
- conditional on the covariates. Available methods include:
- \begin{itemize}
- \item {\tt "mahalanobis"}: the Mahalanobis distance measure.
- \item binomial generalized linear models with one of the following
- link functions:
- \begin{itemize}
- \item \texttt{"logit"}: logistic link
- \item {\tt "linear.logit"}: logistic link with linear propensity
- score)\footnote{The linear propensity scores are obtained by
- transforming back onto a linear scale.}
- \item \texttt{"probit"}: probit link
- \item {\tt "linear.probit"}: probit link with linear propensity
- score
- \item {\tt "cloglog"}: complementary log-log link
- \item {\tt "linear.cloglog"}: complementary log-log link with linear
- propensity score
- \item {\tt "log"}: log link
- \item {\tt "linear.log"}: log link with linear propensity score
- \item {\tt "cauchit"} Cauchy CDF link
- \item {\tt "linear.cauchit"} Cauchy CDF link with linear propensity
- score
- \end{itemize}
- \item Choose one of the following generalized additive models (see
- {\tt help(gam)} for more options).
- \begin{itemize}
- \item \texttt{"GAMlogit"}: logistic link
- \item {\tt "GAMlinear.logit"}: logistic link with linear propensity
- score
- \item \texttt{"GAMprobit"}: probit link
- \item {\tt "GAMlinear.probit"}: probit link with linear propensity
- score
- \item {\tt "GAMcloglog"}: complementary log-log link
- \item {\tt "GAMlinear.cloglog"}: complementary log-log link with
- linear propensity score
- \item {\tt "GAMlog"}: log link
- \item {\tt "GAMlinear.log"}: log link with linear propensity score,
- \item {\tt "GAMcauchit"}: Cauchy CDF link
- \item {\tt "GAMlinear.cauchit"}: Cauchy CDF link with linear
- propensity score
-\end{itemize}
-\item \texttt{"nnet"}: neural network model. See {\tt help(nnet)} for
- more options.
-\item \texttt{"rpart"}: classification trees. See {\tt help(rpart)}
- for more options.
- \end{itemize}
-
-\item \texttt{distance.options}: optional arguments for estimating the
- distance measure. The input to this argument should be a list. For
- example, if the distance measure is estimated with a logistic
- regression, users can increase the maximum IWLS iterations by
- \texttt{distance.options = list(maxit = 5000)}. Find additional
- options for general linear models using {\tt help(glm)} or {\tt
- help(family)}, for general additive models using {\tt help(gam)},
- for neutral network models {\tt help(nnet)}, and for classification
- trees {\tt help(rpart)}.
-
-\item \texttt{discard}: specifies whether to discard units that fall
- outside some measure of support of the distance measure (default =
- \texttt{"none"}, discard no units). Discarding units may change the
- quantity of interest being estimated. Enter a logical vector
- indicating which unit should be discarded or choose from the
- following options:
- \begin{itemize}
- \item \texttt{"none"}: no units will be discarded before matching.
- Use this option when the units to be matched are substantially
- similar, such as in the case of matching treatment and control
- units from a field experiment that was close to (but not fully)
- randomized (e.g., \citealt{Imai05}), when caliper matching will
- restrict the donor pool, or when you do not wish to change the
- quantity of interest and the parametric methods to be used
- post-matching can be trusted to extrapolate.
- \item \texttt{"hull.both"}: all units that are not within the convex
- hull will be discarded. We recommend that this option be used
- with observational data sets.
- \item \texttt{"both"}: all units (treated and control) that are
- outside the support of the distance measure will be discarded.
- \item \texttt{"hull.control"}: only control units that are not
- within the convex hull of the treated units will be discarded.
- \item \texttt{"control"}: only control units outside the support of
- the distance measure of the treated units will be discarded. Use
- this option when the average treatment effect on the treated is of
- most interest and when you are unwilling to discard
- non-overlapping treatment units (which would change the quantity
- of interest).
- \item \texttt{"hull.treat"}: only treated units that are not within
- the convex hull of the control units will be discarded.
- \item \texttt{"treat"}: only treated units outside the support of
- the distance measure of the control units will be discarded. Use
- this option when the average treatment effect on the control units
- is of most interest and when unwilling to discard control units.
- \end{itemize}
-
-\item \texttt{reestimate}: If {\tt FALSE} (default), the model for the
- distance measure will not be re-estimated after units are discarded.
- The input must be a logical value. Re-estimation may be desirable
- for efficiency reasons, especially if many units were discarded and
- so the post-discard samples are quite different from the original
- samples.
-
-\end{itemize}
-
-\paragraph{Additional Arguments for Subclassification}
-\label{subsubsec:inputs-subclass}
-
-\begin{itemize}
-\item \texttt{sub.by}: criteria for subclassification. Choose from:
- \texttt{"treat"} (default), the number of treatment units;
- \texttt{"control"}, the number of control units; or \texttt{"all"},
- the total number of units.
-\item \texttt{subclass}: either a scalar specifying the number of
- subclasses, or a vector of probabilities bounded between 0 and 1,
- which create quantiles of the distance measure using the units in
- the group specified by \texttt{sub.by} (default = \texttt{subclass =
- 6}).
-\end{itemize}
-
-\paragraph{Additional Arguments for Nearest Neighbor Matching}
-\label{subsubsec:inputs-nearest}
-
-\begin{itemize}
-\item \texttt{m.order}: the order in which to match treatment units
- with control units.
- \begin{itemize}
- \item {\tt "largest"} (default): matches from the largest value of
- the distance measure to the smallest.
- \item {\tt "smallest"}: matches from the smallest value of the
- distance measure to the largest.
- \item {\tt "random"}: matches in random order.
- \end{itemize}
-\item \texttt{replace}: logical value indicating whether each control
- unit can be matched to more than one treated unit (default = {\tt
- replace = FALSE}, each control unit is used at most once -- i.e.,
- sampling without replacement). For matching with replacement, use
- \texttt{replace = TRUE}.
-\item \texttt{ratio}: the number of control units to match to each
- treated unit (default = {\tt 1}). If matching is done without
- replacement and there are fewer control units than {\tt ratio} times
- the number of eligible treated units (i.e., there are not enough
- control units for the specified method), then the higher ratios will
- have \texttt{NA} in place of the matching unit number in
- \texttt{match.matrix}.
-\item \texttt{exact}: variables on which to perform exact matching
- within the nearest neighbor matching (default = {\tt NULL}, no exact
- matching). If \texttt{exact} is specified, only matches that
- exactly match on the covariates in \texttt{exact} will be allowed.
- Within the matches that match on the variables in \texttt{exact},
- the match with the closest distance measure will be chosen.
- \texttt{exact} should be entered as a vector of variable names
- (e.g., \texttt{exact = c("X1", "X2")}).
-\item \texttt{caliper}: the number of standard deviations of the
- distance measure within which to draw control units (default = {\tt
- 0}, no caliper matching). If a caliper is specified, a control
- unit within the caliper for a treated unit is randomly selected as
- the match for that treated unit. If \texttt{caliper != 0}, there
- are two additional options:
- \begin{itemize}
- \item \texttt{calclosest}: whether to take the nearest available
- match if no matches are available within the \texttt{caliper}
- (default = {\tt FALSE}).
- \item \texttt{mahvars}: variables on which to perform
- Mahalanobis-metric matching within each caliper (default = {\tt
- NULL}). Variables should be entered as a vector of variable
- names (e.g., \texttt{mahvars = c("X1", "X2")}). If
- \texttt{mahvars} is specified without \texttt{caliper}, the
- caliper is set to 0.25.
- \end{itemize}
-\item \texttt{subclass} and \texttt{sub.by}: See the options for
- subclassification for more details on these options. If a
- \texttt{subclass} is specified within \texttt{method = "nearest"},
- the matched units will be placed into subclasses after the nearest
- neighbor matching is completed.
-\end{itemize}
-
-\paragraph{Additional Arguments for Optimal Matching}
-\label{subsubsec:inputs-optimal}
-
-\begin{itemize}
-\item {\tt ratio}: the number of control units to be matched to each
- treatment unit (default = {\tt 1}).
-\item {\tt ...}: additional inputs that can be passed to the {\tt
- fullmatch()} function in the {\tt optmatch} package. See {\tt
- help(fullmatch)} or
- \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html}
- for details.
-\end{itemize}
-
-\paragraph{Additional Arguments for Full Matching}
-\label{subsubsec:inputs-full}
-
-\begin{itemize}
-\item {\tt ...}: additional inputs that can be passed to the {\tt
- fullmatch()} function in the {\tt optmatch} package. See {\tt
- help(fullmatch)} or
- \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html} for details.
-\end{itemize}
-
-\paragraph{Additional Arguments for Genetic Matching}
-\label{subsubsec:inputs-genetic}
-
-The available options are listed below.
-\begin{itemize}
-\item {\tt ratio}: the number of control units to be matched to each
- treatment unit (default = {\tt 1}).
-\item {\tt ...}: additional minor inputs that can be passed to the
- {\tt GenMatch()} function in the {\tt Matching} package. See {\tt
- help(GenMatch)} or
- \hlink{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}
- for details.
-\end{itemize}
-
-\subsubsection{Output Values}
-\label{sec:outputs}
-
-Regardless of the type of matching performed, the \texttt{matchit}
-output object contains the following elements:\footnote{When
- inapplicable or unnecessary, these elements may equal {\tt NULL}.
- For example, when exact matching, {\tt match.matrix = NULL}.}
-
-\begin{itemize}
-\item \texttt{call}: the original {\tt matchit()} call.
-
-\item \texttt{formula}: the formula used to specify the model for
- estimating the distance measure.
-
-\item \texttt{model}: the output of the model used to estimate
- the distance measure. \texttt{summary(m.out\$model)} will give the
- summary of the model where \texttt{m.out} is the output object from
- \texttt{matchit()}.
-
-\item \texttt{match.matrix}: an $n_1 \times$ \texttt{ratio} matrix
- where:
- \begin{itemize}
- \item the row names represent the names of the treatment units
- (which match the row names of the data frame specified in
- \texttt{data}).
- \item each column stores the name(s) of the control unit(s) matched
- to the treatment unit of that row. For example, when the
- \texttt{ratio} input for nearest neighbor or optimal matching is
- specified as 3, the three columns of \texttt{match.matrix}
- represent the three control units matched to one treatment unit).
- \item \texttt{NA} indicates that the treatment unit was not matched.
- \end{itemize}
-
-\item \texttt{discarded}: a vector of length $n$ that displays whether
- the units were ineligible for matching due to common support
- restrictions. It equals \texttt{TRUE} if unit $i$ was discarded,
- and it is set to \texttt{FALSE} otherwise.
-
-\item \texttt{distance}: a vector of length $n$ with the estimated
- distance measure for each unit.
-
-\item \texttt{weights}: a vector of length $n$ with the weights
- assigned to each unit in the matching process. Unmatched units have
- weights equal to $0$. Matched treated units have weight $1$. Each
- matched control unit has weight proportional to the number of
- treatment units to which it was matched, and the sum of the control
- weights is equal to the number of uniquely matched control units.
-
-\item \texttt{subclass}: the subclass index in an ordinal scale from 1
- to the total number of subclasses as specified in \texttt{subclass}
- (or the total number of subclasses from full or exact matching).
- Unmatched units have \texttt{NA}.
-
-\item \texttt{q.cut}: the subclass cut-points that classify the
- distance measure.
-
-\item \texttt{treat}: the treatment indicator from \texttt{data} (the
- left-hand side of \texttt{formula}).
-
-\item \texttt{X}: the covariates used for estimating the distance
- measure (the right-hand side of \texttt{formula}). When applicable,
- \texttt{X} is augmented by covariates contained in \texttt{mahvars}
- and \texttt{exact}.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-If you use \MatchIt, please cite
-\begin{verse}
-\bibentry{HoImaKin07}
-and
-\bibentry{HoImaKin05b}
-\end{verse}
-
-The {\tt convex.hull} discard option is implemented via the {\tt
- WhatIf} package. If you use this option, please cite
-\begin{verse}
-\bibentry{StoKinZen06}
-and either
-\bibentry{KinZen06a}
-or
-\bibentry{KinZen06b}
-\end{verse}
-
-Generalized linear distance measures are implemented via the {\tt
-stats} package. If you use this distance measure, please cite
-\begin{verse}
-\bibentry{VenRip02}
-\end{verse}
-
-Generalized additive distance measures are implemented via the {\tt
- mcgv} package. If you use this distance measure, please cite
-\begin{verse}
-\bibentry{HasTib90}
-\end{verse}
-
-The neural network distance measure is implemented via the {\tt nnet}
-package. If you use this distance measure, please cite
-\begin{verse}
-\bibentry{Ripley96}
-\end{verse}
-
-The classification trees distance measure is implemented via the {\tt
- rpart} package. If you use this distance measure, please cite
-\begin{verse}
-\bibentry{BreFriOls84}
-\end{verse}
-
-Full and optimal matching are implemented via the {\tt optmatch}
-package by Ben Hansen. If you use either of these methods, please cite
-\begin{verse}
-\bibentry{Hansen04}
-\end{verse}
-
-Genetic matching is implemented via the {\tt Matching} package by
-Jasjeet Sekhon. If you use this method, please cite
-\begin{verse}
-\bibentry{DiaSek05}
-\end{verse}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "matchit"
-%%% End:
diff --git a/inst/doc/commands/mi.tex b/inst/doc/commands/mi.tex
deleted file mode 100644
index 7d2ecbc..0000000
--- a/inst/doc/commands/mi.tex
+++ /dev/null
@@ -1,36 +0,0 @@
-\section{{\tt mi}: Create a list of multiply imputed data frames}
-\label{mi.command}
-
-\subsubsection{Description}
-
-Use {\tt mi()} to create a list of multiply imputed data frames for
-use with {\tt zelig()}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-mi(data1, data2, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt data1}: the first multiply imputed data frame.
-\item {\tt data2}: the second multiply imputed data frame.
-\item {\tt \dots}: additional multiply imputed data frames.
-\end{itemize}
-
-\subsubsection{Output Values}
-A list of data frames with class {\tt "mi"}
-\subsubsection{See Also}
-\begin{itemize}
-\item See \Sref{mi.ex} to use {\tt mi()} with {\tt zelig()}.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/model.end.aux b/inst/doc/commands/model.end.aux
deleted file mode 100644
index de40dad..0000000
--- a/inst/doc/commands/model.end.aux
+++ /dev/null
@@ -1,44 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.2}{\tt model.end}: Cleaning up after optimization}{339}{section.13.2}}
-\newlabel{model.end}{{13.2}{339}{{\tt model.end}: Cleaning up after optimization\relax }{section.13.2}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{339}{section*.406}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{339}{section*.407}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{339}{section*.408}}
-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{339}{section*.409}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{339}{section*.410}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{339}{section*.411}}
-\@setckpt{commands/model.end}{
-\setcounter{page}{340}
-\setcounter{equation}{0}
-\setcounter{enumi}{2}
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diff --git a/inst/doc/commands/model.end.tex b/inst/doc/commands/model.end.tex
deleted file mode 100644
index 3433a60..0000000
--- a/inst/doc/commands/model.end.tex
+++ /dev/null
@@ -1,49 +0,0 @@
-\section{{\tt model.end}: Cleaning up after optimization}
-\label{model.end}
-
-\subsubsection{Description}
-
-The {\tt model.end()} function creates a list of regression output from
-{\tt optim()} output. The list includes coefficients (from the {\tt optim()
-par} output), a variance-covariance matrix (from the {\tt optim()} Hessian output),
-and any terms, contrasts, or xlevels (from the model frame). Use {\tt
-model.end()} after calling {\tt optim()}, but before assigning a class to the
-regression output.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-model.end(res, mf)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt res}: the output from {\tt optim()} or another fitting-algorithm.
-\item {\tt mf}: the model frame output by {\tt model.frame()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-A list of regression output, including:
-\begin{itemize}
-\item {\tt coefficients}: the optimized parameters.
-\item {\tt variance}: the variance-covariance matrix (the negative
- inverse of the Hessian matrix returned from the optimization
- procedure).
-\item {\tt terms}: the terms object. See {\tt help(terms.object)}
- for more information.
-\item {\tt \dots}: additional elements passed from {\tt res}.
-\end{itemize}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{s:new} for an overview of how to write a new model.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/model.frame.multiple.aux b/inst/doc/commands/model.frame.multiple.aux
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-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.3}{\tt model.frame.multiple}: Extracting the ``environment'' of a model formula}{340}{section.13.3}}
-\newlabel{model.frame.multiple}{{13.3}{340}{{\tt model.frame.multiple}: Extracting the ``environment'' of a model formula\relax }{section.13.3}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{340}{section*.412}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{340}{section*.413}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{340}{section*.414}}
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-\@writefile{toc}{\contentsline {subsubsection}{See Also}{341}{section*.417}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{341}{section*.418}}
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diff --git a/inst/doc/commands/model.frame.multiple.tex b/inst/doc/commands/model.frame.multiple.tex
deleted file mode 100644
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--- a/inst/doc/commands/model.frame.multiple.tex
+++ /dev/null
@@ -1,80 +0,0 @@
-\section{{\tt model.frame.multiple}: Extracting the ``environment'' of
-a model formula}
-\label{model.frame.multiple}
-
-\subsubsection{Description}
-Use {\tt model.frame.multiple()} after {\tt parse.par()} to create a
-data frame of the unique variables identified in the formula (or list
-of formulas).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-model.frame.multiple(formula, data, eqn = NULL, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
- \item {\tt formula}: a list of formulas of class {\tt "multiple"},
-returned from {\tt parse.par()}.
- \item {\tt data}: a data frame containing all the variables used in
-{\tt formula}.
-\item {\tt eqn}: an optional character string or vector of character strings
-specifying the equations for which you would like to extract variables. Defaults
-to {\tt NULL}, which pulls out all the variables for all equations in {\tt
-formula}.
- \item {\tt \dots}: additional arguments passed to {\tt
-model.frame.default()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output is a data frame (with a terms attribute) containing all the
-unique explanatory and response variables identified in the list of
-formulas. By default, missing ({\tt NA}) values are listwise deleted.
-
-If {\tt as.factor()} appears on the left-hand side, the response
-variables will be returned as an indicator (0/1) matrix with columns
-corresponding to the unique levels in the factor variable.
-
-If any formula contains a {\tt tag()}, {\tt model.frame.multiple()}
-will return the original variable in the data frame and use the {\tt
-tag()} information in the terms attribute only.
-
-\subsubsection{Examples}
-\begin{verbatim}
-formulae <- list(import ~ coop + cost + target,
- export ~ coop + cost + target)
-fml <- parse.formula(formulae, model = "bivariate.logit")
-D <- model.frame(fml, data = mydata)
-\end{verbatim}
-Since the output from {\tt parse.formula()} is of class {\tt
-"multiple"}, you do not need to call {\tt model.frame.multiple()}
-explicitly, but can use the generic {\tt model.frame()} instead.
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{parse.formula} for {\tt parse.formula()}
-\item \Sref{ui} for an overview of the user-interface.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/model.matrix.multiple.aux b/inst/doc/commands/model.matrix.multiple.aux
deleted file mode 100644
index c56268e..0000000
--- a/inst/doc/commands/model.matrix.multiple.aux
+++ /dev/null
@@ -1,45 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.4}{\tt model.matrix.multiple}: Design matrix for multivariate models}{342}{section.13.4}}
-\newlabel{model.matrix.multiple}{{13.4}{342}{{\tt model.matrix.multiple}: Design matrix for multivariate models\relax }{section.13.4}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{342}{section*.419}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{342}{section*.420}}
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-\@writefile{toc}{\contentsline {subsubsection}{Examples}{343}{section*.423}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{343}{section*.424}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{343}{section*.425}}
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diff --git a/inst/doc/commands/model.matrix.multiple.tex b/inst/doc/commands/model.matrix.multiple.tex
deleted file mode 100644
index ec6b9d6..0000000
--- a/inst/doc/commands/model.matrix.multiple.tex
+++ /dev/null
@@ -1,111 +0,0 @@
-\section{{\tt model.matrix.multiple}: Design matrix for multivariate models}
-\label{model.matrix.multiple}
-
-\subsubsection{Description}
-Use {\tt model.matrix.multiple()} after {\tt parse.formula()} to
-create a design matrix for multiple-equation models.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-model.matrix(object, data, shape = "compact", eqn = NULL, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
- \item {\tt object}: the list of formulas output from \texttt{parse.formula()}.
- \item {\tt data}: a data frame created with {\tt model.frame.multiple()}.
- \item {\tt shape}: a character string specifying the shape of the
- outputed matrix. Available options are:
-
-\begin{itemize}
-
-\item {\tt "compact"}: (default) the output matrix will be an $n \times v$,
-where $v$ is the number of unique variables in all of the equations
-(including the intercept term).
-
-\item {\tt "array"}: the output is an $n \times K \times J$ array where $J$ is the
-total number of equations and $K$ is the total number of parameters
-across all the equations. If a variable is not in a certain equation,
-it is observed as a vector of 0s.
-
-\item {\tt "stacked"}: the output will be a $2n \times K$ matrix where $K$ is the total number of parameters across all the equations.
-
-\end{itemize}
-
- \item {\tt eqn}: a character string or a vector of character
- strings identifying the equations from which to construct the design
- matrix. The defaults to {\tt NULL}, which only uses the systematic
- parameters (for which {\tt DepVar = TRUE} in the appropriate {\tt
-describe.model()}) .
-\item {\tt \dots}: additional arguments passed to {\tt model.matrix.default()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-A design matrix or array, depending on the options chosen in {\tt
-shape}, with appropriate terms attributes.
-
-\subsubsection{Examples}
-Let's say that the name of the model is {\tt "bivariate.probit"}, and
-the corresponding describe function is {\tt
-describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
-as systematic components, and an ancillary parameter {\tt rho}, which
-may be parameterized, but is estimated as a scalar by default. Let
-{\tt par} be the parameter vector (including parameters for {\tt
-rho}), {\tt formulae} a user-specified formula given in one of the
-formats in Table \ref{good.formulas}, and {\tt mydata} the user
-specified data frame.
-
-Acceptable combinations of {\tt parse.par()} and {\tt model.matrix()}
-are as follows:
-\begin{verbatim}
-## Setting up
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = mydata)
-terms <- terms(D)
-
-## Intuitive option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
-eta <- X %*% Beta
-
-## Memory-efficient (compact) option (default)
-Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
-eta <- X %*% Beta
-
-## Computationally-efficient (array) option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
-eta <- apply(X, 3, '%*%', Beta)
-\end{verbatim}
-In each case, {\tt eta} is an $n \times 2$ matrix with columns
-corresponding to the linear predictors for {\tt mu1} and {\tt mu2},
-respectively.
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{parse.par} for selecting and shaping parameter vectors.
-\item \Sref{s:new} for examples of how to write new models.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/names.tex b/inst/doc/commands/names.tex
deleted file mode 100644
index 2390432..0000000
--- a/inst/doc/commands/names.tex
+++ /dev/null
@@ -1,57 +0,0 @@
-\section{{\tt names}: Identifying the Structure of Zelig Objects}
-
-\subsubsection{Description}
- The function \texttt{names()} returns the named elements within an
- object. For objects created using \texttt{zelig()} and
- \texttt{sim()}, \texttt{names()} returns nested lists as
- object-level arguments. Use the \texttt{\$} operator to extract
-elements
- from objects.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> names(x) # Lists elements.
-> names(x) <- "value" # Assigns names to x.
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt x}: any R object.
-\item {\tt "value"}: a character vector of up to the same length as
- \texttt{x} (or \texttt{NULL} to clear the names from the object).
-\end{itemize}
-
-\subsubsection{Output Values}
-For the generic function \texttt{names}, \texttt{NULL} or a character
-vector of the same length as \texttt{x}.
-
-\subsubsection{Example}
-Attach sample data:
-\begin{verbatim}
-> data(turnout)
-\end{verbatim}
-Return the variables in the data frame:
-\begin{verbatim}
-> names(turnout)
-\end{verbatim}
-Generate Zelig objects:
-\begin{verbatim}
-> z.out <- zelig(vote ~ age + income, model = "logit", data = turnout)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Extract names from objects of class zelig:
-\begin{verbatim}
-> names(z.out)
-> names(s.out)
-> names(summary(s.out))
-\end{verbatim}
-
-\subsubsection{See Also}
-
-Additional information is available in {\tt help(names)}.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/network.tex b/inst/doc/commands/network.tex
deleted file mode 100644
index 477748f..0000000
--- a/inst/doc/commands/network.tex
+++ /dev/null
@@ -1,49 +0,0 @@
-\subsection{ \texttt{network}: Format Individual Matricies into a Data Frame for Social Network Analysis }\label{network}
-
-\subsubsection{Description}
-This function accepts individual matricies as its inputs, combining
-the input matricies into a single data frame which can then
-be used in the {\tt data} argument for social network analysis (models
-\verb|"netlm"| and \verb|"netlogit"|) in Zelig.\\
-
-\subsubsection{Usage}
-\begin{verbatim}
-network(...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{enumerate}
-\item[...] Sociomatricies. These can be given as named arguments and should be given in the order the in which the user wishes them to appear in the dataframe.
-\end{enumerate}
-
-\subsubsection{Details}
-The \verb|network()| function creates a dataframe which contains
-matricies instead of vectors as its variables. Inputs to the function
-should all be square matricies and can be given as named arguments.
-
-\noindent A matrix within the data frame can be called as a named
-argument with
-\begin{verbatim}
-> data.name$matrix.name
-\end{verbatim} %$
-or numerically with
-\begin{verbatim}
-> data.name[i]
-\end{verbatim}
-
-
-\subsubsection{Example}
-
-Attach sample data (individual matricies from the {\tt sna.ex} dataset):
-\begin{verbatim}
-data(Var1, Var2, Var3, Var4, Var5)
-\end{verbatim}
-
-To make a dataframe formated for social network analysis:
-\begin{verbatim}
-> friendship <- network(Var1, Var2, Var3, Var4, Var5)
-\end{verbatim}
-
-\subsubsection{Contributors}
-This function was written by Skyler J. Cranmer.
diff --git a/inst/doc/commands/parse.formula.aux b/inst/doc/commands/parse.formula.aux
deleted file mode 100644
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--- a/inst/doc/commands/parse.formula.aux
+++ /dev/null
@@ -1,47 +0,0 @@
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-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{345}{section*.432}}
-\@writefile{lot}{\contentsline {table}{\numberline {13.1}{\ignorespaces Examples of acceptable short-hand for user-specified formulas, using bivariate probit as an example}}{346}{table.13.1}}
-\newlabel{good.formulas}{{13.1}{346}{Contributors\relax }{table.13.1}{}}
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diff --git a/inst/doc/commands/parse.formula.tex b/inst/doc/commands/parse.formula.tex
deleted file mode 100644
index 968b1ad..0000000
--- a/inst/doc/commands/parse.formula.tex
+++ /dev/null
@@ -1,113 +0,0 @@
-\section{{\tt parse.formula}: Parsing the inputs}
-\label{parse.formula}
-
-\subsubsection{Description}
-Parse the input formula (or list of formulas) into the standard format
-described below. Since labels for this format will vary by model,
-{\tt parse.formula()} will evaluate a function {\tt describe.model()},
-where {\tt model} is given as an input to {\tt parse.formula()}.
-
-If the {\tt check.model()} function has more than one parameter for
-which {\tt ExpVar = TRUE} and {\tt DepVar = TRUE}, then the
-user-specified equations must have labels to match those parameters,
-else {\tt parse.formula()} should return an error. In addition, if the
-formula entries are not unambiguous, then {\tt parse.formula()} should
-return an error.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> fml <- parse.formula(formula, model, data = NULL)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
- \item {\tt formula}: either a single formula or a list of formula
-objects.
- \item {\tt model}: a character string specifying the name of the model.
- \item {\tt data}: an optional data frame for models that require a
-factor response variable.
-\end{itemize}
-
-\subsubsection{Output Values}
-The output is a list of formula objects with class {\tt ("multiple",
-"list")}. Let's say that the name of the model is {\tt
-"bivariate.probit"}, and the corresponding describe function is {\tt
-describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
-as systematic components, and an ancillary parameter {\tt rho}, which
-may be parameterized, but is estimated as a scalar by default. Given
-this model, Table \ref{good.formulas} gives acceptable user inputs.
-
-\subsubsection{Examples}
-\begin{verbatim}
-formulae <- list(cbind(import, export) ~ coop + cost + target)
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = mydata)
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{ui} for commented examples of how {\tt parse.formula()}
-and {\tt describe.model()} work together.
- \item \Sref{tag} for constraints between coefficients in a multiple
-equation context.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-\clearpage
-\begin{landscape}
-\begin{table}[h!]
-\caption{Examples of acceptable short-hand for user-specified
-formulas, using bivariate probit as an example}
-\label{good.formulas}
-\begin{center}
-\begin{tabular}{lll}
-\\
- & User Input & Output from {\tt parse.formula()} \\
-\hline
-\\
-Same covariates, & \verb|cbind(y1, y2) ~ x1 + x2 * x3| & \verb|list(mu1 = y1 ~ x1 + x2 * x3,|\\
-separate effects & & \verb| mu2 = y2 ~ x1 + x2 * x3,| \\
-& & \verb| rho = ~ 1)| \\
-\\
-With $\rho$ as a & \verb|list(cbind(y1, y2) ~ x1 + x2,| &
-\verb|list(mu1 = y1 ~ x1 + x2,|\\
-systematic equation & \verb| rho = ~ x4 + x5)| & \verb| mu2 = y2 ~ x1 + x2,|\\
-& & \verb| rho = ~ x4 + x5)| \\
-\\
-With constraints & \verb|list(mu1 = y1 ~ x1 + tag(x2, "x2"),| &
-\verb|list(mu1 = y1 ~ x1 + tag(x2, "x2"),| \\
-(same variable) & \verb| mu2 = y2 ~ x3 + tag(x2, "x2"))| &
-\verb| mu2 = y2 ~ x3 + tag(x2, "x2"),|\\
-& & \verb| rho = ~ 1)|\\
-\\
-With constraints & \verb|list(mu1 = y1 ~ x1 + tag(x2, "z1"),| &
-\verb|list(mu1 = y1 ~ x1 + tag(x2, "z1"),| \\
-(different variables) & \verb| mu2 = y2 ~ x3 + tag(x4, "z1"))| &
-\verb| mu2 = y2 ~ x3 + tag(x4, "z1"),|\\
-& & \verb| rho = ~ 1)|\\
-\end{tabular}
-\end{center}
-\end{table}
-\end{landscape}
-
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/parse.par.aux b/inst/doc/commands/parse.par.aux
deleted file mode 100644
index a11ebdb..0000000
--- a/inst/doc/commands/parse.par.aux
+++ /dev/null
@@ -1,45 +0,0 @@
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-\@writefile{toc}{\contentsline {section}{\numberline {13.6}{\tt parse.par}: Select and reshape parameter vectors}{347}{section.13.6}}
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-\@writefile{toc}{\contentsline {subsubsection}{See Also}{348}{section*.438}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{348}{section*.439}}
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diff --git a/inst/doc/commands/parse.par.tex b/inst/doc/commands/parse.par.tex
deleted file mode 100644
index a806b9d..0000000
--- a/inst/doc/commands/parse.par.tex
+++ /dev/null
@@ -1,95 +0,0 @@
-\section{{\tt parse.par}: Select and reshape parameter vectors}\label{parse.par}
-
-\subsubsection{Description}
-
-The {\tt parse.par()} function reshapes parameter vectors for
-compatability with the output matrix from {\tt
-model.matrix.multiple()}. (See\Sref{model.matrix.multiple}.) Use {\tt
-parse.par()} to identify sets of parameters; for example, within
-optimization functions that require vector input, or within {\tt qi()}
-functions that take matrix input of all parameters as a lump.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-parse.par(par, terms, shape = "matrix", eqn = NULL)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt par}: the vector (or matrix) of parameters.
-\item {\tt terms}: the terms from either {\tt model.frame.*()} or
- {\tt model.matrix.*()}.
-\item {\tt shape}: a character string (either {\tt "matrix"} or {\tt "vector"})
-that identifies the type of output structure.
-\item {\tt eqn}: a character string (or strings) that identify the
-parameters that you would like to subset from the larger {\tt par}
-structure.
-\end{itemize}
-
-\subsubsection{Output Values}
-A matrix or vector of the sub-setted (and reshaped) parameters for the specified
-parameters given in {\tt eqn}. By default, {\tt eqn = NULL}, such that all systematic
-components are selected. (Systematic components have {\tt ExpVar =
-TRUE} in the appropriate {\tt describe.model()} function.)
-
-If an ancillary parameter (for which {\tt ExpVar = FALSE} in
-{\tt describe.model()}) is specified in {\tt eqn}, it is
-always returned as a vector (ignoring {\tt shape}). (Ancillary
-parameters are all parameters that have intercept only formulas.)
-
-\subsubsection{Examples}
-Let's say that the name of the model is {\tt "bivariate.probit"}, and
-the corresponding describe function is {\tt
-describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
-as systematic components, and an ancillary parameter {\tt rho}, which
-may be parameterized, but is estimated as a scalar by default. Let
-{\tt par} be the parameter vector (including parameters for {\tt
-rho}), {\tt formulae} a user-specified formula given in one of the
-formats in Table \ref{good.formulas}, and {\tt mydata} the user
-specified data frame.
-
-Acceptable combinations of {\tt parse.par()} and {\tt model.matrix()}
-are as follows:
-\begin{verbatim}
-## Setting up
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = mydata)
-terms <- terms(D)
-
-## Intuitive option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
-eta <- X %*% Beta
-
-## Memory-efficient (compact) option (default)
-Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
-eta <- X %*% Beta
-
-## Computationally-efficient (array) option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
-eta <- apply(X, 3, '%*%', Beta)
-\end{verbatim}
-In each case, {\tt eta} is an $n \times 2$ matrix with columns
-corresponding to the linear predictors for {\tt mu1} and {\tt mu2},
-respectively.
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{model.matrix.multiple} for a description of how multiple
-equation models work with {\tt model.matrix()}.
-\item \Sref{constraints} for more detail on the three combinations
-(intuitive, memory-efficient, and computationally-efficient) methods
-of multiplying matrices of parameters and variables.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/plot.ci.tex b/inst/doc/commands/plot.ci.tex
deleted file mode 100644
index 99ebfd0..0000000
--- a/inst/doc/commands/plot.ci.tex
+++ /dev/null
@@ -1,86 +0,0 @@
-\section{{\tt plot.ci}: Plotting Vertical confidence Intervals}\label{plot.ci}
-
-\subsubsection{Description}
-The \texttt{plot.ci()} command generates vertical confidence intervals
-for linear or generalized linear univariate response models.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> plot.ci(object, CI = 95, qi = "ev", col = c("red", "blue"), ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt object}: stored output from {\tt sim()}. The {\tt x} and optional
- {\tt x1} values used to generate the {\tt sim()} output object must
- have more than one observation.
-\item {\tt CI}: the selected confidence interval. Defaults to 95
- percent.
-\item {\tt qi}: the selected quantity of interest. Defaults to
- expected values.
-\item {\tt col}: a vector of at most two colors for plotting the
- expected value given by {\tt x} and the alternative set of expected
- values given by {\tt x1} in {\tt sim()}. If the quantity of
- interest selected is not the expected value, or {\tt x1 = NULL},
- only the first color will be used.
-\item {\tt \dots}: Additional parameters, such as {\tt xlab}, {\tt
- ylab}, and {\tt main}, passed to {\tt plot()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-For all univariate response models, {\tt plot.ci()} returns vertical
-confidence intervals over a specified range of one explanatory
-variable. You may save this plot using the commands described in
-\Sref{ss:output}.
-
-\subsubsection{Example}
-
-This demo is available within R as {\tt demo(vertci)}. Load the
-sample data and estimate the model.
-\begin{verbatim}
-data(turnout)
-z.out <- zelig(vote ~ race + educate + age + I(age^2) + income,
- model = "logit", data = turnout)
-\end{verbatim}
-Establish a range for {\tt `age'}, the key explanatory variable, and
-create two {\tt setx} objects that hold the other explanatory
-variables to fixed, while allowing age to range from 18 to 95, inclusive.
-\begin{verbatim}
-age.range <- 18:95
-x.low <- setx(z.out, educate = 12, age = age.range)
-x.high <- setx(z.out, educate = 16, age = age.range)
-\end{verbatim}
-Simulate quantities of interest.
-\begin{verbatim}
-s.out <- sim(z.out, x = x.low, x1 = x.high)
-\end{verbatim}
-Plot these results using the {\tt plot.ci()} function:
-\begin{verbatim}
-plot.ci(s.out, xlab = "Age in Years",
- ylab = "Predicted Probability of Voting",
- main = "Effect of Education and Age on Voting Behavior")
-legend(45, 0.52, legend = c("College Education (16 years)",
- "High School Education (12 years)"), col = c("blue","red"),
- lty = c("solid"))
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item The {\tt help(plot)} and {\tt help(lines)} reference pages.
- \item \Sref{s:plot} for an overview of plotting procedures
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau created the R procedure to
-generate vertical confidence intervals for simulated quantities of
-interest.
-
-Sample data are a selection of $2,000$ observations from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/plot.zelig.tex b/inst/doc/commands/plot.zelig.tex
deleted file mode 100644
index 90d7ff5..0000000
--- a/inst/doc/commands/plot.zelig.tex
+++ /dev/null
@@ -1,68 +0,0 @@
-\section{{\tt plot}: Graphing Quantities of Interest}\label{ss:plot.zelig}
-
-\subsubsection{Description}
-The \texttt{plot()} command generates default plots for {\tt
- sim()} objects with one-observation values in {\tt x} and {\tt x1}.
- Additional default plotting methods for other data structures are
- described in {\tt help(plot)}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> plot(s.out, alt.col = "red", user.par = FALSE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item{\tt s.out}: stored output from {\tt sim()}. If the {\tt x} or
- {\tt x1} values stored in the object contain more than one
- observation, {\tt plot.zelig()} will return an error. For linear or
- generalized linear models with more than one observation in {\tt x}
- and optionally {\tt x1}, you may use {\tt plot.ci()} (see \Sref{plot.ci}).
-\item{\tt alt.col}: alternative color used for contrast in the plots.
- The primary color is black. Type {\tt colors()} to see a list of
- available alternative colors.
-\item{\tt user.par}: a logical value indicating whether to use the
- default Zelig plotting parameters ({\tt user.par = FALSE}) or
- user-defined parameters ({\tt user.par = TRUE}), set using the {\tt
- par()} function prior to plotting.
-\item{\tt \dots}: Additional parameters passed to {\tt plot()}.
- Because {\tt plot.zelig()} primarily produces diagnostic plots, many
- of these parameters are hard-coded for convenience and
- presentation. If you wish to produce plots using different
- parameters, you may follow the directions in \Sref{s:plot}.
-\end{itemize}
-
-\subsubsection{Output Values}
-Depending on the class of model selected, \texttt{plot.zelig()} will
-return an on-screen window with graphs of the various quantities of
-interest. You may save these plots using the commands described in
-\Sref{ss:output}.
-
-\subsubsection{Examples}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "logit", data = mydata)
-> x.out <- setx(z.out, x1 = 10)
-> x.alt <- setx(z.out, x1 = 0)
-> s.out <- sim(z.out, x = x.out, x1 = x.alt)
-> plot(s.out)
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item The {\tt help(plot)} and {\tt help(lines)} reference pages for
- optional arguments passed to {\tt \dots}.
-\item The {\tt help(par)} reference page for user-specified plotting
- parameters.
-\item \Sref{s:plot} for an overview of plotting procedures
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau created the plots generated by
-{\tt plot.zelig()}. These plots are individually available in the
-base, MASS, and vcd libraries.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/print.aux b/inst/doc/commands/print.aux
deleted file mode 100644
index cc5c7ae..0000000
--- a/inst/doc/commands/print.aux
+++ /dev/null
@@ -1,44 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {10.6}{\tt print}: Printing Quantities of Interest}{103}{section.10.6}}
-\newlabel{ss:print}{{10.6}{103}{{\tt print}: Printing Quantities of Interest\relax }{section.10.6}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{103}{section*.65}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{103}{section*.66}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{103}{section*.67}}
-\@writefile{toc}{\contentsline {subsubsection}{Examples}{103}{section*.68}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{103}{section*.69}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{103}{section*.70}}
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diff --git a/inst/doc/commands/print.tex b/inst/doc/commands/print.tex
deleted file mode 100644
index edd4134..0000000
--- a/inst/doc/commands/print.tex
+++ /dev/null
@@ -1,51 +0,0 @@
-\section{{\tt print}: Printing Quantities of Interest}
-\label{ss:print}
-
-\subsubsection{Description}
-The {\tt print} command formats summary output and allows users to
-specify the number of decimal places as an optional argument.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> print(x, digits = 3, print.x = FALSE)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt x}: the object to be printed may be {\tt z.out} output
- from {\tt zelig()}, {\tt x.out} output from {\tt setx()}, {\tt s.out}
- output from {\tt sim()}, or other R data structures.
-\item {\tt digits}: the minimum number of significant digits to return
- for all elements of $x < 0$. By default, {\tt print()} avoids
- scientific notation, but setting the number of digits to 1 will
- frequently force output in scientific notation. The number of {\tt
- digits} is not the number of significant digits for all output
- values, but the minimum number of significant digits for the
- smallest value in {\tt x} between -1 and 1; this governs the number
- of significant digits in the rest of the values with decimal output.
-\item {\tt print.x}: a logical value for {\tt sim()} output, which
- specifies whether to print a summary ({\tt print.x = FALSE}, the
- default) of the {\tt x} and {\tt x1} \emph{inputs} to {\tt sim()},
- or the complete set of inputs (optionally, {\tt print.x = TRUE}).
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{verbatim}
-> print(summary(z.out), digits = 2)
-> print(summary(s.out), digits = 3, print.x = TRUE)
-\end{verbatim}
-
-\subsubsection{See Also}
-
-Advanced users may wish to refer to {\tt help(print)}.
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau added print methods for {\tt
- sim()} output, and {\tt summary()} output for Zelig objects.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/put.start.aux b/inst/doc/commands/put.start.aux
deleted file mode 100644
index 6e5b3e1..0000000
--- a/inst/doc/commands/put.start.aux
+++ /dev/null
@@ -1,44 +0,0 @@
-\relax
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-\newlabel{put.start}{{13.7}{349}{{\tt put.start}: Set specific starting values for certain parameters\relax }{section.13.7}{}}
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-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{349}{section*.443}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{349}{section*.444}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{349}{section*.445}}
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diff --git a/inst/doc/commands/put.start.tex b/inst/doc/commands/put.start.tex
deleted file mode 100644
index 1d7d9af..0000000
--- a/inst/doc/commands/put.start.tex
+++ /dev/null
@@ -1,44 +0,0 @@
-\section{{\tt put.start}: Set specific starting values for certain parameters}
-\label{put.start}
-
-\subsubsection{Description}
-
-After calling {\tt set.start()} to create default starting values, use
-{\tt put.start()} to change starting values for specific parameters or
-parameter sets.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-put.start(start.val, value, terms, eqn)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt start.val}: the vector of starting values created by {\tt
-set.start()}.
-\item {\tt value}: the scalar or vector of replacement starting
-values.
-\item {\tt terms}: the terms output from {\tt model.frame.multiple()}.
-\item {\tt eqn}: the parameters for which you would like to replace
-the default values with {\tt value}.
-\end{itemize}
-
-\subsubsection{Output Values}
-A vector of starting values (of the same length as {\tt start.val}).
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{set.start} to set default starting values.
-\item \Sref{s:new} for an overview of the procedure to add
- models to Zelig.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/replicate.tex b/inst/doc/commands/replicate.tex
deleted file mode 100644
index 78c3f3e..0000000
--- a/inst/doc/commands/replicate.tex
+++ /dev/null
@@ -1,82 +0,0 @@
-\section{{\tt repl}: Replicating Analyses}\label{ss:replicate}
-
-\subsubsection{Description}
-The {\tt repl()} command included with Zelig takes {\tt zelig()}
-or {\tt sim()} output objects and replicates (literally, re-runs) the
-entire analysis. The results should be an output object identical to
-the original input object in the case of {\tt zelig()} output. In the
-case of {\tt sim()} output, the replicated analyses may differ
-slightly due to stochastic randomness in the simulation procedure.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> repl(z.out, data = NULL)
-> repl(s.out, data = NULL, prev = NULL, x = NULL, x1 = NULL,
- bootfn = NULL)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt z.out}: Stored output from {\tt zelig()}.
-\item {\tt s.out}: Stored output from {\tt sim()}
-\item {\tt data}: You may manually input the data frame name rather
- than allowing replicate to draw the data frame name from the object
- to be replicated.
-\item {\tt prev}: When replicating {\tt sim()} output, you may
- optionally use the previously simulated parameters to calculate the
- quantities of interest rather than simulating a new set of
- parameters. For all models, this should produce identical
- quantities of interest. If the parameters were
- bootstrapped in the original analysis, this will save a considerable
- amount of time.
-\item {\tt x}: When replicating {\tt sim()} output, you may
- optionally use an alternative {\tt setx()} value for the {\tt x}
- input.
-\item {\tt x1}: When replicating {\tt sim()} output, you may
- optionally use an alternative {\tt setx()} object for the {\tt x1}
- input to replicating the {\tt sim()} object.
-\item {\tt bootfn}: When replicating {\tt sim()} output with
- bootstrapped parameters, you should manually specify the {\tt
- bootfn} if a non-default option was used.
-\end{itemize}
-
-\subsubsection{Output Values}
-For {\tt zelig()} output, {\tt repl()} will create output that is in
-every way identical to the original input. You may check to see
-whether they are identical by using the {\tt identical()} command.
-
-For {\tt sim()} output, {\tt repl()} output will be will be
-identical to the original object if you choose not to simulate new
-parameters, and instead choose to calculate quantities of interest
-using the previously simulated parameters (using the {\tt prev}
-option. If you choose to simulate new parameters, the summary
-statistics for each quantity of interest should be identical, up to a
-random approximation error. As the number of simulations increases,
-this error decreases.
-
-\subsubsection{Examples}
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout[1:1000,])
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-> z.rep <- repl(z.out)
-> identical(z.out$coef, z.rep$coef)
-> z.alt <- repl(z.out, data = turnout[1001:2000,])
-> s.rep <- repl(s.out, prev = s.out$par)
-> identical(s.out$ev, s.rep$ev)
-\end{verbatim} %$
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{load.data} for loading data from disk.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/rocplot.tex b/inst/doc/commands/rocplot.tex
deleted file mode 100644
index b21ab71..0000000
--- a/inst/doc/commands/rocplot.tex
+++ /dev/null
@@ -1,91 +0,0 @@
-\section{{\tt rocplot}: Receiver Operator Characteristic Plots}\label{ss:rocplot}
-
-\subsubsection{Description}
-The \texttt{rocplot()} command generates a receiver operator
-characteristic plot to compare the in-sample (default) or
-out-of-sample fit for two logit or probit regressions.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-rocplot(y1, y2, fitted1, fitted2, cutoff = seq(from=0, to=1, length=100),
- lty1 = "solid", lty2 = "dashed", lwd1 = par("lwd"), lwd2 = par("lwd"),
- col1 = par("col"), col2 = par("col"), plot = TRUE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt y1}: Response variable for the first model.
-\item {\tt y2}: Response variable for the second model.
-\item {\tt fitted1}: Fitted values for the first model. These values
- may represent either the in-sample or out-of-sample fitted values.
-\item {\tt fitted2}: Fitted values for the second model.
-\item {\tt cutoff}: A vector of cut-off values between 0 and 1, at
- which to evaluate the proportion of 0s and 1s correctly predicted by
- the first and second model. By default, this is 100 increments
- between 0 and 1, inclusive.
-\item {\tt lty1}, {\tt lty2}: The line type for the first model ({\tt
- lty1}) and the second model ({\tt lty2}), defaulting to solid and
- dashed, respectively.
-\item {\tt lwd1}, {\tt lwd2}: The width of the line for the first
- model ({\tt lwd1}) and the second model ({\tt lwd2}), defaulting to
- 1 for both.
-\item {\tt col1}, {\tt col2}: The colors of the line for the first
- model ({\tt col1}) and the second model ({\tt col2}), defaulting to
- black for both.
-\item{\tt plot}: By default, {\tt plot = TRUE}, which generates a plot to the
- selected device. If {\tt FALSE}, {\tt rocplot()} returns a list of output (see below).
-\item {\tt \dots}: Additional parameters passed to plot, including
- {\tt xlab}, {\tt ylab}, and {\tt main}.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-If {\tt plot = TRUE}, {\tt rocplot()} generates an ROC plot for two
-logit or probit models. You may save this plot using the commands
-described in \Sref{ss:output}.
-
-If {\tt plot = FALSE}, {\tt rocplot()} returns a list with the following
-elements:
-\begin{itemize}
-\item {\tt roc1}: A matrix containing a vector of x-coordinates and
- y-coordinates corresponding to the number of ones and zeros correctly
- predicted for the first model.
-\item {\tt roc2}: A matrix containing a vector of x-coordinates and
- y-coordinates corresponding to the number of ones and zeros correctly
- predicted for the second model.
-\item {\tt area1}: The area under the first ROC curve, calculated using
- Reimann sums.
-\item {\tt area2}: The area under the second ROC curve, calculated using
- Reimann sums.
-\end{itemize}
-
-\subsubsection{Example}
-You may view this example using {\tt demo(roc)}.
-\begin{verbatim}
-> data(turnout)
-> z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
- data = turnout)
-> z.out2 <- zelig(vote ~ race + educate, model = "logit",
- data = turnout)
-> rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item The {\tt help(plot)} and {\tt help(lines)} reference pages.
- \item \Sref{s:plot} for an overview of plotting procedures.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau.
-
-Sample data are a selection of $2,000$ observations from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/commands/set.start.aux b/inst/doc/commands/set.start.aux
deleted file mode 100644
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diff --git a/inst/doc/commands/set.start.tex b/inst/doc/commands/set.start.tex
deleted file mode 100644
index 70c68f9..0000000
--- a/inst/doc/commands/set.start.tex
+++ /dev/null
@@ -1,53 +0,0 @@
-\section{{\tt set.start}: Set starting values for all parameters}
-\label{set.start}
-
-\subsubsection{Description}
-
-After using {\tt parse.par()} and {\tt model.matrix()}, use {\tt
-set.start()} to set starting values for all parameters. By default,
-starting values are set to 0. If you wish to select alternative
-starting values for certain parameters, use {\tt put.start()} after
-{\tt set.start()}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-set.start(start.val = NULL, terms)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt start.val}: user-specified starting values. If {\tt NULL}
-(default), the default starting values for all parameters are set to
-0.
-\item {\tt terms}: the terms output from {\tt
-model.frame.multiple()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-A named vector of starting values for all parameters specified in {\tt
-terms}, defaulting to 0.
-
-\subsubsection{Example}
-\begin{verbatim}
-fml <- parse.formula(formula, model = "bivariate.probit")
-D <- model.frame(fml, data = data)
-terms <- terms(D)
-start.val <- set.start(start.val = NULL, terms)
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{put.start} to change starting values for specific
-parameter sets.
-\item \Sref{s:new} for detailed examples of writing new models.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/setx.tex b/inst/doc/commands/setx.tex
deleted file mode 100644
index 9b066ef..0000000
--- a/inst/doc/commands/setx.tex
+++ /dev/null
@@ -1,149 +0,0 @@
-\section{{\tt setx}: Setting Explanatory Variable Values}\label{ss:setx}
-
-\subsubsection{Description}
-The \texttt{setx()} command uses the variables identified in the
-\texttt{formula} generated by \texttt{zelig()} and sets the values of
-the explanatory variables to the selected values. Use \texttt{setx()}
-after {\tt zelig()} and before \texttt{sim()} to simulate quantities
-of interest.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> x.out <- setx(z.out, fn = list(numeric = mean, ordered = median,
- other = mode), data = NULL, cond = FALSE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-
-\item {\tt z.out}: the saved output from \texttt{zelig()}.
-
-\item {\tt fn}: a list of three functions to apply to three types of
- variables
- \begin{itemize}
- \item {\tt numeric} variables are set to their mean by default, but
- you may select any other mathematical function.
- \item {\tt ordered} factors are set to their median by default, and
- most mathematical operations will work on them. (If you select
- {\tt ordered = mean}, however, {\tt setx()} will default to median
- with a warning.)
- \item {\tt other} variables may consist of unordered factors,
- character strings, or logical variables. The {\tt other}
- variables may only be set to their mode. (If you wish to set one
- of the other variables to a specific value, you may do so using
- {\tt \dots} or by making them numeric and using \texttt{fn}.)
- \end{itemize}
- In the special case {\tt fn = NULL}, {\tt setx()} will return all of
- the observations without applying any function to the data.
-\item {\tt data}: a new data frame used to set the values of
- explanatory variables. If {\tt data = NULL} (the default), the data
- frame called in \texttt{zelig()} is used.
-\item {\tt cond}: a logical value indicating whether the usual
- unconditional prediction is used (the default) or whether the optional
- conditional prediction should be performed (choose {\tt cond =
- TRUE}). If you choose {\tt cond = TRUE}, {\tt setx()} will coerce
- {\tt fn = NULL} and ignore the arguments in {\tt \dots}.
-\item {\tt \dots}: user-defined values of specific variables that
- overwrite the default values set by the function. For example,
- adding {\tt var1 = mean(data\$var1)} sets the value of \texttt{var1}
- to the mean of {\tt data\$var1}; {\tt x1 = 12} explicitly sets the value
- of {\tt x1} to 12. In addition, you may specify one explanatory
- variable as a range of values, creating one observation for every
- unique value in the range of values. The other variables are set to
- the values given by {\tt fn}.
-\end{itemize}
-
-\subsubsection{Output Values}
-\begin{itemize}
-\item For unconditional prediction, {\tt x.out} is a model matrix
- based on the specified values for the explanatory variables. For
- multiple analyses (i.e., when choosing the {\tt by} option in {\tt
- zelig()}, {\tt setx()} returns the selected values calculated over
- the entire data frame. If you wish to calculate values over just
- one subset of the data frame, the 5th subset for example, you may
- use:
-\begin{verbatim}
-> x.out <- setx(z.out[[5]])
-\end{verbatim}
-
-\item For conditional prediction, {\tt x.out} includes the model matrix
- and the dependent variable(s). For multiple analyses (when choosing
- the {\tt by} option in {\tt zelig()}), {\tt setx()} returns the
- observed explanatory variables in each subset.
-\end{itemize}
-
-\subsubsection{Example: Unconditional Prediction}
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Using all observations:
-\begin{verbatim}
-> x.out <- setx(z.out, fn = NULL)
-\end{verbatim}
-Defining a user defined function for {\tt fn}:
-\begin{verbatim}
-> quants <- function(x) {
-+ quantile(x, 0.25)
-+ }
-> x.out <- setx(z.out, fn = list(numeric = quants))
-\end{verbatim}
-
-\subsubsection{Example: Conditional Prediction With MatchIt Data}
-Use {\tt demo(match)} to view this example.
-\begin{verbatim}
-> data(lalonde)
-> match.out <- matchit(treat ~ age + educ + black + hispan + married +
- nodegree + re74 + re75, data = lalonde)
-> z.out <- zelig(re78 ~ pscore, data = match.data(match, "control"),
- model = "ls")
-> x.out <- setx(z.out, data = match.data(match, "treat"), fn = NULL,
- cond = TRUE)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Example: Conditional Prediction With Multiple Analyses}
-Use {\tt demo(conditional)} to view this example:
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ age + educate + income, by = "race",
- data = turnout, model = "probit")
-
-# Calculate the ATE (average treatment effect) for race == "white":
-> x.white <- setx(z.out$others, data = turnout[turnout$race == "white",],
- fn = NULL, cond = TRUE)
-> s.others <- sim(z.out$others, x = x.white)
-
-# Calculate the ATE for race == "others":
-> x.others <- setx(z.out$white, data = turnout[turnout$race == "others",],
- fn = NULL, cond = TRUE)
-> s.others <- sim(z.out$white, x = x.others)
-\end{verbatim} %$
-
-\subsubsection{See Also}
- \begin{itemize}
- \item \Sref{s:commands} for an overview of the Zelig simulation
- procedure.
- \item \Sref{s:models} for an overview of supported models.
- \item \Sref{s:syntax} for an overview of R syntax.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau ported {\tt setx()} to R, following
-the logic of Clarify (for Stata). See King, Tomz, and Wittenberg,
-2000.\nocite{KinTomWit00}
-
-Sample data are a selection of $2,000$ observations from
-
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/sim.tex b/inst/doc/commands/sim.tex
deleted file mode 100644
index 0f46809..0000000
--- a/inst/doc/commands/sim.tex
+++ /dev/null
@@ -1,152 +0,0 @@
-\section{{\tt sim}: Simulating Quantities of Interest}\label{ss:sim}
-
-\subsubsection{Description}
-Simulate quantities of interest from the estimated model output from
-\texttt{zelig()} given specified values of explanatory variables
-established in \texttt{setx()}. For classical \emph{maximum
- likelihood} models, {\tt sim()} uses asymptotic normal approximation
-to the log-likelihood. For \emph{Bayesian models}, Zelig simulates
-quantities of interest from the posterior density, whenever possible.
-For \emph{robust Bayesian models}, simulations are drawn from the
-identified class of Bayesian posteriors. Alternatively, you may
-generate quantities of interest using bootstrapped parameters.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> s.out <- sim(z.out, x, x1 = NULL, num = c(1000, 100), prev = NULL,
- bootstrap = FALSE, bootfn = NULL, cond.data = NULL, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
- \item {\tt z.out}: the output object from \texttt{zelig()}.
- \item {\tt x}: values of explanatory variables used
- for simulation, generated by {\tt setx()}.
- \item {\tt x1}: optional values of explanatory variables (generated
- by a second call of {\tt setx()}), used to simulate first
- differences and risk ratios. (Not available for conditional prediction.)
- \item {\tt num}: the number of simulations, i.e., posterior draws.
- If the \texttt{num} argument is omitted, \texttt{sim()} draws 1,000
- simulations if {\tt bootstrap = FALSE} (the default), or
- 100 simulations if {\tt bootstrap = TRUE}. You may increase this
- value to improve accuracy. (Not available for conditional prediction.)
- \item {\tt bootstrap:} a logical value indicating if parameters
- should be generated by re-fitting the model for bootstrapped
- data, rather than from the likelihood or posterior. (Not
- available for conditional prediction.)
- \item {\tt bootfn:} a function which governs how the data is
- sampled, re-fits the model, and returns the bootstrapped model
- parameters. If {\tt bootstrap = TRUE} and {\tt bootfn = NULL},
- {\tt sim()} will sample observations from the original data (with
- replacement) until it creates a sampled dataset with the same
- number of observations as the original data. Alternative
- bootstrap methods (for which users have to specify their own {\tt
-bootfn}) include sampling the residuals rather than the
- observations, weighted sampling, and parametric bootstrapping.
- (Not available for conditional prediction.)
- \item {\tt \dots}: additional optional arguments passed to
- \texttt{boot()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-The output stored in {\tt s.out} varies by model. Use the {\tt
- names()} command to view the output stored in {\tt s.out}. Common
-elements include:
-\begin{itemize}
- \item {\tt x}: the {\tt setx()} values for the explanatory variables,
- used to calculate the quantities of interest (expected values,
- predicted values, etc.).
- \item {\tt x1}: the optional {\tt setx()} object used to simulate
- first differences, and other model-specific quantities of
- interest, such as risk-ratios.
- \item {\tt call}: the options selected for {\tt sim()}, used to
- replicate quantities of interest.
- \item {\tt zelig.call}: the original command and options for {\tt
- zelig()}, used to replicate analyses.
- \item {\tt num}: the number of simulations requested.
- \item {\tt par}: the parameters (coefficients, and additional
- model-specific parameters). You may wish to use the same set of
- simulated parameters to calculate quantities of interest rather
- than simulating another set.
- \item {\tt qi\$ev}: simulations of the expected values given the
- model and {\tt x}.
- \item {\tt qi\$pr}: simulations of the predicted values given by the
- fitted values.
- \item {\tt qi\$fd}: simulations of the first differences (or risk
- difference for binary models) for the given {\tt x} and {\tt x1}.
- The difference is calculated by subtracting the expected values
- given {\tt x} from the expected values given {\tt x1}. (If do not
- specify {\tt x1}, you will not get first differences or risk
- ratios.)
- \item {\tt qi\$rr}: simulations of the risk ratios for binary and
- multinomial models. See specific models for details.
- \item {\tt qi\$ate.ev}: simulations of the average expected
- treatment effect for the treatment group, using conditional
- prediction. Let $t_i$ be a binary explanatory variable defining
- the treatment ($t_i=1$) and control ($t_i=0$) groups. Then the
- average expected treatment effect for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \},
- \end{equation*}
- where $Y_i(t_i=1)$ is the value of the dependent variable for
- observation $i$ in the treatment group. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
- \item {\tt qi\$ate.pr}: simulations of the average predicted
- treatment effect for the treatment group, using conditional
- prediction. Let $t_i$ be a binary explanatory variable defining
- the treatment ($t_i=1$) and control ($t_i=0$) groups. Then the
- average predicted treatment effect for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \},
- \end{equation*}
- where $Y_i(t_i=1)$ is the value of the dependent variable for
- observation $i$ in the treatment group. Variation in the
- simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-In the case of censored $Y$ in the exponential, Weibull, and lognormal
-models, {\tt sim()} first imputes the uncensored values for $Y$ before
-calculating the ATE.
-
-You may use the {\tt \$} operator to extract any of the
-above from {\tt s.out}. For example, {\tt s.out\$qi\$ev} extracts the
-simulated expected values.
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{s:commands} for an overview of the Zelig simulation procedure.
- \item \Sref{s:models} for an overview of supported models.
- \item \Sref{s:syntax} for an overview of R syntax.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau generalized {\tt sim()} from a
-similar procedure used in Clarify (for Stata, see King, Tomz, and
-Wittenberg, 2000) and added procedures to bootstrap quantities of
-interest, and perform conditional prediction.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/summary.tex b/inst/doc/commands/summary.tex
deleted file mode 100644
index 60857a8..0000000
--- a/inst/doc/commands/summary.tex
+++ /dev/null
@@ -1,125 +0,0 @@
-\section{{\tt summary}: Summarizing Zelig Output}
-\label{ss:summary}
-
-\subsubsection{Description}
-
-Summarize R objects using using the generic function {\tt summary()}.
-R automatically recognizes the type of object (lists, data frames,
-variables, etc.) and selects the appropriate {\tt summary()} function.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> summary(z.out, subset = NULL, ...)
-> summary(s.out, subset = NULL, CI = 95,
- stats = c("mean", "sd", "min", "max"), ...)
-\end{verbatim}
-To set the desired number of digits in the summary output, use {\tt
- options(digits = x)}, where {\tt x} is the desired number of digits,
-prior to using the {\tt summary()} command.
-
-\subsubsection{Arguments for multiply-imputed {\tt zelig()} output}
-For {\tt zelig()} output created with $m$ multiply-imputed data sets,
-the {\tt z.out} object contains one set of output for each imputed
-data set. Options for multiply-imputed {\tt zelig()} output include:
- \begin{itemize}
- \item {\tt subset}: specifies which of the $m$ sets of output to
- summarize. Possible arguments include:
- \begin{itemize}
- \item {\tt NULL}: (default) which produces one summary by
- averaging the coefficients and standard errors from all the
- imputed data sets using the Rubin rules (\hlink{King,
- Honaker, Joseph, Scheve
- (2000)}{http://gking.harvard.edu/files/abs/evil-abs.shtml},
- p. 53).
- \item a numeric vector: consisting of any set of numbers in
- $[1,m]$. For example, you may use {\tt summary(z.out,
- subset = 2:4)} to view the output from the second, third,
- and fourth imputed data sets only.
- \end{itemize}
- \item{\dots}: additional options passed to {\tt print()}.
- \end{itemize}
-
-\subsubsection{Arguments for subsetted {\tt zelig()} output}
-If you selected the {\tt by} option, {\tt zelig()} creates an {\tt
- z.out} object with one set of regression output for each of the
-unique values in the {\tt by} variable. Options for subsetted
- analyses are:
-\begin{itemize}
-\item {\tt subset}: specifies which regression output to summarize.
- You cannot summarize multiple analyses generated using the {\tt
- "by"} option in one summary, as each of the levels in the {\tt by}
- variable may have a different number of associated observations. (A
- weighted summary would also be inappropriate, as the weighted
- coefficients would be identical to the coefficients generated
- without subsetting.)
-\begin{itemize}
-\item numeric vector: specifying which sets of regression output to
- summarize. By default, {\tt summary()} for subsetted output
- sequentially summarizes the regression output for each unique value
- in the {\tt by} variable. You may choose to summarize just the
- fifteenth unique value by typing: {\tt summary(z.out, subset =
- 15)}.
-\end{itemize}
-\item {\tt \dots}: Additional arguments passed to {\tt print()}.
-\end{itemize}
-
-\subsubsection{Arguments for {\tt sim()} output}
-Summaries of {\tt sim()} output includes these additional options:
- \begin{itemize}
- \item {\tt subset}: Only valid for {\tt sim()} output created
- using more than one observation in {\tt x}. You may select to
- summarize the quantities of interest created by each
- observation in {\tt x} using one of the following options:
- \begin{itemize}
- \item {\tt NULL}: (default) produces one summary per quantity
- of interest by combining the simulations from each
- observation into a single set and then summarizing.
- \item {\tt all}: summarizes the quantity of interest
- produced by each observation separately.
- \item a numeric vector: specifying the observations to summarize
- \end{itemize}
- \item {\tt CI}: A value between 0 and 100, for the percentage
- confidence interval. The default value is 95, for a 95 percent
- confidence interval.
- \item {\tt stats}: A vector specifying the statistics to
- calculate for each quantity of interest. The default values
- are {\tt c("mean", "sd", "min", "max")}.
- \end{itemize}
-Using {\tt summary()} on {\tt sim()} output from multiple analyses
-will automatically weight the quantities of interest according to the
-proportion of observations in each strata.
-
-\subsubsection{Output Values}
-\begin{itemize}
-\item For {\tt zelig()} output objects, {\tt summary()} returns a
- formatted summary of the regression output, including the usual
- table of coefficients, standard errors, and $t$-values. Additional
- output depends on the statistical model, the names of which may
- be viewed using the {\tt names(summary(z.out))} command.
-\item For {\tt sim()} output objects, {\tt summary()} returns summary
- statistics for each quantity of interest, including a default 95\%
- confidence interval. Use {\tt names(summary(s.out))} to see the
- available output. Note that {\tt qi.stats} contains a useful matrix
- of summary statistics for each of the quantities of interest. For
- example, {\tt summary(s.out)\$qi.stats\$ev}, extracts the summary
- matrix of expected values.
-\end{itemize}
-
-\subsubsection{See Also}
-
-Advanced users may wish to refer to {\tt help.zelig("print")},
-as well as {\tt help(summary)}, {\tt help(summary.lm)}, {\tt
- help(summary.glm)}, and {\tt help(anova)} in the base package.
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau added summary methods for
-certain {\tt zelig()} models, and for {\tt sim()} output.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/commands/tag.aux b/inst/doc/commands/tag.aux
deleted file mode 100644
index 63eacb6..0000000
--- a/inst/doc/commands/tag.aux
+++ /dev/null
@@ -1,45 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.9}{\tt tag}: Constrain parameter effects across equations}{351}{section.13.9}}
-\newlabel{tag}{{13.9}{351}{{\tt tag}: Constrain parameter effects across equations\relax }{section.13.9}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{351}{section*.453}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{351}{section*.454}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{351}{section*.455}}
-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{351}{section*.456}}
-\@writefile{toc}{\contentsline {subsubsection}{Examples}{351}{section*.457}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{351}{section*.458}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{351}{section*.459}}
-\@setckpt{commands/tag}{
-\setcounter{page}{352}
-\setcounter{equation}{0}
-\setcounter{enumi}{2}
-\setcounter{enumii}{3}
-\setcounter{enumiii}{2}
-\setcounter{enumiv}{0}
-\setcounter{footnote}{0}
-\setcounter{mpfootnote}{0}
-\setcounter{part}{3}
-\setcounter{chapter}{13}
-\setcounter{section}{9}
-\setcounter{subsection}{0}
-\setcounter{subsubsection}{0}
-\setcounter{paragraph}{0}
-\setcounter{subparagraph}{0}
-\setcounter{figure}{0}
-\setcounter{table}{1}
-\setcounter{LT at tables}{0}
-\setcounter{LT at chunks}{0}
-\setcounter{FancyVerbLine}{1}
-\setcounter{NAT at ctr}{42}
-\setcounter{parentequation}{0}
-\setcounter{Item}{269}
-\setcounter{Hfootnote}{10}
-\setcounter{lpart}{0}
-\setcounter{lchapter}{1}
-\setcounter{lsection}{0}
-\setcounter{lsubsection}{0}
-\setcounter{lsubsubsection}{0}
-\setcounter{lparagraph}{0}
-\setcounter{lsubparagraph}{0}
-\setcounter{lsubsubparagraph}{0}
-\setcounter{section at level}{3}
-}
diff --git a/inst/doc/commands/tag.tex b/inst/doc/commands/tag.tex
deleted file mode 100644
index 5632b00..0000000
--- a/inst/doc/commands/tag.tex
+++ /dev/null
@@ -1,54 +0,0 @@
-\section{{\tt tag}: Constrain parameter effects across equations}
-\label{tag}
-
-\subsubsection{Description}
-Use {\tt tag()} to identify parameters and constrain their effects
-across equations in multiple-equation models.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-tag(x, label)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt x}: the variable to be constrained.
-\item {\tt label}: the name that the constrained variable takes.
-\end{itemize}
-
-\subsubsection{Output Values}
-While there is no specific output from {\tt tag()} itself, {\tt
-parse.formula()} uses {\tt tag()} to identify parameter constraints
-across equations, when a model takes more than one systematic
-component.
-
-\subsubsection{Examples}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{ui} for an overview of the multiple-equation user-interface.
-\item \Sref{parse.formula} for more examples of acceptable uses for
-{\tt tag()} in formulas.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/ternaryplot.tex b/inst/doc/commands/ternaryplot.tex
deleted file mode 100644
index 7be782d..0000000
--- a/inst/doc/commands/ternaryplot.tex
+++ /dev/null
@@ -1,81 +0,0 @@
-\section{{\tt ternaryplot}: Ternary Diagram for 3D Data}
-\label{ternaryplot}
-
-\subsubsection{Description}
-Visualizes compositional, 3-dimensional data in an equilateral
-triangle. A point's coordinates are found by computing the gravity center
-of mass points using the data entries as weights. Thus, the coordinates
-of a point $P(a,b,c)$ for $a + b + c = 1$ are: $P(b + \frac{c}{2}, \frac{c
-\sqrt{3}}{2})$.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-ternaryplot(x, scale = 1, dimnames = NULL,
- dimnames.position = c("corner","edge","none"),
- dimnames.color = "black", id = NULL, id.color = "black",
- coordinates = FALSE, grid = TRUE, grid.color = "gray",
- labels = c("inside", "outside", "none"),
- labels.color = "darkgray", border = "black", bg = "white",
- pch = 19, cex = 1, prop.size = FALSE, col = "red",
- main = "ternary plot", ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt x}: a matrix with three columns.
-\item {\tt scale}: row sums scale to be used.
-\item {\tt dimnames}: dimension labels (defaults to the column names
-of {\tt x}).
-\item {\tt dimnames.position, dimnames.color}: position and color of dimension labels.
-\item {\tt id}: optional labels to be plotted below the plot
-symbols. {\tt coordinates} and {\tt id} are mutual exclusive.
-\item {\tt id.color}: color of these labels.
-\item {\tt coordinates}: if {\tt TRUE}, the coordinates of the points are
- plotted below them. {\tt coordinates} and {\tt id} are mutual exclusive.
-\item {\tt grid}: if {\tt TRUE}, a grid is plotted. May optionally
- be a string indicating the line type (default: {\tt "dotted"}).
-\item {\tt grid.color}: grid color.
-\item {\tt labels, labels.color}: position and color of the grid labels.
-\item {\tt border}: color of the triangle border.
-\item {\tt bg}: triangle background.
-\item {\tt pch}: plotting character. Defaults to filled dots.
-\item {\tt cex}: a numerical value giving the amount by which plotting text
- and symbols should be scaled relative to the default. Ignored for
- the symbol size if {\tt prop.size} is not {\tt FALSE}.
-\item {\tt prop.size}: if {\tt TRUE}, the symbol size is plotted
- proportional to the row sum of the three variables, i.e. represents
- the weight of the observation.
-\item {\tt col}: plotting color.
- \item {\tt main}: main title.
-\item {\tt \dots}: additional graphics parameters (see {\tt par}).
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{verbatim}
-data(mexico)
-z.out <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
- model = "mlogit", data = mexico)
-x.out <- setx(z.out)
-s.out <- sim(z.out, x = x.out)
-
-ternaryplot(s.out$qi$ev, pch = ".", col = "blue",
- main = "1988 Mexican Presidential Election")
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{ternarypoints} for a way to add points to an existing
-ternary diagram.
-\item \Sref{s:plot} for an overview of plotting procedures.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-This function was originally written by David Meyer for the vcd
-library, version 0.1, which uses plot graphics, and differs from
-the current implementation in vcd, which uses grid graphics.
-For additional information on vcd, please consult
-\begin{verse}
-\bibentry{Friendly00}.
-\end{verse}
-
diff --git a/inst/doc/commands/ternarypoints.tex b/inst/doc/commands/ternarypoints.tex
deleted file mode 100644
index 13e1167..0000000
--- a/inst/doc/commands/ternarypoints.tex
+++ /dev/null
@@ -1,63 +0,0 @@
-\section{{\tt ternarypoints}: Adding Points to Ternary Diagrams}
-\label{ternarypoints}
-
-\subsubsection{Description}
-Use {\tt ternarypoints()} to add points to a ternary diagram generated
-using the {\tt ternaryplot()} function in the vcd library. Use
-ternary diagrams to plot expected values for multinomial choice models
-with three categories in the dependent variable.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> ternarypoints(object, pch = 19, col = "blue", ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt object}: The input object must be a matrix with three
- columns.
-\item {\tt pch}: The selected type of point. By default, {\tt pch =
- 19}, solid disks.
-\item {\tt col}: The color of the points. By default, {\tt col =
- "blue"}.
-\item {\tt \dots}: Additional parameters passed to {\tt points()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-The {\tt ternarypoints()} command adds points to a previously existing
-ternary diagram. Use {\tt ternaryplot()} to generate the main ternary diagram.
-
-\subsubsection{Examples}
-\begin{verbatim}
-> ternaryplot(s.out$qi$ev)
-> ternarypoints((s.out$qi$ev + s.out$qi$fd))
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{ternaryplot} to create a ternary diagram, to which you
-may add points.
- \item \Sref{s:plot} for an overview of plotting procedures
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau created the {\tt
- ternarypoints()} function to work with {\tt ternaryplot()}.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/zelig.tex b/inst/doc/commands/zelig.tex
deleted file mode 100644
index 723fd94..0000000
--- a/inst/doc/commands/zelig.tex
+++ /dev/null
@@ -1,193 +0,0 @@
-\section{{\tt zelig}: Estimating a Statistical Model}\label{ss:zelig}
-
-\subsubsection{Description}
-The \texttt{zelig()} command estimates a variety of statistical models. Use
-{\tt zelig()} output with {\tt setx()} and {\tt sim()} to compute
-quantities of interest, such as predicted probabilities, expected
-values, and first differences, along with the associated measures of
-uncertainty (standard errors and confidence intervals).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(formula, model, data, by = NULL, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item{\tt formula}: a symbolic representation of the model to be
- estimated, in the form {\tt y \~\, x1 + x2}, where {\tt y} is the
- dependent variable and {\tt x1} and {\tt x2} are the explanatory
- variables, and {\tt y}, {\tt x1}, and {\tt x2} are contained in the
- same dataset. (You may include more than two explanatory variables,
- of course.) The {\tt +} symbol means ``inclusion'' not
- ``addition.'' You may also include interaction terms and main
- effects in the form {\tt x1*x2} without computing them in prior
- steps; {\tt I(x1*x2)} to include only the interaction term and
- exclude the main effects; and quadratic terms in the form {\tt
- I(x1\^{}2)}.
-\item{\tt model}: the name of a statistical model, enclosed in {\tt ""}.
- Currently, the following models are supported:
- \begin{itemize}
- \item {\tt "arima"}: ARIMA regression (see \Sref{arima})
- \item {\tt "blogit"}: Bivariate logistic regression. (see
- \Sref{blogit})
- \item {\tt "bprobit"}: Bivariate probit regression. (see
- \Sref{bprobit})
- \item {\tt "exp"}: Exponential duration regression. (see
- \Sref{exp})
- \item {\tt "factor.bayes"}: Bayesian factor analysis. (see
-\Sref{factor.bayes})
- \item {\tt "factor.mix"}: Mixed data Bayesian factor analysis.
-(see \Sref{factor.mix})
- \item {\tt "factor.ord"}: Bayesian factor analysis for ordinal
-variables. (see \Sref{factor.ord})
- \item {\tt "ei.dynamic"}: Quinn's dyanamic ecological inference
-model for $2 \times 2$ tables. (see \Sref{ei.dynamic})
- \item {\tt "ei.hier"}: Bayesian hierarchical ecological inference
-model for a cross-section of $2 \times 2$ tables. (see
-\Sref{ei.hier})
- \item {\tt "ei.RxC"}: Non-linear least squares approximation to
-the hierarchical Multinomial-Dirichlet ecological inference model in
-contingency tables with more than 2 rows or columns. (see \Sref{eiRxC})
- \item {\tt "gamma"}: Gamma regression. (see
- \Sref{gamma})
- \item {\tt "irt1d"}: Bayesian unidimensional item response theory model.
-(see \Sref{irt1d})
- \item {\tt "irtkd"}: Bayesian $k$-dimensional item response theory
-model. (see \Sref{irtkd})
- \item {\tt "logit"}: Logistic regression. (see
- \Sref{logit})
- \item {\tt "logit.bayes"}: Bayesian logistic regression. (see
-\Sref{logit.bayes})
- \item {\tt "lognorm"}: Log-normal duration regression. (see
- \Sref{lognorm})
- \item {\tt "ls"}: Linear least squares regression. (see
- \Sref{ls})
- \item {\tt "mlogit"}: Multinomial logistic regression. (see
- \Sref{mlogit})
- \item {\tt "mlogit.bayes"}: Bayesian multinomial logistic
-regression. (see \Sref{mlogit.bayes})
-% \item {\tt "mloglm"}: Multinomial log-linear regression. (see
-% \Sref{mloglm})
-% \item {\tt "mprobit"}: Multinomial probit regression. (see
-% \Sref{mprobit})
- \item {\tt "negbin"}: Negative binomial event count regression.
- (see \Sref{negbin})
- \item {\tt "netls"}: Network least squares regression (see
-\Sref{netls})
-\item {\tt "netlogit"}: Network logistic regression (see
-\Sref{netlogit})
-
- \item {\tt "negbin"}: Negative binomial event count regression.
- (see \Sref{negbin})
- \item {\tt "normal"}: Normal linear regression. (see
- \Sref{normal})
- \item {\tt "normal.bayes"}: Bayesian Normal regression (see \Sref{normal.bayes})
- \item {\tt "ologit"}: Ordinal logistic regression. (see
- \Sref{ologit})
- \item {\tt "oprobit"}: Ordinal probit regression. (see
- \Sref{oprobit})
- \item {\tt "oprobit.bayes"}: Bayesian ordinal probit regression.
-(see \Sref{oprobit.bayes})
- \item {\tt "poisson"}: Poisson event count regression. (see
- \Sref{poisson})
- \item {\tt "poisson.bayes"}: Bayesian Poisson regression. (see \Sref{poisson.bayes})
- \item {\tt "probit"}: Probit regression. (see
- \Sref{probit})
- \item {\tt "probit.bayes"}: Bayesian Probit regression. (see \Sref{probit.bayes})
- \item {\tt "relogit"}: Rare events logistic regression. (see
- \Sref{relogit})
- \item {\tt "tobit"}: Tobit regression. (see \Sref{tobit})
- \item {\tt "tobit.bayes"}: Bayesian tobit regression. (see \Sref{tobit.bayes})
- \item {\tt "weibull"}: Weibull regression. (see
- \Sref{weibull})
- \end{itemize}
-\item {\tt data}: the name of a data frame containing the variables
- referenced in the formula, or a list of multiply imputed data frames
- each having the same variable names and number of rows (created by
- {\tt mi()}).
-\item {\tt by}: \label{by} A factor variable contained in {\tt
- data}. Zelig will subset the data frame based on the levels in
- the {\tt by} variable, and estimate a model for each subset. This a
- particularly powerful option which will allow you to save a
- considerable amount of effort. For example, to run the same model
- on all fifty states, you could type:
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "state")
-\end{verbatim}
- You may also use {\tt by} to run models by
- \hlink{MatchIt}{http://gking.harvard.edu/matchit/} subclass.
-
-\item{\tt save.data}: the logical variable indicating whether the
- original data frame should be saved within the output of
- \texttt{zelig()}. If {\tt FALSE} (default), only the name of the
- data frame will be stored.
-
-\item{\tt \dots}: additional arguments passed to \texttt{zelig()},
- depending on the model to be estimated.
-\end{itemize}
-
-\subsubsection{Output Values}
-Depending on the class of model selected, \texttt{zelig()} will return
-an object with elements including \texttt{coefficients}, \texttt{residuals},
-and \texttt{formula} which may be summarized using
-\texttt{summary(z.out)} or individually extracted using, for example,
-\texttt{z.out\$coefficients}. See the specific models listed above
-for additional output values, or simply type {\tt names(z.out)}.
-
-\subsubsection{Examples}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "logit", data = mydata)
-> z.out <- zelig(log(y) ~ x1 + x2, model = "ls", data = mydata)
-> z.out <- zelig(y ~ x1 + I(x2^2), model = "ologit", data = mydata)
-> z.out <- zelig(y ~ x1 * x2, model = "probit", data = mydata)
-> z.out <- zelig(y ~ x1 * x2, model = "probit",
- data = mi(mydata1, mydata2, mydata3, mydata4, mydata5))
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{s:commands} for an overview of the Zelig simulation procedure.
- \item \Sref{s:models} for an overview of supported models.
- \item \Sref{s:syntax} for an overview of R syntax.
- \item \Sref{factors} for how to create factor variables.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau created the {\tt zelig()}
-framework for handling a selection of the statistical models contained
-in the base, survreg, VGAM, and nnet packages for R. For additional
-details, please refer to the appropriate model reference page.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commandsRd/CVS.tex b/inst/doc/commandsRd/CVS.tex
deleted file mode 100644
index 776df6c..0000000
--- a/inst/doc/commandsRd/CVS.tex
+++ /dev/null
@@ -1,2 +0,0 @@
-\section{{\tt }: }\label{ss:}
-
diff --git a/inst/doc/commandsRd/MatchIt.url.tex b/inst/doc/commandsRd/MatchIt.url.tex
deleted file mode 100644
index 34cd196..0000000
--- a/inst/doc/commandsRd/MatchIt.url.tex
+++ /dev/null
@@ -1,6 +0,0 @@
-\section{{\tt MatchIt.url}: Table of links for Zelig}\label{ss:MatchIt.url}
-\keyword{datasets}{MatchIt.url}
-\begin{Description}\relax
-Table of linds for \code{help.zelig} for the companion MatchIt package.
-\end{Description}
-
diff --git a/inst/doc/commandsRd/PErisk.tex b/inst/doc/commandsRd/PErisk.tex
deleted file mode 100644
index e54e5f0..0000000
--- a/inst/doc/commandsRd/PErisk.tex
+++ /dev/null
@@ -1,67 +0,0 @@
-\section{{\tt PErisk}: Political Economic Risk Data from 62 Countries in 1987}\label{ss:PErisk}
-\keyword{datasets}{PErisk}
-\begin{Description}\relax
-Political Economic Risk Data from 62 Countries in 1987.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(PErisk)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A data frame with 62 observations on the following 6 variables.
-All data points are from 1987. See Quinn (2004) for more
-details.
-
-country: a factor with levels 'Argentina' 'Australia' 'Austria'
-'Bangladesh' 'Belgium' 'Bolivia' 'Botswana' 'Brazil' 'Burma'
-'Cameroon' 'Canada' 'Chile' 'Colombia' 'Congo-Kinshasa'
-'Costa Rica' 'Cote d'Ivoire' 'Denmark' 'Dominican Republic'
-'Ecuador' 'Finland' 'Gambia, The' 'Ghana' 'Greece' 'Hungary'
-'India' 'Indonesia' 'Iran' 'Ireland' 'Israel' 'Italy' 'Japan'
-'Kenya' 'Korea, South' 'Malawi' 'Malaysia' 'Mexico' 'Morocco'
-'New Zealand' 'Nigeria' 'Norway' 'Papua New Guinea'
-'Paraguay' 'Philippines' 'Poland' 'Portugal' 'Sierra Leone'
-'Singapore' 'South Africa' 'Spain' 'Sri Lanka' 'Sweden'
-'Switzerland' 'Syria' 'Thailand' 'Togo' 'Tunisia' 'Turkey'
-'United Kingdom' 'Uruguay' 'Venezuela' 'Zambia' 'Zimbabwe'
-
-courts: an ordered factor with levels '0' < '1'.'courts' is an
-indicator of whether the country in question is judged to
-have an independent judiciary. From Henisz (2002).
-
-barb2: a numeric vector giving the natural log of the black market
-premium in each country. The black market premium is coded as
-the black market exchange rate (local currency per dollar)
-divided by the official exchange rate minus 1. From
-Marshall, Gurr, and Harff (2002).
-
-prsexp2: an ordered factor with levels '0' < '1' < '2' < '3' < '4'
-< '5', giving the lack of expropriation risk. From Marshall,
-Gurr, and Harff (2002).
-
-prscorr2: an ordered factor with levels '0' < '1' < '2' < '3' < '4'
-< '5', measuring the lack of corruption. From Marshall, Gurr,
-and Harff (2002).
-
-gdpw2: a numeric vector giving the natural log of real GDP per
-worker in 1985 international prices. From Alvarez et al.
-(1999).
-\end{Format}
-\begin{Source}\relax
-Mike Alvarez, Jose Antonio Cheibub, Fernando Limongi, and Adam
-Przeworski. 1999. ``ACLP Political and Economic Database.'' <URL:
-http://www.ssc.upenn.edu/~cheibub/data/>.
-
-Witold J. Henisz. 2002. ``The Political Constraint Index (POLCON)
-Dataset.'' \ <URL:
-http://www-management.wharton.upenn.edu/henisz/POLCON/ContactInfo.
-html>.
-
-Monty G. Marshall, Ted Robert Gurr, and Barbara Harff. 2002.
-``State Failure Task Force Problem Set.'' <URL:
-http://www.cidcm.umd.edu/inscr/stfail/index.htm>.
-\end{Source}
-\begin{References}\relax
-Kevin M. Quinn. 2004. ``Bayesian Factor Analysis for Mixed Ordinal
-and Continuous Response.'' \emph{Political Analyis}. Vol. 12, pp.338--353.
-\end{References}
-
diff --git a/inst/doc/commandsRd/SupremeCourt.tex b/inst/doc/commandsRd/SupremeCourt.tex
deleted file mode 100644
index fa043da..0000000
--- a/inst/doc/commandsRd/SupremeCourt.tex
+++ /dev/null
@@ -1,26 +0,0 @@
-\section{{\tt SupremeCourt}: U.S. Supreme Court Vote Matrix}\label{ss:SupremeCourt}
-\keyword{datasets}{SupremeCourt}
-\begin{Description}\relax
-This dataframe contains a matrix votes cast by U.S. Supreme Court
-justices in all cases in the 2000 term.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(SupremeCourt)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-The dataframe has contains data for justices Rehnquist, Stevens,
-O'Connor, Scalia, Kennedy, Souter, Thomas, Ginsburg, and Breyer
-for the 2000 term of the U.S. Supreme Court. It contains data
-from 43 non-unanimous cases. The votes are coded liberal (1) and
-conservative (0) using the protocol of Spaeth (2003). The unit
-of analysis is the case citation (ANALU=0). We are concerned with
-formally decided cases issued with written opinions, after full
-oral argument and cases decided by an equally divided vote
-(DECTYPE=1,5,6,7).
-\end{Format}
-\begin{Source}\relax
-Harold J. Spaeth (2005). ``Original United States Supreme Court
-Database: 1953-2004 Terms.''
-<URL:http://www.as.uky.edu/polisci/ulmerproject/sctdata.htm>.
-\end{Source}
-
diff --git a/inst/doc/commandsRd/Weimar.tex b/inst/doc/commandsRd/Weimar.tex
deleted file mode 100644
index 2768278..0000000
--- a/inst/doc/commandsRd/Weimar.tex
+++ /dev/null
@@ -1,26 +0,0 @@
-\section{{\tt Weimar}: 1932 Weimar election data}\label{ss:Weimar}
-\keyword{datasets}{Weimar}
-\begin{Description}\relax
-This data set contains election results for 10 kreise (equivalent to precincts) from the 1932 Weimar (German) election.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(Weimar)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 11 variables and 10 observations. The variables are {
-{Nazi}{Number of votes for the Nazi party}
-{Government}{Number of votes for the Government}
-{Communists}{Number of votes for the Communist party}
-{FarRight}{Number of votes for far right parties}
-{Other}{Number of votes for other parties, and non-voters}
-{shareunemployed}{Proportion unemployed}
-{shareblue}{Proportion working class}
-{sharewhite}{Proportion white-collar workers}
-{sharedomestic}{Proportion domestic servants}
-{shareprotestants}{Proportion Protestant}
-}
-\end{Format}
-\begin{Source}\relax
-ICPSR
-\end{Source}
-
diff --git a/inst/doc/commandsRd/Zelig.tex b/inst/doc/commandsRd/Zelig.tex
deleted file mode 100644
index 70e3e5c..0000000
--- a/inst/doc/commandsRd/Zelig.tex
+++ /dev/null
@@ -1,62 +0,0 @@
-\section{{\tt zelig}: Estimating a Statistical Model}\label{ss:zelig}
-\aliasA{Zelig}{zelig}{Zelig}
-\keyword{file}{zelig}
-\begin{Description}\relax
-The \code{zelig} command estimates a variety of statistical
-models. Use \code{zelig} output with \code{setx} and \code{sim} to compute
-quantities of interest, such as predicted probabilities, expected values, and
-first differences, along with the associated measures of uncertainty
-(standard errors and confidence intervals).
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-z.out <- zelig(formula, model, data, by, ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{formula}] a symbolic representation of the model to be
-estimated, in the form \code{y \textasciitilde{}\\, x1 + x2}, where \code{y} is the
-dependent variable and \code{x1} and \code{x2} are the explanatory
-variables, and \code{y}, \code{x1}, and \code{x2} are contained in the
-same dataset. (You may include more than two explanatory variables,
-of course.) The \code{+} symbol means ``inclusion'' not
-``addition.'' You may also include interaction terms and main
-effects in the form \code{x1*x2} without computing them in prior
-steps; \code{I(x1*x2)} to include only the interaction term and
-exclude the main effects; and quadratic terms in the form
-\code{I(x1\textasciicircum{}2)}.
-\item[\code{model}] the name of a statistical model, enclosed in \code{""}.
-Type \code{help.zelig("models")} to see a list of currently supported
-models.
-\item[\code{data}] the name of a data frame containing the variables
-referenced in the formula, or a list of multiply imputed data frames
-each having the same variable names and row numbers (created by \code{mi}).
-\item[\code{by}] a factor variable contained in \code{data}. Zelig will subset
-the data frame based on the levels in the \code{by} variable, and
-estimate a model for each subset. This a particularly powerful option
-which will allow you to save a considerable amount of effort. For
-example, to run the same model on all fifty states, you could type:
-\code{z.out <- zelig(y \textasciitilde{} x1 + x2, data = mydata, model = "ls", by = "state")}
-You may also use \code{by} to run models using MatchIt subclass.
-\item[\code{...}] additional arguments passed to \code{zelig},
-depending on the model to be estimated.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-Depending on the class of model selected, \code{zelig} will return
-an object with elements including \code{coefficients}, \code{residuals},
-and \code{formula} which may be summarized using
-\code{summary(z.out)} or individually extracted using, for example,
-\code{z.out\$coefficients}. See the specific models listed above
-for additional output values, or simply type \code{names(z.out)}.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual is available at
-\url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/Zelig.url.tex b/inst/doc/commandsRd/Zelig.url.tex
deleted file mode 100644
index b4beedf..0000000
--- a/inst/doc/commandsRd/Zelig.url.tex
+++ /dev/null
@@ -1,6 +0,0 @@
-\section{{\tt Zelig.url}: Table of links for Zelig}\label{ss:Zelig.url}
-\keyword{datasets}{Zelig.url}
-\begin{Description}\relax
-Table of linds for \code{help.zelig} for the core Zelig package.
-\end{Description}
-
diff --git a/inst/doc/commandsRd/approval.tex b/inst/doc/commandsRd/approval.tex
deleted file mode 100644
index a4caeb3..0000000
--- a/inst/doc/commandsRd/approval.tex
+++ /dev/null
@@ -1,20 +0,0 @@
-\section{{\tt approval}: U.S. Presidential Approval Data}\label{ss:approval}
-\keyword{datasets}{approval}
-\begin{Description}\relax
-Monthy public opinion data for 2001-2006.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(approval)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 8 variables ("month", "year", "approve",
-"disapprove", "unsure", "sept.oct.2001", "iraq.war", and "avg.price")
-and 65 observations.
-\end{Format}
-\begin{Source}\relax
-ICPSR
-\end{Source}
-\begin{References}\relax
-Stuff here
-\end{References}
-
diff --git a/inst/doc/commandsRd/coalition.tex b/inst/doc/commandsRd/coalition.tex
deleted file mode 100644
index 4cfc6d6..0000000
--- a/inst/doc/commandsRd/coalition.tex
+++ /dev/null
@@ -1,31 +0,0 @@
-\section{{\tt coalition}: Coalition Dissolution in Parliamentary Democracies}\label{ss:coalition}
-\keyword{datasets}{coalition}
-\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. For
-parsimony, country indicator variables are omitted in the sample data.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(coalition)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 7 variables ("duration", "ciep12", "invest",
-"fract", "polar", "numst2", "crisis") 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}
-
diff --git a/inst/doc/commandsRd/current.packages.tex b/inst/doc/commandsRd/current.packages.tex
deleted file mode 100644
index 47b8fd3..0000000
--- a/inst/doc/commandsRd/current.packages.tex
+++ /dev/null
@@ -1,35 +0,0 @@
-\section{{\tt current.packages}: Find all packages in a dependency chain}\label{ss:current.packages}
-\keyword{file}{current.packages}
-\begin{Description}\relax
-Use \code{current.packages} to find all the packages suggested or
-required by a given package, and the currently installed version number
-for each.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-current.packages(package)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{package}] a character string corresponding to the name of an
-installed package
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-A matrix containing the current version number of the packages
-suggested or required by \code{package}.
-\end{Value}
-\begin{Author}\relax
-Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{packageDescription}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-## Not run:
-current.packages("Zelig")
-## End(Not run)\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/dims.tex b/inst/doc/commandsRd/dims.tex
deleted file mode 100644
index 67acdd9..0000000
--- a/inst/doc/commandsRd/dims.tex
+++ /dev/null
@@ -1,37 +0,0 @@
-\section{{\tt dims}: Return Dimensions of Vectors, Arrays, and Data Frames}\label{ss:dims}
-\keyword{file}{dims}
-\begin{Description}\relax
-Retrieve the dimensions of a vector, array, or data frame.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-dims(x)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{x}] An R object. For example, a vector, matrix, array, or data
-frame.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-The function \code{dims} performs exactly the same as \code{dim}, and
-additionally returns the \code{length} of vectors (treating them as
-one-dimensional arrays).
-\end{Value}
-\begin{Author}\relax
-Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{dim}, \code{length}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-a <- 1:12
-dims(a)
-
-a <- matrix(1, nrow = 4, ncol = 9)
-dims(a)
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/eidat.tex b/inst/doc/commandsRd/eidat.tex
deleted file mode 100644
index 40c02cf..0000000
--- a/inst/doc/commandsRd/eidat.tex
+++ /dev/null
@@ -1,14 +0,0 @@
-\section{{\tt eidat}: Simulation Data for Ecological Inference}\label{ss:eidat}
-\keyword{datasets}{eidat}
-\begin{Description}\relax
-This dataframe contains a simulated data set to illustrate the models
-for ecological inference.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(eidat)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 4 variables ("t0", "t1", "x0", "x1") and 10
-observations.
-\end{Format}
-
diff --git a/inst/doc/commandsRd/friendship.tex b/inst/doc/commandsRd/friendship.tex
deleted file mode 100644
index 034ceeb..0000000
--- a/inst/doc/commandsRd/friendship.tex
+++ /dev/null
@@ -1,15 +0,0 @@
-\section{{\tt friendship}: Simulated Example of Schoolchildren Friendship Network}\label{ss:friendship}
-\keyword{datasets}{friendship}
-\begin{Description}\relax
-This data set contains five sociomatrices of simulated data on friendship ties among schoolchildren.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(friendship)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-Each variable in the dataset is a 15 by 15 matrix representing some form of social network tie held by the fictitious children. The matrices are labeled "friends", "advice", "prestige", "authority", and "perpower".
-\end{Format}
-\begin{Source}\relax
-fictitious
-\end{Source}
-
diff --git a/inst/doc/commandsRd/gsource.tex b/inst/doc/commandsRd/gsource.tex
deleted file mode 100644
index 863a91c..0000000
--- a/inst/doc/commandsRd/gsource.tex
+++ /dev/null
@@ -1,49 +0,0 @@
-\section{{\tt gsource}: Read Data As a Space-Delimited Table}\label{ss:gsource}
-\keyword{file}{gsource}
-\begin{Description}\relax
-The \code{gsource} function allows you to read a space delimited table
-as a data frame. Unlike \code{scan}, you may use \code{gsource} in a
-\code{source}ed file, and unlike \code{read.table}, you may use
-\code{gsource} to include a small (or large) data set in a file that
-also contains other commands.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-gsource(var.names = NULL, variables)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{var.names}] An optional vector of character strings representing
-the column names. By default, \code{var.names = NULL}.
-\item[\code{variables}] A single character string representing the data.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-The output from \code{gsource} is a data frame, which you may save to
-an object in your workspace.
-\end{Value}
-\begin{Author}\relax
-Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{read.table}, \code{scan}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-## Not run:
-data <- gsource(variables = "
- 1 2 3 4 5
- 6 7 8 9 10
- 3 4 5 1 3
- 6 7 8 1 9 ")
-
-data <- gsource(var.names = "Vote Age Party", variables = "
- 0 23 Democrat
- 0 27 Democrat
- 1 45 Republican
- 1 65 Democrat ")
-## End(Not run)
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/help.zelig.tex b/inst/doc/commandsRd/help.zelig.tex
deleted file mode 100644
index da777c7..0000000
--- a/inst/doc/commandsRd/help.zelig.tex
+++ /dev/null
@@ -1,29 +0,0 @@
-\section{{\tt help.zelig}: HTML Help for Zelig Commands and Models}\label{ss:help.zelig}
-\keyword{documentation}{help.zelig}
-\begin{Description}\relax
-The \code{help.zelig} command launches html help for Zelig commands
-and supported models. The full manual is available online at
-\url{http://gking.harvard.edu/zelig}.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-help.zelig(...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{...}] a Zelig command or model.
-\code{help.zelig(command)} will take you to an index of Zelig
-commands and \code{help.zelig(model)} will take you to a list of
-models.
-\end{ldescription}
-\end{Arguments}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau<\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The complete document is available online at
-\url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/hoff.tex b/inst/doc/commandsRd/hoff.tex
deleted file mode 100644
index 203f2ca..0000000
--- a/inst/doc/commandsRd/hoff.tex
+++ /dev/null
@@ -1,24 +0,0 @@
-\section{{\tt hoff}: Social Security Expenditure Data}\label{ss:hoff}
-\keyword{datasets}{hoff}
-\begin{Description}\relax
-This data set contains annual social security expenditure (as percent
-of budget lagged by two years), the
-relative frequency of mentions social justice received in the party's
-platform in each year, and whether the president is Republican or
-Democrat.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(hoff)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 5 variables ("year", "L2SocSec", "Just503D", "Just503R", "RGovDumy") and 36 observations.
-\end{Format}
-\begin{Source}\relax
-ICPSR (replication dataset s1109)
-\end{Source}
-\begin{References}\relax
-Gary King and Michael Laver. ``On Party Platforms, Mandates, and
-Government Spending,'' \emph{American Political Science Review},
-Vol. 87, No. 3 (September, 1993): pp. 744-750.
-\end{References}
-
diff --git a/inst/doc/commandsRd/immigration.tex b/inst/doc/commandsRd/immigration.tex
deleted file mode 100644
index 56a5f4f..0000000
--- a/inst/doc/commandsRd/immigration.tex
+++ /dev/null
@@ -1,28 +0,0 @@
-\section{{\tt immigration}: Individual Preferences Over Immigration Policy}\label{ss:immigration}
-\aliasA{immi1}{immigration}{immi1}
-\aliasA{immi2}{immigration}{immi2}
-\aliasA{immi3}{immigration}{immi3}
-\aliasA{immi4}{immigration}{immi4}
-\aliasA{immi5}{immigration}{immi5}
-\keyword{datasets}{immigration}
-\begin{Description}\relax
-These five datasets are part of a larger set of 10 multiply
-imputed data sets describing individual preferences toward immigration
-policy. Imputation was performed via Amelia.
-\end{Description}
-\begin{Format}\relax
-Each multiply-inputed data set consists of a table with 7 variables
-("ipip", "wage1992", "prtyid",
-"ideol", "gender") and 2,485 observations. For variable descriptions,
-please refer to Scheve and
-Slaugher, 2001.
-\end{Format}
-\begin{Source}\relax
-National Election Survey
-\end{Source}
-\begin{References}\relax
-Scheve, Kenneth and Matthew Slaughter (2001). ``Labor Market Competition
-and Individual Preferences Over Immigration Policy,'' \emph{The Review of
-Economics and Statistics}, vol. 83, no. 1, pp. 133-145.
-\end{References}
-
diff --git a/inst/doc/commandsRd/macro.tex b/inst/doc/commandsRd/macro.tex
deleted file mode 100644
index 32e2cbc..0000000
--- a/inst/doc/commandsRd/macro.tex
+++ /dev/null
@@ -1,28 +0,0 @@
-\section{{\tt macro}: Macroeconomic Data}\label{ss:macro}
-\keyword{datasets}{macro}
-\begin{Description}\relax
-Selected macroeconomic indicators for Austria, Belgium, Canada,
-Denmark, Finland, France, Italy, Japan, the Netherlands, Norway,
-Sweden, the United Kingdom, the United States, and West Germany for
-the period 1966-1990.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(macro)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 6 variables ("country", "year", "gdp",
-"unem", "capmob", and "trade") and 350 observations.
-\end{Format}
-\begin{Source}\relax
-ICPSR
-\end{Source}
-\begin{References}\relax
-King, Gary, Michael Tomz and Jason Wittenberg. ICPSR Publication
-Related Archive, 1225.
-
-King, Gary, Michael Tomz and Jason Wittenberg (2000).
-``Making the Most of Statistical Analyses: Improving Interpretation and
-Presentation,'' \emph{American Journal of Political Science}, vol. 44,
-pp. 341-355.
-\end{References}
-
diff --git a/inst/doc/commandsRd/match.data.tex b/inst/doc/commandsRd/match.data.tex
deleted file mode 100644
index 09d1d89..0000000
--- a/inst/doc/commandsRd/match.data.tex
+++ /dev/null
@@ -1,43 +0,0 @@
-\section{{\tt match.data}: Output matched data sets}\label{ss:match.data}
-\keyword{methods}{match.data}
-\begin{Description}\relax
-The code \code{match.data} creates output data sets from the \code{matchit}
-matching algorithm.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-match.data <- match.data(object, group = "all")
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{object}] Stored output from \code{matchit}.
-\item[\code{group}] Which units to output. Selecting "all" (default) gives all
-matched units (treated and control), "treat" gives just the matched
-treated units, and "control" gives just the matched control units.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-The \code{match.data} command generates a matched data set from
-the output of the \code{matchit} function, according to the options
-selected in the \code{group} argument. The matched data set contains
-the additional variables:
-\begin{ldescription}
-\item[\code{pscore}] The propensity score for each unit.
-\item[\code{psclass}] The subclass index for each unit (if applicable).
-\item[\code{psweights}] The weight for each unit (generated from the matching
-procedure).
-\end{ldescription}
-
-See the \code{matchit} documentation for more details on these items.
-\end{Value}
-\begin{Author}\relax
-Daniel Ho <\email{deho at fas.harvard.edu}>; Kosuke Imai
-<\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Elizabeth Stuart<\email{stuart at stat.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The complete documentation for \code{matchit} is available online at
-\url{http://gking.harvard.edu/matchit}.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/mexico.tex b/inst/doc/commandsRd/mexico.tex
deleted file mode 100644
index 90d1bfa..0000000
--- a/inst/doc/commandsRd/mexico.tex
+++ /dev/null
@@ -1,24 +0,0 @@
-\section{{\tt mexico}: Voting Data from the 1988 Mexican Presidental Election}\label{ss:mexico}
-\keyword{datasets}{mexico}
-\begin{Description}\relax
-This dataset contains voting data for the 1988 Mexican presidential
-election.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(mexico)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 33 variables and 1,359 observations.
-\end{Format}
-\begin{Source}\relax
-ICPSR
-\end{Source}
-\begin{References}\relax
-King, Gary, Michael Tomz and Jason Wittenberg (2000).
-``Making the Most of Statistical Analyses: Improving Interpretation and
-Presentation,'' \emph{American Journal of Political Science}, vol. 44,
-pp. 341-355.
-
-King, Tomz and Wittenberg. ICPSR Publication Related Archive, 1255.
-\end{References}
-
diff --git a/inst/doc/commandsRd/mi.aux b/inst/doc/commandsRd/mi.aux
deleted file mode 100644
index bb7623b..0000000
--- a/inst/doc/commandsRd/mi.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {11.2}{\tt mi}: Bundle multiply imputed data sets as a list}{114}{section.11.2}}
-\newlabel{ss:mi}{{11.2}{114}{{\tt mi}: Bundle multiply imputed data sets as a list\relax }{section.11.2}{}}
-\@setckpt{commandsRd/mi}{
-\setcounter{page}{115}
-\setcounter{equation}{0}
-\setcounter{enumi}{4}
-\setcounter{enumii}{3}
-\setcounter{enumiii}{2}
-\setcounter{enumiv}{0}
-\setcounter{footnote}{2}
-\setcounter{mpfootnote}{0}
-\setcounter{part}{3}
-\setcounter{chapter}{11}
-\setcounter{section}{2}
-\setcounter{subsection}{0}
-\setcounter{subsubsection}{0}
-\setcounter{paragraph}{0}
-\setcounter{subparagraph}{0}
-\setcounter{figure}{0}
-\setcounter{table}{0}
-\setcounter{LT at tables}{0}
-\setcounter{LT at chunks}{0}
-\setcounter{FancyVerbLine}{0}
-\setcounter{NAT at ctr}{0}
-\setcounter{parentequation}{0}
-\setcounter{Item}{216}
-\setcounter{Hfootnote}{10}
-\setcounter{lpart}{0}
-\setcounter{lchapter}{0}
-\setcounter{lsection}{0}
-\setcounter{lsubsection}{0}
-\setcounter{lsubsubsection}{7}
-\setcounter{lparagraph}{0}
-\setcounter{lsubparagraph}{0}
-\setcounter{lsubsubparagraph}{0}
-\setcounter{section at level}{1}
-}
diff --git a/inst/doc/commandsRd/mi.tex b/inst/doc/commandsRd/mi.tex
deleted file mode 100644
index a11b51b..0000000
--- a/inst/doc/commandsRd/mi.tex
+++ /dev/null
@@ -1,39 +0,0 @@
-\section{{\tt mi}: Bundle multiply imputed data sets as a list}\label{ss:mi}
-\keyword{methods}{mi}
-\begin{Description}\relax
-The code \code{mi} bundles multiply imputed data sets as a
-list for further analysis.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
- mi(...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{...}] multiply imputed data sets, separated by commas. The
-arguments can be tagged by \code{name=data} where \code{name} is the
-element named used for the data set \code{data}.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-The list containing each multiply imputed data set as an
-element. The class name is \code{mi}. The list can be inputted into
-\code{zelig} for statistical analysis with multiply imputed data
-sets. See \code{zelig} for details.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual is available at
-\url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
- data(immi1, immi2, immi3, immi4, immi5)
- mi(immi1, immi2, immi3, immi4, immi5)
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/mid.tex b/inst/doc/commandsRd/mid.tex
deleted file mode 100644
index 02688ca..0000000
--- a/inst/doc/commandsRd/mid.tex
+++ /dev/null
@@ -1,28 +0,0 @@
-\section{{\tt mid}: Militarized Interstate Disputes}\label{ss:mid}
-\keyword{datasets}{mid}
-\begin{Description}\relax
-A small sample from the militarized interstate disputes database,
-available at \url{http://pss.la.psu.edu/MID_DATA.HTM}.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(mid)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 6 variables ("conflict", "major", "contig",
-"power", "maxdem", "mindem", and "years") and 3,126 observations. For
-full variable descriptions, please see King and Zeng, 2001.
-\end{Format}
-\begin{Source}\relax
-Militarized Interstate Disputes database
-\end{Source}
-\begin{References}\relax
-King, Gary, and Lanche Zeng (2001). ``Explaining Rare Events in
-International Relations,'' \emph{International Organization}, vol. 55,
-no. 3, pp. 693-715.
-
-Jones, Daniel M., Stuart A. Bremer and David Singer (1996). ``Militarized
-Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical
-Patterns,'' \emph{Conflict Management and Peace Science}, vol. 15,
-no. 2, pp. 163-213.
-\end{References}
-
diff --git a/inst/doc/commandsRd/model.end.tex b/inst/doc/commandsRd/model.end.tex
deleted file mode 100644
index e6e4326..0000000
--- a/inst/doc/commandsRd/model.end.tex
+++ /dev/null
@@ -1,44 +0,0 @@
-\section{{\tt model.end}: Cleaning up after optimization}\label{ss:model.end}
-\keyword{utilities}{model.end}
-\begin{Description}\relax
-The \code{model.end} function creates a list of regression output from \code{\LinkA{optim}{optim}}
-output. The list includes coefficients (from the \code{\LinkA{optim}{optim}} \code{par} output), a
-variance-covariance matrix (from the \code{\LinkA{optim}{optim}} Hessian output), and any terms, contrasts, or
-xlevels (from the model frame). Use \code{model.end} after calling \code{\LinkA{optim}{optim}}, but before
-assigning a
-class to the regression output.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-model.end(res, mf)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{res}] the output from \code{\LinkA{optim}{optim}} or another fitting-algorithm
-\item[\code{mf}] the model frame output by \code{\LinkA{model.frame}{model.frame}}
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-A list of regression output, including:
-\begin{ldescription}
-\item[\code{coefficients}] the optimized parameters
-\item[\code{variance}] the variance-covariance matrix (the negative
-inverse of the Hessian matrix returned from the optimization
-procedure)
-\item[\code{terms}] the terms object. See \code{\LinkA{terms.object}{terms.object}}
-for more information
-\item[\code{...}] additional elements passed from \code{res}
-\end{ldescription}
-
-normal-bracket37bracket-normal
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
-<\email{falimadhi at iq.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual at \url{http://gking.harvard.edu/zelig} for examples.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/model.frame.multiple.tex b/inst/doc/commandsRd/model.frame.multiple.tex
deleted file mode 100644
index 1c30acd..0000000
--- a/inst/doc/commandsRd/model.frame.multiple.tex
+++ /dev/null
@@ -1,56 +0,0 @@
-\section{{\tt model.frame.multiple}: Extracting the ``environment'' of a model formula}\label{ss:model.frame.multiple}
-\keyword{utilities}{model.frame.multiple}
-\begin{Description}\relax
-Use \code{model.frame.multiple} after \code{\LinkA{parse.par}{parse.par}} to create a
-data frame of the unique variables identified in the formula (or list
-of formulas).
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-model.frame.multiple(formula, data, eqn = NULL, ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{formula}] a list of formulas of class \code{"multiple"}, returned from \code{\LinkA{parse.par}{parse.par}}
-\item[\code{data}] a data frame containing all the variables used in \code{formula}
-\item[\code{eqn}] an optional character string or vector of character strings specifying
-the equations (specified in \code{describe.mymodel}) for which you would like to
-pull out the relevant variables.
-\item[\code{...}] additional arguments passed to
-\code{\LinkA{model.frame.default}{model.frame.default}}
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-The output is a data frame (with a terms attribute) containing all the
-unique explanatory and response variables identified in the list of
-formulas. By default, missing (\code{NA}) values are listwise deleted.
-
-If \code{as.factor} appears on the left-hand side, the response
-variables will be returned as an indicator (0/1) matrix with columns
-corresponding to the unique levels in the factor variable.
-
-If any formula contains more than one \code{tag} statement, \code{model.frame.multiple}
-will return the original variable in the data frame and use the \code{tag} information in the terms
-attribute only.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
-<\email{falimadhi at iq.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{\LinkA{model.matrix.default}{model.matrix.default}}, \code{\LinkA{parse.formula}{parse.formula}} and the full Zelig manual at
-\url{http://gking.harvard.edu/zelig}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-## Not run:
-data(sanction)
-formulae <- list(import ~ coop + cost + target,
- export ~ coop + cost + target)
-fml <- parse.formula(formulae, model = "bivariate.logit")
-D <- model.frame(fml, data = sanction)
-## End(Not run)\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/model.matrix.multiple.tex b/inst/doc/commandsRd/model.matrix.multiple.tex
deleted file mode 100644
index da76b16..0000000
--- a/inst/doc/commandsRd/model.matrix.multiple.tex
+++ /dev/null
@@ -1,83 +0,0 @@
-\section{{\tt model.matrix.multiple}: Design matrix for multivariate models}\label{ss:model.matrix.multiple}
-\keyword{utilities}{model.matrix.multiple}
-\begin{Description}\relax
-Use \code{model.matrix.multiple} after \code{\LinkA{parse.formula}{parse.formula}} to
-create a design matrix for multiple-equation models.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-model.matrix.multiple(object, data, shape = "compact", eqn = NULL, ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{object}] the list of formulas output from \code{\LinkA{parse.formula}{parse.formula}}
-\item[\code{data}] a data frame created with \code{\LinkA{model.frame.multiple}{model.frame.multiple}}
-\item[\code{shape}] a character string specifying the shape of the outputed matrix. Available options are
-\Itemize{
-\item["compact"] (default) the output matrix will be an \eqn{n \times v}{n x v},
-where \eqn{v}{} is the number of unique variables in all of the equations
-(including the intercept term)
-\item["array"] the output is an \eqn{n \times K \times J}{n x K x J} array where \eqn{J}{} is the
-total number of equations and \eqn{K}{} is the total number of parameters
-across all the equations. If a variable is not in a certain equation,
-it is observed as a vector of 0s.
-\item["stacked"] the output will be a \eqn{2n \times K}{2n x K} matrix where \eqn{K}{} is the total number of
-parameters across all the equations.
-}
-\item[\code{eqn}] a character string or a vector of character strings identifying the equations from which to
-construct the design matrix. The defaults to \code{NULL}, which only uses the systematic
-parameters (for which \code{DepVar = TRUE} in the appropriate \code{describe.model} function)
-\item[\code{...}] additional arguments passed to \code{model.matrix.default}
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-A design matrix or array, depending on the options chosen in \code{shape}, with appropriate terms
-attributes.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
-<\email{falimadhi at iq.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{\LinkA{parse.par}{parse.par}}, \code{\LinkA{parse.formula}{parse.formula}} and the full Zelig manual at
-\url{http://gking.harvard.edu/zelig}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-
-# Let's say that the name of the model is "bivariate.probit", and
-# the corresponding describe function is describe.bivariate.probit(),
-# which identifies mu1 and mu2 as systematic components, and an
-# ancillary parameter rho, which may be parameterized, but is estimated
-# as a scalar by default. Let par be the parameter vector (including
-# parameters for rho), formulae a user-specified formula, and mydata
-# the user specified data frame.
-
-# Acceptable combinations of parse.par() and model.matrix() are as follows:
-## Setting up
-## Not run:
-data(sanction)
-formulae <- cbind(import, export) ~ coop + cost + target
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = sanction)
-terms <- attr(D, "terms")
-
-## Intuitive option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
-eta <- X
-
-## Memory-efficient (compact) option (default)
-Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
-eta <- X
-
-## Computationally-efficient (array) option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
-eta <- apply(X, 3, '
-## End(Not run)\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/network.aux b/inst/doc/commandsRd/network.aux
deleted file mode 100644
index d287bdd..0000000
--- a/inst/doc/commandsRd/network.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {11.3}{\tt network}: Format matricies into a data frame for social network analysis}{115}{section.11.3}}
-\newlabel{ss:network}{{11.3}{115}{{\tt network}: Format matricies into a data frame for social network analysis\relax }{section.11.3}{}}
-\@setckpt{commandsRd/network}{
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diff --git a/inst/doc/commandsRd/network.tex b/inst/doc/commandsRd/network.tex
deleted file mode 100644
index 03cce83..0000000
--- a/inst/doc/commandsRd/network.tex
+++ /dev/null
@@ -1,42 +0,0 @@
-\section{{\tt network}: Format matricies into a data frame for social network analysis}\label{ss:network}
-\keyword{methods}{network}
-\begin{Description}\relax
-This function accepts individual matricies as its inputs,
-combining the input matricies into a single data frame which can then be
-used in the \code{data} argument for social network analysis (models
-\code{"netlm"} and \code{"netlogit"}) in Zelig.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-network(...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{...}] matricies representing variables, with rows and columns corresponding to
-individuals. These can be given as named arguments and should be
-given in the order the in which the user wishes them to appear in
-the output data frame.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-The \code{network} function creates a data frame which
-contains matricies instead of vectors as its variables. Inputs to the
-function should all be square matricies and can be given as named
-arguments.
-\end{Value}
-\begin{Author}\relax
-Skyler J. Cranmer
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual is available at
-\url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}## Not run:
-## Let Var1, Var2, Var3, Var4, and Var5 be matrices
-friendship <- network(Var1, Var2, Var3, Var4, Var5)
-## End(Not run)
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/newpainters.tex b/inst/doc/commandsRd/newpainters.tex
deleted file mode 100644
index 882a6a4..0000000
--- a/inst/doc/commandsRd/newpainters.tex
+++ /dev/null
@@ -1,32 +0,0 @@
-\section{{\tt newpainters}: The Discretized Painter's Data of de Piles}\label{ss:newpainters}
-\keyword{datasets}{newpainters}
-\begin{Description}\relax
-The original painters data contain the subjective assessment,
-on a 0 to 20 integer scale, of 54 classical painters. The
-newpainters data discretizes the subjective assessment by
-quartiles with thresholds 25\%, 50\%, 75\%. The painters were
-assessed on four characteristics: composition, drawing,
-colour and expression. The data is due to the Eighteenth century
-art critic, de Piles.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(newpainters)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 5 variables ("Composition", "Drawing", "Colour",
-"Expression", and "School") and 54 observations.
-\end{Format}
-\begin{Source}\relax
-A. J. Weekes (1986).``A Genstat Primer''. Edward Arnold.
-
-M. Davenport and G. Studdert-Kennedy (1972). ``The statistical
-analysis of aesthetic judgement: an exploration.'' \emph{Applied
-Statistics}, vol. 21, pp. 324--333.
-
-I. T. Jolliffe (1986) ``Principal Component Analysis.'' Springer.
-\end{Source}
-\begin{References}\relax
-Venables, W. N. and Ripley, B. D. (2002) ``Modern Applied
-Statistics with S,'' Fourth edition. Springer.
-\end{References}
-
diff --git a/inst/doc/commandsRd/parse.formula.tex b/inst/doc/commandsRd/parse.formula.tex
deleted file mode 100644
index 83e689d..0000000
--- a/inst/doc/commandsRd/parse.formula.tex
+++ /dev/null
@@ -1,77 +0,0 @@
-\section{{\tt parse.formula}: Parsing user-input formulas into multiple syntax}\label{ss:parse.formula}
-\keyword{utilities}{parse.formula}
-\begin{Description}\relax
-Parse the input formula (or list of formulas) into the
-standard format described below. Since labels for this format will vary
-by model, \code{parse.formula} will evaluate a function \code{describe.model},
-where \code{model} is given as an input to \code{parse.formula}.
-
-If the \code{describe.model} function has more than one parameter for
-which \code{ExpVar = TRUE} and \code{DepVar = TRUE}, then the
-user-specified equations must have labels to match those parameters,
-else \code{parse.formula} should return an error. In addition, if the
-formula entries are not unambiguous, then \code{parse.formula} returns an error.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-parse.formula(formula, model, data = NULL)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{formula}] either a single formula or a list of \code{formula} objects
-\item[\code{model}] a character string specifying the name of the model
-\item[\code{data}] an optional data frame for models that require a factor response variable
-\end{ldescription}
-\end{Arguments}
-\begin{Details}\relax
-Acceptable user inputs are as follows:
-
-\Tabular{lll}{
-& User Input & Output from \code{parse.formula}\\
-& & \\
-Same covariates, & cbind(y1, y2) ~ x1 + x2 * x3 & list(mu1 = y1 ~ x1 + x2 * x3,\\
-separate effects & & mu2 = y2 ~ x1 + x2 * x3,\\
-& & rho = ~ 1)\\
-& & \\
-With \code{rho} as a & list(cbind(y1, y2) ~ x1 + x2, & list(mu1 = y1 ~ x1 + x2,\\
-systematic equation & rho = ~ x4 + x5) & mu2 = y2 ~ x1 + x2,\\
-& & rho = ~ x4 + x5)\\
-& & \\
-With constraints & list(mu1 = y1 ~ x1 + tag(x2, "x2"), & list(mu1 = y1 ~ x1 + tag(x2, "x2"),\\
-(same variable) & mu2 = y2 ~ x3 + tag(x2, "x2")) & mu2 = y2 ~ x3 + tag(x2, "x2"),\\
-& & rho = ~ 1)\\
-& & \\
-With constraints & list(mu1 = y1 ~ x1 + tag(x2, "z1"), & list(mu1 = y1 ~ x1 + tag(x2, "z1"),\\
-(different variables) & mu2 = y2 ~ x3 + tag(x4, "z1")) & mu2 = y2 ~ x3 + tag(x4, "z1"),\\
-& & rho = ~ 1)\\
-}
-\end{Details}
-\begin{Value}
-The output is a list of formula objects with class
-\code{c("multiple", "list")}. Let's say that the name of the model is
-\code{"bivariate.probit"}, and the corresponding describe function is
-\code{describe.bivariate.probit}, which identifies \code{mu1} and
-\code{mu2} as systematic components, and an ancillary parameter \code{rho}, which
-may be parameterized, but is estimated as a scalar by default.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
-<\email{falimadhi at iq.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{\LinkA{parse.par}{parse.par}}, \code{\LinkA{model.frame.multiple}{model.frame.multiple}},
-\code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}, and the full Zelig manual at
-\url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-## Not run:
-data(sanction)
-formulae <- list(cbind(import, export) ~ coop + cost + target)
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = sanction)
-## End(Not run)\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/parse.par.tex b/inst/doc/commandsRd/parse.par.tex
deleted file mode 100644
index 877e3d7..0000000
--- a/inst/doc/commandsRd/parse.par.tex
+++ /dev/null
@@ -1,82 +0,0 @@
-\section{{\tt parse.par}: Select and reshape parameter vectors}\label{ss:parse.par}
-\keyword{utilities}{parse.par}
-\begin{Description}\relax
-The \code{parse.par} function reshapes parameter vectors for
-comfortability with the output matrix from \code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}.
-Use \code{parse.par} to identify sets of parameters; for example, within
-optimization functions that require vector input, or within \code{qi}
-functions that take matrix input of all parameters as a lump.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-parse.par(par, terms, shape = "matrix", eqn = NULL)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{par}] the vector (or matrix) of parameters
-\item[\code{terms}] the terms from either \code{\LinkA{model.frame.multiple}{model.frame.multiple}} or
-\code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}
-\item[\code{shape}] a character string (either \code{"matrix"} or \code{"vector"})
-that identifies the type of output structure
-\item[\code{eqn}] a character string (or strings) that identify the
-parameters that you would like to subset from the larger \code{par}
-structure
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-A matrix or vector of the sub-setted (and reshaped) parameters for the specified
-parameters given in \code{"eqn"}. By default, \code{eqn = NULL}, such that all systematic
-components are selected. (Systematic components have \code{ExpVar = TRUE} in the appropriate
-\code{describe.model} function.)
-
-If an ancillary parameter (for which \code{ExpVar = FALSE} in
-\code{describe.model}) is specified in \code{eqn}, it is
-always returned as a vector (ignoring \code{shape}). (Ancillary
-parameters are all parameters that have intercept only formulas.)
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
-<\email{falimadhi at iq.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}, \code{\LinkA{parse.formula}{parse.formula}} and the full Zelig manual at
-\url{http://gking.harvard.edu/zelig}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-# Let's say that the name of the model is "bivariate.probit", and
-# the corresponding describe function is describe.bivariate.probit(),
-# which identifies mu1 and mu2 as systematic components, and an
-# ancillary parameter rho, which may be parameterized, but is estimated
-# as a scalar by default. Let par be the parameter vector (including
-# parameters for rho), formulae a user-specified formula, and mydata
-# the user specified data frame.
-
-# Acceptable combinations of parse.par() and model.matrix() are as follows:
-## Setting up
-## Not run:
-data(sanction)
-formulae <- cbind(import, export) ~ coop + cost + target
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = sanction)
-terms <- attr(D, "terms")
-
-## Intuitive option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
-eta <- X
-
-## Memory-efficient (compact) option (default)
-Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
-eta <- X
-
-## Computationally-efficient (array) option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
-eta <- apply(X, 3, '
-## End(Not run)\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/plot.ci.aux b/inst/doc/commandsRd/plot.ci.aux
deleted file mode 100644
index 326d849..0000000
--- a/inst/doc/commandsRd/plot.ci.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {11.4}{\tt plot.ci}: Plotting Vertical confidence Intervals}{116}{section.11.4}}
-\newlabel{ss:plot.ci}{{11.4}{116}{{\tt plot.ci}: Plotting Vertical confidence Intervals\relax }{section.11.4}{}}
-\@setckpt{commandsRd/plot.ci}{
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diff --git a/inst/doc/commandsRd/plot.ci.tex b/inst/doc/commandsRd/plot.ci.tex
deleted file mode 100644
index 20f73dd..0000000
--- a/inst/doc/commandsRd/plot.ci.tex
+++ /dev/null
@@ -1,68 +0,0 @@
-\section{{\tt plot.ci}: Plotting Vertical confidence Intervals}\label{ss:plot.ci}
-\keyword{hplot}{plot.ci}
-\begin{Description}\relax
-The \code{plot.ci} command generates vertical
-confidence intervals for linear or generalized linear univariate
-response models.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-plot.ci(x, CI = 95, qi = "ev", main = "", ylab = NULL, xlab = NULL,
- xlim = NULL, ylim = NULL, col = c("red", "blue"), ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{x}] stored output from \code{sim}. The \code{x\$x} and optional
-\code{x\$x1} values used to generate the \code{sim} output object must
-have more than one observation.
-\item[\code{CI}] the selected confidence interval. Defaults to 95
-percent.
-\item[\code{qi}] the selected quantity of interest. Defaults to
-expected values.
-\item[\code{main}] a title for the plot.
-\item[\code{ylab}] label for the y-axis.
-\item[\code{xlab}] label for the x-axis.
-\item[\code{xlim}] limits on the x-axis.
-\item[\code{ylim}] limits on the y-axis.
-\item[\code{col}] a vector of at most two colors for plotting the
-expected value given by \code{x} and the alternative set of expected
-values given by \code{x1} in \code{sim}. If the quantity of
-interest selected is not the expected value, or \code{x1 = NULL},
-only the first color will be used.
-\item[\code{...}] Additional parameters passed to \code{plot}.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-For all univariate response models, {\tt plot.ci()} returns vertical
-confidence intervals over a specified range of one explanatory
-variable. You may save this plot using the commands described in the
-Zelig manual (\url{http://gking.harvard.edu/zelig}).
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual is available at
-\url{http://gking.harvard.edu/zelig}, and users may also wish to see
-\code{plot}, \code{lines}.
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-data(turnout)
-z.out <- zelig(vote ~ race + educate + age + I(age^2) + income,
- model = "logit", data = turnout)
-age.range <- 18:95
-x.low <- setx(z.out, educate = 12, age = age.range)
-x.high <- setx(z.out, educate = 16, age = age.range)
-s.out <- sim(z.out, x = x.low, x1 = x.high)
-plot.ci(s.out, xlab = "Age in Years",
- ylab = "Predicted Probability of Voting",
- main = "Effect of Education and Age on Voting Behavior")
-legend(45, 0.52, legend = c("College Education (16 years)",
- "High School Education (12 years)"), col = c("blue","red"),
- lty = c("solid"))
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/plot.zelig.aux b/inst/doc/commandsRd/plot.zelig.aux
deleted file mode 100644
index 7a64e65..0000000
--- a/inst/doc/commandsRd/plot.zelig.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {10.5}{\tt plot.zelig}: Graphing Quantities of Interest}{102}{section.10.5}}
-\newlabel{ss:plot.zelig}{{10.5}{102}{{\tt plot.zelig}: Graphing Quantities of Interest\relax }{section.10.5}{}}
-\@setckpt{commandsRd/plot.zelig}{
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diff --git a/inst/doc/commandsRd/plot.zelig.tex b/inst/doc/commandsRd/plot.zelig.tex
deleted file mode 100644
index 59e2843..0000000
--- a/inst/doc/commandsRd/plot.zelig.tex
+++ /dev/null
@@ -1,48 +0,0 @@
-\section{{\tt plot.zelig}: Graphing Quantities of Interest}\label{ss:plot.zelig}
-\aliasA{plot}{plot.zelig}{plot}
-\keyword{hplot}{plot.zelig}
-\begin{Description}\relax
-The \code{zelig} method for the generic \code{plot}
-command generates default plots for \code{\LinkA{sim}{sim}} output with
-one-observation values in \code{x} and \code{x1}.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-## S3 method for class 'zelig':
-plot(x, xlab = "", user.par = FALSE, ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{x}] stored output from \code{\LinkA{sim}{sim}}. If the \code{x\$x} or
-\code{x\$x1} values stored in the object contain more than one
-observation, \code{plot.zelig} will return an error. For linear or
-generalized linear models with more than one observation in \code{x\$x}
-and optionally \code{x\$x1}, you may use \code{\LinkA{plot.ci}{plot.ci}}.
-\item[\code{xlab}] a character string for the x-axis label for all graphs.
-\item[\code{user.par}] a logical value indicating whether to use the default
-Zelig plotting parameters (\code{user.par = FALSE}) or
-user-defined parameters (\code{user.par = TRUE}), set using the
-\code{par} function prior to plotting.
-\item[\code{...}] Additional parameters passed to \code{plot.default}.
-Because \code{plot.zelig} primarily produces diagnostic plots, many
-of these parameters are hard-coded for convenience and
-presentation.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-Depending on the class of model selected, \code{plot.zelig} will
-return an on-screen window with graphs of the various quantities of
-interest. You may save these plots using the commands described in
-the Zelig manual (available at \url{http://gking.harvard.edu/zelig}).
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual at
-\url{http://gking.harvard.edu/zelig} and \code{plot}, \code{lines},
-and \code{par}.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/put.start.tex b/inst/doc/commandsRd/put.start.tex
deleted file mode 100644
index 0a27044..0000000
--- a/inst/doc/commandsRd/put.start.tex
+++ /dev/null
@@ -1,33 +0,0 @@
-\section{{\tt put.start}: Set specific starting values for certain parameters}\label{ss:put.start}
-\keyword{utilities}{put.start}
-\begin{Description}\relax
-After calling \code{\LinkA{set.start}{set.start}} to create default starting values, use \code{put.start}
-to change starting values for specific parameters or parameter sets.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-put.start(start.val, value, terms, eqn)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{start.val}] the vector of starting values created by \code{\LinkA{set.start}{set.start}}
-\item[\code{value}] the scalar or vector of replacement starting values
-\item[\code{terms}] the terms output from \code{\LinkA{model.frame.multiple}{model.frame.multiple}}
-\item[\code{eqn}] character vector of the parameters for which you would like to replace
-the default values with \code{value}
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-A vector of starting values (of the same length as \code{start.val})
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
-<\email{falimadhi at iq.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{\LinkA{set.start}{set.start}}, and the full Zelig manual at
-\url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/repl.aux b/inst/doc/commandsRd/repl.aux
deleted file mode 100644
index 84819ae..0000000
--- a/inst/doc/commandsRd/repl.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {10.7}{\tt repl}: Replicating Analyses}{104}{section.10.7}}
-\newlabel{ss:repl}{{10.7}{104}{{\tt repl}: Replicating Analyses\relax }{section.10.7}{}}
-\@setckpt{commandsRd/repl}{
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diff --git a/inst/doc/commandsRd/repl.tex b/inst/doc/commandsRd/repl.tex
deleted file mode 100644
index cddfe5d..0000000
--- a/inst/doc/commandsRd/repl.tex
+++ /dev/null
@@ -1,89 +0,0 @@
-\section{{\tt repl}: Replicating Analyses}\label{ss:repl}
-\methaliasA{repl.default}{repl}{repl.default}
-\methaliasA{repl.zelig}{repl}{repl.zelig}
-\keyword{file}{repl}
-\begin{Description}\relax
-The generic function \code{repl} command takes
-\code{\LinkA{zelig}{zelig}} or
-\code{\LinkA{sim}{sim}} output objects and replicates (literally, re-runs)
-the entire analysis. The results should be an output
-object
-identical to the original input object in the case of
-\code{\LinkA{zelig}{zelig}} output. In the case of \code{\LinkA{sim}{sim}}
-output, the replicated analyses may differ slightly due to
-stochastic randomness in the simulation procedure.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-repl(object, data, ...)
-## Default S3 method:
-repl(object, data = NULL, ...)
-## S3 method for class 'zelig':
-repl(object, data = NULL, prev = NULL, x = NULL, x1 = NULL,
- bootfn = NULL, ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{object}] Stored output from either \code{\LinkA{zelig}{zelig}} or
-\code{\LinkA{sim}{sim}}.
-\item[\code{data}] You may manually input the data frame name rather
-than allowing \code{repl} to draw the data frame name from the object
-to be replicated.
-\item[\code{prev}] When replicating \code{\LinkA{sim}{sim}} output, you may
-optionally use the previously simulated parameters to calculate the
-quantities of interest rather than simulating a new set of
-parameters. For all models, this should produce identical
-quantities of interest. In addition, for if the parameters were
-bootstrapped in the original analysis, this will save a considerable
-amount of time.
-\item[\code{x}] When replicating \code{\LinkA{sim}{sim}} output, you may
-optionally use an alternative \code{\LinkA{setx}{setx}} value for the \code{x}
-input.
-\item[\code{x1}] When replicating \code{\LinkA{sim}{sim}} output, you may
-optionally use an alternative \code{\LinkA{setx}{setx}} object for the \code{x1}
-input to replicating the \code{\LinkA{sim}{sim}} object.
-\item[\code{bootfn}] When replicating \code{\LinkA{sim}{sim}} output with
-bootstrapped parameters, you should manually specify the
-\code{bootfn} if a non-default option was used.
-\item[\code{...}] Additional arguments passed to either \code{\LinkA{zelig}{zelig}} or
-\code{\LinkA{sim}{sim}}.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-For \code{\LinkA{zelig}{zelig}} output, \code{repl} will create output that is in
-every way identical to the original input. You may check to see
-whether they are identical by using the \code{identical} command.
-
-For \code{\LinkA{sim}{sim}} output, \code{repl} output will be will be
-identical to the original object if you choose not to simulate new
-parameters, and instead choose to calculate quantities of interest
-using the previously simulated parameters (using the \code{prev}
-option. If you choose to simulate new parameters, the summary
-statistics for each quantity of interest should be identical, up to a
-random approximation error. As the number of simulations increases,
-this error decreases.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{\LinkA{zelig}{zelig}}, \code{\LinkA{setx}{setx}}, and
-\code{\LinkA{sim}{sim}}. In addition, the full Zelig manual may be
-accessed online at \url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-data(turnout)
-z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout[1:1000,])
-x.out <- setx(z.out)
-s.out <- sim(z.out, x = x.out)
-z.rep <- repl(z.out)
-identical(z.out$coef, z.rep$coef)
-z.alt <- repl(z.out, data = turnout[1001:2000,])
-s.rep <- repl(s.out, prev = s.out$par)
-identical(s.out$ev, s.rep$ev)
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/rocplot.aux b/inst/doc/commandsRd/rocplot.aux
deleted file mode 100644
index 1e5aa03..0000000
--- a/inst/doc/commandsRd/rocplot.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {11.5}{\tt rocplot}: Receiver Operator Characteristic Plots}{118}{section.11.5}}
-\newlabel{ss:rocplot}{{11.5}{118}{{\tt rocplot}: Receiver Operator Characteristic Plots\relax }{section.11.5}{}}
-\@setckpt{commandsRd/rocplot}{
-\setcounter{page}{120}
-\setcounter{equation}{0}
-\setcounter{enumi}{4}
-\setcounter{enumii}{3}
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diff --git a/inst/doc/commandsRd/rocplot.tex b/inst/doc/commandsRd/rocplot.tex
deleted file mode 100644
index f933f24..0000000
--- a/inst/doc/commandsRd/rocplot.tex
+++ /dev/null
@@ -1,87 +0,0 @@
-\section{{\tt rocplot}: Receiver Operator Characteristic Plots}\label{ss:rocplot}
-\aliasA{ROC}{rocplot}{ROC}
-\aliasA{roc}{rocplot}{roc}
-\aliasA{ROCplot}{rocplot}{ROCplot}
-\keyword{file}{rocplot}
-\begin{Description}\relax
-The \code{rocplot} command generates a receiver operator
-characteristic plot to compare the in-sample (default) or out-of-sample
-fit for two logit or probit regressions.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-rocplot(y1, y2, fitted1, fitted2, cutoff = seq(from=0, to=1, length=100),
- lty1 = "solid", lty2 = "dashed", lwd1 = par("lwd"), lwd2 = par("lwd"),
- col1 = par("col"), col2 = par("col"), main, xlab, ylab,
- plot = TRUE, ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{y1}] Response variable for the first model.
-\item[\code{y2}] Response variable for the second model.
-\item[\code{fitted1}] Fitted values for the first model. These values
-may represent either the in-sample or out-of-sample fitted values.
-\item[\code{fitted2}] Fitted values for the second model.
-\item[\code{cutoff}] A vector of cut-off values between 0 and 1, at
-which to evaluate the proportion of 0s and 1s correctly predicted by
-the first and second model. By default, this is 100 increments
-between 0 and 1, inclusive.
-\item[\code{lty1, lty2}] The line type for the first model (\code{lty1}) and
-the second model (\code{lty2}), defaulting to solid and dashed,
-respectively.
-\item[\code{lwd1, lwd2}] The width of the line for the first model
-(\code{lwd1}) and the second model (\code{lwd2}), defaulting to 1 for both.
-\item[\code{col1, col2}] The colors of the line for the first
-model (\code{col1}) and the second model (\code{col2}), defaulting to
-black for both.
-\item[\code{main}] a title for the plot. Defaults to \code{ROC Curve}.
-\item[\code{xlab}] a label for the x-axis. Defaults to \code{Proportion of 1's
- Correctly Predicted}.
-\item[\code{ylab}] a label for the y-axis. Defaults to \code{Proportion of 0's
- Correctly Predicted}.
-\item[\code{plot}] defaults to \code{TRUE}, which generates a plot to the
-selected device. If \code{FALSE}, returns a list of
-items (see below).
-\item[\code{...}] Additional parameters passed to plot, including
-\code{xlab}, \code{ylab}, and \code{main}.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-If \code{plot = TRUE}, \code{rocplot} generates an ROC plot for
-two logit or probit models. If \code{plot = FALSE}, \code{rocplot}
-returns a list with the following elements: normal-bracket63bracket-normal
-\begin{ldescription}
-\item[\code{roc1}] a matrix containing a vector of x-coordinates and
-y-coordinates corresponding to the number of ones and zeros correctly
-predicted for the first model.
-\item[\code{roc2}] a matrix containing a vector of x-coordinates and
-y-coordinates corresponding to the number of ones and zeros correctly
-predicted for the second model.
-\item[\code{area1}] the area under the first ROC curve, calculated using
-Reimann sums.
-\item[\code{area2}] the area under the second ROC curve, calculated using
-Reimann sums.
-\end{ldescription}
-
-normal-bracket63bracket-normal
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual (available at
-\url{http://gking.harvard.edu/zelig}), \code{plot}, \code{lines}.
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-data(turnout)
-z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
- data = turnout)
-z.out2 <- zelig(vote ~ race + educate, model = "logit",
- data = turnout)
-rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/sanction.tex b/inst/doc/commandsRd/sanction.tex
deleted file mode 100644
index 7aec455..0000000
--- a/inst/doc/commandsRd/sanction.tex
+++ /dev/null
@@ -1,24 +0,0 @@
-\section{{\tt sanction}: Multilateral Economic Sanctions}\label{ss:sanction}
-\keyword{datasets}{sanction}
-\begin{Description}\relax
-Data on bilateral sanctions behavior for selected years during the
-general period 1939-1983. This data contains errors that have since
-been corrected. Please contact Lisa Martin before using this data for
-publication.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(sanction)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 8 variables ("mil", "coop", "target",
-"import", "export", "cost", "num", and "ncost") and 78 observations.
-For full variable description, see Martin, 1992.
-\end{Format}
-\begin{Source}\relax
-Martin, 1992
-\end{Source}
-\begin{References}\relax
-Martin, Lisa (1992). \emph{Coercive Cooperation: Explaining Multilateral
-Economic Sanctions}, Princeton: Princeton University Press.
-\end{References}
-
diff --git a/inst/doc/commandsRd/set.start.tex b/inst/doc/commandsRd/set.start.tex
deleted file mode 100644
index a81b9dd..0000000
--- a/inst/doc/commandsRd/set.start.tex
+++ /dev/null
@@ -1,42 +0,0 @@
-\section{{\tt set.start}: Set starting values for all parameters}\label{ss:set.start}
-\keyword{utilities}{set.start}
-\begin{Description}\relax
-After using \code{\LinkA{parse.par}{parse.par}} and \code{\LinkA{model.matrix.multiple}{model.matrix.multiple}}, use
-\code{set.start} to set starting values for all parameters. By default, starting values are set to 0. If
-you wish to select alternative starting values for certain parameters, use \code{\LinkA{put.start}{put.start}} after
-\code{set.start}.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-set.start(start.val = NULL, terms)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{start.val}] user-specified starting values. If \code{NULL} (default), the default
-starting values for all parameters are set to 0.
-\item[\code{terms}] the terms output from \code{\LinkA{model.frame.multiple}{model.frame.multiple}}
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-A named vector of starting values for all parameters specified in \code{terms}, defaulting to 0.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>; Ferdinand Alimadhi
-<\email{falimadhi at iq.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{\LinkA{put.start}{put.start}}, \code{\LinkA{parse.par}{parse.par}}, \code{\LinkA{model.frame.multiple}{model.frame.multiple}}, and the
-full Zelig manual at \url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-## Not run:
-fml <- parse.formula(formula, model = "bivariate.probit")
-D <- model.frame(fml, data = data)
-terms <- attr(D, "terms")
-start.val <- set.start(start.val = NULL, terms)
-## End(Not run)\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/setx.aux b/inst/doc/commandsRd/setx.aux
deleted file mode 100644
index 56c1ac2..0000000
--- a/inst/doc/commandsRd/setx.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {10.2}{\tt setx}: Setting Explanatory Variable Values}{93}{section.10.2}}
-\newlabel{ss:setx}{{10.2}{93}{{\tt setx}: Setting Explanatory Variable Values\relax }{section.10.2}{}}
-\@setckpt{commandsRd/setx}{
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diff --git a/inst/doc/commandsRd/setx.tex b/inst/doc/commandsRd/setx.tex
deleted file mode 100644
index c729e8b..0000000
--- a/inst/doc/commandsRd/setx.tex
+++ /dev/null
@@ -1,113 +0,0 @@
-\section{{\tt setx}: Setting Explanatory Variable Values}\label{ss:setx}
-\keyword{file}{setx}
-\begin{Description}\relax
-The \code{setx} command uses the variables identified in
-the \code{formula} generated by \code{zelig} and sets the values of
-the explanatory variables to the selected values. Use \code{setx}
-after \code{zelig} and before \code{sim} to simulate quantities of
-interest.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-x.out <- setx(object, fn = list(numeric = mean, ordered = median,
- others = mode),
- data = NULL, cond = FALSE, ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{object}] the saved output from \code{\LinkA{zelig}{zelig}}.
-\item[\code{fn}] a list of functions to apply to three types of variables:
-\item[numeric] \code{numeric} variables are set to their mean by
-default, but you may select any mathematical function to apply to
-numeric variables.
-\item[ordered] \code{ordered} factors are set to their meidan by
-default, and most mathematical operations will work on them. If
-you select \code{ordered = mean}, however, \code{setx} will
-default to median with a warning.
-\item[other] variables may consist of unordered factors, character
-strings, or logical variables. The \code{other} variables may
-only be set to their mode. If you wish to set one of the other
-variables to a specific value, you may do so using \code{...}
-below.
-In the special case \code{fn = NULL}, \code{setx} will return all
-of the observations without applying any function to the data.
-\item[\code{data}] a new data frame used to set the values of
-explanatory variables. If \code{data = NULL} (the default), the
-data frame called in \code{zelig} is used.
-\item[\code{cond}] a logical value indicating whether unconditional
-(default) or conditional (choose \code{cond = TRUE}) prediction
-should be performed. If you choose \code{cond = TRUE}, \code{setx}
-will coerce \code{fn = NULL} and ignore the additional arguments in
-\code{...}. If \code{cond = TRUE} and \code{data = NULL},
-\code{setx} will prompt you for a data frame.
-\item[\code{...}] user-defined values of specific variables
-overwriting the default values set by the function \code{fn}. For
-example, adding \code{var1 = mean(data\$var1)} or \code{x1 = 12}
-explicitly sets the value of \code{x1} to 12. In addition, you may
-specify one explanatory variable as a range of values, creating one
-observation for every unique value in the range of values.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-For unconditional prediction, \code{x.out} is a model matrix based
-on the specified values for the explanatory variables. For multiple
-analyses (i.e., when choosing the \code{by} option in \code{\LinkA{zelig}{zelig}},
-\code{setx} returns the selected values calculated over the entire
-data frame. If you wish to calculate values over just one subset of
-the data frame, the 5th subset for example, you may use:
-\code{x.out <- setx(z.out[[5]])}
-
-For conditional prediction, \code{x.out} includes the model matrix
-and the dependent variables. For multiple analyses (when choosing
-the \code{by} option in \code{zelig}), \code{setx} returns the
-observed explanatory variables in each subset.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual may be accessed online at
-\url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-# Unconditional prediction:
-data(turnout)
-z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-x.out <- setx(z.out)
-s.out <- sim(z.out, x = x.out)
-
-# Unconditional prediction with all observations:
-x.out <- setx(z.out, fn = NULL)
-s.out <- sim(z.out, x = x.out)
-
-# Unconditional prediction with out of sample data:
-z.out <- zelig(vote ~ race + educate, model = "logit",
- data = turnout[1:1000,])
-x.out <- setx(z.out, data = turnout[1001:2000,])
-s.out <- sim(z.out, x = x.out)
-
-# Using a user-defined function in fn:
-## Not run:
-quants <- function(x)
- quantile(x, 0.25)
-x.out <- setx(z.out, fn = list(numeric = quants))
-## End(Not run)
-
-# Conditional prediction:
-## Not run:
-library(MatchIt)
-data(lalonde)
-match.out <- matchit(treat ~ age + educ + black + hispan + married +
- nodegree + re74 + re75, data = lalonde)
-z.out <- zelig(re78 ~ distance, data = match.data(match.out, "control"),
- model = "ls")
-x.out <- setx(z.out, fn = NULL, data = match.data(match.out, "treat"),
- cond = TRUE)
-s.out <- sim(z.out, x = x.out)
-## End(Not run)
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/sim.aux b/inst/doc/commandsRd/sim.aux
deleted file mode 100644
index 1db6466..0000000
--- a/inst/doc/commandsRd/sim.aux
+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {10.4}{\tt sim}: Simulating Quantities of Interest}{99}{section.10.4}}
-\newlabel{ss:sim}{{10.4}{99}{{\tt sim}: Simulating Quantities of Interest\relax }{section.10.4}{}}
-\@setckpt{commandsRd/sim}{
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diff --git a/inst/doc/commandsRd/sim.tex b/inst/doc/commandsRd/sim.tex
deleted file mode 100644
index 8fe7036..0000000
--- a/inst/doc/commandsRd/sim.tex
+++ /dev/null
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-\section{{\tt sim}: Simulating Quantities of Interest}\label{ss:sim}
-\keyword{file}{sim}
-\begin{Description}\relax
-Simulate quantities of interest from the estimated model
-output from \code{zelig()} given specified values of explanatory
-variables established in \code{setx()}. For classical \emph{maximum
-likelihood} models, \code{sim()} uses asymptotic normal
-approximation to the log-likelihood. For \emph{Bayesian models},
-Zelig simulates quantities of interest from the posterior density,
-whenever possible. For \emph{robust Bayesian models}, simulations
-are drawn from the identified class of Bayesian posteriors.
-Alternatively, you may generate quantities of interest using
-bootstrapped parameters.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-s.out <- sim(object, x, x1 = NULL, num = c(1000, 100), prev = NULL,
- bootstrap = FALSE, bootfn = NULL, ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{object}] the output object from \code{\LinkA{zelig}{zelig}}.
-\item[\code{x}] values of explanatory variables used for simulation,
-generated by \code{\LinkA{setx}{setx}}.
-\item[\code{x1}] optional values of explanatory variables (generated by a
-second call of \code{\LinkA{setx}{setx}}), used to simulate first
-differences and risk ratios. (Not available for conditional
-prediction.)
-\item[\code{num}] the number of simulations, i.e., posterior draws. If the
-\code{num} argument is omitted, \code{sim} draws 1,000
-simulations by if \code{bootstrap = FALSE} (the default), or 100
-simulations if \code{bootstrap = TRUE}. You may increase this
-value to improve accuracy. (Not available for conditional
-prediction.)
-\item[\code{bootstrap}] a logical value indicating if parameters
-should be generated by re-fitting the model for bootstrapped
-data, rather than from the likelihood or posterior. (Not
-available for conditional prediction.)
-\item[\code{bootfn}] a function which governs how the data is
-sampled, re-fits the model, and returns the bootstrapped model
-parameters. If \code{bootstrap = TRUE} and \code{bootfn = NULL},
-\code{\LinkA{sim}{sim}} will sample observations from the original data
-(with
-replacement) until it creates a sampled dataset with the same
-number of observations as the original data. Alternative
-bootstrap methods include sampling the residuals rather than the
-observations, weighted sampling, and parametric bootstrapping.
-(Not available for conditional prediction.)
-\item[\code{...}] additional optional arguments passed to
-\code{boot}.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-The output stored in \code{s.out} varies by model. Use the
-\code{names} command to view the output stored in \code{s.out}.
-Common elements include: normal-bracket109bracket-normal
-\begin{ldescription}
-\item[\code{x}] the \code{\LinkA{setx}{setx}} values for the explanatory variables,
-used to calculate the quantities of interest (expected values,
-predicted values, etc.).
-\item[\code{x1}] the optional \code{\LinkA{setx}{setx}} object used to simulate
-first differences, and other model-specific quantities of
-interest, such as risk-ratios.
-\item[\code{call}] the options selected for \code{\LinkA{sim}{sim}}, used to
-replicate quantities of interest.
-\item[\code{zelig.call}] the original command and options for
-\code{\LinkA{zelig}{zelig}}, used to replicate analyses.
-\item[\code{num}] the number of simulations requested.
-\item[\code{par}] the parameters (coefficients, and additional
-model-specific parameters). You may wish to use the same set of
-simulated parameters to calculate quantities of interest rather
-than simulating another set.
-\item[\code{qi\$ev}] simulations of the expected values given the
-model and \code{x}.
-\item[\code{qi\$pr}] simulations of the predicted values given by the
-fitted values.
-\item[\code{qi\$fd}] simulations of the first differences (or risk
-difference for binary models) for the given \code{x} and \code{x1}.
-The difference is calculated by subtracting the expected values
-given \code{x} from the expected values given \code{x1}. (If do not
-specify \code{x1}, you will not get first differences or risk
-ratios.)
-\item[\code{qi\$rr}] simulations of the risk ratios for binary and
-multinomial models. See specific models for details.
-\item[\code{qi\$ate.ev}] simulations of the average expected
-treatment effect for the treatment group, using conditional
-prediction. Let \eqn{t_i}{} be a binary explanatory variable defining
-the treatment (\eqn{t_i=1}{}) and control (\eqn{t_i=0}{}) groups. Then the
-average expected treatment effect for the treatment group is
-\deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) -
-E[Y_i(t_i=0)] \mid t_i=1 \,],}{}
-where \eqn{Y_i(t_i=1)}{} is the value of the dependent variable for
-observation \eqn{i}{} in the treatment group. Variation in the
-simulations are due to uncertainty in simulating \eqn{E[Y_i(t_i=0)]}{},
-the counterfactual expected value of \eqn{Y_i}{} for observations in the
-treatment group, under the assumption that everything stays the
-same except that the treatment indicator is switched to \eqn{t_i=0}{}.
-\item[\code{qi\$ate.pr}] simulations of the average predicted
-treatment effect for the treatment group, using conditional
-prediction. Let \eqn{t_i}{} be a binary explanatory variable defining
-the treatment (\eqn{t_i=1}{}) and control (\eqn{t_i=0}{}) groups. Then the
-average predicted treatment effect for the treatment group is
-\deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) -
-\widehat{Y_i(t_i=0)} \mid t_i=1 \,],}{}
-where \eqn{Y_i(t_i=1)}{} is the value of the dependent variable for
-observation \eqn{i}{} in the treatment group. Variation in the
-simulations are due to uncertainty in simulating
-\eqn{\widehat{Y_i(t_i=0)}}{}, the counterfactual predicted value of
-\eqn{Y_i}{} for observations in the treatment group, under the
-assumption that everything stays the same except that the
-treatment indicator is switched to \eqn{t_i=0}{}.
-\end{ldescription}
-
-normal-bracket109bracket-normal
-
-In the case of censored $Y$ in the exponential, Weibull, and lognormal
-models, \code{sim} first imputes the uncensored values for $Y$ before
-calculating the ATE.
-
-You may use the \code{\$} operator to extract any of the
-above from \code{s.out}. For example, \code{s.out\$qi\$ev} extracts the
-simulated expected values.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig at \url{http://gking.harvard.edu/zelig}, and \code{boot}.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/sna.ex.tex b/inst/doc/commandsRd/sna.ex.tex
deleted file mode 100644
index 2ccc739..0000000
--- a/inst/doc/commandsRd/sna.ex.tex
+++ /dev/null
@@ -1,15 +0,0 @@
-\section{{\tt sna.ex}: Simulated Example of Social Network Data}\label{ss:sna.ex}
-\keyword{datasets}{sna.ex}
-\begin{Description}\relax
-This data set contains five sociomatrices of simulated data social network data.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(sna.ex)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-Each variable in the dataset is a 25 by 25 matrix of simulated social network data. The matrices are labeled "Var1", "Var2", "Var3", "Var4", and "Var5".
-\end{Format}
-\begin{Source}\relax
-fictitious
-\end{Source}
-
diff --git a/inst/doc/commandsRd/summary.aux b/inst/doc/commandsRd/summary.aux
deleted file mode 100644
index 7db5746..0000000
--- a/inst/doc/commandsRd/summary.aux
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@@ -1,36 +0,0 @@
-\relax
-\@setckpt{commandsRd/summary}{
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diff --git a/inst/doc/commandsRd/summary.zelig.tex b/inst/doc/commandsRd/summary.zelig.tex
deleted file mode 100644
index 949f5cb..0000000
--- a/inst/doc/commandsRd/summary.zelig.tex
+++ /dev/null
@@ -1,51 +0,0 @@
-\section{{\tt summary.zelig}: Summary of Simulated Quantities of Interest}\label{ss:summary.zelig}
-\aliasA{summary}{summary.zelig}{summary}
-\keyword{file}{summary.zelig}
-\begin{Description}\relax
-Summarizes the object of class \code{\LinkA{zelig}{zelig}} (output
-from \code{\LinkA{sim}{sim}}) which contains simulated quantities of
-interst.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-## S3 method for class 'zelig':
-summary(object, subset = NULL, CI = 95, stats = c("mean", "sd"), ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{object}] output object from \code{\LinkA{sim}{sim}} (of class
-\code{"zelig"}).
-\item[\code{subset}] takes one of three values:
-\item[NULL] (default) for more than one observation, summarizes all the
-observations at once for each quantity of interest.
-\item[a numeric vector] indicates which observations to summarize,
-and summarizes each one independently.
-\item[all] summarizes all the observations independently.
-
-\item[\code{stats}] summary statistics to be calculated.
-\item[\code{CI}] a confidence interval to be calculated.
-\item[\code{...}] further arguments passed to or from other methods.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-\begin{ldescription}
-\item[\code{sim}] number of simulations, i.e., posterior draws.
-\item[\code{x}] values of explanatory variables used for simulation.
-\item[\code{x1}] values of explanatory variables used for simulation of first
-differences etc.
-\item[\code{qi.stats}] summary of quantities of interst. Use
-\code{\LinkA{names}{names}} to view the model-specific items available in
-\code{qi.stats}.
-\end{ldescription}
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{\LinkA{zelig}{zelig}}, \code{\LinkA{setx}{setx}}, \code{\LinkA{sim}{sim}},
-and \code{\LinkA{names}{names}}, and the full Zelig manual at
-\url{http://gking.harvard.edu/zelig}.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/swiss.tex b/inst/doc/commandsRd/swiss.tex
deleted file mode 100644
index 0cbf5f7..0000000
--- a/inst/doc/commandsRd/swiss.tex
+++ /dev/null
@@ -1,40 +0,0 @@
-\section{{\tt swiss}: Swiss Fertility and Socioeconomic Indicators (1888) Data}\label{ss:swiss}
-\keyword{datasets}{swiss}
-\begin{Description}\relax
-Standardized fertility measure and socio-economic indicators for
-each of 47 French-speaking provinces of Switzerland at about 1888.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(swiss)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A data frame with 47 observations on 6 variables, each of which
-is in percent, i.e., in [0,100].
-
-[,1] Fertility Ig, "common standardized fertility measure"
-[,2] Agriculture
-[,3] Examination
-nation
-[,4] Education
-[,5] Catholic
-[,6] Infant.Mortality live births who live less than 1 year.
-
-All variables but 'Fert' give proportions of the population.
-\end{Format}
-\begin{Source}\relax
-Project "16P5", pages 549-551 in
-
-Mosteller, F. and Tukey, J. W. (1977) ``Data Analysis and
-Regression: A Second Course in Statistics''. Addison-Wesley,
-Reading Mass.
-
-indicating their source as "Data used by permission of Franice van
-de Walle. Office of Population Research, Princeton University,
-1976. Unpublished data assembled under NICHD contract number No
-1-HD-O-2077."
-\end{Source}
-\begin{References}\relax
-Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) ``The New S
-Language''. Wadsworth \& Brooks/Cole.
-\end{References}
-
diff --git a/inst/doc/commandsRd/ternaryplot.aux b/inst/doc/commandsRd/ternaryplot.aux
deleted file mode 100644
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+++ /dev/null
@@ -1,38 +0,0 @@
-\relax
-\@writefile{toc}{\contentsline {section}{\numberline {11.6}{\tt ternaryplot}: Ternary diagram}{120}{section.11.6}}
-\newlabel{ss:ternaryplot}{{11.6}{120}{{\tt ternaryplot}: Ternary diagram\relax }{section.11.6}{}}
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diff --git a/inst/doc/commandsRd/ternaryplot.tex b/inst/doc/commandsRd/ternaryplot.tex
deleted file mode 100644
index c525fe0..0000000
--- a/inst/doc/commandsRd/ternaryplot.tex
+++ /dev/null
@@ -1,77 +0,0 @@
-\section{{\tt ternaryplot}: Ternary diagram}\label{ss:ternaryplot}
-\keyword{hplot}{ternaryplot}
-\begin{Description}\relax
-Visualizes compositional, 3-dimensional data in an equilateral triangle
-(from the vcd library, Version 0.1-3.3, Date 2004-04-21), using plot graphics.
-Differs from implementation in vcd (0.9-7), which uses grid graphics.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-ternaryplot(x, scale = 1, dimnames = NULL, dimnames.position = c("corner","edge","none"),
- dimnames.color = "black", id = NULL, id.color = "black", coordinates = FALSE,
- grid = TRUE, grid.color = "gray", labels = c("inside", "outside", "none"),
- labels.color = "darkgray", border = "black", bg = "white", pch = 19, cex = 1,
- prop.size = FALSE, col = "red", main = "ternary plot", ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{x}] a matrix with three columns.
-\item[\code{scale}] row sums scale to be used.
-\item[\code{dimnames}] dimension labels (defaults to the column names of
-\code{x}).
-\item[\code{dimnames.position, dimnames.color}] position and color of dimension labels.
-\item[\code{id}] optional labels to be plotted below the plot
-symbols. \code{coordinates} and \code{id} are mutual exclusive.
-\item[\code{id.color}] color of these labels.
-\item[\code{coordinates}] if \code{TRUE}, the coordinates of the points are
-plotted below them. \code{coordinates} and \code{id} are mutual exclusive.
-\item[\code{grid}] if \code{TRUE}, a grid is plotted. May optionally
-be a string indicating the line type (default: \code{"dotted"}).
-\item[\code{grid.color}] grid color.
-\item[\code{labels, labels.color}] position and color of the grid labels.
-\item[\code{border}] color of the triangle border.
-\item[\code{bg}] triangle background.
-\item[\code{pch}] plotting character. Defaults to filled dots.
-\item[\code{cex}] a numerical value giving the amount by which plotting text
-and symbols should be scaled relative to the default. Ignored for
-the symbol size if \code{prop.size} is not \code{FALSE}.
-\item[\code{prop.size}] if \code{TRUE}, the symbol size is plotted
-proportional to the row sum of the three variables, i.e. represents
-the weight of the observation.
-\item[\code{col}] plotting color.
-\item[\code{main}] main title.
-\item[\code{...}] additional graphics parameters (see \code{par})
-\end{ldescription}
-\end{Arguments}
-\begin{Details}\relax
-A points' coordinates are found by computing the gravity center
-of mass points using the data entries as weights. Thus, the coordinates
-of a point P(a,b,c), \eqn{a + b + c = 1}{}, are: P(b + c/2, c * sqrt(3)/2).
-\end{Details}
-\begin{Author}\relax
-David Meyer\\
-\email{david.meyer at ci.tuwien.ac.at}
-\end{Author}
-\begin{References}\relax
-M. Friendly (2000),
-\emph{Visualizing Categorical Data}. SAS Institute, Cary, NC.
-\end{References}
-\begin{SeeAlso}\relax
-\code{\LinkA{ternarypoints}{ternarypoints}}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-data(mexico)
-if (require(VGAM)) {
-z.out <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
- model = "mlogit", data = mexico)
-x.out <- setx(z.out)
-s.out <- sim(z.out, x = x.out)
-
-ternaryplot(s.out$qi$ev, pch = ".", col = "blue",
- main = "1988 Mexican Presidential Election")
-}
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/ternarypoints.aux b/inst/doc/commandsRd/ternarypoints.aux
deleted file mode 100644
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-\relax
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-\newlabel{ss:ternarypoints}{{11.7}{122}{{\tt ternarypoints}: Adding Points to Ternary Diagrams\relax }{section.11.7}{}}
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diff --git a/inst/doc/commandsRd/ternarypoints.tex b/inst/doc/commandsRd/ternarypoints.tex
deleted file mode 100644
index 0ab5675..0000000
--- a/inst/doc/commandsRd/ternarypoints.tex
+++ /dev/null
@@ -1,39 +0,0 @@
-\section{{\tt ternarypoints}: Adding Points to Ternary Diagrams}\label{ss:ternarypoints}
-\keyword{aplot}{ternarypoints}
-\begin{Description}\relax
-Use \code{ternarypoints} to add points to a ternary diagram generated
-using the \code{ternaryplot} function in the vcd library. Use
-ternary diagrams to plot expected values for multinomial choice models
-with three categories in the dependent variable.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-ternarypoints(object, pch = 19, col = "blue", ...)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{object}] The input object must be a matrix with three
-columns.
-\item[\code{pch}] The selected type of point. By default, \code{pch =
- 19}, solid disks.
-\item[\code{col}] The color of the points. By default, \code{col =
- "blue"}.
-\item[\code{...}] Additional parameters passed to \code{points}.
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-The \code{ternarypoints} command adds points to a previously existing
-ternary diagram. Use \code{ternaryplot} in the \code{vcd} library to
-generate the main ternary diagram.
-\end{Value}
-\begin{Author}\relax
-Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
-<\email{king at harvard.edu}>; Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-The full Zelig manual at
-\url{http://gking.harvard.edu/zelig}, \code{points}, and
-\code{\LinkA{ternaryplot}{ternaryplot}}.
-\end{SeeAlso}
-
diff --git a/inst/doc/commandsRd/tobin.tex b/inst/doc/commandsRd/tobin.tex
deleted file mode 100644
index ade51e9..0000000
--- a/inst/doc/commandsRd/tobin.tex
+++ /dev/null
@@ -1,23 +0,0 @@
-\section{{\tt tobin}: Tobin's Tobit Data}\label{ss:tobin}
-\keyword{datasets}{tobin}
-\begin{Description}\relax
-Economists fit a parametric censored data model called the
-`tobit'. These data are from Tobin's original paper.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(tobin)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A data frame with 20 observations on the following 3 variables.
-
-durable: Durable goods purchase
-
-age: Age in years
-
-quant: Liquidity ratio (x 1000)
-\end{Format}
-\begin{Source}\relax
-J. Tobin, Estimation of relationships for limited dependent
-variables, Econometrica, v26, 24-36, 1958.
-\end{Source}
-
diff --git a/inst/doc/commandsRd/turnout.tex b/inst/doc/commandsRd/turnout.tex
deleted file mode 100644
index b1ea522..0000000
--- a/inst/doc/commandsRd/turnout.tex
+++ /dev/null
@@ -1,25 +0,0 @@
-\section{{\tt turnout}: Turnout Data Set from the National Election Survey}\label{ss:turnout}
-\keyword{datasets}{turnout}
-\begin{Description}\relax
-This data set contains individual-level turnout data. It pools several
-American National Election Surveys conducted during the 1992 presidential
-election year. Only the first 2,000 observations (from a total of 15,837
-observations) are included in the sample data.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}data(turnout)\end{verbatim}
-\end{Usage}
-\begin{Format}\relax
-A table containing 5 variables ("race", "age", "educate",
-"income", and "vote") and 2,000 observations.
-\end{Format}
-\begin{Source}\relax
-National Election Survey
-\end{Source}
-\begin{References}\relax
-King, Gary, Michael Tomz, Jason Wittenberg (2000).
-``Making the Most of Statistical Analyses: Improving Interpretation and
-Presentation,'' \emph{American Journal of Political Science}, vol. 44,
-pp.341--355.
-\end{References}
-
diff --git a/inst/doc/commandsRd/user.prompt.tex b/inst/doc/commandsRd/user.prompt.tex
deleted file mode 100644
index 74c9a56..0000000
--- a/inst/doc/commandsRd/user.prompt.tex
+++ /dev/null
@@ -1,25 +0,0 @@
-\section{{\tt user.prompt}: Pause in demo files}\label{ss:user.prompt}
-\keyword{file}{user.prompt}
-\begin{Description}\relax
-Use \code{user.prompt} while writing demo files to force users to hit
-return before continuing.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
-user.prompt()
-\end{verbatim}
-\end{Usage}
-\begin{Author}\relax
-Olivia Lau <\email{olau at fas.harvard.edu}>
-\end{Author}
-\begin{SeeAlso}\relax
-\code{readline}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}
-## Not run:
-user.prompt()
-## End(Not run)
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/commandsRd/zeligVDC.tex b/inst/doc/commandsRd/zeligVDC.tex
deleted file mode 100644
index f21c05b..0000000
--- a/inst/doc/commandsRd/zeligVDC.tex
+++ /dev/null
@@ -1,62 +0,0 @@
-\section{{\tt zeligDescribeModelXML}: Zelig interface functions}\label{ss:zeligDescribeModelXML}
-\aliasA{zeligGetSpecial}{zeligDescribeModelXML}{zeligGetSpecial}
-\aliasA{zeligInstalledModels}{zeligDescribeModelXML}{zeligInstalledModels}
-\aliasA{zeligListModels}{zeligDescribeModelXML}{zeligListModels}
-\aliasA{zeligModelDependency}{zeligDescribeModelXML}{zeligModelDependency}
-\keyword{IO}{zeligDescribeModelXML}
-\keyword{print}{zeligDescribeModelXML}
-\begin{Description}\relax
-Zelig interface functions. Used by VDC DSB to communicate with Zelig.
-\end{Description}
-\begin{Usage}
-\begin{verbatim}
- zeligDescribeModelXML(modelName,force=FALSE,schemaVersion="1.1")
- zeligInstalledModels(inZeligOnly=TRUE,schemaVersion="1.1")
- zeligListModels(inZeligOnly=TRUE)
- zeligModelDependency(modelName,repos)
- zeligGetSpecial(modelName)
-\end{verbatim}
-\end{Usage}
-\begin{Arguments}
-\begin{ldescription}
-\item[\code{modelName}] Name of model as returned by zeligInstalledModels or zeligListModels.
-\item[\code{inZeligOnly}] Flag, include only models in official Zelig distribution
-\item[\code{repos}] URL of default repository to use
-\item[\code{schemaVersion}] version of Zelig schema
-\item[\code{force}] generate a description even if no custom description supplied
-\end{ldescription}
-\end{Arguments}
-\begin{Value}
-Use zeligInstalledModels and zeligListModels to determine what models are available in zelig
-for a particular schema level. Use zmodel2string(zeligDescribeModel()) to generate an XML
-instance describing a model. Use zeligModelDependencies to generate a list of package
-dependencies for models. Use zeligGetSpecial to get the name special function, if any,
-to apply to the outcome variables. All functions return NULL if results are
-not available for that model.
-\end{Value}
-\begin{Author}\relax
-Micah Altman
-\email{thedata-users\@lists.sourceforge.net}
-\url{http://thedata.org}
-\end{Author}
-\begin{SeeAlso}\relax
-\LinkA{zelig}{zelig}
-\end{SeeAlso}
-\begin{Examples}
-\begin{ExampleCode}## Not run:
- # show all available models
- zeligListModels(inZeligOnly=FALSE)
- # show installed models
- zeligInstalledModels()
- # show dependency for normal.bayes
- zeligModelDependency("normal.bayes","http://cran.r-project.org/")
- # description of logit
- cat(zeligDescribeModelXML("ologit"))
- # special function for factor analysis
- zeligGetSpecial("factor.mix")
-## End(Not run)
-
-
-\end{ExampleCode}
-\end{Examples}
-
diff --git a/inst/doc/models/contributors_systemfit.tex b/inst/doc/contributors_systemfit.tex
similarity index 100%
rename from inst/doc/models/contributors_systemfit.tex
rename to inst/doc/contributors_systemfit.tex
diff --git a/inst/doc/ei.RxC.Rnw b/inst/doc/ei.RxC.Rnw
index 2b5f23c..6d22263 100644
--- a/inst/doc/ei.RxC.Rnw
+++ b/inst/doc/ei.RxC.Rnw
@@ -15,9 +15,8 @@ if(!length(grep("package:Zelig",pkg)))
library(Zelig)
@
-\section{\texttt{ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological
- Inference Model for $R \times C$ Tables}
-\label{eiRxC}
+\section{{\tt ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological
+ Inference Model for $R \times C$ Tables}\label{eiRxC}
Given $n$ contingency tables, each with observed marginals (column and
row totals), ecological inference ({\sc ei}) estimates the internal
@@ -231,4 +230,4 @@ $R \times C$ {\sc ei} model for Zelig.
for (pkg in torm)
detach(pos=match(pkg,search()))
@
- \end{document}
+\end{document}
diff --git a/inst/doc/ei.RxC.pdf b/inst/doc/ei.RxC.pdf
index 45a1821..7c4d7ae 100644
Binary files a/inst/doc/ei.RxC.pdf and b/inst/doc/ei.RxC.pdf differ
diff --git a/inst/doc/ei.RxC.tex b/inst/doc/ei.RxC.tex
deleted file mode 100644
index 94c428d..0000000
--- a/inst/doc/ei.RxC.tex
+++ /dev/null
@@ -1,238 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Hierarchical Multinomial-Dirichlet Ecological Inference Model}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,ecological, many contingency tables, Gibbs sampling}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological
- Inference Model for $R \times C$ Tables}
-\label{eiRxC}
-
-Given $n$ contingency tables, each with observed marginals (column and
-row totals), ecological inference ({\sc ei}) estimates the internal
-cell values in each table. The hierarchical Multinomial-Dirichlet
-model estimates cell counts in $R \times C$ tables. The model is
-implemented using a nonlinear least squares approximation and, with
-bootstrapping for standard errors, had good frequentist properties.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(T0, T1, T2, T3) ~ X0 + X1,
- covar = NULL,
- model = "eiRxC", data = mydata)
-> x.out <- setx(z.out, fn = NULL)
-> s.out <- sim(z.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\begin{itemize}
-\item \texttt{T0}, \texttt{T1}, \texttt{T2},\ldots, \texttt{TC}:
- numeric vectors (either counts, or proportions that sum to one for
- each row) containing the column margins of the units to be analyzed.
-
-\item \texttt{X0}, {\tt X1}, {\tt X2},\ldots,{\tt XR}: numeric vectors
- (either counts, or proportions that sum to one for each row)
- containing the row margins of the units to be analyzed.
-
-\item {\tt covar}: (optional) a covariate that varies across tables,
-specified as \verb|covar = ~ Z1|, for example. (The model only
-accepts one covariate.)
-\end{itemize}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples: No covariate \\
- Attaching the example dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(Weimar)
-\end{Sinput}
-\end{Schunk}
-Estimating the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(Nazi, Government, Communists, FarRight,
-+ Other) ~ shareunemployed + shareblue + sharewhite + shareself +
-+ sharedomestic, model = "ei.RxC", data = Weimar)
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Setting values for in-sample simulations given marginal values:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Estimate fractions of different social groups that support political parties:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Summarizing fractions of different social groups that support political parties:
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-
- \item Example of covariates being present in the model \\
-
- Using the example dataset Weimar and estimating the model
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(Nazi, Government, Communists, FarRight,
-+ Other) ~ shareunemployed + shareblue + sharewhite + shareself +
-+ sharedomestic, covar = ~shareprotestants, model = "ei.RxC",
-+ data = Weimar)
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Set the covariate to its default (mean/median) value
-
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Estimate fractions of different social groups that support political parties:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Summarizing fractions of different social groups that support political parties:
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\clearpage
-
-\subsubsection{Model}
-Consider the following $5 \times 5$ contingency table for the voting
-patterns in Weimar Germany. For each geographical unit $i$ ($i = 1,
-\dots, p$), the marginals $T_{1i}$,\dots, $T_{Ci}$, $X_{1i}$,\dots,
-$X_{Ri}$ are known for each of the $p$ electoral precincts, and we
-would like to estimate ($\beta_i^{rc}, r=1,\dots,R, c=1,\dots,C-1$)
-which are the fractions of people in social class $r$ who vote for
-party $c$, for all $r$ and $c$.
-\begin{table}[!h]
- \begin{center}
- \begin{tabular}{l|ccccc|c}
- & Nazi & Government & Communists & Far Right & Other \\
- \hline
- Unemployed & $\beta_{11}^{i}$ & $\beta_{12}^{i}$ & $\beta_{13}^{i}$ & $\beta_{14}^{i}$ & $1-\sum_{c=1}^4 \beta_{1c}^i$ & $X_1^i$ \\
- Blue & $\beta_{21}^{i}$ & $\beta_{22}^{i}$ & $\beta_{23}^{i}$ & $\beta_{24}^{i}$ & $1-\sum_{c=1}^4 \beta_{2c}^i$ & $X_2^i$ \\
- White & $\beta_{31}^{i}$ & $\beta_{32}^{i}$ & $\beta_{33}^{i}$ & $\beta_{34}^{i}$ & $1-\sum_{c=1}^4 \beta_{3c}^i$ & $X_3^i$ \\
- Self & $\beta_{41}^{i}$ & $\beta_{42}^{i}$ & $\beta_{43}^{i}$ & $\beta_{44}^{i}$ & $1-\sum_{c=1}^4 \beta_{4c}^i$ & $X_4^i$ \\
- Domestic & $\beta_{51}^{i}$ & $\beta_{52}^{i}$ & $\beta_{53}^{i}$ & $\beta_{54}^{i}$ & $1-\sum_{c=1}^4 \beta_{5c}^i$ & $X_5^i$ \\
- \hline
- & $T_{1i}$ & $T_{2i}$ & $T_{3i}$ & $T_{4i}$ & $1-\sum_{c=1}^4 \beta_{ci}$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent
-The marginal values $X_{1i},\dots,X_{Ri}$, $T_{1i},\dots,T_{Ci}$ may be
-observed as counts or fractions.
-
-Let $T_i^{'}=(T_{1i}^{'},T_{2i}^{'},\dots,T_{Ci}^{'})$ be the number
-of voting age persons who turn out to vote for different parties.
-There are three levels of hierarchy in the
-Multinomial-Dirichlet {\sc ei} model. At the first stage, we model
-the data as:
-\begin{itemize}
-
-\item The \emph{stochastic component} is described $T_i^{'}$ which
-follows a multinomial distribution:
-\begin{eqnarray*}
-T_i^{'} &\sim & \textrm{Multinomial}(\Theta_{1i},\dots,\Theta_{Ci})
-\end{eqnarray*}
-
-\item The \emph{systematic components} are
-\begin{eqnarray*}
-\Theta_{ci}=\sum_{r=1}^{R}\beta_{rc}^{i}X_{ri} & \textrm{for} & c=1,\dots,C
-\end{eqnarray*}
-
-\end{itemize}
-
-At the second stage, we use an optional covariate to model
-$\Theta_{ci}$'s and $\beta_{qrc}^{i}$:
-\begin{itemize}
-\item The \emph{stochastic component} is described by
-$\beta_{r}^{i}=(\beta_{r1},\beta_{r2},\dots,\beta_{r,C-1})$ for
- $i=1,\dots,,p$ and $r=1,\dots,R$, which follows a Dirichlet distribution:
-
- \begin{eqnarray*}
- \beta_r^{i} &\sim & \textrm{Dirichlet}(\alpha_{r1}^{i},\dots,\alpha_{rc}^{i})
- \end{eqnarray*}
-
-\item The \emph{systematic components} are
-\begin{eqnarray*}
- \alpha_{rc}^{i}=\frac{d_{r}exp(\gamma_{rc}+\delta_{rc}Z_{i})}{d_r(1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i))} = \frac{exp(\gamma_{rc}+\delta_{rc}Z_i)}{1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i)}
-\end{eqnarray*}
-for $i=1, \dots,p$, $r=1,\dots,R$, and $c=1, \dots, C-1$.
-
-In the third stage, we assume that the regression parameters (the
-$\gamma_{rc}$'s and $\delta_{rc}$'s) are \emph{a priori} independent,
-and put a flat prior on these regression parameters. The parameters
-$d_{r}$ for $r=1,\dots,R$ are assumed to follow exponential distributions with
-mean $\frac{1}{\lambda}$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-> z.out <- zelig(cbind(T0, T1, T2) ~ X0 + X1 + X2,
- model = "eiRxC", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)}. For example,
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object
- \texttt{z.out\$coefficients} are the estimates of $\gamma_{ij}$ (and
- also $\delta_{ij}$, if covariates are present). The parameters are
- returned as a single vector of length $R\times (C-1)$. If there is a
- covariate, $\delta$ is concatenated to it.
-
-\item From the \texttt{sim()} output object, you may extract the
- parameters $\beta_{ij}$ corresponding to the estimated fractions of
- different social groups that support different political parties, by
- using \texttt{s.out\$qi\$ev}. For each precinct, that will be a
- matrix with dimensions: simulations $\times R \times C$.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Please cite the model as
-\begin{verse}
-\bibentry{RosJiaKin01}.
-\end{verse}
-
-Jason Wittenberg, Ferdinand Alimadhi, and Olivia Lau implemented the
-$R \times C$ {\sc ei} model for Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/ei.dynamic.Rnw b/inst/doc/ei.dynamic.Rnw
index b8ad6d3..e2c2013 100644
--- a/inst/doc/ei.dynamic.Rnw
+++ b/inst/doc/ei.dynamic.Rnw
@@ -18,10 +18,8 @@ library(Zelig)
%%% the following inputs do not work
%\SweaveInput{ei.hier}
%\SweaveInput{eiRxC}
-\section{\texttt{ei.dynamic}: Quinn's Dynamic Ecological Inference
-Model}
-
-\label{ei.dynamic}
+\section{{\tt ei.dynamic}: Quinn's Dynamic Ecological Inference
+Model}\label{ei.dynamic}
Given contingency tables with observed marginals, ecological inference
({\sc ei}) models estimate each internal cell value for each table.
diff --git a/inst/doc/ei.dynamic.pdf b/inst/doc/ei.dynamic.pdf
index 3f18d9a..15761a7 100644
Binary files a/inst/doc/ei.dynamic.pdf and b/inst/doc/ei.dynamic.pdf differ
diff --git a/inst/doc/ei.dynamic.tex b/inst/doc/ei.dynamic.tex
deleted file mode 100644
index ddb4402..0000000
--- a/inst/doc/ei.dynamic.tex
+++ /dev/null
@@ -1,306 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Quinn's Dynamic Ecological Inference}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,ecological, contingency tables, Gibbs sampling}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-%%% the following inputs do not work
-%\SweaveInput{ei.hier}
-%\SweaveInput{eiRxC}
-\section{\texttt{ei.dynamic}: Quinn's Dynamic Ecological Inference
-Model}
-
-\label{ei.dynamic}
-
-Given contingency tables with observed marginals, ecological inference
-({\sc ei}) models estimate each internal cell value for each table.
-Quinn's dynamic {\sc ei} model estimates a dynamic Bayesian model for
-$2 \times 2$ tables with temporal dependence across tables (units).
-The model is implemented using a Markov Chain Monte Carlo algorithm
-(via a combination of slice and Gibbs sampling).
-For a hierarchical Bayesian implementation of {\sc ei} see Quinn's
-dynamic {\sc ei} model (\Sref{ei.hier}). For contingency tables larger than
-2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "MCMCei.dynamic", data = mydata)
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\begin{itemize}
-\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
-proportions) containing the column margins of the units to be
-analyzed.
-
-\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
-proportions) containing the row margins of the units to be
-analyzed.
-
-\item \texttt{N}: total counts in each contingency table (unit). If
-\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
-you must specify \texttt{N}.
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional inputs for
-\texttt{ei.dynamic} to monitor the convergence of the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 5,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 50,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed of 12345.
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to
-specify priors and other parameters:
-
-\begin{itemize}
-\item \texttt{W}: a $p \times p$ numeric matrix describing the
-structure of the temporal dependence among elements of $\theta_0$ and
-$\theta_1$. The default value is 0, which constructs a weight matrix
-corresponding to random walk priors for $\theta_0$ and $\theta_1$
-(assuming that the tables are equally spaced throughout time, and that
-the elements of \texttt{t0},
-\texttt{t1},\texttt{x0},\texttt{x1} are temporally ordered).
-
-\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$. The default is 0.825.
-
-\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$. The default is 0.0105.
-
-\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$. The default is 0.825.
-
-\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$. The default is 0.0105.
-\end{itemize}
-
-\noindent Users may wish to refer to \texttt{help(MCMCdynamicEI)} for more options.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples \\
- Attaching the example dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(eidat)
-\end{Sinput}
-\end{Schunk}
-Estimating the model using \texttt{ei.dynamic}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.dynamic",
-+ data = eidat, mcmc = 40000, thin = 10, burnin = 10000, verbose = TRUE)
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Setting values for in-sample simulations given the marginal values
-of {\tt t0}, {\tt t1}, {\tt x0}, and {\tt x1}:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-\end{Sinput}
-\end{Schunk}
-In-sample simulations from the posterior distribution:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-Summarizing in-sample simulations at aggregate level
-weighted by the count in each unit:
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-Summarizing in-sample simulations at unit level for the first 5 units:
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out, subset = 1:5)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-\clearpage
-\subsubsection{Model}
-Consider the following $2 \times 2$ contingency table for the racial
-voting example. For each geographical unit $i = 1, \dots, p$, the
-marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
-would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
-\begin{table}[h!]
- \begin{center}
- \begin{tabular}{l|cc|c}
- & No Vote & Vote & \\
- \hline
- Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
- White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
- \hline
- & $t_i^0$ & $t_i^1$ & $N_i$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$, $t_i^1$ are
-observed as either counts or fractions. If fractions, the counts can
-be obtained by multiplying by the total counts per table $N_i =
-n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$, and rounding to the
-nearest integer. Although there are four internal cells, only two
-unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
-$n_{i}^{11} = s_i^1 - n_{i}^{10}$. \\
-
-The hierarchical Bayesian model for ecological inference in
-$2 \times 2$ is illustrated as following:
-\begin{itemize}
-\item The \emph{stochastic component} of the model assumes that
-\begin{eqnarray*}
-n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
-x_i^0, \beta_i^b\right) ,\\
-n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
-x_i^1, \beta_i^w\right) \\
-\end{eqnarray*}
-where $\beta_{i}^{b}$ is the fraction of the black voters who vote
-and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
-and $\beta_i^w$ as well as their aggregate summaries are the focus
-of inference.
-
-\item The \emph{systematic component} of the model is
-\begin{eqnarray*}
-\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
-\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
-\end{eqnarray*}
-The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
-and $\theta_i^1$ now take value on the real line. (Future versions may allow
-$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
-
-\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
- are given by
-\begin{eqnarray*}
-\theta_{i}^{0} \mid\sigma_{0}^{2}&\propto & \frac{1}{\sigma_0^{p}} \exp
-\left( -\frac{1}{2\sigma_{0}^2}\theta_0' P \theta_0 \right) \\
-\theta_{i}^{1} \mid\sigma_{1}^{2}&\propto & \frac{1}{\sigma_1^{p}} \exp
-\left( -\frac{1}{2\sigma_{1}^2}\theta_1' P \theta_1 \right)
-\end{eqnarray*}
-where $P$ is a $p \times p$ matrix whose off diagonal elements
-$P_{ts}$ ($t \ne s$) equal $-W_{ts}$ (the negative values of the corresponding
-elements of the weight matrix $W$), and diagonal elements
-$P_{tt}=\sum_{s\ne t} W_{ts}$. Scale parameters $\sigma_{0}^2$ and
-$\sigma_{1}^2$ have hyperprior distributions as given below.
-
-\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
-given by
-\begin{eqnarray*}
-\sigma_{0}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right) ,\\
-\sigma_{1}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right) ,
-\end{eqnarray*}
-where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
-(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$
- are the scale parameters. \\
-
-The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
-and $\sigma_{1}^{2}$ are chosen such that the prior distributions for
-$\beta^b$ and $\beta^w$ are flat.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-
-\begin{verbatim}
-> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "ei.dynamic", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)}, see the draws from the
-posterior distribution of the quantities of interest by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out},
-you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the parameters.
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{N}: the total counts when the inputs are fractions.
-\item \texttt{seed}: the random seed used in the model.
-\end{itemize}
-
-\item From \texttt{summary(z.out)}, you may extract:
-\begin{itemize}
-\item \texttt{summary}: a matrix containing the summary information of the
-posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
-the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
-the posterior distribution. The first $p$ rows correspond to
-$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
-\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
-$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
-\texttt{p1table}$i$. The last four rows contain information about
-$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
-variances of $\theta_0$ and $\theta_1$.
-\end{itemize}
-\item From the \texttt{sim()} output object \texttt{s.out}, you may
-extract quantities of interest arranged as arrays indexed by
-simulation $\times$ column $\times$ row $\times$ observation, where
-column and row refer to the column dimension and the row dimension of
-the ecological table, respectively. In this model, only $2 \times 2$
-contingency tables are analyzed, hence column$=2$ and row$=2$ in all
-cases. Available quantities are:
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values of each internal
-cell given the observed marginals.
-\item \texttt{qi\$pr}: the simulated expected values of each internal
-cell given the observed marginals.
-\end{itemize}
-
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The dynamic {\sc ei} model was developed in
-\begin{verse}
-\bibentry{Qui04}.
-\end{verse}
-The \input{contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.dynamic} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
diff --git a/inst/doc/ei.hier.Rnw b/inst/doc/ei.hier.Rnw
index 75538ae..f765a76 100644
--- a/inst/doc/ei.hier.Rnw
+++ b/inst/doc/ei.hier.Rnw
@@ -16,10 +16,8 @@ library(Zelig)
@
%\SweaveInput{ei.dynamic}
-\section{\texttt{ei.hier}: Hierarchical Ecological Inference Model for
-$2 \times 2$ Tables}
-
-\label{ei.hier}
+\section{{\tt ei.hier}: Hierarchical Ecological Inference Model for
+$2 \times 2$ Tables} \label{ei.hier}
Given contingency tables with observed marginals, ecological inference
({\sc ei}) models estimate each internal cell value for each table.
diff --git a/inst/doc/ei.hier.pdf b/inst/doc/ei.hier.pdf
index 10bdb8c..0a8be95 100644
Binary files a/inst/doc/ei.hier.pdf and b/inst/doc/ei.hier.pdf differ
diff --git a/inst/doc/ei.hier.tex b/inst/doc/ei.hier.tex
deleted file mode 100644
index 4e9d54a..0000000
--- a/inst/doc/ei.hier.tex
+++ /dev/null
@@ -1,309 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Hierarchical Ecological Inference Model}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,ecological, two contingency tables, Gibbs sampling}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-%\SweaveInput{ei.dynamic}
-\section{\texttt{ei.hier}: Hierarchical Ecological Inference Model for
-$2 \times 2$ Tables}
-
-\label{ei.hier}
-
-Given contingency tables with observed marginals, ecological inference
-({\sc ei}) models estimate each internal cell value for each table.
-The hierarchical {\sc ei} model estimates a Bayesian model for $2
-\times 2$ tables. The model is implemented using a Markov Chain Monte
-Carlo algorithm (via a combination of slice and Gibbs sampling).
-For a Bayesian implementation of {\sc ei} that accounts for temporal
-dependence, see Quinn's dynamic {\sc ei} model (\Sref{ei.dynamic}).
-For contingency tables larger than
-2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "MCMCei.hier", data = mydata)
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\begin{itemize}
-\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
-proportions) containing the column margins of the units to be
-analyzed.
-
-\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
-proportions) containing the row margins of the units to be
-analyzed.
-
-\item \texttt{N}: total counts per contingency table (unit). If
-\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
-you must specify \texttt{N}.
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional inputs for
-\texttt{ei.hier} to monitor the convergence of the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 5,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 50,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is \texttt{NA} which
-corresponds to a random seed of 12345.
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to specify
- prior parameters used in the model:
-
-\begin{itemize}
-\item \texttt{m0}: prior mean of $\mu_{0}$ (defaults to 0).
-
-\item \texttt{M0}: prior variance of $\mu_{0}$ (defaults to 2.287656).
-
-\item \texttt{m1}: prior mean of $\mu_{1}$ (defaults to 0).
-
-\item \texttt{M1}: prior variance of $\mu_{1}$ (defaults to 2.287656).
-
-\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$ (defaults to 0.825).
-
-\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$ (defaults to 0.0105).
-
-\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$ (defaults to 0.825).
-
-\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$ (defaults to 0.0105).
-\end{itemize}
-
-\noindent Users may wish to refer to \texttt{help(MCMChierEI)} for more information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples \\
- Attaching the example dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(eidat)
-> eidat
-\end{Sinput}
-\end{Schunk}
-Estimating the model using \texttt{ei.hier}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.hier", data = eidat,
-+ mcmc = 40000, thin = 10, burnin = 10000, verbose = TRUE)
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Setting values for in-sample simulations given marginal values
-of {\tt x0}, {\tt x1}, {\tt t0}, and {\tt t1}:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-\end{Sinput}
-\end{Schunk}
-
-In-sample simulations from the posterior distribution:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-Summarizing in-sample simulations at aggregate level
-weighted by the count in each unit:
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-Summarizing in-sample simulations at unit level for the first 5 units:
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out, subset = 1:5)
-\end{Sinput}
-\end{Schunk}
-
-\end{enumerate}
-\clearpage
-\subsubsection{Model}
-Consider the following $2 \times 2$ contingency table for the racial
-voting example. For each geographical unit $i = 1, \dots, p$, the
-marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
-would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
-
-\begin{table}[!h]
- \begin{center}
- \begin{tabular}{l|cc|c}
- & No Vote & Vote & \\
- \hline
- Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
- White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
- \hline
- & $t_i^0$ & $t_i^1$ & $N_i$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$,
-$t_i^1$ are observed as either counts or fractions. If fractions, the
-counts can be obtained by multiplying by the total counts per table
-$N_i = n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$ and rounding to the
-nearest integer. Although there are four internal cells, only two
-unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
-$n_{i}^{11} = s_i^1 - n_{i}^{10}$.
-
-The hierarchical Bayesian model for ecological inference in $2 \times
-2$ is illustrated as following:
-\begin{itemize}
-\item The \emph{stochastic component} of the model assumes that
-\begin{eqnarray*}
-n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
-x_i^0, \beta_i^b\right) ,\\
-n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
-x_i^1, \beta_i^w\right) \\
-\end{eqnarray*}
-where $\beta_{i}^{b}$ is the fraction of the black voters who vote
-and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
-and $\beta_i^w$ as well as their aggregate level summaries are the focus
-of inference.
-
-\item The \emph{systematic component} is
-\begin{eqnarray*}
-\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
-\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
-\end{eqnarray*}
-The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
-and $\theta_i^1$ now take value on the real line. (Future versions may allow
-$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
-
-\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
- are given by
-\begin{eqnarray*}
-\theta_{i}^{0} \mid\mu_{0},\sigma_{0}^{2}&\sim&\textrm{Normal}\left( \mu
-_{0},\sigma_{0}^{2}\right),\\
-\theta_{i}^{1} \mid\mu_{1},\sigma_{1}^{2}&\sim&\textrm{Normal}\left( \mu
-_{1},\sigma_{1}^{2}\right)
-\end{eqnarray*}
-where $\mu_{0}$ and $\mu_{1}$ are the means, and $\sigma_{0}^{2}$
-and $\sigma_{1}^{2}$ are the variances of the two corresponding
-(independent) normal distributions.
-
-\item The \emph{hyperpriors} for $\mu_{0}$ and $\mu_{1}$ are given by
-\begin{eqnarray*}
-\mu_{0} &\sim& \textrm{Normal} \left( m_{0}, M_{0} \right) ,\\
-\mu_{1} &\sim& \textrm{Normal} \left( m_{1}, M_{1} \right) ,
-\end{eqnarray*}
-where $m_{0}$ and $m_{1}$ are the means of the (independent) normal
-distributions while $M_{0}$ and $M_{1}$ are the variances.
-
-\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
-given by
-\begin{eqnarray*}
-\sigma_{0}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right),\\
-\sigma_{1}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right),
-\end{eqnarray*}
-where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
-(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$ are
-the scale parameters.
-
-The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
-and $\sigma_{1}^{2}$ are chosen such that the prior distributions of
-$\beta^b$ and $\beta^w$ are flat.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "ei.hier", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)},
-see the draws from the posterior distribution of
-the quantities of interest by using \texttt{z.out\$coefficients},
-and a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out},
-you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the parameters.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{N}: the total counts when the inputs are fractions.
-\item \texttt{seed}: the random seed used in the model.
-\end{itemize}
-
-\item From \texttt{summary(z.out)}, you may extract:
-\begin{itemize}
-\item \texttt{summary}: a matrix containing the summary information of the
-posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
-the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
-the posterior distribution. The first $p$ rows correspond to
-$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
-\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
-$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
-\texttt{p1table}$i$. The last four rows contain information about
-$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
-variances of $\theta_0$ and $\theta_1$.
-\end{itemize}
-\item From the \texttt{sim()} output object \texttt{s.out}, you may
-extract quantities of interest arranged as arrays indexed by
-simulation $\times$ column $\times$ row $\times$ observation, where
-column and row refer to the column dimension and the row dimension of
-the contingency table, respectively. In this model, only $2 \times 2$
-contingency tables are analyzed, hence column$=2$ and row$=2$ in all
-cases. Available quantities are:
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values of each internal cell
-given the observed marginals.
-\item \texttt{qi\$pr}: the simulated expected values of each internal cell
-given the observed marginals.
-\end{itemize}
-
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The hierarchical {\sc ei} \input{contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.hier} to work with Zelig.
-i
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/exp.pdf b/inst/doc/exp.pdf
index 28fd25b..820d729 100644
Binary files a/inst/doc/exp.pdf and b/inst/doc/exp.pdf differ
diff --git a/inst/doc/exp.tex b/inst/doc/exp.tex
deleted file mode 100644
index 7c3bb10..0000000
--- a/inst/doc/exp.tex
+++ /dev/null
@@ -1,304 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Exponential Regression for Duration Dependent Variables}
-%\VignetteDepends{Zelig, survival}
-%\VignetteKeyWords{model,exponential,regression, time}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt exp}: Exponential Regression for Duration Dependent Variables}\label{exp}
-
-Use the exponential duration regression model if you have a dependent
-variable representing a duration (time until an event). The model
-assumes a constant hazard rate for all events. The dependent variable
-may be censored (for observations have not yet been completed when
-data were collected).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X, model = "exp", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Exponential models require that the dependent variable be in the form
-{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
-For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
-duration (lifetime, for example), and the associated $c_i$ is a binary
-variable such that $c_i = 1$ if the duration is not censored ({\it
- e.g.}, the subject dies during the study) or $c_i = 0$ if the
-duration is censored ({\it e.g.}, the subject is still alive at the
-end of the study and is know to live at least as long as $y_i$). If
-$c_i$ is omitted, all Y are assumed to be completed; that is, time
-defaults to 1 for all observations.
-
-\subsubsection{Input Values}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for exponential regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) and the options selected in
-{\tt cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "exp", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(coalition)
-\end{Sinput}
-\end{Schunk}
-
-Estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp",
-+ data = coalition)
-\end{Sinput}
-\end{Schunk}
-View the regression output:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out, numst2 = 0)
-> x.high <- setx(z.out, numst2 = 1)
-\end{Sinput}
-\end{Schunk}
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-\end{Sinput}
-\end{Schunk}
-Summarize quantities of interest and produce some plots:
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/exp-ExamplePlot}
-\end{center}
-
-\subsubsection{Model}
-
-Let $Y_i^*$ be the survival time for observation $i$. This variable
-might be censored for some observations at a fixed time $y_c$ such
-that the fully observed dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c \\
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by the distribution
- of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
- the exponential distribution whose density function is given by
- \begin{equation*}
- f(y_i^*\mid \lambda_i) = \frac{1}{\lambda_i} \exp\left(-\frac{y_i^*}{\lambda_i}\right)
- \end{equation*}
- for $y_i^*\ge 0$ and $\lambda_i>0$. The mean of this distribution is
- $\lambda_i$.
-
- In addition, survival models like the exponential have three
- additional properties. The hazard function $h(t)$ measures the
- probability of not surviving past time $t$ given survival up to
- $t$. In general, the hazard function is equal to $f(t)/S(t)$ where
- the survival function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the
- fraction still surviving at time $t$. The cumulative hazard
- function $H(t)$ describes the probability of dying before time $t$.
- In general, $H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case
- of the exponential model,
-\begin{eqnarray*}
-h(t) &=& \frac{1}{\lambda_i} \\
-S(t) &=& \exp\left( -\frac{t}{\lambda_i} \right) \\
-H(t) &=& \frac{t}{\lambda_i}
-\end{eqnarray*}
-For the exponential model, the hazard function $h(t)$ is constant over
-time. The Weibull model and lognormal models allow the hazard
-function to vary as a function of elapsed time (see \Sref{weibull} and
-\Sref{lognorm} respectively).
-
-\item The \emph{systematic component} $\lambda_i$ is modeled as
- \begin{equation*}
- \lambda_i = \exp(x_i \beta),
- \end{equation*}
- where $x_i$ is the vector of explanatory variables, and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the exponential model are
- simulations of the expected duration given $x_i$ and draws of
- $\beta$ from its posterior, $$E(Y) = \lambda_i = \exp(x_i \beta).$$
-
-\item The predicted values ({\tt qi\$pr}) are draws from the
- exponential distribution with rate equal to the expected value.
-
-\item The first difference (or difference in expected values, {\tt
- qi\$ev.diff}), is
-\begin{equation}
-\textrm{FD} \; = \; E(Y \mid x_1) - E(Y \mid x),
-\end{equation}
-where $x$ and $x_1$ are different vectors of values for the
-explanatory variables.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is \begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) - E[Y_i(t_i=0)]
- \right\}, \end{equation*} where $t_i$ is a binary explanatory
- variable defining the treatment ($t_i=1$) and control ($t_i=0$)
- groups. When $Y_i(t_i=1)$ is censored rather than observed, we
- replace it with a simulation from the model given available
- knowledge of the censoring process. Variation in the simulations
- is due to two factors: uncertainty in the imputation process for
- censored $y_i^*$ and uncertainty in simulating $E[Y_i(t_i=0)]$, the
- counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the same
- except that the treatment indicator is switched to $t_i=0$.
-
- \item In conditional prediction models, the average predicted
- treatment effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\}, \end{equation*} where $t_i$ is a
- binary explanatory variable defining the treatment ($t_i=1$) and
- control ($t_i=0$) groups. When $Y_i(t_i=1)$ is censored rather than
- observed, we replace it with a simulation from the model given
- available knowledge of the censoring process. Variation in the
- simulations is due to two factors: uncertainty in the imputation
- process for censored $y_i^*$ and uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
- for observations in the treatment group, under the assumption that
- everything stays the same except that the treatment indicator is
- switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
- C) \~\, X, model = "exp", data)}, then you may examine the
-available information in \texttt{z.out} by using
-\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
- z.out\$coefficients}, and a default summary of information
-through \texttt{summary(z.out)}. Other elements available through
-the {\tt \$} operator are listed below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt icoef}: parameter estimates for the intercept and scale
- parameter. While the scale parameter varies for the Weibull
- distribution, it is fixed to 1 for the exponential distribution
- (which is modeled as a special case of the Weibull).
- \item {\tt var}: the variance-covariance matrix for the estimates
- of $\beta$.
- \item {\tt loglik}: a vector containing the log-likelihood for the
- model and intercept only (respectively).
- \item {\tt linear.predictors}: the vector of
- $x_{i}\beta$.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item Most of this may be conveniently summarized using {\tt
- summary(z.out)}. From {\tt summary(z.out)}, you may
- additionally extract:
- \begin{itemize}
- \item {\tt table}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics. For
- example, {\tt summary(z.out)\$table}
- \end{itemize}
-
-\item From the {\tt sim()} output stored in {\tt s.out}:
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from a
- distribution defined by the expected values.
- \item {\tt qi\$fd}: the simulated first differences between the
- simulated expected values for {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The exponential function is part of the survival library by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library and
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/factor.bayes.pdf b/inst/doc/factor.bayes.pdf
index 199d174..7746ab3 100644
Binary files a/inst/doc/factor.bayes.pdf and b/inst/doc/factor.bayes.pdf differ
diff --git a/inst/doc/factor.bayes.tex b/inst/doc/factor.bayes.tex
deleted file mode 100644
index 0de5d80..0000000
--- a/inst/doc/factor.bayes.tex
+++ /dev/null
@@ -1,283 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Factor Analysis}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,latent factors,continuous,explanatory variables, normal, Gibbs}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{factor.bayes}: Bayesian Factor Analysis}
-\label{factor.bayes}
-
-Given some unobserved explanatory variables and observed dependent
-variables, the Normal theory factor analysis model estimates the
-latent factors. The model is implemented using a Markov Chain Monte
-Carlo algorithm (Gibbs sampling with data augmentation). For factor
-analysis with ordinal dependent variables, see ordered factor analysis
-(\Sref{factor.ord}), and for a mix of types of dependent variables,
-see the mixed factor analysis model (\Sref{factor.mix}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 2,
- model = "factor.bayes", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} takes the following functions for {\tt factor.bayes}:
-\begin{itemize}
-\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: variables of interest in
-factor analysis (manifest variables), assumed to be normally
-distributed. The model requires a minimum of three manifest variables.
-
-\item \texttt{factors}: number of the factors to be fitted (defaults to 2).
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional arguments for
-model specification:
-\begin{itemize}
-\item \texttt{lambda.constraints}: list containing the equality or
-inequality constraints on the factor loadings. Choose from one of the
-following forms:
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-
-\item \texttt{std.var}: defaults to {\tt FALSE} (manifest variables
-are rescaled to zero mean, but retain observed variance). If
-\texttt{TRUE}, the manifest variables are rescaled to be mean zero and
-unit variance.
-
-\end{itemize}
- In addition, {\tt zelig()} accepts the following additional inputs
-for {\tt bayes.factor}:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{Lambda.start}: starting values of the factor loading
-matrix $\Lambda$, either a scalar (all unconstrained loadings are set
-to that value), or a matrix with compatible dimensions. The default
-is \texttt{NA}, where the start value are set to be 0 for
-unconstrained factor loadings, and 0.5 or $-$0.5 for constrained
-factor loadings (depending on the nature of the constraints).
-
-\item \texttt{Psi.start}: starting values for the uniquenesses, either
-a scalar (the starting values for all diagonal elements of $\Psi$ are
-set to be this value), or a vector with length equal to the number of
-manifest variables. In the latter case, the starting values of the
-diagonal elements of $\Psi$ take the values of \texttt{Psi.start}. The
-default value is \texttt{NA} where the starting values of the all the
-uniquenesses are set to be 0.5.
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-stores the posterior draws of the factor scores. (Storing factor
-scores may take large amount of memory for a large number of draws
-or observations.)
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to
-specify prior parameters:
-
-\begin{itemize}
-
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, that value will be the prior mean for
-all the factor loadings. Defaults to 0.
-
-\item \texttt{L0}: precision parameter of the Normal prior
-for the factor loadings, either a scalar or a matrix with the same
-dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
-the precision matrix will be a diagonal matrix with the diagonal
-elements set to that value. The default value is 0, which leads to an
-improper prior.
-
-\item \texttt{a0}: the shape parameter of the Inverse Gamma prior for
-the uniquenesses is \texttt{a0/2}. It can take a scalar value or a
-vector. The default value is 0.001.
-
-\item \texttt{b0}: the shape parameter of the Inverse Gamma prior for
-the uniquenesses is \texttt{b0/2}. It can take a scalar value or a
-vector. The default value is 0.001.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCfactanal)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(swiss)
-> names(swiss) <- c("Fert", "Agr", "Exam", "Educ", "Cath", "InfMort")
-\end{Sinput}
-\end{Schunk}
-Factor analysis:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
-+ model = "factor.bayes", data = swiss, factors = 2, verbose = TRUE,
-+ a0 = 1, b0 = 0.15, burnin = 5000, mcmc = 50000)
-\end{Sinput}
-\end{Schunk}
-%it is not in the demo and it fails
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> algor <- try(geweke.diag(z.out$coefficients), silent = T)
-> if (class(algor) == "try-error") print(algor)
-\end{Sinput}
-\end{Schunk}
-
-Since the algorithm did not converge, we now add some constraints on
-$\Lambda$.
-
-\item {Putting Constraints on $\Lambda$} \\
-Put constraints on Lambda to optimize the algorithm:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
-+ model = "factor.bayes", data = swiss, factors = 2, lambda.constraints = list(Exam = list(1,
-+ "+"), Exam = list(2, "-"), Educ = c(2, 0), InfMort = c(1,
-+ 0)), verbose = TRUE, a0 = 1, b0 = 0.15, burnin = 5000,
-+ mcmc = 50000)
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-Suppose for observation $i$ we observe $K$ variables and hypothesize
-that there are $d$ underlying factors such that:
-\begin{eqnarray*}
-Y_i = \Lambda \phi_i+\epsilon_i
-\end{eqnarray*}
-where $Y_{i}$ is the vector of $K$ manifest variables for observation
-$i$. $\Lambda$ is the $K \times d$ factor loading matrix and $\phi_i$
-is the $d$-vector of latent factor scores. Both $\Lambda$ and $\phi$
-need to be estimated.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by:
-\begin{eqnarray*}
-\epsilon_{i} \sim \textrm{Normal}(0, \Psi).
-\end{eqnarray*}
-where $\Psi$ is a diagonal, positive definite matrix. The diagonal elements
-of $\Psi$ are referred to as uniquenesses.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_i = E(Y_i) = \Lambda\phi_i
-\end{eqnarray*}
-
-\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The independent conjugate \emph{prior} for each $\Psi_{ii}$ is given by
-\begin{eqnarray*}
-\Psi_{ii} \sim \textrm{InverseGamma}(\frac{a_0}{2}, \frac{b_0}{2}), \textrm{ for }
-i = 1, \ldots, k.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is
-\begin{eqnarray*}
-\phi_i &\sim& \textrm{Normal}(0, I_d), \textrm{ for } i = 1, \ldots, n.
-\end{eqnarray*}
-where $I_d$ is a $ d\times d $ identity matrix.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings and the uniquenesses. If
-\texttt{store.scores = TRUE}, the estimated factors scores are also
-contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for factor analysis models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian factor analysis
-\input{contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.bayes} to work with Zelig.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
- \end{document}
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/factor.mix.pdf b/inst/doc/factor.mix.pdf
index 4c78bf6..ac7f256 100644
Binary files a/inst/doc/factor.mix.pdf and b/inst/doc/factor.mix.pdf differ
diff --git a/inst/doc/factor.mix.tex b/inst/doc/factor.mix.tex
deleted file mode 100644
index 8c5c9dd..0000000
--- a/inst/doc/factor.mix.tex
+++ /dev/null
@@ -1,295 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Mixed Data Factor Analysis}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,mixed, factors latent, continuous, ordinal,Gibbs}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-\
-\section{\texttt{factor.mix}: Mixed Data Factor Analysis}\label{factor.mix}
-
-Mixed data factor analysis takes both continuous and ordinal dependent
-variables and estimates a model for a given number of latent factors.
-The model is estimated using a Markov Chain Monte Carlo algorithm
-(Gibbs sampler with data augmentation). Alternative models include
-Bayesian factor analysis for continuous variables
-(\Sref{factor.bayes}) and Bayesian factor analysis for ordinal
-variables (\Sref{factor.ord}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
- model = "factor.mix", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments for {\tt factor.mix}:
-\begin{itemize}
-
-\item \texttt{Y1}, {\tt Y2}, \texttt{Y3}, {\tt \dots}: The dependent variables of
-interest, which can be a mix of ordinal and continuous variables. You
-must have more dependent variables than factors.
-
-\item \texttt{factors}: The number of the factors to be fitted.
-
-\end{itemize}
-
-
-\subsubsection{Additional Inputs}
-
-The model accepts the following additional arguments to monitor
-convergence:
-\begin{itemize}
-\item \texttt{lambda.constraints}: A list that contains the equality or
-inequality constraints on the factor loadings.
-
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-Unlike Bayesian factor analysis for continuous variables
-(\Sref{factor.bayes}), the first column of $\Lambda$ corresponds to
-negative item difficulty parameters and should not be constrained in
-general.
-
-\item \texttt{std.mean}: defaults to {\tt TRUE}, which rescales the
-continuous manifest variables to have mean 0.
-
-\item \texttt{std.var}: defaults to {\tt TRUE}. which rescales the
-continuous manifest variables to have unit variance.
-\end{itemize}
-
-
-\noindent \texttt{factor.mix} accepts the following additional arguments
-to monitor the sampling scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded. The default value is 1,000.
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin.
- The default value is 20,000.
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: tuning parameter, which can be either a
-scalar or a vector of length $K$. The value of the tuning parameter
-must be positive. The default value is \texttt{1.2}.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen. The default
-is \texttt{FALSE}.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{lambda.start}: starting values of the factor loading
-matrix $\Lambda$ for the Markov chain, either a scalar (starting
-values of the unconstrained loadings will be set to that value), or a
-matrix with compatible dimensions. The default is \texttt{NA}, where
-the start values for the first column of $\Lambda$ are set based on
-the observed pattern, while for the rest of the columns of $\Lambda$,
-the start values are set to be 0 for unconstrained factor loadings,
-and 1 or $-$1 for constrained factor loadings (depending on the nature
-of the constraints).
-
-\item \texttt{psi.start}: starting values for the diagonals of the error variance
-(uniquenesses) matrix. Since the starting values for the ordinal
-variables are constrained to 1 (to identify the model), you may only
-specify the starting values for the continuous variables. For the
-continuous variables, you may specify {\tt psi.start} as a scalar or a
-vector with length equal to the number of continuous variables. If a
-scalar, that starting value is recycled for all continuous variables.
-If a vector, the starting values should correspond to each of the
-continuous variables. The default value is \texttt{NA}, which means
-the starting values of all the continuous variable uniqueness are set
-to 0.5.
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, storing the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-the posterior draws of the factor scores are stored. (Storing factor
-scores may take large amount of memory for a a large number of draws
-or observations.)
-
-\end{itemize}
-Use the following additional arguments to specify prior parameters used in the model:
-\begin{itemize}
-
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, then that value will be the prior mean
-for all the factor loadings. The default value is 0.
-
-\item \texttt{L0}: precision parameter of Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as $\Lambda$.
-If a scalar value, then the precision matrix will be
-a diagonal matrix with the diagonal elements set to that value.
-The default value is 0 which leads to an improper prior.
-
-\item \texttt{a0}: {\tt a0/2} is the shape parameter of the Inverse Gamma priors for
-the uniquenesses. It can take a scalar value or a vector. The default
-value is 0.001.
-
-\item \texttt{b0}: {\tt b0/2} is the shape parameter of the Inverse Gamma priors for
-the uniquenesses. It can take a scalar value or a vector. The default
-value is 0.001.
-
-\end{itemize}
-Zelig users may wish to refer to \texttt{help(MCMCmixfactanal)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(PErisk)
-\end{Sinput}
-\end{Schunk}
-Factor analysis for mixed data using \texttt{factor.mix}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(courts, barb2, prsexp2, prscorr2, gdpw2) ~
-+ NULL, data = PErisk, model = "factor.mix", factors = 1, burnin = 5000,
-+ mcmc = 1e+05, thin = 50, verbose = TRUE, L0 = 0.25, tune = 1.2)
-\end{Sinput}
-\end{Schunk}
-
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a $K$-vector of observed variables for observation $i$,
-The $k$th variable can be either continuous or ordinal. When $Y_{ik}$ is an
-ordinal variable, it takes value from 1 to $J_k$ for $k=1,\ldots, K$ and for
-$i =1, \ldots, n$. The distribution of $Y_{ik}$ is assumed to be
-governed by another $K$-vector of unobserved continuous variable $Y_{ik}^*$.
-There are $d$ underlying factors. When $Y_{ik}$ is continuous, we let
-$Y_{ik}^*=Y_{ik}$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described in terms of $Y_i^*$:
-\begin{eqnarray*}
-Y_{i}^* &\sim& \textrm{Normal}_K (\mu_i, I_K),
-\end{eqnarray*}
-where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
-
-For ordinal $Y_{ik}$,
-\begin{eqnarray*}
-Y_{ik} = j \quad {\rm if} \quad \gamma_{(j-1),k} \le Y_{ik}^* \le
-\gamma_{jk} \quad \textrm{ for } \quad j=1,\ldots, J_k; k=1,\ldots, K.
-\end{eqnarray*}
-where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for the $k$th
-variable with the following constraints, $\gamma_{lk} < \gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k k}=\infty$ for any $k=1, \ldots, K$.
-It follows that the probability of observing $Y_{ik}$ belonging to category
-$j$ is,
-\begin{eqnarray*}
-\Pr(Y_{ik}=j) &=&\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \quad
-\textrm{ for } j=1,\ldots,J_k
-\end{eqnarray*}
-where $\Phi(\cdot\mid\mu_{ik})$ is the cumulative distribution function of the
-Normal distribution with mean $\mu_{ik}$ and variance 1.
-
-\item The \emph{systematic component} is given by,
-\begin{eqnarray*}
-\mu_i &=& \Lambda\phi_i,
-\end{eqnarray*}
-where $\Lambda$ is a $K \times d$ matrix of factor loadings for each variable,
-$\phi_i$ is a $d$-vector of factor scores for observation $i$. Note both
-$\Lambda$ and $\phi$ are estimated..
-
-\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} &\sim& \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is,
-\begin{eqnarray*}
-\phi_{i} \sim \textrm{Normal}(0, I_{d-1}), \quad {\rm for} \quad i=2, \ldots, n.
-\end{eqnarray*}
-where $I_{d-1}$ is a $ (d-1) \times (d-1) $ identity matrix. Note the
-first element of $\phi_i$ is 1.
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.mix", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings, the estimated cut points $\gamma$ for each
-variable. Note the first element of $\gamma$ is normalized to be 0. If
-\texttt{store.scores = TRUE}, the estimated factors scores are also contained in
-\texttt{coefficients}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()} procedure is
-not applicable for factor analysis models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-Factor analysis for mixed dependent variables \input{contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.mix} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/factor.ord.pdf b/inst/doc/factor.ord.pdf
index d071f59..aeca568 100644
Binary files a/inst/doc/factor.ord.pdf and b/inst/doc/factor.ord.pdf differ
diff --git a/inst/doc/factor.ord.tex b/inst/doc/factor.ord.tex
deleted file mode 100644
index 78b8111..0000000
--- a/inst/doc/factor.ord.tex
+++ /dev/null
@@ -1,274 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Ordinal Data Factor Analysis}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,factors latent, ordinal,Gibbs}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-\section{\texttt{factor.ord}: Ordinal Data Factor Analysis}
-\label{factor.ord}
-
-Given some unobserved explanatory variables and observed ordinal
-dependent variables, this model estimates latent factors using a Gibbs
-sampler with data augmentation. For factor analysis for continuous
-data, see \Sref{factor.bayes}. For factor analysis for mixed data
-(including both continuous and ordinal variables), see
-\Sref{factor.mix}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
- model = "factor.ord", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments for {\tt factor.ord}: :
-\begin{itemize}
-\item \texttt{Y1, Y2}, and \texttt{Y3}: variables of interest in
-factor analysis (manifest variables), assumed to be ordinal
-variables. The number of manifest variables must be greater than the
-number of the factors.
-
-\item \texttt{factors}: number of the factors to be fitted (defaults
-to 1).
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following arguments for model
-specification:
-\begin{itemize}
-\item \texttt{lambda.constraints}: list that contains the equality or
-inequality constraints on the factor loadings. A typical entry in the list
-has one of the following forms:
-\begin{itemize}
-\item {\tt varname = list()}: by default, no constraints are imposed.
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c};
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-%Unlike \texttt{factanal} the $\Lambda$ matrix
-%has \texttt{factors+1} columns.
-The first column of $\Lambda$ should not be constrained in general.
-
-\item \texttt{drop.constantvars}: defaults to {\tt TRUE}, dropping the
-manifest variables that have no variation before fitting the model.
-
-\end{itemize}
-
-The model accepts the following arguments to monitor the convergence
-of the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: tuning parameter for Metropolis-Hasting sampling,
-either a scalar or a vector of length $K$. The value of the tuning
-parameter must be positive. The default value is 1.2.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
-progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The
-default is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{Lambda.start}: starting values of the factor loading
-matrix $\Lambda$ for the Markov chain, either a scalar (all
-unconstrained loadings are set to that value), or a matrix with
-compatible dimensions. The default is {\tt NA}, such that the start
-values for the first column are set based on the observed pattern,
-while the remaining columns have start values set to 0 for
-unconstrained factor loadings, and -1 or 1 for constrained loadings
-(depending on the nature of the constraints).
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-stores the posterior draws of the factor scores. (Storing factor
-scores may take large amount of memory for a a large number of draws
-or observations.)
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, that value will be the prior mean for
-all the factor loadings. Defaults to 0.
-
-\item \texttt{L0}: precision parameter of the Normal prior
-for the factor loadings, either a scalar or a matrix with the same
-dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
-the precision matrix will be a diagonal matrix with the diagonal
-elements set to that value. The default value is 0, which leads to an
-improper prior.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCordfactanal)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(newpainters)
-\end{Sinput}
-\end{Schunk}
-
-Factor analysis for ordinal data using \texttt{factor.ord}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(Composition, Drawing, Colour, Expression) ~
-+ NULL, data = newpainters, model = "factor.ord", factors = 1,
-+ L0 = 0.5, burin = 5000, mcmc = 30000, thin = 5, tune = 1.2,
-+ verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of $K$ observed ordinal variables for
-observation $i$, each ordinal variable $k$ for $k=1,\ldots, K$ takes
-integer value $j=1, \ldots, J_k$. The distribution of $Y_i$ is assumed
-to be governed by another $k$-vector of unobserved continuous variable
-$Y_i^*$. There are $d$ underlying factors.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described in terms of the latent
-variable $Y_i^*$:
-\begin{eqnarray*}
-Y_{i}^* \sim \textrm{Normal}_K(\mu_i, I_K),
-\end{eqnarray*}
-where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i$ is the mean vector
-for $Y_i^*$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
-
-Instead of $Y_{ik}^*$, we observe ordinal variable $Y_{ik}$,
-\begin{eqnarray*}
-Y_{ik} = j \textrm{ if } \gamma_{(j-1),k} \le Y_{ik}^* \le \gamma_{jk}
-\textrm{ for } \quad j=1,\ldots, J_k, k=1,\ldots, K.
-\end{eqnarray*}
-where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for
-the $k$th variable with the following constraints, $\gamma_{lk} <
-\gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k
-k}=\infty$ for any $k=1, \ldots, K$. It follows that the probability
-of observing $Y_{ik}$ belonging to category $j$ is,
-\begin{eqnarray*}
-\Pr(Y_{ik}=j) =\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \textrm{ for } j=1,\ldots,J_k
-\end{eqnarray*}
-where $\Phi(\cdot \mid\mu_{ik})$ is the cumulative distribution
-function of the Normal distribution with mean $\mu_{ik}$ and variance
-1.
-
-\item The \emph{systematic component} is given by,
-\begin{eqnarray*}
-\mu_i = \Lambda\phi_i,
-\end{eqnarray*}
-where $\Lambda$ is a $K \times d$ matrix of factor loadings for each
-variable, $\phi_i$ is a $d$-vector of factor scores for observation
-$i$. Note both $\Lambda$ and $\phi$ need to be estimated.
-
-\item The independent conjugate \emph{prior} for each element of $\Lambda$,
-$\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is,
-\begin{eqnarray*}
-\phi_{i(2:d)} \sim \textrm{Normal}(0, I_{d-1}), \textrm{ for } \quad i=2, \ldots, n.
-\end{eqnarray*}
-where $I_{d-1}$ is a $ (d-1)\times (d-1) $ identity matrix. Note the
-first element of $\phi_i$ is 1.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.ord", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior
-distribution of the \texttt{coefficients} by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings, the estimated cut points $\gamma$ for each
-variable. Note the first element of $\gamma$ is normalized to be 0. If
-\texttt{store.scores=TRUE}, the estimated factors scores are also contained in
-\texttt{coefficients}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()} procedure is
-not applicable for factor analysis models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-The \texttt{factor.ord} \input{contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.ord} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/forMake.sh b/inst/doc/forMake.sh
deleted file mode 100755
index ac0dfb8..0000000
--- a/inst/doc/forMake.sh
+++ /dev/null
@@ -1,93 +0,0 @@
-#!/bin/sh
-
-
-##
-## This file is called by Makefile. It does the followings:
-## 1.run pdf latex to each tex file produced by R CMD build
-## 2.remove header and footer or each tex file so that it's ready for inclusion by zelig.tex
-## 3.convert all Rd files to tex file so that they are ready for inclusion by zelig.tex
-##
-
-## get name of the models and save them in a file
-echo "library(Zelig); a<-zeligListModels(); write(a,file='tmp.txt');" | R --vanilla --slave
-
-## at this point, Sweave should have been run and produced tex files
-for f in `cat tmp.txt`
- do
-if [ "$f" != "beta" ]
-then
- #if [ "$RBUILD" != "TRUE" ]
- # then
- echo "Sweave(\"$f.Rnw\")" | R --slave
- #fi
-
- ## create pdfs for all the tex file produced by sweave
- pdflatex $f.tex
-
- ## remove header and footer from the tex file so that they are ready
- ## for inclusion in big zelig manual
- perl -i -ne 'print unless /^\\include{zinput}$/ .. /^\\begin{document}$/' $f.tex
- perl -i -p -e 's#\\end{document}# #i' $f.tex
-fi
-done
-
-## run R CMD Rdconv to rd files
-## create commandsRd folder if does not exists
-
-if [ ! -d commandsRd ]
-then
- mkdir commandsRd
-fi
-Rdfiles=`ls ../../man`
-for rd in ${Rdfiles}
- do
- newname=`basename ${rd} .Rd`
- R CMD Rdconv -t=latex ../../man/${rd} -o commandsRd/${newname}.tex
-
- ### perl -i -pe 's#HeaderA{#section{{\\tt #i' commandsRd/${newname}.tex
- perl -i -pe 's#HeaderA{(.*)}{(.*)}{(.*)}#section{{\\tt \1}: \2}\\label{ss:\3}#i' commandsRd/${newname}.tex
-done
-
-
-## create the big Zelig manual
-pdflatex zelig
-bibtex zelig
-pdflatex zelig
-pdflatex zelig
-pdflatex zelig
-
-
-#for f in `cat tmp.txt`
-#do
-#rm -f $f.tex
-#done
-
-## remove the files with the name of the models
-rm tmp.txt
-
-
-
-## do some cleanup
-rm -f *.aux *.toc *.log *.out *.blg *.bbl
-
-
-
-
-
-
-
-
-#### here is how u delete the \end{document}
-#### perl -i.old -p -e 's#\\end{document}# #i' try
-
-#### delete first 10 lines
-#### perl -i.old -ne 'print unless 1 .. 10' foo.txt
-
-#### \SweaveOpts{result=hide}
-#### perl -i.old -p -e 's#\\include{zinput}#\\SweaveOpts{results=hide}\n\\include{zinput}#i' *.Rnw
-
-### change dependencies line
-### perl -i.old -p -e 's#See Package dependencies#Zelig#i' *.Rnw
-
-#### thats how u remove the header from tex files
-#### perl -i.old -ne 'print unless /^\\include{zinput}$/ .. /^\\begin{document}$/' try
diff --git a/inst/doc/gam.logit.Rnw b/inst/doc/gam.logit.Rnw
new file mode 100644
index 0000000..7c722bd
--- /dev/null
+++ b/inst/doc/gam.logit.Rnw
@@ -0,0 +1,294 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/gam}
+\include{zinput}
+%\VignetteIndexEntry{gam.logit: Generalized Additive Model for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model, gam.logit, gam}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+
+\section{{\tt gam.logit}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Where {\tt s()} indicates a variable to be estimated via nonparametric smooth. All variables for which {\tt s()} is not specified, are estimated via standard parametric methods.
+
+\subsubsection{Additional Inputs}
+In addition to the standard inputs, {\tt zelig()} takes the following additional options for GAM models.
+\begin{itemize}
+\item {\tt method}: Controls the fitting method to be used. Fitting methods are selected via a list environment within {\tt method=gam.method()}. See {\tt gam.method()} for details.
+\item {\tt scale}: Generalized Cross Validation (GCV) is used if {\tt scale = 0} (see the ``Model'' section for details) except for Logit models where a Un-Biased Risk Estimator (UBRE) (also see the ``Model'' section for details) is used with a scale parameter assumed to be 1. If {\tt scale} is greater than 1, it is assumed to be the scale parameter/variance and UBRE is used. If {\tt scale} is negative GCV is used.
+\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
+\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
+\item {\tt sp}: A vector of smoothing parameters for each term.
+\item {\tt \ldots}: additional options passed to the {\tt gam.logit} model. See the {\tt mgcv} library for details.
+\end{itemize}
+
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Create some count data:
+
+<<echo=FALSE, results=hide>>=
+library(Zelig)
+<<echo=FALSE, results=hide>>=
+set.seed(0)
+n<-400
+sig<-2
+x0 <- runif(n, 0, 1)
+x1 <- runif(n, 0, 1)
+x2 <- runif(n, 0, 1)
+x3 <- runif(n, 0, 1)
+f0 <- function(x) 2 * sin(pi * x)
+f1 <- function(x) exp(2 * x)
+f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
+f3 <- function(x) 0*x
+f <- f0(x0) + f1(x1) + f2(x2)
+g <- (f-5)/3
+g <- binomial()$linkinv(g)
+y <- rbinom(g,1,g)
+my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+
+@
+\begin{Schunk}
+\begin{Sinput}
+> set.seed(0); n <- 400; sig <- 2;
+> x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
+> x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
+> g <- (f-5)/3
+> g <- binomial()$linkinv(g)
+> y <- rbinom(g,1,g)
+> my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+\end{Sinput}
+\end{Schunk}
+
+Estimate the model, summarize the results, and plot nonlinearities:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.logit", data=my.data)
+summary(z.out)
+plot(z.out,pages=1,residuals=TRUE)
+@
+Note that the {\tt plot()} function can be used after model estimation and before simulation to view the nonlinear relationships in the independent variables:
+\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=6, height=6>>=
+plot(z.out,pages=1,residuals=TRUE)
+@
+\label{fig:plotgam}
+\end{figure}
+
+Set values for the explanatory variables to their default (mean/mode) values, then simulate, summarize and plot quantities of interest:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+ x.out <- setx(z.out)
+ s.out <- sim(z.out, x = x.out)
+ summary(s.out)
+ plot(s.out)
+@
+\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low values (20th percentile) and high values (80th percentile) of the explanatory variable {\tt x3} while all the other variables are held at their default (mean/mode) values.
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
+x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
+
+s.out <- sim(z.out, x=x.high, x1=x.low)
+summary(s.out)
+plot(s.out)
+
+@
+%\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+@
+\item Variations in GAM model specification. Note that {\tt setx} and {\tt sim} work as shown in the above examples for any GAM model. As such, in the interest of parsimony, I will not re-specify the simulations of quantities of interest.
+
+An extra ridge penalty (useful with convergence problems):
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
+ model="gam.logit", data=my.data)
+summary(z.out)
+plot(z.out,pages=1,residuals=TRUE)
+
+@
+Set the smoothing parameter for the first term, estimate the rest:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
+ model="gam.logit", data=my.data)
+summary(z.out)
+plot(z.out,pages=1)
+
+@
+Set lower bounds on smoothing parameters:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
+ model="gam.logit", data=my.data)
+summary(z.out)
+plot(z.out, pages=1)
+
+@
+A GAM with 3df regression spline term \& 2 penalized terms:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
+ model="gam.logit", data=my.data)
+summary(z.out)
+plot(z.out,pages=1)
+
+@
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+
+
+GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt 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.
+\begin{itemize}
+\item The logistic distribution has \emph{stochastic component}
+\begin{eqnarray*}
+Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
+& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
+\end{eqnarray*}
+where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_{i} = \frac{1}{1 + \exp \left(-x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right)},
+\end{equation*}
+where $x_{i}$ is the vector of covariates, $\beta$ is the vector of coefficients and $f_j(Z_j)$ for $j=1, \ldots J$ is the set of smooth terms..
+\end{itemize}
+
+Generalized additive models (GAMs) are similar in many respects to generalized linear models (GLMs). Specifically, GAMs are generally fit by penalized maximum likelihood estimation and GAMs have (or can have) a parametric component identical to that of a GLM. The difference is that GAMs also include in their linear predictors a specified sum of smooth functions.
+
+In this GAM implementation, smooth functions are represented using penalized regression splines. Two techniques may be used to estimate smoothing parameters: Generalized Cross Validation (GCV),
+\begin{equation}
+n \frac{D}{(n-DF)^2},
+\end{equation}
+or an Un-Biased Risk Estimator (UBRE) (which is effectively just a rescaled AIC),
+\begin{equation}
+\frac{D}{n} + 2 s \frac{DF}{n-s},
+\end{equation}
+where $D$ is the deviance, $n$ is the number of observations, $s$ is the scale parameter, and $DF$ is the effective degrees of freedom of the model. The use of GCV or UBRE can be set by the user with the {\tt scale} command described in the ``Additional Inputs'' section and in either case, smoothing parameters are chosen to minimize the GCV or UBRE score for the model.
+
+Estimation for GAM models proceeds as follows: first, basis functions and a set (one or more) of quadratic penalty coefficient matrices are constructed for each smooth term. Second, a model matrix is is obtained for the parametric component of the GAM. These matrices are combined to produce a complete model matrix and a set of penalty matrices for the smooth terms. Iteratively Reweighted Least Squares (IRLS) is then used to estimate the model; at each iteration of the IRLS, a penalized w [...]
+
+Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
+
+
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the {\tt gam.logit} 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,
+\begin{equation*}
+\pi_{i} = \frac{1}{1 + \exp \left(-x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right)},
+\end{equation*}
+
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference ({\tt qi\$fd}) for the GAM logit model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
+\end{equation*}
+for $w=\{X, Z\}$.
+\end{itemize}
+
+
+
+\subsubsection{Output Values}
+
+
+
+The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IRLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt method}: the fitting method used.
+\item {\tt converged}: logical indicating weather the model converged or not.
+\item {\tt smooth}: information about the smoothed parameters.
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt data}: the input data frame.
+\item {\tt model}: the model matrix used.
+
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt p.coeff}: the coefficients of the parametric components of the model.
+\item {\tt se}: the standard errors of the entire model.
+\item {\tt p.table}: the coefficients, standard errors, and associated $t$ statistics for the parametric portion of the model.
+\item {\tt s.table}: the table of estimated degrees of freedom, estimated rank, $F$ statistics, and $p$-values for the nonparametric portion of the model.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+
+
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{References}
+
+\begin{quote} Wood, Simon N. (2006) \emph{Generalized Additive Models: An Introduction with R.} CRC Press \\ \end{quote}
+
+\begin{quote} Wood, Simon N. (2004) ``Stable and efficient multiple smoothing parameter estimation for generalized additive models.''\emph{Journal of the American Statistical Association.} 99: 673-686.\\ \end{quote}
+
+\begin{quote} Wood, Simon N. (2000) ``Modeling and Smoothing Parameter Estimation with Multiple Quadratic Penalties.'' \emph{Journal of the Royal Statistical Society.} 62(2): 413-428.\\ \end{quote}
+
+\subsubsection{Contributors}
+The {\tt gam.logit} model and all other GAMs currently in Zelig are from the {\tt mgcv} package by Simon N. Wood. Please cite the model as \\
+
+\begin{quote} Wood, Simon N. (2006) \emph{Generalized Additive Models: An Introduction with R.} CRC Press \\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(gam)} and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages. Skyler J. Cranmer added Zelig functionality.
+
+
+<<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/gam.logit.pdf b/inst/doc/gam.logit.pdf
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@@ -0,0 +1,286 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/gam}
+\include{zinput}
+%\VignetteIndexEntry{Generalized Additive Model for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model, gam, gam.normal}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt gam.normal}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Where {\tt s()} indicates a variable to be estimated via nonparametric smooth. All variables for which {\tt s()} is not specified, are estimated via standard parametric methods.
+
+\subsubsection{Additional Inputs}
+In addition to the standard inputs, {\tt zelig()} takes the following additional options for GAM models.
+\begin{itemize}
+\item {\tt method}: Controls the fitting method to be used. Fitting methods are selected via a list environment within {\tt method=gam.method()}. See {\tt gam.method()} for details.
+\item {\tt scale}: Generalized Cross Validation (GCV) is used if {\tt scale = 0} (see the ``Model'' section for details) except for Normal models where a Un-Biased Risk Estimator (UBRE) (also see the ``Model'' section for details) is used with a scale parameter assumed to be 1. If {\tt scale} is greater than 1, it is assumed to be the scale parameter/variance and UBRE is used. If {\tt scale} is negative GCV is used.
+\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
+\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
+\item {\tt sp}: A vector of smoothing parameters for each term.
+\item {\tt \ldots}: additional options passed to the {\tt gam.normal} model. See the {\tt mgcv} library for details.
+\end{itemize}
+
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example:
+
+Create some data:
+
+\begin{Schunk}
+\begin{Sinput}
+> set.seed(0); n <- 400; sig <- 2;
+> x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
+> x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
+> f0 <- function(x) 2 * sin(pi * x)
+> f1 <- function(x) exp(2 * x)
+> f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 * (10 *
++ x)^3 * (1 - x)^10
+> f3 <- function(x) 0 * x
+> f <- f0(x0) + f1(x1) + f2(x2)
+> e <- rnorm(n, 0, sig); y <- f + e
+> my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+\end{Sinput}
+\end{Schunk}
+
+<<echo=FALSE, results=hide>>=
+library(Zelig)
+<<echo=FALSE, results=hide>>=
+set.seed(0); n<-400; sig<-2
+set.seed(0); n <- 400; sig <- 2;
+x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
+x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
+f0 <- function(x) 2 * sin(pi * x)
+f1 <- function(x) exp(2 * x)
+f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 * (10 * x)^3 * (1 - x)^10
+f3 <- function(x) 0 * x
+f <- f0(x0) + f1(x1) + f2(x2)
+e <- rnorm(n, 0, sig); y <- f + e
+my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+
+@
+Estimate the model, summarize the results, and plot nonlinearities:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.normal", data=my.data)
+summary(z.out)
+plot(z.out,pages=1,residuals=TRUE)
+@
+Note that the {\tt plot()} function can be used after model estimation and before simulation to view the nonlinear relationships in the independent variables:
+\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=6, height=6>>=
+plot(z.out,pages=1,residuals=TRUE)
+@
+\label{fig:plotgam}
+\end{figure}
+
+Set values for the explanatory variables to their default (mean/mode) values, then simulate, summarize and plot quantities of interest:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+ x.out <- setx(z.out)
+ s.out <- sim(z.out, x = x.out)
+ summary(s.out)
+ plot(s.out)
+@
+\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low values (20th percentile) and high values (80th percentile) of the explanatory variable {\tt x3} while all the other variables are held at their default (mean/mode) values.
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
+x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
+
+s.out <- sim(z.out, x=x.high, x1=x.low)
+summary(s.out)
+plot(s.out)
+
+@
+%\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+@
+\item Variations in GAM model specification. Note that {\tt setx} and {\tt sim} work as shown in the above examples for any GAM model. As such, in the interest of parsimony, I will not re-specify the simulations of quantities of interest.
+
+An extra ridge penalty (useful with convergence problems):
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
+ model="gam.normal", data=my.data)
+summary(z.out)
+plot(z.out,pages=1,residuals=TRUE)
+
+@
+Set the smoothing parameter for the first term, estimate the rest:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
+ model="gam.normal", data=my.data)
+summary(z.out)
+plot(z.out,pages=1)
+
+@
+Set lower bounds on smoothing parameters:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
+ model="gam.normal", data=my.data)
+summary(z.out)
+plot(z.out, pages=1)
+
+@
+A GAM with 3df regression spline term \& 2 penalized terms:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
+ model="gam.normal", data=my.data)
+summary(z.out)
+plot(z.out,pages=1)
+
+@
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+
+
+GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt gam.normal} a normal link function is used. Specifically, let $Y_{i}$ be the continuous dependent variable for observation $i$.
+\begin{itemize}
+\item The \emph{stochastic component} is described by a univariate normal model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
+\begin{equation*}
+Y_{i} \sim \text{Normal} ( \mu_{i}, \sigma^2).\\
+\end{equation*}
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\mu_i = x_i\beta + \sum_{j=1}^{J} f_j (Z_j).
+\end{equation*}
+where $ x_i$ is the vector of $k$ explanatory variables, $\beta$ is the vector of coefficients and $f_j(Z_j)$ for $j=1, \ldots J$ is the set of smooth terms.
+\end{itemize}
+
+
+Generalized additive models (GAMs) are similar in many respects to generalized linear models (GLMs). Specifically, GAMs are generally fit by penalized maximum likelihood estimation and GAMs have (or can have) a parametric component identical to that of a GLM. The difference is that GAMs also include in their linear predictors a specified sum of smooth functions.
+
+In this GAM implementation, smooth functions are represented using penalized regression splines. Two techniques may be used to estimate smoothing parameters: Generalized Cross Validation (GCV),
+\begin{equation}
+n \frac{D}{(n-DF)^2},
+\end{equation}
+or an Un-Biased Risk Estimator (UBRE) (which is effectively just a rescaled AIC),
+\begin{equation}
+\frac{D}{n} + 2 s \frac{DF}{n-s},
+\end{equation}
+where $D$ is the deviance, $n$ is the number of observations, $s$ is the scale parameter, and $DF$ is the effective degrees of freedom of the model. The use of GCV or UBRE can be set by the user with the {\tt scale} command described in the ``Additional Inputs'' section and in either case, smoothing parameters are chosen to minimize the GCV or UBRE score for the model.
+
+Estimation for GAM models proceeds as follows: first, basis functions and a set (one or more) of quadratic penalty coefficient matrices are constructed for each smooth term. Second, a model matrix is is obtained for the parametric component of the GAM. These matrices are combined to produce a complete model matrix and a set of penalty matrices for the smooth terms. Iteratively Reweighted Least Squares (IRLS) is then used to estimate the model; at each iteration of the IRLS, a penalized w [...]
+
+Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
+
+
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the {\tt gam.normal} 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,
+\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
+\begin{equation*}
+FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
+\end{equation*}
+for $w=\{X, Z\}$.
+\end{itemize}
+
+
+
+\subsubsection{Output Values}
+
+
+
+The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IRLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt method}: the fitting method used.
+\item {\tt converged}: logical indicating weather the model converged or not.
+\item {\tt smooth}: information about the smoothed parameters.
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt data}: the input data frame.
+\item {\tt model}: the model matrix used.
+
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt p.coeff}: the coefficients of the parametric components of the model.
+\item {\tt se}: the standard errors of the entire model.
+\item {\tt p.table}: the coefficients, standard errors, and associated $t$ statistics for the parametric portion of the model.
+\item {\tt s.table}: the table of estimated degrees of freedom, estimated rank, $F$ statistics, and $p$-values for the nonparametric portion of the model.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+
+
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{References}
+
+\begin{quote} Wood, Simon N. (2006) \emph{Generalized Additive Models: An Introduction with R.} CRC Press \\ \end{quote}
+
+\begin{quote} Wood, Simon N. (2004) ``Stable and efficient multiple smoothing parameter estimation for generalized additive models.''\emph{Journal of the American Statistical Association.} 99: 673-686.\\ \end{quote}
+
+\begin{quote} Wood, Simon N. (2000) ``Modeling and Smoothing Parameter Estimation with Multiple Quadratic Penalties.'' \emph{Journal of the Royal Statistical Society.} 62(2): 413-428.\\ \end{quote}
+
+\subsubsection{Contributors}
+The {\tt gam.normal} model and all other GAMs currently in Zelig are from the {\tt mgcv} package by Simon N. Wood. Please cite the model as \\
+
+\begin{quote} Wood, Simon N. (2006) \emph{Generalized Additive Models: An Introduction with R.} CRC Press \\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(gam)} and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages. Skyler J. Cranmer added Zelig functionality.
+<<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/gam.normal.pdf b/inst/doc/gam.normal.pdf
new file mode 100644
index 0000000..c8861c6
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diff --git a/inst/doc/gam.poisson.Rnw b/inst/doc/gam.poisson.Rnw
new file mode 100644
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--- /dev/null
+++ b/inst/doc/gam.poisson.Rnw
@@ -0,0 +1,290 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/gam}
+\include{zinput}
+%\VignetteIndexEntry{Generalized Additive Model for Count Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model, gam, gam.poisson}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt gam.poisson}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Where {\tt s()} indicates a variable to be estimated via nonparametric smooth. All variables for which {\tt s()} is not specified, are estimated via standard parametric methods.
+
+\subsubsection{Additional Inputs}
+In addition to the standard inputs, {\tt zelig()} takes the following additional options for GAM models.
+\begin{itemize}
+\item {\tt method}: Controls the fitting method to be used. Fitting methods are selected via a list environment within {\tt method=gam.method()}. See {\tt gam.method()} for details.
+\item {\tt scale}: Generalized Cross Validation (GCV) is used if {\tt scale = 0} (see the ``Model'' section for details) except for Poisson models where a Un-Biased Risk Estimator (UBRE) (also see the ``Model'' section for details) is used with a scale parameter assumed to be 1. If {\tt scale} is greater than 1, it is assumed to be the scale parameter/variance and UBRE is used. If {\tt scale} is negative GCV is used.
+\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
+\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
+\item {\tt sp}: A vector of smoothing parameters for each term.
+\item {\tt \ldots}: additional options passed to the {\tt gam.poisson} model. See the {\tt mgcv} library for details.
+\end{itemize}
+
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Create some count data:
+
+<<echo=FALSE, results=hide>>=
+library(Zelig)
+<<echo=FALSE, results=hide>>=
+set.seed(0); n<-400; sig<-2
+x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1); x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
+f0 <- function(x) 2 * sin(pi * x)
+f1 <- function(x) exp(2 * x)
+f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
+f3 <- function(x) 0*x
+f <- f0(x0) + f1(x1) + f2(x2)
+g<-exp(f/4)
+y<-rpois(rep(1,n),g)
+my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+
+@
+\begin{Schunk}
+\begin{Sinput}
+> set.seed(0); n <- 400; sig <- 2;
+> x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
+> x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
+> f0 <- function(x) 2 * sin(pi * x)
+> f1 <- function(x) exp(2 * x)
+> f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 * (10 *
++ x)^3 * (1 - x)^10
+> f3 <- function(x) 0 * x
+> f <- f0(x0) + f1(x1) + f2(x2)
+> g <- exp(f/4); y <- rpois(rep(1, n), g)
+> my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+\end{Sinput}
+\end{Schunk}
+
+Estimate the model, summarize the results, and plot nonlinearities:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.poisson", data=my.data)
+summary(z.out)
+plot(z.out,pages=1,residuals=TRUE)
+@
+Note that the {\tt plot()} function can be used after model estimation and before simulation to view the nonlinear relationships in the independent variables:
+\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=6, height=6>>=
+plot(z.out,pages=1,residuals=TRUE)
+@
+\label{fig:plotgam}
+\end{figure}
+
+Set values for the explanatory variables to their default (mean/mode) values, then simulate, summarize and plot quantities of interest:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+ x.out <- setx(z.out)
+ s.out <- sim(z.out, x = x.out)
+ summary(s.out)
+ plot(s.out)
+@
+\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low values (20th percentile) and high values (80th percentile) of the explanatory variable {\tt x3} while all the other variables are held at their default (mean/mode) values.
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
+x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
+
+s.out <- sim(z.out, x=x.high, x1=x.low)
+summary(s.out)
+plot(s.out)
+
+@
+%\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+@
+\item Variations in GAM model specification. Note that {\tt setx} and {\tt sim} work as shown in the above examples for any GAM model. As such, in the interest of parsimony, I will not re-specify the simulations of quantities of interest.
+
+An extra ridge penalty (useful with convergence problems):
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
+ model="gam.poisson", data=my.data)
+summary(z.out)
+plot(z.out,pages=1,residuals=TRUE)
+
+@
+Set the smoothing parameter for the first term, estimate the rest:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
+ model="gam.poisson", data=my.data)
+summary(z.out)
+plot(z.out,pages=1)
+
+@
+Set lower bounds on smoothing parameters:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
+ model="gam.poisson", data=my.data)
+summary(z.out)
+plot(z.out, pages=1)
+
+@
+A GAM with 3df regression spline term \& 2 penalized terms:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
+ model="gam.poisson", data=my.data)
+summary(z.out)
+plot(z.out,pages=1)
+
+@
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+
+
+GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt 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.
+\begin{itemize}
+\item The Poisson distribution has \emph{stochastic component}
+\begin{equation*}
+Y_{i} \sim \text{Poisson} ( \lambda_{i}),\\
+\end{equation*}
+where $\lambda_{i}$ is the mean and variance parameter.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\lambda_i = \exp \left(x_i\beta + \sum_{j=1}^{J} f_j(Z_j) \right).
+\end{equation*}
+where $ x_i$ is the vector of explanatory variables, $\beta$ is the vector of coefficients and $f_j(Z_j)$ for $j=1, \ldots J$ is the set of smooth terms.
+\end{itemize}
+
+Generalized additive models (GAMs) are similar in many respects to generalized linear models (GLMs). Specifically, GAMs are generally fit by penalized maximum likelihood estimation and GAMs have (or can have) a parametric component identical to that of a GLM. The difference is that GAMs also include in their linear predictors a specified sum of smooth functions.
+
+In this GAM implementation, smooth functions are represented using penalized regression splines. Two techniques may be used to estimate smoothing parameters: Generalized Cross Validation (GCV),
+\begin{equation}
+n \frac{D}{(n-DF)^2},
+\end{equation}
+or an Un-Biased Risk Estimator (UBRE) (which is effectively just a rescaled AIC),
+\begin{equation}
+\frac{D}{n} + 2 s \frac{DF}{n-s},
+\end{equation}
+where $D$ is the deviance, $n$ is the number of observations, $s$ is the scale parameter, and $DF$ is the effective degrees of freedom of the model. The use of GCV or UBRE can be set by the user with the {\tt scale} command described in the ``Additional Inputs'' section and in either case, smoothing parameters are chosen to minimize the GCV or UBRE score for the model.
+
+Estimation for GAM models proceeds as follows: first, basis functions and a set (one or more) of quadratic penalty coefficient matrices are constructed for each smooth term. Second, a model matrix is is obtained for the parametric component of the GAM. These matrices are combined to produce a complete model matrix and a set of penalty matrices for the smooth terms. Iteratively Reweighted Least Squares (IRLS) is then used to estimate the model; at each iteration of the IRLS, a penalized w [...]
+
+Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
+
+
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the {\tt gam.poisson} 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,
+\begin{equation*}
+E(Y) = \lambda_{i} = \exp \left(x_{i}\beta \sum_{j=1}^{J} f_j(Z_j) \right).
+\end{equation*}
+
+
+\item The predicted value ({\tt qi\$pr}) is a random draw from the Poisson distribution defined by mean $\lambda_i$.
+
+\item The first difference ({\tt qi\$fd}) for the GAM Poisson model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
+\end{equation*}
+for $w=\{X, Z\}$.
+\end{itemize}
+
+
+
+\subsubsection{Output Values}
+
+
+
+The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IRLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt method}: the fitting method used.
+\item {\tt converged}: logical indicating weather the model converged or not.
+\item {\tt smooth}: information about the smoothed parameters.
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt data}: the input data frame.
+\item {\tt model}: the model matrix used.
+
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt p.coeff}: the coefficients of the parametric components of the model.
+\item {\tt se}: the standard errors of the entire model.
+\item {\tt p.table}: the coefficients, standard errors, and associated $t$ statistics for the parametric portion of the model.
+\item {\tt s.table}: the table of estimated degrees of freedom, estimated rank, $F$ statistics, and $p$-values for the nonparametric portion of the model.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+
+
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{References}
+
+\begin{quote} Wood, Simon N. (2006) \emph{Generalized Additive Models: An Introduction with R.} CRC Press \\ \end{quote}
+
+\begin{quote} Wood, Simon N. (2004) ``Stable and efficient multiple smoothing parameter estimation for generalized additive models.''\emph{Journal of the American Statistical Association.} 99: 673-686.\\ \end{quote}
+
+\begin{quote} Wood, Simon N. (2000) ``Modeling and Smoothing Parameter Estimation with Multiple Quadratic Penalties.'' \emph{Journal of the Royal Statistical Society.} 62(2): 413-428.\\ \end{quote}
+
+\subsubsection{Contributors}
+The {\tt gam.poisson} model and all other GAMs currently in Zelig are from the {\tt mgcv} package by Simon N. Wood. Please cite the model as \\
+
+\begin{quote} Wood, Simon N. (2006) \emph{Generalized Additive Models: An Introduction with R.} CRC Press \\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(gam)} and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages. Skyler J. Cranmer added Zelig functionality.
+<<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/gam.poisson.pdf b/inst/doc/gam.poisson.pdf
new file mode 100644
index 0000000..8c5a2ab
Binary files /dev/null and b/inst/doc/gam.poisson.pdf differ
diff --git a/inst/doc/gam.probit.Rnw b/inst/doc/gam.probit.Rnw
new file mode 100644
index 0000000..70a0ec2
--- /dev/null
+++ b/inst/doc/gam.probit.Rnw
@@ -0,0 +1,286 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/gam}
+\include{zinput}
+%\VignetteIndexEntry{Generalized Additive Model for Dichotomous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model, gam, gam.probit}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt gam.probit}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Where {\tt s()} indicates a variable to be estimated via nonparametric smooth. All variables for which {\tt s()} is not specified, are estimated via standard parametric methods.
+
+\subsubsection{Additional Inputs}
+In addition to the standard inputs, {\tt zelig()} takes the following additional options for GAM models.
+\begin{itemize}
+\item {\tt method}: Controls the fitting method to be used. Fitting methods are selected via a list environment within {\tt method=gam.method()}. See {\tt gam.method()} for details.
+\item {\tt scale}: Generalized Cross Validation (GCV) is used if {\tt scale = 0} (see the ``Model'' section for details) except for Logit models where a Un-Biased Risk Estimator (UBRE) (also see the ``Model'' section for details) is used with a scale parameter assumed to be 1. If {\tt scale} is greater than 1, it is assumed to be the scale parameter/variance and UBRE is used. If {\tt scale} is negative GCV is used.
+\item {\tt knots}: An optional list of knot values to be used for the construction of basis functions.
+\item {\tt H}: A user supplied fixed quadratic penalty on the parameters of the GAM can be supplied with this as its coefficient matrix. For example, ridge penalties can be added to the parameters of the GAM to aid in identification on the scale of the linear predictor.
+\item {\tt sp}: A vector of smoothing parameters for each term.
+\item {\tt \ldots}: additional options passed to the {\tt gam.probit} model. See the {\tt mgcv} library for details.
+\end{itemize}
+
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Create some count data:
+
+<<echo=FALSE, results=hide>>=
+library(Zelig)
+<<echo=FALSE, results=hide>>=
+set.seed(0); n<-400; sig<-2
+x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1); x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
+f0 <- function(x) 2 * sin(pi * x)
+f1 <- function(x) exp(2 * x)
+f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
+f3 <- function(x) 0*x
+f <- f0(x0) + f1(x1) + f2(x2)
+
+g <- (f-5)/3
+g <- binomial()$linkinv(g)
+y <- rbinom(g,1,g)
+my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+
+@
+\begin{Schunk}
+\begin{Sinput}
+> set.seed(0); n <- 400; sig <- 2;
+> x0 <- runif(n, 0, 1); x1 <- runif(n, 0, 1)
+> x2 <- runif(n, 0, 1); x3 <- runif(n, 0, 1)
+> g <- (f-5)/3
+> g <- binomial()$linkinv(g)
+> y <- rbinom(g,1,g)
+> my.data <- as.data.frame(cbind(y, x0, x1, x2, x3))
+\end{Sinput}
+\end{Schunk}
+
+Estimate the model, summarize the results, and plot nonlinearities:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), model="gam.probit", data=my.data)
+summary(z.out)
+plot(z.out,pages=1,residuals=TRUE)
+@
+Note that the {\tt plot()} function can be used after model estimation and before simulation to view the nonlinear relationships in the independent variables:
+\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=6, height=6>>=
+plot(z.out,pages=1,residuals=TRUE)
+@
+\label{fig:plotgam}
+\end{figure}
+
+Set values for the explanatory variables to their default (mean/mode) values, then simulate, summarize and plot quantities of interest:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+ x.out <- setx(z.out)
+ s.out <- sim(z.out, x = x.out)
+ summary(s.out)
+ plot(s.out)
+@
+\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low values (20th percentile) and high values (80th percentile) of the explanatory variable {\tt x3} while all the other variables are held at their default (mean/mode) values.
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.high <- setx(z.out, x3= quantile(my.data$x3, 0.8))
+x.low <- setx(z.out, x3 = quantile(my.data$x3, 0.2))
+
+s.out <- sim(z.out, x=x.high, x1=x.low)
+summary(s.out)
+plot(s.out)
+
+@
+%\SweaveOpts{echo=false}
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+@
+\item Variations in GAM model specification. Note that {\tt setx} and {\tt sim} work as shown in the above examples for any GAM model. As such, in the interest of parsimony, I will not re-specify the simulations of quantities of interest.
+
+An extra ridge penalty (useful with convergence problems):
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3), H=diag(0.5,37),
+ model="gam.probit", data=my.data)
+summary(z.out)
+plot(z.out,pages=1,residuals=TRUE)
+
+@
+Set the smoothing parameter for the first term, estimate the rest:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),
+ model="gam.probit", data=my.data)
+summary(z.out)
+plot(z.out,pages=1)
+
+@
+Set lower bounds on smoothing parameters:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(y~s(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),
+ model="gam.probit", data=my.data)
+summary(z.out)
+plot(z.out, pages=1)
+
+@
+A GAM with 3df regression spline term \& 2 penalized terms:
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <-zelig(y~s(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),
+ model="gam.probit", data=my.data)
+summary(z.out)
+plot(z.out,pages=1)
+
+@
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+
+
+GAM models use families the same way GLM models do: they specify the distribution and link function to use in model fitting. In the case of {\tt 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.
+\begin{itemize}
+\item The normal distribution has \emph{stochastic component}
+\begin{equation*}
+Y_{i} \sim \text{Bernoulli} ( \pi_{i})\\
+\end{equation*}
+where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_{i} = \pmb{\Phi} \left( x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right),
+\end{equation*}
+where $ \pmb{\Phi}(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance and $f_j(Z_j)$ for $j=1, \ldots J$ is the set of smooth terms.
+\end{itemize}
+
+Generalized additive models (GAMs) are similar in many respects to generalized linear models (GLMs). Specifically, GAMs are generally fit by penalized maximum likelihood estimation and GAMs have (or can have) a parametric component identical to that of a GLM. The difference is that GAMs also include in their linear predictors a specified sum of smooth functions.
+
+In this GAM implementation, smooth functions are represented using penalized regression splines. Two techniques may be used to estimate smoothing parameters: Generalized Cross Validation (GCV),
+\begin{equation}
+n \frac{D}{(n-DF)^2},
+\end{equation}
+or an Un-Biased Risk Estimator (UBRE) (which is effectively just a rescaled AIC),
+\begin{equation}
+\frac{D}{n} + 2 s \frac{DF}{n-s},
+\end{equation}
+where $D$ is the deviance, $n$ is the number of observations, $s$ is the scale parameter, and $DF$ is the effective degrees of freedom of the model. The use of GCV or UBRE can be set by the user with the {\tt scale} command described in the ``Additional Inputs'' section and in either case, smoothing parameters are chosen to minimize the GCV or UBRE score for the model.
+
+Estimation for GAM models proceeds as follows: first, basis functions and a set (one or more) of quadratic penalty coefficient matrices are constructed for each smooth term. Second, a model matrix is is obtained for the parametric component of the GAM. These matrices are combined to produce a complete model matrix and a set of penalty matrices for the smooth terms. Iteratively Reweighted Least Squares (IRLS) is then used to estimate the model; at each iteration of the IRLS, a penalized w [...]
+
+Further details of the GAM fitting process are given in Wood (2000, 2004, 2006).
+
+
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the {\tt gam.probit} 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,
+\begin{equation*}
+\pi_{i} = \pmb{\Phi} \left( x_{i}\beta + \sum_{j=1}^{J} f_j(Z_j) \right).
+\end{equation*}
+
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference ({\tt qi\$fd}) for the GAM probit model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y| w_{1}) - \text{Pr}(Y| w)
+\end{equation*}
+for $w=\{X, Z\}$.
+\end{itemize}
+
+
+
+\subsubsection{Output Values}
+
+
+
+The output of each Zelig command contains useful information which you may view. For example, if you run {\tt z.out <- zelig(y \~{} x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IRLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt method}: the fitting method used.
+\item {\tt converged}: logical indicating weather the model converged or not.
+\item {\tt smooth}: information about the smoothed parameters.
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt data}: the input data frame.
+\item {\tt model}: the model matrix used.
+
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt p.coeff}: the coefficients of the parametric components of the model.
+\item {\tt se}: the standard errors of the entire model.
+\item {\tt p.table}: the coefficients, standard errors, and associated $t$ statistics for the parametric portion of the model.
+\item {\tt s.table}: the table of estimated degrees of freedom, estimated rank, $F$ statistics, and $p$-values for the nonparametric portion of the model.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+
+
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{References}
+
+\begin{quote} Wood, Simon N. (2006) \emph{Generalized Additive Models: An Introduction with R.} CRC Press \\ \end{quote}
+
+\begin{quote} Wood, Simon N. (2004) ``Stable and efficient multiple smoothing parameter estimation for generalized additive models.''\emph{Journal of the American Statistical Association.} 99: 673-686.\\ \end{quote}
+
+\begin{quote} Wood, Simon N. (2000) ``Modeling and Smoothing Parameter Estimation with Multiple Quadratic Penalties.'' \emph{Journal of the Royal Statistical Society.} 62(2): 413-428.\\ \end{quote}
+
+\subsubsection{Contributors}
+The {\tt gam.probit} model and all other GAMs currently in Zelig are from the {\tt mgcv} package by Simon N. Wood. Please cite the model as \\
+
+\begin{quote} Wood, Simon N. (2006) \emph{Generalized Additive Models: An Introduction with R.} CRC Press \\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(gam)} and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages. Skyler J. Cranmer added Zelig functionality.
+<<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/gam.probit.pdf b/inst/doc/gam.probit.pdf
new file mode 100644
index 0000000..32c5f7e
Binary files /dev/null and b/inst/doc/gam.probit.pdf differ
diff --git a/inst/doc/gamma.Rnw b/inst/doc/gamma.Rnw
index c94ac50..e37b840 100644
--- a/inst/doc/gamma.Rnw
+++ b/inst/doc/gamma.Rnw
@@ -122,7 +122,7 @@ for $\alpha, \lambda_i, y_i > 0$. \\
\begin{itemize}
\item The expected values ({\tt qi\$ev}) are simulations of the mean
of the stochastic component given draws of $\alpha$ and
- $\beta$ from their posteriors: $$E(Y) = \alpha_i \lambda.$$
+ $\beta$ from their posteriors: $$E(Y) = \alpha \lambda_i.$$
\item The predicted values ({\tt qi\$pr}) are draws from the gamma
distribution for each given set of parameters $(\alpha, \lambda_i)$.
\item If {\tt x1} is specified, {\tt sim()} also returns the
@@ -203,7 +203,7 @@ below.
\item {\tt qi\$ev}: the simulated expected values for the specified
values of {\tt x}.
\item {\tt qi\$pr}: the simulated predicted values drawn from a
- distribution defined by $(\alpha_i, \lambda)$.
+ distribution defined by $(\alpha, \lambda_i)$.
\item {\tt qi\$fd}: the simulated first difference in the expected
values for the specified values in {\tt x} and {\tt x1}.
\item {\tt qi\$att.ev}: the simulated average expected treatment
diff --git a/inst/doc/gamma.pdf b/inst/doc/gamma.pdf
index 6f03b9c..841c54a 100644
Binary files a/inst/doc/gamma.pdf and b/inst/doc/gamma.pdf differ
diff --git a/inst/doc/gamma.tex b/inst/doc/gamma.tex
deleted file mode 100644
index 31a70fb..0000000
--- a/inst/doc/gamma.tex
+++ /dev/null
@@ -1,266 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Gamma Regression for Continuous, Positive Dependent Variables}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,regression,gamma distribution}
-%\VignettePackage{Zelig, stats}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt gamma}: Gamma Regression for Continuous, Positive Dependent Variables}\label{gamma}
-
-Use the gamma regression model if you have a positive-valued dependent
-variable such as the number of years a parliamentary cabinet endures,
-or the seconds you can stay airborne while jumping. The gamma
-distribution assumes that all waiting times are complete by the end
-of the study (censoring is not allowed).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "gamma", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out, x1 = NULL)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for gamma regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(coalition)
-\end{Sinput}
-\end{Schunk}
-Estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition)
-\end{Sinput}
-\end{Schunk}
-View the regression output:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out, numst2 = 0)
-> x.high <- setx(z.out, numst2 = 1)
-\end{Sinput}
-\end{Schunk}
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/gamma-ExamplePlot}
-\end{center}
-
-\subsubsection{Model}
-
-\begin{itemize}
-\item The Gamma distribution with scale parameter $\alpha$ has a
-\emph{stochastic component}:
-\begin{eqnarray*}
-Y &\sim& \textrm{Gamma}(y_i \mid \lambda_i, \alpha) \\
-f(y) &=& \frac{1}{\alpha^{\lambda_i} \, \Gamma \lambda_i} \, y_i^{\lambda_i
- - 1} \exp -\left\{ \frac{y_i}{\alpha} \right\}
-\end{eqnarray*}
-for $\alpha, \lambda_i, y_i > 0$. \\
-
-\item The \emph{systematic component} is given by
-\begin{equation*}
- \lambda_i = \frac{1}{x_i \beta}
-\end{equation*}
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) are simulations of the mean
- of the stochastic component given draws of $\alpha$ and
- $\beta$ from their posteriors: $$E(Y) = \alpha_i \lambda.$$
-\item The predicted values ({\tt qi\$pr}) are draws from the gamma
- distribution for each given set of parameters $(\alpha, \lambda_i)$.
-\item If {\tt x1} is specified, {\tt sim()} also returns the
- differences in the expected values ({\tt qi\$fd}), $$E(Y \mid x_1) -
- E(Y \mid x)$$.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "gamma", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: the vector of fitted values.
- \item {\tt linear.predictors}: the vector of $x_{i}\beta$.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from a
- distribution defined by $(\alpha_i, \lambda)$.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- values for the specified values in {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The gamma model is part of the stats package by William N. Venables and
-Brian D. Ripley. Users should cite this model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\begin{verse}
-\bibentry{McCNel89}.
-\end{verse}
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/index.shtml b/inst/doc/index.shtml
index d05b287..21055a4 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:2.8-4</b>
+<b>Version:2.8-5</b>
<table border='0' cellpadding='0' cellspacing='8'><tr><td valign='top' width='200'>
<p !style="float:left; width: 200; margin: 1.5em;"> <a
@@ -58,7 +58,7 @@ Bayesian, robust Bayesian, and nonparametric theories of
inference.</p>
<ul>
-<li>Documentation: <a href="/zelig/docs/">Read on-line</a> or <a
+<li>Documentation: <a href="/zelig/docs/zelig.html">Read on-line</a> or <a
href="/zelig/docs/zelig.pdf">Download PDF</a></li>
<li><a href="/zelig/docs/Installation.html">Installation</a>, <a
diff --git a/inst/doc/intro.tex b/inst/doc/intro.tex
index 2786a1c..142c1cd 100644
--- a/inst/doc/intro.tex
+++ b/inst/doc/intro.tex
@@ -131,6 +131,22 @@ per day.) You can also browse or search our
\hlink{archive}{http://lists.hmdc.harvard.edu/lists/zelig/} of
previous messages before posting your query.
+\section{How to Cite Zelig}
+
+Please cite Zelig with reference to these two sources:
+\begin{quote}
+ Kosuke Imai, Gary King, and Olivia Lau. 2007. ``Zelig: Everyone's
+ Statistical Software,'' \url{http://GKing.harvard.edu/zelig}.
+\end{quote}
+\begin{quote}
+ Kosuke Imai, Gary King, and Olivia Lau. 2007. ``Toward A Common
+ Framework for Statistical Analysis and Development,''
+ \url{http://gking.harvard.edu/files/abs/z-abs.shtml}.
+\end{quote}
+
+To refer to a particular Zelig model, please refer to the ``how to
+cite'' portion at the end of each model documentation section.
+
%%% Local Variables:
%%% mode: latex
%%% TeX-master: "zelig"
diff --git a/inst/doc/irt1d.pdf b/inst/doc/irt1d.pdf
index c8bbf75..e5c06ab 100644
Binary files a/inst/doc/irt1d.pdf and b/inst/doc/irt1d.pdf differ
diff --git a/inst/doc/irt1d.tex b/inst/doc/irt1d.tex
deleted file mode 100644
index e3b9f8d..0000000
--- a/inst/doc/irt1d.tex
+++ /dev/null
@@ -1,274 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{ One Dimensional Item Response Mode}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,item response,dichotomous, Gibbs}
-%\VignettePackage{Zelig, stats}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{irt1d}: One Dimensional Item Response Model}
-
-\label{irt1d}
-
-Given several observed dependent variables and an unobserved
-explanatory variable, item response theory estimates the latent
-variable (ideal points). The model is estimated using the Markov
-Chain Monte Carlo algorithm via a Gibbs sampler and data augmentation.
-Use this model if you believe that the ideal points lie in one
-dimension, and see the $k$-dimensional item response model
-(\Sref{irtkd}) for $k$ hypothesized latent variables.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\texttt{irt1d} accepts the following argument:
-\begin{itemize}
-\item \texttt{Y1, Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
-subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and
-so on.
-\end{itemize}
-
-\subsubsection{Additional arguments}
-
-\texttt{irt1d} accepts the following additional arguments for model specification:
-
-\begin{itemize}
-\item \texttt{theta.constraints}: a list specifying possible equality
-or inequality constraints on the ability parameters $\theta$. A
-typical entry takes one of the following forms:
-
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = c}: constrains the ability parameter for the subject named
-\texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = "+"}: constrains the ability parameter for the subject named
-\texttt{varname} to be positive.
-
-\item \texttt{varname = "-"}: constrains the ability parameter for the
-subject named {\tt varname} to be negative.
-
-\end{itemize}
-\end{itemize}
-
-The model also accepts the following arguments to monitor the sampling
-scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{theta.start}: starting values for the subject abilities
-(ideal points), either a scalar or a vector with length equal to the
-number of subjects. If a scalar, that value will be the starting value
-for all subjects. The default is \texttt{NA}, which sets the starting
-values based on an eigenvalue-eigenvector decomposition of the
-agreement score matrix formed from the model response matrix
-(\texttt{cbind(Y1, Y2, ...)}).
-
-\item \texttt{alpha.start}: starting values for the difficulty
-parameters $\alpha$, either a scalar or a vector with length equal to
-the number of the items. If a scalar, the value will be the starting
-value for all $\alpha$. The default is \texttt{NA}, which sets the
-starting values based on a series of probit regressions that condition
-on \texttt{theta.start}.
-
-\item \texttt{beta.start}: starting values for the $\beta$ discrimination
-parameters, either a scalar or a vector with length equal to the
-number of the items. If a scalar, the value will be the starting value
-for all $\beta$. The default is \texttt{NA}, which sets the starting
-values based on a series of probit regressions conditioning on
-\texttt{theta.start}.
-
-\item \texttt{store.item}: defaults to {\tt TRUE}, storing the
-posterior draws of the item parameters. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
-items with no variation before fitting the model.
-
-\end{itemize}
-
-\noindent \texttt{irt1d} accepts the following additional arguments to
-specify prior parameters used in the model:
-
-\begin{itemize}
-
-\item \texttt{t0}: prior mean of the subject abilities
-(ideal points). The default is 0.
-
-\item \texttt{T0}: prior precision of the subject abilities
-(ideal points). The default is 0.
-
-\item \texttt{ab0}: prior mean of $(\alpha, \beta)$. It can be a scalar or
-a vector of length 2. If it takes a scalar value, then the prior means for
-both $\alpha$ and $\beta$ will be set to that value. The default is 0.
-
-\item \texttt{AB0}: prior precision of $(\alpha, \beta)$. It can be
-a scalar or a $2 \times 2$ matrix. If it takes a scalar value,
-then the prior precision will be \texttt{diag(AB0,2)}. The prior precision
-is assumed to be same for all the items. The default is 0.25.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCirt1d)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(SupremeCourt)
-> names(SupremeCourt) <- c("Rehnquist", "Stevens", "OConnor", "Scalia",
-+ "Kennedy", "Souter", "Thomas", "Ginsburg", "Breyer")
-\end{Sinput}
-\end{Schunk}
-Fitting a one-dimensional item response theory model using \texttt{irt1d}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
-+ Souter, Thomas, Ginsburg, Breyer) ~ NULL, data = SupremeCourt,
-+ model = "irt1d", B0.alpha = 0.2, B0.beta = 0.2, burnin = 500,
-+ mcmc = 10000, thin = 20, verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
-$i=1, \ldots, n$. The choice $Y_{ij}$ is assumed to be determined by
-an unobserved utility $Z_{ij}$, which is a function of the subject $i$'s
-abilities (ideal points) $\theta_i$ and item parameters $\alpha_j$ and
-$\beta_j$ as follows:
-\begin{eqnarray*}
-Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
-\end{eqnarray*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij}) \\
-& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
-
-The error term in the unobserved utility equation is independently
-and identically distributed with
-\begin{eqnarray*}
-\epsilon_{ij} \sim \textrm{Normal}(0, 1).
-\end{eqnarray*}
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\pi_{ij}= \Phi(-\alpha_j + \beta_j' \theta_i),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-normal distribution with mean 0 and variance 1, $\theta_i$ is the
-subject ability (ideal point) parameter, and $\alpha_j$ and $\beta_j$
-are the item parameters. Both subject abilities and item parameters
-are estimated from the model, such that the model is identified by
-placing constraints on the subject ability parameters.
-
-\item The \emph{prior} for $\theta_i$ is given by
-\begin{eqnarray*}
-\theta_i &\sim& \textrm{Normal} \left( t_{0},T_{0}^{-1}\right)
-\end{eqnarray*}
-
-\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
-\begin{eqnarray*}
-(\alpha_j, \beta_j)' &\sim& \textrm{Normal}\left( ab_{0},AB_{0}^{-1}\right)
-\end{eqnarray*}
-where $ab_0$ is a 2-vector of prior means and $AB_0$ is a $2 \times 2$ prior
-precision matrix.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated subject abilities(ideal points). If
-\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and
-$\beta$ are also contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for item response models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-The unidimensional item-response \input{contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{irt1d} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
diff --git a/inst/doc/irtkd.pdf b/inst/doc/irtkd.pdf
index ce5fa57..16f1015 100644
Binary files a/inst/doc/irtkd.pdf and b/inst/doc/irtkd.pdf differ
diff --git a/inst/doc/irtkd.tex b/inst/doc/irtkd.tex
deleted file mode 100644
index 709501b..0000000
--- a/inst/doc/irtkd.tex
+++ /dev/null
@@ -1,279 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{K-Dimensional Item Response Model}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,item response,dichotomous, Gibbs}
-%\VignettePackage{Zelig, stats}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-\section{\texttt{irtkd}: $k$-Dimensional Item Response Theory Model}
-
-\label{irtkd}
-
-Given several observed dependent variables and an unobserved
-explanatory variable, item response theory estimates the latent
-variable (ideal points). The model is estimated using the Markov
-Chain Monte Carlo algorithm, via a combination of Gibbs sampling and
-data augmentation. Use this model if you believe that the ideal
-points lie in $k$ dimensions. See the unidimensional item response
-model (\Sref{irt1d}) for a single hypothesized latent variable.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, dimensions = 1,
- model = "irtkd", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\texttt{irtkd} accepts the following arguments:
-\begin{itemize}
-\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
-subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and so on.
-
-\item \texttt{dimensions}: The number of dimensions in the latent space. The
-default is 1.
-
-\end{itemize}
-
-\subsubsection{Additional arguments}
-
-\texttt{irtkd} accepts the following additional arguments for model specification:
-\begin{itemize}
-\item \texttt{item.constraints}: a list of lists specifying possible
-simple equality or inequality constraints on the item parameters.
-A typical entry has one of the following forms:
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be equal
-to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be negative.
-\end{itemize}
-In a $k$ dimensional model, the first item parameter for item $i$ is
-the difficulty parameter $\alpha_i$, the second item parameter is the
-discrimination parameter on dimension 1, $(\beta_{i,1})$, the third
-item parameter is the discrimination parameter on dimension 2,
-$(\beta_{i,2}),\ldots$, and $(k+1)$th item parameter is the
-discrimination parameter on dimension $k$, $(\beta_{i,k})$. The item
-difficulty parameter($\alpha$) should not be constrained in general.
-\end{itemize}
-
-\noindent \texttt{irtkd} accepts the following additional arguments
-to monitor the sampling scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen. The default
-is \texttt{FALSE}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{alphabeta.start}: starting values for the item parameters
-$\alpha$ and $\beta$, either a scalar or a $(k+1) \times items$
-matrix. If it is a scalar, then that value will be the starting value
-for all the elements of \texttt{alphabeta.start}. The default is
-\texttt{NA} which sets the starting values for the unconstrained
-elements based on a series of proportional odds logistic
-regressions. The starting values for the inequality constrained
-elements are set to be either 1.0 or -1.0 depending on the nature of
-the constraints.
-
-\item \texttt{store.item}: defaults to {\tt FALSE}. If {\tt TRUE} stores the
-posterior draws of the item parameters. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{store.ability}: defaults to {\tt TRUE}, storing the
-posterior draws of the subject abilities. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
-items with no variation before fitting the model.
-\end{itemize}
-
-\noindent \texttt{irtkd} accepts the following additional arguments to
-specify prior parameters used in the model:
-
-\begin{itemize}
-
-\item \texttt{b0}: prior mean of $(\alpha, \beta)$, either as a scalar or
-a vector of compatible length. If a scalar value, then the prior means
-for both $\alpha$ and $\beta$ will be set to that value. The default
-is 0.
-
-\item \texttt{B0}: prior precision for $(\alpha, \beta)$, either a
-scalar or a $(k+1) \times items$ matrix. If a scalar value, the prior
-precision will be a blocked diagonal matrix with elements
-\texttt{diag(B0,items)}. The prior precision is assumed to be same for
-all the items. The default is 0.25.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCirtKd)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(SupremeCourt)
-> names(SupremeCourt) <- c("Rehnquist", "Stevens", "OConnor", "Scalia",
-+ "Kennedy", "Souter", "Thomas", "Ginsburg", "Breyer")
-\end{Sinput}
-\end{Schunk}
-Fitting a one-dimensional item response theory model using \texttt{irtkd}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
-+ Souter, Thomas, Ginsburg, Breyer) ~ NULL, dimensions = 1,
-+ data = SupremeCourt, model = "irtkd", B0 = 0.25, burnin = 5000,
-+ mcmc = 50000, thin = 10, verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
-$i = 1, \ldots, n$. The choice $Y_{ij}$ is assumed to be
-determined by unobserved utility $Z_{ij}$, which is a function
-of subject abilities (ideal points) $\theta_i$ and item parameters
-$\alpha_j$ and $\beta_j$,
-\begin{eqnarray*}
-Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
-\end{eqnarray*}
-In the $k$-dimensional item response theory model, each subject's ability is
-represented by a $k$-vector, $\theta_i$. Each item has a difficulty
-parameter $\alpha_j$ and a $k$-dimensional discrimination parameter
-$\beta_j$. In one-dimensional item response theory model, $k = 1$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij})\\
-& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
-
-The error term in the unobserved utility equation has
-a standard normal distribution,
-\begin{eqnarray*}
-\epsilon_{ij} \sim \textrm{Normal}(0, 1).
-\end{eqnarray*}
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{ij} &=& \Phi(-\alpha_j + \beta_j \theta_i),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-normal distribution with mean 0 and variance 1, while $\theta_i$
-contains the $k$-dimensional subject abilities(ideal points), and
-$\alpha_{j}$ and $\beta_j$ are the item parameters. Both subject
-abilities and item parameters need to estimated from the model. The
-model is identified by placing constraints on the item parameters.
-
-\item The \emph{prior} for $\theta_i$ is given by
-\begin{eqnarray*}
-\theta_i &\sim& \textrm{Normal}_k(0, I_k)
-\end{eqnarray*}
-
-\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
-\begin{eqnarray*}
-(\alpha_j, \beta_j)' &\sim& \textrm{Normal}_{k+1} \left( b_{0_j}, B_{0_j}^{-1}\right)
-\end{eqnarray*}
-where $b_{0_j}$ is a $(k+1)$-vector of prior mean and $B_{0_j}$ is a $(k+1)
-\times (k+1)$ prior precision matrix which is assumed to be diagonal.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irtkd", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated subject abilities(ideal points). If
-\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and $\beta$
- are also contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for item response models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-The $k$ dimensional item response \input{contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{irtkd} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/logit.bayes.pdf b/inst/doc/logit.bayes.pdf
index e980cc9..a883740 100644
Binary files a/inst/doc/logit.bayes.pdf and b/inst/doc/logit.bayes.pdf differ
diff --git a/inst/doc/logit.bayes.tex b/inst/doc/logit.bayes.tex
deleted file mode 100644
index b932eca..0000000
--- a/inst/doc/logit.bayes.tex
+++ /dev/null
@@ -1,288 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Logistic Regression for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model,bayes,dichotomous, Metropolis}
-%\VignettePackage{Zelig, MCMCpack}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-\section{\texttt{logit.bayes}: Bayesian Logistic Regression}
-
-\label{logit.bayes}
-
-Logistic regression specifies a dichotomous dependent variable as a
-function of a set of explanatory variables using a random walk
-Metropolis algorithm. For a maximum likelihood implementation, see
-\Sref{logit}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: Metropolis tuning parameter, either
-a positive scalar or a vector of length $k$, where $k$ is the number
-of coefficients. The tuning parameter should be set such that the
-acceptance rate of the Metropolis algorithm is satisfactory (typically
-between 0.20 and 0.5) before using the posterior density for
-inference. The default value is 1.1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The
-default is \texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the maximum
-likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will
-be the prior mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0, which leads to an improper prior.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(logit.bayes)} for more information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(turnout)
-\end{Sinput}
-\end{Schunk}
-Estimating the logistic regression using \texttt{logit.bayes}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(vote ~ race + educate, model = "logit.bayes",
-+ data = turnout, verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-Convergence diagnostics before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their sample averages:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in individual's probability of
-voting when education is set to be low (25th percentile) versus
-high (75th percentile) while all the other variables held at their
-default values.
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the binary dependent variable for observation $i$ which takes
-the value of either 0 or 1.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \rm{Bernoulli}(\pi_{i})\\
-& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
-\end{eqnarray*}
-where $\pi_{i}=\Pr(Y_{i}=1)$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{i}= \frac{1}{1+\exp(-x_{i} \beta)},
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the logit model are
-simulations of the predicted probability of a success:
-\begin{eqnarray*}
-E(Y) = \pi_{i}= \frac{1}{1 + \exp(-x_{i} \beta)},
-\end{eqnarray*}
-given the posterior draws of $\beta$ from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
-distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference (\texttt{qi\$fd}) for the logit model is defined
-as
-\begin{eqnarray*}
-\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr})is defined as
-\begin{eqnarray*}
-\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "logit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values(probabilities) for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
-simulated from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian logistic regression \input{contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{logit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/logit.gee.Rnw b/inst/doc/logit.gee.Rnw
new file mode 100644
index 0000000..4cc60c0
--- /dev/null
+++ b/inst/doc/logit.gee.Rnw
@@ -0,0 +1,394 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/logit_gee}
+\include{zinput}
+%\VignetteIndexEntry{Generalized Estimating Equation for Logistic Regression}
+%\VignetteDepends{Zelig, gee}
+%\VignetteKeyWords{model,generalized estimating equation, logistic regression}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{\texttt{logit.gee}: Generalized Estimating Equation for Logistic Regression}
+\label{logit.gee}
+
+The GEE logit estimates the same model as the standard logistic
+regression (appropriate when you have a dichotomous dependent
+variable and a set of explanatory variables). Unlike in logistic
+regression, GEE logit allows for dependence within clusters, such as
+in longitudinal data, although its use is not limited to just
+panel data. The user must first specify a ``working''
+correlation matrix for the clusters, which models the dependence of each observation with other observations in the same cluster. The ``working'' correlation matrix is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster and the elements of the matrix are correlations between within-cluster observations. The appeal of GEE models is that it gives consistent estimates of the parameters and consistent estimates of
+the standard errors can be obtained using a robust ``sandwich''
+estimator even if the ``working'' correlation matrix is incorrectly
+specified. If the ``working'' correlation matrix is correctly specified, GEE models will give more efficient estimates of the parameters.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "logit.gee",
+ id = "X3", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\noindent where \texttt{id} is a variable which identifies the clusters. The data should be sorted by \texttt{id} and should be ordered within each cluster when appropriate.
+
+\subsubsection{Additional Inputs}
+
+\begin{itemize}
+\item \texttt{robust}: defaults to \texttt{TRUE}. If \texttt{TRUE}, consistent standard errors are estimated using a ``sandwich'' estimator.
+\end{itemize}
+Use the following arguments to specify the structure of the ``working'' correlations within clusters:
+
+\begin{itemize}
+\item \texttt{corstr}: defaults to {\tt "independence"}. It can take on the following arguments:
+\begin{itemize}
+\item Independence (\texttt{corstr = "independence"}): ${\rm
+ cor}(y_{it}, y_{it'})=0$, $\forall t, t'$ with $t\ne t'$. It assumes that there is no correlation within the clusters and the model becomes equivalent to standard logistic regression. The ``working'' correlation matrix is the identity matrix.
+\item Fixed (\texttt{corstr = "fixed"}): If selected, the user must define the ``working'' correlation matrix with the \texttt{R} argument rather than estimating it from the model.
+\item Stationary $m$ dependent (\texttt{corstr = "stat\_M\_dep"}):
+ $${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
+ \alpha_{|t-t'|} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
+ & |t-t'| > m
+ \end{array}\right.$$
+ If (\texttt{corstr = "stat\_M\_dep"}), you must also specify \texttt{Mv} = $m$, where $m$
+is the number of periods $t$ of dependence. Choose this option when the correlations are assumed to be the same for observations of the same $|t-t'|$ periods apart for $|t-t'| \leq m$.
+\begin{center}
+Sample ``working'' correlation for Stationary 2 dependence ({\tt Mv}=2)\\
+\bigskip
+$\left( \begin{array}{ccccc}
+1 & \alpha_1 & \alpha_2 & 0 & 0 \\
+\alpha_1 & 1 & \alpha_1 & \alpha_2 & 0 \\
+\alpha_2 & \alpha_1 & 1 & \alpha_1 & \alpha_2 \\
+0 & \alpha_2 & \alpha_1 & 1 & \alpha_1 \\
+0 & 0 & \alpha_2 & \alpha_1 & 1
+\end{array} \right) $
+\end{center}
+
+\item Non-stationary $m$ dependent (\texttt{corstr =
+ "non\_stat\_M\_dep"}):
+$${\rm cor}(y_{it}, y_{it'})=\left\{\begin{array}{ccc}
+ \alpha_{tt'} & {\rm if} & |t-t'|\le m \\ 0 & {\rm if}
+ & |t-t'| > m
+ \end{array}\right.$$
+If (\texttt{corstr = "non\_stat\_M\_dep"}), you must also specify
+\texttt{Mv} = $m$, where $m$ is the number of periods $t$ of
+dependence. This option relaxes the assumption that the
+correlations are the same for all observations of the same $|t-t'|$
+periods apart.
+\begin{center}
+Sample ``working'' correlation for Non-stationary 2 dependence ({\tt Mv}=2)\\
+\bigskip
+$\left( \begin{array}{ccccc}
+1 & \alpha_{12} & \alpha_{13} & 0 & 0 \\
+\alpha_{12} & 1 & \alpha_{23} & \alpha_{24} & 0 \\
+\alpha_{13} & \alpha_{23} & 1 & \alpha_{34} & \alpha_{35} \\
+0 & \alpha_{24} & \alpha_{34} & 1 & \alpha_{45} \\
+0 & 0 & \alpha_{35} & \alpha_{45} & 1
+\end{array} \right) $
+\end{center}
+\item Exchangeable (\texttt{corstr = "exchangeable"}): ${\rm
+ cor}(y_{it}, y_{it'})=\alpha$, $\forall t, t'$ with $t\ne t'$. Choose this option if the correlations are assumed to be the same for all observations within the cluster.
+\begin{center}
+Sample ``working'' correlation for Exchangeable\\
+\bigskip
+$\left( \begin{array}{ccccc}
+1 & \alpha & \alpha & \alpha & \alpha \\
+\alpha & 1 & \alpha & \alpha & \alpha \\
+\alpha & \alpha & 1 & \alpha & \alpha \\
+\alpha & \alpha & \alpha & 1 & \alpha \\
+\alpha & \alpha & \alpha & \alpha & 1
+\end{array} \right) $
+\end{center}
+\item Stationary $m$th order autoregressive (\texttt{corstr = "AR-M"}): If (\texttt{corstr = "AR-M"}), you must also specify \texttt{Mv} = $m$, where $m$
+is the number of periods $t$ of dependence. For example, the first order autoregressive model (AR-1) implies ${\rm cor}(y_{it},
+ y_{it'})=\alpha^{|t-t'|}, \forall t, t'$ with $t\ne t'$. In AR-1, observation 1 and observation 2 have a correlation of $\alpha$. Observation 2 and observation 3 also have a correlation of $\alpha$. Observation 1 and observation 3 have a correlation of $\alpha^2$, which is a function of how 1 and 2 are correlated ($\alpha$) multiplied by how 2 and 3 are correlated ($\alpha$). Observation 1 and 4 have a correlation that is a function of the correlation between 1 and 2, 2 and 3, and [...]
+\begin{center}
+Sample ``working'' correlation for Stationary AR-1 ({\tt Mv}=1)\\
+\bigskip
+$\left( \begin{array}{ccccc}
+1 & \alpha & \alpha^2 & \alpha^3 & \alpha^4 \\
+\alpha & 1 & \alpha & \alpha^2 & \alpha^3 \\
+\alpha^2 & \alpha & 1 & \alpha & \alpha^2 \\
+\alpha^3 & \alpha^2 & \alpha & 1 & \alpha \\
+\alpha^4 & \alpha^3 & \alpha^2 & \alpha & 1
+\end{array} \right) $
+\end{center}
+\item Unstructured (\texttt{corstr = "unstructured"}): ${\rm
+ cor}(y_{it}, y_{it'})=\alpha_{tt'}$, $\forall t, t'$ with
+ $t\ne t'$. No constraints are placed on the correlations, which are then estimated from the data.
+\end{itemize}
+\item \texttt{Mv}: defaults to 1. It specifies the number of periods of correlation and only needs to be specified when \texttt{corstr} is {\tt "stat\_M\_dep"}, {\tt "non\_stat\_M\_dep"}, or {\tt "AR-M"}.
+\item \texttt{R}: defaults to \texttt{NULL}. It specifies a user-defined correlation matrix rather than estimating it from the data. The argument is used only when \texttt{corstr} is {\tt "fixed"}. The input is a $T \times T$ matrix of correlations, where $T$ is the size of the largest cluster.
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item {Example with Stationary 3 Dependence}
+
+Attaching the sample turnout dataset:
+<<Example.data>>=
+data(turnout)
+@
+Variable identifying clusters
+<<Example.cluster>>=
+turnout$cluster <- rep(c(1:200),10)
+@
+Sorting by cluster
+<<Example.sort>>=
+sorted.turnout <- turnout[order(turnout$cluster),]
+@
+Estimating parameter values for the logistic regression:
+<<Example.zelig>>=
+z.out1 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "stat_M_dep", Mv=3)
+@
+Setting values for the explanatory variables to their default values:
+<<Example.setx>>=
+x.out1 <- setx(z.out1)
+@
+Simulating quantities of interest from the posterior distribution.
+<<Example.sim>>=
+s.out1 <- sim(z.out1, x = x.out1)
+@
+<<Example.summary.sim>>=
+summary(s.out1)
+@
+\begin{center}
+<<label=ExamplePlot,fig=true,echo=true>>=
+plot(s.out1)
+@
+\end{center}
+
+\item {Simulating First Differences}
+
+Estimating the risk difference (and risk ratio) between low education
+(25th percentile) and high education (75th percentile) while all the
+other variables held at their default values.
+
+<<FirstDifference.setx>>=
+x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
+x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
+@
+
+<<FirstDifference.sim>>=
+s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
+@
+<<FirstDifference.summary.sim>>=
+summary(s.out2)
+@
+\begin{center}
+<<label=FirstDifferencePlot,fig=true,echo=true>>=
+plot(s.out2)
+@
+\end{center}
+
+\item Example with Fixed Correlation Structure
+
+User-defined correlation structure
+<<Example2.corr>>=
+corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
+diag(corr.mat) <- 1
+@
+Generating empirical estimates:
+<<Example2.zelig>>=
+z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout, robust = TRUE, corstr = "fixed", R=corr.mat)
+@
+Viewing the regression output:
+<<Example2.summary>>=
+summary(z.out2)
+@
+\end{enumerate}
+
+\subsubsection{The Model}
+Suppose we have a panel dataset, with $Y_{it}$ denoting the binary
+dependent variable for unit $i$ at time $t$ which takes the value of
+either 0 or 1. $Y_{i}$ 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.
+
+
+\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
+\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.
+
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_{it} \; = \; \frac{1}{1 + \exp(-x_{it} \beta)}.
+\end{equation*}
+where $x_{it}$ is the vector of $k$ explanatory variables for unit $i$ at time $t$
+and $\beta$ is the vector of coefficients.
+
+
+\item 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.
+
+\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
+ 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
+\begin{equation*}
+\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{\sum_{i=1}^n \sum_{t=1}^T tr_{it}}\sum_{i:tr_{it}=1}^n \sum_{t:tr_{it}=1}^T \left\{ Y_{it}(tr_{it}=1) -
+ E[Y_{it}(tr_{it}=0)] \right\},
+ \end{equation*}
+ where $tr_{it}$ is a binary explanatory variable defining the treatment
+ ($tr_{it}=1$) and control ($tr_{it}=0$) groups. Variation in the
+ simulations are due to uncertainty in simulating $E[Y_{it}(tr_{it}=0)]$,
+ the counterfactual expected value of $Y_{it}$ for observations in the
+ treatment group, under the assumption that everything stays the
+ same except that the treatment indicator is switched to $tr_{it}=0$.
+
+\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
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt residuals}: the working residuals in the final iteration
+ of the fit.
+ \item {\tt fitted.values}: the vector of fitted values for the
+ systemic component, $\pi_{it}$.
+ \item {\tt linear.predictors}: the vector of $x_{it}\beta$
+ \item {\tt max.id}: the size of the largest cluster.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, $p$-values, and $z$-statistics.
+ \item {\tt working.correlation}: the ``working'' correlation matrix
+ \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as matrices indexed by simulation
+ $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+ Available quantities are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}.
+ \item {\tt qi\$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}
+
+\subsubsection{Contributors}
+
+%The logit function is part of the base package by William N. Venables
+%and Brian D. Ripley. Please cite this model as:
+%\begin{verse}
+%\bibentry{VenRip02}.
+%\end{verse}
+%
+%Advanced users may wish to refer to
+%\texttt{help(glm)} and \texttt{help(family)}, as well as
+%\begin{verse}
+%\bibentry{McCNel89}.
+%\end{verse}
+%
+%Robust standard errors are implemented via the sandwich package
+%by Achim Zeileis. Please cite as
+%\begin{verse}
+%\bibentry{Zeileis04}
+%\end{verse}
+%
+%Sample data are a selection of $2,000$ observations from
+%\begin{verse}
+%\bibentry{KinTomWit00}.
+%\end{verse}
+%
+%Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
+%%\bibliographystyle{apsr}
+%%\bibliography{gee}
+
+<<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
new file mode 100644
index 0000000..f831d00
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diff --git a/inst/doc/logit.tex b/inst/doc/logit.tex
deleted file mode 100644
index 338103f..0000000
--- a/inst/doc/logit.tex
+++ /dev/null
@@ -1,323 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Logistic Regression for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,logistic,dichotomous, regression}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt logit}: Logistic Regression for Dichotomous Dependent
-Variables}\label{logit}
-
-Logistic regression specifies a dichotomous dependent variable as a
-function of a set of explanatory variables. For a Bayesian
-implementation, see \Sref{logit.bayes}.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out, x1 = NULL)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for logistic regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame) The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Basic Example}
-
-Attaching the sample turnout dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(turnout)
-\end{Sinput}
-\end{Schunk}
-Estimating parameter values for the logistic regression:
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their default values:
-\begin{Schunk}
-\begin{Sinput}
-> x.out1 <- setx(z.out1)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution.
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out1, x = x.out1)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out1)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/logit-ExamplePlot}
-\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.
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
-> x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
-\end{Sinput}
-\end{Schunk}
-
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out2)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/logit-FirstDifferencesPlot}
-\end{center}
-
-
-\item {Presenting Results: An ROC Plot} \label{ROC}
-
- One can use an ROC plot to evaluate the fit of alternative model
- specifications. (Use {\tt demo(roc)} to view this example, or see
- King and Zeng (2002)\nocite{KinZen02}.)
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
-+ data = turnout)
-> z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/logit-ROCPlot}
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i$ be the binary dependent variable for observation $i$ which
-takes the value of either 0 or 1.
-\begin{itemize}
-
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_i &\sim& \textrm{Bernoulli}(y_i \mid \pi_i) \\
- &=& \pi_i^{y_i} (1-\pi_i)^{1-y_i}
-\end{eqnarray*}
-where $\pi_i=\Pr(Y_i=1)$.
-
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
-\end{equation*}
-where $x_i$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the logit model are
- simulations of the predicted probability of a success: $$E(Y) =
- \pi_i= \frac{1}{1 + \exp(-x_i \beta)},$$ given draws of $\beta$ from
- its sampling distribution.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
- distribution with mean equal to the simulated expected value $\pi_i$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)}\right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "logit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_i$.
- \item {\tt linear.predictors}: the vector of $x_{i}\beta$
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt data}: the name of the input data frame.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values for the
- specified values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the expected
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The logit function is part of the base package by William N. Venables
-and Brian D. Ripley. Please cite this model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\begin{verse}
-\bibentry{McCNel89}.
-\end{verse}
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}
-\end{verse}
-
-Sample data are a selection of $2,000$ observations from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/lognorm.pdf b/inst/doc/lognorm.pdf
index 8eef0cf..8dd6c14 100644
Binary files a/inst/doc/lognorm.pdf and b/inst/doc/lognorm.pdf differ
diff --git a/inst/doc/lognorm.tex b/inst/doc/lognorm.tex
deleted file mode 100644
index 1e60d18..0000000
--- a/inst/doc/lognorm.tex
+++ /dev/null
@@ -1,293 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Log-Normal Regression for Duration Dependent Variables}
-%\VignetteDepends{Zelig, survival}
-%\VignetteKeyWords{model,log, normal,bounded, regression, time durarion}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt lognorm}: Log-Normal Regression for Duration
-Dependent Variables}\label{lognorm}
-
-The log-normal model describes an event's duration, the dependent
-variable, as a function of a set of explanatory variables. The
-log-normal model may take time censored dependent variables, and
-allows the hazard rate to increase and decrease.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X, model = "lognorm", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Log-normal models require that the dependent variable be in the form
-{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
-For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
-duration (lifetime, for example) of each subject, and the associated
-$c_i$ is a binary variable such that $c_i = 1$ if the duration is not
-censored ({\it e.g.}, the subject dies during the study) or $c_i = 0$
-if the duration is censored ({\it e.g.}, the subject is still alive at
-the end of the study). If $c_i$ is omitted, all Y are assumed to be
-completed; that is, time defaults to 1 for all observations.
-
-\subsubsection{Input Values}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for lognormal regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
-cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "exp", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(coalition)
-\end{Sinput}
-\end{Schunk}
-Estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm",
-+ data = coalition)
-\end{Sinput}
-\end{Schunk}
-View the regression output:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out, numst2 = 0)
-> x.high <- setx(z.out, numst2 = 1)
-\end{Sinput}
-\end{Schunk}
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt
- qi\$fd}):
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/lognormal-ExamplePlot}
-\end{center}
-
-\subsubsection{Model}
-
-Let $Y_i^*$ be the survival time for observation $i$ with the density
-function $f(y)$ and the corresponding distribution function
-$F(t)=\int_{0}^t f(y) dy$. This variable might be censored for some
-observations at a fixed time $y_c$ such that the fully observed
-dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c \\
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by the distribution
- of the partially observed variable, $Y^*$. For the lognormal model,
-there are two equivalent representations:
- \begin{eqnarray*}
- Y_i^* \; \sim \; \textrm{LogNormal}(\mu_i, \sigma^2) & \textrm{ or
-} & \log(Y_i^*) \; \sim \; \textrm{Normal}(\mu_i, \sigma^2)
-\end{eqnarray*}
-where the parameters $\mu_i$ and $\sigma^2$ are the mean and variance
-of the Normal distribution. (Note that the output from {\tt zelig()}
-parameterizes {\tt scale}$ = \sigma$.)
-
- In addition, survival models like the lognormal have three additional
-properties. The hazard function $h(t)$ measures the probability of not surviving
- past time $t$ given survival up to $t$. In general, the hazard
- function is equal to $f(t)/S(t)$ where the survival function $S(t) =
- 1 - \int_{0}^t f(s) ds$ represents the fraction still surviving at
- time $t$. The cumulative hazard function $H(t)$ describes the
- probability of dying before time $t$. In general, $H(t)=
-\int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the lognormal model,
-\begin{eqnarray*}
-h(t) &=& \frac{1}{\sqrt{2 \pi} \, \sigma t \, S(t)}
-\exp\left\{-\frac{1}{2 \sigma^2} (\log \lambda t)^2\right\} \\
-S(t) &=& 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \\
-H(t) &=& -\log \left\{ 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \right\}
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function for the Normal
-distribution.
-
-\item The \emph{systematic component} is described as:
-\begin{equation*}
-\mu_i = x_i \beta .
-\end{equation*}
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the lognormal model are
- simulations of the expected duration:
-\begin{equation*}
-E(Y) = \exp\left(\mu_i + \frac{1}{2}\sigma^2 \right),
-\end{equation*}
-given draws of $\beta$ and $\sigma$ from their sampling distributions.
-
-\item The predicted value is a draw from the log-normal distribution
- given simulations of the parameters $(\lambda_i, \sigma)$.
-
-\item The first difference ({\tt qi\$fd}) is
-\begin{equation*}
-\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is \begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) - E[Y_i(t_i=0)] \}, \end{equation*} where $t_i$ is a binary explanatory variable
- defining the treatment ($t_i=1$) and control ($t_i=0$) groups. When
- $Y_i(t_i=1)$ is censored rather than observed, we replace it with a
- simulation from the model given available knowledge of the censoring
- process. Variation in the simulations is due to two factors:
- uncertainty in the imputation process for censored $y_i^*$ and
- uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
- expected value of $Y_i$ for observations in the treatment group,
- under the assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
-\begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i} \sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
-\widehat{Y_i(t_i=0)} \},
-\end{equation*}
-where $t_i$ is a binary explanatory
- variable defining the treatment ($t_i=1$) and control ($t_i=0$)
- groups. When $Y_i(t_i=1)$ is censored rather than observed, we
- replace it with a simulation from the model given available
- knowledge of the censoring process. Variation in the simulations
- are due to two factors: uncertainty in the imputation process for
- censored $y_i^*$ and uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
- for observations in the treatment group, under the assumption that
- everything stays the same except that the treatment indicator is
- switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
- C) \~\, X, model = "lognorm", data)}, then you may examine the
-available information in \texttt{z.out} by using
-\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
- z.out\$coefficients}, and a default summary of information
-through \texttt{summary(z.out)}. Other elements available through
-the {\tt \$} operator are listed below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt icoef}: parameter estimates for the intercept and $\sigma$.
- \item {\tt var}: Variance-covariance matrix.
- \item {\tt loglik}: Vector containing the log-likelihood for the
- model and intercept only (respectively).
- \item {\tt linear.predictors}: the vector of
- $x_{i}\beta$.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item Most of this may be conveniently summarized using {\tt
- summary(z.out)}. From {\tt summary(z.out)}, you may
- additionally extract:
- \begin{itemize}
- \item {\tt table}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by $(\lambda_i, \sigma)$.
- \item {\tt qi\$fd}: the simulated first differences between the
- simulated expected values for {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The exponential function is part of the survival library by by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library, and
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
-
-
-
-
-
diff --git a/inst/doc/ls.pdf b/inst/doc/ls.pdf
index 5506d73..eedc4ce 100644
Binary files a/inst/doc/ls.pdf and b/inst/doc/ls.pdf differ
diff --git a/inst/doc/ls.tex b/inst/doc/ls.tex
deleted file mode 100644
index f0b3bea..0000000
--- a/inst/doc/ls.tex
+++ /dev/null
@@ -1,285 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,least squares,continuous, regression}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt ls}: Least Squares Regression for Continuous
-Dependent Variables}
-\label{ls}
-
-Use least squares regression analysis to estimate the best linear
-predictor for the specified dependent variables.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "ls", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for least squares regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors based on
-sandwich estimators (see \cite{Zeileis04}, \cite{Huber81}, and
-\cite{White80}). The default type of robust standard error is
-heteroskedastic consistent (HC), \emph{not} heteroskedastic and
-autocorrelation consistent (HAC).
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: choose from
-\begin{itemize}
-\item {\tt "vcovHC"}: (the default if {\tt robust = TRUE}), HC standard errors.
-\item {\tt "vcovHAC"}: HAC standard errors without weights.
-\item {\tt "kernHAC"}: HAC standard errors using the weights given in
-\cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the weights given in
-\cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: only applies to the HAC methods above. Defaults to
-{\tt NULL} (the observations are chronologically ordered as in the
-original data). Optionally, you may specify a time index (either as
-{\tt order.by = z}, where {\tt z} exists outside the data frame; or
-as {\tt order.by = \~{}z}, where {\tt z} is a variable in the data
-frame). The observations are chronologically ordered by the size of
-{\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}\begin{enumerate}
-\item Basic Example with First Differences
-
-Attach sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(macro)
-\end{Sinput}
-\end{Schunk}
-Estimate model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)
-\end{Sinput}
-\end{Schunk}
-Summarize regression coefficients:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out1)
-\end{Sinput}
-\end{Schunk}
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) values for the trade variable:
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8))
-> x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2))
-\end{Sinput}
-\end{Schunk}
-Generate first differences for the effect of high versus low trade on
-GDP:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out1)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/ls-ExamplesPlot}
-\end{center}
-
-\item Using Dummy Variables
-
-Estimate a model with fixed effects for each country (see
-\Sref{factors} for help with dummy variables). Note that you do not
-need to create dummy variables, as the program will automatically
-parse the unique values in the selected variable into discrete levels.
-\begin{Schunk}
-\begin{Sinput}
-> z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country),
-+ model = "ls", data = macro)
-\end{Sinput}
-\end{Schunk}
-Set values for the explanatory variables, using the default mean/mode
-values, with country set to the United States and Japan, respectively:
-\begin{Schunk}
-\begin{Sinput}
-> x.US <- setx(z.out2, country = "United States")
-> x.Japan <- setx(z.out2, country = "Japan")
-\end{Sinput}
-\end{Schunk}
-Simulate quantities of interest:
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out2)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/ls-DummyPlot}
-\end{center}
-
-\end{enumerate}
-
-\subsubsection{Model}
-\begin{itemize}
-\item The \emph{stochastic component} is described by a density
- with mean $\mu_i$ and the common variance $\sigma^2$
- \begin{equation*}
- Y_i \; \sim \; f(y_i \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, $\sum_{i=1}^n (Y_i-x_i \beta)^2$.
-\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 = "ls", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
- \item 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 parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \begin{equation*}
- \hat{\beta} \; = \; \left(\sum_{i=1}^n x_i' x_i\right)^{-1} \sum x_i y_i
- \end{equation*}
- \item {\tt sigma}: the square root of the estimate variance of the
- random error $e$:
- \begin{equation*}
- \hat{\sigma} \; = \; \frac{\sum (Y_i-x_i\hat{\beta})^2}{n-k}
- \end{equation*}
- \item {\tt r.squared}: the fraction of the variance explained by
- the model.
- \begin{equation*}
- R^2 \; = \; 1 - \frac{\sum (Y_i-x_i\hat{\beta})^2}{\sum (y_i -
- \bar{y})^2}
- \end{equation*}
- \item {\tt adj.r.squared}: the above $R^2$ statistic, penalizing
- for an increased number of explanatory variables.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output 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}
-
-\subsubsection{Contributors}
-
-The least squares regression is part of the stats package by William N.
-Venables and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, advanced users may wish to refer to
-\texttt{help(lm)} and \texttt{help(lm.fit)}.
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
-
- \end{document}
diff --git a/inst/doc/makeDocs b/inst/doc/makeDocs
new file mode 100755
index 0000000..95589b8
--- /dev/null
+++ b/inst/doc/makeDocs
@@ -0,0 +1,195 @@
+#!/bin/sh
+
+##
+## This file is called by Makefile. It does the followings:
+## 1.run pdf latex to each tex file produced by R CMD build
+## OR run Sweave to each file if issued from command line (not called by "make" during R build)
+## 2.remove header and footer or each tex file so that it's ready for inclusion by zelig.tex
+## 3.convert all Rd files to tex file so that they are ready for inclusion by zelig.tex
+##
+
+STATICTEXFILES="acknowledgments.tex advancedR.tex graphs.tex install.tex intro.tex Zcommands.tex addingModels.tex newModels.tex syntax.tex faq.tex"
+
+## get name of the models and save them in a file
+## for now exclude models which are not complete
+##
+## echo "library(Zelig); models<-setdiff(zeligListModels(),c('aov')); write(models,file='models.txt');" | R --vanilla --slave
+
+## at this point, Sweave should have been run and produced tex files
+for f in `cat models.txt`
+ do
+ #if this script is called from make, then Rbuild would have
+ # run vignettes, otherwise this script should run them.
+ # to distinguish this to cases we test the number of params
+ # 0 - when called from command line
+ # 1 - when called from make
+ #
+ if [ $# -eq 0 ]
+ then
+ echo "Sweave(\"$f.Rnw\")" | R --slave
+ fi
+
+ ## create pdfs for all the tex file produced by sweave
+ pdflatex $f.tex
+
+ ## remove header and footer from the tex file so that they are ready
+ ## for inclusion in big zelig manual
+ perl -i -ne 'print unless /^\\include{zinput}$/ .. /^\\begin{document}$/' $f.tex
+ perl -i -p -e 's#\\end{document}# #i' $f.tex
+done
+
+## create pdf file for a007.Rnw, just to pass the local R CMD check
+pdflatex a007.tex
+
+## run R CMD Rdconv to rd files
+## create commandsRd folder if does not exists
+
+if [ ! -d commandsRd ]
+then
+ mkdir commandsRd
+fi
+
+cp zinput.tex commandsRd
+cp Rd.sty commandsRd
+cp upquote.sty commandsRd
+
+Rdfiles=`ls ../../man/*.Rd`
+for rd in ${Rdfiles}
+ do
+ echo $rd
+ newname=`basename ${rd} .Rd`
+ echo $newname
+ R CMD Rdconv -t=latex ${rd} -o commandsRd/${newname}.tex
+
+ ### perl -i -pe 's#HeaderA{#section{{\\tt #i' commandsRd/${newname}.tex
+ perl -i -pe 's#HeaderA{(.*)}{(.*)}{(.*)}#section{{\\tt \1}: \2}\\label{ss:\3}#i' commandsRd/${newname}.tex
+
+done
+
+
+## create the big Zelig manual
+pdflatex zelig
+bibtex zelig
+pdflatex zelig
+pdflatex zelig
+pdflatex zelig
+
+
+## time to create pdfs for commands in commandsRd (for garys website help)
+cd commandsRd
+for rd in ${Rdfiles}
+do
+ newname=`basename ${rd} .Rd`
+ ## add header
+ perl -i -pe 'print q{\\include{zinput} \\begin{document}} if $. == 1; close ARGV if eof' ${newname}.tex
+ ## add footer
+ echo "\\end{document}" >> ${newname}.tex
+ ## create pdfs
+ pdflatex ${newname}
+done
+cd ..
+
+
+
+# some static tex to pdf
+# put them in a folder first
+
+
+if [ ! -d static ]
+then
+ mkdir static
+fi
+
+
+
+for stfile in $STATICTEXFILES
+do
+ cp $stfile static
+done
+
+cp zinput.tex static
+cp Rd.sty static
+cp upquote.sty static
+cp -r figs static
+
+cd static
+for stfile in ${STATICTEXFILES}
+do
+
+ ## add header
+ perl -i -pe 'print q{\\include{zinput} \\begin{document}} if $. == 1; close ARGV if eof' ${stfile}
+
+ ## replace \chapter with \title ## it was not a good idea
+ ### perl -i -pe 's#\\chapter#\\title#i' ${stfile}
+
+ ## add footer
+ echo "\\end{document}" >> ${stfile}
+ ## create pdfs
+ pdflatex ${stfile}
+done
+
+cd ..
+
+## generate html help for models and the full html file
+
+###
+###if [ -f "zeligmodels.html" ]
+###then
+### rm "zeligmodels.html"
+###fi
+
+###echo "<ul id=models>" >> zeligmodels.html
+###for f in `cat models.txt`
+### do
+###echo "<li><a href=$f.pdf>$f</a></li>" >> zeligmodels.html
+###done
+
+###echo "</ul>" >> zeligmodels.html
+
+cat zeligheader.html zeligmodels.html zeligfooter.html > zelig.html
+
+
+
+
+## some other pdf files
+
+
+
+## remove the files with the name of the models
+#rm models.txt
+
+## do some cleanup
+rm -f *.aux *.toc *.log *.out *.blg *.bbl
+
+## clean commandsRd directory
+rm -f commandsRd/*.aux commandsRd/*.out commandsRd/*.log
+## clean statis folder
+rm -f static/*.aux static/*.out static/*.log static/*.tex static/Rd.sty static/zinput.*
+
+
+##rm zeligmodels.html
+for f in `cat models.txt`
+do
+ rm -f $f.tex
+done
+###rm models.txt
+
+#### here is how u delete the \end{document}
+#### perl -i.old -p -e 's#\\end{document}# #i' try
+
+#### delete first 10 lines
+#### perl -i.old -ne 'print unless 1 .. 10' foo.txt
+
+#### \SweaveOpts{result=hide}
+#### perl -i.old -p -e 's#\\include{zinput}#\\SweaveOpts{results=hide}\n\\include{zinput}#i' *.Rnw
+
+### change dependencies line
+### perl -i.old -p -e 's#See Package dependencies#Zelig#i' *.Rnw
+
+#### thats how u remove the header from tex files
+#### perl -i.old -ne 'print unless /^\\include{zinput}$/ .. /^\\begin{document}$/' try
+
+
+### here is how to add some tex at the beginning of each file
+
+### perl -i -ple 'print q{TEXTTOBEADDED} if $. == 1; close ARGV if eof' *.tex
diff --git a/inst/doc/mlogit.bayes.pdf b/inst/doc/mlogit.bayes.pdf
index b9e8398..0e758df 100644
Binary files a/inst/doc/mlogit.bayes.pdf and b/inst/doc/mlogit.bayes.pdf differ
diff --git a/inst/doc/mlogit.bayes.tex b/inst/doc/mlogit.bayes.tex
deleted file mode 100644
index af34de7..0000000
--- a/inst/doc/mlogit.bayes.tex
+++ /dev/null
@@ -1,316 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, bayes,multinomial, logistic regression}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{mlogit.bayes}: Bayesian Multinomial Logistic
-Regression}
-
-\label{mlogit.bayes}
-
-Use Bayesian multinomial logistic regression to model unordered
-categorical variables. The dependent variable may be in the format of
-either character strings or integer values. The model is estimated
-via a random walk Metropolis algorithm or a slice sampler. See
-\Sref{mlogit} for the maximum-likelihood estimation of this model.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "mlogit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-{\tt zelig()} accepts the following arguments for {\tt mlogit.bayes}:
-\begin{itemize}
-\item \texttt{baseline}: either a character string or numeric value
-(equal to one of the observed values in the dependent variable)
-specifying a baseline category. The default value is \texttt{NA}
-which sets the baseline to the first alphabetical or numerical unique
-value of the dependent variable.
-\end{itemize}
-
-The model accepts the following additional arguments to monitor the
-Markov chains:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{mcmc.method}: either {\tt "MH"} or {\tt "slice"}, specifying whether
-to use Metropolis Algorithm or slice sampler. The default value is
-\texttt{"MH"}.
-
-\item \texttt{tune}: tuning parameter for the Metropolis-Hasting step,
-either a scalar or a numeric vector (for $k$ coefficients, enter a $k$
-vector). The tuning parameter should be set such that the acceptance
-rate is satisfactory (between 0.2 and 0.5). The default value is 1.1.
-
-\item \texttt{verbose}: defaults to \texttt{FALSE}.
-If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
-printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or a vector (for $k$ coefficients, enter a $k$
-vector). The default is \texttt{NA} where the maximum likelihood
-estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following arguments to specify the priors for the model:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a scalar or
-vector. If a scalar, that value will be the prior mean for all the
-coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix with the dimensions equal to the number of
-coefficients or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0 which
-leads to an improper prior.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCmnl)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(mexico)
-\end{Sinput}
-\end{Schunk}
-Estimating multinomial logistics regression using \texttt{mlogit.bayes}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes",
-+ data = mexico)
-\end{Sinput}
-\end{Schunk}
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their sample averages:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution
-given \texttt{x.out}.
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in the probabilities of
-voting different candidates when \texttt{pristr} (the strength of the
-PRI) is set to be weak (equal to 1) versus strong (equal to 3)
-while all the other variables held at their default values.
-\begin{Schunk}
-\begin{Sinput}
-> x.weak <- setx(z.out, pristr = 1)
-> x.strong <- setx(z.out, pristr = 3)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.strong, x1 = x.weak)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the (unordered) categorical dependent variable for observation
-$i$ which takes an integer values $j=1, \ldots, J$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by:
-\begin{eqnarray*}
-Y_{i} &\sim& \textrm{Multinomial}(Y_i \mid \pi_{ij}).
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1, \ldots, J$.
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\pi_{ij}=\frac{\exp(x_i\beta_j)}{\sum_{k=1}^J \exp(x_i\beta_k)},
-\textrm{ for } j=1,\ldots, J-1,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta_j$ is the vector of coefficient for
-category $j$. Category $J$ is assumed to be the baseline category.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta_j \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
-\textrm{ for } j = 1, \ldots, J-1,
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the multinomial logistics
- regression model are the predicted probability of belonging to each
- category:
-\begin{eqnarray*}
-\Pr(Y_i=j)=\pi_{ij}=\frac{\exp(x_i \beta_j)}{\sum_{k=1}^J \exp(x_J
-\beta_k)}, \quad \textrm{ for } j=1,\ldots, J-1,
-\end{eqnarray*}
-and
-\begin{eqnarray*}
-\Pr(Y_i=J)=1-\sum_{j=1}^{J-1}\Pr(Y_i=j)
-\end{eqnarray*}
-given the posterior draws of $\beta_j$ for all categories from the
-MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are the draws of
-$Y_i$ from a multinomial distribution whose parameters are the expected
-values(\texttt{qi\$ev}) computed based on the posterior draws
-of $\beta$ from the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) in category $j$ for the
-multinomial logistic model is defined as
-\begin{eqnarray*}
-\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
-\begin{eqnarray*}
-\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "mlogit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated coefficients $\beta$ for each category except the baseline
-category.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values(probabilities) of
-each of the $J$ categories given the specified values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values drawn from the
-multinomial distribution defined by the expected values(\texttt{qi\$ev})
-given the specified values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values of each of the $J$ categories for the values specified in
-\texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the
-expected values of each of the $J$ categories simulated
-from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian multinomial logistic regression \input{contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{mlogit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
diff --git a/inst/doc/mlogit.pdf b/inst/doc/mlogit.pdf
index 7ae233f..0e5f714 100644
Binary files a/inst/doc/mlogit.pdf and b/inst/doc/mlogit.pdf differ
diff --git a/inst/doc/mlogit.tex b/inst/doc/mlogit.tex
deleted file mode 100644
index 82e1ba8..0000000
--- a/inst/doc/mlogit.tex
+++ /dev/null
@@ -1,296 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}
-%\VignetteDepends{Zelig, VGAM}
-%\VignetteKeyWords{model, bayes,multinomial, logistic regression}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-\section{{\tt mlogit}: Multinomial Logistic Regression for
-Dependent Variables with Unordered Categorical Values}\label{mlogit}
-Use the multinomial logit distribution to model unordered categorical
-variables. The dependent variable may be in the format of either
-character strings or integer values. See
-%\Sref{mlogit.bayes}
-for a Bayesian version of this model.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "mlogit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Input Values}
-
-If the user wishes to use the same formula across all levels, then
-\verb|formula <- as.factor(Y) ~ X1 + X2| may be used.
-If the user wants to use different formula for each level then the following
-syntax should be used:
-\begin{verbatim}
-formulae <- list(list(id(Y, "apples")~ X1,
- id(Y, "bananas")~ X1 + X2)
-\end{verbatim}
-where Y above is supposed to be a factor variable with levels {apples,bananas,oranges}.
-By default, oranges is the last level and omitted. (You cannot
-specify a different base level at this time.)
-For $J$ equations, there must be $J + 1$ levels.
-
-\subsubsection{Examples} \label{ternary}
-
-\begin{enumerate}
-
-\item {The same formula for each level}
-
-Load the sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(mexico)
-\end{Sinput}
-\end{Schunk}
-Estimate the empirical model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
-+ model = "mlogit", data = mexico)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their default values, with {\tt pristr}
-(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
-equal to 3 (strong) in the alternative values:
-\begin{Schunk}
-\begin{Sinput}
-> x.weak <- setx(z.out1, pristr = 1)
-> x.strong <- setx(z.out1, pristr = 3)
-\end{Sinput}
-\end{Schunk}
-Generate simulated predicted probabilities {\tt qi\$ev} and differences in
-the predicted probabilities {\tt qi\$fd}:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out1, x = x.strong, x1 = x.weak)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-Generate simulated predicted probabilities {\tt qi\$ev} for the
-alternative values:
-\begin{Schunk}
-\begin{Sinput}
-> ev.weak <- s.out1$qi$ev + s.out1$qi$fd
-\end{Sinput}
-\end{Schunk}
-Plot the differences in the predicted probabilities.
-\begin{Schunk}
-\begin{Sinput}
-> library(vcd)
-\end{Sinput}
-\end{Schunk}
-%%%%does not work but the demo gives same error message
-%%%funlegend=ternarypoints,legendargs=parpoint
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> ternaryplot(x = s.out1$qi$ev, pch = ".", col = "blue", main = "1988 Mexican Presidential Election")
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/mlogit-TernaryPlot}
-\end{center}
-\item {Different formula for each level}
-
-Estimate the empirical model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out2 <- zelig(list(id(vote88, "1") ~ pristr + othcok, id(vote88,
-+ "2") ~ othsocok), model = "mlogit", data = mexico)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their default values, with {\tt pristr}
-(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
-equal to 3 (strong) in the alternative values:
-\begin{Schunk}
-\begin{Sinput}
-> x.weak <- setx(z.out2, pristr = 1)
-> x.strong <- setx(z.out2, pristr = 3)
-\end{Sinput}
-\end{Schunk}
-Generate simulated predicted probabilities {\tt qi\$ev} and differences in
-the predicted probabilities {\tt qi\$fd}:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out2, x = x.strong, x1 = x.weak)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-Generate simulated predicted probabilities {\tt qi\$ev} for the
-alternative values:
-\begin{Schunk}
-\begin{Sinput}
-> ev.weak <- s.out1$qi$ev + s.out1$qi$fd
-\end{Sinput}
-\end{Schunk}
-Using the vcd package, plot the differences in the predicted probabilities.
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> ternaryplot(s.out1$qi$ev, pch = ".", col = "blue", main = "1988 Mexican Presidential Election")
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/mlogit-LevelsTernaryPlot}
-\end{center}
-
-
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i$ be the unordered categorical dependent variable that takes
-one of the values from 1 to $J$, where $J$ is the total number of
-categories.
-
-\begin{itemize}
-\item The stochastic component is given by
- \begin{equation*}
- Y_i \; \sim \; \textrm{Multinomial}(y_{i} \mid \pi_{ij}),
- \end{equation*}
- where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1,\dots,J$.
-
-\item The systemic component is given by:
- \begin{equation*}
- \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
- \exp(x_{i}\beta_{k})},
- \end{equation*}
- where $x_i$ is the vector of explanatory variables for observation
- $i$, and $\beta_j$ is the vector of coefficients for category $j$.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the predicted probability
- for each category:
-\begin{equation*}
- E(Y) \; = \; \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
- \exp(x_{i}\beta_{k})}.
-\end{equation*}
-
-\item The predicted value ({\tt qi\$pr}) is a draw from the
- multinomial distribution defined by the predicted probabilities.
-
-\item The first difference in predicted
- probabilities ({\tt qi\$fd}), for each category is given by:
-\begin{equation*}
-\textrm{FD}_j = \Pr(Y=j \mid x_1) - \Pr(Y=j \mid x) \quad {\rm for}
-\quad j=1,\dots,J.
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "mlogit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
- predictors $x_i \beta_j$.
- \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times J$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
-\end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models,
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Further Information}
-
-The multinomial logit model is part of the VGAM package by Thomas Yee.
-Please cite the model as:
-\begin{verse}
-\bibentry{YeeHas03}.
-\end{verse}
-
-In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM library. Additional documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
-
-Sample data are from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
- \end{document}
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/mloglm.pdf b/inst/doc/mloglm.pdf
index a352245..f800cf6 100644
Binary files a/inst/doc/mloglm.pdf and b/inst/doc/mloglm.pdf differ
diff --git a/inst/doc/mloglm.tex b/inst/doc/mloglm.tex
deleted file mode 100644
index 9e364a1..0000000
--- a/inst/doc/mloglm.tex
+++ /dev/null
@@ -1,116 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Multinomial Log-Linear Regression for Contingency Table Models}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model, log-linear,multinomial, regression, contingency table}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt mloglm}: Multinomial Log-Linear Regression
-for Contingency Table Models}\label{mloglm}
-
-Log-linear models are for modeling contingency tables, the
-cross-tabulation of discrete individual-level variables. Contingency
-table models take as the ``unit of analysis'' for the purpose of the
-statistical procedure, the cell of a contingency table. The
-``dependent variable'' is then the count within each cell, and the
-explanatory variables indicate what categories the cells fall into.
-These models are highly efficient computationally since there are so
-few ``observations,'' but they are asymptotically equivalent to
-logistic regression models run on the unpacked individual level data.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> estimate <- zelig(Y ~ X1 + X2, model = "mloglm", data = mydata)
-> Xval <- setx(estimate)
-> results <- sim(estimate, x = Xval)
-\end{verbatim}
-
-\subsubsection{Examples}
-
-\subsubsection{Model}
-
-\subsubsection{Quantities of Interest}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{estimate <- zelig(y \~\,
- x, model = "mloglm", data)}, then you may examine the available
-information in \texttt{estimate} by using \texttt{names(estimate)},
-see the {\tt coefficients} by using {\tt estimate\$coefficients}, and
-a default summary of information through \texttt{summary(estimate)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt estimate}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt deviance}: the residual deviance.
- \item {\tt fitted.values}: the $n \times m$ matrix of in-sample
- fitted values.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt edf}: the effective degrees of freedom.
- \item {\tt AIC}: Akaike's An Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt Hessian}: the Hessian matrix.
- \end{itemize}
-
-\item From {\tt summary(estimate)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output stored in {\tt results}:
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected (or fitted values) for
- the specified values of {\tt x}.
- \item {\tt qi\$rd}: the difference in the expected values (or first
- difference) for the values specified in {\tt x} and
- {\tt x1}.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The multinomial logit model is part of the nnet library by Brian D.
-Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{Ripley1996}.
-\end{verse}
-
-Advanced users may wish to refer to the R-help for
-\texttt{help(multinom)} and
-\begin{verse}
-\bibentry{VenRip2002}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
- \end{document}
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/models/arima.tex b/inst/doc/models/arima.tex
deleted file mode 100644
index 249f808..0000000
--- a/inst/doc/models/arima.tex
+++ /dev/null
@@ -1,290 +0,0 @@
-\section{{\tt ARIMA} : ARIMA Models for Time Series Data}
-\label{ARIMA}
-
-Use auto-regressive, integrated, moving-average (ARIMA) models for
-time series data. A time series is a set of observations ordered
-according to the time they were observed. Because the value observed
-at time $t$ may depend on values observed at previous time points,
-time series data may violate independence assumptions. An ARIMA($p$,
-$d$, $q$) model can account for temporal dependence in several ways.
-First, the time series is differenced to render it stationary, by
-taking $d$ differences. Second, the time dependence of the stationary
-process is modeled by including $p$ auto-regressive and $q$
-moving-average terms, in addition to any time-varying covariates. For
-a cyclical time series, these steps can be repeated according to the
-period of the cycle, whether quarterly or monthly or another time
-interval. ARIMA models are extremely flexible for continuous data.
-Common formulations include, ARIMA(0, 0, 0) for least squares
-regression (see \Sref{ls}), ARIMA(1, 0, 0), for an AR1 model, and
-ARIMA(0, 0, 1) for an MA1 model. For a more comprehensive review of
-ARIMA models, see \cite{Enders04}.
-
-\subsubsection*{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Diff(Y, d, ds=NULL, per=NULL) ~ lag.y(p, ps=NULL)
- + lag.eps(q, qs=NULL) + X1 + X2,
- model="arima", data=mydata, ...)
-> x.out <- setx(z.out, X1 = list(time, value), cond = FALSE)
-> s.out <- sim(z.out, x=x.out, x1=NULL)
-\end{verbatim}
-
-\subsubsection*{Inputs}
-In addition to independent variables, {\tt zelig()} accepts the
-following arguments to specify the {\tt ARIMA} model:
-\begin{itemize}
-\item {\tt Diff(Y, d, ds, per)} for a dependent variable Y sets the
-number of non-seasonal differences ({\tt d}), the number of seasonal differences ({\tt
-ds}), and the period of the season ({\tt per}).
-\item {\tt lag.y(p, ps)} sets the number of lagged observations of the
-dependent variable for non-seasonal ({\tt p}) and seasonal ({\tt ps})
-components.
-\item {\tt lag.eps(q, qs)} sets the number of lagged innovations, or
-differences between the observed value of the time series and the
-expected value of the time series for non-seasonal ({\tt q}) and
-seasonal ({\tt qs}) components.
-\end{itemize}
-In addition the user can control the estimation of the time
-series with the following terms:
-\begin{itemize}
-\item $\hdots$: Additional inputs. See {\tt help(arima)} in the stats
-library for further information.
-\end{itemize}
-
-\subsubsection*{Stationarity}
-A stationary time series has finite variance, correlations between
-observations that are not time-dependent, and a constant expected
-value for all components of the time series \citep[p.\ 12]{BroDav91}.
-Users should ensure that the time series being analyzed is stationary
-before specifying a model. The following commands provide diagnostics
-to determine if a time series {\tt Y} is stationary.
-\begin{itemize}
-\item {\tt pp.test(Y)}: Tests the null hypothesis that the time series
- is non-stationary.
-\item {\tt kpss.test(Y)}: Tests the null hypothesis that the time
- series model is stationary.
-\end{itemize}
-The following commands provide graphical means of diagnosing whether a
-given time series is stationary.
-\begin{itemize}
-\item {\tt ts.plot(Y)}: Plots the observed time series.
-\item {\tt acf(Y)}: Provides the sample auto-correlation function
-(correlogram) for the time series.
-\item {\tt pacf(Y)}: Provides the sample partial auto-correlation
- function (PACF) for the time series.
-\end{itemize}
-These latter two plots are also useful in determining the $p$
-autoregressive terms and the $q$ lagged error terms. See
-\cite{Enders04} for a complete description of how to utilize ACF and
-PACF plots to determine the order of an ARIMA model.
-
-\subsubsection*{Examples}
-
-
-\begin{enumerate}
-
-\item No covariates\newline
-
-Estimate the ARIMA model, and summarize the results.
-\begin{verbatim}
-> data(approval)
-> z.out1 <- zelig(Diff(approve, 1) ~ lag.eps(2) + lag.y(2),
- data = approval, model = "arima")
-> summary(z.out1)
-\end{verbatim}
-Set the number of time periods (ahead) for the prediction to run.
-\begin{verbatim}
-> x.out1 <- setx(z.out1, pred.ahead = 10)
-\end{verbatim}
-Simulate the predicted quantities of interest.
-\begin{verbatim}
-> s.out1 <- sim(z.out1, x = x.out1)
-\end{verbatim}
-Summarize and plot the results.
-\begin{verbatim}
-> summary(z.out1)
-> plot(s.out1, lty.set = 2)
-\end{verbatim}
-
-\item Calculating a treatment effect\newline
-
-Estimate an ARIMA model with exogenous regressors, in addition to
-lagged errors and lagged values of the dependent variable.
-\begin{verbatim}
-> z.out2 <- zelig(Diff(approve, 1) ~ iraq.war + sept.oct.2001 + avg.price
- + lag.y(2)+ lag.eps(1), data = approval, model = "arima")
-\end{verbatim}
-To calculate a treatment effect, provide one counterfactual value for
-one time period for one of the exogenous regressors (this is the
-counterfactual treatment).
-\begin{verbatim}
-> x.out2 <- setx(z.out2, sept.oct.2001 = list(time = 45, value = 0),
- cond = TRUE)
-\end{verbatim}
-Simulate the quantities of interest.
-\begin{verbatim}
-> s.out2 <- sim(z.out2, x = x.out2)
-\end{verbatim}
-Summarize and plot the quantities of interest.
-\begin{verbatim}
-> summary(s.out2)
-> plot(s.out2)
-\end{verbatim}
-
-\item Calculating first differences\newline
-
-Continuing the example from above, calculate first differences by
-selecting several counterfactual values for one of the exogenous
-regressors.
-\begin{verbatim}
-> x.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value = 0))
-> x1.out3 <- setx(z.out2, sept.oct.2001 = list(time = 45:50, value = 1))
-\end{verbatim}
-Simulating the quantities of interest.
-\begin{verbatim}
-> s.out3 <- sim(z.out2, x = x.out3, x1 = x1.out3)
-\end{verbatim}
-Summarizing and plotting the quantities of interest. Choosing {\tt
-pred.se = TRUE} only displays the uncertainty resulting from parameter
-estimation.
-\begin{verbatim}
-> summary(s.out3)
-> plot(s.out3, pred.se = FALSE)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection*{Model}
-Suppose we observe a time series $Y$, with observations $Y_i$ where
-$i$ denotes the time at which the observation was recorded. The first
-step in the ARIMA procedure is to ensure that this series is
-stationary. If initial diagnostics indicate non-stationarity, then we
-take additional differences until the diagnostics indicate
-stationarity. Formally, define the difference operator, $\nabla^
-{d}$, as follows. When $d = 1$, $\nabla^{1} Y = Y_i - Y_{i-1}$, for
-all observations in the series. When $d=2$, $\nabla^2 Y = (Y_{i} -
-Y_{i-1}) - (Y_{i-1} - Y_{i-2})$. This is analogous to a polynomial
-expansion, $Y_{i} - 2Y_{i-1} + Y_{i-2}$. Higher orders of
-differencing ($d > 2$) following the same function. Let $Y^*$
-represent the stationary time series derived from the initial time
-series by differencing $Y$ $d$ times. In the second step, lagged
-values of $Y^{*}$ and errors $\mu - Y^*_i$ are used to model the time
-series. ARIMA utilizes a state space representation of the ARIMA
-model to assemble the likelihood and then utilizes maximum likelihood
-to estimate the parameters of the model. See \cite{BroDav91} Chapter
-12 for further details.
-\begin{itemize}
-\item A stationary time series $Y_i^*$ that has been differenced $d$
-times has \textit{stochastic component}:
-\begin{equation*}
-Y_i^* \sim \text{Normal} (\mu_i, \sigma^2),
-\end{equation*}
-where $\mu_i$ and $\sigma^2$ are the mean and variance of the Normal
-distribution, respectively.
-\item The \textit{systematic component}, $\mu_i$ is modeled as
-\begin{equation*}
-\mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots + \alpha_p Y_{i-p}^*
-+ \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
-\end{equation*}
-where $x_i$ are the explanatory variables with associated parameter
-vector $\beta$; $Y^*$ the lag-$p$ observations from the stationary time
-series with associated parameter vector $\alpha$; and $\epsilon_i$ the
-lagged errors or innovations of order $q$, with associated parameter vector
-$\gamma$.
-\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*}
-\text{E(}Y_\text{i}) = \mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots + \alpha_p Y_{i-p}^*
-+ \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
-\end{equation*}
-given draws of $\beta$, $\alpha$, and $\gamma$ from their estimated
-distribution.
-\item The first difference ({\tt qi\$fd}) is:
-\begin{equation*}
-\text{FD}_i= E(Y | x_{1i}) - E(Y|x_{i})
-\end{equation*}
-\item The treatment effect ({\tt qi\$t.eff}), obtained with {\tt
-setx(\dots, cond = TRUE)}, represents the difference
-between the observed time series and the expected value of a time
-series with counterfactual values of the external regressors. Formally,
-\begin{equation*}
-\text{t.eff}_\text{i} = Y_i - E[Y_i | x_{i}]
-\end{equation*}
-Zelig will not estimate both first differences and treatment effects.
-\end{itemize}
-
-\subsubsection*{Output Values}
-The output of each Zelig command contains useful information which the
-user may view. For example, if the user runs {\tt z.out <-
-zelig(Diff(Y,1) + lag.y(1) + lag.eps(1) + X1, model = "arima", data)}
-then the user may examine the available information in {\tt z.out} by
-using {\tt names(z.out)}, see the coefficients by using {\tt
-z.out\$coef} and a default summary of information through {\tt
-summary(z.out)}. {\tt tsdiag(z.out)} returns a plot of the residuals,
-the ACF of the residuals, and a plot displaying the $p$-values for the
-Ljung-Box statistic. Other elements, available through the \$
-operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
-\begin{itemize}
-\item {\tt coef}: parameter estimates for the explanatory variables,
-lagged observations of the time series, and lagged innovations.
-\item {\tt sigma2}: maximum likelihood estimate of the variance of
-the stationary time series.
-\item {\tt var.coef}: variance-covariance matrix for the parameters.
-\item {\tt loglik}: maximized log-likelihood.
-\item {\tt aic}: Akaike Information Criterion (AIC) for the maximized
- log-likelihood.
-\item {\tt residuals}: Residuals from the fitted model.
-\item {\tt arma}: A vector with seven elements corresponding to the AR
-and MA, the seasonal AR and MA, the period of the seasonal component,
-and the number of non-seasonal and seasonal differences of the
-dependent variable.
-\item {\tt data}: the name of the input data frame.
-\end{itemize}
-\item From the {\tt sim()} output object {\tt s.out} you may extract
- quantities of interest arranged as matrices, with the rows
- indicating the number of the simulations, and the columns
- representing the simulated value of the dependent variable for the
- counterfactual value at that time period. {\tt summary(s.out)}
- provides a summary of the simulated values, while {\tt plot(s.out)}
- provides a graphical representation of the simulations. Available
- quantities are:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}.
-\item {\tt qi\$fd} : the simulated first difference for the values
- that are specified in {\tt x} and {\tt x1}.
-\item{ \tt qi\$t.eff}: the simulated treatment effect, difference
-between the observed {\tt y} and the expected values given the
-counterfactual values specified in {\tt x}.
-\end{itemize}
-\end{itemize}
-
-\subsection*{See Also}
-
-Additional options for ARIMA models may be found using {\tt
-help(arima)}.
-
-For an accessible introduction to identifying the order of an ARIMA
-model consult:
-\begin{verse}
-\bibentry{Enders04}.
-\end{verse}
-In addition, advanced users may wish to become more familiar with the state-space
-representation of an ARIMA process:
-\begin{verse}
-\bibentry{BroDav91}.
-\end{verse}
-
-\subsubsection*{Contributors}
-The ARIMA function is part of the stats package by William Venables
-and Brian Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-Justin Grimmer added Zelig functionality.
-
diff --git a/inst/doc/models/blogit.tex b/inst/doc/models/blogit.tex
deleted file mode 100644
index 8f4a71b..0000000
--- a/inst/doc/models/blogit.tex
+++ /dev/null
@@ -1,326 +0,0 @@
-\section{{\tt blogit}: Bivariate Logistic Regression for Two
-Dichotomous Dependent Variables}\label{blogit}
-
-Use the bivariate logistic regression model if you have two binary
-dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
-function of some explanatory variables. Each pair of dependent
-variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
-Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
-$(Y_{i1}=0, Y_{i2}=0)$. The joint probability for each of these four
-outcomes is modeled with three systematic components: the marginal
-Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the odds ratio $\psi$,
-which describes the dependence of one marginal on the other. Each of
-these systematic components may be modeled as functions of (possibly
-different) sets of explanatory variables.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2 ,
- mu2 = Y2 ~ X1 + X3),
- model = "blogit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Input Values}
-
-In every bivariate logit specification, there are three equations which
-correspond to each dependent variable ($Y_1$, $Y_2$), and $\psi$, the
-odds ratio. You should provide a list of formulas for each equation or,
-you may use {\tt cbind()} if the right hand side is the same for both equations
-\begin{verbatim}
-formulae <- list(cbind(Y1,Y2) ~ X1 + X2)
-\end{verbatim}
-which means that all the explanatory variables in equations 1 and 2
-(corresponding to $Y_1$ and $Y_2$) are included, but only an intercept
-is estimated (all explanatory variables are omitted) for equation 3
-($\psi$).
-
-You may use the function {\tt tag()} to constrain variables across
-equations:
-\begin{verbatim}
-formulae <- list(mu1 = y1 ~ x1 + tag(x3, "x3"),
- mu2 = y2 ~ x2 + tag(x3, "x3"))
-\end{verbatim}
-where {\tt tag()} is a special function that constrains variables to
-have the same effect across equations. Thus, the coefficient for {\tt
-x3} in equation {\tt mu1} is constrained to be equal to the
-coefficient for {\tt x3} in equation {\tt mu2}.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-
-\item {Basic Example} \label{basic.bl}
-
-Load the data and estimate the model:
-\begin{verbatim}
-> data(sanction)
-> z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
- model = "blogit", data = sanction)
-\end{verbatim}
-By default, {\tt zelig()} estimates two effect parameters
-for each explanatory variable in addition to the odds ratio parameter;
-this formulation is parametrically independent (estimating
-unconstrained effects for each explanatory variable), but
-stochastically dependent because the models share an odds ratio.
-\newline \newline Generate baseline values for the explanatory
-variables (with cost set to 1, net gain to sender) and alternative
-values (with cost set to 4, major loss to sender):
-\begin{verbatim}
-> x.low <- setx(z.out1, cost = 1)
-> x.high <- setx(z.out1, cost = 4)
-\end{verbatim}
-Simulate fitted values and first differences:
-\begin{verbatim}
-> s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
-> summary(s.out1)
-> plot(s.out1)
-\end{verbatim}
-
-\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.logit}
-
-Load the sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Estimate the statistical model, with {\tt import} a function of {\tt
- coop} in the first equation and {\tt export} a function of {\tt
- cost} and {\tt target} in the second equation:
-\begin{verbatim}
-> z.out2 <- zelig(list(import ~ coop, export ~ cost + target),
- model = "blogit", data = sanction)
-> summary(z.out2)
-\end{verbatim}
-Set the explanatory variables to their means:
-\begin{verbatim}
-> x.out2 <- setx(z.out2)
-\end{verbatim}
-Simulate draws from the posterior distribution:
-\begin{verbatim}
-> s.out2 <- sim(z.out2, x = x.out2)
-> summary(s.out2)
-> plot(s.out2)
-\end{verbatim}
-
-\item Joint Estimation of a Parametrically and Stochastically
-Dependent Model
-\label{pdep.l}
-
-Load the sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-A bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
-some or all explanatory variables, {\it and} the effects of the shared
-explanatory variables are jointly estimated. For example,
-\begin{verbatim}
-> z.out3 <- zelig(list(import ~ tag(coop,"coop") + tag(cost,"cost") +
- tag(target,"target"),
- export ~ tag(coop,"coop") + tag(cost,"cost") +
- tag(target,"target")),
- model = "blogit", data = sanction)
-> summary(z.out3)
-\end{verbatim}
-Note that this model only returns one parameter estimate for each of
-{\tt coop}, {\tt cost}, and {\tt target}. Contrast this to
-Example~\ref{basic.bl} which returns two parameter estimates for each
-of the explanatory variables. \newline \newline Set values for the
-explanatory variables:
-\begin{verbatim}
-> x.out3 <- setx(z.out3, cost = 1:4)
-\end{verbatim}
-Draw simulated expected values:
-\begin{verbatim}
-> s.out3 <- sim(z.out3, x = x.out3)
-> summary(s.out3)
-\end{verbatim}
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-For each observation, define two binary dependent variables, $Y_1$ and
-$Y_2$, each of which take the value of either 0 or 1 (in the
-following, we suppress the observation index). We model the joint
-outcome $(Y_1$, $Y_2)$ using a marginal probability for each dependent
-variable, and the odds ratio, which parameterizes the relationship
-between the two dependent variables. Define $Y_{rs}$ such that it is
-equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise, where $r$ and
-$s$ take a value of either 0 or 1. Then, the model is defined as follows,
-
-\begin{itemize}
-
-\item The \emph{stochastic component} is
-\begin{eqnarray*}
- Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
- Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
- Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
-\end{eqnarray*}
-where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
-$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$.
-
-
-\item The \emph{systematic components} model the marginal probabilities,
- $\pi_j=\Pr(Y_j=1)$, as well as the odds ratio. The odds ratio
- is defined as $\psi = \pi_{00} \pi_{01}/\pi_{10}\pi_{11}$ and
- describes the relationship between the two outcomes. Thus, for each
- observation we have
-\begin{eqnarray*}
-\pi_j & = & \frac{1}{1 + \exp(-x_j \beta_j)} \quad \textrm{ for} \quad
-j=1,2, \\
-\psi &= & \exp(x_3 \beta_3).
-\end{eqnarray*}
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the bivariate logit model
- are the predicted joint probabilities. Simulations of $\beta_1$,
- $\beta_2$, and $\beta_3$ (drawn from their sampling distributions)
- are substituted into the systematic components $(\pi_1, \pi_2,
- \psi)$ to find simulations of the predicted joint probabilities:
-\begin{eqnarray*}
-\pi_{11} & = & \left\{ \begin{array}{ll}
- \frac{1}{2}(\psi - 1)^{-1} - {a - \sqrt{a^2 + b}} &
- \textrm{for} \; \psi \ne 1 \\
- \pi_1 \pi_2 & \textrm{for} \; \psi = 1
- \end{array} \right., \\
-\pi_{10} &=& \pi_1 - \pi_{11}, \\
-\pi_{01} &=& \pi_2 - \pi_{11}, \\
-\pi_{00} &=& 1 - \pi_{10} - \pi_{01} - \pi_{11},
-\end{eqnarray*}
-where $a = 1 + (\pi_1 + \pi_2)(\psi - 1)$, $b = -4 \psi(\psi - 1)
-\pi_1 \pi_2$, and the joint probabilities for each observation must sum
-to one. For $n$ simulations, the expected values form an $n \times 4$
-matrix for each observation in {\tt x}.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the
- multinomial distribution given the expected joint probabilities.
-
-\item The first differences ({\tt qi\$fd}) for each
- of the predicted joint probabilities are given by $$\textrm{FD}_{rs}
- = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s \mid x).$$
-
-\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
- probabilities are given by
-\begin{equation*}
-\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
- E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
- \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 $E[Y_{ij}(t_i=0)]$,
- 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 $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
- \widehat{Y_{ij}(t_i=0)} \right\} \textrm{ for } j = 1,2,
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating
- $\widehat{Y_{ij}(t_i=0)}$, 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 $t_i=0$.
-\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 = "blogit", 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
-obtain 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 object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times 3$ matrix of the linear
- predictors $x_j \beta_j$.
- \item {\tt residuals}: an $n \times 3$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays indexed by simulation
- $\times$ quantity $\times$ {\tt x}-observation (for more than one
- {\tt x}-observation; otherwise the quantities are matrices).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected joint probabilities (or expected
- values) for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
- distribution defined by the expected joint probabilities.
- \item {\tt qi\$fd}: the simulated first difference in the
- expected joint probabilities for the values specified in {\tt x} and
- {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio in the predicted
- probabilities for given {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The bivariate logit function is part of the VGAM package by Thomas Yee.
-Please cite the model as:
-
-\begin{verse}
-\bibentry{YeeHas03}.
-\end{verse}
-
-In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM library. Additional
-documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
diff --git a/inst/doc/models/bprobit.tex b/inst/doc/models/bprobit.tex
deleted file mode 100644
index 1b173f1..0000000
--- a/inst/doc/models/bprobit.tex
+++ /dev/null
@@ -1,357 +0,0 @@
-\section{{\tt bprobit}: Bivariate Probit Regression for Two
-Dichotomous Dependent Variables}\label{bprobit}
-
-Use the bivariate probit regression model if you have two binary
-dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
-function of some explanatory variables. Each pair of dependent
-variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
-Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
-$(Y_{i1}=0, Y_{i2}=0)$. The joint probability for each of these four
-outcomes is modeled with three systematic components: the marginal
-Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the correlation parameter
-$\rho$ for the two marginal distributions. Each of these systematic
-components may be modeled as functions of (possibly different) sets of
-explanatory variables.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2,
- mu2 = Y2 ~ X1 + X3,
- rho = ~ 1),
- model = "bprobit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Input Values}
-
-In every bivariate probit specification, there are three equations
-which correspond to each dependent variable ($Y_1$, $Y_2$), and the
-correlation parameter $\rho$. Since the correlation parameter does
-not correspond to one of the dependent variables, the model estimates
-$\rho$ as a constant by default. Hence, only two formulas (for
-$\mu_1$ and $\mu_2$) are required. If the explanatory variables for
-$\mu_1$ and $\mu_2$ are the same and effects are estimated separately
-for each parameter, you may use the following short hand:
-\begin{verbatim}
-fml <- list(cbind(Y1,Y2) ~ X1 + X2)
-\end{verbatim}
-which has the same meaning as:
-\begin{verbatim}
-fml <- list(mu1 = Y1 ~ X1 + X2,
- mu2 = Y2 ~ X1 + X2,
- rho = ~ 1)
-\end{verbatim}
-You may use the function {\tt tag()} to constrain variables across
-equations. The {\tt tag()} function takes a variable and a label for
-the effect parameter. Below, the constrained effect of {\tt
-x3} in both equations is called the {\tt age} parameter:
-\begin{verbatim}
-fml <- list(mu1 = y1 ~ x1 + tag(x3, "age"),
- mu2 = y2 ~ x2 + tag(x3, "age"))
-\end{verbatim}
-You may also constrain different variables across different equations
-to have the same effect.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-
-\item {Basic Example} \label{basic.bp}
-
-Load the data and estimate the model:
-\begin{verbatim}
-> data(sanction)
-> z.out1 <- zelig(cbind(import, export) ~ coop + cost + target,
- model = "bprobit", data = sanction)
-\end{verbatim}
-By default, {\tt zelig()} estimates two effect parameters
-for each explanatory variable in addition to the correlation coefficient;
-this formulation is parametrically independent (estimating
-unconstrained effects for each explanatory variable), but
-stochastically dependent because the models share a correlation parameter.
-\newline \newline Generate baseline values for the explanatory
-variables (with cost set to 1, net gain to sender) and alternative
-values (with cost set to 4, major loss to sender):
-\begin{verbatim}
-> x.low <- setx(z.out1, cost = 1)
-> x.high <- setx(z.out1, cost = 4)
-\end{verbatim}
-Simulate fitted values and first differences:
-\begin{verbatim}
-> s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
-> summary(s.out1)
-> plot(s.out1)
-\end{verbatim}
-
-\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.probit}
-
-Load the sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Estimate the statistical model, with {\tt import} a function of {\tt
- coop} in the first equation and {\tt export} a function of {\tt
- cost} and {\tt target} in the second equation:
-\begin{verbatim}
-> fml2 <- list(mu1 = import ~ coop,
- mu2 = export ~ cost + target)
-> z.out2 <- zelig(fml2, model = "bprobit", data = sanction)
-> summary(z.out2)
-\end{verbatim}
-Set the explanatory variables to their means:
-\begin{verbatim}
-> x.out2 <- setx(z.out2)
-\end{verbatim}
-Simulate draws from the posterior distribution:
-\begin{verbatim}
-> s.out2 <- sim(z.out2, x = x.out2)
-> summary(s.out2)
-> plot(s.out2)
-\end{verbatim}
-
-\item Joint Estimation of a Parametrically and Stochastically
-Dependent Model
-\label{pdep.p}
-
-Load the sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-A bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
-some or all explanatory variables, {\it and} the effects of the shared
-explanatory variables are jointly estimated. For example,
-\begin{verbatim}
-> fml3 <- list(mu1 = import ~ tag(coop,"coop") + tag(cost,"cost") +
- tag(target,"target"),
- mu2 = export ~ tag(coop,"coop") + tag(cost,"cost") +
- tag(target,"target"))
-> z.out3 <- zelig(fml3, model = "bprobit", data = sanction)
-> summary(z.out3)
-\end{verbatim}
-Note that this model only returns one parameter estimate for each of
-{\tt coop}, {\tt cost}, and {\tt target}. Contrast this to
-Example~\ref{basic.bp} which returns two parameter estimates for each
-of the explanatory variables. \newline \newline Set values for the
-explanatory variables:
-\begin{verbatim}
-> x.out3 <- setx(z.out3, cost = 1:4)
-\end{verbatim}
-Draw simulated expected values:
-\begin{verbatim}
-> s.out3 <- sim(z.out3, x = x.out3)
-> summary(s.out3)
-\end{verbatim}
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-For each observation, define two binary dependent variables, $Y_1$ and
-$Y_2$, each of which take the value of either 0 or 1 (in the
-following, we suppress the observation index $i$). We model the joint
-outcome $(Y_1$, $Y_2)$ using two marginal probabilities for each
-dependent variable, and the correlation parameter, which describes how
-the two dependent variables are related.
-%Define $Y_{rs}$ such that it
-%is equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise where $r$
-%and $s$ take a value of either 0 or 1. Then, the model is defined as
-%follows,
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by two latent (unobserved)
- continuous variables which follow the bivariate Normal distribution:
-\begin{eqnarray*}
- \left ( \begin{array}{c}
- Y_1^* \\
- Y_2^*
- \end{array}
- \right ) &\sim &
- N_2 \left \{ \left (
- \begin{array}{c}
- \mu_1 \\ \mu_2
- \end{array} \right ), \left( \begin{array}{cc}
- 1 & \rho \\
- \rho & 1
- \end{array} \right) \right\},
-\end{eqnarray*}
-where $\mu_j$ is a mean for $Y_j^*$ and $\rho$ is a scalar correlation
-parameter. The following observation mechanism links the observed
-dependent variables, $Y_j$, with these latent variables
-\begin{eqnarray*}
-Y_j & = & \left \{ \begin{array}{cc}
- 1 & {\rm if} \; Y_j^* \ge 0, \\
- 0 & {\rm otherwise.}
- \end{array}
- \right.
-\end{eqnarray*}
-
-%Alternatively, the stochastic component for the observed dependent
-%variables can be written as
-%\begin{eqnarray*}
-% Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
-% Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
-% Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
-%\end{eqnarray*}
-%where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
-%$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$. Each of these joint
-%probabilities is modeled using the bivariate normal cumulative
-%distribution function.
-
-\item The \emph{systemic components} for each observation are
- \begin{eqnarray*}
- \mu_j & = & x_{j} \beta_j \quad {\rm for} \quad j=1,2, \\
- \rho & = & \frac{\exp(x_3 \beta_3) - 1}{\exp(x_3 \beta_3) + 1}.
-\end{eqnarray*}
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-For $n$ simulations, expected values form an $n \times 4$
-matrix.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the binomial probit model
- are the predicted joint probabilities. Simulations of $\beta_1$,
- $\beta_2$, and $\beta_3$ (drawn form their sampling distributions)
- are substituted into the systematic components, to find simulations
- of the predicted joint probabilities $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$:
-\begin{eqnarray*}
-\pi_{11} &= \Pr(Y_1^* \geq 0 , Y_2^* \geq 0) &= \int_0^{\infty}
-\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^* \\
-\pi_{10} &= \Pr(Y_1^* \geq 0 , Y_2^* < 0) &= \int_0^{\infty}
-\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
-\pi_{01} &= \Pr(Y_1^* < 0 , Y_2^* \geq 0) &= \int_{-\infty}^{0}
-\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
-\pi_{11} &= \Pr(Y_1^* < 0 , Y_2^* < 0) &= \int_{-\infty}^{0}
-\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
-\end{eqnarray*}
-where $r$ and $s$ may take a value of either 0 or 1, $\phi_2$ is the
-bivariate Normal density.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the
- multinomial distribution given the expected joint probabilities.
-
-\item The first difference ({\tt qi\$fd}) in each of the predicted joint
- probabilities are given by
- $$\textrm{FD}_{rs} = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s
- \mid x).$$
-
-\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
- probabilities are given by
-\begin{equation*}
-\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}.
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
- E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
- \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 $E[Y_{ij}(t_i=0)]$,
- 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 $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
- \widehat{Y_{ij}(t_i=0)}\right\} \textrm{ for } j = 1,2,
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating
- $\widehat{Y_{ij}(t_i=0)}$, 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 $t_i=0$.
-
-\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 = "bprobit", 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
-obtain a default summary of information through
-\texttt{summary(z.out)}. Other elements available through the {\tt
- \$} operator are listed below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times 3$ matrix of the linear
- predictors $x_j \beta_j$.
- \item {\tt residuals}: an $n \times 3$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.
-\end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays indexed by simulation
- $\times$ quantity $\times$ {\tt x}-observation (for more than one
- {\tt x}-observation; otherwise the quantities are matrices). Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values (joint predicted
- probabilities) for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
- distribution defined by the joint predicted probabilities.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio in the predicted
- probabilities for given {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The bivariate probit function is part of the VGAM package by Thomas Yee.
-Please cite this model as:
-\begin{verse}
-\bibentry{YeeHas03}.
-\end{verse}
-
-In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM package. Additional documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
diff --git a/inst/doc/models/coda_diag.tex b/inst/doc/models/coda_diag.tex
deleted file mode 100644
index c915940..0000000
--- a/inst/doc/models/coda_diag.tex
+++ /dev/null
@@ -1,32 +0,0 @@
-\subsubsection{Convergence}
-
-Users should verify that the Markov Chain converges to its stationary
-distribution. After running the \texttt{zelig()} function but before
-performing \texttt{setx()}, users may conduct the following
-convergence diagnostics tests:
-
-\begin{itemize}
-\item \texttt{geweke.diag(z.out\$coefficients)}: The Geweke diagnostic tests
-the null hypothesis that the Markov chain is in the stationary distribution
-and produces z-statistics for each estimated parameter.
-
-\item \texttt{heidel.diag(z.out\$coefficients)}: The Heidelberger-Welch
-diagnostic first tests the null hypothesis that the Markov Chain is in the
-stationary distribution and produces p-values for each estimated parameter.
- Calling \texttt{heidel.diag()} also produces
-output that indicates whether the mean of a marginal posterior distribution
-can be estimated with sufficient precision, assuming that the Markov Chain is
-in the stationary distribution.
-
-\item \texttt{raftery.diag(z.out\$coefficients)}: The Raftery diagnostic
-indicates how long the Markov Chain should run before considering draws from
-the marginal posterior distributions sufficiently representative of the
-stationary distribution.
-\end{itemize}
-
-\noindent If there is evidence of non-convergence, adjust the values
-for \texttt{burnin} and \texttt{mcmc} and rerun \texttt{zelig()}.
-
-Advanced users may wish to refer to \texttt{help(geweke.diag)},
-\texttt{help(heidel.diag)}, and \texttt{help(raftery.diag)} for more
-information about these diagnostics.
diff --git a/inst/doc/models/contributors.tex b/inst/doc/models/contributors.tex
deleted file mode 100644
index c2830e4..0000000
--- a/inst/doc/models/contributors.tex
+++ /dev/null
@@ -1,15 +0,0 @@
-function is part of the MCMCpack library by Andrew D.
-Martin and Kevin M. Quinn. If you use this model, please cite:
-\begin{verse}
-\bibentry{MarQui05}.
-\end{verse}
-The convergence diagnostics are part of the CODA library
-by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. These diagnostics
-should be cited as:
-\begin{verse}
-\bibentry{PluBesCowVin05}.
-\end{verse}
-\noindent Sample data are adapted from
-\begin{verse}
-\bibentry{MarQui05}.
-\end{verse}
diff --git a/inst/doc/models/contributors2.tex b/inst/doc/models/contributors2.tex
deleted file mode 100644
index 0958836..0000000
--- a/inst/doc/models/contributors2.tex
+++ /dev/null
@@ -1,11 +0,0 @@
-function is part of the MCMCpack library by Andrew D.
-Martin and Kevin M. Quinn. If you use this model, please cite:
-\begin{verse}
-\bibentry{MarQui05}
-\end{verse}
-The convergence diagnostics are part of the CODA library
-by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. These diagnostics
-should be cited as:
-\begin{verse}
-\bibentry{PluBesCowVin05}
-\end{verse}
diff --git a/inst/doc/models/ei.dynamic.tex b/inst/doc/models/ei.dynamic.tex
deleted file mode 100644
index 3fd28e7..0000000
--- a/inst/doc/models/ei.dynamic.tex
+++ /dev/null
@@ -1,283 +0,0 @@
-\section{\texttt{ei.dynamic}: Quinn's Dynamic Ecological Inference
-Model}
-
-\label{ei.dynamic}
-
-Given contingency tables with observed marginals, ecological inference
-({\sc ei}) models estimate each internal cell value for each table.
-Quinn's dynamic {\sc ei} model estimates a dynamic Bayesian model for
-$2 \times 2$ tables with temporal dependence across tables (units).
-The model is implemented using a Markov Chain Monte Carlo algorithm
-(via a combination of slice and Gibbs sampling).
-For a hierarchical Bayesian implementation of {\sc ei} see Quinn's
-dynamic {\sc ei} model (\Sref{ei.hier}). For contingency tables larger than
-2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "MCMCei.dynamic", data = mydata)
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\begin{itemize}
-\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
-proportions) containing the column margins of the units to be
-analyzed.
-
-\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
-proportions) containing the row margins of the units to be
-analyzed.
-
-\item \texttt{N}: total counts in each contingency table (unit). If
-\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
-you must specify \texttt{N}.
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional inputs for
-\texttt{ei.dynamic} to monitor the convergence of the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 5,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 50,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed of 12345.
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to
-specify priors and other parameters:
-
-\begin{itemize}
-\item \texttt{W}: a $p \times p$ numeric matrix describing the
-structure of the temporal dependence among elements of $\theta_0$ and
-$\theta_1$. The default value is 0, which constructs a weight matrix
-corresponding to random walk priors for $\theta_0$ and $\theta_1$
-(assuming that the tables are equally spaced throughout time, and that
-the elements of \texttt{t0},
-\texttt{t1},\texttt{x0},\texttt{x1} are temporally ordered).
-
-\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$. The default is 0.825.
-
-\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$. The default is 0.0105.
-
-\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$. The default is 0.825.
-
-\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$. The default is 0.0105.
-\end{itemize}
-
-\noindent Users may wish to refer to \texttt{help(MCMCdynamicEI)} for more options.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples \\
- Attaching the example dataset:
-\begin{verbatim}
-> data(eidat)
-\end{verbatim}
-Estimating the model using \texttt{ei.dynamic}:
-\begin{verbatim}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.dynamic",
- data = eidat, mcmc = 40000, thin = 10, burnin = 10000,
- verbose = TRUE)
-> summary(z.out)
-\end{verbatim}
-
-Setting values for in-sample simulations given the marginal values
-of {\tt t0}, {\tt t1}, {\tt x0}, and {\tt x1}:
-\begin{verbatim}
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-\end{verbatim}
-
-In-sample simulations from the posterior distribution:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-Summarizing in-sample simulations at aggregate level
-weighted by the count in each unit:
-\begin{verbatim}
-> summary(s.out)
-\end{verbatim}
-
-Summarizing in-sample simulations at unit level for the first 5 units:
-\begin{verbatim}
-> summary(s.out, subset = 1:5)
-\end{verbatim}
-
-\end{enumerate}
-\clearpage
-\subsubsection{Model}
-Consider the following $2 \times 2$ contingency table for the racial
-voting example. For each geographical unit $i = 1, \dots, p$, the
-marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
-would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
-\begin{table}[h!]
- \begin{center}
- \begin{tabular}{l|cc|c}
- & No Vote & Vote & \\
- \hline
- Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
- White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
- \hline
- & $t_i^0$ & $t_i^1$ & $N_i$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$, $t_i^1$ are
-observed as either counts or fractions. If fractions, the counts can
-be obtained by multiplying by the total counts per table $N_i =
-n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$, and rounding to the
-nearest integer. Although there are four internal cells, only two
-unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
-$n_{i}^{11} = s_i^1 - n_{i}^{10}$. \\
-
-The hierarchical Bayesian model for ecological inference in
-$2 \times 2$ is illustrated as following:
-\begin{itemize}
-\item The \emph{stochastic component} of the model assumes that
-\begin{eqnarray*}
-n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
-x_i^0, \beta_i^b\right) ,\\
-n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
-x_i^1, \beta_i^w\right) \\
-\end{eqnarray*}
-where $\beta_{i}^{b}$ is the fraction of the black voters who vote
-and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
-and $\beta_i^w$ as well as their aggregate summaries are the focus
-of inference.
-
-\item The \emph{systematic component} of the model is
-\begin{eqnarray*}
-\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
-\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
-\end{eqnarray*}
-The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
-and $\theta_i^1$ now take value on the real line. (Future versions may allow
-$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
-
-\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
- are given by
-\begin{eqnarray*}
-\theta_{i}^{0} \mid\sigma_{0}^{2}&\propto & \frac{1}{\sigma_0^{p}} \exp
-\left( -\frac{1}{2\sigma_{0}^2}\theta_0' P \theta_0 \right) \\
-\theta_{i}^{1} \mid\sigma_{1}^{2}&\propto & \frac{1}{\sigma_1^{p}} \exp
-\left( -\frac{1}{2\sigma_{1}^2}\theta_1' P \theta_1 \right)
-\end{eqnarray*}
-where $P$ is a $p \times p$ matrix whose off diagonal elements
-$P_{ts}$ ($t \ne s$) equal $-W_{ts}$ (the negative values of the corresponding
-elements of the weight matrix $W$), and diagonal elements
-$P_{tt}=\sum_{s\ne t} W_{ts}$. Scale parameters $\sigma_{0}^2$ and
-$\sigma_{1}^2$ have hyperprior distributions as given below.
-
-\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
-given by
-\begin{eqnarray*}
-\sigma_{0}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right) ,\\
-\sigma_{1}^{2} & \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right) ,
-\end{eqnarray*}
-where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
-(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$
- are the scale parameters. \\
-
-The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
-and $\sigma_{1}^{2}$ are chosen such that the prior distributions for
-$\beta^b$ and $\beta^w$ are flat.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "ei.dynamic", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)}, see the draws from the
-posterior distribution of the quantities of interest by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out},
-you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the parameters.
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{N}: the total counts when the inputs are fractions.
-\item \texttt{seed}: the random seed used in the model.
-\end{itemize}
-
-\item From \texttt{summary(z.out)}, you may extract:
-\begin{itemize}
-\item \texttt{summary}: a matrix containing the summary information of the
-posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
-the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
-the posterior distribution. The first $p$ rows correspond to
-$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
-\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
-$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
-\texttt{p1table}$i$. The last four rows contain information about
-$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
-variances of $\theta_0$ and $\theta_1$.
-\end{itemize}
-\item From the \texttt{sim()} output object \texttt{s.out}, you may
-extract quantities of interest arranged as arrays indexed by
-simulation $\times$ column $\times$ row $\times$ observation, where
-column and row refer to the column dimension and the row dimension of
-the ecological table, respectively. In this model, only $2 \times 2$
-contingency tables are analyzed, hence column$=2$ and row$=2$ in all
-cases. Available quantities are:
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values of each internal
-cell given the observed marginals.
-\item \texttt{qi\$pr}: the simulated expected values of each internal
-cell given the observed marginals.
-\end{itemize}
-
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The dynamic {\sc ei} model was developed in
-\begin{verse}
-\bibentry{Qui04}.
-\end{verse}
-The \input{models/contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.dynamic} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/ei.hier.tex b/inst/doc/models/ei.hier.tex
deleted file mode 100644
index 787a298..0000000
--- a/inst/doc/models/ei.hier.tex
+++ /dev/null
@@ -1,287 +0,0 @@
-\section{\texttt{ei.hier}: Hierarchical Ecological Inference Model for
-$2 \times 2$ Tables}
-
-\label{ei.hier}
-
-Given contingency tables with observed marginals, ecological inference
-({\sc ei}) models estimate each internal cell value for each table.
-The hierarchical {\sc ei} model estimates a Bayesian model for $2
-\times 2$ tables. The model is implemented using a Markov Chain Monte
-Carlo algorithm (via a combination of slice and Gibbs sampling).
-For a Bayesian implementation of {\sc ei} that accounts for temporal
-dependence, see Quinn's dynamic {\sc ei} model (\Sref{ei.dynamic}).
-For contingency tables larger than
-2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "MCMCei.hier", data = mydata)
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\begin{itemize}
-\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
-proportions) containing the column margins of the units to be
-analyzed.
-
-\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
-proportions) containing the row margins of the units to be
-analyzed.
-
-\item \texttt{N}: total counts per contingency table (unit). If
-\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
-you must specify \texttt{N}.
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional inputs for
-\texttt{ei.hier} to monitor the convergence of the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 5,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 50,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is \texttt{NA} which
-corresponds to a random seed of 12345.
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to specify
- prior parameters used in the model:
-
-\begin{itemize}
-\item \texttt{m0}: prior mean of $\mu_{0}$ (defaults to 0).
-
-\item \texttt{M0}: prior variance of $\mu_{0}$ (defaults to 2.287656).
-
-\item \texttt{m1}: prior mean of $\mu_{1}$ (defaults to 0).
-
-\item \texttt{M1}: prior variance of $\mu_{1}$ (defaults to 2.287656).
-
-\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$ (defaults to 0.825).
-
-\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{0}^{2}$ (defaults to 0.0105).
-
-\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$ (defaults to 0.825).
-
-\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
-prior on $\sigma_{1}^{2}$ (defaults to 0.0105).
-\end{itemize}
-
-\noindent Users may wish to refer to \texttt{help(MCMChierEI)} for more information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples \\
- Attaching the example dataset:
- \begin{verbatim}
- > data(eidat)
- \end{verbatim}
-Estimating the model using \texttt{ei.hier}:
-\begin{verbatim}
-> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.hier",
- data = eidat, mcmc = 40000, thin = 10, burnin = 10000,
- verbose = TRUE)
-> summary(z.out)
-\end{verbatim}
-
-Setting values for in-sample simulations given marginal values
-of {\tt x0}, {\tt x1}, {\tt t0}, and {\tt t1}:
-\begin{verbatim}
-> x.out <- setx(z.out, fn = NULL, cond = TRUE)
-\end{verbatim}
-
-In-sample simulations from the posterior distribution:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-Summarizing in-sample simulations at aggregate level
-weighted by the count in each unit:
-\begin{verbatim}
-> summary(s.out)
-\end{verbatim}
-
-Summarizing in-sample simulations at unit level for the first 5 units:
-\begin{verbatim}
-> summary(s.out, subset = 1:5)
-\end{verbatim}
-
-\end{enumerate}
-\clearpage
-\subsubsection{Model}
-Consider the following $2 \times 2$ contingency table for the racial
-voting example. For each geographical unit $i = 1, \dots, p$, the
-marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
-would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
-
-\begin{table}[!h]
- \begin{center}
- \begin{tabular}{l|cc|c}
- & No Vote & Vote & \\
- \hline
- Black & $n_i^{00}$ & $n_i^{01}$ & $x_i^0$ \\
- White & $n_i^{10}$ & $n_i^{11}$ & $x_i^1$ \\
- \hline
- & $t_i^0$ & $t_i^1$ & $N_i$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$,
-$t_i^1$ are observed as either counts or fractions. If fractions, the
-counts can be obtained by multiplying by the total counts per table
-$N_i = n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$ and rounding to the
-nearest integer. Although there are four internal cells, only two
-unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
-$n_{i}^{11} = s_i^1 - n_{i}^{10}$.
-
-The hierarchical Bayesian model for ecological inference in $2 \times
-2$ is illustrated as following:
-\begin{itemize}
-\item The \emph{stochastic component} of the model assumes that
-\begin{eqnarray*}
-n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
-x_i^0, \beta_i^b\right) ,\\
-n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
-x_i^1, \beta_i^w\right) \\
-\end{eqnarray*}
-where $\beta_{i}^{b}$ is the fraction of the black voters who vote
-and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$
-and $\beta_i^w$ as well as their aggregate level summaries are the focus
-of inference.
-
-\item The \emph{systematic component} is
-\begin{eqnarray*}
-\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
-\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1}
-\end{eqnarray*}
-The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$,
-and $\theta_i^1$ now take value on the real line. (Future versions may allow
-$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
-
-\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
- are given by
-\begin{eqnarray*}
-\theta_{i}^{0} \mid\mu_{0},\sigma_{0}^{2}&\sim&\textrm{Normal}\left( \mu
-_{0},\sigma_{0}^{2}\right),\\
-\theta_{i}^{1} \mid\mu_{1},\sigma_{1}^{2}&\sim&\textrm{Normal}\left( \mu
-_{1},\sigma_{1}^{2}\right)
-\end{eqnarray*}
-where $\mu_{0}$ and $\mu_{1}$ are the means, and $\sigma_{0}^{2}$
-and $\sigma_{1}^{2}$ are the variances of the two corresponding
-(independent) normal distributions.
-
-\item The \emph{hyperpriors} for $\mu_{0}$ and $\mu_{1}$ are given by
-\begin{eqnarray*}
-\mu_{0} &\sim& \textrm{Normal} \left( m_{0}, M_{0} \right) ,\\
-\mu_{1} &\sim& \textrm{Normal} \left( m_{1}, M_{1} \right) ,
-\end{eqnarray*}
-where $m_{0}$ and $m_{1}$ are the means of the (independent) normal
-distributions while $M_{0}$ and $M_{1}$ are the variances.
-
-\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
-given by
-\begin{eqnarray*}
-\sigma_{0}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right),\\
-\sigma_{1}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right),
-\end{eqnarray*}
-where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
-(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$ are
-the scale parameters.
-
-The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
-and $\sigma_{1}^{2}$ are chosen such that the prior distributions of
-$\beta^b$ and $\beta^w$ are flat.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL,
- model = "ei.hier", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)},
-see the draws from the posterior distribution of
-the quantities of interest by using \texttt{z.out\$coefficients},
-and a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out},
-you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the parameters.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{N}: the total counts when the inputs are fractions.
-\item \texttt{seed}: the random seed used in the model.
-\end{itemize}
-
-\item From \texttt{summary(z.out)}, you may extract:
-\begin{itemize}
-\item \texttt{summary}: a matrix containing the summary information of the
-posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
-the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
-the posterior distribution. The first $p$ rows correspond to
-$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
-\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
-$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
-\texttt{p1table}$i$. The last four rows contain information about
-$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
-variances of $\theta_0$ and $\theta_1$.
-\end{itemize}
-\item From the \texttt{sim()} output object \texttt{s.out}, you may
-extract quantities of interest arranged as arrays indexed by
-simulation $\times$ column $\times$ row $\times$ observation, where
-column and row refer to the column dimension and the row dimension of
-the contingency table, respectively. In this model, only $2 \times 2$
-contingency tables are analyzed, hence column$=2$ and row$=2$ in all
-cases. Available quantities are:
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values of each internal cell
-given the observed marginals.
-\item \texttt{qi\$pr}: the simulated expected values of each internal cell
-given the observed marginals.
-\end{itemize}
-
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The hierarchical {\sc ei} \input{models/contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.hier} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/eiRxC.tex b/inst/doc/models/eiRxC.tex
deleted file mode 100644
index 37e708e..0000000
--- a/inst/doc/models/eiRxC.tex
+++ /dev/null
@@ -1,212 +0,0 @@
-\section{\texttt{ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological
- Inference Model for $R \times C$ Tables}
-\label{eiRxC}
-
-Given $n$ contingency tables, each with observed marginals (column and
-row totals), ecological inference ({\sc ei}) estimates the internal
-cell values in each table. The hierarchical Multinomial-Dirichlet
-model estimates cell counts in $R \times C$ tables. The model is
-implemented using a nonlinear least squares approximation and, with
-bootstrapping for standard errors, had good frequentist properties.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(T0, T1, T2, T3) ~ X0 + X1,
- covar = NULL,
- model = "eiRxC", data = mydata)
-> x.out <- setx(z.out, fn = NULL)
-> s.out <- sim(z.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\begin{itemize}
-\item \texttt{T0}, \texttt{T1}, \texttt{T2},\ldots, \texttt{TC}:
- numeric vectors (either counts, or proportions that sum to one for
- each row) containing the column margins of the units to be analyzed.
-
-\item \texttt{X0}, {\tt X1}, {\tt X2},\ldots,{\tt XR}: numeric vectors
- (either counts, or proportions that sum to one for each row)
- containing the row margins of the units to be analyzed.
-
-\item {\tt covar}: (optional) a covariate that varies across tables,
-specified as \verb|covar = ~ Z1|, for example. (The model only
-accepts one covariate.)
-\end{itemize}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
- \item Basic examples: No covariate \\
- Attaching the example dataset:
- \begin{verbatim}
- > data(Weimar)
- \end{verbatim}
-Estimating the model:
-\begin{verbatim}
-> z.out <- zelig(cbind(Nazi, Government, Communists, FarRight, Other) ~
- shareunemployed + shareblue + sharewhite + shareself +
- sharedomestic, model = "ei.RxC", data = Weimar)
-> summary(z.out)
-\end{verbatim}
-
-Setting values for in-sample simulations given marginal values:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-
-Estimate fractions of different social groups that support political parties:
-\begin{verbatim}
-> s.out <- sim(z.out)
-\end{verbatim}
-
-Summarizing fractions of different social groups that support political parties:
-\begin{verbatim}
-> summary(s.out)
-\end{verbatim}
-
- \item Example of covariates being present in the model \\
- Attaching the example dataset:
- \begin{verbatim}
- > data(Weimar)
- \end{verbatim}
-Estimating the model:
-\begin{verbatim}
-> z.out <- zelig(cbind(Nazi, Government, Communists, FarRight, Other) ~
- shareunemployed + shareblue + sharewhite + shareself +
- sharedomestic,
- covar = ~ shareprotestants,
- model = "ei.RxC", data = Weimar)
-> summary(z.out)
-\end{verbatim}
-
-Set the covariate to its default (mean/median) value
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-
-Estimate fractions of different social groups that support political parties:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-Summarizing fractions of different social groups that support political parties:
-\begin{verbatim}
-> summary(s.out)
-\end{verbatim}
-\end{enumerate}
-
-\clearpage
-
-\subsubsection{Model}
-Consider the following $5 \times 5$ contingency table for the voting
-patterns in Weimar Germany. For each geographical unit $i$ ($i = 1,
-\dots, p$), the marginals $T_{1i}$,\dots, $T_{Ci}$, $X_{1i}$,\dots,
-$X_{Ri}$ are known for each of the $p$ electoral precincts, and we
-would like to estimate ($\beta_i^{rc}, r=1,\dots,R, c=1,\dots,C-1$)
-which are the fractions of people in social class $r$ who vote for
-party $c$, for all $r$ and $c$.
-\begin{table}[!h]
- \begin{center}
- \begin{tabular}{l|ccccc|c}
- & Nazi & Government & Communists & Far Right & Other \\
- \hline
- Unemployed & $\beta_{11}^{i}$ & $\beta_{12}^{i}$ & $\beta_{13}^{i}$ & $\beta_{14}^{i}$ & $1-\sum_{c=1}^4 \beta_{1c}^i$ & $X_1^i$ \\
- Blue & $\beta_{21}^{i}$ & $\beta_{22}^{i}$ & $\beta_{23}^{i}$ & $\beta_{24}^{i}$ & $1-\sum_{c=1}^4 \beta_{2c}^i$ & $X_2^i$ \\
- White & $\beta_{31}^{i}$ & $\beta_{32}^{i}$ & $\beta_{33}^{i}$ & $\beta_{34}^{i}$ & $1-\sum_{c=1}^4 \beta_{3c}^i$ & $X_3^i$ \\
- Self & $\beta_{41}^{i}$ & $\beta_{42}^{i}$ & $\beta_{43}^{i}$ & $\beta_{44}^{i}$ & $1-\sum_{c=1}^4 \beta_{4c}^i$ & $X_4^i$ \\
- Domestic & $\beta_{51}^{i}$ & $\beta_{52}^{i}$ & $\beta_{53}^{i}$ & $\beta_{54}^{i}$ & $1-\sum_{c=1}^4 \beta_{5c}^i$ & $X_5^i$ \\
- \hline
- & $T_{1i}$ & $T_{2i}$ & $T_{3i}$ & $T_{4i}$ & $1-\sum_{c=1}^4 \beta_{ci}$
- \end{tabular}
- \end{center}
-\end{table}
-
-\noindent
-The marginal values $X_{1i},\dots,X_{Ri}$, $T_{1i},\dots,T_{Ci}$ may be
-observed as counts or fractions.
-
-Let $T_i^{'}=(T_{1i}^{'},T_{2i}^{'},\dots,T_{Ci}^{'})$ be the number
-of voting age persons who turn out to vote for different parties.
-There are three levels of hierarchy in the
-Multinomial-Dirichlet {\sc ei} model. At the first stage, we model
-the data as:
-\begin{itemize}
-
-\item The \emph{stochastic component} is described $T_i^{'}$ which
-follows a multinomial distribution:
-\begin{eqnarray*}
-T_i^{'} &\sim & \textrm{Multinomial}(\Theta_{1i},\dots,\Theta_{Ci})
-\end{eqnarray*}
-
-\item The \emph{systematic components} are
-\begin{eqnarray*}
-\Theta_{ci}=\sum_{r=1}^{R}\beta_{rc}^{i}X_{ri} & \textrm{for} & c=1,\dots,C
-\end{eqnarray*}
-
-\end{itemize}
-
-At the second stage, we use an optional covariate to model
-$\Theta_{ci}$'s and $\beta_{qrc}^{i}$:
-\begin{itemize}
-\item The \emph{stochastic component} is described by
-$\beta_{r}^{i}=(\beta_{r1},\beta_{r2},\dots,\beta_{r,C-1})$ for
- $i=1,\dots,,p$ and $r=1,\dots,R$, which follows a Dirichlet distribution:
-
- \begin{eqnarray*}
- \beta_r^{i} &\sim & \textrm{Dirichlet}(\alpha_{r1}^{i},\dots,\alpha_{rc}^{i})
- \end{eqnarray*}
-
-\item The \emph{systematic components} are
-\begin{eqnarray*}
- \alpha_{rc}^{i}=\frac{d_{r}exp(\gamma_{rc}+\delta_{rc}Z_{i})}{d_r(1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i))} = \frac{exp(\gamma_{rc}+\delta_{rc}Z_i)}{1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i)}
-\end{eqnarray*}
-for $i=1, \dots,p$, $r=1,\dots,R$, and $c=1, \dots, C-1$.
-
-In the third stage, we assume that the regression parameters (the
-$\gamma_{rc}$'s and $\delta_{rc}$'s) are \emph{a priori} independent,
-and put a flat prior on these regression parameters. The parameters
-$d_{r}$ for $r=1,\dots,R$ are assumed to follow exponential distributions with
-mean $\frac{1}{\lambda}$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-> z.out <- zelig(cbind(T0, T1, T2) ~ X0 + X1 + X2,
- model = "eiRxC", data = mydata)
-\end{verbatim}
-
-\noindent then you may examine the available information in
-\texttt{z.out} by using \texttt{names(z.out)}. For example,
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object
- \texttt{z.out\$coefficients} are the estimates of $\gamma_{ij}$ (and
- also $\delta_{ij}$, if covariates are present). The parameters are
- returned as a single vector of length $R\times (C-1)$. If there is a
- covariate, $\delta$ is concatenated to it.
-
-\item From the \texttt{sim()} output object, you may extract the
- parameters $\beta_{ij}$ corresponding to the estimated fractions of
- different social groups that support different political parties, by
- using \texttt{s.out\$qi\$ev}. For each precinct, that will be a
- matrix with dimensions: simulations $\times R \times C$.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Please cite the model as
-\begin{verse}
-\bibentry{RosJiaKin01}.
-\end{verse}
-
-Jason Wittenberg, Badri Narayan Bhaskar, Ferdinand Alimadhi, and
-Olivia Lau implemented the $R \times C$ {\sc ei} model for Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/exp.tex b/inst/doc/models/exp.tex
deleted file mode 100644
index a627925..0000000
--- a/inst/doc/models/exp.tex
+++ /dev/null
@@ -1,275 +0,0 @@
-\section{{\tt exp}: Exponential Regression for Duration
-Dependent Variables}\label{exp}
-
-Use the exponential duration regression model if you have a dependent
-variable representing a duration (time until an event). The model
-assumes a constant hazard rate for all events. The dependent variable
-may be censored (for observations have not yet been completed when
-data were collected).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X, model = "exp", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Exponential models require that the dependent variable be in the form
-{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
-For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
-duration (lifetime, for example), and the associated $c_i$ is a binary
-variable such that $c_i = 1$ if the duration is not censored ({\it
- e.g.}, the subject dies during the study) or $c_i = 0$ if the
-duration is censored ({\it e.g.}, the subject is still alive at the
-end of the study and is know to live at least as long as $y_i$). If
-$c_i$ is omitted, all Y are assumed to be completed; that is, time
-defaults to 1 for all observations.
-
-\subsubsection{Input Values}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for exponential regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) and the options selected in
-{\tt cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "exp", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-\begin{verbatim}
-> data(coalition)
-\end{verbatim}
-Estimate the model:
-\begin{verbatim}
-> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp",
- data = coalition)
-\end{verbatim}
-View the regression output:
-\begin{verbatim}
-> summary(z.out)
-\end{verbatim}
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-\begin{verbatim}
-> x.low <- setx(z.out, numst2 = 0)
-> x.high <- setx(z.out, numst2 = 1)
-\end{verbatim}
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-\end{verbatim}
-Summarize quantities of interest and produce some plots:
-\begin{verbatim}
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-
-\subsubsection{Model}
-
-Let $Y_i^*$ be the survival time for observation $i$. This variable
-might be censored for some observations at a fixed time $y_c$ such
-that the fully observed dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c \\
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by the distribution
- of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
- the exponential distribution whose density function is given by
- \begin{equation*}
- f(y_i^*\mid \lambda_i) = \frac{1}{\lambda_i} \exp\left(-\frac{y_i^*}{\lambda_i}\right)
- \end{equation*}
- for $y_i^*\ge 0$ and $\lambda_i>0$. The mean of this distribution is
- $\lambda_i$.
-
- In addition, survival models like the exponential have three
- additional properties. The hazard function $h(t)$ measures the
- probability of not surviving past time $t$ given survival up to
- $t$. In general, the hazard function is equal to $f(t)/S(t)$ where
- the survival function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the
- fraction still surviving at time $t$. The cumulative hazard
- function $H(t)$ describes the probability of dying before time $t$.
- In general, $H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case
- of the exponential model,
-\begin{eqnarray*}
-h(t) &=& \frac{1}{\lambda_i} \\
-S(t) &=& \exp\left( -\frac{t}{\lambda_i} \right) \\
-H(t) &=& \frac{t}{\lambda_i}
-\end{eqnarray*}
-For the exponential model, the hazard function $h(t)$ is constant over
-time. The Weibull model and lognormal models allow the hazard
-function to vary as a function of elapsed time (see \Sref{weibull} and
-\Sref{lognorm} respectively).
-
-\item The \emph{systematic component} $\lambda_i$ is modeled as
- \begin{equation*}
- \lambda_i = \exp(x_i \beta),
- \end{equation*}
- where $x_i$ is the vector of explanatory variables, and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the exponential model are
- simulations of the expected duration given $x_i$ and draws of
- $\beta$ from its posterior, $$E(Y) = \lambda_i = \exp(x_i \beta).$$
-
-\item The predicted values ({\tt qi\$pr}) are draws from the
- exponential distribution with rate equal to the expected value.
-
-\item The first difference (or difference in expected values, {\tt
- qi\$ev.diff}), is
-\begin{equation}
-\textrm{FD} \; = \; E(Y \mid x_1) - E(Y \mid x),
-\end{equation}
-where $x$ and $x_1$ are different vectors of values for the
-explanatory variables.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is \begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) - E[Y_i(t_i=0)]
- \right\}, \end{equation*} where $t_i$ is a binary explanatory
- variable defining the treatment ($t_i=1$) and control ($t_i=0$)
- groups. When $Y_i(t_i=1)$ is censored rather than observed, we
- replace it with a simulation from the model given available
- knowledge of the censoring process. Variation in the simulations
- is due to two factors: uncertainty in the imputation process for
- censored $y_i^*$ and uncertainty in simulating $E[Y_i(t_i=0)]$, the
- counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the same
- except that the treatment indicator is switched to $t_i=0$.
-
- \item In conditional prediction models, the average predicted
- treatment effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\}, \end{equation*} where $t_i$ is a
- binary explanatory variable defining the treatment ($t_i=1$) and
- control ($t_i=0$) groups. When $Y_i(t_i=1)$ is censored rather than
- observed, we replace it with a simulation from the model given
- available knowledge of the censoring process. Variation in the
- simulations is due to two factors: uncertainty in the imputation
- process for censored $y_i^*$ and uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
- for observations in the treatment group, under the assumption that
- everything stays the same except that the treatment indicator is
- switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
- C) \~\, X, model = "exp", data)}, then you may examine the
-available information in \texttt{z.out} by using
-\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
- z.out\$coefficients}, and a default summary of information
-through \texttt{summary(z.out)}. Other elements available through
-the {\tt \$} operator are listed below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt icoef}: parameter estimates for the intercept and scale
- parameter. While the scale parameter varies for the Weibull
- distribution, it is fixed to 1 for the exponential distribution
- (which is modeled as a special case of the Weibull).
- \item {\tt var}: the variance-covariance matrix for the estimates
- of $\beta$.
- \item {\tt loglik}: a vector containing the log-likelihood for the
- model and intercept only (respectively).
- \item {\tt linear.predictors}: the vector of
- $x_{i}\beta$.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item Most of this may be conveniently summarized using {\tt
- summary(z.out)}. From {\tt summary(z.out)}, you may
- additionally extract:
- \begin{itemize}
- \item {\tt table}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics. For
- example, {\tt summary(z.out)\$table}
- \end{itemize}
-
-\item From the {\tt sim()} output stored in {\tt s.out}:
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from a
- distribution defined by the expected values.
- \item {\tt qi\$fd}: the simulated first differences between the
- simulated expected values for {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The exponential function is part of the survival library by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library and
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/models/factor.bayes.tex b/inst/doc/models/factor.bayes.tex
deleted file mode 100644
index c8ae634..0000000
--- a/inst/doc/models/factor.bayes.tex
+++ /dev/null
@@ -1,260 +0,0 @@
-\section{\texttt{factor.bayes}: Bayesian Factor Analysis}\label{factor.bayes}
-
-Given some unobserved explanatory variables and observed dependent
-variables, the Normal theory factor analysis model estimates the
-latent factors. The model is implemented using a Markov Chain Monte
-Carlo algorithm (Gibbs sampling with data augmentation). For factor
-analysis with ordinal dependent variables, see ordered factor analysis
-(\Sref{factor.ord}), and for a mix of types of dependent variables,
-see the mixed factor analysis model (\Sref{factor.mix}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 2,
- model = "factor.bayes", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} takes the following functions for {\tt factor.bayes}:
-\begin{itemize}
-\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: variables of interest in
-factor analysis (manifest variables), assumed to be normally
-distributed. The model requires a minimum of three manifest variables.
-
-\item \texttt{factors}: number of the factors to be fitted (defaults to 2).
-
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional arguments for
-model specification:
-\begin{itemize}
-\item \texttt{lambda.constraints}: list containing the equality or
-inequality constraints on the factor loadings. Choose from one of the
-following forms:
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-
-\item \texttt{std.var}: defaults to {\tt FALSE} (manifest variables
-are rescaled to zero mean, but retain observed variance). If
-\texttt{TRUE}, the manifest variables are rescaled to be mean zero and
-unit variance.
-
-\end{itemize}
- In addition, {\tt zelig()} accepts the following additional inputs
-for {\tt bayes.factor}:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{Lambda.start}: starting values of the factor loading
-matrix $\Lambda$, either a scalar (all unconstrained loadings are set
-to that value), or a matrix with compatible dimensions. The default
-is \texttt{NA}, where the start value are set to be 0 for
-unconstrained factor loadings, and 0.5 or $-$0.5 for constrained
-factor loadings (depending on the nature of the constraints).
-
-\item \texttt{Psi.start}: starting values for the uniquenesses, either
-a scalar (the starting values for all diagonal elements of $\Psi$ are
-set to be this value), or a vector with length equal to the number of
-manifest variables. In the latter case, the starting values of the
-diagonal elements of $\Psi$ take the values of \texttt{Psi.start}. The
-default value is \texttt{NA} where the starting values of the all the
-uniquenesses are set to be 0.5.
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-stores the posterior draws of the factor scores. (Storing factor
-scores may take large amount of memory for a large number of draws
-or observations.)
-
-\end{itemize}
-
-\noindent The model also accepts the following additional arguments to
-specify prior parameters:
-
-\begin{itemize}
-
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, that value will be the prior mean for
-all the factor loadings. Defaults to 0.
-
-\item \texttt{L0}: precision parameter of the Normal prior
-for the factor loadings, either a scalar or a matrix with the same
-dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
-the precision matrix will be a diagonal matrix with the diagonal
-elements set to that value. The default value is 0, which leads to an
-improper prior.
-
-\item \texttt{a0}: the shape parameter of the Inverse Gamma prior for
-the uniquenesses is \texttt{a0/2}. It can take a scalar value or a
-vector. The default value is 0.001.
-
-\item \texttt{b0}: the shape parameter of the Inverse Gamma prior for
-the uniquenesses is \texttt{b0/2}. It can take a scalar value or a
-vector. The default value is 0.001.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCfactanal)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(swiss)
-> names(swiss) <- c("Fert","Agr","Exam","Educ","Cath","InfMort")
-\end{verbatim}
-Factor analysis:
-\begin{verbatim}
-> z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
- model = "factor.bayes", data = swiss, factors = 2,
- verbose = TRUE, a0 = 1, b0 = 0.15,
- burnin = 5000, mcmc = 50000)
-\end{verbatim}
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-\end{verbatim} %$
-
-Since the algorithm did not converge, we now add some constraints on
-$\Lambda$.
-
-\item {Putting Constraints on $\Lambda$} \\
-Put constraints on Lambda to optimize the algorithm:
-\begin{verbatim}
-z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,
- model = "factor.bayes", data = swiss, factors = 2,
- lambda.constraints = list(Exam=list(1,"+"),
- Exam=list(2,"-"), Educ=c(2,0),
- InfMort=c(1,0)),
- verbose = TRUE, a0 = 1, b0 = 0.15,
- burnin = 5000, mcmc = 50000)
-
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-\end{enumerate}
-
-\subsubsection{Model}
-Suppose for observation $i$ we observe $K$ variables and hypothesize
-that there are $d$ underlying factors such that:
-\begin{eqnarray*}
-Y_i = \Lambda \phi_i+\epsilon_i
-\end{eqnarray*}
-where $Y_{i}$ is the vector of $K$ manifest variables for observation
-$i$. $\Lambda$ is the $K \times d$ factor loading matrix and $\phi_i$
-is the $d$-vector of latent factor scores. Both $\Lambda$ and $\phi$
-need to be estimated.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by:
-\begin{eqnarray*}
-\epsilon_{i} \sim \textrm{Normal}(0, \Psi).
-\end{eqnarray*}
-where $\Psi$ is a diagonal, positive definite matrix. The diagonal elements
-of $\Psi$ are referred to as uniquenesses.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_i = E(Y_i) = \Lambda\phi_i
-\end{eqnarray*}
-
-\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The independent conjugate \emph{prior} for each $\Psi_{ii}$ is given by
-\begin{eqnarray*}
-\Psi_{ii} \sim \textrm{InverseGamma}(\frac{a_0}{2}, \frac{b_0}{2}), \textrm{ for }
-i = 1, \ldots, k.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is
-\begin{eqnarray*}
-\phi_i &\sim& \textrm{Normal}(0, I_d), \textrm{ for } i = 1, \ldots, n.
-\end{eqnarray*}
-where $I_d$ is a $ d\times d $ identity matrix.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings and the uniquenesses. If
-\texttt{store.scores = TRUE}, the estimated factors scores are also
-contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for factor analysis models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian factor analysis \input{models/contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/factor.mix.tex b/inst/doc/models/factor.mix.tex
deleted file mode 100644
index ea04815..0000000
--- a/inst/doc/models/factor.mix.tex
+++ /dev/null
@@ -1,272 +0,0 @@
-\section{\texttt{factor.mix}: Mixed Data Factor Analysis}\label{factor.mix}
-
-Mixed data factor analysis takes both continuous and ordinal dependent
-variables and estimates a model for a given number of latent factors.
-The model is estimated using a Markov Chain Monte Carlo algorithm
-(Gibbs sampler with data augmentation). Alternative models include
-Bayesian factor analysis for continuous variables
-(\Sref{factor.bayes}) and Bayesian factor analysis for ordinal
-variables (\Sref{factor.ord}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
- model = "factor.mix", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments for {\tt factor.mix}:
-\begin{itemize}
-
-\item \texttt{Y1}, {\tt Y2}, \texttt{Y3}, {\tt \dots}: The dependent variables of
-interest, which can be a mix of ordinal and continuous variables. You
-must have more dependent variables than factors.
-
-\item \texttt{factors}: The number of the factors to be fitted.
-
-\end{itemize}
-
-
-\subsubsection{Additional Inputs}
-
-The model accepts the following additional arguments to monitor
-convergence:
-\begin{itemize}
-\item \texttt{lambda.constraints}: A list that contains the equality or
-inequality constraints on the factor loadings.
-
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-Unlike Bayesian factor analysis for continuous variables
-(\Sref{factor.bayes}), the first column of $\Lambda$ corresponds to
-negative item difficulty parameters and should not be constrained in
-general.
-
-\item \texttt{std.mean}: defaults to {\tt TRUE}, which rescales the
-continuous manifest variables to have mean 0.
-
-\item \texttt{std.var}: defaults to {\tt TRUE}. which rescales the
-continuous manifest variables to have unit variance.
-\end{itemize}
-
-
-\noindent \texttt{factor.mix} accepts the following additional arguments
-to monitor the sampling scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded. The default value is 1,000.
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin.
- The default value is 20,000.
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: tuning parameter, which can be either a
-scalar or a vector of length $K$. The value of the tuning parameter
-must be positive. The default value is \texttt{1.2}.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen. The default
-is \texttt{FALSE}.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{lambda.start}: starting values of the factor loading
-matrix $\Lambda$ for the Markov chain, either a scalar (starting
-values of the unconstrained loadings will be set to that value), or a
-matrix with compatible dimensions. The default is \texttt{NA}, where
-the start values for the first column of $\Lambda$ are set based on
-the observed pattern, while for the rest of the columns of $\Lambda$,
-the start values are set to be 0 for unconstrained factor loadings,
-and 1 or $-$1 for constrained factor loadings (depending on the nature
-of the constraints).
-
-\item \texttt{psi.start}: starting values for the diagonals of the error variance
-(uniquenesses) matrix. Since the starting values for the ordinal
-variables are constrained to 1 (to identify the model), you may only
-specify the starting values for the continuous variables. For the
-continuous variables, you may specify {\tt psi.start} as a scalar or a
-vector with length equal to the number of continuous variables. If a
-scalar, that starting value is recycled for all continuous variables.
-If a vector, the starting values should correspond to each of the
-continuous variables. The default value is \texttt{NA}, which means
-the starting values of all the continuous variable uniqueness are set
-to 0.5.
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, storing the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-the posterior draws of the factor scores are stored. (Storing factor
-scores may take large amount of memory for a a large number of draws
-or observations.)
-
-\end{itemize}
-Use the following additional arguments to specify prior parameters used in the model:
-\begin{itemize}
-
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, then that value will be the prior mean
-for all the factor loadings. The default value is 0.
-
-\item \texttt{L0}: precision parameter of Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as $\Lambda$.
-If a scalar value, then the precision matrix will be
-a diagonal matrix with the diagonal elements set to that value.
-The default value is 0 which leads to an improper prior.
-
-\item \texttt{a0}: {\tt a0/2} is the shape parameter of the Inverse Gamma priors for
-the uniquenesses. It can take a scalar value or a vector. The default
-value is 0.001.
-
-\item \texttt{b0}: {\tt b0/2} is the shape parameter of the Inverse Gamma priors for
-the uniquenesses. It can take a scalar value or a vector. The default
-value is 0.001.
-
-\end{itemize}
-Zelig users may wish to refer to \texttt{help(MCMCmixfactanal)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(PErisk)
-\end{verbatim}
-Factor analysis for mixed data using \texttt{factor.mix}:
-\begin{verbatim}
-> z.out<-zelig(cbind(courts, barb2, prsexp2, prscorr2, gdpw2) ~ NULL,
- data = PErisk, model = "factor.mix", factors = 1,
- burnin = 5000, mcmc = 100000, thin = 50, verbose = TRUE,
- L0 = 0.25)
-\end{verbatim}
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim}
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a $K$-vector of observed variables for observation $i$,
-The $k$th variable can be either continuous or ordinal. When $Y_{ik}$ is an
-ordinal variable, it takes value from 1 to $J_k$ for $k=1,\ldots, K$ and for
-$i =1, \ldots, n$. The distribution of $Y_{ik}$ is assumed to be
-governed by another $K$-vector of unobserved continuous variable $Y_{ik}^*$.
-There are $d$ underlying factors. When $Y_{ik}$ is continuous, we let
-$Y_{ik}^*=Y_{ik}$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described in terms of $Y_i^*$:
-\begin{eqnarray*}
-Y_{i}^* &\sim& \textrm{Normal}_K (\mu_i, I_K),
-\end{eqnarray*}
-where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
-
-For ordinal $Y_{ik}$,
-\begin{eqnarray*}
-Y_{ik} = j \quad {\rm if} \quad \gamma_{(j-1),k} \le Y_{ik}^* \le
-\gamma_{jk} \quad \textrm{ for } \quad j=1,\ldots, J_k; k=1,\ldots, K.
-\end{eqnarray*}
-where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for the $k$th
-variable with the following constraints, $\gamma_{lk} < \gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k k}=\infty$ for any $k=1, \ldots, K$.
-It follows that the probability of observing $Y_{ik}$ belonging to category
-$j$ is,
-\begin{eqnarray*}
-\Pr(Y_{ik}=j) &=&\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \quad
-\textrm{ for } j=1,\ldots,J_k
-\end{eqnarray*}
-where $\Phi(\cdot\mid\mu_{ik})$ is the cumulative distribution function of the
-Normal distribution with mean $\mu_{ik}$ and variance 1.
-
-\item The \emph{systematic component} is given by,
-\begin{eqnarray*}
-\mu_i &=& \Lambda\phi_i,
-\end{eqnarray*}
-where $\Lambda$ is a $K \times d$ matrix of factor loadings for each variable,
-$\phi_i$ is a $d$-vector of factor scores for observation $i$. Note both
-$\Lambda$ and $\phi$ are estimated..
-
-\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} &\sim& \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is,
-\begin{eqnarray*}
-\phi_{i} \sim \textrm{Normal}(0, I_{d-1}), \quad {\rm for} \quad i=2, \ldots, n.
-\end{eqnarray*}
-where $I_{d-1}$ is a $ (d-1) \times (d-1) $ identity matrix. Note the
-first element of $\phi_i$ is 1.
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.mix", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings, the estimated cut points $\gamma$ for each
-variable. Note the first element of $\gamma$ is normalized to be 0. If
-\texttt{store.scores = TRUE}, the estimated factors scores are also contained in
-\texttt{coefficients}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()} procedure is
-not applicable for factor analysis models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-Factor analysis for mixed dependent variables \input{models/contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.mix} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/models/factor.ord.tex b/inst/doc/models/factor.ord.tex
deleted file mode 100644
index 11437b2..0000000
--- a/inst/doc/models/factor.ord.tex
+++ /dev/null
@@ -1,244 +0,0 @@
-\section{\texttt{factor.ord}: Ordinal Data Factor Analysis}\label{factor.ord}
-
-Given some unobserved explanatory variables and observed ordinal
-dependent variables, this model estimates latent factors using a Gibbs
-sampler with data augmentation. For factor analysis for continuous
-data, see \Sref{factor.bayes}. For factor analysis for mixed data
-(including both continuous and ordinal variables), see
-\Sref{factor.mix}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1,
- model = "factor.ord", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments for {\tt factor.ord}: :
-\begin{itemize}
-\item \texttt{Y1, Y2}, and \texttt{Y3}: variables of interest in
-factor analysis (manifest variables), assumed to be ordinal
-variables. The number of manifest variables must be greater than the
-number of the factors.
-
-\item \texttt{factors}: number of the factors to be fitted (defaults
-to 1).
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following arguments for model
-specification:
-\begin{itemize}
-\item \texttt{lambda.constraints}: list that contains the equality or
-inequality constraints on the factor loadings. A typical entry in the list
-has one of the following forms:
-\begin{itemize}
-\item {\tt varname = list()}: by default, no constraints are imposed.
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c};
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be positive;
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th loading for the variable named \texttt{varname} to be negative.
-\end{itemize}
-%Unlike \texttt{factanal} the $\Lambda$ matrix
-%has \texttt{factors+1} columns.
-The first column of $\Lambda$ should not be constrained in general.
-
-\item \texttt{drop.constantvars}: defaults to {\tt TRUE}, dropping the
-manifest variables that have no variation before fitting the model.
-
-\end{itemize}
-
-The model accepts the following arguments to monitor the convergence
-of the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: tuning parameter for Metropolis-Hasting sampling,
-either a scalar or a vector of length $K$. The value of the tuning
-parameter must be positive. The default value is 1.2.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
-progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The
-default is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{Lambda.start}: starting values of the factor loading
-matrix $\Lambda$ for the Markov chain, either a scalar (all
-unconstrained loadings are set to that value), or a matrix with
-compatible dimensions. The default is {\tt NA}, such that the start
-values for the first column are set based on the observed pattern,
-while the remaining columns have start values set to 0 for
-unconstrained factor loadings, and -1 or 1 for constrained loadings
-(depending on the nature of the constraints).
-
-\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
-posterior draws of the factor loadings.
-
-\item \texttt{store.scores}: defaults to {\tt FALSE}. If {\tt TRUE},
-stores the posterior draws of the factor scores. (Storing factor
-scores may take large amount of memory for a a large number of draws
-or observations.)
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{l0}: mean of the Normal prior for the factor
-loadings, either a scalar or a matrix with the same dimensions as
-$\Lambda$. If a scalar value, that value will be the prior mean for
-all the factor loadings. Defaults to 0.
-
-\item \texttt{L0}: precision parameter of the Normal prior
-for the factor loadings, either a scalar or a matrix with the same
-dimensions as $\Lambda$. If \texttt{L0} takes a scalar value, then
-the precision matrix will be a diagonal matrix with the diagonal
-elements set to that value. The default value is 0, which leads to an
-improper prior.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCordfactanal)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(newpainters)
-\end{verbatim}
-Factor analysis for ordinal data using \texttt{factor.ord}:
-\begin{verbatim}
-> z.out <- zelig(cbind(Composition,Drawing,Colour,Expression)~NULL,
- data=newpainters, model="factor.ord",
- factors=1, L0=0.5,
- burin=1000,mcmc=10000, thin=5, verbose=TRUE)
-
-\end{verbatim}
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of $K$ observed ordinal variables for
-observation $i$, each ordinal variable $k$ for $k=1,\ldots, K$ takes
-integer value $j=1, \ldots, J_k$. The distribution of $Y_i$ is assumed
-to be governed by another $k$-vector of unobserved continuous variable
-$Y_i^*$. There are $d$ underlying factors.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described in terms of the latent
-variable $Y_i^*$:
-\begin{eqnarray*}
-Y_{i}^* \sim \textrm{Normal}_K(\mu_i, I_K),
-\end{eqnarray*}
-where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i$ is the mean vector
-for $Y_i^*$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
-
-Instead of $Y_{ik}^*$, we observe ordinal variable $Y_{ik}$,
-\begin{eqnarray*}
-Y_{ik} = j \textrm{ if } \gamma_{(j-1),k} \le Y_{ik}^* \le \gamma_{jk}
-\textrm{ for } \quad j=1,\ldots, J_k, k=1,\ldots, K.
-\end{eqnarray*}
-where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for
-the $k$th variable with the following constraints, $\gamma_{lk} <
-\gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k
-k}=\infty$ for any $k=1, \ldots, K$. It follows that the probability
-of observing $Y_{ik}$ belonging to category $j$ is,
-\begin{eqnarray*}
-\Pr(Y_{ik}=j) =\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \textrm{ for } j=1,\ldots,J_k
-\end{eqnarray*}
-where $\Phi(\cdot \mid\mu_{ik})$ is the cumulative distribution
-function of the Normal distribution with mean $\mu_{ik}$ and variance
-1.
-
-\item The \emph{systematic component} is given by,
-\begin{eqnarray*}
-\mu_i = \Lambda\phi_i,
-\end{eqnarray*}
-where $\Lambda$ is a $K \times d$ matrix of factor loadings for each
-variable, $\phi_i$ is a $d$-vector of factor scores for observation
-$i$. Note both $\Lambda$ and $\phi$ need to be estimated.
-
-\item The independent conjugate \emph{prior} for each element of $\Lambda$,
-$\Lambda_{ij}$ is given by
-\begin{eqnarray*}
-\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
-\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
-\end{eqnarray*}
-
-\item The \emph{prior} for $\phi_i$ is,
-\begin{eqnarray*}
-\phi_{i(2:d)} \sim \textrm{Normal}(0, I_{d-1}), \textrm{ for } \quad i=2, \ldots, n.
-\end{eqnarray*}
-where $I_{d-1}$ is a $ (d-1)\times (d-1) $ identity matrix. Note the
-first element of $\phi_i$ is 1.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.ord", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior
-distribution of the \texttt{coefficients} by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated factor loadings, the estimated cut points $\gamma$ for each
-variable. Note the first element of $\gamma$ is normalized to be 0. If
-\texttt{store.scores=TRUE}, the estimated factors scores are also contained in
-\texttt{coefficients}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()} procedure is
-not applicable for factor analysis models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-The \texttt{factor.ord} \input{models/contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.ord} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/gamma.tex b/inst/doc/models/gamma.tex
deleted file mode 100644
index 0bf6773..0000000
--- a/inst/doc/models/gamma.tex
+++ /dev/null
@@ -1,235 +0,0 @@
-\section{{\tt gamma}: Gamma Regression for Continuous, Positive Dependent Variables}\label{gamma}
-
-Use the gamma regression model if you have a positive-valued dependent
-variable such as the number of years a parliamentary cabinet endures,
-or the seconds you can stay airborne while jumping. The gamma
-distribution assumes that all waiting times are complete by the end
-of the study (censoring is not allowed).
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "gamma", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out, x1 = NULL)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for gamma regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-\begin{verbatim}
-> data(coalition)
-\end{verbatim}
-Estimate the model:
-\begin{verbatim}
-> z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition)
-\end{verbatim}
-View the regression output:
-\begin{verbatim}
-> summary(z.out)
-\end{verbatim}
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-\begin{verbatim}
-> x.low <- setx(z.out, numst2 = 0)
-> x.high <- setx(z.out, numst2 = 1)
-\end{verbatim}
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-
-\subsubsection{Model}
-
-\begin{itemize}
-\item The Gamma distribution with scale parameter $\alpha$ has a
-\emph{stochastic component}:
-\begin{eqnarray*}
-Y &\sim& \textrm{Gamma}(y_i \mid \lambda_i, \alpha) \\
-f(y) &=& \frac{1}{\alpha^{\lambda_i} \, \Gamma \lambda_i} \, y_i^{\lambda_i
- - 1} \exp -\left\{ \frac{y_i}{\alpha} \right\}
-\end{eqnarray*}
-for $\alpha, \lambda_i, y_i > 0$. \\
-
-\item The \emph{systematic component} is given by
-\begin{equation*}
- \lambda_i = \frac{1}{x_i \beta}
-\end{equation*}
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) are simulations of the mean
- of the stochastic component given draws of $\alpha$ and
- $\beta$ from their posteriors: $$E(Y) = \alpha_i \lambda.$$
-\item The predicted values ({\tt qi\$pr}) are draws from the gamma
- distribution for each given set of parameters $(\alpha, \lambda_i)$.
-\item If {\tt x1} is specified, {\tt sim()} also returns the
- differences in the expected values ({\tt qi\$fd}), $$E(Y \mid x_1) -
- E(Y \mid x)$$.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "gamma", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: the vector of fitted values.
- \item {\tt linear.predictors}: the vector of $x_{i}\beta$.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from a
- distribution defined by $(\alpha_i, \lambda)$.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- values for the specified values in {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The gamma model is part of the stats package by William N. Venables and
-Brian D. Ripley. Users should cite this model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\begin{verse}
-\bibentry{McCNel89}.
-\end{verse}
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/models/irt1d.tex b/inst/doc/models/irt1d.tex
deleted file mode 100644
index 047aff7..0000000
--- a/inst/doc/models/irt1d.tex
+++ /dev/null
@@ -1,248 +0,0 @@
-\section{\texttt{irt1d}: One Dimensional Item Response Model}
-
-\label{irt1d}
-
-Given several observed dependent variables and an unobserved
-explanatory variable, item response theory estimates the latent
-variable (ideal points). The model is estimated using the Markov
-Chain Monte Carlo algorithm via a Gibbs sampler and data augmentation.
-Use this model if you believe that the ideal points lie in one
-dimension, and see the $k$-dimensional item response model
-(\Sref{irtkd}) for $k$ hypothesized latent variables.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\texttt{irt1d} accepts the following argument:
-\begin{itemize}
-\item \texttt{Y1, Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
-subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and
-so on.
-\end{itemize}
-
-\subsubsection{Additional arguments}
-
-\texttt{irt1d} accepts the following additional arguments for model specification:
-
-\begin{itemize}
-\item \texttt{theta.constraints}: a list specifying possible equality
-or inequality constraints on the ability parameters $\theta$. A
-typical entry takes one of the following forms:
-
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = c}: constrains the ability parameter for the subject named
-\texttt{varname} to be equal to \texttt{c}.
-
-\item \texttt{varname = "+"}: constrains the ability parameter for the subject named
-\texttt{varname} to be positive.
-
-\item \texttt{varname = "-"}: constrains the ability parameter for the
-subject named {\tt varname} to be negative.
-
-\end{itemize}
-\end{itemize}
-
-The model also accepts the following arguments to monitor the sampling
-scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{theta.start}: starting values for the subject abilities
-(ideal points), either a scalar or a vector with length equal to the
-number of subjects. If a scalar, that value will be the starting value
-for all subjects. The default is \texttt{NA}, which sets the starting
-values based on an eigenvalue-eigenvector decomposition of the
-agreement score matrix formed from the model response matrix
-(\texttt{cbind(Y1, Y2, ...)}).
-
-\item \texttt{alpha.start}: starting values for the difficulty
-parameters $\alpha$, either a scalar or a vector with length equal to
-the number of the items. If a scalar, the value will be the starting
-value for all $\alpha$. The default is \texttt{NA}, which sets the
-starting values based on a series of probit regressions that condition
-on \texttt{theta.start}.
-
-\item \texttt{beta.start}: starting values for the $\beta$ discrimination
-parameters, either a scalar or a vector with length equal to the
-number of the items. If a scalar, the value will be the starting value
-for all $\beta$. The default is \texttt{NA}, which sets the starting
-values based on a series of probit regressions conditioning on
-\texttt{theta.start}.
-
-\item \texttt{store.item}: defaults to {\tt TRUE}, storing the
-posterior draws of the item parameters. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
-items with no variation before fitting the model.
-
-\end{itemize}
-
-\noindent \texttt{irt1d} accepts the following additional arguments to
-specify prior parameters used in the model:
-
-\begin{itemize}
-
-\item \texttt{t0}: prior mean of the subject abilities
-(ideal points). The default is 0.
-
-\item \texttt{T0}: prior precision of the subject abilities
-(ideal points). The default is 0.
-
-\item \texttt{ab0}: prior mean of $(\alpha, \beta)$. It can be a scalar or
-a vector of length 2. If it takes a scalar value, then the prior means for
-both $\alpha$ and $\beta$ will be set to that value. The default is 0.
-
-\item \texttt{AB0}: prior precision of $(\alpha, \beta)$. It can be
-a scalar or a $2 \times 2$ matrix. If it takes a scalar value,
-then the prior precision will be \texttt{diag(AB0,2)}. The prior precision
-is assumed to be same for all the items. The default is 0.25.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCirt1d)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(SupremeCourt)
-\end{verbatim}
-Fitting a one-dimensional item response theory model using \texttt{irt1d}:
-\begin{verbatim}
-> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
- Souter, Thomas, Ginsburg, Breyer) ~ NULL,
- data = SupremeCourt, model = "irt1d",
- B0.alpha = 0.2, B0.beta = 0.2, burnin = 500, mcmc = 10000,
- thin = 20, verbose = TRUE)
-\end{verbatim}
-
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
-$i=1, \ldots, n$. The choice $Y_{ij}$ is assumed to be determined by
-an unobserved utility $Z_{ij}$, which is a function of the subject $i$'s
-abilities (ideal points) $\theta_i$ and item parameters $\alpha_j$ and
-$\beta_j$ as follows:
-\begin{eqnarray*}
-Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
-\end{eqnarray*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij}) \\
-& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
-
-The error term in the unobserved utility equation is independently
-and identically distributed with
-\begin{eqnarray*}
-\epsilon_{ij} \sim \textrm{Normal}(0, 1).
-\end{eqnarray*}
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\pi_{ij}= \Phi(-\alpha_j + \beta_j' \theta_i),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-normal distribution with mean 0 and variance 1, $\theta_i$ is the
-subject ability (ideal point) parameter, and $\alpha_j$ and $\beta_j$
-are the item parameters. Both subject abilities and item parameters
-are estimated from the model, such that the model is identified by
-placing constraints on the subject ability parameters.
-
-\item The \emph{prior} for $\theta_i$ is given by
-\begin{eqnarray*}
-\theta_i &\sim& \textrm{Normal} \left( t_{0},T_{0}^{-1}\right)
-\end{eqnarray*}
-
-\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
-\begin{eqnarray*}
-(\alpha_j, \beta_j)' &\sim& \textrm{Normal}\left( ab_{0},AB_{0}^{-1}\right)
-\end{eqnarray*}
-where $ab_0$ is a 2-vector of prior means and $AB_0$ is a $2 \times 2$ prior
-precision matrix.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated subject abilities(ideal points). If
-\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and
-$\beta$ are also contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for item response models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-The unidimensional item-response \input{models/contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{irt1d} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/models/irtkd.tex b/inst/doc/models/irtkd.tex
deleted file mode 100644
index 67315ec..0000000
--- a/inst/doc/models/irtkd.tex
+++ /dev/null
@@ -1,250 +0,0 @@
-\section{\texttt{irtkd}: $k$-Dimensional Item Response Theory Model}
-
-\label{irtkd}
-
-Given several observed dependent variables and an unobserved
-explanatory variable, item response theory estimates the latent
-variable (ideal points). The model is estimated using the Markov
-Chain Monte Carlo algorithm, via a combination of Gibbs sampling and
-data augmentation. Use this model if you believe that the ideal
-points lie in $k$ dimensions. See the unidimensional item response
-model (\Sref{irt1d}) for a single hypothesized latent variable.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, dimensions = 1,
- model = "irtkd", data = mydata)
-\end{verbatim}
-
-\subsubsection{Inputs}
-\texttt{irtkd} accepts the following arguments:
-\begin{itemize}
-\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for
-subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and so on.
-
-\item \texttt{dimensions}: The number of dimensions in the latent space. The
-default is 1.
-
-\end{itemize}
-
-\subsubsection{Additional arguments}
-
-\texttt{irtkd} accepts the following additional arguments for model specification:
-\begin{itemize}
-\item \texttt{item.constraints}: a list of lists specifying possible
-simple equality or inequality constraints on the item parameters.
-A typical entry has one of the following forms:
-\begin{itemize}
-
-\item {\tt varname = list()}: by default, no constraints are
-imposed.
-
-\item \texttt{varname = list(d, c)}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be equal
-to \texttt{c}.
-
-\item \texttt{varname = list(d, "+")}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be positive;
-
-\item \texttt{varname = list(d, "-")}: constrains the
-$d$th item parameter for the item named \texttt{varname} to be negative.
-\end{itemize}
-In a $k$ dimensional model, the first item parameter for item $i$ is
-the difficulty parameter $\alpha_i$, the second item parameter is the
-discrimination parameter on dimension 1, $(\beta_{i,1})$, the third
-item parameter is the discrimination parameter on dimension 2,
-$(\beta_{i,2}),\ldots$, and $(k+1)$th item parameter is the
-discrimination parameter on dimension $k$, $(\beta_{i,k})$. The item
-difficulty parameter($\alpha$) should not be constrained in general.
-\end{itemize}
-
-\noindent \texttt{irtkd} accepts the following additional arguments
-to monitor the sampling scheme for the Markov chain:
-
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 20,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen. The default
-is \texttt{FALSE}.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{alphabeta.start}: starting values for the item parameters
-$\alpha$ and $\beta$, either a scalar or a $(k+1) \times items$
-matrix. If it is a scalar, then that value will be the starting value
-for all the elements of \texttt{alphabeta.start}. The default is
-\texttt{NA} which sets the starting values for the unconstrained
-elements based on a series of proportional odds logistic
-regressions. The starting values for the inequality constrained
-elements are set to be either 1.0 or -1.0 depending on the nature of
-the constraints.
-
-\item \texttt{store.item}: defaults to {\tt FALSE}. If {\tt TRUE} stores the
-posterior draws of the item parameters. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{store.ability}: defaults to {\tt TRUE}, storing the
-posterior draws of the subject abilities. (For a large number of draws or
-a large number observations, this may take a lot of memory.)
-
-\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
-items with no variation before fitting the model.
-\end{itemize}
-
-\noindent \texttt{irtkd} accepts the following additional arguments to
-specify prior parameters used in the model:
-
-\begin{itemize}
-
-\item \texttt{b0}: prior mean of $(\alpha, \beta)$, either as a scalar or
-a vector of compatible length. If a scalar value, then the prior means
-for both $\alpha$ and $\beta$ will be set to that value. The default
-is 0.
-
-\item \texttt{B0}: prior precision for $(\alpha, \beta)$, either a
-scalar or a $(k+1) \times items$ matrix. If a scalar value, the prior
-precision will be a blocked diagonal matrix with elements
-\texttt{diag(B0,items)}. The prior precision is assumed to be same for
-all the items. The default is 0.25.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCirtKd)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(SupremeCourt)
-\end{verbatim}
-Fitting a one-dimensional item response theory model using \texttt{irtkd}:
-\begin{verbatim}
-> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy, Souter,
- Thomas, Ginsburg, Breyer) ~ NULL, dimensions = 1,
- data = SupremeCourt, model = "irtkd", B0 = 0.25,
- burnin = 5000, mcmc = 50000,
- thin = 10, verbose = TRUE)
-\end{verbatim}
-
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
-$i = 1, \ldots, n$. The choice $Y_{ij}$ is assumed to be
-determined by unobserved utility $Z_{ij}$, which is a function
-of subject abilities (ideal points) $\theta_i$ and item parameters
-$\alpha_j$ and $\beta_j$,
-\begin{eqnarray*}
-Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
-\end{eqnarray*}
-In the $k$-dimensional item response theory model, each subject's ability is
-represented by a $k$-vector, $\theta_i$. Each item has a difficulty
-parameter $\alpha_j$ and a $k$-dimensional discrimination parameter
-$\beta_j$. In one-dimensional item response theory model, $k = 1$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{ij} & \sim & \textrm{Bernoulli}(\pi_{ij})\\
-& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
-
-The error term in the unobserved utility equation has
-a standard normal distribution,
-\begin{eqnarray*}
-\epsilon_{ij} \sim \textrm{Normal}(0, 1).
-\end{eqnarray*}
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{ij} &=& \Phi(-\alpha_j + \beta_j \theta_i),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-normal distribution with mean 0 and variance 1, while $\theta_i$
-contains the $k$-dimensional subject abilities(ideal points), and
-$\alpha_{j}$ and $\beta_j$ are the item parameters. Both subject
-abilities and item parameters need to estimated from the model. The
-model is identified by placing constraints on the item parameters.
-
-\item The \emph{prior} for $\theta_i$ is given by
-\begin{eqnarray*}
-\theta_i &\sim& \textrm{Normal}_k(0, I_k)
-\end{eqnarray*}
-
-\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
-\begin{eqnarray*}
-(\alpha_j, \beta_j)' &\sim& \textrm{Normal}_{k+1} \left( b_{0_j}, B_{0_j}^{-1}\right)
-\end{eqnarray*}
-where $b_{0_j}$ is a $(k+1)$-vector of prior mean and $B_{0_j}$ is a $(k+1)
-\times (k+1)$ prior precision matrix which is assumed to be diagonal.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irtkd", data)
-\end{verbatim}
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated subject abilities(ideal points). If
-\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and $\beta$
- are also contained in \texttt{coefficients}.
-
-\item \texttt{data}: the name of the input data frame.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item Since there are no explanatory variables, the \texttt{sim()}
-procedure is not applicable for item response models.
-
-\end{itemize}
-
-\subsubsection{Contributors}
-The $k$ dimensional item response \input{models/contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{irtkd} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/logit.bayes.tex b/inst/doc/models/logit.bayes.tex
deleted file mode 100644
index 6b1880a..0000000
--- a/inst/doc/models/logit.bayes.tex
+++ /dev/null
@@ -1,238 +0,0 @@
-\section{\texttt{logit.bayes}: Bayesian Logistic Regression}
-
-\label{logit.bayes}
-
-Logistic regression specifies a dichotomous dependent variable as a
-function of a set of explanatory variables using a random walk
-Metropolis algorithm. For a maximum likelihood implementation, see
-\Sref{logit}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: Metropolis tuning parameter, either
-a positive scalar or a vector of length $k$, where $k$ is the number
-of coefficients. The tuning parameter should be set such that the
-acceptance rate of the Metropolis algorithm is satisfactory (typically
-between 0.20 and 0.5) before using the posterior density for
-inference. The default value is 1.1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress
- of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The
-default is \texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the maximum
-likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will
-be the prior mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0, which leads to an improper prior.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(logit.bayes)} for more information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(turnout)
-\end{verbatim}
-Estimating the logistic regression using \texttt{logit.bayes}:
-\begin{verbatim}
-> z.out < - zelig(vote ~ race + educate, model = "logit.bayes",
- data = turnout, verbose = TRUE)
-\end{verbatim}
-Convergence diagnostics before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-Setting values for the explanatory variables to their sample averages:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{verbatim}
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in individual's probability of
-voting when education is set to be low (25th percentile) versus
-high (75th percentile) while all the other variables held at their
-default values.
-\begin{verbatim}
-> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
-> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the binary dependent variable for observation $i$ which takes
-the value of either 0 or 1.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \rm{Bernoulli}(\pi_{i})\\
-& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
-\end{eqnarray*}
-where $\pi_{i}=\Pr(Y_{i}=1)$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{i}= \frac{1}{1+\exp(-x_{i} \beta)},
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the logit model are
-simulations of the predicted probability of a success:
-\begin{eqnarray*}
-E(Y) = \pi_{i}= \frac{1}{1 + \exp(-x_{i} \beta)},
-\end{eqnarray*}
-given the posterior draws of $\beta$ from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
-distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference (\texttt{qi\$fd}) for the logit model is defined
-as
-\begin{eqnarray*}
-\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr})is defined as
-\begin{eqnarray*}
-\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "logit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values(probabilities) for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
-simulated from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian logistic regression \input{models/contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{logit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/logit.tex b/inst/doc/models/logit.tex
deleted file mode 100644
index b4a4a48..0000000
--- a/inst/doc/models/logit.tex
+++ /dev/null
@@ -1,270 +0,0 @@
-\section{{\tt logit}: Logistic Regression for Dichotomous Dependent
-Variables}\label{logit}
-
-Logistic regression specifies a dichotomous dependent variable as a
-function of a set of explanatory variables. For a Bayesian
-implementation, see \Sref{logit.bayes}.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "logit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out, x1 = NULL)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for logistic regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame) The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item {Basic Example}
-
-Attaching the sample turnout dataset:
-\begin{verbatim}
-> data(turnout)
-\end{verbatim}
-Estimating parameter values for the logistic regression:
-\begin{verbatim}
-> z.out1 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-> summary(z.out1)
-\end{verbatim}
-Setting values for the explanatory variables to their default values:
-\begin{verbatim}
-> x.out1 <- setx(z.out1)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution.
-\begin{verbatim}
-> s.out1 <- sim(z.out1, x = x.out)
-> summary(s.out1)
-> plot(s.out1)
-\end{verbatim}
-
-\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.
-\begin{verbatim}
-> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
-> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
-\end{verbatim}
-
-\begin{verbatim}
-> s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
-> summary(s.out2)
-> plot(s.out2)
-\end{verbatim}
-
-\item {Presenting Results: An ROC Plot} \label{ROC}
-
- One can use an ROC plot to evaluate the fit of alternative model
- specifications. (Use {\tt demo(roc)} to view this example, or see
- King and Zeng (2002)\nocite{KinZen02}.)
-\begin{verbatim}
-> z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
- data = turnout)
-> z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-> rocplot(z.out1, z.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i$ be the binary dependent variable for observation $i$ which
-takes the value of either 0 or 1.
-\begin{itemize}
-
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_i &\sim& \textrm{Bernoulli}(y_i \mid \pi_i) \\
- &=& \pi_i^{y_i} (1-\pi_i)^{1-y_i}
-\end{eqnarray*}
-where $\pi_i=\Pr(Y_i=1)$.
-
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
-\end{equation*}
-where $x_i$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the logit model are
- simulations of the predicted probability of a success: $$E(Y) =
- \pi_i= \frac{1}{1 + \exp(-x_i \beta)},$$ given draws of $\beta$ from
- its sampling distribution.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
- distribution with mean equal to the simulated expected value $\pi_i$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)}\right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\, x,
- model = "logit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_i$.
- \item {\tt linear.predictors}: the vector of $x_{i}\beta$
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt data}: the name of the input data frame.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values for the
- specified values of {\tt x}.
- \item {\tt qi\$fd}: the simulated first difference in the expected
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the expected
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The logit function is part of the base package by William N. Venables
-and Brian D. Ripley. Please cite this model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\begin{verse}
-\bibentry{McCNel89}.
-\end{verse}
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}
-\end{verse}
-
-Sample data are a selection of $2,000$ observations from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/models/lognormal.tex b/inst/doc/models/lognormal.tex
deleted file mode 100644
index 97649ea..0000000
--- a/inst/doc/models/lognormal.tex
+++ /dev/null
@@ -1,262 +0,0 @@
-\section{{\tt lognorm}: Log-Normal Regression for Duration
-Dependent Variables}\label{lognorm}
-
-The log-normal model describes an event's duration, the dependent
-variable, as a function of a set of explanatory variables. The
-log-normal model may take time censored dependent variables, and
-allows the hazard rate to increase and decrease.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X, model = "lognorm", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Log-normal models require that the dependent variable be in the form
-{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
-For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
-duration (lifetime, for example) of each subject, and the associated
-$c_i$ is a binary variable such that $c_i = 1$ if the duration is not
-censored ({\it e.g.}, the subject dies during the study) or $c_i = 0$
-if the duration is censored ({\it e.g.}, the subject is still alive at
-the end of the study). If $c_i$ is omitted, all Y are assumed to be
-completed; that is, time defaults to 1 for all observations.
-
-\subsubsection{Input Values}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for lognormal regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
-cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "exp", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-\begin{verbatim}
-> data(coalition)
-\end{verbatim}
-Estimate the model:
-\begin{verbatim}
-> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm",
- data = coalition)
-\end{verbatim}
-View the regression output:
-\begin{verbatim}
-> summary(z.out)
-\end{verbatim}
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-\begin{verbatim}
-> x.low <- setx(z.out, numst2 = 0)
-> x.high <- setx(z.out, numst2 = 1)
-\end{verbatim}
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt
- qi\$fd}):
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-
-\subsubsection{Model}
-
-Let $Y_i^*$ be the survival time for observation $i$ with the density
-function $f(y)$ and the corresponding distribution function
-$F(t)=\int_{0}^t f(y) dy$. This variable might be censored for some
-observations at a fixed time $y_c$ such that the fully observed
-dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c \\
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by the distribution
- of the partially observed variable, $Y^*$. For the lognormal model,
-there are two equivalent representations:
- \begin{eqnarray*}
- Y_i^* \; \sim \; \textrm{LogNormal}(\mu_i, \sigma^2) & \textrm{ or
-} & \log(Y_i^*) \; \sim \; \textrm{Normal}(\mu_i, \sigma^2)
-\end{eqnarray*}
-where the parameters $\mu_i$ and $\sigma^2$ are the mean and variance
-of the Normal distribution. (Note that the output from {\tt zelig()}
-parameterizes {\tt scale}$ = \sigma$.)
-
- In addition, survival models like the lognormal have three additional
-properties. The hazard function $h(t)$ measures the probability of not surviving
- past time $t$ given survival up to $t$. In general, the hazard
- function is equal to $f(t)/S(t)$ where the survival function $S(t) =
- 1 - \int_{0}^t f(s) ds$ represents the fraction still surviving at
- time $t$. The cumulative hazard function $H(t)$ describes the
- probability of dying before time $t$. In general, $H(t)=
-\int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the lognormal model,
-\begin{eqnarray*}
-h(t) &=& \frac{1}{\sqrt{2 \pi} \, \sigma t \, S(t)}
-\exp\left\{-\frac{1}{2 \sigma^2} (\log \lambda t)^2\right\} \\
-S(t) &=& 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \\
-H(t) &=& -\log \left\{ 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \right\}
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function for the Normal
-distribution.
-
-\item The \emph{systematic component} is described as:
-\begin{equation*}
-\mu_i = x_i \beta .
-\end{equation*}
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the lognormal model are
- simulations of the expected duration:
-\begin{equation*}
-E(Y) = \exp\left(\mu_i + \frac{1}{2}\sigma^2 \right),
-\end{equation*}
-given draws of $\beta$ and $\sigma$ from their sampling distributions.
-
-\item The predicted value is a draw from the log-normal distribution
- given simulations of the parameters $(\lambda_i, \sigma)$.
-
-\item The first difference ({\tt qi\$fd}) is
-\begin{equation*}
-\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is \begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) - E[Y_i(t_i=0)] \}, \end{equation*} where $t_i$ is a binary explanatory variable
- defining the treatment ($t_i=1$) and control ($t_i=0$) groups. When
- $Y_i(t_i=1)$ is censored rather than observed, we replace it with a
- simulation from the model given available knowledge of the censoring
- process. Variation in the simulations is due to two factors:
- uncertainty in the imputation process for censored $y_i^*$ and
- uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
- expected value of $Y_i$ for observations in the treatment group,
- under the assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
-\begin{equation*}
- \frac{1}{\sum_{i=1}^n t_i} \sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
-\widehat{Y_i(t_i=0)} \},
-\end{equation*}
-where $t_i$ is a binary explanatory
- variable defining the treatment ($t_i=1$) and control ($t_i=0$)
- groups. When $Y_i(t_i=1)$ is censored rather than observed, we
- replace it with a simulation from the model given available
- knowledge of the censoring process. Variation in the simulations
- are due to two factors: uncertainty in the imputation process for
- censored $y_i^*$ and uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
- for observations in the treatment group, under the assumption that
- everything stays the same except that the treatment indicator is
- switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(Surv(Y,
- C) \~\, X, model = "lognorm", data)}, then you may examine the
-available information in \texttt{z.out} by using
-\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
- z.out\$coefficients}, and a default summary of information
-through \texttt{summary(z.out)}. Other elements available through
-the {\tt \$} operator are listed below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt icoef}: parameter estimates for the intercept and $\sigma$.
- \item {\tt var}: Variance-covariance matrix.
- \item {\tt loglik}: Vector containing the log-likelihood for the
- model and intercept only (respectively).
- \item {\tt linear.predictors}: the vector of
- $x_{i}\beta$.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item Most of this may be conveniently summarized using {\tt
- summary(z.out)}. From {\tt summary(z.out)}, you may
- additionally extract:
- \begin{itemize}
- \item {\tt table}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by $(\lambda_i, \sigma)$.
- \item {\tt qi\$fd}: the simulated first differences between the
- simulated expected values for {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The exponential function is part of the survival library by by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library, and
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
diff --git a/inst/doc/models/ls.tex b/inst/doc/models/ls.tex
deleted file mode 100644
index 4bc67cc..0000000
--- a/inst/doc/models/ls.tex
+++ /dev/null
@@ -1,241 +0,0 @@
-\section{{\tt ls}: Least Squares Regression for Continuous
-Dependent Variables}
-\label{ls}
-
-Use least squares regression analysis to estimate the best linear
-predictor for the specified dependent variables.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "ls", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for least squares regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors based on
-sandwich estimators (see \cite{Zeileis04}, \cite{Huber81}, and
-\cite{White80}). The default type of robust standard error is
-heteroskedastic consistent (HC), \emph{not} heteroskedastic and
-autocorrelation consistent (HAC).
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: choose from
-\begin{itemize}
-\item {\tt "vcovHC"}: (the default if {\tt robust = TRUE}), HC standard errors.
-\item {\tt "vcovHAC"}: HAC standard errors without weights.
-\item {\tt "kernHAC"}: HAC standard errors using the weights given in
-\cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the weights given in
-\cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: only applies to the HAC methods above. Defaults to
-{\tt NULL} (the observations are chronologically ordered as in the
-original data). Optionally, you may specify a time index (either as
-{\tt order.by = z}, where {\tt z} exists outside the data frame; or
-as {\tt order.by = \~{}z}, where {\tt z} is a variable in the data
-frame). The observations are chronologically ordered by the size of
-{\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}\begin{enumerate}
-\item Basic Example with First Differences
-
-Attach sample data:
-\begin{verbatim}
-> data(macro)
-\end{verbatim}
-Estimate model:
-\begin{verbatim}
-> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)
-\end{verbatim}
-Summarize regression coefficients:
-\begin{verbatim}
-> summary(z.out1)
-\end{verbatim}
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) values for the trade variable:
-\begin{verbatim}
-> x.high <- setx(z.out1, trade = quantile(trade, 0.8))
-> x.low <- setx(z.out1, trade = quantile(trade, 0.2))
-\end{verbatim}
-Generate first differences for the effect of high versus low trade on
-GDP:
-\begin{verbatim}
-> s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
-> summary(s.out1)
-> plot(s.out1)
-\end{verbatim}
-
-\item Using Dummy Variables
-
-Estimate a model with fixed effects for each country (see
-\Sref{factors} for help with dummy variables). Note that you do not
-need to create dummy variables, as the program will automatically
-parse the unique values in the selected variable into discrete levels.
-\begin{verbatim}
-> z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country),
- model = "ls", data = macro)
-\end{verbatim}
-Set values for the explanatory variables, using the default mean/mode
-values, with country set to the United States and Japan, respectively:
-\begin{verbatim}
-> x.US <- setx(z.out2, country = "United States")
-> x.Japan <- setx(z.out2, country = "Japan")
-\end{verbatim}
-Simulate quantities of interest:
-\begin{verbatim}
-> s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
-> plot(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-\begin{itemize}
-\item The \emph{stochastic component} is described by a density
- with mean $\mu_i$ and the common variance $\sigma^2$
- \begin{equation*}
- Y_i \; \sim \; f(y_i \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, $\sum_{i=1}^n (Y_i-x_i \beta)^2$.
-\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 = "ls", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
- \item 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 parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \begin{equation*}
- \hat{\beta} \; = \; \left(\sum_{i=1}^n x_i' x_i\right)^{-1} \sum x_i y_i
- \end{equation*}
- \item {\tt sigma}: the square root of the estimate variance of the
- random error $e$:
- \begin{equation*}
- \hat{\sigma} \; = \; \frac{\sum (Y_i-x_i\hat{\beta})^2}{n-k}
- \end{equation*}
- \item {\tt r.squared}: the fraction of the variance explained by
- the model.
- \begin{equation*}
- R^2 \; = \; 1 - \frac{\sum (Y_i-x_i\hat{\beta})^2}{\sum (y_i -
- \bar{y})^2}
- \end{equation*}
- \item {\tt adj.r.squared}: the above $R^2$ statistic, penalizing
- for an increased number of explanatory variables.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output 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}
-
-\subsubsection{Contributors}
-
-The least squares regression is part of the stats package by William N.
-Venables and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, advanced users may wish to refer to
-\texttt{help(lm)} and \texttt{help(lm.fit)}.
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
-
-
-
diff --git a/inst/doc/models/mlogit.bayes.tex b/inst/doc/models/mlogit.bayes.tex
deleted file mode 100644
index 8a93800..0000000
--- a/inst/doc/models/mlogit.bayes.tex
+++ /dev/null
@@ -1,274 +0,0 @@
-\section{\texttt{mlogit.bayes}: Bayesian Multinomial Logistic
-Regression}
-
-\label{mlogit.bayes}
-
-Use Bayesian multinomial logistic regression to model unordered
-categorical variables. The dependent variable may be in the format of
-either character strings or integer values. The model is estimated
-via a random walk Metropolis algorithm or a slice sampler. See
-\Sref{mlogit} for the maximum-likelihood estimation of this model.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "mlogit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-{\tt zelig()} accepts the following arguments for {\tt mlogit.bayes}:
-\begin{itemize}
-\item \texttt{baseline}: either a character string or numeric value
-(equal to one of the observed values in the dependent variable)
-specifying a baseline category. The default value is \texttt{NA}
-which sets the baseline to the first alphabetical or numerical unique
-value of the dependent variable.
-\end{itemize}
-
-The model accepts the following additional arguments to monitor the
-Markov chains:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{mcmc.method}: either {\tt "MH"} or {\tt "slice"}, specifying whether
-to use Metropolis Algorithm or slice sampler. The default value is
-\texttt{"MH"}.
-
-\item \texttt{tune}: tuning parameter for the Metropolis-Hasting step,
-either a scalar or a numeric vector (for $k$ coefficients, enter a $k$
-vector). The tuning parameter should be set such that the acceptance
-rate is satisfactory (between 0.2 and 0.5). The default value is 1.1.
-
-\item \texttt{verbose}: defaults to \texttt{FALSE}.
-If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
-printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or a vector (for $k$ coefficients, enter a $k$
-vector). The default is \texttt{NA} where the maximum likelihood
-estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following arguments to specify the priors for the model:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a scalar or
-vector. If a scalar, that value will be the prior mean for all the
-coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix with the dimensions equal to the number of
-coefficients or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0 which
-leads to an improper prior.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCmnl)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(mexico)
-\end{verbatim}
-Estimating multinomial logistics regression using \texttt{mlogit.bayes}:
-\begin{verbatim}
-> z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes",
- data = mexico)
-\end{verbatim}
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-Setting values for the explanatory variables to their sample averages:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution
-given \texttt{x.out}.
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{verbatim}
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in the probabilities of
-voting different candidates when \texttt{pristr} (the strength of the
-PRI) is set to be weak (equal to 1) versus strong (equal to 3)
-while all the other variables held at their default values.
-
-\begin{verbatim}
-> x.weak <- setx(z.out, pristr = 1)
-> x.strong <- setx(z.out, pristr = 3)
-> s.out2 <- sim(z.out, x = x.strong, x1 = x.weak)
-> summary(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the (unordered) categorical dependent variable for observation
-$i$ which takes an integer values $j=1, \ldots, J$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by:
-\begin{eqnarray*}
-Y_{i} &\sim& \textrm{Multinomial}(Y_i \mid \pi_{ij}).
-\end{eqnarray*}
-where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1, \ldots, J$.
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\pi_{ij}=\frac{\exp(x_i\beta_j)}{\sum_{k=1}^J \exp(x_i\beta_k)},
-\textrm{ for } j=1,\ldots, J-1,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta_j$ is the vector of coefficient for
-category $j$. Category $J$ is assumed to be the baseline category.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta_j \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
-\textrm{ for } j = 1, \ldots, J-1,
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the multinomial logistics
- regression model are the predicted probability of belonging to each
- category:
-\begin{eqnarray*}
-\Pr(Y_i=j)=\pi_{ij}=\frac{\exp(x_i \beta_j)}{\sum_{k=1}^J \exp(x_J
-\beta_k)}, \quad \textrm{ for } j=1,\ldots, J-1,
-\end{eqnarray*}
-and
-\begin{eqnarray*}
-\Pr(Y_i=J)=1-\sum_{j=1}^{J-1}\Pr(Y_i=j)
-\end{eqnarray*}
-given the posterior draws of $\beta_j$ for all categories from the
-MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are the draws of
-$Y_i$ from a multinomial distribution whose parameters are the expected
-values(\texttt{qi\$ev}) computed based on the posterior draws
-of $\beta$ from the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) in category $j$ for the
-multinomial logistic model is defined as
-\begin{eqnarray*}
-\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
-\begin{eqnarray*}
-\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "mlogit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
-summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated coefficients $\beta$ for each category except the baseline
-category.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values(probabilities) of
-each of the $J$ categories given the specified values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values drawn from the
-multinomial distribution defined by the expected values(\texttt{qi\$ev})
-given the specified values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values of each of the $J$ categories for the values specified in
-\texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the
-expected values of each of the $J$ categories simulated
-from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian multinomial logistic regression \input{models/contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{mlogit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/models/mlogit.tex b/inst/doc/models/mlogit.tex
deleted file mode 100644
index 628a00b..0000000
--- a/inst/doc/models/mlogit.tex
+++ /dev/null
@@ -1,249 +0,0 @@
-\section{{\tt mlogit}: Multinomial Logistic Regression for
-Dependent Variables with Unordered Categorical Values}\label{mlogit}
-
-Use the multinomial logit distribution to model unordered categorical
-variables. The dependent variable may be in the format of either
-character strings or integer values. See \Sref{mlogit.bayes} for a
-Bayesian version of this model.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "mlogit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Input Values}
-
-If the user wishes to use the same formula across all levels, then
-\verb|formula <- as.factor(Y) ~ X1 + X2| may be used.
-If the user wants to use different formula for each level then the following
-syntax should be used:
-\begin{verbatim}
-formulae <- list(list(id(Y, "apples")~ X1,
- id(Y, "bananas")~ X1 + X2)
-\end{verbatim}
-where Y above is supposed to be a factor variable with levels {apples,bananas,oranges}.
-By default, oranges is the last level and omitted. (You cannot
-specify a different base level at this time.)
-For $J$ equations, there must be $J + 1$ levels.
-
-\subsubsection{Examples} \label{ternary}
-
-\begin{enumerate}
-
-\item {The same formula for each level}
-
-Load the sample data:
-\begin{verbatim}
-> data(mexico)
-\end{verbatim}
-Estimate the empirical model:
-\begin{verbatim}
-> z.out1 <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
- model = "mlogit", data = mexico)
-\end{verbatim}
-Set the explanatory variables to their default values, with {\tt pristr}
-(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
-equal to 3 (strong) in the alternative values:
-\begin{verbatim}
-> x.weak <- setx(z.out1, pristr = 1)
-> x.strong <- setx(z.out1, pristr = 3)
-\end{verbatim}
-Generate simulated predicted probabilities {\tt qi\$ev} and differences in
-the predicted probabilities {\tt qi\$fd}:
-\begin{verbatim}
-> s.out1 <- sim(z.out1, x = x.strong, x1 = x.weak)
-> summary(s.out1)
-\end{verbatim}
-Generate simulated predicted probabilities {\tt qi\$ev} for the
-alternative values:
-\begin{verbatim}
-> ev.weak <- s.out1$qi$ev + s.out1$qi$fd
-\end{verbatim}
-Plot the differences in the predicted probabilities.
-\begin{verbatim}
-> library(vcd)
-> ternaryplot(s.out1$qi$ev, pch = ".", col = "blue",
- main = "1988 Mexican Presidential Election")
-> ternarypoints(ev.weak, pch = ".", col = "red")
-\end{verbatim}
-
-\item {Different formula for each level}
-
-Estimate the empirical model:
-\begin{verbatim}
-> z.out2 <- zelig(list(id(vote88, "1") ~ pristr + othcok,
- id(vote88, "2") ~ othsocok),
- model = "mlogit", data = mexico)
-\end{verbatim}
-Set the explanatory variables to their default values, with {\tt pristr}
-(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
-equal to 3 (strong) in the alternative values:
-\begin{verbatim}
-> x.weak <- setx(z.out2, pristr = 1)
-> x.strong <- setx(z.out2, pristr = 3)
-\end{verbatim}
-Generate simulated predicted probabilities {\tt qi\$ev} and differences in
-the predicted probabilities {\tt qi\$fd}:
-\begin{verbatim}
-> s.out1 <- sim(z.out2, x = x.strong, x1 = x.weak)
-> summary(s.out1)
-\end{verbatim}
-Generate simulated predicted probabilities {\tt qi\$ev} for the
-alternative values:
-\begin{verbatim}
-> ev.weak <- s.out2$qi$ev + s.out2$qi$fd
-\end{verbatim}
-Plot the differences in the predicted probabilities.
-\begin{verbatim}
-> library(vcd)
-> ternaryplot(s.out2$qi$ev, pch = ".", col = "blue",
- main = "1988 Mexican Presidential Election")
-> ternarypoints(ev.weak, pch = ".", col = "red")
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i$ be the unordered categorical dependent variable that takes
-one of the values from 1 to $J$, where $J$ is the total number of
-categories.
-
-\begin{itemize}
-\item The stochastic component is given by
- \begin{equation*}
- Y_i \; \sim \; \textrm{Multinomial}(y_{i} \mid \pi_{ij}),
- \end{equation*}
- where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1,\dots,J$.
-
-\item The systemic component is given by:
- \begin{equation*}
- \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
- \exp(x_{i}\beta_{k})},
- \end{equation*}
- where $x_i$ is the vector of explanatory variables for observation
- $i$, and $\beta_j$ is the vector of coefficients for category $j$.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the predicted probability
- for each category:
-\begin{equation*}
- E(Y) \; = \; \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
- \exp(x_{i}\beta_{k})}.
-\end{equation*}
-
-\item The predicted value ({\tt qi\$pr}) is a draw from the
- multinomial distribution defined by the predicted probabilities.
-
-\item The first difference in predicted
- probabilities ({\tt qi\$fd}), for each category is given by:
-\begin{equation*}
-\textrm{FD}_j = \Pr(Y=j \mid x_1) - \Pr(Y=j \mid x) \quad {\rm for}
-\quad j=1,\dots,J.
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "mlogit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
- predictors $x_i \beta_j$.
- \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times J$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
-\end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models,
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Further Information}
-
-The multinomial logit model is part of the VGAM package by Thomas Yee.
-Please cite the model as:
-\begin{verse}
-\bibentry{YeeHas03}.
-\end{verse}
-
-In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM library. Additional documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
-
-Sample data are from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/mloglm.tex b/inst/doc/models/mloglm.tex
deleted file mode 100644
index 998d479..0000000
--- a/inst/doc/models/mloglm.tex
+++ /dev/null
@@ -1,105 +0,0 @@
-\section{{\tt mloglm}: Multinomial Log-Linear Regression
-for Contingency Table Models}\label{mloglm}
-
-Log-linear models are for modeling contingency tables, the
-cross-tabulation of discrete individual-level variables. Contingency
-table models take as the ``unit of analysis'' for the purpose of the
-statistical procedure, the cell of a contingency table. The
-``dependent variable'' is then the count within each cell, and the
-explanatory variables indicate what categories the cells fall into.
-These models are highly efficient computationally since there are so
-few ``observations,'' but they are asymptotically equivalent to
-logistic regression models run on the unpacked individual level data.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> estimate <- zelig(Y ~ X1 + X2, model = "mloglm", data = mydata)
-> Xval <- setx(estimate)
-> results <- sim(estimate, x = Xval)
-\end{verbatim}
-
-\subsubsection{Examples}
-
-\subsubsection{Model}
-
-\subsubsection{Quantities of Interest}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{estimate <- zelig(y \~\,
- x, model = "mloglm", data)}, then you may examine the available
-information in \texttt{estimate} by using \texttt{names(estimate)},
-see the {\tt coefficients} by using {\tt estimate\$coefficients}, and
-a default summary of information through \texttt{summary(estimate)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt estimate}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt deviance}: the residual deviance.
- \item {\tt fitted.values}: the $n \times m$ matrix of in-sample
- fitted values.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt edf}: the effective degrees of freedom.
- \item {\tt AIC}: Akaike's An Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt Hessian}: the Hessian matrix.
- \end{itemize}
-
-\item From {\tt summary(estimate)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output stored in {\tt results}:
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected (or fitted values) for
- the specified values of {\tt x}.
- \item {\tt qi\$rd}: the difference in the expected values (or first
- difference) for the values specified in {\tt x} and
- {\tt x1}.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The multinomial logit model is part of the nnet library by Brian D.
-Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{Ripley1996}.
-\end{verse}
-
-Advanced users may wish to refer to the R-help for
-\texttt{help(multinom)} and
-\begin{verse}
-\bibentry{VenRip2002}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/models/negbin.tex b/inst/doc/models/negbin.tex
deleted file mode 100644
index 917ab1a..0000000
--- a/inst/doc/models/negbin.tex
+++ /dev/null
@@ -1,237 +0,0 @@
-\section{{\tt negbin}: Negative Binomial Regression for Event
-Count Dependent Variables}\label{negbin}
-
-Use the negative binomial regression if you have a count of events for
-each observation of your dependent variable. The negative binomial
-model is frequently used to estimate over-dispersed event count
-models.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "negbin", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for negative binomial regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Example}
-
-Load sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Estimate the model:
-\begin{verbatim}
-> z.out <- zelig(num ~ target + coop, model = "negbin", data = sanction)
-> summary(z.out)
-\end{verbatim}
-Set values for the explanatory variables to their default mean values:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulate fitted values:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-\end{verbatim}
-
-\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.
-
-\begin{itemize}
-\item The negative binomial distribution is derived by letting the
- mean of the Poisson distribution vary according to a fixed
- parameter $\zeta$ given by the Gamma distribution. The
- \emph{stochastic component} is given by
- \begin{eqnarray*}
- Y_i \mid \zeta_i & \sim & \textrm{Poisson}(\zeta_i \mu_i),\\
- \zeta_i & \sim & \frac{1}{\theta}\textrm{Gamma}(\theta).
- \end{eqnarray*}
- The marginal distribution of $Y_i$ is then the negative binomial
- with mean $\mu_i$ and variance $\mu_i + \mu_i^2/\theta$:
- \begin{eqnarray*}
- Y_i & \sim & \textrm{NegBin}(\mu_i, \theta), \\
- & = & \frac{\Gamma (\theta + y_i)}{y! \, \Gamma(\theta)}
- \frac{\mu_i^{y_i} \, \theta^{\theta}}{(\mu_i + \theta)^{\theta + y_i}},
- \end{eqnarray*}
- where $\theta$ is the systematic parameter of the Gamma
- distribution modeling $\zeta_i$.
-
- \item The \emph{systematic component} is given by
- \begin{equation*}
- \mu_i = \exp(x_i \beta)
- \end{equation*}
- where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
- the vector of coefficients.
- \end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) are simulations of the mean
- of the stochastic component. Thus, $$E(Y) = \mu_i = \exp(x_i
- \beta),$$ given simulations of $\beta$.
-
-\item The predicted value ({\tt qi\$pr}) drawn from the distribution
- defined by the set of parameters $(\mu_i, \theta)$.
-
-\item The first difference ({\tt qi\$fd}) is
-\begin{equation*}
-\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
-\end{equation*}
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "negbin", 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 theta}: the maximum likelihood estimate for the
- stochastic parameter $\theta$.
- \item {\tt SE.theta}: the standard error for {\tt theta}.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: a vector of the fitted values for the systemic
- component $\lambda$.
- \item {\tt linear.predictors}: a vector of $x_{i} \beta$.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values given the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by $(\mu_i, \theta)$.
- \item {\tt qi\$fd}: the simulated first differences in the
- simulated expected values given the specified values of {\tt x}
- and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The negative binomial model is part of the MASS library by William N.
-Venables and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, advanced users may wish to refer to {\tt help(glm.nb)} in
-the MASS library and \cite{McCNel89}.
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
diff --git a/inst/doc/models/netlogit.tex b/inst/doc/models/netlogit.tex
deleted file mode 100644
index 9b79327..0000000
--- a/inst/doc/models/netlogit.tex
+++ /dev/null
@@ -1,165 +0,0 @@
-\section{{\tt netlogit}: Network Logistic Regression for
-Dichotomous Proximity Matrix Dependent Variables}\label{netlogit}
-
-Use network logistic squares regression analysis for a dependent
-variable that is a binary-valued proximity matrix (a.k.a.\
-sociomatrices, adjacency matrices, or matrix representations of
-directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netlogit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Load the sample data and format it for social network analysis:
-\begin{verbatim}
-data(friendship)
-\end{verbatim}
-Estimate model:
-\begin{verbatim}
-> z.out <- zelig(friends ~ advice + prestige + perpower,
- model = "netlogit", data = friendship)
-> summary(z.out)
-\end{verbatim}
-Setting values for the explanatory variables to their default values:
-\begin{verbatim}
-x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the sampling distribution.
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low personal
-power (25th percentile) and high education (75th percentile) while all
-the other variables are held at their default values.
-
-\begin{verbatim}
-> x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
-> x.low <- setx(z.out, educate = quantile(friendship$perpower, prob = 0.25))
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-> plot(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-The {\tt netlogit} model performs a logistic 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 logistic regression
-element-wise on the appropriately vectorized matrices. Sociomatrices
-are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
-represent the sociomatrix. The vectorization which produces the $Y$
-vector from the $\mathbf{Y}$ matrix is preformed by simple
-row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
-$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
-element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
-element of $Y$ and so on. Once the input matrices are vectorized,
-standard logistic regression is performed.
-
-Let $Y_{i}$ be the binary dependent variable, produced by vectorizing
-a binary sociomatrix, for observation $i$ which takes the value of
-either 0 or 1.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
-& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
-\end{eqnarray*}
-where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
-\end{equation*}
-where $x_{i}$ is the vector of $k$ covariates for observation $i$ and
-$\beta$ is the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network logistic regression are the
-same as those for the standard logistic regression.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the netlogit model are
-simulations of the predicted probability of a success:
-\begin{equation*}
-E(Y) = \pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
-\end{equation*}
-given draws of $\beta$ from its sampling distribution.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
-distribution with mean equal to the simulated expected value
-$\pi_{i}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
-\end{equation*}
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, you run {\tt 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.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the
-explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of
-the IWLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike's Information Criterion (minus twice the
-maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the
-maximized log-likelihood plus the number of coefficients times log
-$n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-\end{itemize}
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled
-covariances.
-\item {\tt ctable}: a $2 \times 2$ table of predicted vs.\ actual
-values. Each cell tabulates whether the predicted value $\widehat{Y}
-\in \{0,1\}$ corresponds to the observed $Y \in \{0,1\}$.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the
-specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected
-probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-The network logistic regression is part of the sna package by Carter
-T. Butts. Please cite the model as \\
-\begin{verse}
-\bibentry{ButCar01}.
-\end{verse}
-In addition, advanced users may wish to refer to {\tt help(netlogit)}.
-Sample data are fictional. Skyler J.\ Cranmer added Zelig functionality.
diff --git a/inst/doc/models/netls.tex b/inst/doc/models/netls.tex
deleted file mode 100644
index 74c907b..0000000
--- a/inst/doc/models/netls.tex
+++ /dev/null
@@ -1,154 +0,0 @@
-\section{{\tt netls}: Network Least Squares Regression for Continuous
-Proximity Matrix Dependent Variables}\label{netls}
-
-Use network least squares regression analysis to estimate the
-best linear predictor when the dependent variable is a
-continuously-valued proximity matrix (a.k.a.\ sociomatrices, adjacency
-matrices, or matrix representations of directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netls", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example with First Differences
-
-Load sample data and format it for social network analysis:
-\begin{verbatim}
-> data(sna.ex)
-\end{verbatim}
-Estimate model:
-\begin{verbatim}
-> z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "netls", data = sna.ex)
-\end{verbatim}
-
-Summarize regression results:
-\begin{verbatim}
-> summary(z.out)
-\end{verbatim}
-
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) for the second
-explanatory variable (Var3).
-\begin{verbatim}
-> x.high <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.8))
-> x.low <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.2))
-\end{verbatim}
-Generate first differences for the effect of high versus low values of
-Var3 on the outcome variable.
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-The {\tt netls} model performs a least squares regression of the
-sociomatrix $\mathbf{Y}$, a $m \times m$ matrix representing network
-ties, on a set of sociomatrices $\mathbf{X}$. This network regression
-model is a directly analogue to standard least squares regression
-element-wise on the appropriately vectorized matrices. Sociomatrices
-are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
-represent the sociomatrix. The vectorization which produces the $Y$
-vector from the $\mathbf{Y}$ matrix is preformed by simple
-row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
-$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
-element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
-element of $Y$ and so on. Once the input matrices are vectorized,
-standard least squares regression is performed. As such:
-\begin{itemize}
-\item The \emph{stochastic component} is described by a density with
-mean $\mu_{i}$ and the common variance $\sigma^{2}$
-\begin{equation*}
-Y_{i} \sim f(y_{i} | \mu_{i}, \sigma^{2}).
-\end{equation*}
-
-\item The \emph{systematic component} models the conditional mean as
-\begin{equation*}
-\mu_{i} = x_{i}\beta
-\end{equation*}
-where $x_{i}$ is the vector of covariates, and $\beta$ is the vector of coefficients.
-\end{itemize}
-The least squares estimator is the best linear predictor of a
-dependent variable given $x_{i}$, and minimizes the sum of squared
-errors $\sum_{i = 1}^{n} (Y_{i} - x_{i}\beta)^{2}$.
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network least squares regression
-are the same as those for the standard least squares regression.
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations from
-the stochastic component,
-\begin{equation*}
-E(Y) = x_{i}\beta,
-\end{equation*}
-given a draw of $\beta$ from its sampling distribution.
-
-\item The first difference ({\tt qi\$fd}) is:
-\begin{equation*}
-FD = E(Y | x_{1}) - E(Y | x)
-\end{equation*}
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, you run {\tt z.out <- zelig(y ~ x,
-model="netls", data)}, then you may examine the available information
-in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by
-using {\tt z.out\$coefficients}, and a default summary of information
-through {\tt summary(z.out)}. Other elements available through the
-{\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-\end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\begin{equation*}
-\hat{\beta} = \left( \sum_{i = 1}^{n} x'_{i}x_{i} \right)^{-1} \sum x_{i}y_{i}
-\end{equation*}
-\item {\tt sigma}: the square root of the estimate variance of the
-random error $\varepsilon$:
-\begin{equation*}
-\hat{\sigma} = \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{n - k}
-\end{equation*}
-\item {\tt r.squared}: the fraction of the variance explained by the model.
-\begin{equation*}
-R^{2} = 1 - \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{\sum (y_{i}
-- \bar{y})^{2}}
-\end{equation*}
-\item {\tt adj.r.squared}: the above $R^{2}$ statistic, penalizing for
-an increased number of explanatory variables.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences (or differences in
-expected values) for the specified values of {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-The network least squares regression is part of the sna package by
-Carter T. Butts. Please cite the model as
-\begin{verse}
-\bibentry{ButCar01}.
-\end{verse}
-In addition, advanced users may wish to refer to {\tt
-help(netlm)}. Sample data are fictional. Skyler J. Cranmer added Zelig
-functionality.
diff --git a/inst/doc/models/normal.bayes.tex b/inst/doc/models/normal.bayes.tex
deleted file mode 100644
index 54a9769..0000000
--- a/inst/doc/models/normal.bayes.tex
+++ /dev/null
@@ -1,230 +0,0 @@
-\section{\texttt{normal.bayes}: Bayesian Normal Linear Regression}
-\label{normal.bayes}
-
-Use Bayesian regression to specify a continuous dependent variable as
-a linear function of specified explanatory variables. The model is
-implemented using a Gibbs sampler. See \Sref{normal} for the
-maximum-likelihood implementation or \Sref{ls} for the ordinary least
-squares variation.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "normal.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the convergence of the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
-progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA}, which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, which uses the
-least squares estimates as the starting values.
-
-\end{itemize}
-
-Use the following arguments to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will
-be the prior mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCregress)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(macro)
-\end{verbatim}
-Estimating linear regression using \texttt{normal.bayes}:
-\begin{verbatim}
-> z.out < - zelig(unem ~ gdp + capmob + trade, model = "normal.bayes",
- data = macro, verbose = TRUE)
-\end{verbatim}
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} $%
-Setting values for the explanatory variables to their sample averages:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}:
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{verbatim}
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) trade on GDP:
-\begin{verbatim}
-> x.high <- setx(z.out, trade = quantile(macro$trade, prob = 0.8))
-> x.low <- setx(z.out, trade = quantile(macro$trade, prob = 0.2))
-\end{verbatim}
-Estimating the first difference for the effect of
-high versus low trade on unemployment rate:
-\begin{verbatim}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
-\end{eqnarray*}
-where $\epsilon_{i}=Y_i-\mu_i$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i}= x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are given by
-\begin{eqnarray*}
-\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
-\sigma^{2} & \sim & {\rm InverseGamma}\left( \frac{c_0}{2},
-\frac{d_0}{2} \right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory
-variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
-a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape and
-scale parameters for $\sigma^{2}$. Note that $\beta$ and $\sigma^2$
-are assumed to be \emph{a priori} independent.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the linear regression model are
-calculated as following:
-\begin{eqnarray*}
-E(Y) = x_{i} \beta,
-\end{eqnarray*}
-given posterior draws of $\beta$ based on the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) for the linear regression model
-is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1} \{
-Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "normal.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-The Bayesian regression \input{models/contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{normal.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/normal.tex b/inst/doc/models/normal.tex
deleted file mode 100644
index d0f7630..0000000
--- a/inst/doc/models/normal.tex
+++ /dev/null
@@ -1,273 +0,0 @@
-\section{{\tt normal}: Normal Regression for Continuous Dependent Variables}
-\label{normal}
-
-The Normal regression model is a close variant of the more standard
-least squares regression model (see \Sref{ls}). Both models specify a
-continuous dependent variable as a linear function of a set of
-explanatory variables. The Normal model reports maximum likelihood
-(rather than least squares) estimates. The two models differ only in
-their estimate for the stochastic parameter $\sigma$.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "normal", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for normal regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item Basic Example with First Differences
-
-Attach sample data:
-\begin{verbatim}
-> data(macro)
-\end{verbatim}
-Estimate model:
-\begin{verbatim}
-> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal",
- data = macro)
-\end{verbatim}
-Summarize of regression coefficients:
-\begin{verbatim}
-> summary(z.out1)
-\end{verbatim}
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) values for trade:
-\begin{verbatim}
-> x.high <- setx(z.out, trade = quantile(macro$trade, 0.8))
-> x.low <- setx(z.out, trade = quantile(macro$trade, 0.2))
-\end{verbatim}
-Generate first differences for the effect of high versus low trade on
-GDP:
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out1)
-\end{verbatim}
-A visual summary of quantities of interest:
-\begin{verbatim}
-> plot(s.out1)
-\end{verbatim}
-
-\item Using Dummy Variables
-
-Estimate a model with a dummy variable for each year and country (see
-\ref{factors} for help with dummy variables). Note that you do not
-need to create dummy variables, as the program will automatically
-parse the unique values in the selected variables into dummy
-variables.
-\begin{verbatim}
-> z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(year)
- + as.factor(country), model = "normal", data = macro)
-\end{verbatim}
-Set values for the explanatory variables, using the default mean/mode
-variables, with country set to the United States and Japan,
-respectively:
-\begin{verbatim}
-> x.US <- setx(z.out2, country = "United States")
-> x.Japan <- setx(z.out2, country = "Japan")
-\end{verbatim}
-Simulate quantities of interest:
-\begin{verbatim}
-> s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
-> summary(s.out2)
-> plot(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i$ be the continuous dependent variable for observation $i$.
-\begin{itemize}
-\item The \emph{stochastic component} is described by a univariate normal
- model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
- \begin{equation*}
- Y_i \; \sim \; \textrm{Normal}(\mu_i, \sigma^2).
- \end{equation*}
-
-\item The \emph{systematic component} is
- \begin{equation*}
- \mu_i \;= \; x_i \beta,
- \end{equation*}
- where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations
- from the the stochastic component, $$E(Y) = \mu_i = x_i \beta,$$
- given a draw of $\beta$ from its posterior.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from the distribution
- defined by the set of parameters $(\mu_i, \sigma)$.
-
-\item The first difference ({\tt qi\$fd}) is:
-\begin{equation*}
-\textrm{FD}\; = \;E(Y \mid x_1) - E(Y \mid x)
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "normal", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: fitted values. For the normal model,
- these are identical to the {\tt linear predictors}.
- \item {\tt linear.predictors}: fitted values. For the normal
- model, these are identical to {\tt fitted.values}.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by $(\mu_i, \sigma)$.
- \item {\tt qi\$fd}: the simulated first difference in the simulated
- expected values for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The Normal model is part of the stats package by William N. Venables
-and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Advanced users may wish to refer to
-\texttt{help(glm)}, \texttt{help(family)}, and \cite{McCNel89}.
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/models/ologit.tex b/inst/doc/models/ologit.tex
deleted file mode 100644
index 588e5b9..0000000
--- a/inst/doc/models/ologit.tex
+++ /dev/null
@@ -1,244 +0,0 @@
-\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
-Categorical Dependent Variables}\label{ologit}
-
-Use the ordinal logit regression model if your dependent variable is
-ordered and categorical, either in the form of integer values or character strings.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-If {\tt Y} takes discrete integer values, the {\tt as.factor()}
-command will order automatically order the values. If {\tt Y} takes
-on values composed of character strings, such as ``strongly agree'',
-``agree'', and ``disagree'', {\tt as.factor()} will order the values
-in the order in which they appear in {\tt Y}. You will need to
-replace your dependent variable with a factored variable prior to
-estimating the model through {\tt zelig()}. See Section \ref{factors}
-for more information on creating ordered factors and Example
-\ref{ord.fact} below.
-
-\subsubsection{Example}
-
-\begin{enumerate}
-
-\item {Creating An Ordered Dependent Variable} \label{ord.fact}
-
-Load the sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Create an ordered dependent variable:
-\begin{verbatim}
-> sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
- levels = c("net gain", "little effect",
- "modest loss", "major loss"))
-\end{verbatim}
-Estimate the model:
-\begin{verbatim}
-> z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
-\end{verbatim}
-Set the explanatory variables to their observed values:
-\begin{verbatim}
-> x.out <- setx(z.out, fn = NULL)
-\end{verbatim}
-Simulate fitted values given {\tt x.out} and view the results:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-\end{verbatim}
-
-\item {First Differences}
-
-Load the sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Estimate the empirical model and returning the coefficients:
-\begin{verbatim}
-> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
- data = sanction)
-summary(z.out)
-\end{verbatim}
-Set the explanatory variables to their means, with {\tt mil} set
-to 0 (no military action in addition to sanctions) in the baseline
-case and set to 1 (military action in addition to sanctions) in the
-alternative case:
-\begin{verbatim}
-> x.low <- setx(z.out, mil = 0)
-> x.high <- setx(z.out, mil = 1)
-\end{verbatim}
-Generate simulated fitted values and first differences, and view the results:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-> summary(s.out)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be the ordered categorical dependent variable for
-observation $i$ that takes one of the integer values from $1$ to $J$
-where $J$ is the total number of categories.
-
-\begin{itemize}
-\item The \emph{stochastic component} begins with an unobserved continuous
- variable, $Y^*_i$, which follows the standard logistic distribution
- with a parameter $\mu_i$,
- \begin{equation*}
- Y_i^* \; \sim \; \textrm{Logit}(y_i^* \mid \mu_i),
- \end{equation*}
- to which we add an observation mechanism
- \begin{equation*}
- Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
- \quad {\rm for} \quad j=1,\dots,J.
- \end{equation*}
- where $\tau_l$ (for $l=0,\dots,J$) are the threshold parameters with
- $\tau_l < \tau_m$ for all $l<m$ and $\tau_0=-\infty$ and
- $\tau_J=\infty$.
-
-\item The \emph{systematic component} has the following form, given
- the parameters $\tau_j$ and $\beta$, and the explanatory variables $x_i$:
- \begin{equation*}
- \Pr(Y \le j) \; = \; \Pr(Y^* \le \tau_j) \; = \frac{\exp(\tau_j -
- x_i \beta)}{1+\exp(\tau_j -x_i \beta)},
- \end{equation*}
- which implies:
- \begin{equation*}
- \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}{1 + \exp(\tau_j -
- x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
- \exp(\tau_{j-1} - x_i \beta)}.
- \end{equation*}
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the ordinal logit model
- are simulations of the predicted probabilities for each category:
-\begin{equation*}
-E(Y = j) \; = \; \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}
-{1 + \exp(\tau_j - x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
- \exp(\tau_{j-1} - x_i \beta)},
-\end{equation*}
-given a draw of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from the logit
- distribution described by $\mu_i$, and observed as one of $J$
- discrete outcomes.
-
-\item The difference in each of the predicted probabilities ({\tt
- qi\$fd}) is given by
- \begin{equation*}
- \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
- j=1,\dots,J.
- \end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "ologit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt zeta}: a vector containing the estimated class
- boundaries $\tau_j$.
- \item {\tt deviance}: the residual deviance.
- \item {\tt fitted.values}: the $n \times J$ matrix of in-sample
- fitted values.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt edf}: the effective degrees of freedom.
- \item {\tt Hessian}: the Hessian matrix.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, and $t$-statistics.
- \end{itemize}
-
- \item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The ordinal logit model is part of the MASS library by William N.
-Venables and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, advanced users may wish to refer to
-{\tt help(polr)} in the MASS libary and \cite{McCNel89}.
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
diff --git a/inst/doc/models/oprobit.bayes.tex b/inst/doc/models/oprobit.bayes.tex
deleted file mode 100644
index c2b0657..0000000
--- a/inst/doc/models/oprobit.bayes.tex
+++ /dev/null
@@ -1,273 +0,0 @@
-\section{\texttt{oprobit.bayes}: Bayesian Ordered Probit Regression}
-
-\label{oprobit.bayes}
-
-Use the ordinal probit regression model if your dependent variables are ordered and
-categorical. They may take either integer values or character strings. The model
-is estimated using a Gibbs sampler with data augmentation. For a
-maximum-likelihood implementation of this models, see \Sref{oprobit}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "oprobit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-
-\subsubsection{Additional Inputs}
-
-{\tt zelig()} accepts the following arguments to monitor the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item{\texttt{tune}}: tuning parameter for the Metropolis-Hasting step.
-The default value is \texttt{NA} which corresponds to 0.05 divided by
-the number of categories in the response variable.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, which uses the
-maximum likelihood estimates as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will be the prior
-mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with dimensions equal to the number of
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0 which leads to an improper prior.
-\end{itemize}
-
-\noindent Zelig users may wish to refer to \texttt{help(MCMCoprobit)}
-for more information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Estimating ordered probit regression using \texttt{oprobit.bayes}:
-\begin{verbatim}
-> z.out <- zelig(ncost ~ mil + coop, model = "oprobit.bayes",
- data = sanction, verbose=TRUE)
-\end{verbatim}
-
-Creating an ordered dependent variable:
-\begin{verbatim}
-sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
- levels = c("net gain", "little effect",
- "modest loss", "major loss"))
-\end{verbatim}
-
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-Setting values for the explanatory variables to their sample averages:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution given:
-\texttt{x.out}.
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{verbatim}
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in the probabilities of
-incurring different level of cost when there is no military action versus
-military action while all the other variables held at their
-default values.
-
-\begin{verbatim}
-> x.high <- setx(z.out, mil=0)
-> x.low <- setx(z.out, mil=1)
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_{i}$ be the ordered categorical dependent variable for
-observation $i$ which takes an integer value $j=1, \ldots, J$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by an unobserved
-continuous variable, $Y_i^*$,
-\begin{eqnarray*}
-Y_{i}^* \sim \textrm{Normal}(\mu_i, 1).
-\end{eqnarray*}
-Instead of $Y_i^*$, we observe categorical variable $Y_i$,
-\begin{eqnarray*}
-Y_i = j \quad \textrm{ if } \tau_{j-1} \le Y_i^* \le \tau_j \textrm{
-for } j=1,\ldots, J.
-\end{eqnarray*}
-where $\tau_j$ for $j=0,\ldots, J$ are the threshold parameters with
-the following constraints, $\tau_l < \tau_m$ for $l < m$, and
-$\tau_0=-\infty, \tau_J=\infty$.
-
-The probability of observing $Y_i$ equal to category $j$ is,
-\begin{eqnarray*}
-\Pr(Y_i=j) &=& \Phi(\tau_j \mid \mu_i)-\Phi(\tau_{j-1} \mid \mu_i)
-\textrm{ for } j=1,\ldots, J
-\end{eqnarray*}
-where $\Phi(\cdot \mid \mu_i)$ is the cumulative distribution function
-of the Normal distribution with mean $\mu_i$ and variance 1.
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\mu_{i}= x_i \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the ordered probit model are
-the predicted probability of belonging to each category:
-\begin{eqnarray*}
-\Pr(Y_i=j)= \Phi(\tau_j \mid x_i \beta)-\Phi(\tau_{j-1} \mid x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ and threshold parameters $\tau$
-from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are the observed values of
-$Y_i$ given the observation scheme and the posterior draws of $\beta$
-and cut points $\tau$ from the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) in category $j$ for the
-ordered probit model is defined as
-\begin{eqnarray*}
-\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
-\begin{eqnarray*}
-\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j} \{
-Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of observations in the treatment group that belong to category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of observations in the treatment group that belong to category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "oprobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior
-distribution of the \texttt{coefficients} by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated coefficients $\beta$ and threshold parameters $\tau$.
-Note, element $\tau_1$ is normalized to 0 and is not returned in the
-\texttt{coefficients} object.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values (probabilities) of
-each of the $J$ categories for the specified values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values (observed values)
- for the specified values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values of each of the $J$ categories for the values specified in
-\texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the
-expected values of each of the $J$ categories simulated
-from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian ordinal probit regression \input{models/contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{oprobit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/models/oprobit.tex b/inst/doc/models/oprobit.tex
deleted file mode 100644
index e654e40..0000000
--- a/inst/doc/models/oprobit.tex
+++ /dev/null
@@ -1,252 +0,0 @@
-\section{{\tt oprobit}: Ordinal Probit Regression for Ordered
-Categorical Dependent Variables}\label{oprobit}
-
-Use the ordinal probit regression model if your dependent variables
-are ordered and categorical. They may take on either integer values
-or character strings. For a Bayesian implementation of this model,
-see \Sref{oprobit.bayes}.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "oprobit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-If {\tt Y} takes discrete integer values, the {\tt as.factor()}
-command will order it automatically. If {\tt Y} takes on values
-composed of character strings, such as ``strongly agree'', ``agree'',
-and ``disagree'', {\tt as.factor()} will order the values in the order
-in which they appear in {\tt Y}. You will need to replace your
-dependent variable with a factored variable prior to estimating the
-model through {\tt zelig()}. See \Sref{factors} for more information
-on creating ordered factors and Example \ref{ord.fact.p} below.
-
-\subsubsection{Example}
-\begin{enumerate}
-\item {Creating An Ordered Dependent Variable} \label{ord.fact.p}
-
-Load the sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Create an ordered dependent variable:
-\begin{verbatim}
-> sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
- levels = c("net gain", "little effect",
- "modest loss", "major loss"))
-\end{verbatim}
-Estimate the model:
-\begin{verbatim}
-> z.out <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanction)
-\end{verbatim}
-Set the explanatory variables to their observed values:
-\begin{verbatim}
-> x.out <- setx(z.out, fn = NULL)
-\end{verbatim}
-Simulate fitted values given {\tt x.out} and view the results:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-
-\item {First Differences}
-
-Load the sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Estimate the empirical model and returning the coefficients:
-\begin{verbatim}
-> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "oprobit",
- data = sanction)
-> summary(z.out)
-\end{verbatim}
-Set the explanatory variables to their means, with {\tt mil} set
-to 0 (no military action in addition to sanctions) in the baseline
-case and set to 1 (military action in addition to sanctions) in the
-alternative case:
-\begin{verbatim}
-> x.low <- setx(z.out, mil = 0)
-> x.high <- setx(z.out, mil = 1)
-\end{verbatim}
-Generate simulated fitted values and first differences, and view the results:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
- Let $Y_i$ be the ordered categorical dependent variable for
- observation $i$ that takes one of the integer values from $1$ to $J$
- where $J$ is the total number of categories.
-\begin{itemize}
-\item The \emph{stochastic component} is described by an unobserved continuous
- variable, $Y^*_i$, which follows the normal distribution with mean
- $\mu_i$ and unit variance
- \begin{equation*}
- Y_i^* \; \sim \; N(\mu_i, 1).
- \end{equation*}
- The observation mechanism is
- \begin{equation*}
- Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
- \quad {\rm for} \quad j=1,\dots,J.
- \end{equation*}
- where $\tau_k$ for $k=0,\dots,J$ is the threshold parameter with the
- following constraints; $\tau_l < \tau_m$ for all $l<m$ and
- $\tau_0=-\infty$ and $\tau_J=\infty$.
-
- Given this observation mechanism, the probability for each category,
- is given by
- \begin{equation*}
- \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i) - \Phi(\tau_{j-1} \mid
- \mu_i) \quad {\rm for} \quad j=1,\dots,J
- \end{equation*}
- where $\Phi(\mu_i)$ is the cumulative distribution function for the
- Normal distribution with mean $\mu_i$ and unit variance.
-
-\item The \emph{systematic component} is given by
- \begin{equation*}
- \mu_i \; = \; x_i \beta
- \end{equation*}
- where $x_i$ is the vector of explanatory variables and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the ordinal probit model
- are simulations of the predicted probabilities for each category:
-\begin{equation*}
- E(Y_i = j) \; = \; \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i)
- - \Phi(\tau_{j-1} \mid \mu_i) \quad {\rm for} \quad j=1,\dots,J,
-\end{equation*}
-given draws of $\beta$ from its posterior.
-
-\item The predicted value ({\tt qi\$pr}) is the observed value of
- $Y_i$ given the underlying standard normal distribution described by
- $\mu_i$.
-
-\item The difference in each of the predicted probabilities ({\tt
- qi\$fd}) is given by
- \begin{equation*}
- \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
- j=1,\dots,J.
- \end{equation*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "oprobit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
- predictors $x_i \beta_j$.
- \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times J$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
-\end{itemize}
-
- \item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The ordinal probit function is part of the VGAM package by Thomas Yee.
-Please cite the model as:
-\begin{verse}
-\bibentry{YeeHas03}.
-\end{verse}
-
-In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM library. Additional
-documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
diff --git a/inst/doc/models/poisson.bayes.tex b/inst/doc/models/poisson.bayes.tex
deleted file mode 100644
index 010f873..0000000
--- a/inst/doc/models/poisson.bayes.tex
+++ /dev/null
@@ -1,230 +0,0 @@
-\section{\texttt{poisson.bayes}: Bayesian Poisson Regression}
-
-\label{poisson.bayes}
-
-Use the Poisson regression model if the observations of your dependent
-variable represents the number of independent events that occur during
-a fixed period of time. The model is fit using a random walk
-Metropolis algorithm. For a maximum-likelihood estimation of this
-model see \Sref{poisson}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "poisson.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following argument to monitor the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: Metropolis tuning parameter, either
-a positive scalar or a vector of length $k$, where $k$ is the number
-of coefficients. The tuning parameter should be set such that the
-acceptance rate of the Metropolis algorithm is satisfactory (typically
-between 0.20 and 0.5). The default value is 1.1.
-
-\item \texttt{verbose}: default to {\tt FALSE}.
-If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
-printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the
-maximum likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will be the prior mean for
-all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCpoisson)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Estimating the Poisson regression using \texttt{poisson.bayes}:
-\begin{verbatim}
-> z.out <- zelig(num ~ target + coop, model = "poisson.bayes",
- data = sanction, verbose = TRUE)
-\end{verbatim}
-Checking convergence diagnostics before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-Setting values for the explanatory variables to their sample averages:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{verbatim}
-\item {Simulating First Differences} \\
-Estimating the first difference in the number of countries imposing sanctions
-when the number of targets is set to be its maximum versus its minimum :
-\begin{verbatim}
-> x.max <- setx(z.out, target = max(sanction$target))
-> x.min <- setx(z.out, target = min(sanction$target))
-> s.out2 <- sim(z.out, x = x.max, x1 = x.min)
-> summary(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the number of independent events that occur during
-a fixed time period.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \textrm{Poisson}(\lambda_i)
-\end{eqnarray*}
-where $\lambda_i$ is the mean and variance parameter.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\lambda_{i}= \exp(x_{i} \beta)
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the Poisson model are
-calculated as following:
-\begin{eqnarray*}
-E(Y\mid X) = \lambda_i = \exp(x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ based on the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Poisson
-distribution with parameter $\lambda_i$.
-
-\item The first difference (\texttt{qi\$fd}) for the Poisson model is defined
-as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}\{Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]\},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "poisson.bayes", data)
-\end{verbatim}
-
-\noindent you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian Poisson regression \input{models/contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{poisson.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/models/poisson.tex b/inst/doc/models/poisson.tex
deleted file mode 100644
index 65456f4..0000000
--- a/inst/doc/models/poisson.tex
+++ /dev/null
@@ -1,219 +0,0 @@
-\section{{\tt poisson}: Poisson Regression for Event Count
-Dependent Variables}\label{poisson}
-
-Use the Poisson regression model if the observations of your dependent
-variable represents the number of independent events that occur during
-a fixed period of time (see the negative binomial model, \Sref{negbin},
-for over-dispersed event counts.) For a Bayesian implementation of
-this model, see \Sref{poisson.bayes}.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "poisson", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for poisson regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Example}
-
-Load sample data:
-\begin{verbatim}
-> data(sanction)
-\end{verbatim}
-Estimate Poisson model:
-\begin{verbatim}
-> z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction)
-> summary(z.out)
-\end{verbatim}
-Set values for the explanatory variables to their default mean values:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulate fitted values:
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-
-\subsubsection{Model}
-Let $Y_i$ be the number of independent events that occur during a
-fixed time period. This variable can take any non-negative integer.
-
-\begin{itemize}
-\item The Poisson distribution has \emph{stochastic component}
- \begin{equation*}
- Y_i \; \sim \; \textrm{Poisson}(\lambda_i),
- \end{equation*}
- where $\lambda_i$ is the mean and variance parameter.
-
-\item The \emph{systematic component} is
- \begin{equation*}
- \lambda_i \; = \; \exp(x_i \beta),
- \end{equation*}
- where $x_i$ is the vector of explanatory variables, and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-
-\item The expected value ({\tt qi\$ev}) is the mean of simulations
- from the stochastic component, $$E(Y) = \lambda_i = \exp(x_i
- \beta),$$ given draws of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is a random draw from the
- poisson distribution defined by mean $\lambda_i$.
-
-\item The first difference in the expected values ({\tt qi\$fd}) is given by:
-\begin{equation*}
-\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
-\end{equation*}
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "poisson", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: a vector of the fitted values for the systemic
- component $\lambda$.
- \item {\tt linear.predictors}: a vector of $x_{i}\beta$.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values given the
- specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distributions defined by $\lambda_i$.
- \item {\tt qi\$fd}: the simulated first differences in the expected
- values given the specified values of {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The Poisson model is part of the stats package by William N. Venables
-and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Advanced users may wish to refer to {\tt help(glm)} and {\tt
- help(family)}, as well as \cite{McCNel89}.
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/models/probit.bayes.tex b/inst/doc/models/probit.bayes.tex
deleted file mode 100644
index 7b49344..0000000
--- a/inst/doc/models/probit.bayes.tex
+++ /dev/null
@@ -1,253 +0,0 @@
-\section{\texttt{probit.bayes}: Bayesian Probit Regression}
-
-\label{probit.bayes}
-
-Use the probit regression model for model binary dependent variables
-specified as a function of a set of explanatory variables. The model
-is estimated using a Gibbs sampler. For other models suitable for
-binary response variables, see Bayesian logistic
-regression(\Sref{logit.bayes}), maximum likelihood logit regression
-(\Sref{logit}), and maximum likelihood probit regression
-(\Sref{probit}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "probit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Using the following arguments to monitor the Markov chains:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the
-maximum likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will be the prior
-mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0, which leads to an improper prior.
-\end{itemize}
-
-Use the following arguments to specify optional output for the model:
-\begin{itemize}
-\item \texttt{bayes.resid}: defaults to {\tt FALSE}. If {\tt TRUE},
-the latent Bayesian residuals for all observations are returned.
-Alternatively, users can specify a vector of observations for which the
-latent residuals should be returned.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCprobit)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(turnout)
-\end{verbatim}
-Estimating the probit regression using \texttt{probit.bayes}:
-\begin{verbatim}
-> z.out < - zelig(vote ~ race + educate, model = "probit.bayes",
- data = turnout, verbose = TRUE)
-\end{verbatim}
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-Setting values for the explanatory variables to their sample averages:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution given:
-\texttt{x.out}.
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{verbatim}
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in individual's probability of
-voting when education is set to be low (25th percentile) versus
-high (75th percentile) while all the other variables are held at their
-default values:
-\begin{verbatim}
-> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
-> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the binary dependent variable for observation $i$ which
-takes the value of either 0 or 1.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \textrm{Bernoulli}(\pi_{i})\\
-& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
-\end{eqnarray*}
-where $\pi_{i}=\Pr(Y_{i}=1)$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{i}= \Phi(x_i \beta),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-Normal distribution with mean 0 and variance 1, $x_{i}$ is the vector
-of $k$ explanatory variables for observation $i$, and $\beta$ is the
-vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0}, B_{0}^{-1} \right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the probit model are
-the predicted probability of a success:
-\begin{eqnarray*}
-E(Y \mid X) = \pi_{i}= \Phi(x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
-distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference (\texttt{qi\$fd}) for the probit model is defined
-as
-\begin{eqnarray*}
-\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr})is defined as
-\begin{eqnarray*}
-\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "probit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{bayes.residuals}: When \texttt{bayes.residual} is \texttt{TRUE}
-or a set of observation numbers is given, this object contains the
-posterior draws of the latent Bayesian residuals of all the observations
-or the observations specified by the user.
-
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values (probabilities) for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
-simulated from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Bayesian probit regression \input{models/contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{probit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/probit.tex b/inst/doc/models/probit.tex
deleted file mode 100644
index 701d78c..0000000
--- a/inst/doc/models/probit.tex
+++ /dev/null
@@ -1,220 +0,0 @@
-\section{{\tt probit}: Probit Regression for Dichotomous Dependent Variables}\label{probit}
-
-Use probit regression to model binary dependent variables
-specified as a function of a set of explanatory variables. For a
-Bayesian implementation of this model, see \Sref{probit.bayes}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "probit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out, x1 = NULL)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for probit regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}
-Attach the sample turnout dataset:
-\begin{verbatim}
-> data(turnout)
-\end{verbatim}
-Estimate parameter values for the probit regression:
-\begin{verbatim}
-> z.out <- zelig(vote ~ race + educate, model = "probit", data = turnout)
-> summary(z.out)
-\end{verbatim}
-Set values for the explanatory variables to their default values.
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulate quantities of interest from the posterior distribution.
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-\end{verbatim}
-
-\subsubsection{Model}
-Let $Y_i$ be the observed binary dependent variable for observation
-$i$ which takes the value of either 0 or 1.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{equation*}
-Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
-\end{equation*}
-where $\pi_i=\Pr(Y_i=1)$.
-
-\item The \emph{systematic component} is
-\begin{equation*}
- \pi_i \; = \; \Phi (x_i \beta)
-\end{equation*}
-where $\Phi(\mu)$ is the cumulative distribution function of the
-Normal distribution with mean 0 and unit variance.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-
-\item The expected value ({\tt qi\$ev}) is a simulation of predicted
- probability of success $$E(Y) = \pi_i = \Phi(x_i
- \beta),$$ given a draw of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is a draw from a Bernoulli
- distribution with mean $\pi_i$.
-
-\item The first difference ({\tt qi\$fd}) in expected values is
- defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) / \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "probit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: a vector of the in-sample fitted values.
- \item {\tt linear.predictors}: a vector of $x_{i}\beta$.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt data}: the name of the input data frame.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values, or predicted
- probabilities, for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distributions defined by the predicted probabilities.
- \item {\tt qi\$fd}: the simulated first differences in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the predicted
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The probit model is part of the base package by William N. Venables
-and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, 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 Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}.
-\end{verse}
-
-Sample data are a selection of $2,000$ observations from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/models/relogit.tex b/inst/doc/models/relogit.tex
deleted file mode 100644
index 6149389..0000000
--- a/inst/doc/models/relogit.tex
+++ /dev/null
@@ -1,398 +0,0 @@
-\section{{\tt relogit}: Rare Events Logistic Regression for
-Dichotomous Dependent Variables}
-\label{relogit}
-
-The {\tt relogit} procedure estimates the same model as standard
-logistic regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables; see \Sref{logit}), but
-the estimates are corrected for the bias that occurs when the
-sample is small or the observed events are rare (i.e., if the
-dependent variable has many more 1s than 0s or the reverse). The {\tt
- relogit} procedure also optionally uses prior correction for
-case-control sampling designs.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "relogit", tau = NULL,
- case.correct = c("prior", "weighting"),
- bias.correct = TRUE, robust = FALSE,
- data = mydata, ...)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-The {\tt relogit} procedure supports four optional arguments in
-addition to the standard arguments for {\tt zelig()}. You may
-additionally use:
-\begin{itemize}
-\item {\tt tau}: a vector containing either one or two values for
- $\tau$, the true population fraction of ones. Use, for example,
- {\tt tau = c(0.05, 0.1)} to specify that the lower bound on {\tt
- tau} is 0.05 and the upper bound is 0.1. If left unspecified, only
-finite-sample bias correction is performed, not case-control correction.
-\item {\tt case.correct}: if {\tt tau} is specified, choose a method
-to correct for case-control sampling design: {\tt "prior"} (default)
-or {\tt "weighting"}.
-\item {\tt bias.correct}: a logical value of {\tt TRUE} (default) or
- {\tt FALSE} indicating whether the intercept should be corrected for
- finite sample (rare events) bias.
-\item {\tt robust}: defaults to {\tt FALSE} (except when {\tt
-case.control = "weighting"}; the default in this case becomes {\tt
-robust = TRUE}). If {\tt TRUE} is selected, {\tt zelig()} computes
-robust standard errors via the {\tt sandwich} package (see
-\cite{Zeileis04}). The default type of robust standard error is
-heteroskedastic and autocorrelation consistent (HAC), and assumes that
-observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame) The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-Note that if {\tt tau = NULL, bias.correct = FALSE, robust = FALSE},
-the {\tt relogit} procedure performs a standard logistic regression
-without any correction.
-
-\subsubsection*{Example 1: One Tau with Prior Correction and Bias Correction}
-
-Due to memory and space considerations, the data used here are a
-sample drawn from the full data set used in King and Zeng,
-2001,\nocite{KinZen01b} The proportion of militarized interstate
-conflicts to the absence of disputes is $\tau = 1,042 / 303,772
-\approx 0.00343$. To estimate the model,
-\begin{verbatim}
-> data(mid)
-> z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
- data = mid, model = "relogit", tau = 1042/303772)
-\end{verbatim}
-Summarize the model output:
-\begin{verbatim}
-> summary(z.out1)
-\end{verbatim}
-Set the explanatory variables to their means:
-\begin{verbatim}
-> x.out1 <- setx(z.out1)
-\end{verbatim}
-Simulate quantities of interest:
-\begin{verbatim}
-> s.out1 <- sim(z.out1, x = x.out1)
-> summary(s.out1)
-> plot(s.out1)
-\end{verbatim}
-
-\subsubsection*{Example 2: One Tau with Weighting, Robust Standard
-Errors, and Bias Correction}
-
-Suppose that we wish to perform case control correction using
-weighting (rather than the default prior correction). To
-estimate the model:
-\begin{verbatim}
-> data(mid)
-> z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
- data = mid, model = "relogit", tau = 1042/303772,
- case.control = "weighting", robust = TRUE)
-\end{verbatim}
-Summarize the model output:
-\begin{verbatim}
-> summary(z.out2)
-\end{verbatim}
-Set the explanatory variables to their means:
-\begin{verbatim}
-> x.out2 <- setx(z.out2)
-\end{verbatim}
-Simulate quantities of interest:
-\begin{verbatim}
-> s.out2 <- sim(z.out2, x = x.out2)
-> summary(s.out2)
-\end{verbatim}
-
-\subsubsection*{Example 3: Two Taus with Bias Correction and Prior Correction}
-
-Suppose that we did not know that $\tau \approx 0.00343$, but only
-that it was somewhere between $(0.002, 0.005)$. To estimate a model
-with a range of feasible estimates for $\tau$ (using the default prior
-correction method for case control correction):
-\begin{verbatim}
-> data(mid)
-> z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem
- + years, data = mid, model = "relogit",
- tau = c(0.002, 0.005))
-\end{verbatim}
-Summarize the model output:
-\begin{verbatim}
-> summary(z.out2)
-\end{verbatim}
-Set the explanatory variables to their means:
-\begin{verbatim}
-> x.out2 <- setx(z.out2)
-\end{verbatim}
-Simulate quantities of interest:
-\begin{verbatim}
-> s.out <- sim(z.out2, x = x.out2)
-> summary(s.out2)
-> plot(s.out2)
-\end{verbatim}
-The cost of giving a range of values for $\tau$ is that point
-estimates are not available for quantities of interest. Instead,
-quantities are presented as confidence intervals with significance
-less than or equal to a specified level (e.g., at least 95\% of the
-simulations are contained in the nominal 95\% confidence interval).
-
-\subsubsection{Model}
-
-\begin{itemize}
-\item Like the standard logistic regression, the \emph{stochastic
- component} for the rare events logistic regression is:
-\begin{equation*}
- Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
-\end{equation*}
-where $Y_i$ is the binary dependent variable, and takes a value of
-either 0 or 1.
-
-\item The \emph{systematic component} is:
- \begin{equation*}
- \pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
- \end{equation*}
-
-
-\item If the sample is generated via a case-control (or choice-based)
- design, such as when drawing all events (or ``cases'') and a sample
- from the non-events (or ``controls'') and going backwards to collect
- the explanatory variables, you must correct for selecting on the
- dependent variable. While the slope coefficients are approximately
- unbiased, the constant term may be significantly biased. Zelig has
-two methods for case control correction:
-\begin{enumerate}
-\item The ``prior correction'' method
-adjusts the intercept term. Let $\tau$ be the true population
-fraction of events, $\bar{y}$ the fraction of events in the sample,
-and $\hat{\beta_0}$ the uncorrected intercept term. The corrected
-intercept $\beta_0$ is:
-\begin{equation*}
-\beta = \hat{\beta_0} - \ln \left[ \bigg( \frac{1 - \tau}{\tau}
- \bigg) \bigg( \frac{\bar{y}}{1 - \bar{y}} \bigg) \right].
-\end{equation*}
-
-\item The ``weighting'' method performs a weighted logistic regression to
-correct for a case-control sampling design. Let the 1 subscript
-denote observations for which the dependent variable is observed as a
-1, and the 0 subscript denote observations for which the dependent
-variable is observed as a 0. Then the vector of weights $w_i$
-\begin{eqnarray*}
-w_1 &=& \frac{\tau}{\bar{y}} \\
-w_0 &=& \frac{(1 - \tau)}{(1 - \bar{y})} \\
-w_i &=& w_1 Y_i + w_0 (1 - Y_i)
-\end{eqnarray*}
-\end{enumerate}
- If $\tau$ is unknown, you may alternatively specify an upper and
- lower bound for the possible range of $\tau$. In this case, the
- {\tt relogit} procedure uses ``robust Bayesian'' methods to generate
- a confidence interval (rather than a point estimate) for each
- quantity of interest. The nominal coverage of the confidence
- interval is at least as great as the actual coverage.
-
-\item By default, estimates of the the coefficients $\beta$ are
- bias-corrected to account for finite sample or rare events bias. In
- addition, quantities of interest, such as predicted probabilities,
- are also corrected of rare-events bias. If $\widehat{\beta}$ are
-the uncorrected logit coefficients and bias($\widehat{\beta}$) is the
-bias term, the corrected coefficients $\tilde{\beta}$ are
-\begin{equation*}
-\widehat{\beta} - \textrm{bias}(\widehat{\beta}) = \tilde{\beta}
-\end{equation*}
-The bias term is
-\begin{equation*}
-\textrm{bias}(\widehat{\beta}) = (X'WX)^{-1} X'W \xi
-\end{equation*}
-where
-\begin{eqnarray*}
-\xi_i &=& 0.5 Q_{ii} \Big( (1 + w-1)\widehat{\pi}_i - w_1 \Big) \\
-Q &=& X(X'WX)^{-1} X' \\
-W = \textrm{diag}\{\widehat{\pi}_i (1 - \widehat{\pi}_i) w_i\}
-\end{eqnarray*}
-where $w_i$ and $w_1$ are given in the ``weighting'' section above.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item For either one or no $\tau$:
- \begin{itemize}
- \item The expected values ({\tt qi\$ev}) for the rare events logit
- are simulations of the predicted probability $$E(Y) = \pi_i =
- \frac{1}{1 + \exp(-x_i \beta)},$$
- given draws of $\beta$ from its posterior.
- \item The predicted value ({\tt qi\$pr}) is a draw from a binomial
- distribution with mean equal to the simulated $\pi_i$.
- \item The first difference ({\tt qi\$fd}) is defined as
- \begin{equation*}
- \textrm{FD} = \Pr(Y = 1 \mid x_1, \tau) - \Pr(Y = 1 \mid x, \tau).
- \end{equation*}
- \item The risk ratio ({\tt qi\$rr}) is defined as
- \begin{equation*}
- \textrm{RR} = \Pr(Y = 1 \mid x_1, \tau) \ / \ \Pr(Y = 1 \mid x, \tau).
- \end{equation*}
- \end{itemize}
- \item For a range of $\tau$ defined by $[\tau_1, \tau_2]$, each of
- the quantities of interest are $n \times 2$ matrices, which report
- the lower and upper bounds, respectively, for a confidence interval
- with nominal coverage at least as great as the actual coverage. At
- worst, these bounds are conservative estimates for the likely range
- for each quantity of interest. Please refer to \hlink{King and
- Zeng
- (2002)}{http://gking.harvard.edu/files/1s.pdf}\nocite{KinZen02b}
- for the specific method of calculating bounded quantities of
- interest.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "relogit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt bias.correct}: {\tt TRUE} if bias correction was
-selected, else {\tt FALSE}.
- \item {\tt prior.correct}: {\tt TRUE} if prior correction was
-selected, else {\tt FALSE}.
- \item {\tt weighting}: {\tt TRUE} if weighting was selected, else
-{\tt FALSE}.
- \item {\tt tau}: the value of {\tt tau} for which case control
-correction was implemented.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_i$.
- \item {\tt linear.predictors}: the vector of $x_{i} \beta$
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
- Note that for a range of $\tau$, each of the above items may be
- extracted from the {\tt "lower.estimate"} and {\tt
- "upper.estimate"} objects in your {\tt zelig} output. Use {\tt
- lower <- z.out\$lower.estimate}, and then {\tt
- lower\$coefficients} to extract the coefficients for the
- empirical estimate generated for the smaller of the two $\tau$.
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values, or predicted
- probabilities, for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from Binomial
- distributions given the predicted probabilities.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the predicted
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Differences with Stata Version}
-The Stata version of ReLogit and the R implementation differ slightly
-in their coefficient estimates due to differences in the matrix
-inversion routines implemented in R and Stata. Zelig uses
-orthogonal-triangular decomposition (through {\tt lm.influence()}) to
-compute the bias term, which is more numerically stable than
-standard matrix calculations.
-
-\subsubsection{Contributors}
-
-Please cite the rare events logit model as:
-\begin{verse}
-\bibentry{KinZen01b}.
-\end{verse}
-\begin{verse}
-\bibentry{KinZen01}.
-\end{verse}
-\begin{verse}
-\bibentry{KinZen02b}.
-\end{verse}
-
-Sample data are a selection of observations from
-\begin{verse}
-\bibentry{KinZen01b}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/models/tobit.bayes.tex b/inst/doc/models/tobit.bayes.tex
deleted file mode 100644
index 282c722..0000000
--- a/inst/doc/models/tobit.bayes.tex
+++ /dev/null
@@ -1,262 +0,0 @@
-\section{\texttt{tobit.bayes}: Bayesian Linear Regression for a
-Censored Dependent Variable} \label{tobit.bayes}
-
-Bayesian tobit regression estimates a linear regression model with a
-censored dependent variable using a Gibbs sampler. The dependent
-variable may be censored from below and/or from above. For other
-linear regression models with fully observed dependent variables, see
-Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
-regression (\Sref{normal}), or least squares (\Sref{ls}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
- model = "tobit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments to specify how the
-dependent variable is censored.
-\begin{itemize}
-\item \texttt{below}: point at which the dependent variable is censored
-from below. If the dependent variable is only censored from above, set
-\texttt{below = -Inf}. The default value is 0.
-\item \texttt{above}: point at which the dependent variable is censored
-from above. If the dependent variable is only censored from below, set
-\texttt{above = Inf}. The default value is \texttt{Inf}.
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the convergence of the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, such that the
-least squares estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will be the prior mean for
-all coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCtobit)} for more
-information.
-
-\input{models/coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(tobin)
-\end{verbatim}
-Estimating linear regression using \texttt{tobit.bayes}:
-\begin{verbatim}
-> z.out < - zelig(durable ~ age + quant, model = "tobit.bayes",
- data = tobin, verbose=TRUE)
-\end{verbatim}
-Checking for convergence before summarizing the estimates:
-\begin{verbatim}
-> geweke.diag(z.out$coefficients)
-> heidel.diag(z.out$coefficients)
-> raftery.diag(z.out$coefficients)
-> summary(z.out)
-\end{verbatim} %$
-Setting values for the explanatory variables to their sample averages:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{verbatim}
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
-
-\begin{verbatim}
-> x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
-> x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
-\end{verbatim}
-Estimating the first difference for the effect of
-high versus low liquidity ratio on duration(\texttt{durable}):
-\begin{verbatim}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i^*$ be the dependent variable which is not directly observed. Instead,
-we observe $Y_i$ which is defined as following:
-\begin{equation*}
-Y_i = \left\{
-\begin{array}{lcl}
-Y_i^* &\textrm{if} & c_1<Y_i^*<c_2 \\
-c_1 &\textrm{if} & c_1 \ge Y_i^* \\
-c_2 &\textrm{if} & c_2 \le Y_i^*
-\end{array}\right.
-\end{equation*}
-where $c_1$ is the lower bound below which $Y_i^*$ is censored, and
-$c_2$ is the upper bound above which $Y_i^*$ is censored.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
-\end{eqnarray*}
-where $\epsilon_{i}=Y^*_i-\mu_i$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i}= x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-
-\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are
-given by
-\begin{eqnarray*}
-\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
-\sigma^{2} & \sim & \textrm{InverseGamma} \left( \frac{c_0}{2}, \frac{d_0}{2}
-\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory
-variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
-a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape
-and scale parameters for $\sigma^{2}$. Note that $\beta$ and
-$\sigma^2$ are assumed \emph{a priori} independent.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the tobit regression model is
-calculated as following. Let
-\begin{eqnarray*}
-\Phi_1 &=& \Phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
-\Phi_2 &=& \Phi\left(\frac{(c_2 - x \beta)}{\sigma}\right) \\
-\phi_1 &=& \phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
-\phi_2 &=& \phi\left(\frac{(c_2 - x \beta)}{\sigma}\right)
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the (cumulative) Normal density function and
-$\phi(\cdot)$ is the Normal probability density function of the
-standard normal distribution. Then the expected values are
-\begin{eqnarray*}
-E(Y|x) &=& P(Y^* \le c_1|x) c_1+P(c_1<Y^*<c_2|x) E(Y^* \mid c_1<Y^*<c_2, x)+P(Y^* \ge c_2) c_2 \\
- &=& \Phi_{1}c_1 + x \beta(\Phi_{2}-\Phi_{1}) + \sigma (\phi_1 -\phi_2) + (1-\Phi_2) c_2,
-\end{eqnarray*}
-
-\item The first difference (\texttt{qi\$fd}) for the tobit regression
-model is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid x_{1})-E(Y\mid x).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "tobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected value for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values given the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian tobit regression \input{models/contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{tobit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-%\end{document}
diff --git a/inst/doc/models/tobit.tex b/inst/doc/models/tobit.tex
deleted file mode 100644
index 6893553..0000000
--- a/inst/doc/models/tobit.tex
+++ /dev/null
@@ -1,208 +0,0 @@
-\section{\texttt{tobit}: Linear Regression for a
-Left-Censored Dependent Variable} \label{tobit}
-
-Tobit regression estimates a linear regression model for a
-left-censored dependent variable, where the dependent variable is
-censored from below. While the classical tobit model has values
-censored at 0, you may select another censoring point. For other
-linear regression models with fully observed dependent variables, see
-Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
-regression (\Sref{normal}), or least squares (\Sref{ls}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
- model = "tobit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments to specify how the
-dependent variable is censored.
-\begin{itemize}
-\item \texttt{below}: (defaults to 0) The point at which the dependent
-variable is censored from below. If any values in the dependent
-variable are observed to be less than the censoring point, it is
-assumed that that particular observation is censored from below at the
-observed value. (See \Sref{tobit.bayes} for a Bayesian
-implementation that supports both left and right censoring.)
- \item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) and the options selected in
-{\tt cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "tobit", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(survreg)} for more
-information.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{verbatim}
-> data(tobin)
-\end{verbatim}
-Estimating linear regression using \texttt{tobit}:
-\begin{verbatim}
-> z.out < - zelig(durable ~ age + quant, model = "tobit", data = tobin)
-\end{verbatim}
-Setting values for the explanatory variables to their sample averages:
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-\begin{verbatim}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{verbatim}
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
-
-\begin{verbatim}
-> x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
-> x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
-\end{verbatim}
-Estimating the first difference for the effect of
-high versus low liquidity ratio on duration(\texttt{durable}):
-\begin{verbatim}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-\end{verbatim}
-\end{enumerate}
-
-\subsubsection{Model}
-\begin{itemize}
-\item Let $Y_i^*$ be a latent dependent variable which is distributed with
-\emph{stochastic} component
-\begin{eqnarray*}
-Y_i^* & \sim & \textrm{Normal}(\mu_i, \sigma^2) \\
-\end{eqnarray*}
-where $\mu_i$ is a vector means and $\sigma^2$ is a scalar variance
-parameter. $Y_i^*$ is not directly observed, however. Rather we
-observed $Y_i$ which is defined as:
-\begin{equation*}
-Y_i = \left\{
-\begin{array}{lcl}
-Y_i^* &\textrm{if} & c <Y_i^* \\
-c &\textrm{if} & c \ge Y_i^*
-\end{array}\right.
-\end{equation*}
-where $c$ is the lower bound below which $Y_i^*$ is censored.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i} &=& x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the tobit regression
-model are the same as the expected value of $Y*$:
-\begin{equation*}
-E(Y^* | X) = \mu_{i} = x_{i} \beta
-\end{equation*}
-
-\item The first difference (\texttt{qi\$fd}) for the tobit regression
-model is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y^* \mid x_{1}) - E(Y^* \mid x).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[E[Y^*_{i}(t_{i}=1)]-E[Y^*_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "tobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected value for the specified
- values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
- values given the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The tobit function is part of the survival library by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library and
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-%\end{document}
diff --git a/inst/doc/models/weibull.tex b/inst/doc/models/weibull.tex
deleted file mode 100644
index 7b27063..0000000
--- a/inst/doc/models/weibull.tex
+++ /dev/null
@@ -1,267 +0,0 @@
-\section{{\tt weibull}: Weibull Regression for Duration
-Dependent Variables}\label{weibull}
-
-Choose the Weibull regression model if the values in your dependent
-variable are duration observations. The Weibull model relaxes the
-exponential model's (see \Sref{exp}) assumption of constant hazard,
-and allows the hazard rate to increase or decrease monotonically with
-respect to elapsed time.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X1 + X2, model = "weibull", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Weibull 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, time defaults to 1 for all observations.
-
-\subsubsection{Input Values}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for weibull regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
-cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "exp", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-\begin{verbatim}
-> data(coalition)
-\end{verbatim}
-Estimate the model:
-\begin{verbatim}
-> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "weibull",
- data = coalition)
-\end{verbatim}
-View the regression output:
-\begin{verbatim}
-> summary(z.out)
-\end{verbatim}
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-\begin{verbatim}
-> x.low <- setx(z.out, numst2 = 0)
-> x.high <- setx(z.out, numst2 = 1)
-\end{verbatim}
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
-\begin{verbatim}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-> summary(s.out)
-> plot(s.out)
-\end{verbatim}
-
-\subsubsection{Model}
-Let $Y_i^*$ be the survival time for observation $i$. This variable
-might be censored for some observations at a fixed time $y_c$ such
-that the fully observed dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by the distribution
- of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
- the Weibull distribution whose density function is given by
- \begin{equation*}
- f(y_i^*\mid \lambda_i, \alpha) = \frac{\alpha}{\lambda_i^\alpha}
- y_i^{* \alpha-1} \exp \left\{ -\left( \frac{y_i^*}{\lambda_i}
-\right)^{\alpha} \right\}
- \end{equation*}
- for $y_i^* \ge 0$, the scale parameter $\lambda_i > 0$, and the shape
- parameter $\alpha > 0$. The mean of this distribution is $\lambda_i
- \Gamma(1 + 1 / \alpha)$. When $\alpha = 1$, the distribution reduces to
- the exponential distribution (see Section~\ref{exp}). (Note that
-the output from {\tt zelig()} parameterizes {\tt scale}$ = 1 / \alpha$.)
-
-In addition, survival models like the Weibull have three additional
-properties. The hazard function $h(t)$ measures the probability of
-not surviving past time $t$ given survival up to $t$. In general,
-the hazard function is equal to $f(t)/S(t)$ where the survival
-function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the fraction still
-surviving at time $t$. The cumulative hazard function $H(t)$
-describes the probability of dying before time $t$. In general,
-$H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the Weibull
-model,
-\begin{eqnarray*}
-h(t) &=& \frac{\alpha}{\lambda_i^{\alpha}} t^{\alpha - 1} \\
-S(t) &=& \exp \left\{ -\left( \frac{t}{\lambda_i} \right)^{\alpha} \right\} \\
-H(t) &=& \left( \frac{t}{\lambda_i} \right)^{\alpha}
-\end{eqnarray*}
-For the Weibull model, the hazard function $h(t)$ can increase or
-decrease monotonically over time.
-
-\item The \emph{systematic component} $\lambda_i$ is modeled as
- \begin{equation*}
- \lambda_i = \exp(x_i \beta),
- \end{equation*}
- where $x_i$ is the vector of explanatory variables, and $\beta$ is
- the vector of coefficients.
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the Weibull model are
- simulations of the expected duration:
-\begin{equation*}
-E(Y) = \lambda_i \, \Gamma (1 + \alpha^{-1}),
-\end{equation*}
-given draws of $\beta$ and $\alpha$ from their sampling
-distributions.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from a distribution
- defined by $(\lambda_i, \alpha)$.
-
-\item The first difference ({\tt qi\$fd}) in expected value is
-\begin{equation*}
-\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. When
- $Y_i(t_i=1)$ is censored rather than observed, we replace it with
- a simulation from the model given available knowledge of the
- censoring process. Variation in the simulations are due to
- uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
- expected value of $Y_i$ for observations in the treatment group,
- under the assumption that everything stays the same except that
- the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. When
- $Y_i(t_i=1)$ is censored rather than observed, we replace it with
- a simulation from the model given available knowledge of the
- censoring process. Variation in the simulations are due to
- uncertainty in simulating $\widehat{Y_i(t_i=0)}$, the
- counterfactual predicted value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "weibull", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt icoef}: parameter estimates for the intercept and ``scale''
- parameter $1 / \alpha$.
- \item {\tt var}: the variance-covariance matrix.
- \item {\tt loglik}: a vector containing the log-likelihood for the
- model and intercept only (respectively).
- \item {\tt linear.predictors}: a vector of the
- $x_{i}\beta$.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-\item Most of this may be conveniently summarized using {\tt
- summary(z.out)}. From {\tt summary(z.out)}, you may
- additionally extract:
- \begin{itemize}
- \item {\tt table}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from a
- distribution defined by $(\lambda_i, \alpha)$.
- \item {\tt qi\$fd}: the simulated first differences between the
- simulated expected values for {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The Weibull model is part of the survival library by Terry Therneau,
-ported to R by Thomas Lumley. Advanced users may wish to refer to
-\texttt{help(survfit)} in the survival library, and
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/negbin.pdf b/inst/doc/negbin.pdf
index d43be6e..5079fc8 100644
Binary files a/inst/doc/negbin.pdf and b/inst/doc/negbin.pdf differ
diff --git a/inst/doc/negbin.tex b/inst/doc/negbin.tex
deleted file mode 100644
index 4af95d8..0000000
--- a/inst/doc/negbin.tex
+++ /dev/null
@@ -1,264 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Negative Binomial Regression for Event Count Dependent Variables}
-%\VignetteDepends{Zelig, MASS}
-%\VignetteKeyWords{model, binomial,negative, regression, count}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-
-\section{{\tt negbin}: Negative Binomial Regression for Event
-Count Dependent Variables}\label{negbin}
-
-Use the negative binomial regression if you have a count of events for
-each observation of your dependent variable. The negative binomial
-model is frequently used to estimate over-dispersed event count
-models.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "negbin", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for negative binomial regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Example}
-
-Load sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(sanction)
-\end{Sinput}
-\end{Schunk}
-Estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(num ~ target + coop, model = "negbin", data = sanction)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set values for the explanatory variables to their default mean values:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulate fitted values:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-
-\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.
-
-\begin{itemize}
-\item The negative binomial distribution is derived by letting the
- mean of the Poisson distribution vary according to a fixed
- parameter $\zeta$ given by the Gamma distribution. The
- \emph{stochastic component} is given by
- \begin{eqnarray*}
- Y_i \mid \zeta_i & \sim & \textrm{Poisson}(\zeta_i \mu_i),\\
- \zeta_i & \sim & \frac{1}{\theta}\textrm{Gamma}(\theta).
- \end{eqnarray*}
- The marginal distribution of $Y_i$ is then the negative binomial
- with mean $\mu_i$ and variance $\mu_i + \mu_i^2/\theta$:
- \begin{eqnarray*}
- Y_i & \sim & \textrm{NegBin}(\mu_i, \theta), \\
- & = & \frac{\Gamma (\theta + y_i)}{y! \, \Gamma(\theta)}
- \frac{\mu_i^{y_i} \, \theta^{\theta}}{(\mu_i + \theta)^{\theta + y_i}},
- \end{eqnarray*}
- where $\theta$ is the systematic parameter of the Gamma
- distribution modeling $\zeta_i$.
-
- \item The \emph{systematic component} is given by
- \begin{equation*}
- \mu_i = \exp(x_i \beta)
- \end{equation*}
- where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
- the vector of coefficients.
- \end{itemize}
-
-\subsubsection{Quantities of Interest}
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) are simulations of the mean
- of the stochastic component. Thus, $$E(Y) = \mu_i = \exp(x_i
- \beta),$$ given simulations of $\beta$.
-
-\item The predicted value ({\tt qi\$pr}) drawn from the distribution
- defined by the set of parameters $(\mu_i, \theta)$.
-
-\item The first difference ({\tt qi\$fd}) is
-\begin{equation*}
-\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
-\end{equation*}
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "negbin", 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 theta}: the maximum likelihood estimate for the
- stochastic parameter $\theta$.
- \item {\tt SE.theta}: the standard error for {\tt theta}.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: a vector of the fitted values for the systemic
- component $\lambda$.
- \item {\tt linear.predictors}: a vector of $x_{i} \beta$.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values given the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by $(\mu_i, \theta)$.
- \item {\tt qi\$fd}: the simulated first differences in the
- simulated expected values given the specified values of {\tt x}
- and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The negative binomial model is part of the MASS library by William N.
-Venables and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, advanced users may wish to refer to {\tt help(glm.nb)} in
-the MASS library and \cite{McCNel89}.
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
-
-
-
-
-
diff --git a/inst/doc/netcloglog.Rnw b/inst/doc/netcloglog.Rnw
new file mode 100644
index 0000000..913ae5a
--- /dev/null
+++ b/inst/doc/netcloglog.Rnw
@@ -0,0 +1,166 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/netcloglog}
+\include{zinput}
+%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignettePackage{Zelig}
+\begin{document}
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\subsection{{\tt netcloglog}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+data(friendship)
+
+
+@
+Estimate model:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(friends ~ advice + prestige + perpower, model = "netcloglog", data = friendship)
+summary(z.out)
+
+@
+Setting values for the explanatory variables to their default values:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+ x.out <- setx(z.out)
+
+@
+Simulating quantities of interest from the posterior distribution.
+<<echo=TRUE, results=hide, fig=FALSE>>=
+s.out <- sim(z.out, x = x.out)
+summary(s.out)
+plot(s.out)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE, echo=false,width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low personal power (25th percentile) and high personal power (75th percentile) while all the other variables are held at their default values.
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
+x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.25))
+
+s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+summary(s.out2)
+plot(s.out2)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12,echo=false, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+The {\tt 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 [...]
+
+Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{equation*}
+Y_{i} \sim \text{Bernoulli} ( \pi_{i})\\
+\end{equation*}
+where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_{i} = 1 - \exp[\exp (-x_i \beta)]
+\end{equation*}
+where $x_i$ the vector of $k$ explanatory variables for observation $i$ and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network complementary log log regression are the same as those for the standard complementary log log regression.
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the {\tt netcloglogt} model are simulations of the predicted probability of a success:
+\begin{equation*}
+E(Y) = \pi_{i} = 1 - \exp[\exp (-x_i \beta)],
+\end{equation*}
+given draws of $\beta$ from its sampling distribution.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference ({\tt qi\$fd}) for the network complementary log log model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
+\end{equation*}
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The network complementary log log regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts. Please cite the model as \\
+
+\begin{quote} Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' \emph{CASOS working paper, Carnegie Mellon University.}\\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(netbinom)}. Sample data are fictional. Skyler J. Cranmer added Zelig functionality.
+
+\end{document}
diff --git a/inst/doc/netcloglog.pdf b/inst/doc/netcloglog.pdf
new file mode 100644
index 0000000..96c9c3d
Binary files /dev/null and b/inst/doc/netcloglog.pdf differ
diff --git a/inst/doc/netgamma.Rnw b/inst/doc/netgamma.Rnw
new file mode 100644
index 0000000..65ae2f5
--- /dev/null
+++ b/inst/doc/netgamma.Rnw
@@ -0,0 +1,173 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/netgamma}
+\include{zinput}
+%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignettePackage{Zelig}
+\begin{document}
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\subsection{{\tt netgamma}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following additional options for network gamma regression:
+
+\begin{itemize}
+\item {\tt LF}: specifies the link function to be used for the network gamma regression. Default is {\tt LF="inverse"}, but {\tt LF} can also be set to {\tt "identity"} or {\tt "log"} by the user.
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+data(friendship)
+
+
+
+@
+Estimate model:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(per ~ perpower, LF="inverse", model="netgamma", data=friendship)
+summary(z.out)
+
+@
+Setting values for the explanatory variables to their default values:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.out <- setx(z.out)
+
+@
+Simulating quantities of interest from the posterior distribution.
+<<echo=TRUE, results=hide, fig=FALSE>>=
+s.out <- sim(z.out, x = x.out)
+summary(s.out)
+plot(s.out)
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE, width=12, echo=false, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+
+\item Simulating First Differences
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.low <- setx(z.out, numst2 = 0)
+x.high <- setx(z.out, numst2 = 1)
+
+s.out2 <- sim(z.out, x = x.low, x1 = x.high)
+summary(s.out2)
+plot(s.out2)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE, echo=false, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+The {\tt 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 [...]
+
+Let $Y_{i}$ be the dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$.
+\begin{itemize}
+\item The Gamma distribution with scale parameter $\alpha$ has a \emph{stochastic component} given by
+\begin{eqnarray*}
+Y &\sim \text{Gamma} ( y_{i} | \lambda_i, \alpha)\\
+f(y) & = \frac{1}{\alpha^{\lambda_i}\Gamma \lambda_i} y_i^{\lambda_i - 1} \exp - \left[ \frac{y_i}{\alpha} \right]
+\end{eqnarray*}
+for $\alpha, \lambda_i, y_i > 0$.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\lambda_{i} = \frac{1}{x_{i}\beta}.
+\end{equation*}
+
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network gamma regression are the same as those for the standard gamma regression.
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) are simulations of the mean of the stochastic component given draws of $\alpha$ and $\beta$ from their posteriors:
+\begin{equation*}
+E(Y) = \alpha_i \lambda.
+\end{equation*}
+
+\item The predicted values ({\tt qi\$pr}) are draws from the gamma distribution for each set of parameters $(\alpha, \lambda_i)$.
+
+\item The first difference ({\tt qi\$fd}) for the network gamma model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y | x_{1}) - \text{Pr}(Y | x)
+\end{equation*}
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values drawn from a distribution defined by $(\alpha_i, \lambda)$.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+
+
+\subsubsection{Contributors}
+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. Please cite the model as \\
+
+\begin{quote} Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' \emph{CASOS working paper, Carnegie Mellon University.}\\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(netgamma)}. Sample data are fictional. Skyler J. Cranmer added Zelig functionality.
+
+\end{document}
diff --git a/inst/doc/netgamma.pdf b/inst/doc/netgamma.pdf
new file mode 100644
index 0000000..02a020f
Binary files /dev/null and b/inst/doc/netgamma.pdf differ
diff --git a/inst/doc/netlogit.Rnw b/inst/doc/netlogit.Rnw
index 5373bfe..ff9fa7a 100644
--- a/inst/doc/netlogit.Rnw
+++ b/inst/doc/netlogit.Rnw
@@ -1,199 +1,171 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/netlogit}
-\include{zinput}
-%\VignetteIndexEntry{Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model, network,logistic, regression, dichotomous}
-%\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 netlogit}: Network Logistic Regression for
-Dichotomous Proximity Matrix Dependent Variables}\label{netlogit}
-
-Use network logistic squares regression analysis for a dependent
-variable that is a binary-valued proximity matrix (a.k.a.\
-sociomatrices, adjacency matrices, or matrix representations of
-directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netlogit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Load the sample data and format it for social network analysis:
-<<BasicExample.data>>=
-data(friendship)
-@
-Estimate model:
-<<BasicExample.zelig>>=
- z.out <- zelig(friends ~ advice + prestige + perpower,
- model = "netlogit", data = friendship)
- summary(z.out)
-@
-Setting values for the explanatory variables to their default values:
-<<BasicExample.setx>>=
-x.out <- setx(z.out)
-@
-Simulating quantities of interest from the sampling distribution.
-<<BasicExample.sim>>=
- s.out <- sim(z.out, x = x.out)
- summary(s.out)
-@
-\begin{center}
-<<label=BasicExamplePlot, fig=true, echo=true>>=
- plot(s.out)
-@
-\end{center}
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low personal
-power (25th percentile) and high education (75th percentile) while all
-the other variables are held at their default values.
-
-<<FirstDifferences.setx>>=
- x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
- x.low <- setx(z.out, educate = quantile(friendship$perpower, prob = 0.25))
-@
-<<FirstDifferences.sim>>=
- s.out2 <- sim(z.out, x = x.high, x1 = x.low)
- summary(s.out2)
-@
-\begin{center}
-<<label=FirstDifferencesPlot,fig=true,echo=true>>=
- plot(s.out2)
-@
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
-The {\tt netlogit} model performs a logistic 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 logistic regression
-element-wise on the appropriately vectorized matrices. Sociomatrices
-are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
-represent the sociomatrix. The vectorization which produces the $Y$
-vector from the $\mathbf{Y}$ matrix is preformed by simple
-row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
-$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
-element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
-element of $Y$ and so on. Once the input matrices are vectorized,
-standard logistic regression is performed.
-
-Let $Y_{i}$ be the binary dependent variable, produced by vectorizing
-a binary sociomatrix, for observation $i$ which takes the value of
-either 0 or 1.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
-& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
-\end{eqnarray*}
-where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
-\end{equation*}
-where $x_{i}$ is the vector of $k$ covariates for observation $i$ and
-$\beta$ is the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network logistic regression are the
-same as those for the standard logistic regression.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the netlogit model are
-simulations of the predicted probability of a success:
-\begin{equation*}
-E(Y) = \pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
-\end{equation*}
-given draws of $\beta$ from its sampling distribution.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
-distribution with mean equal to the simulated expected value
-$\pi_{i}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
-\end{equation*}
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, you run {\tt 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.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the
-explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of
-the IWLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike's Information Criterion (minus twice the
-maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the
-maximized log-likelihood plus the number of coefficients times log
-$n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-\end{itemize}
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled
-covariances.
-\item {\tt ctable}: a $2 \times 2$ table of predicted vs.\ actual
-values. Each cell tabulates whether the predicted value $\widehat{Y}
-\in \{0,1\}$ corresponds to the observed $Y \in \{0,1\}$.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the
-specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected
-probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-The network logistic regression is part of the sna package by Carter
-T. Butts. Please cite the model as \\
-\begin{verse}
-\bibentry{ButCar01}.
-\end{verse}
-In addition, advanced users may wish to refer to {\tt help(netlogit)}.
-Sample data are fictional. Skyler J.\ Cranmer added Zelig functionality.
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
+\SweaveOpts{results=hide, prefix.string=vigpics/netlogit}
+\include{zinput}
+%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignettePackage{Zelig}
+\begin{document}
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\subsection{{\tt netlogit}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+data(friendship)
+
+
+
+@
+Estimate model:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(friends ~ advice + prestige + perpower, model = "netlogit", data = friendship)
+summary(z.out)
+
+@
+Setting values for the explanatory variables to their default values:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.out <- setx(z.out)
+
+@
+Simulating quantities of interest from the posterior distribution.
+<<echo=TRUE, results=hide, fig=FALSE>>=
+s.out <- sim(z.out, x = x.out)
+summary(s.out)
+plot(s.out)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE, echo=false,width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low personal power (25th percentile) and high personal power (75th percentile) while all the other variables are held at their default values.
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
+x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.25))
+
+s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+summary(s.out2)
+plot(s.out2)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE,echo=false, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+
+
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+The {\tt 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 [...]
+
+Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
+& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
+\end{eqnarray*}
+where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
+\end{equation*}
+where $x_{i}$ is the vector of $k$ covariates for observation $i$ and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network logistic regression are the same as those for the standard logistic regression.
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the {\tt netlogit} model are simulations of the predicted probability of a success:
+\begin{equation*}
+E(Y) = \pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)},
+\end{equation*}
+given draws of $\beta$ from its sampling distribution.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference ({\tt qi\$fd}) for the network logit model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
+\end{equation*}
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The network logistic regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts. Please cite the model as \\
+
+\begin{quote} Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' \emph{CASOS working paper, Carnegie Mellon University.}\\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(netlogit)}. Sample data are fictional. Skyler J. Cranmer added Zelig functionality.
+
+\end{document}
diff --git a/inst/doc/netlogit.pdf b/inst/doc/netlogit.pdf
index 4a860dc..25e59ed 100644
Binary files a/inst/doc/netlogit.pdf and b/inst/doc/netlogit.pdf differ
diff --git a/inst/doc/netlogit.tex b/inst/doc/netlogit.tex
deleted file mode 100644
index ef428e2..0000000
--- a/inst/doc/netlogit.tex
+++ /dev/null
@@ -1,206 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model, network,logistic, regression, dichotomous}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt netlogit}: Network Logistic Regression for
-Dichotomous Proximity Matrix Dependent Variables}\label{netlogit}
-
-Use network logistic squares regression analysis for a dependent
-variable that is a binary-valued proximity matrix (a.k.a.\
-sociomatrices, adjacency matrices, or matrix representations of
-directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netlogit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example
-
-Load the sample data and format it for social network analysis:
-\begin{Schunk}
-\begin{Sinput}
-> data(friendship)
-\end{Sinput}
-\end{Schunk}
-Estimate model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(friends ~ advice + prestige + perpower, model = "netlogit",
-+ data = friendship)
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their default values:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the sampling distribution.
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/netlogit-BasicExamplePlot}
-\end{center}
-
-\item Simulating First Differences
-
-Estimating the risk difference (and risk ratio) between low personal
-power (25th percentile) and high education (75th percentile) while all
-the other variables are held at their default values.
-
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out, perpower = quantile(friendship$perpower,
-+ prob = 0.75))
-> x.low <- setx(z.out, educate = quantile(friendship$perpower,
-+ prob = 0.25))
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out2)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/netlogit-FirstDifferencesPlot}
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
-The {\tt netlogit} model performs a logistic 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 logistic regression
-element-wise on the appropriately vectorized matrices. Sociomatrices
-are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
-represent the sociomatrix. The vectorization which produces the $Y$
-vector from the $\mathbf{Y}$ matrix is preformed by simple
-row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
-$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
-element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
-element of $Y$ and so on. Once the input matrices are vectorized,
-standard logistic regression is performed.
-
-Let $Y_{i}$ be the binary dependent variable, produced by vectorizing
-a binary sociomatrix, for observation $i$ which takes the value of
-either 0 or 1.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim \text{Bernoulli} (y_{i} | \pi_{i})\\
-& = \pi_{i}^{y_{i}} (1 - \pi_{i})^{1 - y_{i}}
-\end{eqnarray*}
-where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
-\end{equation*}
-where $x_{i}$ is the vector of $k$ covariates for observation $i$ and
-$\beta$ is the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network logistic regression are the
-same as those for the standard logistic regression.
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the netlogit model are
-simulations of the predicted probability of a success:
-\begin{equation*}
-E(Y) = \pi_{i} = \frac{1}{1 + \exp(-x_{i}\beta)}.
-\end{equation*}
-given draws of $\beta$ from its sampling distribution.
-
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
-distribution with mean equal to the simulated expected value
-$\pi_{i}$.
-
-\item The first difference ({\tt qi\$fd}) for the logit model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
-\end{equation*}
-\end{itemize}
-
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, you run {\tt 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.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the
-explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of
-the IWLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike's Information Criterion (minus twice the
-maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the
-maximized log-likelihood plus the number of coefficients times log
-$n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-\end{itemize}
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled
-covariances.
-\item {\tt ctable}: a $2 \times 2$ table of predicted vs.\ actual
-values. Each cell tabulates whether the predicted value $\widehat{Y}
-\in \{0,1\}$ corresponds to the observed $Y \in \{0,1\}$.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the
-specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected
-probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-The network logistic regression is part of the sna package by Carter
-T. Butts. Please cite the model as \\
-\begin{verse}
-\bibentry{ButCar01}.
-\end{verse}
-In addition, advanced users may wish to refer to {\tt help(netlogit)}.
-Sample data are fictional. Skyler J.\ Cranmer added Zelig functionality.
- \end{document}
diff --git a/inst/doc/netls.pdf b/inst/doc/netls.pdf
index b04a91d..e82af6f 100644
Binary files a/inst/doc/netls.pdf and b/inst/doc/netls.pdf differ
diff --git a/inst/doc/netls.tex b/inst/doc/netls.tex
deleted file mode 100644
index 3e734b8..0000000
--- a/inst/doc/netls.tex
+++ /dev/null
@@ -1,182 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model, network,least squares, regression,continuous, proximity matrix}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt netls}: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables}\label{netls}
-
-Use network least squares regression analysis to estimate the
-best linear predictor when the dependent variable is a
-continuously-valued proximity matrix (a.k.a.\ sociomatrices, adjacency
-matrices, or matrix representations of directed graphs).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "netls", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Examples}
-\begin{enumerate}
-\item Basic Example with First Differences
-
-Load sample data and format it for social networkx analysis:
-%library sna required
-\begin{Schunk}
-\begin{Sinput}
-> data(sna.ex)
-\end{Sinput}
-\end{Schunk}
-Estimate model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(Var1 ~ Var2 + Var3 + Var4, model = "netls", data = sna.ex)
-\end{Sinput}
-\end{Schunk}
-
-Summarize regression results:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) for the second
-explanatory variable (Var3).
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.8))
-> x.low <- setx(z.out, Var3 = quantile(sna.ex$Var3, 0.2))
-\end{Sinput}
-\end{Schunk}
-Generate first differences for the effect of high versus low values of
-Var3 on the outcome variable.
-\begin{Schunk}
-\begin{Sinput}
-> try(s.out <- sim(z.out, x = x.high, x1 = x.low))
-> try(summary(s.out))
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/netls-ExamplesPlot}
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
-The {\tt netls} model performs a least squares regression of the
-sociomatrix $\mathbf{Y}$, a $m \times m$ matrix representing network
-ties, on a set of sociomatrices $\mathbf{X}$. This network regression
-model is a directly analogue to standard least squares regression
-element-wise on the appropriately vectorized matrices. Sociomatrices
-are vectorized by creating $Y$, an $m^{2} \times 1$ vector to
-represent the sociomatrix. The vectorization which produces the $Y$
-vector from the $\mathbf{Y}$ matrix is preformed by simple
-row-concatenation of $\mathbf{Y}$. For example if $\mathbf{Y}$ is a
-$15 \times 15$ matrix, the $\mathbf{Y}_{1,1}$ element is the first
-element of $Y$, and the $\mathbf{Y}_{21}$ element is the second
-element of $Y$ and so on. Once the input matrices are vectorized,
-standard least squares regression is performed. As such:
-\begin{itemize}
-\item The \emph{stochastic component} is described by a density with
-mean $\mu_{i}$ and the common variance $\sigma^{2}$
-\begin{equation*}
-Y_{i} \sim f(y_{i} | \mu_{i}, \sigma^{2}).
-\end{equation*}
-
-\item The \emph{systematic component} models the conditional mean as
-\begin{equation*}
-\mu_{i} = x_{i}\beta
-\end{equation*}
-where $x_{i}$ is the vector of covariates, and $\beta$ is the vector of coefficients.
-\end{itemize}
-The least squares estimator is the best linear predictor of a
-dependent variable given $x_{i}$, and minimizes the sum of squared
-errors $\sum_{i = 1}^{n} (Y_{i} - x_{i}\beta)^{2}$.
-
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network least squares regression
-are the same as those for the standard least squares regression.
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations from
-the stochastic component,
-\begin{equation*}
-E(Y) = x_{i}\beta,
-\end{equation*}
-given a draw of $\beta$ from its sampling distribution.
-
-\item The first difference ({\tt qi\$fd}) is:
-\begin{equation*}
-FD = E(Y | x_{1}) - E(Y | x)
-\end{equation*}
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, you run {\tt z.out <- zelig(y ~ x,
-model="netls", data)}, then you may examine the available information
-in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by
-using {\tt z.out\$coefficients}, and a default summary of information
-through {\tt summary(z.out)}. Other elements available through the
-{\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
-\end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\begin{equation*}
-\hat{\beta} = \left( \sum_{i = 1}^{n} x'_{i}x_{i} \right)^{-1} \sum x_{i}y_{i}
-\end{equation*}
-\item {\tt sigma}: the square root of the estimate variance of the
-random error $\varepsilon$:
-\begin{equation*}
-\hat{\sigma} = \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{n - k}
-\end{equation*}
-\item {\tt r.squared}: the fraction of the variance explained by the model.
-\begin{equation*}
-R^{2} = 1 - \frac{\sum (Y_{i} - x_{i} \hat{\beta} ) ^{2}}{\sum (y_{i}
-- \bar{y})^{2}}
-\end{equation*}
-\item {\tt adj.r.squared}: the above $R^{2}$ statistic, penalizing for
-an increased number of explanatory variables.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
-\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences (or differences in
-expected values) for the specified values of {\tt x} and {\tt x1}.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-The network least squares regression is part of the sna package by
-Carter T. Butts. Please cite the model as
-\begin{verse}
-\bibentry{ButCar01}.
-\end{verse}
-In addition, advanced users may wish to refer to {\tt
-help(netlm)}. Sample data are fictional. Skyler J. Cranmer added Zelig
-functionality.
- \end{document}
diff --git a/inst/doc/netnormal.Rnw b/inst/doc/netnormal.Rnw
new file mode 100644
index 0000000..215bca1
--- /dev/null
+++ b/inst/doc/netnormal.Rnw
@@ -0,0 +1,151 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/netnormal}
+\include{zinput}
+%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignettePackage{Zelig}
+\begin{document}
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\subsection{{\tt netnormal}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following additional options for network normal regression:
+
+\begin{itemize}
+\item {\tt LF}: specifies the link function to be used for the network normal regression. Default is {\tt LF="identity"}, but {\tt LF} can also be set to {\tt "log"} or {\tt "inverse"} by the user.
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+data(friendship)
+
+
+
+@
+Estimate model:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(perpower ~ friends + advice + prestige, model = "netnormal", data = friendship)
+summary(z.out)
+
+@
+Setting values for the explanatory variables to their default values:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.out <- setx(z.out)
+
+@
+Simulate fitted values.
+<<echo=TRUE, results=hide, fig=FALSE>>=
+s.out <- sim(z.out, x = x.out)
+summary(s.out)
+plot(s.out)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE,echo=false, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+The {\tt 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 $ [...]
+
+Let $Y_{i}$ be the continuous dependent variable, produced by vectorizing a continuous proximity matrix, for observation $i$.
+\begin{itemize}
+\item The \emph{stochastic component} is described by a univariate normal model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
+\begin{equation*}
+Y_{i} \sim \text{Normal} ( \mu_{i}, \sigma^2).\\
+\end{equation*}
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\mu_i = x_i\beta.
+\end{equation*}
+where $ x_i$ is the vector of $k$ explanatory variables and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network Normal regression are the same as those for the standard Normal regression.
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) for the {\tt netnormal} model is the mean of simulations from the stochastic component,
+\begin{equation*}
+E(Y) = \mu_{i} = x_{i}\beta,
+\end{equation*}
+given a draw of $\beta$ from its posterior.
+
+\item The predicted value ({\tt qi\$pr}) is a draw from the distribution defined by the set of parameters $(\mu_i, \sigma^2)$.
+
+\item The first difference ({\tt qi\$fd}) for the network Normal model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y| x_{1}) - \text{Pr}(Y| x)
+\end{equation*}
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the systemic component $\lambda$.
+\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
+\item {\tt linear.predictors}: fitted values. For the normal model, these are identical to fitted values.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distribution defined by $(\mu_i, \sigma^2)$.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+
+\subsubsection{Contributors}
+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. Please cite the model as \\
+
+\begin{quote} Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' \emph{CASOS working paper, Carnegie Mellon University.}\\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(netnormal)}. Sample data are fictional. Skyler J. Cranmer added Zelig functionality.
+
+\end{document}
diff --git a/inst/doc/netnormal.pdf b/inst/doc/netnormal.pdf
new file mode 100644
index 0000000..c8dfdf3
Binary files /dev/null and b/inst/doc/netnormal.pdf differ
diff --git a/inst/doc/netpoisson.Rnw b/inst/doc/netpoisson.Rnw
new file mode 100644
index 0000000..8765123
--- /dev/null
+++ b/inst/doc/netpoisson.Rnw
@@ -0,0 +1,153 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/netpoisson}
+\include{zinput}
+%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignettePackage{Zelig}
+\begin{document}
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\subsection{{\tt netpoisson}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+In addition to the standard inputs, {\tt zelig()} takes the following additional options for network poisson regression:
+
+\begin{itemize}
+\item {\tt LF}: specifies the link function to be used for the network poisson regression. Default is {\tt LF="log"}, but {\tt LF} can also be set to {\tt "sqrt"} by the user.
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+data(friendship)
+
+
+
+@
+Estimate model:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(count ~ advice + prestige + perpower, model = "netpoisson", data = friendship)
+summary(z.out)
+
+@
+Setting values for the explanatory variables to their default values:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.out <- setx(z.out)
+
+@
+Simulate fitted values.
+<<echo=TRUE, results=hide, fig=FALSE>>=
+s.out <- sim(z.out, x = x.out)
+summary(s.out)
+plot(s.out)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE,echo=false, width=12, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+The {\tt 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 [...]
+
+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}
+\item The Poisson distribution has \emph{stochastic component}
+\begin{equation*}
+Y_{i} \sim \text{Poisson} ( \lambda_{i}),\\
+\end{equation*}
+where $\lambda_{i}$ is the mean and variance parameter.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\lambda_i = \exp(x_i\beta).
+\end{equation*}
+where $ x_i$ is the vector of explanatory variables and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+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,
+\begin{equation*}
+E(Y) = \lambda_{i} = \exp(x_{i}\beta),
+\end{equation*}
+given draws of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is a random draw from the Poisson distribution defined by mean $\lambda_i$.
+
+\item The first difference ({\tt qi\$fd}) for the network Poisson model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y| x_{1}) - \text{Pr}(Y| x)
+\end{equation*}
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the systemic component $\lambda$.
+\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+
+\subsubsection{Contributors}
+The network Poisson 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. Please cite the model as \\
+
+\begin{quote} Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' \emph{CASOS working paper, Carnegie Mellon University.}\\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(netpoisson)}. Sample data are fictional. Skyler J. Cranmer added Zelig functionality.
+
+\end{document}
diff --git a/inst/doc/netpoisson.pdf b/inst/doc/netpoisson.pdf
new file mode 100644
index 0000000..d417b90
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diff --git a/inst/doc/netprobit.Rnw b/inst/doc/netprobit.Rnw
new file mode 100644
index 0000000..6146f53
--- /dev/null
+++ b/inst/doc/netprobit.Rnw
@@ -0,0 +1,168 @@
+\SweaveOpts{results=hide, prefix.string=vigpics/netprobit}
+\include{zinput}
+%\VignetteIndexEntry{Least Squares Regression for Continuous Dependent Variables}
+%\VignetteDepends{Zelig, stats}
+%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignettePackage{Zelig}
+\begin{document}
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\subsection{{\tt netprobit}: 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)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item Basic Example
+
+Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+data(friendship)
+
+
+@
+Estimate model:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+z.out <- zelig(friends ~ advice + prestige + perpower, model = "netprobit", data = friendship)
+summary(z.out)
+
+@
+Setting values for the explanatory variables to their default values:
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.out <- setx(z.out)
+
+@
+Simulating quantities of interest from the posterior distribution.
+<<echo=TRUE, results=hide, fig=FALSE>>=
+s.out <- sim(z.out, x = x.out)
+summary(s.out)
+plot(s.out)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE,echo=false, width=6, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+@
+\item Simulating First Differences
+
+Estimating the risk difference (and risk ratio) between low personal power (25th percentile) and high personal power (75th percentile) while all the other variables are held at their default values.
+
+<<echo=TRUE, results=hide, fig=FALSE>>=
+x.high <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.75))
+x.low <- setx(z.out, perpower = quantile(friendship$perpower, prob = 0.25))
+
+s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+summary(s.out2)
+plot(s.out2)
+
+@
+
+\begin{figure}[here]
+\centering
+<<fig=TRUE,echo=false, width=6, height=6>>=
+plot(s.out)
+@
+\label{fig:plotgam}
+\end{figure}
+
+\end{enumerate}
+
+
+
+\subsubsection{Model}
+The {\tt 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 $ [...]
+
+Let $Y_{i}$ be the binary dependent variable, produced by vectorizing a binary proximity matrix, for observation $i$ which takes the value of either 0 or 1.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{equation*}
+Y_{i} \sim \text{Bernoulli} ( \pi_{i})\\
+\end{equation*}
+where $\pi_{i} = \text{Pr}(Y_{i} = 1)$.
+\item The \emph{systematic component} is given by:
+\begin{equation*}
+\pi_{i} = \pmb{\Phi}(x_{i}\beta).
+\end{equation*}
+where $ \pmb{\Phi}(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+The quantities of interest for the network probit regression are the same as those for the standard probit regression.
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the {\tt netprobit} model are simulations of the predicted probability of a success:
+\begin{equation*}
+E(Y) = \pi_{i} = \pmb{\Phi}(x_{i}\beta),
+\end{equation*}
+given draws of $\beta$ from its sampling distribution.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference ({\tt qi\$fd}) for the network probit model is defined as
+\begin{equation*}
+FD = \text{Pr}(Y = 1 | x_{1}) - \text{Pr}(Y = 1| x)
+\end{equation*}
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "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.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
+\begin{itemize}
+\item {\tt coefficients}: parameter estimates for the explanatory variables.
+\item {\tt fitted.values}: the vector of fitted values for the explanatory variables.
+\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
+\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+\item {\tt aic}: Akaike's Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
+\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
+\item {\tt df.residual}: the residual degrees of freedom.
+\item {\tt df.null}: the residual degrees of freedom for the null model.
+\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+
+\end{itemize}
+\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
+\begin{itemize}
+\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
+\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
+\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
+\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
+\end{itemize}
+\end{itemize}
+
+
+\subsubsection{Contributors}
+The network probit regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts. Please cite the model as \\
+
+\begin{quote} Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' \emph{CASOS working paper, Carnegie Mellon University.}\\ \end{quote}
+
+\noindent In addition, advanced users may wish to refer to {\tt help(netbinom)}. Sample data are fictional. Skyler J. Cranmer added Zelig functionality.
+
+\end{document}
diff --git a/inst/doc/netprobit.pdf b/inst/doc/netprobit.pdf
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diff --git a/inst/doc/normal.bayes.pdf b/inst/doc/normal.bayes.pdf
index c44ac57..cb4a0b2 100644
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diff --git a/inst/doc/normal.bayes.tex b/inst/doc/normal.bayes.tex
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-
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Normal Linear Regression}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, normal,linear, regression,bayes, continuous}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{normal.bayes}: Bayesian Normal Linear Regression}
-\label{normal.bayes}
-
-Use Bayesian regression to specify a continuous dependent variable as
-a linear function of specified explanatory variables. The model is
-implemented using a Gibbs sampler. See \Sref{normal} for the
-maximum-likelihood implementation or \Sref{ls} for the ordinary least
-squares variation.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "normal.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the convergence of the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
-progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA}, which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, which uses the
-least squares estimates as the starting values.
-
-\end{itemize}
-
-Use the following arguments to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will
-be the prior mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCregress)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(macro)
-\end{Sinput}
-\end{Schunk}
-
-Estimating linear regression using \texttt{normal.bayes}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.bayes",
-+ data = macro, verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-
-Setting values for the explanatory variables to their sample averages:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) trade on GDP:
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out, trade = quantile(macro$trade, prob = 0.8))
-> x.low <- setx(z.out, trade = quantile(macro$trade, prob = 0.2))
-\end{Sinput}
-\end{Schunk}
-Estimating the first difference for the effect of
-high versus low trade on unemployment rate:
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
-\end{eqnarray*}
-where $\epsilon_{i}=Y_i-\mu_i$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i}= x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are given by
-\begin{eqnarray*}
-\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
-\sigma^{2} & \sim & {\rm InverseGamma}\left( \frac{c_0}{2},
-\frac{d_0}{2} \right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory
-variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
-a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape and
-scale parameters for $\sigma^{2}$. Note that $\beta$ and $\sigma^2$
-are assumed to be \emph{a priori} independent.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the linear regression model are
-calculated as following:
-\begin{eqnarray*}
-E(Y) = x_{i} \beta,
-\end{eqnarray*}
-given posterior draws of $\beta$ based on the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) for the linear regression model
-is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1} \{
-Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "normal.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-The Bayesian regression \input{contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{normal.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/normal.pdf b/inst/doc/normal.pdf
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diff --git a/inst/doc/normal.tex b/inst/doc/normal.tex
deleted file mode 100644
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--- a/inst/doc/normal.tex
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-
-\include{zinput}
-%\VignetteIndexEntry{Normal Regression for Continuous Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, normal,regression,continuous, least squares}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-\section{{\tt normal}: Normal Regression for Continuous Dependent Variables}
-\label{normal}
-
-The Normal regression model is a close variant of the more standard
-least squares regression model (see \Sref{ls}). Both models specify a
-continuous dependent variable as a linear function of a set of
-explanatory variables. The Normal model reports maximum likelihood
-(rather than least squares) estimates. The two models differ only in
-their estimate for the stochastic parameter $\sigma$.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "normal", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for normal regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item Basic Example with First Differences
-
-Attach sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(macro)
-\end{Sinput}
-\end{Schunk}
-Estimate model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal",
-+ data = macro)
-\end{Sinput}
-\end{Schunk}
-Summarize of regression coefficients:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out1)
-\end{Sinput}
-\end{Schunk}
-Set explanatory variables to their default (mean/mode) values, with
-high (80th percentile) and low (20th percentile) values for trade:
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8))
-> x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2))
-\end{Sinput}
-\end{Schunk}
-Generate first differences for the effect of high versus low trade on
-GDP:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-%plot does not work
-A visual summary of quantities of interest:
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out1)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/normal-ExamplesPlot}
-\end{center}
-
-\item Using Dummy Variables
-%the code in this section does not work well but there is no demo for this part either
-
-Estimate a model with a dummy variable for each year and country (see
-\ref{factors} for help with dummy variables). Note that you do not
-need to create dummy variables, as the program will automatically
-parse the unique values in the selected variables into dummy
-variables.
-\begin{Schunk}
-\begin{Sinput}
-> z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(year) +
-+ as.factor(country), model = "normal", data = macro)
-\end{Sinput}
-\end{Schunk}
-Set values for the explanatory variables, using the default mean/mode
-variables, with country set to the United States and Japan,
-respectively:
-Simulate quantities of interest:
-%plot does not work
-\begin{center}
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i$ be the continuous dependent variable for observation $i$.
-\begin{itemize}
-\item The \emph{stochastic component} is described by a univariate normal
- model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
- \begin{equation*}
- Y_i \; \sim \; \textrm{Normal}(\mu_i, \sigma^2).
- \end{equation*}
-
-\item The \emph{systematic component} is
- \begin{equation*}
- \mu_i \;= \; x_i \beta,
- \end{equation*}
- where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected value ({\tt qi\$ev}) is the mean of simulations
- from the the stochastic component, $$E(Y) = \mu_i = x_i \beta,$$
- given a draw of $\beta$ from its posterior.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from the distribution
- defined by the set of parameters $(\mu_i, \sigma)$.
-
-\item The first difference ({\tt qi\$fd}) is:
-\begin{equation*}
-\textrm{FD}\; = \;E(Y \mid x_1) - E(Y \mid x)
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "normal", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: fitted values. For the normal model,
- these are identical to the {\tt linear predictors}.
- \item {\tt linear.predictors}: fitted values. For the normal
- model, these are identical to {\tt fitted.values}.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by $(\mu_i, \sigma)$.
- \item {\tt qi\$fd}: the simulated first difference in the simulated
- expected values for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The Normal model is part of the stats package by William N. Venables
-and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Advanced users may wish to refer to
-\texttt{help(glm)}, \texttt{help(family)}, and \cite{McCNel89}.
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/ologit.pdf b/inst/doc/ologit.pdf
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diff --git a/inst/doc/ologit.tex b/inst/doc/ologit.tex
deleted file mode 100644
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-
-\include{zinput}
-%\VignetteIndexEntry{Ordinal Logistic Regression for Ordered Categorical Dependent Variables}
-%\VignetteDepends{Zelig, MASS}
-%\VignetteKeyWords{model, logistic,regression,ordinal, ordered,categorical}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
-Categorical Dependent Variables}\label{ologit}
-
-Use the ordinal logit regression model if your dependent variable is
-ordered and categorical, either in the form of integer values or character strings.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-If {\tt Y} takes discrete integer values, the {\tt as.factor()}
-command will order automatically order the values. If {\tt Y} takes
-on values composed of character strings, such as ``strongly agree'',
-``agree'', and ``disagree'', {\tt as.factor()} will order the values
-in the order in which they appear in {\tt Y}. You will need to
-replace your dependent variable with a factored variable prior to
-estimating the model through {\tt zelig()}. See Section \ref{factors}
-for more information on creating ordered factors and Example
-\ref{ord.fact} below.
-
-\subsubsection{Example}
-
-\begin{enumerate}
-
-\item {Creating An Ordered Dependent Variable} \label{ord.fact}
-
-Load the sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(sanction)
-\end{Sinput}
-\end{Schunk}
-Create an ordered dependent variable:
-\begin{Schunk}
-\begin{Sinput}
-> sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain",
-+ "little effect", "modest loss", "major loss"))
-\end{Sinput}
-\end{Schunk}
-Estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their observed values:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out, fn = NULL)
-\end{Sinput}
-\end{Schunk}
-Simulate fitted values given {\tt x.out} and view the results:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-
-\item {First Differences}
-
-Using the sample data \texttt{sanction}, estimate the empirical model and returning the coefficients:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
-+ data = sanction)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their means, with {\tt mil} set
-to 0 (no military action in addition to sanctions) in the baseline
-case and set to 1 (military action in addition to sanctions) in the
-alternative case:
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out, mil = 0)
-> x.high <- setx(z.out, mil = 1)
-\end{Sinput}
-\end{Schunk}
-Generate simulated fitted values and first differences, and view the results:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_i$ be the ordered categorical dependent variable for
-observation $i$ that takes one of the integer values from $1$ to $J$
-where $J$ is the total number of categories.
-
-\begin{itemize}
-\item The \emph{stochastic component} begins with an unobserved continuous
- variable, $Y^*_i$, which follows the standard logistic distribution
- with a parameter $\mu_i$,
- \begin{equation*}
- Y_i^* \; \sim \; \textrm{Logit}(y_i^* \mid \mu_i),
- \end{equation*}
- to which we add an observation mechanism
- \begin{equation*}
- Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
- \quad {\rm for} \quad j=1,\dots,J.
- \end{equation*}
- where $\tau_l$ (for $l=0,\dots,J$) are the threshold parameters with
- $\tau_l < \tau_m$ for all $l<m$ and $\tau_0=-\infty$ and
- $\tau_J=\infty$.
-
-\item The \emph{systematic component} has the following form, given
- the parameters $\tau_j$ and $\beta$, and the explanatory variables $x_i$:
- \begin{equation*}
- \Pr(Y \le j) \; = \; \Pr(Y^* \le \tau_j) \; = \frac{\exp(\tau_j -
- x_i \beta)}{1+\exp(\tau_j -x_i \beta)},
- \end{equation*}
- which implies:
- \begin{equation*}
- \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}{1 + \exp(\tau_j -
- x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
- \exp(\tau_{j-1} - x_i \beta)}.
- \end{equation*}
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the ordinal logit model
- are simulations of the predicted probabilities for each category:
-\begin{equation*}
-E(Y = j) \; = \; \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}
-{1 + \exp(\tau_j - x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
- \exp(\tau_{j-1} - x_i \beta)},
-\end{equation*}
-given a draw of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from the logit
- distribution described by $\mu_i$, and observed as one of $J$
- discrete outcomes.
-
-\item The difference in each of the predicted probabilities ({\tt
- qi\$fd}) is given by
- \begin{equation*}
- \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
- j=1,\dots,J.
- \end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "ologit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt zeta}: a vector containing the estimated class
- boundaries $\tau_j$.
- \item {\tt deviance}: the residual deviance.
- \item {\tt fitted.values}: the $n \times J$ matrix of in-sample
- fitted values.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt edf}: the effective degrees of freedom.
- \item {\tt Hessian}: the Hessian matrix.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, and $t$-statistics.
- \end{itemize}
-
- \item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The ordinal logit model is part of the MASS library by William N.
-Venables and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, advanced users may wish to refer to
-{\tt help(polr)} in the MASS libary and \cite{McCNel89}.
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
-
-
-
-
-
diff --git a/inst/doc/oprobit.bayes.pdf b/inst/doc/oprobit.bayes.pdf
index 9465dee..20c100e 100644
Binary files a/inst/doc/oprobit.bayes.pdf and b/inst/doc/oprobit.bayes.pdf differ
diff --git a/inst/doc/oprobit.bayes.tex b/inst/doc/oprobit.bayes.tex
deleted file mode 100644
index 3b62857..0000000
--- a/inst/doc/oprobit.bayes.tex
+++ /dev/null
@@ -1,309 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Ordered Probit Regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model, bayes,regression,ordinal, ordered,categorical}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{oprobit.bayes}: Bayesian Ordered Probit Regression}
-
-\label{oprobit.bayes}
-
-Use the ordinal probit regression model if your dependent variables are ordered and
-categorical. They may take either integer values or character strings. The model
-is estimated using a Gibbs sampler with data augmentation. For a
-maximum-likelihood implementation of this models, see \Sref{oprobit}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "oprobit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-
-\subsubsection{Additional Inputs}
-
-{\tt zelig()} accepts the following arguments to monitor the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item{\texttt{tune}}: tuning parameter for the Metropolis-Hasting step.
-The default value is \texttt{NA} which corresponds to 0.05 divided by
-the number of categories in the response variable.
-
-\item \texttt{verbose}: defaults to {\tt FALSE} If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, which uses the
-maximum likelihood estimates as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will be the prior
-mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with dimensions equal to the number of
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0 which leads to an improper prior.
-\end{itemize}
-
-\noindent Zelig users may wish to refer to \texttt{help(MCMCoprobit)}
-for more information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(sanction)
-\end{Sinput}
-\end{Schunk}
-Estimating ordered probit regression using \texttt{oprobit.bayes}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(ncost ~ mil + coop, model = "oprobit.bayes", data = sanction,
-+ verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-
-Creating an ordered dependent variable:
-\begin{Schunk}
-\begin{Sinput}
-> sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain",
-+ "little effect", "modest loss", "major loss"))
-\end{Sinput}
-\end{Schunk}
-
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their sample averages:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution given:
-\texttt{x.out}.
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out, x = x.out)
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in the probabilities of
-incurring different level of cost when there is no military action versus
-military action while all the other variables held at their
-default values.
-
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out, mil = 0)
-> x.low <- setx(z.out, mil = 1)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_{i}$ be the ordered categorical dependent variable for
-observation $i$ which takes an integer value $j=1, \ldots, J$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by an unobserved
-continuous variable, $Y_i^*$,
-\begin{eqnarray*}
-Y_{i}^* \sim \textrm{Normal}(\mu_i, 1).
-\end{eqnarray*}
-Instead of $Y_i^*$, we observe categorical variable $Y_i$,
-\begin{eqnarray*}
-Y_i = j \quad \textrm{ if } \tau_{j-1} \le Y_i^* \le \tau_j \textrm{
-for } j=1,\ldots, J.
-\end{eqnarray*}
-where $\tau_j$ for $j=0,\ldots, J$ are the threshold parameters with
-the following constraints, $\tau_l < \tau_m$ for $l < m$, and
-$\tau_0=-\infty, \tau_J=\infty$.
-
-The probability of observing $Y_i$ equal to category $j$ is,
-\begin{eqnarray*}
-\Pr(Y_i=j) &=& \Phi(\tau_j \mid \mu_i)-\Phi(\tau_{j-1} \mid \mu_i)
-\textrm{ for } j=1,\ldots, J
-\end{eqnarray*}
-where $\Phi(\cdot \mid \mu_i)$ is the cumulative distribution function
-of the Normal distribution with mean $\mu_i$ and variance 1.
-
-\item The \emph{systematic component} is given by
-
-\begin{eqnarray*}
-\mu_{i}= x_i \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k\left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the ordered probit model are
-the predicted probability of belonging to each category:
-\begin{eqnarray*}
-\Pr(Y_i=j)= \Phi(\tau_j \mid x_i \beta)-\Phi(\tau_{j-1} \mid x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ and threshold parameters $\tau$
-from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are the observed values of
-$Y_i$ given the observation scheme and the posterior draws of $\beta$
-and cut points $\tau$ from the MCMC iterations.
-
-\item The first difference (\texttt{qi\$fd}) in category $j$ for the
-ordered probit model is defined as
-\begin{eqnarray*}
-\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
-\begin{eqnarray*}
-\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
-\end{eqnarray*}
-
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j} \{
-Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of observations in the treatment group that belong to category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of observations in the treatment group that belong to category $j$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "oprobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior
-distribution of the \texttt{coefficients} by using
-\texttt{z.out\$coefficients}, and view a default summary of
-information through \texttt{summary(z.out)}. Other elements available
-through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated coefficients $\beta$ and threshold parameters $\tau$.
-Note, element $\tau_1$ is normalized to 0 and is not returned in the
-\texttt{coefficients} object.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values (probabilities) of
-each of the $J$ categories for the specified values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values (observed values)
- for the specified values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values of each of the $J$ categories for the values specified in
-\texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the
-expected values of each of the $J$ categories simulated
-from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian ordinal probit regression \input{contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{oprobit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
diff --git a/inst/doc/oprobit.pdf b/inst/doc/oprobit.pdf
index b8c0dc5..92db5de 100644
Binary files a/inst/doc/oprobit.pdf and b/inst/doc/oprobit.pdf differ
diff --git a/inst/doc/oprobit.tex b/inst/doc/oprobit.tex
deleted file mode 100644
index 6aad487..0000000
--- a/inst/doc/oprobit.tex
+++ /dev/null
@@ -1,291 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Ordinal Probit Regression for Ordered Categorical Dependent Variables}
-%\VignetteDepends{Zelig, MASS}
-%\VignetteKeyWords{model, probit,regression,ordinal, ordered,categorical}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt oprobit}: Ordinal Probit Regression for Ordered
-Categorical Dependent Variables}\label{oprobit}
-
-Use the ordinal probit regression model if your dependent variables
-are ordered and categorical. They may take on either integer values
-or character strings. For a Bayesian implementation of this model,
-see \Sref{oprobit.bayes}.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "oprobit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-If {\tt Y} takes discrete integer values, the {\tt as.factor()}
-command will order it automatically. If {\tt Y} takes on values
-composed of character strings, such as ``strongly agree'', ``agree'',
-and ``disagree'', {\tt as.factor()} will order the values in the order
-in which they appear in {\tt Y}. You will need to replace your
-dependent variable with a factored variable prior to estimating the
-model through {\tt zelig()}. See \Sref{factors} for more information
-on creating ordered factors and Example \ref{ord.fact.p} below.
-
-\subsubsection{Example}
-\begin{enumerate}
-\item {Creating An Ordered Dependent Variable} \label{ord.fact.p}
-
-Load the sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(sanction)
-\end{Sinput}
-\end{Schunk}
-Create an ordered dependent variable:
-\begin{Schunk}
-\begin{Sinput}
-> sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain",
-+ "little effect", "modest loss", "major loss"))
-\end{Sinput}
-\end{Schunk}
-Estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanction)
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their observed values:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out, fn = NULL)
-\end{Sinput}
-\end{Schunk}
-Simulate fitted values given {\tt x.out} and view the results:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.out)
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-%plot does not work but is nnot included in the demo
-\begin{center}
-\end{center}
-
-\item {First Differences}
-
-Using the sample data \texttt{sanction}, let us estimate the empirical model and return the coefficients:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "oprobit",
-+ data = sanction)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their means, with {\tt mil} set
-to 0 (no military action in addition to sanctions) in the baseline
-case and set to 1 (military action in addition to sanctions) in the
-alternative case:
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out, mil = 0)
-> x.high <- setx(z.out, mil = 1)
-\end{Sinput}
-\end{Schunk}
-Generate simulated fitted values and first differences, and view the results:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/oprobit-FirstDifferencesPlot}
-\end{center}
-\end{enumerate}
-
-\subsubsection{Model}
- Let $Y_i$ be the ordered categorical dependent variable for
- observation $i$ that takes one of the integer values from $1$ to $J$
- where $J$ is the total number of categories.
-\begin{itemize}
-\item The \emph{stochastic component} is described by an unobserved continuous
- variable, $Y^*_i$, which follows the normal distribution with mean
- $\mu_i$ and unit variance
- \begin{equation*}
- Y_i^* \; \sim \; N(\mu_i, 1).
- \end{equation*}
- The observation mechanism is
- \begin{equation*}
- Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
- \quad {\rm for} \quad j=1,\dots,J.
- \end{equation*}
- where $\tau_k$ for $k=0,\dots,J$ is the threshold parameter with the
- following constraints; $\tau_l < \tau_m$ for all $l<m$ and
- $\tau_0=-\infty$ and $\tau_J=\infty$.
-
- Given this observation mechanism, the probability for each category,
- is given by
- \begin{equation*}
- \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i) - \Phi(\tau_{j-1} \mid
- \mu_i) \quad {\rm for} \quad j=1,\dots,J
- \end{equation*}
- where $\Phi(\mu_i)$ is the cumulative distribution function for the
- Normal distribution with mean $\mu_i$ and unit variance.
-
-\item The \emph{systematic component} is given by
- \begin{equation*}
- \mu_i \; = \; x_i \beta
- \end{equation*}
- where $x_i$ is the vector of explanatory variables and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the ordinal probit model
- are simulations of the predicted probabilities for each category:
-\begin{equation*}
- E(Y_i = j) \; = \; \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i)
- - \Phi(\tau_{j-1} \mid \mu_i) \quad {\rm for} \quad j=1,\dots,J,
-\end{equation*}
-given draws of $\beta$ from its posterior.
-
-\item The predicted value ({\tt qi\$pr}) is the observed value of
- $Y_i$ given the underlying standard normal distribution described by
- $\mu_i$.
-
-\item The difference in each of the predicted probabilities ({\tt
- qi\$fd}) is given by
- \begin{equation*}
- \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
- j=1,\dots,J.
- \end{equation*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
-\begin{eqnarray*}
-\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
-number of treated observations in category $j$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "oprobit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: the named vector of coefficients.
- \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
- fitted values.
- \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
- predictors $x_i \beta_j$.
- \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.total}: the total degrees of freedom.
- \item {\tt rss}: the residual sum of squares.
- \item {\tt y}: an $n \times J$ matrix of the dependent variables.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
-\begin{itemize}
- \item {\tt coef3}: a table of the coefficients with their associated
- standard errors and $t$-statistics.
- \item {\tt cov.unscaled}: the variance-covariance matrix.
- \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.
-\end{itemize}
-
- \item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as arrays. Available quantities
- are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected probabilities for the
- specified values of {\tt x}, indexed by simulation $\times$
- quantity $\times$ {\tt x}-observation (for more than one {\tt
- x}-observation).
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distribution defined by the expected probabilities, indexed by
- simulation $\times$ {\tt x}-observation.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1},
- indexed by simulation $\times$ quantity $\times$ {\tt
- x}-observation (for more than one {\tt x}-observation).
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The ordinal probit function is part of the VGAM package by Thomas Yee.
-Please cite the model as:
-\begin{verse}
-\bibentry{YeeHas03}.
-\end{verse}
-
-In addition, advanced users may wish to refer to \texttt{help(vglm)}
-in the VGAM library. Additional
-documentation is available at
-\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
- \end{document}
-
-
-
-
-
-
-
diff --git a/inst/doc/poisson.bayes.pdf b/inst/doc/poisson.bayes.pdf
index ee71568..153c517 100644
Binary files a/inst/doc/poisson.bayes.pdf and b/inst/doc/poisson.bayes.pdf differ
diff --git a/inst/doc/poisson.bayes.tex b/inst/doc/poisson.bayes.tex
deleted file mode 100644
index 74ccb31..0000000
--- a/inst/doc/poisson.bayes.tex
+++ /dev/null
@@ -1,273 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Poisson Regression}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, poisson,bayes,count,Metropolis}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{poisson.bayes}: Bayesian Poisson Regression}
-
-\label{poisson.bayes}
-
-Use the Poisson regression model if the observations of your dependent
-variable represents the number of independent events that occur during
-a fixed period of time. The model is fit using a random walk
-Metropolis algorithm. For a maximum-likelihood estimation of this
-model see \Sref{poisson}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "poisson.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Use the following argument to monitor the Markov chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{tune}: Metropolis tuning parameter, either
-a positive scalar or a vector of length $k$, where $k$ is the number
-of coefficients. The tuning parameter should be set such that the
-acceptance rate of the Metropolis algorithm is satisfactory (typically
-between 0.20 and 0.5). The default value is 1.1.
-
-\item \texttt{verbose}: default to {\tt FALSE}.
-If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
-printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default
-is \texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the
-maximum likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will be the prior mean for
-all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCpoisson)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(sanction)
-\end{Sinput}
-\end{Schunk}
-Estimating the Poisson regression using \texttt{poisson.bayes}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(num ~ target + coop, model = "poisson.bayes",
-+ data = sanction, verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-Checking convergence diagnostics before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their sample averages:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\item {Simulating First Differences} \\
-Estimating the first difference in the number of countries imposing sanctions
-when the number of targets is set to be its maximum versus its minimum :
-\begin{Schunk}
-\begin{Sinput}
-> x.max <- setx(z.out, target = max(sanction$target))
-> x.min <- setx(z.out, target = min(sanction$target))
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.max, x1 = x.min)
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the number of independent events that occur during
-a fixed time period.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \textrm{Poisson}(\lambda_i)
-\end{eqnarray*}
-where $\lambda_i$ is the mean and variance parameter.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\lambda_{i}= \exp(x_{i} \beta)
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
-and $\beta$ is the vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the Poisson model are
-calculated as following:
-\begin{eqnarray*}
-E(Y\mid X) = \lambda_i = \exp(x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ based on the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Poisson
-distribution with parameter $\lambda_i$.
-
-\item The first difference (\texttt{qi\$fd}) for the Poisson model is defined
-as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}\{Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]\},
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "poisson.bayes", data)
-\end{verbatim}
-
-\noindent you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian Poisson regression \input{contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{poisson.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
diff --git a/inst/doc/poisson.pdf b/inst/doc/poisson.pdf
index bb0f621..d29b36c 100644
Binary files a/inst/doc/poisson.pdf and b/inst/doc/poisson.pdf differ
diff --git a/inst/doc/poisson.tex b/inst/doc/poisson.tex
deleted file mode 100644
index eea2f10..0000000
--- a/inst/doc/poisson.tex
+++ /dev/null
@@ -1,253 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Poisson Regression for Event Count Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model, poisson,regression, count}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt poisson}: Poisson Regression for Event Count
-Dependent Variables}\label{poisson}
-
-Use the Poisson regression model if the observations of your dependent
-variable represents the number of independent events that occur during
-a fixed period of time (see the negative binomial model, \Sref{negbin},
-for over-dispersed event counts.) For a Bayesian implementation of
-this model, see \Sref{poisson.bayes}.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "poisson", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for poisson regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Example}
-
-Load sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(sanction)
-\end{Sinput}
-\end{Schunk}
-Estimate Poisson model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set values for the explanatory variables to their default mean values:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulate fitted values:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/poisson-ExamplePlot}
-\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.
-
-\begin{itemize}
-\item The Poisson distribution has \emph{stochastic component}
- \begin{equation*}
- Y_i \; \sim \; \textrm{Poisson}(\lambda_i),
- \end{equation*}
- where $\lambda_i$ is the mean and variance parameter.
-
-\item The \emph{systematic component} is
- \begin{equation*}
- \lambda_i \; = \; \exp(x_i \beta),
- \end{equation*}
- where $x_i$ is the vector of explanatory variables, and $\beta$ is
- the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-
-\item The expected value ({\tt qi\$ev}) is the mean of simulations
- from the stochastic component, $$E(Y) = \lambda_i = \exp(x_i
- \beta),$$ given draws of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is a random draw from the
- poisson distribution defined by mean $\lambda_i$.
-
-\item The first difference in the expected values ({\tt qi\$fd}) is given by:
-\begin{equation*}
-\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
-\end{equation*}
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "poisson", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: a vector of the fitted values for the systemic
- component $\lambda$.
- \item {\tt linear.predictors}: a vector of $x_{i}\beta$.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values given the
- specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distributions defined by $\lambda_i$.
- \item {\tt qi\$fd}: the simulated first differences in the expected
- values given the specified values of {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The Poisson model is part of the stats package by William N. Venables
-and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Advanced users may wish to refer to {\tt help(glm)} and {\tt
- help(family)}, as well as \cite{McCNel89}.
-
-Robust standard errors are implemented via the sandwich package
-by Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{Martin92}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
diff --git a/inst/doc/probit.bayes.pdf b/inst/doc/probit.bayes.pdf
index c06dbdc..5999ad0 100644
Binary files a/inst/doc/probit.bayes.pdf and b/inst/doc/probit.bayes.pdf differ
diff --git a/inst/doc/probit.bayes.tex b/inst/doc/probit.bayes.tex
deleted file mode 100644
index ec02b93..0000000
--- a/inst/doc/probit.bayes.tex
+++ /dev/null
@@ -1,300 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Probit Regression for Dichotomous Dependent Variable}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, probit,bayes, regression,dichotomous}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{probit.bayes}: Bayesian Probit Regression}
-
-\label{probit.bayes}
-
-Use the probit regression model for model binary dependent variables
-specified as a function of a set of explanatory variables. The model
-is estimated using a Gibbs sampler. For other models suitable for
-binary response variables, see Bayesian logistic
-regression(\Sref{logit.bayes}), maximum likelihood logit regression
-(\Sref{logit}), and maximum likelihood probit regression
-(\Sref{probit}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "probit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-Using the following arguments to monitor the Markov chains:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number of
-estimated coefficients. The default is \texttt{NA}, such that the
-maximum likelihood estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar value, that value will be the prior
-mean for all the coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar value, that value times an
-identity matrix will be the prior precision parameter. The default is
-0, which leads to an improper prior.
-\end{itemize}
-
-Use the following arguments to specify optional output for the model:
-\begin{itemize}
-\item \texttt{bayes.resid}: defaults to {\tt FALSE}. If {\tt TRUE},
-the latent Bayesian residuals for all observations are returned.
-Alternatively, users can specify a vector of observations for which the
-latent residuals should be returned.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCprobit)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(turnout)
-\end{Sinput}
-\end{Schunk}
-Estimating the probit regression using \texttt{probit.bayes}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(vote ~ race + educate, model = "probit.bayes",
-+ data = turnout, verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their sample averages:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution given:
-\texttt{x.out}
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\item {Simulating First Differences} \\
-Estimating the first difference (and risk ratio) in individual's probability of
-voting when education is set to be low (25th percentile) versus
-high (75th percentile) while all the other variables are held at their
-default values:
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
-> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-
-Let $Y_{i}$ be the binary dependent variable for observation $i$ which
-takes the value of either 0 or 1.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-Y_{i} & \sim & \textrm{Bernoulli}(\pi_{i})\\
-& = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
-\end{eqnarray*}
-where $\pi_{i}=\Pr(Y_{i}=1)$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\pi_{i}= \Phi(x_i \beta),
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the cumulative density function of the standard
-Normal distribution with mean 0 and variance 1, $x_{i}$ is the vector
-of $k$ explanatory variables for observation $i$, and $\beta$ is the
-vector of coefficients.
-
-\item The \emph{prior} for $\beta$ is given by
-\begin{eqnarray*}
-\beta \sim \textrm{Normal}_k \left( b_{0}, B_{0}^{-1} \right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory variables
-and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
-variance-covariance matrix).
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the probit model are
-the predicted probability of a success:
-\begin{eqnarray*}
-E(Y \mid X) = \pi_{i}= \Phi(x_i \beta),
-\end{eqnarray*}
-given the posterior draws of $\beta$ from the MCMC iterations.
-
-\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
-distribution with mean equal to the simulated expected value $\pi_{i}$.
-
-\item The first difference (\texttt{qi\$fd}) for the probit model is defined
-as
-\begin{eqnarray*}
-\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item The risk ratio (\texttt{qi\$rr})is defined as
-\begin{eqnarray*}
-\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\item In conditional prediction models, the average predicted treatment effect
-(\texttt{qi\$att.pr}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "probit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-
-\item \texttt{bayes.residuals}: When \texttt{bayes.residual} is \texttt{TRUE}
-or a set of observation numbers is given, this object contains the
-posterior draws of the latent Bayesian residuals of all the observations
-or the observations specified by the user.
-
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected values (probabilities) for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$pr}: the simulated predicted values for the specified values
-of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values for the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
-simulated from \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
-for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Bayesian probit regression \input{contributors2}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{probit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/probit.pdf b/inst/doc/probit.pdf
index e6287bf..a39f914 100644
Binary files a/inst/doc/probit.pdf and b/inst/doc/probit.pdf differ
diff --git a/inst/doc/probit.tex b/inst/doc/probit.tex
deleted file mode 100644
index 5ae7d19..0000000
--- a/inst/doc/probit.tex
+++ /dev/null
@@ -1,247 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Probit Regression for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model, probit,regression,dichotomous, binary}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt probit}: Probit Regression for Dichotomous Dependent Variables}\label{probit}
-
-Use probit regression to model binary dependent variables
-specified as a function of a set of explanatory variables. For a
-Bayesian implementation of this model, see \Sref{probit.bayes}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "probit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out, x1 = NULL)
-\end{verbatim}
-
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for probit regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE} is
-selected, {\tt zelig()} computes robust standard errors via the {\tt
-sandwich} package (see \cite{Zeileis04}). The default type of robust
-standard error is heteroskedastic and autocorrelation consistent (HAC),
-and assumes that observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame). The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Examples}
-Attach the sample turnout dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(turnout)
-\end{Sinput}
-\end{Schunk}
-Estimate parameter values for the probit regression:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(vote ~ race + educate, model = "probit", data = turnout)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set values for the explanatory variables to their default values.
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulate quantities of interest from the posterior distribution.
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-
-\subsubsection{Model}
-Let $Y_i$ be the observed binary dependent variable for observation
-$i$ which takes the value of either 0 or 1.
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{equation*}
-Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
-\end{equation*}
-where $\pi_i=\Pr(Y_i=1)$.
-
-\item The \emph{systematic component} is
-\begin{equation*}
- \pi_i \; = \; \Phi (x_i \beta)
-\end{equation*}
-where $\Phi(\mu)$ is the cumulative distribution function of the
-Normal distribution with mean 0 and unit variance.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-
-\item The expected value ({\tt qi\$ev}) is a simulation of predicted
- probability of success $$E(Y) = \pi_i = \Phi(x_i
- \beta),$$ given a draw of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is a draw from a Bernoulli
- distribution with mean $\pi_i$.
-
-\item The first difference ({\tt qi\$fd}) in expected values is
- defined as
-\begin{equation*}
-\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-\textrm{RR} = \Pr(Y = 1 \mid x_1) / \Pr(Y = 1 \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "probit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: a vector of the in-sample fitted values.
- \item {\tt linear.predictors}: a vector of $x_{i}\beta$.
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt data}: the name of the input data frame.
- \end{itemize}
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values, or predicted
- probabilities, for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from the
- distributions defined by the predicted probabilities.
- \item {\tt qi\$fd}: the simulated first differences in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the predicted
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The probit model is part of the base package by William N. Venables
-and Brian D. Ripley. Please cite the model as:
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-In addition, 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 Achim Zeileis. Please cite as
-\begin{verse}
-\bibentry{Zeileis04}.
-\end{verse}
-
-Sample data are a selection of $2,000$ observations from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
diff --git a/inst/doc/refman.tex b/inst/doc/refman.tex
index 10f49c6..7135c9f 100644
--- a/inst/doc/refman.tex
+++ b/inst/doc/refman.tex
@@ -7,10 +7,9 @@ the {\tt setx()} command. (Occasionally, you may need to use, for example, {\tt
help(print)} rather than {\tt help.zelig(print)}, to access the R
help page instead of the default Zelig help page.)
-\input{commands/zelig}
+\include{commandsRd/zelig}
\include{commandsRd/setx}
\include{commandsRd/sim}
-\include{commandsRd/sim}
\include{commandsRd/summary}
\include{commandsRd/plot.zelig}
\include{commands/print}
@@ -80,12 +79,7 @@ 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{models/2sls}
-%\include{models/3sls}
-%\include{models/sur}
-
\include{arima}
-%\include{models/beta}
\include{blogit}
\include{bprobit}
\include{ei.dynamic}
@@ -96,9 +90,15 @@ 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{irt1d}
\include{irtkd}
\include{logit}
+\include{logit.gee}
\include{logit.bayes}
\include{lognorm}
\include{ls}
@@ -108,6 +108,11 @@ of simulations increases.
\include{negbin}
\include{netls}
\include{netlogit}
+\include{netcloglog}
+\include{netgamma}
+\include{netnormal}
+\include{netpoisson}
+\include{netprobit}
\include{normal}
\include{normal.bayes}
\include{ologit}
@@ -118,8 +123,11 @@ of simulations increases.
\include{probit}
\include{probit.bayes}
\include{relogit}
+\include{sur}
+\include{threesls}
\include{tobit}
\include{tobit.bayes}
+\include{twosls}
\include{weibull}
\chapter{Commands for Programmers and Contributors}
diff --git a/inst/doc/relogit.pdf b/inst/doc/relogit.pdf
index 68e2ecd..fd69ba0 100644
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diff --git a/inst/doc/relogit.tex b/inst/doc/relogit.tex
deleted file mode 100644
index 9cae9ee..0000000
--- a/inst/doc/relogit.tex
+++ /dev/null
@@ -1,453 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Rare Events Logistic Regression for Dichotomous Dependent Variables}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{model,logistic,regression,dichotomous}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt relogit}: Rare Events Logistic Regression for
-Dichotomous Dependent Variables}
-\label{relogit}
-
-The {\tt relogit} procedure estimates the same model as standard
-logistic regression (appropriate when you have a dichotomous dependent
-variable and a set of explanatory variables; see \Sref{logit}), but
-the estimates are corrected for the bias that occurs when the
-sample is small or the observed events are rare (i.e., if the
-dependent variable has many more 1s than 0s or the reverse). The {\tt
- relogit} procedure also optionally uses prior correction for
-case-control sampling designs.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, model = "relogit", tau = NULL,
- case.correct = c("prior", "weighting"),
- bias.correct = TRUE, robust = FALSE,
- data = mydata, ...)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-The {\tt relogit} procedure supports four optional arguments in
-addition to the standard arguments for {\tt zelig()}. You may
-additionally use:
-\begin{itemize}
-\item {\tt tau}: a vector containing either one or two values for
- $\tau$, the true population fraction of ones. Use, for example,
- {\tt tau = c(0.05, 0.1)} to specify that the lower bound on {\tt
- tau} is 0.05 and the upper bound is 0.1. If left unspecified, only
-finite-sample bias correction is performed, not case-control correction.
-\item {\tt case.correct}: if {\tt tau} is specified, choose a method
-to correct for case-control sampling design: {\tt "prior"} (default)
-or {\tt "weighting"}.
-\item {\tt bias.correct}: a logical value of {\tt TRUE} (default) or
- {\tt FALSE} indicating whether the intercept should be corrected for
- finite sample (rare events) bias.
-\item {\tt robust}: defaults to {\tt FALSE} (except when {\tt
-case.control = "weighting"}; the default in this case becomes {\tt
-robust = TRUE}). If {\tt TRUE} is selected, {\tt zelig()} computes
-robust standard errors via the {\tt sandwich} package (see
-\cite{Zeileis04}). The default type of robust standard error is
-heteroskedastic and autocorrelation consistent (HAC), and assumes that
-observations are ordered by time index.
-
-In addition, {\tt robust} may be a list with the following options:
-\begin{itemize}
-\item {\tt method}: Choose from
-\begin{itemize}
-\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
-errors.
-\item {\tt "kernHAC"}: HAC standard errors using the
-weights given in \cite{Andrews91}.
-\item {\tt "weave"}: HAC standard errors using the
-weights given in \cite{LumHea99}.
-\end{itemize}
-\item {\tt order.by}: defaults to {\tt NULL} (the observations are
-chronologically ordered as in the original data). Optionally, you may
-specify a vector of weights (either as {\tt order.by = z}, where {\tt
-z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
-{\tt z} is a variable in the data frame) The observations are
-chronologically ordered by the size of {\tt z}.
-\item {\tt \dots}: additional options passed to the functions
-specified in {\tt method}. See the {\tt sandwich} library and
-\cite{Zeileis04} for more options.
-\end{itemize}
-\end{itemize}
-Note that if {\tt tau = NULL, bias.correct = FALSE, robust = FALSE},
-the {\tt relogit} procedure performs a standard logistic regression
-without any correction.
-
-\subsubsection*{Example 1: One Tau with Prior Correction and Bias Correction}
-
-Due to memory and space considerations, the data used here are a
-sample drawn from the full data set used in King and Zeng,
-2001,\nocite{KinZen01b} The proportion of militarized interstate
-conflicts to the absence of disputes is $\tau = 1,042 / 303,772
-\approx 0.00343$. To estimate the model,
-\begin{Schunk}
-\begin{Sinput}
-> data(mid)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> z.out1 <- zelig(conflict ~ major + contig + power + maxdem +
-+ mindem + years, data = mid, model = "relogit", tau = 1042/303772)
-\end{Sinput}
-\end{Schunk}
-Summarize the model output:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out1)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their means:
-\begin{Schunk}
-\begin{Sinput}
-> x.out1 <- setx(z.out1)
-\end{Sinput}
-\end{Schunk}
-Simulate quantities of interest:
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out1, x = x.out1)
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out1)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/relogit-Example1Plot}
-\end{center}
-
-\subsubsection*{Example 2: One Tau with Weighting, Robust Standard
-Errors, and Bias Correction}
-
-Suppose that we wish to perform case control correction using
-weighting (rather than the default prior correction). To
-estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out2 <- zelig(conflict ~ major + contig + power + maxdem +
-+ mindem + years, data = mid, model = "relogit", tau = 1042/303772,
-+ case.control = "weighting", robust = TRUE)
-\end{Sinput}
-\end{Schunk}
-Summarize the model output:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out2)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their means:
-\begin{Schunk}
-\begin{Sinput}
-> x.out2 <- setx(z.out2)
-\end{Sinput}
-\end{Schunk}
-Simulate quantities of interest:
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out2, x = x.out2)
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-
-\subsubsection*{Example 3: Two Taus with Bias Correction and Prior Correction}
-
-Suppose that we did not know that $\tau \approx 0.00343$, but only
-that it was somewhere between $(0.002, 0.005)$. To estimate a model
-with a range of feasible estimates for $\tau$ (using the default prior
-correction method for case control correction):
-\begin{Schunk}
-\begin{Sinput}
-> z.out2 <- zelig(conflict ~ major + contig + power + maxdem +
-+ mindem + years, data = mid, model = "relogit", tau = c(0.002,
-+ 0.005))
-\end{Sinput}
-\end{Schunk}
-Summarize the model output:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out2)
-\end{Sinput}
-\end{Schunk}
-Set the explanatory variables to their means:
-\begin{Schunk}
-\begin{Sinput}
-> x.out2 <- setx(z.out2)
-\end{Sinput}
-\end{Schunk}
-Simulate quantities of interest:
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out2, x = x.out2)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out2)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/relogit-Example3Plot}
-\end{center}
-The cost of giving a range of values for $\tau$ is that point
-estimates are not available for quantities of interest. Instead,
-quantities are presented as confidence intervals with significance
-less than or equal to a specified level (e.g., at least 95\% of the
-simulations are contained in the nominal 95\% confidence interval).
-
-\subsubsection{Model}
-
-\begin{itemize}
-\item Like the standard logistic regression, the \emph{stochastic
- component} for the rare events logistic regression is:
-\begin{equation*}
- Y_i \; \sim \; \textrm{Bernoulli}(\pi_i),
-\end{equation*}
-where $Y_i$ is the binary dependent variable, and takes a value of
-either 0 or 1.
-
-\item The \emph{systematic component} is:
- \begin{equation*}
- \pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
- \end{equation*}
-
-
-\item If the sample is generated via a case-control (or choice-based)
- design, such as when drawing all events (or ``cases'') and a sample
- from the non-events (or ``controls'') and going backwards to collect
- the explanatory variables, you must correct for selecting on the
- dependent variable. While the slope coefficients are approximately
- unbiased, the constant term may be significantly biased. Zelig has
-two methods for case control correction:
-\begin{enumerate}
-\item The ``prior correction'' method
-adjusts the intercept term. Let $\tau$ be the true population
-fraction of events, $\bar{y}$ the fraction of events in the sample,
-and $\hat{\beta_0}$ the uncorrected intercept term. The corrected
-intercept $\beta_0$ is:
-\begin{equation*}
-\beta = \hat{\beta_0} - \ln \left[ \bigg( \frac{1 - \tau}{\tau}
- \bigg) \bigg( \frac{\bar{y}}{1 - \bar{y}} \bigg) \right].
-\end{equation*}
-
-\item The ``weighting'' method performs a weighted logistic regression to
-correct for a case-control sampling design. Let the 1 subscript
-denote observations for which the dependent variable is observed as a
-1, and the 0 subscript denote observations for which the dependent
-variable is observed as a 0. Then the vector of weights $w_i$
-\begin{eqnarray*}
-w_1 &=& \frac{\tau}{\bar{y}} \\
-w_0 &=& \frac{(1 - \tau)}{(1 - \bar{y})} \\
-w_i &=& w_1 Y_i + w_0 (1 - Y_i)
-\end{eqnarray*}
-\end{enumerate}
- If $\tau$ is unknown, you may alternatively specify an upper and
- lower bound for the possible range of $\tau$. In this case, the
- {\tt relogit} procedure uses ``robust Bayesian'' methods to generate
- a confidence interval (rather than a point estimate) for each
- quantity of interest. The nominal coverage of the confidence
- interval is at least as great as the actual coverage.
-
-\item By default, estimates of the the coefficients $\beta$ are
- bias-corrected to account for finite sample or rare events bias. In
- addition, quantities of interest, such as predicted probabilities,
- are also corrected of rare-events bias. If $\widehat{\beta}$ are
-the uncorrected logit coefficients and bias($\widehat{\beta}$) is the
-bias term, the corrected coefficients $\tilde{\beta}$ are
-\begin{equation*}
-\widehat{\beta} - \textrm{bias}(\widehat{\beta}) = \tilde{\beta}
-\end{equation*}
-The bias term is
-\begin{equation*}
-\textrm{bias}(\widehat{\beta}) = (X'WX)^{-1} X'W \xi
-\end{equation*}
-where
-\begin{eqnarray*}
-\xi_i &=& 0.5 Q_{ii} \Big( (1 + w-1)\widehat{\pi}_i - w_1 \Big) \\
-Q &=& X(X'WX)^{-1} X' \\
-W = \textrm{diag}\{\widehat{\pi}_i (1 - \widehat{\pi}_i) w_i\}
-\end{eqnarray*}
-where $w_i$ and $w_1$ are given in the ``weighting'' section above.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item For either one or no $\tau$:
- \begin{itemize}
- \item The expected values ({\tt qi\$ev}) for the rare events logit
- are simulations of the predicted probability $$E(Y) = \pi_i =
- \frac{1}{1 + \exp(-x_i \beta)},$$
- given draws of $\beta$ from its posterior.
- \item The predicted value ({\tt qi\$pr}) is a draw from a binomial
- distribution with mean equal to the simulated $\pi_i$.
- \item The first difference ({\tt qi\$fd}) is defined as
- \begin{equation*}
- \textrm{FD} = \Pr(Y = 1 \mid x_1, \tau) - \Pr(Y = 1 \mid x, \tau).
- \end{equation*}
- \item The risk ratio ({\tt qi\$rr}) is defined as
- \begin{equation*}
- \textrm{RR} = \Pr(Y = 1 \mid x_1, \tau) \ / \ \Pr(Y = 1 \mid x, \tau).
- \end{equation*}
- \end{itemize}
- \item For a range of $\tau$ defined by $[\tau_1, \tau_2]$, each of
- the quantities of interest are $n \times 2$ matrices, which report
- the lower and upper bounds, respectively, for a confidence interval
- with nominal coverage at least as great as the actual coverage. At
- worst, these bounds are conservative estimates for the likely range
- for each quantity of interest. Please refer to \hlink{King and
- Zeng
- (2002)}{http://gking.harvard.edu/files/1s.pdf}\nocite{KinZen02b}
- for the specific method of calculating bounded quantities of
- interest.
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the treatment
- ($t_i=1$) and control ($t_i=0$) groups. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. Variation in
- the simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "relogit", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may
- extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt bias.correct}: {\tt TRUE} if bias correction was
-selected, else {\tt FALSE}.
- \item {\tt prior.correct}: {\tt TRUE} if prior correction was
-selected, else {\tt FALSE}.
- \item {\tt weighting}: {\tt TRUE} if weighting was selected, else
-{\tt FALSE}.
- \item {\tt tau}: the value of {\tt tau} for which case control
-correction was implemented.
- \item {\tt residuals}: the working residuals in the final iteration
- of the IWLS fit.
- \item {\tt fitted.values}: the vector of fitted values for the
- systemic component, $\pi_i$.
- \item {\tt linear.predictors}: the vector of $x_{i} \beta$
- \item {\tt aic}: Akaike's Information Criterion (minus twice the
- maximized log-likelihood plus twice the number of coefficients).
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
- Note that for a range of $\tau$, each of the above items may be
- extracted from the {\tt "lower.estimate"} and {\tt
- "upper.estimate"} objects in your {\tt zelig} output. Use {\tt
- lower <- z.out\$lower.estimate}, and then {\tt
- lower\$coefficients} to extract the coefficients for the
- empirical estimate generated for the smaller of the two $\tau$.
-
-\item From {\tt summary(z.out)}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
- \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
- covariances.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values, or predicted
- probabilities, for the specified values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from Binomial
- distributions given the predicted probabilities.
- \item {\tt qi\$fd}: the simulated first difference in the predicted
- probabilities for the values specified in {\tt x} and {\tt x1}.
- \item {\tt qi\$rr}: the simulated risk ratio for the predicted
- probabilities simulated from {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Differences with Stata Version}
-The Stata version of ReLogit and the R implementation differ slightly
-in their coefficient estimates due to differences in the matrix
-inversion routines implemented in R and Stata. Zelig uses
-orthogonal-triangular decomposition (through {\tt lm.influence()}) to
-compute the bias term, which is more numerically stable than
-standard matrix calculations.
-
-\subsubsection{Contributors}
-
-Please cite the rare events logit model as:
-\begin{verse}
-\bibentry{KinZen01b}.
-\end{verse}
-\begin{verse}
-\bibentry{KinZen01}.
-\end{verse}
-\begin{verse}
-\bibentry{KinZen02b}.
-\end{verse}
-
-Sample data are a selection of observations from
-\begin{verse}
-\bibentry{KinZen01b}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
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diff --git a/inst/doc/static/graphs.pdf b/inst/doc/static/graphs.pdf
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diff --git a/inst/doc/static/install.pdf b/inst/doc/static/install.pdf
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index 0000000..067a7c6
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diff --git a/inst/doc/static/intro.pdf b/inst/doc/static/intro.pdf
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index 0000000..e68b969
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diff --git a/inst/doc/static/newModels.pdf b/inst/doc/static/newModels.pdf
new file mode 100644
index 0000000..1594564
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diff --git a/inst/doc/static/syntax.pdf b/inst/doc/static/syntax.pdf
new file mode 100644
index 0000000..1f5adc3
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diff --git a/inst/doc/static/upquote.sty b/inst/doc/static/upquote.sty
new file mode 100644
index 0000000..67b4703
--- /dev/null
+++ b/inst/doc/static/upquote.sty
@@ -0,0 +1,76 @@
+\NeedsTeXFormat{LaTeX2e}[1995/12/01]
+\ProvidesPackage{upquote}[2003/08/11 v1.1 Covington's upright-quote
+ modification to verbatim and verb]
+
+%% Copyright 2000 Michael A. Covington.
+%% Copyright 2003 Michael A. Covington, Frank Mittelbach.
+%%
+%% It may be distributed and/or modified under the
+%% conditions of the LaTeX Project Public License, either version 1.2
+%% of this license or (at your option) any later version.
+%% The latest version of this license is in
+%% http://www.latex-project.org/lppl.txt
+%% and version 1.2 or later is part of all distributions of LaTeX
+%% version 1999/12/01 or later.
+%%
+
+% Problem: Computer Modern Typewriter is the best font for program listings,
+% *but* it prints ` ' as bent opening and closing single quotes.
+% Other fonts, and most programming languages, print ` as a grave
+% accent and ' upright; ' is used both to open and to close quoted
+% strings.
+
+% See also M. Covington, "Computer Languages in Type," Journal of
+% Scholarly Publishing 26.1:34-41 (1994).
+
+% Solution: This package switches the typewriter font to Computer Modern
+% Typewriter (regardless of other fonts in use, so long as this
+% package is called afterward) and modifies the behavior of
+% verbatim, verbatim*, verb, and verb* to print ` ' the desired way.
+% It does not affect \tt, \texttt, etc.
+
+% Author: Michael A. Covington
+% Artificial Intelligence Center
+% The University of Georgia
+% http://www.ai.uga.edu/~mc
+%
+% and
+%
+% Covington Innovations (Consulting and Typesetting)
+% http://www.CovingtonInnovations.com
+
+% The modification is done by adding instructions to \@noligs,
+% which is called by verbatim and verb in order to turn the
+% characters ` < > , ' - into active characters that merely
+% print themselves rather than activating ligatures.
+%
+% What is added is code to redefine ` as grave and ' as upright single quote.
+%
+
+% Bug fix, 2000/12/11: previously, '0 (or ' and any digit) would print as a
+% nonexistent character. (The reason: \char13 or \char18 was combining with
+% the digit to make \char130, etc.) Fixed by adding curly brackets.
+
+
+
+%% Rewritten by FMi 2003/06/19
+%
+%
+% Use textcomp official chars so that change works in various
+% encodings
+%
+% Extend \@noligs (this also works with the latest listings package
+% that recognizes that the package was loaded)
+
+\RequirePackage{textcomp}
+
+\begingroup
+\catcode`'=\active
+\catcode``=\active
+\g at addto@macro\@noligs
+ {\let`\textasciigrave
+ \let'\textquotesingle}
+\endgroup
+
+
+\endinput
diff --git a/inst/doc/models/sur.tex b/inst/doc/sur.Rnw
similarity index 86%
rename from inst/doc/models/sur.tex
rename to inst/doc/sur.Rnw
index 49836a6..4e0bb20 100644
--- a/inst/doc/models/sur.tex
+++ b/inst/doc/sur.Rnw
@@ -1,5 +1,22 @@
-\subsection{\texttt{sur}: Seemingly Unrelated Regression}
+\SweaveOpts{results=hide, prefix.string=vigpics/sur}
+\include{zinput}
+%\VignetteIndexEntry{Seemingly Unrelated Regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,continuous, least squares,error}
+%\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 sur}: Seemingly Unrelated Regression}
\label{sur}
+
\texttt{sur} extends ordinary least squares analysis to estimate system of linear
equations with correlated error terms. The seemingly unrelated regression model can
be viewed as a special case of generalized least squares.
@@ -19,11 +36,11 @@ be viewed as a special case of generalized least squares.
\item \texttt{formula}:a list whose elements are formulae corresponding to
the $M$ equations and their respective dependent and explanatory variables.
For example, when there are no constraints on the coefficients:
-\begin{verbatim}
-> fml <- list ("mu1" = Y1 ~ X1,
+<<Inputs.list1>>=
+ fml <- list ("mu1" = Y1 ~ X1,
"mu2" = Y2 ~ X2,
"mu3" = Y3 ~ X3)
-\end{verbatim}
+@
\texttt{"mu1"} is the label for the first equation with Y1 as the dependent variable
and X1 as the explanatory variable. Similarly \texttt{"mu2"} and \texttt{"mu3"} are the
labels for the Y2 and Y3 equations.
@@ -32,11 +49,11 @@ the special function \texttt{tag}. \texttt{tag} takes two parameters. The first
parameter is the variable whose coefficient needs to be constrained and the second
parameter is label for the constrained coefficient. Each label uniquely identifies
the constrained coefficient. For example:
-\begin{verbatim}
-> fml <- list ("mu1" = Y1 ~ tag(Xc,"constrain1")+ X1,
+<<Inputs.list2>>=
+ fml <- list ("mu1" = Y1 ~ tag(Xc,"constrain1")+ X1,
"mu2" = Y2 ~ tag(Xc,"constrain1")+ X2,
"mu3" = Y3 ~ X3)
-\end{verbatim}
+@
\end{itemize}
\subsubsection{Additional Inputs}
\texttt{sur} takes the following additional inputs for model
@@ -93,30 +110,34 @@ positive semidefinit. Thus, it is doubtful whether formula 2 is really superior
(Theil, 1971, p.322).
\subsubsection{Examples}
Attaching the example dataset:
- \begin{verbatim}
- > data(grunfeld)
- \end{verbatim}
+<<Examples.data>>=
+ data(grunfeld)
+@
Formula:
-\begin{verbatim}
- >formula<-list(mu1=Ige~Fge+Cge,
+<<Examples.formula>>=
+ formula<-list(mu1=Ige~Fge+Cge,
mu2=Iw~Fw+Cw)
-\end{verbatim}
+@
Estimating the model using \texttt{sur}:
-\begin{verbatim}
-> z.out<-zelig(formula,"sur",data=grunfeld)
-> summary(z.out)
-\end{verbatim}
+<<Examples.zelig>>=
+ z.out<-zelig(formula=formula,model="sur",data=grunfeld)
+@
+<<Examples.summary>>=
+summary(z.out)
+@
Set explanatory variables to their default (mean/mode) values
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
+<<Examples.setx>>=
+ x.out <- setx(z.out)
+@
Simulate draws from the posterior distribution:
-\begin{verbatim}
-> s.out <- sim(z.out)
-> summary(s.out)
-\end{verbatim}
+<<Examples.sim>>=
+ s.out <- sim(z.out,x=x.out)
+@
+<<Examples.summary.sim>>=
+summary(s.out)
+@
\clearpage
\subsubsection{Model}
The basic seemingly unrelated regression model assumes that for each individual
@@ -167,12 +188,18 @@ available through the \texttt{\$} operator are listed below:
\item \texttt{rcovest}: residual covariance matrix used for estimation.
\item \texttt{mcelr2}: McElroys R-squared value for the system.
\end{itemize}
-\input{models/systemfit_output}
+\input{systemfit_output}
\begin{itemize}
\item \texttt{maxiter}: maximum number of iterations.
\item \texttt{tol}: tolerance level indicating when to stop the iteration.
\end{itemize}
\subsubsection{Contributors}
-The \texttt{sur} function is adapted from
-\input{models/contributors_systemfit}
+The \texttt{sur} function is adapted from \input{contributors_systemfit}
+<<afterpkgs, echo=FALSE>>=
+ after <- search()
+ torm <- setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+\end{document}
diff --git a/inst/doc/sur.pdf b/inst/doc/sur.pdf
new file mode 100644
index 0000000..78dd220
Binary files /dev/null and b/inst/doc/sur.pdf differ
diff --git a/inst/doc/models/systemfit_output.tex b/inst/doc/systemfit_output.tex
similarity index 100%
rename from inst/doc/models/systemfit_output.tex
rename to inst/doc/systemfit_output.tex
diff --git a/inst/doc/models/3sls.tex b/inst/doc/threesls.Rnw
similarity index 76%
rename from inst/doc/models/3sls.tex
rename to inst/doc/threesls.Rnw
index d5c745e..08d3c63 100644
--- a/inst/doc/models/3sls.tex
+++ b/inst/doc/threesls.Rnw
@@ -1,21 +1,40 @@
-\subsection{\texttt{3sls}: Three Stage Least Squares}
-\label{3sls}
-\texttt{3sls} is a combination of two stage least squares
+\SweaveOpts{results=hide, prefix.string=vigpics/threesls}
+\include{zinput}
+%\VignetteIndexEntry{ Three Stage Least Squares}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,continuous, least squares,error term}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+library(Zelig)
+@
+
+\section{{\tt threesls}: Three Stage Least Squares}
+\label{threesls}
+
+\texttt{threesls} is a combination of two stage least squares
and seemingly unrelated regression. It provides consistent estimates for linear regression models with
explanatory variables correlated with the error term. It also extends ordinary least squares
analysis to estimate system of linear equations with correlated error terms
\subsubsection{Syntax}
-\begin{verbatim}
-> fml <- list ("mu1" = Y1 ~ X1 + Z1,
+<<Syntax.list>>=
+ fml <- list ("mu1" = Y1 ~ X1 + Z1,
"mu2" = Y2 ~ X2 + Z2,
"inst1" = Z1 ~ W1 + X1,
"inst2" = Z2 ~ W2 + X2)
-> z.out <- zelig(formula = fml, model = "3sls", data = mydata)
+@
+\begin{verbatim}
+> z.out <- zelig(formula = fml, model = "treesls", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
\subsubsection{Inputs}
-\texttt{3sls} regression specification requires at least two sets of equations. The first set of $M$ euqations
+\texttt{threesls} regression specification requires at least two sets of equations. The first set of $M$ euqations
corresponds to the $M$ dependent variables ($Y_1,\ldots,Y_M$) to be estimated. The second set of equations ($Z$)
corresponds to the instrumental variables in the $M$ equations.
\begin{itemize}
@@ -23,12 +42,12 @@ corresponds to the instrumental variables in the $M$ equations.
equations. The system of equations is listed first as \texttt{mu}s. The equations
for the instrumental variables are listed next as \texttt{inst}s.
For example:
-\begin{verbatim}
-> fml <- list ("mu1" = Y1 ~ X1 + Z1,
+<<Inputs.list>>=
+ fml <- list ("mu1" = Y1 ~ X1 + Z1,
"mu2" = Y2 ~ X2 + Z2,
"inst1" = Z1 ~ W1 + X1,
"inst2" = Z2 ~ W2 + X2)
-\end{verbatim}
+@
\texttt{"mu1"} is the first equation in the two equation model with \texttt{Y1}
as the dependent variable and \texttt{X1} and \texttt{Z1} as
the explanatory variables. \texttt{"mu2"} is the second equation with
@@ -44,7 +63,7 @@ the error term.
problematic explanatory variables (\texttt{Z})
\end{itemize}
\subsubsection{Additional Inputs}
-\texttt{3sls} takes the following additional inputs for model
+\texttt{threesls} takes the following additional inputs for model
specifications:
\begin{itemize}
\item \texttt{TX}: an optional matrix to transform the regressor
@@ -53,7 +72,7 @@ matrix and, hence, also the coefficient vector (see details). Default is \texttt
\item \texttt{tol}: tolerance level indicating when to stop the iteration.
\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
matrix (see details). Default is equal to 1.
-\item \texttt{formula3sls}: formula for calculating the 3sls estimator, one of ``GLS'',
+\item \texttt{formulathreesls}: formula for calculating the threesls estimator, one of ``GLS'',
``IV'', ``GMM'', ``Schmidt'', or ``Eviews'' (see details.)
\item \texttt{probdfsys}: use the degrees of freedom of the whole system
(in place of the degrees of freedom of the single equation to calculate probability
@@ -100,38 +119,44 @@ for the diagonal elements of the residual covariance matrix. If $i neq j$, only
yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
(Theil, 1971, p.322).
-The formulas to calculate the 3sls estimator lead to identical results
+The formulas to calculate the threesls estimator lead to identical results
if the same instruments are used in all equations. If different instruments
-are used in the different equations, only the GMM-3sls estimator (``GMM'')
-and the 3sls estimator proposed by Schmidt (1990) (``Schmidt'') are consistent, whereas
+are used in the different equations, only the GMM-threesls estimator (``GMM'')
+and the threesls estimator proposed by Schmidt (1990) (``Schmidt'') are consistent, whereas
``GMM'' is efficient relative to ``Schmidt'' (see Schmidt, 1990).
\subsubsection{Examples}
Attaching the example dataset:
- \begin{verbatim}
- > data(kmenta)
- \end{verbatim}
+<<Examples.data>>=
+ data(kmenta)
+@
Formula:
-\begin{verbatim}
- >formula<-list(mu1= q ~ p + d,
+<<Examples.list>>=
+ formula <- list(mu1= q ~ p + d,
mu2=q ~ p + f + a,
inst =~ d + f+ a )
-\end{verbatim}
-Estimating the model using \texttt{3sls}:
-\begin{verbatim}
-> z.out<-zelig(formula=formula, model ="3sls",data=kmenta)
-> summary(z.out)
-\end{verbatim}
+@
+Estimating the model using \texttt{threesls}:
+<<Examples.zelig>>=
+ z.out <- zelig(formula=formula, model ="threesls",data=kmenta)
+ summary(z.out)
+@
Set explanatory variables to their default (mean/mode) values
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
+<<Examples.setx>>=
+ x.out <- setx(z.out)
+@
Simulate draws from the posterior distribution:
-\begin{verbatim}
-> s.out <- sim(z.out)
-> summary(s.out)
-\end{verbatim}
+<<Examples.sim>>=
+ s.out <- sim(z.out, x=x.out)
+ summary(s.out)
+@
+Plot the quantities of interest
+\begin{center}
+<<label=Examplesthreesls, fig=true, echo=false>>=
+plot(s.out)
+@
+\end{center}
\clearpage
\subsubsection{Model}
@@ -145,7 +170,7 @@ For information about seemingly unrelated regression, see
The output of each Zelig command contains useful information which you may
view. For example, if you run:
\begin{verbatim}
-z.out <- zelig(formula=fml, model = "3sls", data)
+z.out <- zelig(formula=fml, model = "threesls", data)
\end{verbatim}
\noindent then you may examine the available information in \texttt{z.out} by
using \texttt{names(z.out)}, see the draws from the posterior distribution of
@@ -156,16 +181,22 @@ available through the \texttt{\$} operator are listed below:
\item \texttt{rcovest}: residual covariance matrix used for estimation.
\item \texttt{mcelr2}: McElroys R-squared value for the system.
\item \texttt{h}: matrix of all (diagonally stacked) instrumental variables.
-\item \texttt{formula3sls}: formula for calculating the 3sls estimator
+\item \texttt{formulathreesls}: formula for calculating the threesls estimator
\end{itemize}
-\input{models/systemfit_output}
+\input{systemfit_output}
\begin{itemize}
\item \texttt{inst*}: instruments of the ith equation.
\item \texttt{h*}: matrix of instrumental variables of the ith equation.
\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
\end{itemize}
\subsubsection{Contributors}
-The \texttt{3sls} function is adapted from
-\input{models/contributors_systemfit}
+The \texttt{threesls} function is adapted from \input{contributors_systemfit}
+<<afterpkgs, echo=FALSE>>=
+ after <- search()
+ torm <- setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+\end{document}
diff --git a/inst/doc/threesls.pdf b/inst/doc/threesls.pdf
new file mode 100644
index 0000000..342450c
Binary files /dev/null and b/inst/doc/threesls.pdf differ
diff --git a/inst/doc/tobit.bayes.pdf b/inst/doc/tobit.bayes.pdf
index abd51cf..48e2316 100644
Binary files a/inst/doc/tobit.bayes.pdf and b/inst/doc/tobit.bayes.pdf differ
diff --git a/inst/doc/tobit.bayes.tex b/inst/doc/tobit.bayes.tex
deleted file mode 100644
index 8530ff4..0000000
--- a/inst/doc/tobit.bayes.tex
+++ /dev/null
@@ -1,306 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Bayesian Linear Regression for a Censored Dependent Variable}
-%\VignetteDepends{Zelig, MCMCpack}
-%\VignetteKeyWords{model, linear,bayes,regression,censored,continuous}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{tobit.bayes}: Bayesian Linear Regression for a
-Censored Dependent Variable} \label{tobit.bayes}
-
-Bayesian tobit regression estimates a linear regression model with a
-censored dependent variable using a Gibbs sampler. The dependent
-variable may be censored from below and/or from above. For other
-linear regression models with fully observed dependent variables, see
-Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
-regression (\Sref{normal}), or least squares (\Sref{ls}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
- model = "tobit.bayes", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments to specify how the
-dependent variable is censored.
-\begin{itemize}
-\item \texttt{below}: point at which the dependent variable is censored
-from below. If the dependent variable is only censored from above, set
-\texttt{below = -Inf}. The default value is 0.
-\item \texttt{above}: point at which the dependent variable is censored
-from above. If the dependent variable is only censored from below, set
-\texttt{above = Inf}. The default value is \texttt{Inf}.
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-Use the following arguments to monitor the convergence of the Markov
-chain:
-\begin{itemize}
-\item \texttt{burnin}: number of the initial MCMC iterations to be
- discarded (defaults to 1,000).
-
-\item \texttt{mcmc}: number of the MCMC iterations after burnin
-(defaults to 10,000).
-
-\item \texttt{thin}: thinning interval for the Markov chain. Only every
- \texttt{thin}-th draw from the Markov chain is kept. The value of
-\texttt{mcmc} must be divisible by this value. The default value is 1.
-
-\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE},
-the progress of the sampler (every $10\%$) is printed to the screen.
-
-\item \texttt{seed}: seed for the random number generator. The default is
-\texttt{NA} which corresponds to a random seed of 12345.
-
-\item \texttt{beta.start}: starting values for the Markov
-chain, either a scalar or vector with length equal to the number
-of estimated coefficients. The default is \texttt{NA}, such that the
-least squares estimates are used as the starting values.
-
-\end{itemize}
-
-Use the following parameters to specify the model's priors:
-\begin{itemize}
-\item \texttt{b0}: prior mean for the coefficients, either a numeric
-vector or a scalar. If a scalar, that value will be the prior mean for
-all coefficients. The default is 0.
-
-\item \texttt{B0}: prior precision parameter for the coefficients,
-either a square matrix (with the dimensions equal to the number of the
-coefficients) or a scalar. If a scalar, that value times an identity
-matrix will be the prior precision parameter. The default is 0, which
-leads to an improper prior.
-
-\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
-prior on the variance of the disturbance terms.
-
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(MCMCtobit)} for more
-information.
-
-\input{coda_diag}
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(tobin)
-\end{Sinput}
-\end{Schunk}
-Estimating linear regression using \texttt{tobit.bayes}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(durable ~ age + quant, model = "tobit.bayes",
-+ data = tobin, verbose = TRUE)
-\end{Sinput}
-\end{Schunk}
-Checking for convergence before summarizing the estimates:
-\begin{Schunk}
-\begin{Sinput}
-> geweke.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> heidel.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> raftery.diag(z.out$coefficients)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their sample averages:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
-
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
-> x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
-\end{Sinput}
-\end{Schunk}
-Estimating the first difference for the effect of
-high versus low liquidity ratio on duration(\texttt{durable}):
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-Let $Y_i^*$ be the dependent variable which is not directly observed. Instead,
-we observe $Y_i$ which is defined as following:
-\begin{equation*}
-Y_i = \left\{
-\begin{array}{lcl}
-Y_i^* &\textrm{if} & c_1<Y_i^*<c_2 \\
-c_1 &\textrm{if} & c_1 \ge Y_i^* \\
-c_2 &\textrm{if} & c_2 \le Y_i^*
-\end{array}\right.
-\end{equation*}
-where $c_1$ is the lower bound below which $Y_i^*$ is censored, and
-$c_2$ is the upper bound above which $Y_i^*$ is censored.
-
-\begin{itemize}
-\item The \emph{stochastic component} is given by
-\begin{eqnarray*}
-\epsilon_{i} & \sim & \textrm{Normal}(0, \sigma^2)
-\end{eqnarray*}
-where $\epsilon_{i}=Y^*_i-\mu_i$.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i}= x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-
-\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are
-given by
-\begin{eqnarray*}
-\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
-\sigma^{2} & \sim & \textrm{InverseGamma} \left( \frac{c_0}{2}, \frac{d_0}{2}
-\right)
-\end{eqnarray*}
-where $b_{0}$ is the vector of means for the $k$ explanatory
-variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
-a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape
-and scale parameters for $\sigma^{2}$. Note that $\beta$ and
-$\sigma^2$ are assumed \emph{a priori} independent.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the tobit regression model is
-calculated as following. Let
-\begin{eqnarray*}
-\Phi_1 &=& \Phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
-\Phi_2 &=& \Phi\left(\frac{(c_2 - x \beta)}{\sigma}\right) \\
-\phi_1 &=& \phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
-\phi_2 &=& \phi\left(\frac{(c_2 - x \beta)}{\sigma}\right)
-\end{eqnarray*}
-where $\Phi(\cdot)$ is the (cumulative) Normal density function and
-$\phi(\cdot)$ is the Normal probability density function of the
-standard normal distribution. Then the expected values are
-\begin{eqnarray*}
-E(Y|x) &=& P(Y^* \le c_1|x) c_1+P(c_1<Y^*<c_2|x) E(Y^* \mid c_1<Y^*<c_2, x)+P(Y^* \ge c_2) c_2 \\
- &=& \Phi_{1}c_1 + x \beta(\Phi_{2}-\Phi_{1}) + \sigma (\phi_1 -\phi_2) + (1-\Phi_2) c_2,
-\end{eqnarray*}
-
-\item The first difference (\texttt{qi\$fd}) for the tobit regression
-model is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y\mid x_{1})-E(Y\mid x).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "tobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected value for the specified
-values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
-values given the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
-for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-Bayesian tobit regression \input{contributors}
-
-\noindent Ben Goodrich and Ying Lu enabled \texttt{tobit.bayes} to work with Zelig.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
- \end{document}
diff --git a/inst/doc/tobit.pdf b/inst/doc/tobit.pdf
index bfd1c0b..b10fe68 100644
Binary files a/inst/doc/tobit.pdf and b/inst/doc/tobit.pdf differ
diff --git a/inst/doc/tobit.tex b/inst/doc/tobit.tex
deleted file mode 100644
index 3481062..0000000
--- a/inst/doc/tobit.tex
+++ /dev/null
@@ -1,235 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Linear regression for Left-Censored Dependet Variable}
-%\VignetteDepends{Zelig, survival}
-%\VignetteKeyWords{model, linear,regression,left-censored,continuous}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{\texttt{tobit}: Linear Regression for a
-Left-Censored Dependent Variable} \label{tobit}
-
-Tobit regression estimates a linear regression model for a
-left-censored dependent variable, where the dependent variable is
-censored from below. While the classical tobit model has values
-censored at 0, you may select another censoring point. For other
-linear regression models with fully observed dependent variables, see
-Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
-regression (\Sref{normal}), or least squares (\Sref{ls}).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf,
- model = "tobit", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Inputs}
-{\tt zelig()} accepts the following arguments to specify how the
-dependent variable is censored.
-\begin{itemize}
-\item \texttt{below}: (defaults to 0) The point at which the dependent
-variable is censored from below. If any values in the dependent
-variable are observed to be less than the censoring point, it is
-assumed that that particular observation is censored from below at the
-observed value. (See \Sref{tobit.bayes} for a Bayesian
-implementation that supports both left and right censoring.)
- \item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) and the options selected in
-{\tt cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "tobit", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-Zelig users may wish to refer to \texttt{help(survreg)} for more
-information.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item {Basic Example} \\
-Attaching the sample dataset:
-\begin{Schunk}
-\begin{Sinput}
-> data(tobin)
-\end{Sinput}
-\end{Schunk}
-Estimating linear regression using \texttt{tobit}:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(durable ~ age + quant, model = "tobit", data = tobin)
-\end{Sinput}
-\end{Schunk}
-Setting values for the explanatory variables to their sample averages:
-\begin{Schunk}
-\begin{Sinput}
-> x.out <- setx(z.out)
-\end{Sinput}
-\end{Schunk}
-Simulating quantities of interest from the posterior distribution given
-\texttt{x.out}.
-\begin{Schunk}
-\begin{Sinput}
-> s.out1 <- sim(z.out, x = x.out)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out1)
-\end{Sinput}
-\end{Schunk}
-\item {Simulating First Differences} \\
-Set explanatory variables to their default(mean/mode) values, with high
-(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
-
-\begin{Schunk}
-\begin{Sinput}
-> x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
-> x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
-\end{Sinput}
-\end{Schunk}
-Estimating the first difference for the effect of
-high versus low liquidity ratio on duration(\texttt{durable}):
-\begin{Schunk}
-\begin{Sinput}
-> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out2)
-\end{Sinput}
-\end{Schunk}
-\end{enumerate}
-
-\subsubsection{Model}
-\begin{itemize}
-\item Let $Y_i^*$ be a latent dependent variable which is distributed with
-\emph{stochastic} component
-\begin{eqnarray*}
-Y_i^* & \sim & \textrm{Normal}(\mu_i, \sigma^2) \\
-\end{eqnarray*}
-where $\mu_i$ is a vector means and $\sigma^2$ is a scalar variance
-parameter. $Y_i^*$ is not directly observed, however. Rather we
-observed $Y_i$ which is defined as:
-\begin{equation*}
-Y_i = \left\{
-\begin{array}{lcl}
-Y_i^* &\textrm{if} & c <Y_i^* \\
-c &\textrm{if} & c \ge Y_i^*
-\end{array}\right.
-\end{equation*}
-where $c$ is the lower bound below which $Y_i^*$ is censored.
-
-\item The \emph{systematic component} is given by
-\begin{eqnarray*}
-\mu_{i} &=& x_{i} \beta,
-\end{eqnarray*}
-where $x_{i}$ is the vector of $k$ explanatory variables for
-observation $i$ and $\beta$ is the vector of coefficients.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values (\texttt{qi\$ev}) for the tobit regression
-model are the same as the expected value of $Y*$:
-\begin{equation*}
-E(Y^* | X) = \mu_{i} = x_{i} \beta
-\end{equation*}
-
-\item The first difference (\texttt{qi\$fd}) for the tobit regression
-model is defined as
-\begin{eqnarray*}
-\text{FD}=E(Y^* \mid x_{1}) - E(Y^* \mid x).
-\end{eqnarray*}
-
-\item In conditional prediction models, the average expected treatment effect
-(\texttt{qi\$att.ev}) for the treatment group is
-\begin{eqnarray*}
-\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[E[Y^*_{i}(t_{i}=1)]-E[Y^*_{i}(t_{i}=0)]],
-\end{eqnarray*}
-where $t_{i}$ is a binary explanatory variable defining the treatment
-($t_{i}=1$) and control ($t_{i}=0$) groups.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may
-view. For example, if you run:
-\begin{verbatim}
-z.out <- zelig(y ~ x, model = "tobit.bayes", data)
-\end{verbatim}
-
-\noindent then you may examine the available information in \texttt{z.out} by
-using \texttt{names(z.out)}, see the draws from the posterior distribution of
-the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a
-default summary of information through \texttt{summary(z.out)}. Other elements
-available through the \texttt{\$} operator are listed below.
-
-\begin{itemize}
-\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
-
-\begin{itemize}
-\item \texttt{coefficients}: draws from the posterior distributions
-of the estimated parameters. The first $k$ columns contain the posterior draws
-of the coefficients $\beta$, and the last column contains the posterior draws
-of the variance $\sigma^2$.
-
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
-\item \texttt{seed}: the random seed used in the model.
-
-\end{itemize}
-
-\item From the \texttt{sim()} output object \texttt{s.out}:
-
-\begin{itemize}
-\item \texttt{qi\$ev}: the simulated expected value for the specified
- values of \texttt{x}.
-
-\item \texttt{qi\$fd}: the simulated first difference in the expected
- values given the values specified in \texttt{x} and \texttt{x1}.
-
-\item \texttt{qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
-
-\end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The tobit function is part of the survival library by Terry
-Therneau, ported to R by Thomas Lumley. Advanced users may wish to
-refer to \texttt{help(survfit)} in the survival library and
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
- \end{document}
diff --git a/inst/doc/models/2sls.tex b/inst/doc/twosls.Rnw
similarity index 81%
rename from inst/doc/models/2sls.tex
rename to inst/doc/twosls.Rnw
index ff54fad..fa8da87 100644
--- a/inst/doc/models/2sls.tex
+++ b/inst/doc/twosls.Rnw
@@ -1,13 +1,29 @@
-\subsection{\texttt{2sls}: Two Stage Least Squares}
-\label{2sls}
-\texttt{2sls} provides consistent estimates for linear regression models with
-some explanatory variable (the instrumental variable)
-correlated with the error term.
+\SweaveOpts{results=hide, prefix.string=vigpics/twosls}
+\include{zinput}
+%\VignetteIndexEntry{ Two Stage Least Squares}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{model,continuous, least squares,instrumental}
+%\VignettePackage{Zelig}
+\begin{document}
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
+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 interst are
+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.
@@ -15,22 +31,22 @@ variable by its predictions from stage one.
\begin{verbatim}
> fml <- list ("mu" = Y ~ X + Z,
"inst" = Z ~ W + X)
-> z.out <- zelig(formula = fml, model = "2sls", data = mydata)
+> z.out <- zelig(formula = fml, model = "twosls", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
\subsubsection{Inputs}
-\texttt{2sls} regression take the following 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}.
For example:
-\begin{verbatim}
-> fml <- list ("mu" = Y ~ X + Z,
+<<Inputs.list>>=
+ fml <- list ("mu" = Y ~ X + Z,
"inst" = Z ~ W + X)
-\end{verbatim}
+@
\begin{itemize}
\item \texttt{Y}: the dependent variable of interest.
\item \texttt{Z}: the instrumental variable.
@@ -38,7 +54,7 @@ For example:
\end{itemize}
\end{itemize}
\subsubsection{Additional Inputs}
-\texttt{2sls} takes the following additional inputs for model
+\texttt{twosls} takes the following additional inputs for model
specifications:
\begin{itemize}
\item \texttt{TX}: an optional matrix to transform the regressor
@@ -92,32 +108,39 @@ positive semidefinit. Thus, it is doubtful whether formula 2 is really superior
\end{itemize}
\subsubsection{Examples}
Attaching the example dataset:
- \begin{verbatim}
- > data(klein)
- \end{verbatim}
+<<Examples.data>>=
+ data(klein)
+@
Formula:
-\begin{verbatim}
- >formula<- list(mu1=C~Wtot + P + P1,
+<<Examples.list>>=
+ formula <- list(mu1=C~Wtot + P + P1,
mu2=I~P + P1 + K1,
mu3=Wp~ X + X1 + Tm,
inst= ~ P1 + K1 + X1 + Tm + Wg + G)
-\end{verbatim}
-Estimating the model using \texttt{2sls}:
-\begin{verbatim}
-> z.out<-zelig(formula=formula, model="2sls",data=klein)
-> summary(z.out)
-\end{verbatim}
+@
+Estimating the model using \texttt{twosls}:
+<<Examples.zelig>>=
+ z.out<-zelig(formula=formula, model="twosls",data=klein)
+ summary(z.out)
+@
Set explanatory variables to their default (mean/mode) values
-\begin{verbatim}
-> x.out <- setx(z.out)
-\end{verbatim}
+<<Examples.setx>>=
+ x.out <- setx(z.out)
+@
Simulate draws from the posterior distribution:
-\begin{verbatim}
-> s.out <- sim(z.out)
-> summary(s.out)
-\end{verbatim}
+<<Examples.sim>>=
+s.out <-sim(z.out,x=x.out)
+ summary(s.out)
+@
+Plot the quantities of interest
+\begin{center}
+<<label=Examplestwosls, fig=true, echo=false>>=
+plot(s.out)
+@
+\end{center}
+
\clearpage
\subsubsection{Model}
Let's consider the following regression model,
@@ -170,7 +193,7 @@ For information about seemingly unrelated regression, see
The output of each Zelig command contains useful information which you may
view. For example, if you run:
\begin{verbatim}
-z.out <- zelig(formula=fml, model = "2sls", data)
+z.out <- zelig(formula=fml, model = "twosls", data)
\end{verbatim}
\noindent then you may examine the available information in \texttt{z.out} by
using \texttt{names(z.out)}, see the draws from the posterior distribution of
@@ -183,13 +206,19 @@ available through the \texttt{\$} operator are listed below:
\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
\end{itemize}
-\input{models/systemfit_output}
+\input{systemfit_output}
\begin{itemize}
\item \texttt{inst*}: instruments of the ith equation.
\item \texttt{h*}: matrix of instrumental variables of the ith equation.
\end{itemize}
\subsubsection{Contributors}
-The \texttt{2sls} function is adapted from
-\input{models/contributors_systemfit}
+The \texttt{twosls} function is adapted from \input{contributors_systemfit}
+<<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/twosls.pdf b/inst/doc/twosls.pdf
new file mode 100644
index 0000000..bc777f4
Binary files /dev/null and b/inst/doc/twosls.pdf differ
diff --git a/inst/doc/weibull.pdf b/inst/doc/weibull.pdf
index f84c227..8c1a9e4 100644
Binary files a/inst/doc/weibull.pdf and b/inst/doc/weibull.pdf differ
diff --git a/inst/doc/weibull.tex b/inst/doc/weibull.tex
deleted file mode 100644
index 2ff799c..0000000
--- a/inst/doc/weibull.tex
+++ /dev/null
@@ -1,298 +0,0 @@
-
-\include{zinput}
-%\VignetteIndexEntry{Weibull Regression for Duration Dependent Variables}
-%\VignetteDepends{Zelig, survival}
-%\VignetteKeyWords{model, weibull,regression,bounded, duration}
-%\VignettePackage{Zelig}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
-\begin{document}
-
-
-\section{{\tt weibull}: Weibull Regression for Duration
-Dependent Variables}\label{weibull}
-
-Choose the Weibull regression model if the values in your dependent
-variable are duration observations. The Weibull model relaxes the
-exponential model's (see \Sref{exp}) assumption of constant hazard,
-and allows the hazard rate to increase or decrease monotonically with
-respect to elapsed time.
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y, C) ~ X1 + X2, model = "weibull", data = mydata)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Weibull 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, time defaults to 1 for all observations.
-
-\subsubsection{Input Values}
-
-In addition to the standard inputs, {\tt zelig()} takes the following
-additional options for weibull regression:
-\begin{itemize}
-\item {\tt robust}: defaults to {\tt FALSE}. If {\tt TRUE}, {\tt
-zelig()} computes robust standard errors based on sandwich estimators
-(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
-cluster}.
-\item {\tt cluster}: if {\tt robust = TRUE}, you may select a
-variable to define groups of correlated observations. Let {\tt x3} be
-a variable that consists of either discrete numeric values, character
-strings, or factors that define strata. Then
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",
- model = "exp", data = mydata)
-\end{verbatim}
-means that the observations can be correlated within the strata defined by
-the variable {\tt x3}, and that robust standard errors should be
-calculated according to those clusters. If {\tt robust = TRUE} but
-{\tt cluster} is not specified, {\tt zelig()} assumes that each
-observation falls into its own cluster.
-\end{itemize}
-
-\subsubsection{Example}
-
-Attach the sample data:
-\begin{Schunk}
-\begin{Sinput}
-> data(coalition)
-\end{Sinput}
-\end{Schunk}
-Estimate the model:
-\begin{Schunk}
-\begin{Sinput}
-> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "weibull",
-+ data = coalition)
-\end{Sinput}
-\end{Schunk}
-View the regression output:
-\begin{Schunk}
-\begin{Sinput}
-> summary(z.out)
-\end{Sinput}
-\end{Schunk}
-Set the baseline values (with the ruling coalition in the minority)
-and the alternative values (with the ruling coalition in the majority)
-for X:
-\begin{Schunk}
-\begin{Sinput}
-> x.low <- setx(z.out, numst2 = 0)
-> x.high <- setx(z.out, numst2 = 1)
-\end{Sinput}
-\end{Schunk}
-Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
-\begin{Schunk}
-\begin{Sinput}
-> s.out <- sim(z.out, x = x.low, x1 = x.high)
-\end{Sinput}
-\end{Schunk}
-\begin{Schunk}
-\begin{Sinput}
-> summary(s.out)
-\end{Sinput}
-\end{Schunk}
-\begin{center}
-\begin{Schunk}
-\begin{Sinput}
-> plot(s.out)
-\end{Sinput}
-\end{Schunk}
-\includegraphics{vigpics/weibull-ExamplePlot}
-\end{center}
-
-\subsubsection{Model}
-Let $Y_i^*$ be the survival time for observation $i$. This variable
-might be censored for some observations at a fixed time $y_c$ such
-that the fully observed dependent variable, $Y_i$, is defined as
-\begin{equation*}
- Y_i = \left\{ \begin{array}{ll}
- Y_i^* & \textrm{if }Y_i^* \leq y_c \\
- y_c & \textrm{if }Y_i^* > y_c
- \end{array} \right.
-\end{equation*}
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by the distribution
- of the partially observed variable $Y^*$. We assume $Y_i^*$ follows
- the Weibull distribution whose density function is given by
- \begin{equation*}
- f(y_i^*\mid \lambda_i, \alpha) = \frac{\alpha}{\lambda_i^\alpha}
- y_i^{* \alpha-1} \exp \left\{ -\left( \frac{y_i^*}{\lambda_i}
-\right)^{\alpha} \right\}
- \end{equation*}
- for $y_i^* \ge 0$, the scale parameter $\lambda_i > 0$, and the shape
- parameter $\alpha > 0$. The mean of this distribution is $\lambda_i
- \Gamma(1 + 1 / \alpha)$. When $\alpha = 1$, the distribution reduces to
- the exponential distribution (see Section~\ref{exp}). (Note that
-the output from {\tt zelig()} parameterizes {\tt scale}$ = 1 / \alpha$.)
-
-In addition, survival models like the Weibull have three additional
-properties. The hazard function $h(t)$ measures the probability of
-not surviving past time $t$ given survival up to $t$. In general,
-the hazard function is equal to $f(t)/S(t)$ where the survival
-function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the fraction still
-surviving at time $t$. The cumulative hazard function $H(t)$
-describes the probability of dying before time $t$. In general,
-$H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the Weibull
-model,
-\begin{eqnarray*}
-h(t) &=& \frac{\alpha}{\lambda_i^{\alpha}} t^{\alpha - 1} \\
-S(t) &=& \exp \left\{ -\left( \frac{t}{\lambda_i} \right)^{\alpha} \right\} \\
-H(t) &=& \left( \frac{t}{\lambda_i} \right)^{\alpha}
-\end{eqnarray*}
-For the Weibull model, the hazard function $h(t)$ can increase or
-decrease monotonically over time.
-
-\item The \emph{systematic component} $\lambda_i$ is modeled as
- \begin{equation*}
- \lambda_i = \exp(x_i \beta),
- \end{equation*}
- where $x_i$ is the vector of explanatory variables, and $\beta$ is
- the vector of coefficients.
-
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The expected values ({\tt qi\$ev}) for the Weibull model are
- simulations of the expected duration:
-\begin{equation*}
-E(Y) = \lambda_i \, \Gamma (1 + \alpha^{-1}),
-\end{equation*}
-given draws of $\beta$ and $\alpha$ from their sampling
-distributions.
-
-\item The predicted value ({\tt qi\$pr}) is drawn from a distribution
- defined by $(\lambda_i, \alpha)$.
-
-\item The first difference ({\tt qi\$fd}) in expected value is
-\begin{equation*}
-\textrm{FD} = E(Y \mid x_1) - E(Y \mid x).
-\end{equation*}
-
-\item In conditional prediction models, the average expected treatment
- effect ({\tt att.ev}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. When
- $Y_i(t_i=1)$ is censored rather than observed, we replace it with
- a simulation from the model given available knowledge of the
- censoring process. Variation in the simulations are due to
- uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
- expected value of $Y_i$ for observations in the treatment group,
- under the assumption that everything stays the same except that
- the treatment indicator is switched to $t_i=0$.
-
-\item In conditional prediction models, the average predicted treatment
- effect ({\tt att.pr}) for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \right\},
- \end{equation*}
- where $t_i$ is a binary explanatory variable defining the
- treatment ($t_i=1$) and control ($t_i=0$) groups. When
- $Y_i(t_i=1)$ is censored rather than observed, we replace it with
- a simulation from the model given available knowledge of the
- censoring process. Variation in the simulations are due to
- uncertainty in simulating $\widehat{Y_i(t_i=0)}$, the
- counterfactual predicted value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you
-may view. For example, if you run \texttt{z.out <- zelig(y \~\,
- x, model = "weibull", data)}, then you may examine the available
-information in \texttt{z.out} by using \texttt{names(z.out)},
-see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
-a default summary of information through \texttt{summary(z.out)}.
-Other elements available through the {\tt \$} operator are listed
-below.
-
-\begin{itemize}
-\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
- \begin{itemize}
- \item {\tt coefficients}: parameter estimates for the explanatory
- variables.
- \item {\tt icoef}: parameter estimates for the intercept and ``scale''
- parameter $1 / \alpha$.
- \item {\tt var}: the variance-covariance matrix.
- \item {\tt loglik}: a vector containing the log-likelihood for the
- model and intercept only (respectively).
- \item {\tt linear.predictors}: a vector of the
- $x_{i}\beta$.
- \item {\tt df.residual}: the residual degrees of freedom.
- \item {\tt df.null}: the residual degrees of freedom for the null
- model.
- \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
- \end{itemize}
-\item Most of this may be conveniently summarized using {\tt
- summary(z.out)}. From {\tt summary(z.out)}, you may
- additionally extract:
- \begin{itemize}
- \item {\tt table}: the parameter estimates with their
- associated standard errors, $p$-values, and $t$-statistics.
- \end{itemize}
-
-\item From the {\tt sim()} output object {\tt s.out}, you may extract
- quantities of interest arranged as matrices indexed by simulation
- $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
- Available quantities are:
-
- \begin{itemize}
- \item {\tt qi\$ev}: the simulated expected values for the specified
- values of {\tt x}.
- \item {\tt qi\$pr}: the simulated predicted values drawn from a
- distribution defined by $(\lambda_i, \alpha)$.
- \item {\tt qi\$fd}: the simulated first differences between the
- simulated expected values for {\tt x} and {\tt x1}.
- \item {\tt qi\$att.ev}: the simulated average expected treatment
- effect for the treated from conditional prediction models.
- \item {\tt qi\$att.pr}: the simulated average predicted treatment
- effect for the treated from conditional prediction models.
- \end{itemize}
-\end{itemize}
-
-\subsubsection{Contributors}
-
-The Weibull model is part of the survival library by Terry Therneau,
-ported to R by Thomas Lumley. Advanced users may wish to refer to
-\texttt{help(survfit)} in the survival library, and
-\begin{verse}
-\bibentry{VenRip02}.
-\end{verse}
-
-Sample data are from
-\begin{verse}
-\bibentry{KinAltBur90}.
-\end{verse}
-
-Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
- \end{document}
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/zelig.html b/inst/doc/zelig.html
new file mode 100644
index 0000000..a42f10a
--- /dev/null
+++ b/inst/doc/zelig.html
@@ -0,0 +1,344 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
+ "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
+ <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+ <head>
+
+<title> Zelig documentation </title>
+ <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+
+<script type="text/javascript" src="http://gking.harvard.edu/gking.js"></script>
+<script type="text/javascript" src="http://gking.harvard.edu/jquery.js"></script>
+
+<script type="text/javascript">
+ $(document).ready(function(){
+ $("#authorcontent").hide();
+
+ // make main index links orange (it was a problem with ie7)
+ $("li.mainindx > a").addClass("orange")
+
+ // close any ul of class "closed"
+ $(".closed/ul").hide()
+
+ // toggle
+ $("li.mainindx > a").click(function(){
+ $(this).next().slideToggle("slow").parent().toggleClass("closed");
+ return false
+ });
+
+ // for each author
+ $("#kosuke").click(function(){
+ $("#authorcontent").slideToggle().html("<p> Assistant Professor, Department of Politics, Princeton University <br/> \
+ Corwin Hall, Department of Politics, Princeton University, Princeton NJ 08544;<br/> \
+ <a href=\"http://Imai.Princeton.Edu\">http://Imai.Princeton.Edu </a>, <a href=\"mailto:KImai at Princeton.Edu\">KImai at Princeton.Edu</a></p>");
+ return false;
+ });
+
+ $("#gary").click(function(){
+ $("#authorcontent").slideToggle().html("<p> David Florence Professor of Government, Harvard University<br/> \
+ Institute for Quantitative Social Sciences, 1737 Cambridge Street, Harvard University, Cambridge MA 02138;<br/> \
+ <a href=\"http://GKing.Harvard.Edu\">http://GKing.Harvard.Edu</a>, <a href=\"mailto:King at Harvard.Edu\">King at Harvard.Edu</a>, (617) 495-2027</p>");
+ return false;
+ });
+
+ $("#olivia").click(function(){
+ $("#authorcontent").slideToggle().html("<p> Ph.D. Candidate, Department of Government, Harvard University<br/>\
+ 1737 Cambridge Street, Cambridge MA 02138;<br/> \
+ <a href=\"http://www.people.fas.harvard.edu/~olau\">http://www.people.fas.harvard.edu/~olau</a>, <a href=\"mailto:Lau at Fas.Harvard.Edu\">Lau at Fas.Harvard.Edu</a></p>");
+ return false;
+ });
+
+ $("#collapseall").click(function(){
+ $("li.mainindx a").next().hide().parent().addClass("closed");
+ return false;
+ });
+
+ $("#expandall").click(function(){
+ $("li.mainindx a").next().show().parent().removeClass("closed");
+ return false;
+ });
+ });
+</script>
+
+
+
+<style type="text/css">
+body {
+background-image: url(http://gking.harvard.edu/pics/temple.jpg)
+}
+
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+<body>
+<script type='text/javascript'>displayHeader()</script>
+<p class="zheader">
+<span id="ztitle">ZELIG: EVERYONE'S STATISTICAL SOFTWARE </span> <br/>
+<span id="authors">
+<a href="" id="kosuke">Kosuke Imai </a> - <a href="" id="gary">Gary King </a> - <a href="" id="olivia">Olivia Lau </a> <br/>
+<span id="authorcontent"></span>
+</span>
+</p>
+<p>[Also available is a downloadable <a href="zelig.pdf"> PDF </a> version of this entire document] </p>
+
+<ul id="menuc">
+<li><a id="collapseall" href=""> collapse all </a> </li>
+<li><a id="expandall" href=""> expand all </a> </li>
+</ul>
+
+<div id="navcontainer">
+
+<ul id="zelig">
+ <li class="mainindx"> <a class="kot" href="">Introduction </a>
+ <ul id="introduction">
+ <li><a href="static/acknowledgments.pdf" title="Acknowledgment">Acknowledgment</a></li>
+ <li><a href="static/intro.pdf" title="Introduction">Introduction</a></li>
+ </ul>
+ </li>
+ <li class="mainindx closed"> <a href="">User's Guide </a>
+ <ul id="usersguide">
+ <li><a href="static/install.pdf" title="Installation">Installation</a></li>
+ <li><a href="static/syntax.pdf" title="Data Analysis Commands">Data Analysis Commands</a></li>
+ <li><a href="static/Zcommands.pdf" title="Statistical Commands">Statistical Commands</a></li>
+ <li><a href="static/graphs.pdf" title="Graphing commands">Graphing Commands</a></li>
+ </ul>
+ </li>
+ <li class="mainindx"> <a href=""> Advanced Zelig uses </a>
+ <ul id="advancedzeliguses">
+ <li><a href="static/advancedR.pdf" title="Advanced R">R programming</a></li>
+ <li><a href="static/newModels.pdf" title="Writing new Models">Writing new Models</a></li>
+ <li><a href="static/addingModels.pdf" title="Adding Models and Methods to Zelig">Adding Models and Methods to Zelig</a></li>
+ </ul>
+ </li>
+ <li class="mainindx"><a href="">Reference Manual </a>
+ <ul id="referencemanual">
+ <li class="mainindx"><a href="">Main Commands</a>
+ <ul id="maincommands">
+ <li><a href="commandsRd/Zelig.pdf" title="zelig">zelig: Estimating a Statistical Model</a></li>
+ <li><a href="commandsRd/setx.pdf" title="setx">setx: Setting Explanatory Variable Values</a></li>
+ <li><a href="commandsRd/sim.pdf" title="sim">sim: Simulating Quantities of Interest</a></li>
+ <li><a href="commandsRd/plot.zelig.pdf" title="plot">plot.zelig: Graphing Quantities of Interest</a></li>
+ <li><a href="commandsRd/repl.pdf" title="repl">repl: Replicating Analyses</a></li>
+ </ul>
+ </li>
+ <li class="mainindx"><a href="">Supplementary Commands </a>
+ <ul id="supplementarycommands">
+ <li><a href="commandsRd/mi.pdf" title="mi">mi: Bundle multiply imputed data sets as a list</a></li>
+ <li><a href="commandsRd/network.pdf" title="network">network: Format matricies into data frame for social network analysis</a></li>
+ <li><a href="commandsRd/plot.ci.pdf" title="plot.ci">plot.ci: Plotting Vertical confidence Intervals</a></li>
+ <li><a href="commandsRd/rocplot.pdf" title="rocplot">rocplot: Receiver Operator Characteristic Plots</a></li>
+ <li><a href="commandsRd/ternaryplot.pdf" title="ternaryplot">ternaryplot: Ternary diagram</a></li>
+ <li><a href="commandsRd/ternarypoints.pdf" title="ternarypoints">ternarypoints: Adding Points To Ternary Diagrams</a></li>
+ </ul>
+ </li>
+
+ <li class="mainindx"><a href="">Models Zelig Can Run </a>
+
+<!-- shell script is expected to produce <ul id="models"> <li> </li> ... </ul> -->
+
+
+
+<ul id="models">
+<li class="mainindx"><a href="">Models for Continous Dependent Variables</a>
+ <ul>
+ <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="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="sur.pdf">sur : Seemingly Unrelated Regression</a></li>
+ <li><a href="threesls.pdf">threesls : Three Stage Least Squares</a></li>
+ <li><a href="tobit.pdf">tobit : Linear regression for Left-Censored Dependent Variable</a></li>
+ <li><a href="tobit.bayes.pdf">tobit.bayes : Bayesian Linear Regression for a Censored Dependent Variable</a></li>
+ <li><a href="twosls.pdf">twosls : Two Stage Least Squares</a></li>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Models for Dichotomous Dependent Variables</a>
+ <ul>
+ <li><a href="blogit.pdf">blogit : Bivariate Logistic Regression for Dichotomous Dependent Variables</a></li>
+ <li><a href="bprobit.pdf">bprobit : Bivariate Probit Regression for Dichotomous Dependent Variables</a></li>
+ <li><a href="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="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.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="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="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="factor.ord.pdf">factor.ord : Ordinal Data Factor Analysis</a></li>
+ <li><a href="ologit.pdf">ologit : Ordinal Logistic Regression for Ordered Categorical Dependent Variables</a></li>
+ <li><a href="oprobit.pdf">oprobit : Ordinal Probit Regression for Ordered Categorical Dependent Variables</a></li>
+ <li><a href="oprobit.bayes.pdf">oprobit.bayes : Bayesian Ordered Probit Regression</a></li>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
+ <ul>
+ <li><a href="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="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>
+<li class="mainindx"><a href="">Multinomial Choice Models</a>
+ <ul>
+ <li><a href="mlogit.pdf">mlogit : Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
+ <li><a href="mlogit.bayes.pdf">mlogit.bayes : Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Event Count Models</a>
+ <ul>
+ <li><a href="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>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Models for Mixed Dependent Variables</a>
+ <ul>
+ <li><a href="factor.mix.pdf">factor.mix : Mixed Data Factor Analysis</a></li>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Ecological inverence models</a>
+ <ul>
+ <li><a href="ei.dynamic.pdf">ei.dynamic : Quinn's Dynamic Ecological Inference Model</a></li>
+ <li><a href="ei.hier.pdf">ei.hier : Hierarchical Ecological Inference Model for 2 x 2 Tables</a></li>
+ <li><a href="ei.RxC.pdf">ei.RxC : Hierarchical Multinomial-Dirichlet Ecological Inference Model for R x C Tables</a></li>
+ </ul>
+</li>
+</ul>
+<!-- shell script is expected to produce <ul id="models"> <li> </li> ... </ul> -->
+ </li>
+
+ <li class="mainindx"><a href="">Commands for Programmers and Contributors </a>
+ <ul id="contribcommands">
+
+ <li><a href="commandsRd/model.end.pdf" title="model.end">model.end: Cleaning up after optimization</a></li>
+ <li><a href="commandsRd/model.frame.multiple.pdf" title="model.frame.multiple">model.frame.multiple: Extracting the "environment" of a model formula</a></li>
+ <li><a href="commandsRd/model.matrix.multiple.pdf" title="model.matrix.multiple">model.matrix.multiple: Desing matrix for multivariate models</a></li>
+ <li><a href="commandsRd/parse.formula.pdf" title="parse.formula">parse.formula: Parsing the inputs</a></li>
+ <li><a href="commandsRd/parse.par.pdf" title="parse.par">parse.par: Select and reshape parameter vectors</a></li>
+ <li><a href="commandsRd/put.start.pdf" title="put.start">put.start: Set specific starting values for certain parameters</a></li>
+ <li><a href="commandsRd/set.start.pdf" title="set.start">set.start: Set starting valus for all parameters</a></li>
+
+ </ul>
+ </li>
+ </ul>
+ </li> <!-- end of refman li -->
+ <li class="mainindx"> <a href=""> Appendices </a>
+ <ul id="Appendices">
+ <li><a href="static/faq.pdf" title="faq">Frequently Asked Questions</a></li>
+ </ul>
+ </li>
+</ul>
+</div>
+
+<address>
+Gary King
+2006-11-05<script type="text/javascript">displayFooter()</script>
+</address>
+</body>
+</html>
+
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diff --git a/inst/doc/zelig.tex b/inst/doc/zelig.tex
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+\include{zinput}
\title{Zelig: Everyone's Statistical Software\thanks{The current
version of this software is available at
\texttt{http://gking.harvard.edu/zelig/}, free of charge and
@@ -62,7 +21,7 @@ Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
\texttt{OLau at Fas.Harvard.Edu}).}}
% rbuild: replace 'Version ' '\\' Version
-\date{Version 2.8-4\\ \today}
+\date{Version 2.8-5\\ \today}
\begin{document}
\maketitle
diff --git a/inst/doc/zeligfooter.html b/inst/doc/zeligfooter.html
new file mode 100644
index 0000000..ce98e4b
--- /dev/null
+++ b/inst/doc/zeligfooter.html
@@ -0,0 +1,33 @@
+<!-- shell script is expected to produce <ul id="models"> <li> </li> ... </ul> -->
+ </li>
+
+ <li class="mainindx"><a href="">Commands for Programmers and Contributors </a>
+ <ul id="contribcommands">
+
+ <li><a href="commandsRd/model.end.pdf" title="model.end">model.end: Cleaning up after optimization</a></li>
+ <li><a href="commandsRd/model.frame.multiple.pdf" title="model.frame.multiple">model.frame.multiple: Extracting the "environment" of a model formula</a></li>
+ <li><a href="commandsRd/model.matrix.multiple.pdf" title="model.matrix.multiple">model.matrix.multiple: Desing matrix for multivariate models</a></li>
+ <li><a href="commandsRd/parse.formula.pdf" title="parse.formula">parse.formula: Parsing the inputs</a></li>
+ <li><a href="commandsRd/parse.par.pdf" title="parse.par">parse.par: Select and reshape parameter vectors</a></li>
+ <li><a href="commandsRd/put.start.pdf" title="put.start">put.start: Set specific starting values for certain parameters</a></li>
+ <li><a href="commandsRd/set.start.pdf" title="set.start">set.start: Set starting valus for all parameters</a></li>
+
+ </ul>
+ </li>
+ </ul>
+ </li> <!-- end of refman li -->
+ <li class="mainindx"> <a href=""> Appendices </a>
+ <ul id="Appendices">
+ <li><a href="static/faq.pdf" title="faq">Frequently Asked Questions</a></li>
+ </ul>
+ </li>
+</ul>
+</div>
+
+<address>
+Gary King
+2006-11-05<script type="text/javascript">displayFooter()</script>
+</address>
+</body>
+</html>
+
diff --git a/inst/doc/zeligheader.html b/inst/doc/zeligheader.html
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+ <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+ <head>
+
+<title> Zelig documentation </title>
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+ $("#authorcontent").slideToggle().html("<p> Assistant Professor, Department of Politics, Princeton University <br/> \
+ Corwin Hall, Department of Politics, Princeton University, Princeton NJ 08544;<br/> \
+ <a href=\"http://Imai.Princeton.Edu\">http://Imai.Princeton.Edu </a>, <a href=\"mailto:KImai at Princeton.Edu\">KImai at Princeton.Edu</a></p>");
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+
+ $("#gary").click(function(){
+ $("#authorcontent").slideToggle().html("<p> David Florence Professor of Government, Harvard University<br/> \
+ Institute for Quantitative Social Sciences, 1737 Cambridge Street, Harvard University, Cambridge MA 02138;<br/> \
+ <a href=\"http://GKing.Harvard.Edu\">http://GKing.Harvard.Edu</a>, <a href=\"mailto:King at Harvard.Edu\">King at Harvard.Edu</a>, (617) 495-2027</p>");
+ return false;
+ });
+
+ $("#olivia").click(function(){
+ $("#authorcontent").slideToggle().html("<p> Ph.D. Candidate, Department of Government, Harvard University<br/>\
+ 1737 Cambridge Street, Cambridge MA 02138;<br/> \
+ <a href=\"http://www.people.fas.harvard.edu/~olau\">http://www.people.fas.harvard.edu/~olau</a>, <a href=\"mailto:Lau at Fas.Harvard.Edu\">Lau at Fas.Harvard.Edu</a></p>");
+ return false;
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+}
+
+#menuc a:hover
+{
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+
+<body>
+<script type='text/javascript'>displayHeader()</script>
+<p class="zheader">
+<span id="ztitle">ZELIG: EVERYONE'S STATISTICAL SOFTWARE </span> <br/>
+<span id="authors">
+<a href="" id="kosuke">Kosuke Imai </a> - <a href="" id="gary">Gary King </a> - <a href="" id="olivia">Olivia Lau </a> <br/>
+<span id="authorcontent"></span>
+</span>
+</p>
+<p>[Also available is a downloadable <a href="zelig.pdf"> PDF </a> version of this entire document] </p>
+
+<ul id="menuc">
+<li><a id="collapseall" href=""> collapse all </a> </li>
+<li><a id="expandall" href=""> expand all </a> </li>
+</ul>
+
+<div id="navcontainer">
+
+<ul id="zelig">
+ <li class="mainindx"> <a class="kot" href="">Introduction </a>
+ <ul id="introduction">
+ <li><a href="static/acknowledgments.pdf" title="Acknowledgment">Acknowledgment</a></li>
+ <li><a href="static/intro.pdf" title="Introduction">Introduction</a></li>
+ </ul>
+ </li>
+ <li class="mainindx closed"> <a href="">User's Guide </a>
+ <ul id="usersguide">
+ <li><a href="static/install.pdf" title="Installation">Installation</a></li>
+ <li><a href="static/syntax.pdf" title="Data Analysis Commands">Data Analysis Commands</a></li>
+ <li><a href="static/Zcommands.pdf" title="Statistical Commands">Statistical Commands</a></li>
+ <li><a href="static/graphs.pdf" title="Graphing commands">Graphing Commands</a></li>
+ </ul>
+ </li>
+ <li class="mainindx"> <a href=""> Advanced Zelig uses </a>
+ <ul id="advancedzeliguses">
+ <li><a href="static/advancedR.pdf" title="Advanced R">R programming</a></li>
+ <li><a href="static/newModels.pdf" title="Writing new Models">Writing new Models</a></li>
+ <li><a href="static/addingModels.pdf" title="Adding Models and Methods to Zelig">Adding Models and Methods to Zelig</a></li>
+ </ul>
+ </li>
+ <li class="mainindx"><a href="">Reference Manual </a>
+ <ul id="referencemanual">
+ <li class="mainindx"><a href="">Main Commands</a>
+ <ul id="maincommands">
+ <li><a href="commandsRd/Zelig.pdf" title="zelig">zelig: Estimating a Statistical Model</a></li>
+ <li><a href="commandsRd/setx.pdf" title="setx">setx: Setting Explanatory Variable Values</a></li>
+ <li><a href="commandsRd/sim.pdf" title="sim">sim: Simulating Quantities of Interest</a></li>
+ <li><a href="commandsRd/plot.zelig.pdf" title="plot">plot.zelig: Graphing Quantities of Interest</a></li>
+ <li><a href="commandsRd/repl.pdf" title="repl">repl: Replicating Analyses</a></li>
+ </ul>
+ </li>
+ <li class="mainindx"><a href="">Supplementary Commands </a>
+ <ul id="supplementarycommands">
+ <li><a href="commandsRd/mi.pdf" title="mi">mi: Bundle multiply imputed data sets as a list</a></li>
+ <li><a href="commandsRd/network.pdf" title="network">network: Format matricies into data frame for social network analysis</a></li>
+ <li><a href="commandsRd/plot.ci.pdf" title="plot.ci">plot.ci: Plotting Vertical confidence Intervals</a></li>
+ <li><a href="commandsRd/rocplot.pdf" title="rocplot">rocplot: Receiver Operator Characteristic Plots</a></li>
+ <li><a href="commandsRd/ternaryplot.pdf" title="ternaryplot">ternaryplot: Ternary diagram</a></li>
+ <li><a href="commandsRd/ternarypoints.pdf" title="ternarypoints">ternarypoints: Adding Points To Ternary Diagrams</a></li>
+ </ul>
+ </li>
+
+ <li class="mainindx"><a href="">Models Zelig Can Run </a>
+
+<!-- shell script is expected to produce <ul id="models"> <li> </li> ... </ul> -->
+
+
+
diff --git a/inst/doc/zeligmodels.html b/inst/doc/zeligmodels.html
new file mode 100644
index 0000000..bbe384e
--- /dev/null
+++ b/inst/doc/zeligmodels.html
@@ -0,0 +1,82 @@
+<ul id="models">
+<li class="mainindx"><a href="">Models for Continous Dependent Variables</a>
+ <ul>
+ <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="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="sur.pdf">sur : Seemingly Unrelated Regression</a></li>
+ <li><a href="threesls.pdf">threesls : Three Stage Least Squares</a></li>
+ <li><a href="tobit.pdf">tobit : Linear regression for Left-Censored Dependent Variable</a></li>
+ <li><a href="tobit.bayes.pdf">tobit.bayes : Bayesian Linear Regression for a Censored Dependent Variable</a></li>
+ <li><a href="twosls.pdf">twosls : Two Stage Least Squares</a></li>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Models for Dichotomous Dependent Variables</a>
+ <ul>
+ <li><a href="blogit.pdf">blogit : Bivariate Logistic Regression for Dichotomous Dependent Variables</a></li>
+ <li><a href="bprobit.pdf">bprobit : Bivariate Probit Regression for Dichotomous Dependent Variables</a></li>
+ <li><a href="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="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.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="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="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="factor.ord.pdf">factor.ord : Ordinal Data Factor Analysis</a></li>
+ <li><a href="ologit.pdf">ologit : Ordinal Logistic Regression for Ordered Categorical Dependent Variables</a></li>
+ <li><a href="oprobit.pdf">oprobit : Ordinal Probit Regression for Ordered Categorical Dependent Variables</a></li>
+ <li><a href="oprobit.bayes.pdf">oprobit.bayes : Bayesian Ordered Probit Regression</a></li>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
+ <ul>
+ <li><a href="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="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>
+<li class="mainindx"><a href="">Multinomial Choice Models</a>
+ <ul>
+ <li><a href="mlogit.pdf">mlogit : Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
+ <li><a href="mlogit.bayes.pdf">mlogit.bayes : Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Event Count Models</a>
+ <ul>
+ <li><a href="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>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Models for Mixed Dependent Variables</a>
+ <ul>
+ <li><a href="factor.mix.pdf">factor.mix : Mixed Data Factor Analysis</a></li>
+ </ul>
+</li>
+<li class="mainindx"><a href="">Ecological inverence models</a>
+ <ul>
+ <li><a href="ei.dynamic.pdf">ei.dynamic : Quinn's Dynamic Ecological Inference Model</a></li>
+ <li><a href="ei.hier.pdf">ei.hier : Hierarchical Ecological Inference Model for 2 x 2 Tables</a></li>
+ <li><a href="ei.RxC.pdf">ei.RxC : Hierarchical Multinomial-Dirichlet Ecological Inference Model for R x C Tables</a></li>
+ </ul>
+</li>
+</ul>
diff --git a/inst/doc/zinput.tex b/inst/doc/zinput.tex
index 00f140f..6932344 100644
--- a/inst/doc/zinput.tex
+++ b/inst/doc/zinput.tex
@@ -1,5 +1,6 @@
\documentclass[oneside,letterpaper,12pt]{book}
-%\usepackage[ae,hyper]{Rd}
+\usepackage{Rd}
+\usepackage{/usr/lib64/R/share/texmf/Sweave}
\usepackage{bibentry}
\usepackage{upquote}
\usepackage{graphicx}
diff --git a/inst/unitTests/report.html b/inst/unitTests/report.html
index 15e4201..bda1f40 100644
--- a/inst/unitTests/report.html
+++ b/inst/unitTests/report.html
@@ -1,10 +1,10 @@
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/html4/transitional.dtd">
-<html><head><title>RUNIT TEST PROTOCOL--Wed Apr 18 12:55:50 2007</title>
+<html><head><title>RUNIT TEST PROTOCOL--Thu May 31 20:13:08 2007</title>
</head>
-<body><h1 TRUE>RUNIT TEST PROTOCOL--Wed Apr 18 12:55:50 2007</h1>
+<body><h1 TRUE>RUNIT TEST PROTOCOL--Thu May 31 20:13:08 2007</h1>
<p>Number of test functions: 2</p>
-<p>Number of deactivated test functions: 1</p>
+<p style=color:black>Number of deactivated test functions: 1</p>
<p>Number of errors: 0</p>
<p>Number of failures: 0</p>
<hr>
@@ -37,8 +37,8 @@
<hr>
<h3 TRUE>Details</h3>
<p><a name="Zelig unit testing"><h5 TRUE>Test Suite: Zelig unit testing</h5>
-</a>Test function regexp: ^test.+<br/>Test file regexp: ^runit.+\.[rR]$<br/>Involved directory:<br/>/nfs/fs1/home/F/falimadh/work/zelig85/zelig/Zelig/tests/../inst/unitTests<br/><ul><li><a href="/nfs/fs1/home/F/falimadh/work/zelig85/zelig/Zelig/tests/../inst/unitTests/runit.multiple.R">Test file: runit.multiple.R</a><ul><li><a name="Zelig unit testing__nfs_fs1_home_F_falimadh_work_zelig85_zelig_Zelig_tests_.._inst_unitTests_runit.multiple.R_test.parse.formula">test.parse.formula: ... OK [...]
-<br/></li><li><a name="Zelig unit testing__nfs_fs1_home_F_falimadh_work_zelig85_zelig_Zelig_tests_.._inst_unitTests_runit.multiple.R_test.terms.multiple">test.terms.multiple: ... OK (0.54 seconds)<br/></a></li></ul></li></ul><hr>
+</a>Test function regexp: ^test.+<br/>Test file regexp: ^runit.+\.[rR]$<br/>Involved directory:<br/>/nfs/fs1/home/F/falimadh/work/zelig85/zelig/Zelig/tests/../inst/unitTests<br/><ul><li><a href="/nfs/fs1/home/F/falimadh/work/zelig85/zelig/Zelig/tests/../inst/unitTests/runit.multiple.R">Test file: runit.multiple.R</a><ul><li><a name="Zelig unit testing__nfs_fs1_home_F_falimadh_work_zelig85_zelig_Zelig_tests_.._inst_unitTests_runit.multiple.R_test.parse.formula">test.parse.formula: ... OK [...]
+<br/></li><li><a name="Zelig unit testing__nfs_fs1_home_F_falimadh_work_zelig85_zelig_Zelig_tests_.._inst_unitTests_runit.multiple.R_test.terms.multiple">test.terms.multiple: ... OK (0.63 seconds)<br/></a></li></ul></li></ul><hr>
<table border="0" width="80%" >
<tr><th>Name</th>
<th>Value</th>
@@ -62,28 +62,28 @@
<td>2</td>
</tr>
<tr><td>minor</td>
-<td>4.1</td>
+<td>5.0</td>
</tr>
<tr><td>year</td>
-<td>2006</td>
+<td>2007</td>
</tr>
<tr><td>month</td>
-<td>12</td>
+<td>04</td>
</tr>
<tr><td>day</td>
-<td>18</td>
+<td>23</td>
</tr>
<tr><td>svn rev</td>
-<td>40228</td>
+<td>41293</td>
</tr>
<tr><td>language</td>
<td>R</td>
</tr>
<tr><td>version.string</td>
-<td>R version 2.4.1 (2006-12-18)</td>
+<td>R version 2.5.0 (2007-04-23)</td>
</tr>
<tr><td>host</td>
-<td>w6.hmdc.harvard.edu</td>
+<td>w5.hmdc.harvard.edu</td>
</tr>
<tr><td>gcc</td>
<td>g++</td>
diff --git a/inst/unitTests/report.txt b/inst/unitTests/report.txt
index 6c0b44e..e30dd2d 100644
--- a/inst/unitTests/report.txt
+++ b/inst/unitTests/report.txt
@@ -1,4 +1,4 @@
-RUNIT TEST PROTOCOL -- Wed Apr 18 12:55:50 2007
+RUNIT TEST PROTOCOL -- Thu May 31 20:13:08 2007
***********************************************
Number of test functions: 2
Number of deactivated test functions: 1
@@ -20,6 +20,6 @@ Involved directory:
/nfs/fs1/home/F/falimadh/work/zelig85/zelig/Zelig/tests/../inst/unitTests
---------------------------
Test file: /nfs/fs1/home/F/falimadh/work/zelig85/zelig/Zelig/tests/../inst/unitTests/runit.multiple.R
-test.parse.formula: ... OK (0.08 seconds)
+test.parse.formula: ... OK (0.09 seconds)
test.process.as.factor : DEACTIVATED, later...when new version of parse.formula...
-test.terms.multiple: ... OK (0.54 seconds)
+test.terms.multiple: ... OK (0.63 seconds)
diff --git a/inst/unitTests/runit.multiple.R b/inst/unitTests/runit.multiple.R
index 9f87cdf..7c8e86b 100644
--- a/inst/unitTests/runit.multiple.R
+++ b/inst/unitTests/runit.multiple.R
@@ -16,9 +16,9 @@ test.parse.formula <- function(){
fml2 <-list(mu1 = import ~ coop,
mu2 = export ~ cost + target)
- ##parsed$fml1<-parse.formula(fml1,model="mlogit",data=mexico)
- ##parsed$fml2<-parse.formula(fml2,model="bprobit")
- ##save(parsed, file="test.parse.formula.RData")
+ ##parsed$fml1<-parse.formula(fml1,model="mlogit",data=mexico)#
+ ##parsed$fml2<-parse.formula(fml2,model="bprobit")#
+ ##save(parsed, file="test.parse.formula.RData")#
load("test.parse.formula.RData")
## testing ...
@@ -83,8 +83,8 @@ test.terms.multiple<-function(){
fml<-listMI[[i]]
objname <- paste("fml",i,sep="")
this.terms <- Zelig:::terms.multiple(fml)
- ####parsed[[objname]]<-this.terms
- ####save(parsed,file="test.terms.multiple.RData" )
+ ##parsed[[objname]]<-this.terms ##
+ ##save(parsed,file="test.terms.multiple.RData" )##
load("test.terms.multiple.RData")
checkEquals(this.terms,parsed[[objname]])
}
diff --git a/inst/zideal/zideal.RData b/inst/zideal/zideal.RData
index 5f5c75e..3396d94 100644
Binary files a/inst/zideal/zideal.RData and b/inst/zideal/zideal.RData differ
diff --git a/inst/zideal/zvcServer.R b/inst/zideal/zvcServer.R
index ee23686..ab2fd8c 100644
--- a/inst/zideal/zvcServer.R
+++ b/inst/zideal/zvcServer.R
@@ -1290,7 +1290,3 @@ return(res)
### > dim(zmatNA) = 38 6
-## file is soruced when R CMD build Zelig
-
-zideal <- create.zelig.all.packages("Zelig")
-save(zideal, file="inst/zideal/zideal.RData")
diff --git a/man/Zelig-package.Rd b/man/Zelig-package.Rd
new file mode 100644
index 0000000..ab2a127
--- /dev/null
+++ b/man/Zelig-package.Rd
@@ -0,0 +1,160 @@
+\name{Zelig-package}
+\alias{Zelig-package}
+\alias{Zelig}
+\docType{package}
+\title{
+Zelig: Everyone's Statistical Software
+}
+\description{
+Zelig is an easy-to-use program that can estimate, and
+help interpret the results of, an enormous range of statistical
+models. It literally is ``everyone's statistical software''
+because Zelig's simple unified framework incorporates everyone
+else's (R) code. We also hope it will become ``everyone's
+statistical software'' for applications and teaching, and so
+have designed Zelig so that anyone can easily use it or add
+their programs to it. Zelig also comes with infrastructure
+that facilitates the use of any existing method, such as by
+allowing multiply imputed data for any 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.
+}
+\details{
+\tabular{ll}{
+Package: \tab Zelig\cr
+Version: \tab 2.8-5\cr
+Date: \tab 2007-06-12\cr
+Depends: \tab R (>= 2.4.0), 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
+License: \tab GPL version 2 or newer\cr
+URL: \tab http://gking.harvard.edu/zelig\cr
+}
+
+Index:
+\preformatted{
+approval U.S. Presidential Approval Data
+coalition Coalition Dissolution in Parliamentary
+ Democracies
+current.packages Find all packages in a dependency chain
+dims Return Dimensions of Vectors, Arrays, and Data
+ Frames
+eidat Simulation Data for Ecological Inference
+friendship Simulated Example of Schoolchildren Friendship
+ Network
+gsource Read Data As a Space-Delimited Table
+help.zelig HTML Help for Zelig Commands and Models
+hoff Social Security Expenditure Data
+immigration Individual Preferences Over Immigration Policy
+macro Macroeconomic Data
+match.data Output matched data sets
+MatchIt.url Table of links for Zelig
+mexico Voting Data from the 1988 Mexican Presidental
+ Election
+mi Bundle multiply imputed data sets as a list
+mid Militarized Interstate Disputes
+model.end Cleaning up after optimization
+model.frame.multiple Extracting the "environment" of a model formula
+model.matrix.multiple Design matrix for multivariate models
+network Format matricies into a data frame for social
+ network analysis
+newpainters The Discretized Painter's Data of de Piles
+parse.formula Parsing user-input formulas into multiple
+ syntax
+parse.par Select and reshape parameter vectors
+PErisk Political Economic Risk Data from 62 Countries
+ in 1987
+plot.ci Plotting Vertical confidence Intervals
+plot.zelig Graphing Quantities of Interest
+put.start Set specific starting values for certain
+ parameters
+repl Replicating Analyses
+rocplot Receiver Operator Characteristic Plots
+sanction Multilateral Economic Sanctions
+set.start Set starting values for all parameters
+setx Setting Explanatory Variable Values
+sim Simulating Quantities of Interest
+sna.ex Simulated Example of Social Network Data
+summary.zelig Summary of Simulated Quantities of Interest
+SupremeCourt U.S. Supreme Court Vote Matrix
+swiss Swiss Fertility and Socioeconomic Indicators
+ (1888) Data
+ternaryplot Ternary diagram
+ternarypoints Adding Points to Ternary Diagrams
+tobin Tobin's Tobit Data
+turnout Turnout Data Set from the National Election
+ Survey
+user.prompt Pause in demo files
+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.url Table of links for Zelig
+zideal Zelig Matrix of Dependencies
+}
+
+Further information is available in the following vignettes:
+
+\tabular{ll}{
+\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{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{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{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.gee} \tab Generalized Estimating Equation for Logistic Regression (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{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{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{probit} \tab Probit Regression for Dichotomous Dependent Variables (source)\cr
+\code{probit.bayes} \tab Bayesian Probit Regression for Dichotomous Dependent Variable (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{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{weibull} \tab Weibull Regression for Duration Dependent Variables (source)\cr
+}
+}
+\author{
+Kosuke Imai <kimai at Princeton.Edu>, Gary King
+<king at harvard.edu>, Olivia Lau <olau at fas.harvard.edu>
+
+Maintainer: Olivia Lau <olau at fas.harvard.edu>
+}
+\keyword{ package }
diff --git a/man/Zelig.Rd b/man/Zelig.Rd
index 3034614..f180fbb 100644
--- a/man/Zelig.Rd
+++ b/man/Zelig.Rd
@@ -1,7 +1,6 @@
\name{zelig}
\alias{zelig}
-\alias{Zelig}
\title{Estimating a Statistical Model}
diff --git a/man/friendship.Rd b/man/friendship.Rd
index aeccfee..cf92655 100644
--- a/man/friendship.Rd
+++ b/man/friendship.Rd
@@ -1,19 +1,28 @@
-\name{friendship}
-
-\alias{friendship}
-
-\title{Simulated Example of Schoolchildren Friendship Network}
-
-\description{
- This data set contains five sociomatrices of simulated data on friendship ties among schoolchildren.}
-
-\usage{data(friendship)}
-
-\format{
-Each variable in the dataset is a 15 by 15 matrix representing some form of social network tie held by the fictitious children. The matrices are labeled "friends", "advice", "prestige", "authority", and "perpower".
-}
-\source{fictitious}
-
-\keyword{datasets}
-
-
+\name{friendship}
+
+\alias{friendship}
+
+\title{Simulated Example of Schoolchildren Friendship Network}
+
+\description{
+ This data set contains six sociomatrices of simulated data on friendship ties among schoolchildren.}
+
+\usage{data(friendship)}
+
+\format{
+Each variable in the dataset is a 15 by 15 matrix representing some form of social network tie held by the fictitious children. The matrices are labeled "friends", "advice", "prestige", "authority", "perpower" and "per".
+
+The sociomatrices were combined into the friendship dataset using the format.network.data function from the netglm package by Skyler Cranmer as shown in the example.
+
+}
+
+\source{fictitious}
+
+\examples{
+ \dontrun{
+ friendship <- format.network.data(friends, advice, prestige, authority, perpower, per)
+ }}
+
+\keyword{datasets}
+
+
diff --git a/man/grunfeld.Rd b/man/grunfeld.Rd
new file mode 100644
index 0000000..e78232f
--- /dev/null
+++ b/man/grunfeld.Rd
@@ -0,0 +1,23 @@
+\name{grunfeld}
+
+\alias{grunfeld}
+
+\title{Simulation Data for model Seemingly Unrelated Regression (sur) that corresponds to method SUR of systemfit}
+
+\description{
+ Dataframe contains 20 annual observations from 1935 to
+ 1954 of 7 variables for two firms General Electric and Westinghouse.
+ Columns are Year; Ige and Iw = Gross investment for GE and
+ W,respectively; Fge and Fw=Market value of Firm as of
+ begin of the year; Cge and Cw= Capital stock measure as of begin of
+ the year.
+}
+
+\usage{data(grunfeld)}
+
+\format{
+ A table containing 7 variables ("Year", "Ige", "Fge", "Cge","Iw", "Fw","Cw")
+ and 20 observations.
+}
+
+\keyword{datasets}
diff --git a/man/klein.Rd b/man/klein.Rd
new file mode 100644
index 0000000..7c1bdfb
--- /dev/null
+++ b/man/klein.Rd
@@ -0,0 +1,25 @@
+\name{klein}
+
+\alias{klein}
+
+\title{Simulation Data for model Two-Stage Least Square (twosls) that corresponds to method 2SLS of systemfit}
+
+\description{
+ Dataframe contains annual observations of US economy from 1920 to
+ 1940. The columns are, Year, C=Consumption, P=Corporate profits,
+ P1=Previous year corporate profit,Wtot=Total wage, Wp=Private wage
+ bill, Wg=Government wage bill,I=Investment,
+ K1=Previous year capital stock,X=GNP,G=Government spending, T=Taxes,
+ X1=Previous year GNP, Tm=Year-1931.
+
+}
+
+\usage{data(klein)}
+
+\format{
+ A table containing 14 variables ("year", "C", "P", "P1","Wtot", "Wp",
+ "Wg", "I", "K1","X", "G", "T", "X1", "Tm") and 21 observations.
+}
+\source{http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm}
+
+\keyword{datasets}
diff --git a/man/kmenta.Rd b/man/kmenta.Rd
new file mode 100644
index 0000000..ef2a032
--- /dev/null
+++ b/man/kmenta.Rd
@@ -0,0 +1,24 @@
+\name{kmenta}
+
+\alias{kmenta}
+
+\title{Simulation Data for model Three-Stage Least Square (threesls) that corresponds to method 3SLS of systemfit}
+
+\description{
+ Dataframe contains 20 annual observations of a supply/demand model
+ with 5 variables. Columns are q=Food consumption per capita,
+ p=Ratio of food price to general consumer prices,
+ d=Disposable income in contstant dollars,
+ f=Ratio of preceding year's prices received by farmers to general consumer prices,
+ a=Time index.
+
+}
+
+\usage{data(kmenta)}
+
+\format{
+ A table containing 5 variables ("q", "p", "d", "f","a")
+ and 20 observations.
+}
+
+\keyword{datasets}
diff --git a/man/zeligDepStatus.Rd b/man/zeligDepStatus.Rd
new file mode 100644
index 0000000..f1198c4
--- /dev/null
+++ b/man/zeligDepStatus.Rd
@@ -0,0 +1,57 @@
+\name{zeligDepStatus}
+\alias{zeligDepStatus}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{Zelig Dependencies Packages Client Status}
+\description{
+ Compares Zelig-matrix of dependencies to the locally installed
+ packages. Finds those packages that \pkg{Zelig} depends on and are not
+ installed in local environment. Also finds those packages that are
+ locally installed but with lower versions than those required in the
+ dependencies matrix.
+ The Zelig-matrix includes any of dependency fields in the
+ \sQuote{DESCRIPTION} files, i.e. depends, imports and suggests,
+ for any packages directly derived from \pkg{Zelig} and for any of the models that \pkg{Zelig} supports.
+}
+\usage{
+zeligDepStatus(lib.loc = NULL)
+}
+%- maybe also 'usage' for other objects documented here.
+\arguments{
+ \item{lib.loc}{a character vector of directory names
+ of \code{R} libraries, or NULL.
+ The default value NULL corresponds to all libraries currently known. If the default is used, the loaded packages are searched before the libraries. }
+}
+
+\value{
+ Returns a matrix of packages that are either installed locally
+ with lower versions, or packages not installed but listed in the
+ Zelig-matrix of dependencies. The matrix rows correspond to the packages and
+ the columns contain the following fields
+ \item{Package}{names of packages.}
+ \item{Version}{versions locally installed}
+ \item{Zideal}{versions required in Zelig-matrix of dependencies.}}
+
+\references{King, Gary. Zelig: Everyones Statistical Software.
+ \url{http://gking.harvard.edu/zelig}.}
+
+\author{Ferdinand Alimadhi and Elena Villalon}
+\seealso{
+\code{\link{installed.packages}}
+\code{\link{packageDescription}}
+\code{\link{zeligDepUpdate}}
+}
+\note{
+If the R version in the local environment is different from the R
+version that \pkg{Zelig}
+depends on, then, it is reported with a message and no futher action is taken.
+If the installed packages have versions higher than the corresponding
+values in the Zelig-matrix, it is reported with a message.}
+
+\examples{
+## find packages in all libraries currently installed
+\dontrun{zstatus <- zeligDepStatus()}
+## find packages only in lib.loc
+\dontrun{zstatus <- zeligDepStatus(lib.loc="~/.R/mylibrary")}
+}
+\keyword{documentation}
+
diff --git a/man/zeligDepUpdate.Rd b/man/zeligDepUpdate.Rd
new file mode 100644
index 0000000..5b888d9
--- /dev/null
+++ b/man/zeligDepUpdate.Rd
@@ -0,0 +1,72 @@
+\name{zeligDepUpdate}
+\alias{zeligDepUpdate}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+ Download Zelig Dependencies Packages
+}
+\description{
+ Compares the packages in Zelig-matrix of dependencies to the
+ locally installed packages. Finds local packages that have lower
+ versions than in the corresponding row of the Zelig-matrix.
+ Also, finds packages that \pkg{Zelig} required but are not
+ 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{\var{repos}} argument or from the \sQuote{URL} column of
+ the Zelig-matrix of dependencies.
+}
+
+\usage{
+zeligDepUpdate(destdir = NULL, installWithVers = FALSE, lib.loc = NULL,
+repos = "http://cran.r-project.org")
+}
+%- maybe also 'usage' for other objects documented here.
+
+\arguments{
+ \item{destdir}{directory to store the compress source-codes of
+ packages that are downloaded from web repositories.}
+ \item{installWithVers}{if TRUE, will invoke the install of the
+ package such that it can be referenced by package version.}
+ \item{lib.loc}{character vector describing the location of R
+ library trees to search through (and update packages therein). }
+ \item{repos}{character vector, the base URL(s) of the repositories to
+ use, i.e. the URL of the CRAN master such as
+ "http://cran.r-project.org", which is the default, or its Statlib
+ mirror, "http://lib.stat.cmu.edu/R/CRAN". Can be NULL to install
+ from local zip files.}
+}
+
+\value{
+ No return value.
+}
+
+\references{
+ King, Gary. Zelig: Everyones Statistical Software.
+ \url{http://gking.harvard.edu/zelig}.
+}
+\author{
+ Ferdinand Alimadhi and Elena Villalon
+}
+\seealso{
+ \code{\link{zeligDepStatus}}
+ \code{\link{install.packages}}
+
+}
+\note{
+ Installs first level dependencies packages of \pkg{Zelig} using R function
+ \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
+ the R version that \pkg{Zelig} depends on, then, it is reported with a message and no futher action is taken.
+}
+
+\examples{
+##checks all libraries curently know for packages
+\dontrun{zeligDepUpdate()}
+##finds packages only in lib.loc
+\dontrun{zeligDepUpdate(lib.loc="~/.R/mylibrary")}
+}
+\keyword{documentation}
+
diff --git a/tests/check.describe.R b/tests/check.describe.R
index eee6b26..0542232 100644
--- a/tests/check.describe.R
+++ b/tests/check.describe.R
@@ -64,7 +64,7 @@ zeligListModels<-function() {
## describe function for this models does not exists
## just for testing purpuses i'm excluding them. <FIXME> urgent
- nonexist <- c("arima","beta", "ei.dynamic","ei.hier","ei.RxC","mloglm","netlogit","netls")
+ nonexist <- c("mloglm")
setdiff(sub("zelig2","", tmp),nonexist)
}
@@ -88,6 +88,7 @@ checkDescribe <- function(){
## when source, this is the function wich will be executed
res <- checkDescribe()
if (length(res)>0) {
- stop("the above models have no mymodel.describe() function os that function did not paseed the test")
+ print(res)
+ stop("the above models have no mymodel.describe() function or that function did not paseed the test")
} else
print ("describe.* functions OK ...")
diff --git a/tests/test.parse.formula.RData b/tests/test.parse.formula.RData
index 42bda4b..1a17384 100644
Binary files a/tests/test.parse.formula.RData and b/tests/test.parse.formula.RData differ
diff --git a/tests/test.terms.multiple.RData b/tests/test.terms.multiple.RData
index a8b2b25..696abad 100644
Binary files a/tests/test.terms.multiple.RData and b/tests/test.terms.multiple.RData differ
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