[r-cran-zelig] 66/102: Import Upstream version 3.3-1
Andreas Tille
tille at debian.org
Sun Jan 8 16:59:29 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 40a1c7d9958391922a233f04e44887b87c86b163
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:40:00 2017 +0100
Import Upstream version 3.3-1
---
DESCRIPTION | 12 +-
NAMESPACE | 1 +
R/chopitcode.R | 2 +-
R/plot.ci.R | 19 +-
R/plot.surv.R | 100 +
R/qi.coxph.R | 8 +-
R/setx.coxph.R | 35 +-
R/summary.MI.R | 7 +-
demo/coxph.R | 32 +
inst/doc/Rplots.pdf | 197932 +++++++++++
inst/doc/Rplots.ps | 327580 ------------------
inst/doc/aov.pdf | Bin 162987 -> 163258 bytes
inst/doc/arima.pdf | Bin 124267 -> 124172 bytes
inst/doc/blogit.pdf | Bin 154166 -> 140644 bytes
inst/doc/bprobit.pdf | Bin 165597 -> 146131 bytes
inst/doc/chopit.pdf | Bin 109231 -> 109230 bytes
inst/doc/cloglog.net.pdf | Bin 107571 -> 107546 bytes
inst/doc/commands/matchitref.tex | 50 +-
inst/doc/commands/model.end.aux | 24 +-
inst/doc/commands/model.frame.multiple.aux | 26 +-
inst/doc/commands/model.matrix.multiple.aux | 26 +-
inst/doc/commands/parse.formula.aux | 30 +-
inst/doc/commands/parse.par.aux | 26 +-
inst/doc/commands/put.start.aux | 24 +-
inst/doc/commands/set.start.aux | 26 +-
inst/doc/commands/tag.aux | 26 +-
inst/doc/commandsRd/MatchIt.url.pdf | Bin 19853 -> 19852 bytes
inst/doc/commandsRd/PErisk.pdf | Bin 42258 -> 42260 bytes
inst/doc/commandsRd/SupremeCourt.pdf | Bin 32131 -> 32130 bytes
inst/doc/commandsRd/Weimar.pdf | Bin 25053 -> 25054 bytes
inst/doc/commandsRd/Zelig-package.pdf | Bin 47952 -> 47952 bytes
inst/doc/commandsRd/Zelig.pdf | Bin 41493 -> 41493 bytes
inst/doc/commandsRd/Zelig.url.pdf | Bin 18734 -> 18736 bytes
inst/doc/commandsRd/approval.pdf | Bin 24314 -> 24313 bytes
inst/doc/commandsRd/bivariate.pdf | Bin 31448 -> 31446 bytes
inst/doc/commandsRd/coalition.pdf | Bin 33900 -> 33898 bytes
inst/doc/commandsRd/coalition2.pdf | Bin 34952 -> 34954 bytes
inst/doc/commandsRd/current.packages.pdf | Bin 38486 -> 38490 bytes
inst/doc/commandsRd/dims.pdf | Bin 38330 -> 38329 bytes
inst/doc/commandsRd/eidat.pdf | Bin 20563 -> 20563 bytes
inst/doc/commandsRd/free1.pdf | Bin 40772 -> 40772 bytes
inst/doc/commandsRd/free2.pdf | Bin 40801 -> 40803 bytes
inst/doc/commandsRd/friendship.pdf | Bin 31868 -> 31869 bytes
inst/doc/commandsRd/grunfeld.pdf | Bin 26300 -> 26300 bytes
inst/doc/commandsRd/gsource.pdf | Bin 43876 -> 43877 bytes
inst/doc/commandsRd/help.zelig.pdf | Bin 33302 -> 33301 bytes
inst/doc/commandsRd/hoff.pdf | Bin 33601 -> 33604 bytes
inst/doc/commandsRd/homerun.pdf | Bin 39387 -> 39386 bytes
inst/doc/commandsRd/immigration.pdf | Bin 33055 -> 33053 bytes
inst/doc/commandsRd/klein.pdf | Bin 27456 -> 27457 bytes
inst/doc/commandsRd/kmenta.pdf | Bin 24920 -> 24919 bytes
inst/doc/commandsRd/macro.pdf | Bin 32698 -> 32698 bytes
inst/doc/commandsRd/match.data.pdf | Bin 35998 -> 35996 bytes
inst/doc/commandsRd/mexico.pdf | Bin 33499 -> 33499 bytes
inst/doc/commandsRd/mi.pdf | Bin 38081 -> 38083 bytes
inst/doc/commandsRd/mid.pdf | Bin 37168 -> 37169 bytes
inst/doc/commandsRd/model.end.pdf | Bin 36596 -> 36597 bytes
inst/doc/commandsRd/model.frame.multiple.pdf | Bin 47452 -> 47452 bytes
inst/doc/commandsRd/model.matrix.multiple.pdf | Bin 53327 -> 53327 bytes
inst/doc/commandsRd/network.pdf | Bin 35147 -> 35147 bytes
inst/doc/commandsRd/newpainters.pdf | Bin 34964 -> 34964 bytes
inst/doc/commandsRd/parse.formula.pdf | Bin 50654 -> 50654 bytes
inst/doc/commandsRd/parse.par.pdf | Bin 52102 -> 52099 bytes
inst/doc/commandsRd/plot.ci.pdf | Bin 51972 -> 51972 bytes
inst/doc/commandsRd/plot.surv.pdf | Bin 0 -> 52326 bytes
inst/doc/commandsRd/plot.surv.tex | 58 +
inst/doc/commandsRd/plot.zelig.pdf | Bin 40109 -> 40111 bytes
inst/doc/commandsRd/put.start.pdf | Bin 34223 -> 34220 bytes
inst/doc/commandsRd/repl.pdf | Bin 49401 -> 49398 bytes
inst/doc/commandsRd/rocplot.pdf | Bin 50646 -> 50650 bytes
inst/doc/commandsRd/sanction.pdf | Bin 31279 -> 31280 bytes
inst/doc/commandsRd/set.start.pdf | Bin 43069 -> 43068 bytes
inst/doc/commandsRd/setx.pdf | Bin 54741 -> 54741 bytes
inst/doc/commandsRd/sim.pdf | Bin 66016 -> 66016 bytes
inst/doc/commandsRd/sna.ex.pdf | Bin 22887 -> 22890 bytes
inst/doc/commandsRd/summary.zelig.pdf | Bin 40776 -> 40772 bytes
inst/doc/commandsRd/swiss.pdf | Bin 29948 -> 29949 bytes
inst/doc/commandsRd/ternaryplot.pdf | Bin 59265 -> 59265 bytes
inst/doc/commandsRd/ternarypoints.pdf | Bin 35079 -> 35073 bytes
inst/doc/commandsRd/tobin.pdf | Bin 24016 -> 24016 bytes
inst/doc/commandsRd/turnout.pdf | Bin 32995 -> 32995 bytes
inst/doc/commandsRd/user.prompt.pdf | Bin 35229 -> 35230 bytes
inst/doc/commandsRd/voteincome.pdf | Bin 30466 -> 30468 bytes
inst/doc/commandsRd/zeligDepStatus.pdf | Bin 44748 -> 44747 bytes
inst/doc/commandsRd/zeligDepUpdate.pdf | Bin 50252 -> 50252 bytes
inst/doc/commandsRd/zeligVDC.pdf | Bin 46762 -> 46762 bytes
inst/doc/coxph.Rnw | 160 +-
inst/doc/coxph.pdf | Bin 162476 -> 187468 bytes
inst/doc/describemymodel.tex | 206 +
inst/doc/ei.RxC.pdf | Bin 96266 -> 96266 bytes
inst/doc/ei.dynamic.pdf | Bin 104820 -> 104824 bytes
inst/doc/ei.hier.pdf | Bin 102357 -> 102365 bytes
inst/doc/exp.pdf | Bin 136472 -> 136578 bytes
inst/doc/factor.bayes.pdf | Bin 91826 -> 91824 bytes
inst/doc/factor.mix.pdf | Bin 96389 -> 96389 bytes
inst/doc/factor.ord.pdf | Bin 94559 -> 94558 bytes
inst/doc/faq.tex | 10 +-
inst/doc/gamma.gee.pdf | Bin 131931 -> 131981 bytes
inst/doc/gamma.mixed.pdf | Bin 103333 -> 103335 bytes
inst/doc/gamma.net.pdf | Bin 132971 -> 133214 bytes
inst/doc/gamma.pdf | Bin 133499 -> 133773 bytes
inst/doc/gamma.survey.pdf | Bin 181776 -> 181582 bytes
inst/doc/index.shtml | 6 +-
inst/doc/irt1d.pdf | Bin 100342 -> 100341 bytes
inst/doc/irtkd.pdf | Bin 102556 -> 102554 bytes
inst/doc/logit.bayes.pdf | Bin 102671 -> 102667 bytes
inst/doc/logit.gam.pdf | Bin 272188 -> 272184 bytes
inst/doc/logit.gee.pdf | Bin 155221 -> 155223 bytes
inst/doc/logit.mixed.pdf | Bin 103224 -> 103230 bytes
inst/doc/logit.net.pdf | Bin 112386 -> 112390 bytes
inst/doc/logit.pdf | Bin 156588 -> 156589 bytes
inst/doc/logit.survey.pdf | Bin 184031 -> 184028 bytes
inst/doc/lognorm.pdf | Bin 137484 -> 137483 bytes
inst/doc/ls.mixed.pdf | Bin 134207 -> 134209 bytes
inst/doc/ls.net.pdf | Bin 109562 -> 109561 bytes
inst/doc/ls.pdf | Bin 185778 -> 185775 bytes
inst/doc/makeDocs | 2 +-
inst/doc/{commands => }/matchitref.tex | 0
inst/doc/mlogit.bayes.pdf | Bin 104389 -> 104390 bytes
inst/doc/mlogit.pdf | Bin 160555 -> 160555 bytes
inst/doc/mloglm.pdf | Bin 56926 -> 56927 bytes
inst/doc/negbin.pdf | Bin 114913 -> 114915 bytes
inst/doc/normal.bayes.pdf | Bin 104934 -> 104932 bytes
inst/doc/normal.gam.pdf | Bin 267607 -> 267608 bytes
inst/doc/normal.gee.pdf | Bin 131592 -> 131599 bytes
inst/doc/normal.net.pdf | Bin 102325 -> 102327 bytes
inst/doc/normal.pdf | Bin 132520 -> 132516 bytes
inst/doc/normal.survey.pdf | Bin 173279 -> 173278 bytes
inst/doc/ologit.pdf | Bin 102240 -> 102237 bytes
inst/doc/oprobit.bayes.pdf | Bin 107638 -> 107639 bytes
inst/doc/oprobit.pdf | Bin 170562 -> 170557 bytes
inst/doc/poisson.bayes.pdf | Bin 100741 -> 100740 bytes
inst/doc/poisson.gam.pdf | Bin 268059 -> 268056 bytes
inst/doc/poisson.gee.pdf | Bin 123351 -> 123349 bytes
inst/doc/poisson.mixed.pdf | Bin 100363 -> 100367 bytes
inst/doc/poisson.net.pdf | Bin 94192 -> 94191 bytes
inst/doc/poisson.pdf | Bin 111099 -> 111099 bytes
inst/doc/poisson.survey.pdf | Bin 165458 -> 165458 bytes
inst/doc/probit.bayes.pdf | Bin 103285 -> 103286 bytes
inst/doc/probit.gam.pdf | Bin 268118 -> 268116 bytes
inst/doc/probit.gee.pdf | Bin 155396 -> 155392 bytes
inst/doc/probit.mixed.pdf | Bin 103781 -> 103779 bytes
inst/doc/probit.net.pdf | Bin 107622 -> 107620 bytes
inst/doc/probit.pdf | Bin 99605 -> 99602 bytes
inst/doc/probit.survey.pdf | Bin 182858 -> 182859 bytes
inst/doc/programmingstatements.tex | 236 +
inst/doc/refman.tex | 5 +-
inst/doc/relogit.pdf | Bin 146463 -> 146466 bytes
inst/doc/{advancedR.tex => robjects.tex} | 236 -
inst/doc/static/Zcommands.pdf | Bin 159937 -> 159936 bytes
inst/doc/static/acknowledgments.pdf | Bin 29859 -> 29857 bytes
inst/doc/static/addingModels.pdf | Bin 87603 -> 87603 bytes
inst/doc/static/advancedR.pdf | Bin 96392 -> 0 bytes
inst/doc/static/describemymodel.pdf | Bin 0 -> 56365 bytes
inst/doc/static/faq.pdf | Bin 104829 -> 105112 bytes
inst/doc/static/graphs.pdf | Bin 173302 -> 173306 bytes
inst/doc/static/install.pdf | Bin 97970 -> 97977 bytes
inst/doc/static/intro.pdf | Bin 72979 -> 72978 bytes
inst/doc/static/matchitref.pdf | Bin 0 -> 91725 bytes
inst/doc/static/newModels.pdf | Bin 135792 -> 135793 bytes
inst/doc/static/programmingstatements.pdf | Bin 0 -> 65125 bytes
inst/doc/static/robjects.pdf | Bin 0 -> 73431 bytes
inst/doc/static/syntax.pdf | Bin 93890 -> 93889 bytes
inst/doc/static/whatsnew.pdf | Bin 0 -> 67241 bytes
inst/doc/sur.pdf | Bin 100655 -> 100653 bytes
inst/doc/threesls.pdf | Bin 137744 -> 137743 bytes
inst/doc/tobit.bayes.pdf | Bin 104466 -> 104467 bytes
inst/doc/tobit.pdf | Bin 94381 -> 94382 bytes
inst/doc/twosls.pdf | Bin 161717 -> 161719 bytes
inst/doc/vigpics/aov-ExamplesPlot1.eps | 686 +-
inst/doc/vigpics/aov-ExamplesPlot1.pdf | 1044 +-
inst/doc/vigpics/aov-ExamplesPlot2.eps | 1406 +-
inst/doc/vigpics/aov-ExamplesPlot2.pdf | 2028 +-
inst/doc/vigpics/aov-ExamplesPlot3.eps | 755 +-
inst/doc/vigpics/aov-ExamplesPlot3.pdf | 1020 +-
inst/doc/vigpics/arima-Example1Plot.eps | 246 +-
inst/doc/vigpics/arima-Example1Plot.pdf | 252 +-
inst/doc/vigpics/arima-Example2Plot.eps | 844 +-
inst/doc/vigpics/arima-Example2Plot.pdf | 1178 +-
inst/doc/vigpics/arima-Example3Plot.eps | 187 +-
inst/doc/vigpics/arima-Example3Plot.pdf | 220 +-
inst/doc/vigpics/blogit-BasicExamplePlot.eps | 3091 +-
inst/doc/vigpics/blogit-BasicExamplePlot.pdf | 1393 +-
inst/doc/vigpics/blogit-JointExamplePlot.eps | 1673 +-
inst/doc/vigpics/blogit-JointExamplePlot.pdf | 759 +-
inst/doc/vigpics/bprobit-BasicExamplePlot.eps | 4019 +-
inst/doc/vigpics/bprobit-BasicExamplePlot.pdf | 1674 +-
inst/doc/vigpics/bprobit-JointEstimationPlot.eps | 2020 +-
inst/doc/vigpics/bprobit-JointEstimationPlot.pdf | 875 +-
inst/doc/vigpics/cloglognet-007.eps | 802 +-
inst/doc/vigpics/cloglognet-007.pdf | 1080 +-
inst/doc/vigpics/cloglognet-009.eps | 802 +-
inst/doc/vigpics/cloglognet-009.pdf | 1080 +-
inst/doc/vigpics/coxph-ExamplePlot.eps | 1315 +-
inst/doc/vigpics/coxph-ExamplePlot.pdf | 2209 +-
inst/doc/vigpics/coxph-ExamplePlot2.eps | 899 +-
inst/doc/vigpics/coxph-ExamplePlot2.pdf | 1359 +-
...xph-ExamplePlot2.eps => coxph-ExamplePlot3.eps} | 1293 +-
inst/doc/vigpics/coxph-ExamplePlot3.pdf | 956 +
inst/doc/vigpics/exp-ExamplePlot.eps | 2225 +-
inst/doc/vigpics/exp-ExamplePlot.pdf | 3124 +-
inst/doc/vigpics/gam-006.pdf | 6 +-
inst/doc/vigpics/gam-008.pdf | 6 +-
inst/doc/vigpics/gam-010.pdf | 6 +-
inst/doc/vigpics/gamma-ExamplePlot.eps | 2197 +-
inst/doc/vigpics/gamma-ExamplePlot.pdf | 3038 +-
inst/doc/vigpics/gamma_gee-ExamplePlot.eps | 1579 +-
inst/doc/vigpics/gamma_gee-ExamplePlot.pdf | 2122 +-
inst/doc/vigpics/gammanet-007.eps | 1579 +-
inst/doc/vigpics/gammanet-007.pdf | 2083 +-
inst/doc/vigpics/gammanet-009.eps | 1579 +-
inst/doc/vigpics/gammanet-009.pdf | 2083 +-
inst/doc/vigpics/gammasurvey-ExistingPlot.eps | 2079 +-
inst/doc/vigpics/gammasurvey-ExistingPlot.pdf | 2928 +-
inst/doc/vigpics/gammasurvey-ReplicatePlot.eps | 2249 +-
inst/doc/vigpics/gammasurvey-ReplicatePlot.pdf | 3088 +-
inst/doc/vigpics/lmmixed-ExamplesPlot.pdf | 6 +-
inst/doc/vigpics/logit-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/logit-FirstDifferencesPlot.pdf | 6 +-
inst/doc/vigpics/logit-ROCPlot.pdf | 6 +-
inst/doc/vigpics/logit_gee-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/logit_gee-FirstDifferencePlot.pdf | 6 +-
inst/doc/vigpics/logitnet-007.pdf | 6 +-
inst/doc/vigpics/logitnet-009.pdf | 6 +-
inst/doc/vigpics/logitsurvey-ExistingPlot.pdf | 6 +-
inst/doc/vigpics/logitsurvey-ReplicatePlot.pdf | 6 +-
inst/doc/vigpics/lognormal-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/ls-DummyPlot.pdf | 6 +-
inst/doc/vigpics/ls-Example4Plot.pdf | 6 +-
inst/doc/vigpics/ls-ExamplesPlot.pdf | 6 +-
inst/doc/vigpics/lsnet-ExamplesPlot.pdf | 6 +-
inst/doc/vigpics/mlogit-LevelsTernaryPlot.pdf | 6 +-
inst/doc/vigpics/mlogit-TernaryPlot.pdf | 6 +-
inst/doc/vigpics/negbin-Example1Plot.pdf | 6 +-
inst/doc/vigpics/normal-ExamplesPlot.pdf | 6 +-
inst/doc/vigpics/normal-ExistingPlot.pdf | 6 +-
inst/doc/vigpics/normal-ReplicatePlot.pdf | 6 +-
inst/doc/vigpics/normal_gee-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/normalnet-007.pdf | 6 +-
inst/doc/vigpics/oprobit-FirstDifferencesPlot.pdf | 6 +-
inst/doc/vigpics/poisson-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/poisson_gee-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/poissonnet-007.pdf | 6 +-
inst/doc/vigpics/poissonsurvey-ExistingPlot.pdf | 6 +-
inst/doc/vigpics/poissonsurvey-ReplicatePlot.pdf | 6 +-
inst/doc/vigpics/probit_gee-ExamplePlot.pdf | 6 +-
.../doc/vigpics/probit_gee-FirstDifferencePlot.pdf | 6 +-
inst/doc/vigpics/probitnet-007.pdf | 6 +-
inst/doc/vigpics/probitnet-009.pdf | 6 +-
inst/doc/vigpics/probitsurvey-ExistingPlot.pdf | 6 +-
inst/doc/vigpics/probitsurvey-ReplicatePlot.pdf | 6 +-
inst/doc/vigpics/relogit-Example1Plot.pdf | 6 +-
inst/doc/vigpics/relogit-Example3Plot.pdf | 6 +-
inst/doc/vigpics/threesls-Examplesthreesls.pdf | 6 +-
inst/doc/vigpics/twosls-Examplestwosls.pdf | 6 +-
inst/doc/vigpics/weibull-ExamplePlot.pdf | 6 +-
inst/doc/weibull.pdf | Bin 137673 -> 137672 bytes
inst/doc/{zelig.tex => whatsnew.tex} | 110 +-
inst/doc/zelig.html | 8 +-
inst/doc/zelig.pdf | 75008 ++--
inst/doc/zelig.tex | 392 +-
inst/doc/zeligfooter.html | 3 +-
inst/doc/zeligheader.html | 5 +-
inst/zideal/zideal.RData | Bin 2014 -> 0 bytes
man/plot.surv.Rd | 66 +
265 files changed, 268708 insertions(+), 403165 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 5ffe762..ff88872 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: Zelig
-Version: 3.2-1
-Date: 2008-04-11
+Version: 3.3-1
+Date: 2008-06-13
Title: Everyone's Statistical Software
Author: Kosuke Imai <kimai at Princeton.Edu>,
Gary King <king at harvard.edu>,
@@ -20,9 +20,9 @@ Description: Zelig is an easy-to-use program that can estimate, and
model, and mimicking the program Clarify (for Stata) that
takes the raw output of existing statistical procedures and
translates them into quantities of direct interest.
-License: GPL (>= 2)
+License: Affero GPL
URL: http://gking.harvard.edu/zelig
-Suggests: VGAM (>= 0.7-1), MCMCpack (>= 0.7-4), mvtnorm, survival,
- sandwich (>= 2.0-0), zoo (>= 1.2-1), coda, nnet, sna, gee,
+Suggests: VGAM (>= 0.7-5), MCMCpack (>= 0.8-2), mvtnorm, survival,
+ sandwich (>= 2.1-0), zoo (>= 1.5-0), coda, nnet, sna, gee,
systemfit, mgcv, lme4, anchors (>= 2.0), survey
-Packaged: Fri Apr 11 02:10:44 2008; king
+Packaged: Fri Jun 13 18:24:07 2008; king
diff --git a/NAMESPACE b/NAMESPACE
index 585d3e0..99bb787 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -24,6 +24,7 @@ export( current.packages,
parse.formula,
parse.par,
plot.ci,
+ plot.surv,
plot.zelig,
put.start,
repl,
diff --git a/R/chopitcode.R b/R/chopitcode.R
index 2ce19dd..a55e0fa 100644
--- a/R/chopitcode.R
+++ b/R/chopitcode.R
@@ -73,7 +73,7 @@ prechop <- function(formula, data, ...) {
for (i in miss2) { D1[[i]] <- rep(NA, length = nrow(D1)) }
data <- rbind(D1, D2)
} else if (is.data.frame(data)) {
- data <- data
+ D1 <- data <- data
}
else { stop("data must be either a data frame or list of data frames") }
out <- chopit(formula, data = data, ...)
diff --git a/R/plot.ci.R b/R/plot.ci.R
index 08b20d4..10604cf 100644
--- a/R/plot.ci.R
+++ b/R/plot.ci.R
@@ -3,15 +3,16 @@ plot.ci <- function(x, CI=95, qi = "ev", main = "",
ylim = NULL, col = c("red", "blue"), ...) {
if (class(x) != "zelig")
stop(" plot.ci() works only for sim() output.")
- if (!(x$zelig.call$model) %in% c("ls", "logit", "probit", "exp", "gamma", "lognorm",
- "weibull", "normal", "poisson", "tobit", "relogit",
- "negbin", "logit.bayes", "probit.bayes",
- "poisson.bayes", "normal.bayes", "tobit.bayes",
- "ls.mixed", "logit.mixed", "probit.mixed",
- "gamma.mixed", "poisson.mixed",
- "logit.gam", "gamma.gee", "normal.gam", "poisson.gam",
- "probit.gam", "logit.gee", "normal.gee",
- "poisson.gee", "probit.gee"))
+ if (!(x$zelig.call$model) %in%
+ c("ls", "logit", "probit", "exp", "gamma", "lognorm",
+ "weibull", "normal", "poisson", "tobit", "relogit",
+ "negbin", "logit.bayes", "probit.bayes",
+ "poisson.bayes", "normal.bayes", "tobit.bayes",
+ "ls.mixed", "logit.mixed", "probit.mixed",
+ "gamma.mixed", "poisson.mixed",
+ "logit.gam", "gamma.gee", "normal.gam", "poisson.gam",
+ "probit.gam", "logit.gee", "normal.gee",
+ "poisson.gee", "probit.gee"))
stop("\n plot.ci() is valid only for non-categorical, univariate response models.")
var <- var1 <- list()
idx1 <- idx <- NULL
diff --git a/R/plot.surv.R b/R/plot.surv.R
new file mode 100644
index 0000000..516ff03
--- /dev/null
+++ b/R/plot.surv.R
@@ -0,0 +1,100 @@
+plot.surv <- function(x,duration,censor,type="line",
+plotcensor=TRUE,plottimes=FALSE,int=c(0.025,0.975),...) {
+
+ s.out <- x
+ nobj <- length(s.out)
+ for (s in 1:nobj) {
+ if (s==1) {
+ survival <- s.out[[s]]$qi$survival
+ survest <- c(1,apply(survival,2,mean))
+
+ survest.lb <- apply(survival,2,function(X){quantile(X,int[1])})
+
+ survest.ub <- apply(survival,2,function(X){quantile(X,int[2])})
+
+ times <- as.numeric(colnames(survival))
+
+ #plot(stepfun(times,survest),xlim=c(0,max(duration)),xlab=xlab,ylab=ylab,main=title)
+ plot(times,survest[-1],type="n",xlim=c(0,max(duration)),
+ ...)
+ ntimes <- length(times)-1
+ for (t in 1:ntimes){
+ u <- survest.ub[t]
+ l <- survest.lb[t]
+ if (type=="poly") {
+ x <- c(times[t],times[t+1],times[t+1],times[t])
+ y <- c(survest.ub[t],survest.ub[t+1],survest.lb[t+1],survest.lb[t])
+ polygon(x,y,density=100,col="grey")
+ }
+ if (type=="line") {
+ segments(times[t],u,times[t],l,col="grey",lwd=.5)
+ }
+ }
+ if (type=="line"){
+ t <- length(times)
+ u <- survest.ub[t]
+ l <- survest.lb[t]
+ segments(times[t],u,times[t],l,col="grey",lwd=.5)
+ }
+
+ }
+ if (s>1){
+ survival <- s.out[[s]]$qi$survival
+ survest <- c(1,apply(survival,2,mean))
+
+ survest.lb <- apply(survival,2,function(X){quantile(X,int[1])})
+
+ survest.ub <- apply(survival,2,function(X){quantile(X,int[2])})
+
+ times <- as.numeric(colnames(survival))
+
+ #lines(times,survest[-1],type="s")
+
+ ntimes <- length(times)-1
+ for (t in 1:ntimes){
+ u <- survest.ub[t]
+ l <- survest.lb[t]
+
+ if (type=="poly") {
+ x <- c(times[t],times[t+1],times[t+1],times[t])
+ y <- c(survest.ub[t],survest.ub[t+1],survest.lb[t+1],survest.lb[t])
+ polygon(x,y,density=100,col="grey")
+ }
+ if (type=="line") {
+ segments(times[t],u,times[t],l,col="grey",lwd=.5)
+ }
+
+ }
+ if (type=="line"){
+ t <- length(times)
+ u <- survest.ub[t]
+ l <- survest.lb[t]
+ segments(times[t],u,times[t],l,col="grey",lwd=.5)
+ }
+
+
+ }
+
+
+
+ }
+
+ for (s in 1:nobj) {
+ survival <- s.out[[s]]$qi$survival
+ survest <- c(1,apply(survival,2,mean))
+
+ survest.lb <- apply(survival,2,function(X){quantile(X,0.025)})
+
+ survest.ub <- apply(survival,2,function(X){quantile(X,0.975)})
+
+ times <- as.numeric(colnames(survival))
+ if (plottimes){lines(stepfun(times,survest),lty=s)}
+ else {lines(times,survest[-1],type="s",lty=s)}
+ }
+ if(plotcensor){
+ rug(duration[censor==0])
+ }
+ }
+
+
+
diff --git a/R/qi.coxph.R b/R/qi.coxph.R
index 413f5d4..fc4ac76 100644
--- a/R/qi.coxph.R
+++ b/R/qi.coxph.R
@@ -66,20 +66,20 @@ qi.coxph <- function(object, simpar, x, x1 = NULL, y = NULL) {
#}
if (!is.null(x))
- x <- as.matrix(x)
+ x.mat <- as.matrix(x)
if (!is.null(x1))
- x1 <- as.matrix(x1)
+ x1.mat <- as.matrix(x1)
coef <- as.matrix(simpar[,1:k])
- eta <- coef%*%t(x)
+ eta <- coef%*%t(x.mat)
risk <- exp(eta)
qi <- qi.name <- list()
## Rate Ratio
if(!is.null(x1)){
- eta1 <- coef%*%t(x1)
+ eta1 <- coef%*%t(x1.mat)
risk1 <- exp(eta1)
qi$hr <- risk1/risk
qi.name$hr <- "Hazard Ratios: h(t|X1)/h(t|X)"
diff --git a/R/setx.coxph.R b/R/setx.coxph.R
index bdb6aa2..9a446c3 100644
--- a/R/setx.coxph.R
+++ b/R/setx.coxph.R
@@ -72,28 +72,34 @@ setx.coxph <- function(object, fn = list(numeric = mean, ordered =
else
dta <- as.data.frame(data)
-################################ create new terms without strata
- if(!is.null(tt.attr$specials$strata)){
- no.strata <- rownames(tt.attr$factors)[-tt.attr$specials$strata]
- ind.var <- paste(no.strata[-tt.attr$response], collapse="+")
- nf <- as.formula(paste(paste(no.strata[tt.attr$response]), paste("~"), ind.var)) #new formula for terms
+################################ create new terms without strata or cluster
+ if(!is.null(tt.attr$specials$strata) | !is.null(tt.attr$specials$cluster)){
+ no.st.cl <- colnames(tt.attr$factors)[-c(tt.attr$specials$strata - 1,
+ tt.attr$specials$cluster - 1)]
+ rhs <- paste(no.st.cl, collapse="+")
+ lhs <- rownames(tt.attr$factors)[1]
+ nf <- as.formula(paste(paste(lhs), paste("~"), rhs)) #new formula for terms
#####extract strata
mf1 <- model.frame(tt, data = dta, na.action = na.pass)
- stratas <- mf1[complete.cases(mf1),tt.attr$specials$strata]
- st <- na.omit(pmatch(names(mc), as.character("strata")))
- if (length(st>0))
+
+ if(!is.null(tt.attr$specials$strata)){
+ stratas <- mf1[complete.cases(mf1),tt.attr$specials$strata]
+ st <- na.omit(pmatch(names(mc), as.character("strata")))
+ if (length(st>0))
strata <- mc[["strata"]]
- else
+ else
strata <- mode(stratas)
-
+ }
+ else
+ strata <- NULL
+
tt <- terms(nf)
tt.attr <- attributes(tt)
}
else
- strata <- NULL
+ strata <- NULL
#################################################
-
## extract variables we need
mf <- model.frame(tt, data = dta, na.action = na.pass)
if(any(class(tt)=="multiple"))
@@ -166,10 +172,7 @@ setx.coxph <- function(object, fn = list(numeric = mean, ordered =
}
data <- data[1:maxl,,drop = FALSE]
- X <- as.data.frame(model.matrix(tt, data = data))[-1]
- k <- length(coef(object))
- if(length(X)>k) #delete cluster variable
- X <- X[-(k+1)]
+ X <- as.data.frame(model.matrix(tt, data = data))[-1] #delete cluster
if(!is.null(strata))
X <- as.data.frame(cbind(X, strata))
class(X) <- c("data.frame", "coxph")
diff --git a/R/summary.MI.R b/R/summary.MI.R
index b8ebe8a..6705679 100644
--- a/R/summary.MI.R
+++ b/R/summary.MI.R
@@ -40,7 +40,10 @@ summary.MI <- function(object, subset = NULL, ...){
}
}
class(ans) <- "summary.MI"
+ } else if (length(M) == 1) {
+ ans <- summary(object[[M]])
+ } else {
+ stop("invalid input for `subset'")
}
- else ans <- summary(object)
- ans
+ return(ans)
}
diff --git a/demo/coxph.R b/demo/coxph.R
index 44b4236..7ba3db9 100644
--- a/demo/coxph.R
+++ b/demo/coxph.R
@@ -53,6 +53,38 @@ summary(s.out2)
user.prompt()
plot(s.out2)
+#### Example 3: Example with Time-Varying Covariates
+
+# Create sample toy dataset (from survival package):
+user.prompt()
+toy <- as.data.frame(list(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8),
+ stop=c(2, 3, 6, 7, 8, 9, 9, 9,14,17),
+ event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0),
+ x=c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0),
+ x1=c(5, 5, 7, 4, 5, 6, 3, 2, 7, 4) ))
+
+# Estimating parameter values for the coxph regression:
+user.prompt()
+z.out3 <- zelig(Surv(start, stop, event) ~ x + x1, model = "coxph", data = toy)
+user.prompt()
+summary(z.out3)
+
+# Setting values for the explanatory variables:
+user.prompt()
+x.low3 <- setx(z.out3, x = 0)
+x.high3 <- setx(z.out3, x = 1)
+
+# Simulating quantities of interest:
+user.prompt()
+s.out3 <- sim(z.out3, x = x.low3, x1 = x.high3)
+user.prompt()
+
+# Viewing the simulated quantites of interest
+summary(s.out3)
+user.prompt()
+plot(s.out3)
+
+
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-ep
-%%Trailer
-%%Pages: 111
-%%EOF
diff --git a/inst/doc/aov.pdf b/inst/doc/aov.pdf
index 3854943..dcbd9ac 100644
Binary files a/inst/doc/aov.pdf and b/inst/doc/aov.pdf differ
diff --git a/inst/doc/arima.pdf b/inst/doc/arima.pdf
index 5cffa09..49fd2fd 100644
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diff --git a/inst/doc/blogit.pdf b/inst/doc/blogit.pdf
index 56d7c65..1bb822e 100644
Binary files a/inst/doc/blogit.pdf and b/inst/doc/blogit.pdf differ
diff --git a/inst/doc/bprobit.pdf b/inst/doc/bprobit.pdf
index 0b4ee7e..2c3bceb 100644
Binary files a/inst/doc/bprobit.pdf and b/inst/doc/bprobit.pdf differ
diff --git a/inst/doc/chopit.pdf b/inst/doc/chopit.pdf
index 51cde82..0f7b4e3 100644
Binary files a/inst/doc/chopit.pdf and b/inst/doc/chopit.pdf differ
diff --git a/inst/doc/cloglog.net.pdf b/inst/doc/cloglog.net.pdf
index 1f99a44..8dd4ede 100644
Binary files a/inst/doc/cloglog.net.pdf and b/inst/doc/cloglog.net.pdf differ
diff --git a/inst/doc/commands/matchitref.tex b/inst/doc/commands/matchitref.tex
index bed2500..ace8f81 100644
--- a/inst/doc/commands/matchitref.tex
+++ b/inst/doc/commands/matchitref.tex
@@ -339,50 +339,16 @@ and
\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}
-
+ WhatIf} package \citep{StoKinZen06,KinZen06a,KinZen06b}.
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}
-
+ stats} package \citep{VenRip02}. Generalized additive distance
+measures are implemented via the {\tt mcgv} package \citep{HasTib90}.
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}
+package \citep{Ripley96}. The classification trees distance measure
+is implemented via the {\tt rpart} package \citep{BreFriOls84}. Full
+and optimal matching are implemented via the {\tt optmatch} package
+\citp{Hansen04}. Genetic matching is implemented via the {\tt
+ Matching} package \citep{DiaSek05}.
%%% Local Variables:
%%% mode: latex
diff --git a/inst/doc/commands/model.end.aux b/inst/doc/commands/model.end.aux
index eded77e..f5be021 100644
--- a/inst/doc/commands/model.end.aux
+++ b/inst/doc/commands/model.end.aux
@@ -1,14 +1,14 @@
\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.2}{\tt model.end}: Cleaning up after optimization}{559}{section.13.2}}
-\newlabel{model.end}{{13.2}{559}{{\tt model.end}: Cleaning up after optimization\relax }{section.13.2}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{559}{section*.699}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{559}{section*.700}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{559}{section*.701}}
-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{559}{section*.702}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{559}{section*.703}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{559}{section*.704}}
+\@writefile{toc}{\contentsline {section}{\numberline {13.1}{\tt model.end}: Cleaning up after optimization}{554}{section.13.1}}
+\newlabel{model.end}{{13.1}{554}{{\tt model.end}: Cleaning up after optimization\relax }{section.13.1}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{554}{section*.688}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{554}{section*.689}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{554}{section*.690}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{554}{section*.691}}
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diff --git a/inst/doc/commandsRd/plot.surv.tex b/inst/doc/commandsRd/plot.surv.tex
new file mode 100644
index 0000000..e1a4aa7
--- /dev/null
+++ b/inst/doc/commandsRd/plot.surv.tex
@@ -0,0 +1,58 @@
+\include{zinput} \begin{document}\section{{\tt plot.surv}: Plotting Confidence Intervals for Survival Curves}\label{ss:plot.surv}
+\keyword{hplot}{plot.surv}
+\begin{Description}\relax
+The \code{plot.surv} command generates confidence intervals for Kaplan-Meier survival curves
+\end{Description}
+\begin{Usage}
+\begin{verbatim}
+ plot.surv(x, duration, censor, type = "line", plotcensor=TRUE,
+ plottimes = FALSE, int = c(0.025,0.975), ...)
+\end{verbatim}
+\end{Usage}
+\begin{Arguments}
+\begin{ldescription}
+\item[\code{x}] output from \code{sim} stored as a list. Each element of the list is the \code{sim} output for a particular survival curve.
+\item[\code{duration}] the duration variable (e.g. lifetime, survival, etc.).
+\item[\code{censor}] the censored data
+\item[\code{type}] the type of confidence interval. Defaults to \code{"line"}, which draws vertical confidence intervals at observed event times. \code{"poly"} draws confidence regions using polygons.
+\item[\code{plotcensor}] default is \code{TRUE}. Plots censoring times as a \code{rug} object.
+\item[\code{plottimes}] default is \code{FALSE}. Plots step function with indicators at observed event times.
+\item[\code{int}] vector of quantile limits for the confidence interval. Default is 95\% interval.
+\item[\code{...}] Additional parameters passed to \code{plot}.
+\end{ldescription}
+\end{Arguments}
+\begin{Value}
+For survival models, {\tt plot.surv()} returns vertical
+confidence intervals or polygon survival regions for Kaplan-Meier survival curves. You may save this plot using the commands described in the
+Zelig manual (\url{http://gking.harvard.edu/zelig}).
+\end{Value}
+\begin{Author}\relax
+John A. Graves <\email{graveja0 at gmail.com}>
+\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}## Not run:
+data(coalition)
+z.out1 <- zelig(Surv(duration,ciep12)~invest+numst2+crisis,
+robust=TRUE,cluster="polar",model="coxph",data=coalition)
+low <- setx(z.out1,numst2=0)
+high <- setx(z.out1,numst2=1
+# Simulate Survival Curves for Each Group
+s.out1 <- sim(z.out1,x=low)
+s.out2 <- sim(z.out1,x=high)
+
+# Organize simulated output as a list
+out <- list(s.out1,s.out2)
+
+plot.surv(x = out, duration = coalition$duration, censor=coalition$ciep12,
+ type="line", plottimes=FALSE, plotcensor=FALSE,
+ main="Survival", xlab="Time", ylab="Survival")
+## End(Not run)
+\end{ExampleCode}
+\end{Examples}
+
+\end{document}
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diff --git a/inst/doc/coxph.Rnw b/inst/doc/coxph.Rnw
index 8c34d3c..7f6796c 100644
--- a/inst/doc/coxph.Rnw
+++ b/inst/doc/coxph.Rnw
@@ -38,22 +38,6 @@ duration is censored ({\it e.g.}, the subject is still alive at the
end of the study). If $c_i$ is omitted, all Y are assumed to be
completed; that is, $c_i$ defaults to 1 for all observations.
-\subsubsection{Stratified Cox Model}
-
-In addition, {\tt zelig()} also supports the stratified Cox model, where the baseline hazards are assumed to be different across different strata but the coefficients are restricted to be the same across strata. Let {\tt id} be a variable that consists of either discrete numeric values, character strings, or factors that define the strata. Then the stratified Cox model can be estimated using {\tt strata()} in the formula. The user can then find quantities of interest for a specific st [...]
-
-\begin{itemize}
-\item If strata were defined by a variable ({\tt strata(id)}), then strata should be defined as {\tt strata = "id=5"}.
-\item If strata were defined by a mathematical expression ({\tt strata(id>10)}), then strata should be defined as {\tt strata = "id>10=TRUE"} or {\tt strata = "id>10=FALSE"}.
-\end{itemize}
-
-\begin{verbatim}
-> z.out <- zelig(Surv(Y,C) ~ X1 + X2 + strata(id), model = "coxph",
- data = mydata)
-> x.out <- setx(z.out, strata = "id=5")
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
\subsubsection{Additional Inputs}
@@ -101,6 +85,76 @@ means that the observations can be correlated within the clusters defined by the
\end{itemize}
\end{itemize}
+\subsubsection{Stratified Cox Model}
+
+In addition, {\tt zelig()} also supports the stratified Cox model, where the baseline hazards are assumed to be different across different strata but the coefficients are restricted to be the same across strata. Let {\tt id} be a variable that consists of either discrete numeric values, character strings, or factors that define the strata. Then the stratified Cox model can be estimated using {\tt strata()} in the formula. The user can then find quantities of interest for a specific st [...]
+
+\begin{itemize}
+\item If strata were defined by a variable ({\tt strata(id)}), then strata should be defined as {\tt strata = "id=5"}.
+\item If strata were defined by a mathematical expression ({\tt strata(id>10)}), then strata should be defined as {\tt strata = "id>10=TRUE"} or {\tt strata = "id>10=FALSE"}.
+\end{itemize}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y,C) ~ X1 + X2 + strata(id), model = "coxph",
+ data = mydata)
+> x.out <- setx(z.out, strata = "id=5")
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+
+\subsubsection{Time-Varying Covariates}
+
+{\tt zelig()} also supports the use of time-varying covariates for the Cox model, where some or all of the covariates change over time for each case. Let ``case'' refer to each unit in the data. Then each case can have one or more ``observations'', where each observation has a different value for one or more covariates for a specific case. \\
+
+\noindent Estimating a time-varying covariate model with {\tt zelig()} involves setting up the data differently to reflect a counting process. In the typical non-time-varying covariate model, the cases include a duration time ($Y$), a censoring mechanism ($C$), and covariates ($X$). A typical dataset would look like this:
+
+\begin{table}[!htp]
+ \begin{center}
+\begin{tabular}{lllll}
+\hline
+Case & Y & C & X1 & X2 \\
+\hline
+1 & 35 & 0 & 4 & 7 \\
+2 & 56 & 1 & 6 & 11\\
+\hline
+\end{tabular}
+ \end{center}
+\end{table}
+
+\noindent The user would then estimate the model and find quantities of interest using the following syntax:
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y,C) ~ X1 + X2, model = "coxph", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\noindent With time-varying covariates, each case is composed of multiple observations with start times, stop times, censoring (event) mechanisms, and covariates. The covariates are assumed to be constant within the intervals defined by the start and stop times. The covariates change only between intervals. Thus, the covariates are constant at each observation. The censoring mechanism equals 1 when an event occurs at the stop time and equals 0 if the observation is censored or if no [...]
+
+\begin{table}[!htp]
+ \begin{center}
+\begin{tabular}{llllll}
+\hline
+Case & Start & Stop & C & X1 & X2 \\
+\hline
+1 & 0 & 26 & 0 & 4 & 7 \\
+1 & 26 & 35 & 0 & 4 & 10 \\
+2 & 0 & 39 & 0 & 6 & 11 \\
+2 & 39 & 56 & 1 & 9 & 5 \\
+\hline
+\end{tabular}
+ \end{center}
+\end{table}
+
+\noindent The user would then estimate the model and find quantities of interest using the following syntax:
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Start,Stop,C) ~ X1 + X2, model = "coxph",
+ data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
\subsubsection{Examples}
\begin{enumerate}
@@ -155,8 +209,40 @@ summary(s.out2)
plot(s.out2)
@
\end{center}
-\end{enumerate}
+\item {Example 3: Example with Time-Varying Covariates}
+
+Create sample toy dataset (from {\tt survival} package):
+<<Example3.data>>=
+toy <- as.data.frame(list(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8),
+ stop=c(2, 3, 6, 7, 8, 9, 9, 9,14,17),
+ event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0),
+ x=c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0),
+ x1=c(5, 5, 7, 4, 5, 6, 3, 2, 7, 4) ))
+@
+Estimating parameter values for the coxph regression:
+<<Example3.zelig>>=
+z.out3 <- zelig(Surv(start, stop, event) ~ x + x1, model = "coxph", data = toy)
+@
+Setting values for the explanatory variables:
+<<Example3.setx>>=
+x.low3 <- setx(z.out3, x = 0)
+x.high3 <- setx(z.out3, x = 1)
+@
+Simulating quantities of interest:
+<<Example3.sim>>=
+s.out3 <- sim(z.out3, x = x.low3, x1 = x.high3)
+@
+<<Example3.summary.sim>>=
+summary(s.out3)
+@
+\begin{center}
+<<label=ExamplePlot3,fig=true,echo=true>>=
+plot(s.out3)
+@
+\end{center}
+
+\end{enumerate}
\subsubsection{The Model}
Let $Y_i^*$ be the survival time for observation $i$. This variable
@@ -182,13 +268,13 @@ h_i(t) = \lambda(t) \times \lambda_i
\end{equation*}
where $\lambda(t)$ is the baseline hazard (when all covariates equal 0), which varies over $t$ but not over $i$, and $\lambda_i$ is the parameterized part of the hazard function, which varies over $i$ but not over $t$ (the proportional hazards assumption).
-The model estimates the parameters without a distributional assumption on the duration times by focusing on the occurrence of events and ignoring the time between events. The data are reconceptualized from duration times to $J$ discrete event times such that each $y_i^*$ corresponds to exactly one event time $t_i$. The model assumes that no two $y_i^*$ have the same event times.
+The model estimates the parameters without a distributional assumption on the duration times by focusing on the occurrence of events and ignoring the time between events. The data are reconceptualized from duration times to $K$ discrete event times such that each $y_i$ corresponds to exactly one event time $t_i$. The model assumes that no two $y_i$ have the same event times.
-For each event time, denote $R_j$ as the set of all observations $k$ that are at risk at $t_i$. Given that an event occurred at $t_i$, we are interested in the conditional probability that the event occurred in observation $i$. The conditional probability is given by
+For each event time, denote $R(t_i)$ as the set of all observations $j$ that are at risk at $t_i$. Given that an event occurred at $t_i$, we are interested in the conditional probability that the event occurred in observation $i$. The conditional probability is given by
\begin{eqnarray*}
-\mathrm{Pr}(y_i^* = t_i \mid \mathrm{an \: event \: at \:}t_i) &=& \frac{h_i(t_i)}{\sum_{k \in R_j} h_k(t_i)}\\
-&=& \frac{\lambda(t_i) \, \lambda_i}{\sum_{k \in R_j} \lambda(t_i) \, \lambda_k}\\
-&=& \frac{\lambda_i}{\sum_{k \in R_j} \lambda_k}
+\mathrm{Pr}(y_i = t_i \mid \mathrm{an \: event \: at \:}t_i) &=& \frac{h_i(t_i)}{\sum_{j \in R(t_i)} h_j(t_i)}\\
+&=& \frac{\lambda(t_i) \, \lambda_i}{\sum_{j \in R(t_i)} \lambda(t_i) \, \lambda_j}\\
+&=& \frac{\lambda_i}{\sum_{j \in R(t_i)} \lambda_j}
\end{eqnarray*}
where the numerator denotes the probability of observation $i$ experiencing the event at $t_i$ and the denominator denotes the probability that an event occurred at $t_i$.
%
@@ -210,29 +296,35 @@ where $x_i$ is the vector of explanatory variables, and $\beta$ is the vector of
\item Each risk set (and thus each event time) contributes one conditional probability to the partial likelihood function, given by
\begin{equation*}
-L(\beta | y) = \prod_{j=1}^J \frac{\exp(x_i \beta)}{\sum_{k \in R_j} \exp(x_k \beta)}
+L(\beta | y) = \prod_{i=1}^K \left[ \frac{\exp(x_i \beta)}{\sum_{j \in R(t_i)} \exp(x_j \beta)} \right] ^{c_i}
+\end{equation*}
+where $c_i$ is the binary censoring variable. Note that event times corresponding to censored observations are not counted since their corresponding terms for the partial likelihood are exponentiated to 0. However, all censored observations are considered part of the risk sets $R(t_i)$ for all event times prior to their censoring, but otherwise do not contribute to the partial likelihood. For an example, see \citet[53]{BoxJon04}.
+
+\item In the case of the Cox model with time-varying covariates, the partial likelihood function is similarly given by
+\begin{equation*}
+L(\beta | y) = \prod_{i=1}^K \left[ \frac{\exp(x_i(t_i) \; \beta)}{\sum_{j \in R(t_i)} \exp(x_j(t_i) \; \beta)} \right] ^{c_i}
\end{equation*}
-where $J$ corresponds to the number of risk sets. Note that censored observations are considered part of the risk sets $R_j$ for all event times prior to their censoring, but otherwise do not contribute to the partial likelihood since they do not have corresponding event times. For an example, see \citet[53]{BoxJon04}.
+where $x_i(t_i)$ is the value of the covariates at time $t_i$. Denote ``cases'' as the units in our data. Each case is composed of one or more observations corresponding to different values in one or more covariates. At each event time $t_i$, the partial likelihood evaluates the hazard of the case in which the event occurred in with its covariate values at $t_i$ (the numerator) and the hazard of all the other cases at risk at $t_i$ (risk set $R(t_i)$) with their covariate values at $t [...]
+
\item Although the model assumes that there are no tied event times, in practice, data often have tied event times due to imprecise measurement. There are three commonly used methods to deal with tied event times.
\begin{itemize}
-\item \textbf{Breslow method}: The Breslow method simply treats the risk set as the same for all tied events in the risk set. Suppose observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. Theoretically, if the event occurred in 1 before in 3, then the risk set for observation 3 would have dropped observation 1. However, since we cannot tell which event occurred first, in the partial likelihood, the risk set for observation 1 and observation 3 are the same, consis [...]
+\item \textbf{Breslow method}: The Breslow method simply treats the risk set as the same for all tied events in the risk set. Suppose observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. Theoretically, if the event occurred in 1 before in 3, then the risk set for observation 3 would have dropped observation 1. However, since we cannot tell which event occurred first, in the partial likelihood, the risk set for observation 1 and observation 3 are the same, consis [...]
\begin{equation*}
-L(\beta | y) = \prod_{j=1}^J \frac{\prod_{i \in D_j}\exp(x_i \beta)}{\left[ \sum_{k \in R_j} \exp(x_k \beta)\right] ^{d_j}}
-\end{equation*}
-where $j$ indexes the $J$ number of risk sets, $k$ denotes the observations in the risk sets, and $i$ denotes the observations for which the events occur .
+L(\beta | y) = \prod_{i=1}^K \left[ \frac{\prod_{i \in D_i}\exp(x_i \beta)}{\left[ \sum_{j \in R(t_i)} \exp(x_j \beta)\right] ^{d_i}} \right] ^ {c_i}
+\end{equation*}
\item \textbf{Efron method}: The Efron method is more precise because it tries to account for how the risk set changes depending on the sequence of tied events. For an intuition behind the Efron approximation, suppose as in the previous example that observations 1 and 3 are tied in a risk set of observations 1, 2, 3, and 4. If the event occurred in 1 before 3, then the risk set for the second event would consist of observations $\{2, 3, 4\}$. On the other hand, if the event occurred i [...]
\begin{equation*}
-L(\beta | y) = \prod_{j=1}^J \left(\frac{\prod_{i \in D_j}\exp(x_i \beta)}{\prod_{r=1}^{d_j} \left[\sum_{k \in R_j} \exp(x_k \beta) - \frac{r-1}{d_j} \sum_{k \in D_j} \exp(x_k \beta)\right] } \right)
+L(\beta | y) = \prod_{i=1}^K \left[\frac{\prod_{i \in D_i}\exp(x_i \beta)}{\prod_{r=1}^{d_i} \left[\sum_{j \in R(t_i)} \exp(x_j \beta) - \frac{r-1}{d_i} \sum_{j \in D_i} \exp(x_j \beta)\right] } \right] ^ {c_i}
\end{equation*}
-where $r$ indexes $D_j$, which is the set of $d_j$ tied events for the $j$th risk set.
+where $r$ indexes $D_i$, which is the set of $d_i$ tied events for the $i$th risk set.
-\item \textbf{Exact discrete method}: Unlike the Breslow and Efron methods, which assume a continuous time process, the exact discrete method assumes a discrete time process where the tied events actually do occur at exactly the same time. The method begins by assuming that the data are grouped into risk sets $R_j$. In each risk set and for each observation, denote a binary dependent variable which takes on the value of 1 for each observation that experiences the event and 0 for each o [...]
+\item \textbf{Exact discrete method}: Unlike the Breslow and Efron methods, which assume a continuous time process, the exact discrete method assumes a discrete time process where the tied events actually do occur at exactly the same time. The method begins by assuming that the data are grouped into risk sets $R(t_i)$. In each risk set and for each observation, denote a binary dependent variable which takes on the value of 1 for each observation that experiences the event and 0 for eac [...]
\begin{eqnarray*}
-\mathrm{Pr}(D_j | d_j) = \frac{\prod_{i \in D_j} \exp(x_i \beta)}{\sum_{m=1}^M \left[\prod_{k \in A_{jm}}\exp(x_k \beta)\right] }
+\mathrm{Pr}(D_i | d_i) = \frac{\prod_{i \in D_i} \exp(x_i \beta)}{\sum_{m=1}^M \left[\prod_{j \in A_{im}}\exp(x_j \beta)\right] }
\end{eqnarray*}
-where $A_{jm}$ is a set of observations that represents one combination of $d_j$ number of 1s in $R_j$. There are $M$ possible combinations for each risk set. The partial likelihood then takes the conditional probability over each $j$ risk set. Note that the exact discrete approximation method is equivalent to a conditional logit model.
+where $A_{im}$ is a set of observations that represents one combination of $d_i$ number of 1s in $R(t_i)$. There are $M$ possible combinations for each risk set. The partial likelihood then takes the conditional probability over each $i$ risk set. Note that the exact discrete approximation method is equivalent to a conditional logit model.
\end{itemize}
\end{itemize}
@@ -306,7 +398,7 @@ To cite the \emph{coxph} Zelig model:
Patrick Lam. 2007. ``coxph: Cox Proportional Hazards Regression for Duration Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
\end{verse}
-\input{citeZelig}
+%\input{citeZelig}
\subsection*{See also}
diff --git a/inst/doc/coxph.pdf b/inst/doc/coxph.pdf
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diff --git a/inst/doc/describemymodel.tex b/inst/doc/describemymodel.tex
new file mode 100644
index 0000000..e2d4705
--- /dev/null
+++ b/inst/doc/describemymodel.tex
@@ -0,0 +1,206 @@
+\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:
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diff --git a/inst/doc/faq.tex b/inst/doc/faq.tex
index 08fafb7..2e905f0 100644
--- a/inst/doc/faq.tex
+++ b/inst/doc/faq.tex
@@ -26,7 +26,15 @@ why you may have installation problems:
download the appropriate version of R to work with the last release
of Zelig. You may have to manually download and install Zelig.
-\item \textbf{On Mac or Linux systems}: If you get the following
+\item \textbf{On Mac}: If the latest version of Zelig is not yet
+ available at CRAN but you would like to install it on your Mac, try
+ typing the following at your R prompt:
+\begin{verbatim}
+ install.packages("Zelig", repos = "http://gking.harvard.edu", type = "source")
+\end{verbatim}
+
+\item \textbf{On Mac or Linux systems}:
+If you get the following
warning message at the end of your installation:
\begin{verbatim}
Installation of package VGAM had non-zero exit status in ...
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index c9e536e..39e2407 100644
--- a/inst/doc/index.shtml
+++ b/inst/doc/index.shtml
@@ -12,8 +12,8 @@
<a href="http://www.people.fas.harvard.edu/~olau/">Olivia Lau</a>
<br /><br />
<!-- rbuild: replace 'Version:' '</b>' version -->
-<b>Version:3.2-1</b>
-
+<b>Version:3.3-1</b>
+<a href="docs/static/whatsnew.pdf"> (What's new) </a>
<table border='0' cellpadding='0' cellspacing='8'><tr><td valign='top' width='200'>
<p !style="float:left; width: 200; margin: 1.5em;"> <a
href="/zelig/zelig.jpg"><img src="/zelig/zelig2.jpg" width="200"
@@ -84,7 +84,7 @@ Common Framework for Statistical Analysis and Development"</span>
Zelig</a></li>
<li><a class="monitor" href="/zelig/" target="ChangeDetectionWiz"
- onClick="doChangeDetectionPopup()"> Track Zelig changes</a></li>
+ onClick="doChangeDetectionPopup()">Be notified of Zelig changes</a></li>
</ul>
</td>
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+++ b/inst/doc/makeDocs
@@ -8,7 +8,7 @@
## 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"
+STATICTEXFILES="acknowledgments.tex robjects.tex programmingstatements.tex graphs.tex matchitref.tex describemymodel.tex install.tex intro.tex Zcommands.tex addingModels.tex newModels.tex syntax.tex faq.tex whatsnew.tex"
## get name of the models and save them in a file
## for now exclude models which are not complete
diff --git a/inst/doc/commands/matchitref.tex b/inst/doc/matchitref.tex
similarity index 100%
copy from inst/doc/commands/matchitref.tex
copy to inst/doc/matchitref.tex
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diff --git a/inst/doc/programmingstatements.tex b/inst/doc/programmingstatements.tex
new file mode 100644
index 0000000..7c618fb
--- /dev/null
+++ b/inst/doc/programmingstatements.tex
@@ -0,0 +1,236 @@
+\chapter{Programming Statements}
+
+This chapter introduces the main programming commands. These include
+functions, if-else statements, for-loops, and special procedures for
+managing the inputs to statistical models.
+
+\section{Functions}
+
+Functions are either built-in or user-defined sets of encapsulated
+commands which may take any number of arguments. Preface a function
+with the {\tt function} statement and use the {\tt <-}
+operator to assign functions to objects in your workspace.
+
+You may use functions to run the same procedure on different objects
+in your workspace. For example,
+\begin{verbatim}
+check <- function(p, q) {
+ result <- (p - q)/q
+ result
+ }
+\end{verbatim}
+is a simple function with arguments {\tt p} and {\tt q} which
+calculates the difference between the $i$th elements of the vector
+{\tt p} and the $i$th element of the vector {\tt q} as a proportion of
+the $i$th element of {\tt q}, and returns the resulting vector. For
+example, {\tt check(p = 10, q = 2)} returns 4. You may omit the
+descriptors as long as you keep the arguments in the correct order:
+{\tt check(10, 2)} also returns 4. You may also use other objects as
+inputs to the function. If {\tt again = 10} and {\tt really = 2},
+then {\tt check(p = again, q = really)} and {\tt check(again, really)}
+also returns 4.
+
+Because functions run commands as a set, you should make sure that
+each command in your function works by testing each line of the
+function at the R prompt.
+
+\section{If-Statements}
+
+Use {\tt if} (and optionally, {\tt else}) to control the flow of R
+functions. For example, let {\tt x} and {\tt y} be scalar numerical
+values:
+\begin{verbatim}
+if (x == y) { # If the logical statement in the ()'s is true,
+ x <- NA # then `x' is changed to `NA' (missing value).
+}
+else { # The `else' statement tells R what to do if
+ x <- x^2 # the if-statement is false.
+}
+\end{verbatim}
+As with a function, use {\tt \{} and {\tt \}} to define the set of commands associated
+with each if and else statement. (If you include if
+statements inside functions, you may have multiple sets of nested
+curly braces.)
+
+\section{For-Loops}
+
+Use {\tt for} to repeat (loop) operations. Avoiding loops by using matrix
+or vector commands is usually faster and more elegant, but loops are
+sometimes necessary to assign values. If you are using a loop to
+assign values to a data structure, you must first initialize an empty
+data structure to hold the values you are assigning.
+
+Select a data structure compatible with the type of output your loop
+will generate. If your loop generates a scalar, store it in a vector
+(with the $i$th value in the vector corresponding to the the $i$th run
+of the loop). If your loop generates vector output, store them as
+rows (or columns) in a matrix, where the $i$th row (or column)
+corresponds to the $i$th iteration of the loop. If your output
+consists of matrices, stack them into an array. For list output (such
+as regression output) or output that changes dimensions in each
+iteration, use a list. To initialize these data structures, use:
+\begin{verbatim}
+> x <- vector() # An empty vector of any length.
+> x <- list() # An empty list of any length.
+\end{verbatim}
+The {\tt vector()} and {\tt list()} commands create a vector or list
+of any length, such that assigning {\tt x[5] <- 15} automatically
+creates a vector with 5 elements, the first four of which are empty
+values ({\tt NA}). In contrast, the {\tt matrix()} and {\tt array()}
+commands create data structures that are restricted to their original
+dimensions.
+\begin{verbatim}
+> x <- matrix(nrow = 5, ncol = 2) # A matrix with 5 rows and 2 columns.
+> x <- array(dim = c(5,2,3)) # A 3D array of 3 stacked 5 by 2 matrices.
+\end{verbatim}
+If you attempt to assign a value at $(100, 200, 20)$ to either of
+these data structures, R will return an error message (``subscript is
+out of bounds''). R does not automatically extend the
+dimensions of either a matrix or an array to accommodate additional
+values.
+
+\paragraph{Example 1: Creating a vector with a logical statement}
+\begin{verbatim}
+x <- array() # Initializes an empty data structure.
+for (i in 1:10) { # Loops through every value from 1 to 10, replacing
+ if (is.integer(i/2)) { # the even values in `x' with i+5.
+ x[i] <- i + 5
+ }
+} # Enclose multiple commands in {}.
+\end{verbatim}
+You may use {\tt for()} inside or outside of functions.
+
+\paragraph{Example 2: Creating dummy variables by hand}
+
+\label{dummy}You may also use a loop to create a matrix of dummy
+variables to append to a data frame. For example, to generate fixed
+effects for each state, let's say that you have {\tt mydata} which
+contains {\tt y}, {\tt x1}, {\tt x2}, {\tt x3}, and {\tt state}, with
+{\tt state} a character variable with 50 unique values. There are
+three ways to create dummy variables: 1) with a built-in R command; 2)
+with one loop; or 3) with 2 for loops.
+\begin{enumerate}
+\item R will create dummy variables on the fly from a single variable
+with distinct values.
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2 + x3 + as.factor(state),
+ data = mydata, model = "ls")
+\end{verbatim}
+This method returns $k - 1$ indicators for $k$ states.
+
+\item Alternatively, you can use a loop to create dummy variables by
+hand. There are two ways to do this, but both start with the same
+initial commands. Using vector commands, first create an index of for
+the states, and initialize a matrix to hold the dummy variables:
+\begin{verbatim}
+idx <- sort(unique(mydata$state))
+dummy <- matrix(NA, nrow = nrow(mydata), ncol = length(idx))
+\end{verbatim} %$
+Now choose between the two methods.
+\begin{enumerate}
+\item The first method is computationally inefficient, but more intuitive for users not
+accustomed to vector operations. The first loop uses {\tt i} as in
+index to loop through all the rows, and the second loop uses {\tt j}
+to loop through all 50 values in the vector {\tt idx}, which
+correspond to columns 1 through 50 in the matrix {\tt dummy}.
+\begin{verbatim}
+for (i in 1:nrow(mydata)) {
+ for (j in 1:length(idx)) {
+ if (mydata$state[i,j] == idx[j]) {
+ dummy[i,j] <- 1
+ }
+ else {
+ dummy[i,j] <- 0
+ }
+ }
+}
+\end{verbatim} % $
+Then add the new matrix of dummy variables to your data frame:
+\begin{verbatim}
+names(dummy) <- idx
+mydata <- cbind(mydata, dummy)
+\end{verbatim}
+
+\item As you become more comfortable with vector operations, you can
+replace the double loop procedure above with one loop:
+\begin{verbatim}
+for (j in 1:length(idx)) {
+ dummy[,j] <- as.integer(mydata$state == idx[j])
+}
+\end{verbatim} %$
+The single loop procedure evaluates each element in {\tt idx} against the vector {\tt mydata\$state}. This creates a vector of $n$ {\tt
+ TRUE}/{\tt FALSE} observations, which you may transform to {\tt
+ 1}'s and {\tt 0}'s using {\tt as.integer()}. Assign the resulting
+ vector to the appropriate column in {\tt dummy}. Combine the {\tt
+dummy} matrix with the data frame as above to complete the procedure.
+\end{enumerate}
+\end{enumerate}
+
+\paragraph{Example 3: Weighted regression with subsets}
+
+Selecting the {\tt by} option in {\tt zelig()} partitions the data
+frame and then automatically loops the specified model through each
+partition. Suppose that {\tt mydata} is a data frame with variables
+{\tt y}, {\tt x1}, {\tt x2}, {\tt x3}, and {\tt state}, with {\tt
+state} a factor variable with 50 unique values. Let's say that you
+would like to run a weighted regression where each observation is
+weighted by the inverse of the standard error on {\tt x1}, estimated
+for that observation's state. In other words, we need
+to first estimate the model for each of the 50 states, calculate 1 /
+{\sc se}({\tt x1}$_j$) for each state $j = 1, \dots, 50$, and then
+assign these weights to each observation in {\tt mydata}.
+\begin{itemize}
+\item Estimate the model separate for each state using the {\tt by}
+option in {\tt zelig()}:
+\begin{verbatim}
+z.out <- zelig(y ~ x1 + x2 + x3, by = "state", data = mydata, model = "ls")
+\end{verbatim}
+Now {\tt z.out} is a list of 50 regression outputs.
+\item Extract the standard error on {\tt x1} for each of the state
+level regressions.
+\begin{verbatim}
+se <- array() # Initalize the empty data structure.
+for (i in 1:50) { # vcov() creates the variance matrix
+ se[i] <- sqrt(vcov(z.out[[i]])[2,2]) # Since we have an intercept, the 2nd
+} # diagonal value corresponds to x1.
+\end{verbatim}
+\item Create the vector of weights.
+\begin{verbatim}
+wts <- 1 / se
+\end{verbatim}
+This vector {\tt wts} has 50 values that correspond to the 50 sets of
+state-level regression output in {\tt z.out}.
+\item To assign the vector of weights to each observation, we need to
+match each observation's state designation to the appropriate state.
+For simplicity, assume that the states are numbered 1 through 50.
+\begin{verbatim}
+mydata$w <- NA # Initalizing the empty variable
+for (i in 1:50) {
+ mydata$w[mydata$state == i] <- wts[i]
+}
+\end{verbatim} %$
+We use {\tt mydata\$state} as the index (inside the square brackets)
+to assign values to {\tt mydata\$w}. Thus, whenever state equals 5
+for an observation, the loop assigns the fifth value in the vector
+{\tt wts} to the variable {\tt w} in {\tt mydata}. If we had 500
+observations in {\tt mydata}, we could use this method to match each
+of the 500 observations to the appropriate {\tt wts}.
+
+If the states are character strings instead of integers, we can use a
+slightly more complex version
+\begin{verbatim}
+mydata$w <- NA
+idx <- sort(unique(mydata$state))
+for (i in 1:length(idx) {
+ mydata$w[mydata$state == idx[i]] <- wts[i]
+}
+\end{verbatim}
+
+\item Now we can run our weighted regression:
+\begin{verbatim}
+z.wtd <- zelig(y ~ x1 + x2 + x3, weights = w, data = mydata,
+ model = "ls")
+\end{verbatim}
+
+\end{itemize}
+
diff --git a/inst/doc/refman.tex b/inst/doc/refman.tex
index 44e7cc4..ffb40fc 100644
--- a/inst/doc/refman.tex
+++ b/inst/doc/refman.tex
@@ -17,10 +17,11 @@ the {\tt setx()} command. (Occasionally, you may need to use, for example, {\tt
\chapter{Supplementary Commands}
-\input{commands/matchit}
+\include{matchit}
\include{commandsRd/mi}
\include{commandsRd/network}
\include{commandsRd/plot.ci}
+\include{commandsRd/plot.surv}
\include{commandsRd/rocplot}
\include{commandsRd/ternaryplot}
\include{commandsRd/ternarypoints}
@@ -151,7 +152,7 @@ of simulations increases.
\chapter{Commands for Programmers and Contributors}
-\input{commands/describe.mymodel}
+\include{describe.mymodel}
\include{commands/model.end}
\include{commands/model.frame.multiple}
\include{commands/model.matrix.multiple}
diff --git a/inst/doc/relogit.pdf b/inst/doc/relogit.pdf
index 8c4fdd2..774ce55 100644
Binary files a/inst/doc/relogit.pdf and b/inst/doc/relogit.pdf differ
diff --git a/inst/doc/advancedR.tex b/inst/doc/robjects.tex
similarity index 59%
rename from inst/doc/advancedR.tex
rename to inst/doc/robjects.tex
index 5f6ddcf..2cc634e 100644
--- a/inst/doc/advancedR.tex
+++ b/inst/doc/robjects.tex
@@ -316,242 +316,6 @@ description of each element.
For almost all data types, you may use {\tt summary()} to get summary
statistics.
-\chapter{Programming Statements}
-
-This chapter introduces the main programming commands. These include
-functions, if-else statements, for-loops, and special procedures for
-managing the inputs to statistical models.
-
-\section{Functions}
-
-Functions are either built-in or user-defined sets of encapsulated
-commands which may take any number of arguments. Preface a function
-with the {\tt function} statement and use the {\tt <-}
-operator to assign functions to objects in your workspace.
-
-You may use functions to run the same procedure on different objects
-in your workspace. For example,
-\begin{verbatim}
-check <- function(p, q) {
- result <- (p - q)/q
- result
- }
-\end{verbatim}
-is a simple function with arguments {\tt p} and {\tt q} which
-calculates the difference between the $i$th elements of the vector
-{\tt p} and the $i$th element of the vector {\tt q} as a proportion of
-the $i$th element of {\tt q}, and returns the resulting vector. For
-example, {\tt check(p = 10, q = 2)} returns 4. You may omit the
-descriptors as long as you keep the arguments in the correct order:
-{\tt check(10, 2)} also returns 4. You may also use other objects as
-inputs to the function. If {\tt again = 10} and {\tt really = 2},
-then {\tt check(p = again, q = really)} and {\tt check(again, really)}
-also returns 4.
-
-Because functions run commands as a set, you should make sure that
-each command in your function works by testing each line of the
-function at the R prompt.
-
-\section{If-Statements}
-
-Use {\tt if} (and optionally, {\tt else}) to control the flow of R
-functions. For example, let {\tt x} and {\tt y} be scalar numerical
-values:
-\begin{verbatim}
-if (x == y) { # If the logical statement in the ()'s is true,
- x <- NA # then `x' is changed to `NA' (missing value).
-}
-else { # The `else' statement tells R what to do if
- x <- x^2 # the if-statement is false.
-}
-\end{verbatim}
-As with a function, use {\tt \{} and {\tt \}} to define the set of commands associated
-with each if and else statement. (If you include if
-statements inside functions, you may have multiple sets of nested
-curly braces.)
-
-\section{For-Loops}
-
-Use {\tt for} to repeat (loop) operations. Avoiding loops by using matrix
-or vector commands is usually faster and more elegant, but loops are
-sometimes necessary to assign values. If you are using a loop to
-assign values to a data structure, you must first initialize an empty
-data structure to hold the values you are assigning.
-
-Select a data structure compatible with the type of output your loop
-will generate. If your loop generates a scalar, store it in a vector
-(with the $i$th value in the vector corresponding to the the $i$th run
-of the loop). If your loop generates vector output, store them as
-rows (or columns) in a matrix, where the $i$th row (or column)
-corresponds to the $i$th iteration of the loop. If your output
-consists of matrices, stack them into an array. For list output (such
-as regression output) or output that changes dimensions in each
-iteration, use a list. To initialize these data structures, use:
-\begin{verbatim}
-> x <- vector() # An empty vector of any length.
-> x <- list() # An empty list of any length.
-\end{verbatim}
-The {\tt vector()} and {\tt list()} commands create a vector or list
-of any length, such that assigning {\tt x[5] <- 15} automatically
-creates a vector with 5 elements, the first four of which are empty
-values ({\tt NA}). In contrast, the {\tt matrix()} and {\tt array()}
-commands create data structures that are restricted to their original
-dimensions.
-\begin{verbatim}
-> x <- matrix(nrow = 5, ncol = 2) # A matrix with 5 rows and 2 columns.
-> x <- array(dim = c(5,2,3)) # A 3D array of 3 stacked 5 by 2 matrices.
-\end{verbatim}
-If you attempt to assign a value at $(100, 200, 20)$ to either of
-these data structures, R will return an error message (``subscript is
-out of bounds''). R does not automatically extend the
-dimensions of either a matrix or an array to accommodate additional
-values.
-
-\paragraph{Example 1: Creating a vector with a logical statement}
-\begin{verbatim}
-x <- array() # Initializes an empty data structure.
-for (i in 1:10) { # Loops through every value from 1 to 10, replacing
- if (is.integer(i/2)) { # the even values in `x' with i+5.
- x[i] <- i + 5
- }
-} # Enclose multiple commands in {}.
-\end{verbatim}
-You may use {\tt for()} inside or outside of functions.
-
-\paragraph{Example 2: Creating dummy variables by hand}
-
-\label{dummy}You may also use a loop to create a matrix of dummy
-variables to append to a data frame. For example, to generate fixed
-effects for each state, let's say that you have {\tt mydata} which
-contains {\tt y}, {\tt x1}, {\tt x2}, {\tt x3}, and {\tt state}, with
-{\tt state} a character variable with 50 unique values. There are
-three ways to create dummy variables: 1) with a built-in R command; 2)
-with one loop; or 3) with 2 for loops.
-\begin{enumerate}
-\item R will create dummy variables on the fly from a single variable
-with distinct values.
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2 + x3 + as.factor(state),
- data = mydata, model = "ls")
-\end{verbatim}
-This method returns $k - 1$ indicators for $k$ states.
-
-\item Alternatively, you can use a loop to create dummy variables by
-hand. There are two ways to do this, but both start with the same
-initial commands. Using vector commands, first create an index of for
-the states, and initialize a matrix to hold the dummy variables:
-\begin{verbatim}
-idx <- sort(unique(mydata$state))
-dummy <- matrix(NA, nrow = nrow(mydata), ncol = length(idx))
-\end{verbatim} %$
-Now choose between the two methods.
-\begin{enumerate}
-\item The first method is computationally inefficient, but more intuitive for users not
-accustomed to vector operations. The first loop uses {\tt i} as in
-index to loop through all the rows, and the second loop uses {\tt j}
-to loop through all 50 values in the vector {\tt idx}, which
-correspond to columns 1 through 50 in the matrix {\tt dummy}.
-\begin{verbatim}
-for (i in 1:nrow(mydata)) {
- for (j in 1:length(idx)) {
- if (mydata$state[i,j] == idx[j]) {
- dummy[i,j] <- 1
- }
- else {
- dummy[i,j] <- 0
- }
- }
-}
-\end{verbatim} % $
-Then add the new matrix of dummy variables to your data frame:
-\begin{verbatim}
-names(dummy) <- idx
-mydata <- cbind(mydata, dummy)
-\end{verbatim}
-
-\item As you become more comfortable with vector operations, you can
-replace the double loop procedure above with one loop:
-\begin{verbatim}
-for (j in 1:length(idx)) {
- dummy[,j] <- as.integer(mydata$state == idx[j])
-}
-\end{verbatim} %$
-The single loop procedure evaluates each element in {\tt idx} against the vector {\tt mydata\$state}. This creates a vector of $n$ {\tt
- TRUE}/{\tt FALSE} observations, which you may transform to {\tt
- 1}'s and {\tt 0}'s using {\tt as.integer()}. Assign the resulting
- vector to the appropriate column in {\tt dummy}. Combine the {\tt
-dummy} matrix with the data frame as above to complete the procedure.
-\end{enumerate}
-\end{enumerate}
-
-\paragraph{Example 3: Weighted regression with subsets}
-
-Selecting the {\tt by} option in {\tt zelig()} partitions the data
-frame and then automatically loops the specified model through each
-partition. Suppose that {\tt mydata} is a data frame with variables
-{\tt y}, {\tt x1}, {\tt x2}, {\tt x3}, and {\tt state}, with {\tt
-state} a factor variable with 50 unique values. Let's say that you
-would like to run a weighted regression where each observation is
-weighted by the inverse of the standard error on {\tt x1}, estimated
-for that observation's state. In other words, we need
-to first estimate the model for each of the 50 states, calculate 1 /
-{\sc se}({\tt x1}$_j$) for each state $j = 1, \dots, 50$, and then
-assign these weights to each observation in {\tt mydata}.
-\begin{itemize}
-\item Estimate the model separate for each state using the {\tt by}
-option in {\tt zelig()}:
-\begin{verbatim}
-z.out <- zelig(y ~ x1 + x2 + x3, by = "state", data = mydata, model = "ls")
-\end{verbatim}
-Now {\tt z.out} is a list of 50 regression outputs.
-\item Extract the standard error on {\tt x1} for each of the state
-level regressions.
-\begin{verbatim}
-se <- array() # Initalize the empty data structure.
-for (i in 1:50) { # vcov() creates the variance matrix
- se[i] <- sqrt(vcov(z.out[[i]])[2,2]) # Since we have an intercept, the 2nd
-} # diagonal value corresponds to x1.
-\end{verbatim}
-\item Create the vector of weights.
-\begin{verbatim}
-wts <- 1 / se
-\end{verbatim}
-This vector {\tt wts} has 50 values that correspond to the 50 sets of
-state-level regression output in {\tt z.out}.
-\item To assign the vector of weights to each observation, we need to
-match each observation's state designation to the appropriate state.
-For simplicity, assume that the states are numbered 1 through 50.
-\begin{verbatim}
-mydata$w <- NA # Initalizing the empty variable
-for (i in 1:50) {
- mydata$w[mydata$state == i] <- wts[i]
-}
-\end{verbatim} %$
-We use {\tt mydata\$state} as the index (inside the square brackets)
-to assign values to {\tt mydata\$w}. Thus, whenever state equals 5
-for an observation, the loop assigns the fifth value in the vector
-{\tt wts} to the variable {\tt w} in {\tt mydata}. If we had 500
-observations in {\tt mydata}, we could use this method to match each
-of the 500 observations to the appropriate {\tt wts}.
-
-If the states are character strings instead of integers, we can use a
-slightly more complex version
-\begin{verbatim}
-mydata$w <- NA
-idx <- sort(unique(mydata$state))
-for (i in 1:length(idx) {
- mydata$w[mydata$state == idx[i]] <- wts[i]
-}
-\end{verbatim}
-
-\item Now we can run our weighted regression:
-\begin{verbatim}
-z.wtd <- zelig(y ~ x1 + x2 + x3, weights = w, data = mydata,
- model = "ls")
-\end{verbatim}
-
-\end{itemize}
-
%%% Local Variables:
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--- a/inst/doc/vigpics/blogit-BasicExamplePlot.eps
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diff --git a/inst/doc/vigpics/relogit-Example1Plot.pdf b/inst/doc/vigpics/relogit-Example1Plot.pdf
index 18429ca..856a63a 100644
--- a/inst/doc/vigpics/relogit-Example1Plot.pdf
+++ b/inst/doc/vigpics/relogit-Example1Plot.pdf
@@ -2,10 +2,10 @@
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diff --git a/inst/doc/vigpics/relogit-Example3Plot.pdf b/inst/doc/vigpics/relogit-Example3Plot.pdf
index cf993a0..8c974c5 100644
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diff --git a/inst/doc/vigpics/threesls-Examplesthreesls.pdf b/inst/doc/vigpics/threesls-Examplesthreesls.pdf
index 23e3c1e..3fd2743 100644
--- a/inst/doc/vigpics/threesls-Examplesthreesls.pdf
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diff --git a/inst/doc/vigpics/twosls-Examplestwosls.pdf b/inst/doc/vigpics/twosls-Examplestwosls.pdf
index e7fca30..2c93c1f 100644
--- a/inst/doc/vigpics/twosls-Examplestwosls.pdf
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diff --git a/inst/doc/vigpics/weibull-ExamplePlot.pdf b/inst/doc/vigpics/weibull-ExamplePlot.pdf
index f374b24..e372682 100644
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diff --git a/inst/doc/weibull.pdf b/inst/doc/weibull.pdf
index c6bf424..b54ecff 100644
Binary files a/inst/doc/weibull.pdf and b/inst/doc/weibull.pdf differ
diff --git a/inst/doc/zelig.tex b/inst/doc/whatsnew.tex
similarity index 89%
copy from inst/doc/zelig.tex
copy to inst/doc/whatsnew.tex
index 4c31157..ec86332 100644
--- a/inst/doc/zelig.tex
+++ b/inst/doc/whatsnew.tex
@@ -1,70 +1,3 @@
-\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
- open-source (under the terms of the GNU GPL, v. 2).}}
-\author{Kosuke
- Imai\thanks{Assistant Professor, Department of Politics, Princeton
- University (Corwin Hall, Department of Politics, Princeton
- University, Princeton NJ 08544; \texttt{http://Imai.Princeton.Edu},
- \texttt{KImai at Princeton.Edu}).}
-\and %
-Gary King\thanks{David Florence Professor of Government, Harvard
- University (Institute for Quantitative Social Sciences, 1737 Cambridge
-Street, Harvard University, Cambridge MA 02138;
- \texttt{http://GKing.Harvard.Edu}, \texttt{King at Harvard.Edu}, (617)
- 495-2027).}
-\and %
-Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
- University (1737 Cambridge Street, Cambridge MA 02138;
- \texttt{http://www.people.fas.harvard.edu/\~\,olau},
- \texttt{OLau at Fas.Harvard.Edu}).}}
-
-% rbuild: replace 'Version ' '\\' Version
-\date{Version 3.2-1\\ \today}
-
-\begin{document}
-\maketitle
-\begin{rawhtml}
- <p>
- [Also available is a downloadable <a href="/zelig/docs/zelig.pdf">PDF</a>
- version of this entire document]
-\end{rawhtml}
-
-\tableofcontents
-
-\nobibliography*
-
-\include{acknowledgments}
-
-\input{intro}
-
-\part[User's Guide]{User's Guide}
-
-\input{install}
-\input{syntax}
-\input{Zcommands}
-\input{graphs}
-
-\part{Advanced Zelig Uses}
-
-\input{advancedR}
-\input{newModels}
-\input{addingModels}
-
-\part{Reference Manual}
-
-\input{refman}
-
-\appendix
-
-\part[Appendices]{Appendices}
-
-%\input{distributions}
-
-\input{faq}
-
-\chapter{What's New? What's Next?}
\section{What's New: Zelig Release Notes}\label{release.notes}
@@ -75,6 +8,36 @@ few days.
\begin{itemize}
+\item {\bf 3.3-1} (June 13, 2008): Bug-fix release for R.2.7.0. A bug
+ fix for {\rm plot.ci()} so that it works with mixed effects models
+ (thanks to Keith Schnakenberg).
+
+\item {\bf 3.3-0} (June 03, 2008): Stable release for R.2.7.0. Updated
+ {\rm coxph} so that it handles time-varying covariates (contributed
+ by Patrick Lam). A new plot function for survival models
+ (contributed by John Graves). First version dependencies are as
+ follows: \newline
+\begin{tabular}{ll}\label{table.compact}
+ "MASS" & "7.2-41" \\
+ "nlme" & "3.1-87" \\
+ "survival" & "2.34" \\
+ "coda" & "0.13-1" \\
+ "sna" & "1.5" \\
+ "boot" & "1.2-31" \\
+ "nnet" & "7.2-41" \\
+ "zoo" & "1.5-0" \\
+ "sandwich" & "2.1-0" \\
+ "lme4" & "0.99875-9" \\
+ "systemfit" & "1.0-2" \\
+ "VGAM" & "0.7-5" \\
+ "MCMCpack" & "0.8-2" \\
+ "mvtnorm" & "0.8-3" \\
+ "gee" & "4.13-13" \\
+ "mgcv" & "1.3-29" \\
+ "anchors" & "1.9-2" \\
+ "survey" & "3.6-13"
+\end{tabular}
+
\item {\bf 3.2-1} (April 10, 2008): Bug-fix release for R.2.6.0-2.6.2.
Fixed the {\rm setx()} bug for multiply imputed data sets. (Thanks
to Steve Shewfelt and Keith Schnakenberg.)
@@ -82,7 +45,7 @@ few days.
\item {\bf 3.2} (April 3, 2008): Stable release for R 2.6.0-2.6.2.
Adding models for survey data -- {\tt normal.survey}, {\tt
logit.survey}, {\tt probit.survey}, {\tt poisson.survey}, {\tt
- gamma.survey}.First version dependencies are as
+ gamma.survey}. First version dependencies are as
follows:\newline
\begin{tabular}{ll}\label{table.compat}
survey & 3.6-13 \\
@@ -449,14 +412,3 @@ units)
If you have suggestions, or packages that you would like to contribute
to Zelig, please email our listserv at \hlink{zelig at lists.gking.harvard.edu}{mailto:zelig at lists.gking.harvard.edu}.
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-
-\end{document}
-
-%%% Local Variables:
-%%% mode: LaTeX
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/zelig.html b/inst/doc/zelig.html
index 545a470..b288a00 100644
--- a/inst/doc/zelig.html
+++ b/inst/doc/zelig.html
@@ -198,7 +198,8 @@ left: 100px;
</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/robjects.pdf" title="Advanced R">R Objects</a></li>
+ <li><a href="static/programmingstatements.pdf" title="Advanced R">Programming statements</a></li>
<li><a href="static/newModels.pdf" title="Writing new Models">Writing new Models</a></li>
<li><a href="static/addingModels.pdf" title="Adding Models and Methods to Zelig">Adding Models and Methods to Zelig</a></li>
</ul>
@@ -216,9 +217,11 @@ left: 100px;
</li>
<li class="mainindx"><a href="">Supplementary Commands </a>
<ul id="supplementarycommands">
+ <li><a href="static/matchitref.pdf" title="">matchit: Create matched data</a></li>
<li><a href="commandsRd/mi.pdf" title="mi">mi: Bundle multiply imputed data sets as a list</a></li>
<li><a href="commandsRd/network.pdf" title="network">network: Format matricies into data frame for social network analysis</a></li>
<li><a href="commandsRd/plot.ci.pdf" title="plot.ci">plot.ci: Plotting Vertical confidence Intervals</a></li>
+ <li><a href="commandsRd/plot.surv.pdf" title="plot.surv">plot.surv: Plotting Coefidence Intervals for Survival Curves</a></li>
<li><a href="commandsRd/rocplot.pdf" title="rocplot">rocplot: Receiver Operator Characteristic Plots</a></li>
<li><a href="commandsRd/ternaryplot.pdf" title="ternaryplot">ternaryplot: Ternary diagram</a></li>
<li><a href="commandsRd/ternarypoints.pdf" title="ternarypoints">ternarypoints: Adding Points To Ternary Diagrams</a></li>
@@ -335,7 +338,7 @@ left: 100px;
<li class="mainindx"><a href="">Commands for Programmers and Contributors </a>
<ul id="contribcommands">
-
+ <li><a href="static/describemymodel.pdf" title="describemymodel">describe: Describe a model's systematic and stochastic parameters</a></li>
<li><a href="commandsRd/model.end.pdf" title="model.end">model.end: Cleaning up after optimization</a></li>
<li><a href="commandsRd/model.frame.multiple.pdf" title="model.frame.multiple">model.frame.multiple: Extracting the "environment" of a model formula</a></li>
<li><a href="commandsRd/model.matrix.multiple.pdf" title="model.matrix.multiple">model.matrix.multiple: Desing matrix for multivariate models</a></li>
@@ -350,6 +353,7 @@ left: 100px;
</li> <!-- end of refman li -->
<li class="mainindx"> <a href=""> Appendices </a>
<ul id="Appendices">
+ <li><a href="static/whatsnew.pdf" title="faq">What's new? What's next? </a></li>
<li><a href="static/faq.pdf" title="faq">Frequently Asked Questions</a></li>
</ul>
</li>
diff --git a/inst/doc/zelig.pdf b/inst/doc/zelig.pdf
index d689ffc..912ce47 100644
--- a/inst/doc/zelig.pdf
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trailer
<<
-/Size 6187
-/Root 6185 0 R
-/Info 6186 0 R
+/Size 6129
+/Root 6127 0 R
+/Info 6128 0 R
>>
startxref
-3660221
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%%EOF
diff --git a/inst/doc/zelig.tex b/inst/doc/zelig.tex
index 4c31157..b6d578c 100644
--- a/inst/doc/zelig.tex
+++ b/inst/doc/zelig.tex
@@ -21,7 +21,7 @@ Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
\texttt{OLau at Fas.Harvard.Edu}).}}
% rbuild: replace 'Version ' '\\' Version
-\date{Version 3.2-1\\ \today}
+\date{Version 3.3-1\\ \today}
\begin{document}
\maketitle
@@ -48,7 +48,8 @@ Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
\part{Advanced Zelig Uses}
-\input{advancedR}
+\input{robjects}
+\input{programmingstatements}
\input{newModels}
\input{addingModels}
@@ -62,393 +63,10 @@ Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
%\input{distributions}
-\input{faq}
-
\chapter{What's New? What's Next?}
+\input{whatsnew}
-\section{What's New: Zelig Release Notes}\label{release.notes}
-
-Releases listed as ``stable releases'' have been tested against prior
-versions of Zelig for consistency and accuracy. Testing distributions
-may contain bugs, but are usually replaced by stable releases within a
-few days.
-
-\begin{itemize}
-
-\item {\bf 3.2-1} (April 10, 2008): Bug-fix release for R.2.6.0-2.6.2.
- Fixed the {\rm setx()} bug for multiply imputed data sets. (Thanks
- to Steve Shewfelt and Keith Schnakenberg.)
-
-\item {\bf 3.2} (April 3, 2008): Stable release for R 2.6.0-2.6.2.
- Adding models for survey data -- {\tt normal.survey}, {\tt
- logit.survey}, {\tt probit.survey}, {\tt poisson.survey}, {\tt
- gamma.survey}.First version dependencies are as
- follows:\newline
-\begin{tabular}{ll}\label{table.compat}
-survey & 3.6-13 \\
-MASS & 7.2-34 \\
-nlme & 3.1-86 \\
-survival & 2.34 \\
-boot & 1.2-30 \\
-nnet & 7.2-34 \\
-zoo & 1.4-0 \\
-sandwich & 2.0-2 \\
-sna & 1.4 \\
-lme4 & 0.99875-9 \\
-coda & 0.12-1 \\
-systemfit & 0.8-5 \\
-VGAM & 0.7-4 \\
-MCMCpack & 0.8-2 \\
-mvtnorm & 0.8-1 \\
-gee & 4.13-13 \\
-mgcv & 1.3-29 \\
-anchors & 2.0
-\end{tabular}
-
-\item {\bf 3.1-1} (January 10, 2008): Bug-fix release for R
- 2.6.0-2.6.1. Fixed bugs, improved the code and the documentation for
- mixed effects models. Thanks to Gregor Gorjanc. Fixed systemfit models
- due to some API changes in systemfit package. Added some other models (including *.mixed models) in plot.ci
-
-\item {\bf 3.1} (November 30, 2007): Stable release for R 2.6.0-2.6.1.
- Adding many new models such as {\tt aov}, {\tt chopit}, {\tt coxph},
- generalized linear mixed-effects models, and gee models. Also,
- several bugs are fixed. First version dependencies are as
- follows:\newline
-\begin{tabular}{ll}\label{table.compat}
-MASS & 7.2-34 \\
-nlme & 3.1-86 \\
-survival & 2.34 \\
-boot & 1.2-30 \\
-nnet & 7.2-34 \\
-zoo & 1.4-0 \\
-sandwich & 2.0-2 \\
-sna & 1.4 \\
-lme4 & 0.99875-9 \\
-coda & 0.12-1 \\
-systemfit & 0.8-5 \\
-VGAM & 0.7-4 \\
-MCMCpack & 0.8-2 \\
-mvtnorm & 0.8-1 \\
-gee & 4.13-13 \\
-mgcv & 1.3-29 \\
-anchors & 2.0
-\end{tabular}
-
-\item {\bf 3.0-1} -- {\bf 3.0-6}: Minor bug fixes. Stable release for
- R 2.5.0-2.5.1.
-
-\item {\bf 3.0} (July 20, 2007): Stable release for R 2.5.0-2.5.1.
- Introducing vignettes for each model. Improving documentation in the
- Zelig web site, improving citation style, improving {\tt
- help.zelig()} function, adding gam models, social network methods,
- logit gee model, adding support for cross-validation procedures and
- diagnostics tools, etc.
-
-\item {\bf 2.8-3} (May 29, 2007): Stable release for R 2.4.0-2.5.0.
-Fixed bugs in {\tt help.zelig()}, and summary for multinomial logit,
-bivariate probit, and bivariate logit with multiple imputation. (Thanks
-to Brant Inman and Javier Marquez.) First version dependencies are as
-follows:\newline
-\begin{tabular}{ll}
- MASS & 7.2-34 \\
- boot & 1.2-27 \\
- VGAM & 0.7-1 \\
- MCMCpack & 0.8-2 \\
- mvtnorm & 0.7-5 \\
- survival & 2.31 \\
- sandwich & 2.0-0 \\
- zoo & 1.2-1 \\
- coda & 0.10-7 \\
- nnet & 7.2-34 \\
- sna & 1.4
-\end{tabular}
-
-\item {\bf 2.8-2} (March 3, 2007): Stable release for R 2.4.0-2.4.1.
-Fixed bug in ARIMA simulation process.
-
-\item {\bf 2.8-1} (February 21, 2007): Stable release for R
- 2.4.0-2.4.1. Made {\tt setx()} compatible with ordred factor variables
- (thanks to Mike Ward and Kirill Kalinin). First order dependencies as
- in version 2.8-1.
-
-\item {\bf 2.8-0} (February 12, 2007): Stable release for R
-2.4.0-2.4.1. Released ARIMA models and network analysis models (least
-squares and logit) for sociomatrices. First level dependencies are as
-follows:\newline
-\begin{tabular}{ll}
- MASS & 7.2-31 \\
- boot & 1.2-27 \\
- VGAM & 0.7-1 \\
- MCMCpack & 0.7-4 \\
- mvtnorm & 0.7-5 \\
- survival & 2.31 \\
- sandwich & 2.0-0 \\
- zoo & 1.2-1 \\
- coda & 0.10-7 \\
- nnet & 7.2-31 \\
- sna & 1.4 \\
-\end{tabular}
-
-\item {\bf 2.7-5} (December 25, 2006): Stable release for R 2.4.0-2.4.1.
-Fixed bug related to {\tt names.default()}, summary for multiple
-imputation methods, and prediction for ordinal response models (thanks to
-Brian Ripley, Chris Lawrence, and Ian Yohai).
-
-\item {\bf 2.7-4} (November 10, 2006): Stable release for R 2.4.0. Fixed
-bugs related to R check.
-
-\item {\bf 2.7-3} (November 9, 2006): Stable release for R 2.4.0. Fixed
-bugs related to R check.
-
-\item {\bf 2.7-2} (November 5, 2006): Stable release for R 2.4.0.
-Temporarily removed {\sc arima} models.
-
-\item {\bf 2.7-1} (November 3, 2006): Stable release for R 2.4.0. Made
- changes regarding the S4 classes in VGAM. The {\sc arima} ({\tt arima})
- model for time series data added by Justin Grimmer. First level
- dependencies are as follows:\newline
-\begin{tabular}{ll}
- MASS & 7.2-29 \\
- boot & 1.2-26 \\
- VGAM & 0.7-1 \\
- MCMCpack & 0.7-4 \\
- mvtnorm & 0.7-5 \\
- survival & 2.29 \\
- sandwich & 2.0-0 \\
- zoo & 1.2-1 \\
- coda & 0.10-7 \\
-\end{tabular}
-
-
-\item {\bf 2.6-5} (September 14, 2006): Stable release for R 2.3.0-2.3.1. Fixed bugs
- in bivariate logit, bivariate probit, multinomial logit, and
- model.matrix.multiple (related to changes in version 2.6-4, but not
- previous versions, thanks to Chris Lawrence). First level
- dependencies are as follows:\newline
-\begin{tabular}{ll}
- MASS & 7.2-27.1 \\
- boot & 1.2-26 \\
- VGAM & 0.6-9 \\
- MCMCpack & 0.7-1 \\
- mvtnorm & 0.7-2 \\
- survival & 2.28 \\
- sandwich & 1.1-1 \\
- zoo & 1.0-6 \\
- coda & 0.10-5 \\
-\end{tabular}
-
-\item {\bf 2.6-4} (September 8, 2006): Stable release for R
-2.3.0-2.3.1. Fixed bugs in {\tt setx()}, and bugs related to {\tt
-multiple} and the multinomial logit model. Added instructions for
-installing Fortran tools for Intel macs. Added the R$\times$C
-ecological inference model. (thanks to Kurt Hornik, Luke Keele, Joerg
-Mueller-Scheessel, and B. Dan Wood)
-
-\item {\bf 2.6-3} (June 19, 2006): Stable release for R 2.0.0-2.3.1.
-Fixed bug in {\sc vdc} interface functions, and {\tt parse.formula()}.
-(thanks to Micah Altman, Christopher N. Lawrence, and Eric Kostello)
-
-\item {\bf 2.6-2} (June 7, 2006): Stable release for R 2.0.0-2.3.1.
-Removed R $\times$ C {\sc ei}. Changed {\tt data = list()} to {\tt
-data = mi()} for multiply-imputed data frames. First level version
-compatibilities are as for version 2.6-1.
-
-\item {\bf 2.6-1} (April 29, 2006): Stable release for R 2.0.0-2.2.1.
-Fixed major bug in ordinal logit and ordinal probit expected value
-simulation procedure (does not affect Bayesian ordinal probit).
-(reported by Ian Yohai) Added the following ecological inference {\sc
-ei} models: Bayesian hierarchical {\sc ei}, Bayesian dynamic {\sc ei},
-and R $\times$ C {\sc ei}. First level version compatibilities (at
-time of release) are as follows:\newline
-\begin{tabular}{ll}
-MASS & 7.2-24 \\
-boot & 1.2-24 \\
-VGAM & 0.6-8 \\
-MCMCpack & 0.7-1 \\
-mvtnorm & 0.7-2 \\
-survival & 2.24 \\
-sandwich & 1.1-1 \\
-zoo & 1.0-6 \\
-coda & 0.10-5 \\
-\end{tabular}
-
-\item {\bf 2.5-4} (March 16, 2006): Stable release for R 2.0.0-2.2.1. Fixed bug
-related to windows build. First-level dependencies are the same as in version
-2.5-1.
-
-\item {\bf 2.5-3} (March 9, 2006): Stable release for R 2.0.0-2.2.1. Fixed bugs
-related to VDC GUI. First level dependencies are the same as in version 2.5-1.
-
-\item {\bf 2.5-2} (February 3, 2006): Stable release for R 2.0.0-2.2.1. Fixed bugs
-related to VDC GUI. First level dependencies are the same as in version 2.5-1.
-
-\item {\bf 2.5-1} (January 31, 2006): Stable release
-for R 2.0.0-2.2.1. Added methods for multiple equation models. Added
-tobit regression. Fixed bugs related to robust estimation and upgrade of sandwich and
-zoo packages. Revised {\tt setx()} to use environments. Added
-{\tt current.packages()} to retrieve version of packages upon which Zelig
-depends. First level version compatibilities (at time of release)
-are as follows: \newline
-\begin{tabular}{ll}
- MASS & 7.2-24 \\
- boot & 1.2-24 \\
- VGAM & 0.6-7 \\
- mvtnorm & 0.7-2 \\
- survival & 2.20 \\
- sandwich & 1.1-0 \\
- zoo & 1.0-4 \\
- MCMCpack & 0.6-6 \\
- coda & 0.10-3 \\
-\end{tabular}
-
-\item \textbf{2.4-7} (December 10, 2005): Stable release for R
-2.0.0-2.2.2. Fixed the environment of {\tt eval()} called within {\tt
- setx.default()} (thanks to Micah Altman).
-
-\item \textbf{2.4-6} (October 27, 2005): Stable release for R
-2.0.0-2.2.2. Fixed bug related to simulation for Bayesian Normal regression.
-
-\item \textbf{2.4-5} (October 18, 2005): Stable release for R 2.0.0-2.2.0.
-Fixed installation instructions.
-
-\item \textbf{2.4-4} (September 29, 2005): Stable release for R
-2.0.0-2.2.0. Fixed {\tt help.zelig()} links.
-
-\item \textbf{2.4-3} (September 29, 2005): Stable release for R
-2.0.0-2.2.0. Revised {\tt matchit()} documentation.
-
-\item \textbf{2.4-2} (August 30, 2005): Stable release for R 2.0.0-2.1.1.
- Fixed bug in {\tt setx()} related to {\tt as.factor()} and {\tt I()}.
-Streamlined {\tt qi.survreg()}.
-
-\item \textbf{2.4-1} (August 15, 2005): Stable release for R 2.0.0-2.1.1. Added the
- following Bayesian models: factor analysis, mixed factor analysis,
- ordinal factor analysis, unidimensional item response theory,
- k-dimensional item response theory, logit, multinomial logit,
- normal, ordinal probit, Poisson, and tobit. Also fixed minor bug in
-formula (long variable names coerced to list).
-
-\item \textbf{2.3-2} (August 5, 2005): Stable release for R 2.0.0-2.1.1.
-Fixed bug in simulation procedure for lognormal model.
-
-\item \textbf{2.3-1} (August 4, 2005): Stable release for R 2.0.0-2.1.1.
-Fixed documentation errors related to model parameterization and code bugs
-related to first differences and conditional prediction for exponential,
-lognormal, and Weibull models. (reported by Alison Post)
-
-\item \textbf{2.2-4} (July 30, 2005): Stable release for R
-2.0.0-2.1.1. Revised relogit, adding option for weighting in addition
-to prior correction. (reported by Martin Pl\"oderl)
-
-\item \textbf{2.2-3} (July 24, 2005): Stable release for R 2.0.0-2.1.1.
-Fixed bug associated with robust standard errors for negative binomial.
-
-\item \textbf{2.2-2} (July 13, 2005): Stable release for R 2.0.0-2.1.1.
-Fixed bug in setx(). (reported by Ying Lu)
-
-\item \textbf{2.2-1} (July 11, 2005): Stable release for R 2.0.0-2.1.0.
-Revised ordinal probit to use MASS library. Added robust standard errors
-for the following regression models: exponential, gamma, logit, lognormal,
-least squares, negative binomial, normal (Gaussian), poisson, probit, and
-weibull.
-
-\item \textbf{2.1-4} (May 22, 2005): Stable release for R 1.9.1-2.1.0. Revised help.zelig()
- to deal with CRAN build of Windows version. Added recode of slots to lists
- in NAMESPACE. Revised {\tt install.R} script to deal with changes to {\tt install.packages()}. (reported by Dan Powers and
-Ying Lu)
-
-\item \textbf{2.1-3} (May 9, 2005): Stable release for R 1.9.1-2.1.0.
-Revised param.lm() function to work with bootstrap simulation. (reported
-by Jens Hainmueller)
-
-\item \textbf{2.1-2} (April 14, 2005): Stable release for R 1.9.1-2.1.0.
-Revised summary.zelig().
-\item \textbf{2.1-1} (April 7, 2005): Stable release for R 1.9.1-2.1.0.
-Fixed bugs in
- NAMESPACE and summary.vglm().
-\item \textbf{2.0-14} (April 5, 2005): Stable release for R
- 1.9.1-2.0.1. Added {\tt summary.vglm()} to ensure the compatibility with VGAM 0.6-2.
-\item \textbf{2.0-13} (March 11, 2005): Stable release for R 1.9.1-2.0.1.
-Fixed bugs in {\tt NAMESPACE} and R-help file for {\tt rocplot()}.
-\item \textbf{2.0-12} (February 20, 2005): Stable release for R
-1.9.1-2.0.1. Added {\tt plot = TRUE} option to {\tt rocplot()}.
-\item \textbf{2.0-11} (January 14, 2005): Stable release for R 1.9.1-2.0.1.
-Changed class name for subsettted models from {\tt "multiple"} to {\tt
-"strata"}, and modified affected functions.
-\item \textbf{2.0-10} (January 5, 2005): Stable release for R 1.9.1 and R
-2.0.0. Fixed bug in ordinal logit simulation procedure. (reported by Ian
-Yohai)
-\item \textbf{2.0-9} (October 21, 2004): Stable release for R 1.9.1
-\emph{and} R 2.0.0 (Linux and Windows). Fixed bug in NAMESPACE file.
-\item \textbf{2.0-8} (October 18, 2004): Stable release for R 1.9.1
-\emph{and} R 2.0.0 (Linux only). Revised for submission to CRAN.
-\item \textbf{2.0-7} (October 14, 2004): Stable release for R 1.9.1 \emph{and} R 2.0.0 (Linux
-only). Fixed bugs in {\tt summary.zelig()}, NAMESPACE, and assorted bugs related to new R
-release. Revised syntax for multiple equation models.
-\item \textbf{2.0-6} (October 4, 2004): Stable release for R 1.9.1.
-Fixed problem with NAMESPACE.
-\item \textbf{2.0-5} (September 25, 2004): Stable release for R 1.9.1.
-Changed installation procedure to source {\tt install.R} from Zelig
-website.
-\item \textbf{2.0-4} (September 22, 2004): Stable release for R 1.9.1. Fixed
-typo in installation directions, implemented NAMESPACE, rationalized {\tt
-summary.zelig()}, and tweaked documentation for least squares.
-\item \textbf{2.0-3} (September 1, 2004): Stable release for
-R 1.9.1. Fixed bug in conditional prediction for survival models.
-\item \textbf{2.0-2} (August 25, 2004): Stable release for R 1.9.1.
-Removed predicted values from {\tt ls}.
-\item \textbf{2.0-1b} (July 16, 2004): Stable release for R 1.9.1. MD5
- checksum problem fixed. Revised {\tt plot.zelig()} command to be a
- generic function with methods assigned by the model. Revised entire
- architecture to accept multiply imputed data sets with strata.
- Added functions to simplify adding models. Completely restructured
- reference manual. Fixed bugs related to conditional prediction in
- setx and summarizing strata in summary.zelig.
- \item \textbf{1.1-2} (June 24, 2004): Stable release for R 1.9.1 (MD5
- checksum problem not fixed, but does not seem to cause problems).
- Fixed bug in {\tt help.zelig()}. (reported by Michael L. Levitan)
-\item \textbf{1.1-1} (June 14, 2004): Stable release for R 1.9.0.
- Revised {\tt zelig()} procedure to use {\tt zelig2model()} wrappers,
- revised {\tt help.zelig()} to use a data file with extension {\tt
- .url.tab}, and revised {\tt setx()} procedure to take a list of
- {\tt fn} to apply to variables, and such that {\tt fn = NULL}
- returns the entire {\tt model.matrix()}.
- \item \textbf{1.0-8} (May 27, 2004): Stable release for R 1.9.0.
- Fixed bug in simulation procedure for survival models. (reported
- by Elizabeth Stuart)
- \item \textbf{1.0-7} (May 26, 2004): Stable release for R 1.9.0. Fixed
- bug in relogit simulation procedure. (reported by Tom Vanwellingham)
- \item \textbf{1.0-6} (May 11, 2004): Stable release for R 1.9.0.
- Fixed bug in setx.default, which had previously failed to ignore
- extraneous variables in data frame. (reported by Steve Purpura)
- \item \textbf{1.0-5} (May 7, 2004): Replaced relogit procedure with
- memory-efficient version. (reported by Tom Vanwellingham)
- \item \textbf{1.0-4} (April 19, 2004): Stable release for R 1.9.0.
- Added vcov.lm method; changed print for summary.relogit.
- \item \textbf{1.0-2} (April 16, 2004): Testing distribution for R 1.9.0.
- \item \textbf{1.0-1} (March, 23, 2004): Stable release for R 1.8.1.
-\end{itemize}
-
-\section{What's Next?}
-
-We have several plans for expanding and improving Zelig. Major
-changes slated for Version 3.0 (and beyond) include:
-
-\begin{itemize}
-\item Hierarchical and multi-level models
-\item Ecological inference models
-\item GEE models
-\item Neural network models
-\item Average treatment effects for everyone (treated and control
-units)
-\item Time-series cross-sectional models (via {\tt nlme})
-\item Generalized boosted regression model (via {\tt gbm})
-\item Saving random seeds to ensure exact replication
-\end{itemize}
-
-If you have suggestions, or packages that you would like to contribute
-to Zelig, please email our listserv at \hlink{zelig at lists.gking.harvard.edu}{mailto:zelig at lists.gking.harvard.edu}.
+\input{faq}
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/zeligfooter.html b/inst/doc/zeligfooter.html
index 7e309c5..da41147 100644
--- a/inst/doc/zeligfooter.html
+++ b/inst/doc/zeligfooter.html
@@ -3,7 +3,7 @@
<li class="mainindx"><a href="">Commands for Programmers and Contributors </a>
<ul id="contribcommands">
-
+ <li><a href="static/describemymodel.pdf" title="describemymodel">describe: Describe a model's systematic and stochastic parameters</a></li>
<li><a href="commandsRd/model.end.pdf" title="model.end">model.end: Cleaning up after optimization</a></li>
<li><a href="commandsRd/model.frame.multiple.pdf" title="model.frame.multiple">model.frame.multiple: Extracting the "environment" of a model formula</a></li>
<li><a href="commandsRd/model.matrix.multiple.pdf" title="model.matrix.multiple">model.matrix.multiple: Desing matrix for multivariate models</a></li>
@@ -18,6 +18,7 @@
</li> <!-- end of refman li -->
<li class="mainindx"> <a href=""> Appendices </a>
<ul id="Appendices">
+ <li><a href="static/whatsnew.pdf" title="faq">What's new? What's next? </a></li>
<li><a href="static/faq.pdf" title="faq">Frequently Asked Questions</a></li>
</ul>
</li>
diff --git a/inst/doc/zeligheader.html b/inst/doc/zeligheader.html
index 3843893..272f6c0 100644
--- a/inst/doc/zeligheader.html
+++ b/inst/doc/zeligheader.html
@@ -198,7 +198,8 @@ left: 100px;
</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/robjects.pdf" title="Advanced R">R Objects</a></li>
+ <li><a href="static/programmingstatements.pdf" title="Advanced R">Programming statements</a></li>
<li><a href="static/newModels.pdf" title="Writing new Models">Writing new Models</a></li>
<li><a href="static/addingModels.pdf" title="Adding Models and Methods to Zelig">Adding Models and Methods to Zelig</a></li>
</ul>
@@ -216,9 +217,11 @@ left: 100px;
</li>
<li class="mainindx"><a href="">Supplementary Commands </a>
<ul id="supplementarycommands">
+ <li><a href="static/matchitref.pdf" title="">matchit: Create matched data</a></li>
<li><a href="commandsRd/mi.pdf" title="mi">mi: Bundle multiply imputed data sets as a list</a></li>
<li><a href="commandsRd/network.pdf" title="network">network: Format matricies into data frame for social network analysis</a></li>
<li><a href="commandsRd/plot.ci.pdf" title="plot.ci">plot.ci: Plotting Vertical confidence Intervals</a></li>
+ <li><a href="commandsRd/plot.surv.pdf" title="plot.surv">plot.surv: Plotting Coefidence Intervals for Survival Curves</a></li>
<li><a href="commandsRd/rocplot.pdf" title="rocplot">rocplot: Receiver Operator Characteristic Plots</a></li>
<li><a href="commandsRd/ternaryplot.pdf" title="ternaryplot">ternaryplot: Ternary diagram</a></li>
<li><a href="commandsRd/ternarypoints.pdf" title="ternarypoints">ternarypoints: Adding Points To Ternary Diagrams</a></li>
diff --git a/inst/zideal/zideal.RData b/inst/zideal/zideal.RData
deleted file mode 100644
index 2a91ef5..0000000
Binary files a/inst/zideal/zideal.RData and /dev/null differ
diff --git a/man/plot.surv.Rd b/man/plot.surv.Rd
new file mode 100644
index 0000000..da65e7d
--- /dev/null
+++ b/man/plot.surv.Rd
@@ -0,0 +1,66 @@
+\name{plot.surv}
+
+\alias{plot.surv}
+
+\title{Plotting Confidence Intervals for Survival Curves}
+
+\description{ The \code{plot.surv} command generates confidence intervals for Kaplan-Meier survival curves}
+
+\usage{
+ plot.surv(x, duration, censor, type = "line", plotcensor=TRUE,
+ plottimes = FALSE, int = c(0.025,0.975), ...)
+}
+
+\arguments{
+\item{x}{output from \code{sim} stored as a list. Each element of the list is the \code{sim} output for a particular survival curve.}
+\item{duration}{the duration variable (e.g. lifetime, survival, etc.).}
+\item{censor}{the censored data}
+\item{type}{the type of confidence interval. Defaults to \code{"line"}, which draws vertical confidence intervals at observed event times. \code{"poly"} draws confidence regions using polygons.}
+\item{plotcensor}{default is \code{TRUE}. Plots censoring times as a \code{rug} object.}
+\item{plottimes}{default is \code{FALSE}. Plots step function with indicators at observed event times.}
+\item{int}{vector of quantile limits for the confidence interval. Default is 95\% interval.}
+\item{\dots}{Additional parameters passed to \code{plot}.}
+}
+
+\value{
+For survival models, {\tt plot.surv()} returns vertical
+confidence intervals or polygon survival regions for Kaplan-Meier survival curves. You may save this plot using the commands described in the
+Zelig manual (\url{http://gking.harvard.edu/zelig}).
+}
+
+\examples{\dontrun{
+data(coalition)
+z.out1 <- zelig(Surv(duration,ciep12)~invest+numst2+crisis,
+robust=TRUE,cluster="polar",model="coxph",data=coalition)
+low <- setx(z.out1,numst2=0)
+high <- setx(z.out1,numst2=1
+# Simulate Survival Curves for Each Group
+s.out1 <- sim(z.out1,x=low)
+s.out2 <- sim(z.out1,x=high)
+
+# Organize simulated output as a list
+out <- list(s.out1,s.out2)
+
+plot.surv(x = out, duration = coalition$duration, censor=coalition$ciep12,
+ type="line", plottimes=FALSE, plotcensor=FALSE,
+ main="Survival", xlab="Time", ylab="Survival")
+}
+}
+
+
+\seealso{
+ The full Zelig manual is available at
+ \url{http://gking.harvard.edu/zelig}, and users may also wish to see
+ \code{plot}, \code{lines}.
+ }
+
+\author{
+ John A. Graves <\email{graveja0 at gmail.com}>
+}
+
+\keyword{hplot}
+
+
+
+
+
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