[r-cran-zelig] 60/102: Import Upstream version 3.1-1
Andreas Tille
tille at debian.org
Sun Jan 8 16:59:07 UTC 2017
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-zelig.
commit c3ffce6230374c530ce299783a7c41e4d5bda7bc
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:39:44 2017 +0100
Import Upstream version 3.1-1
---
DESCRIPTION | 10 +-
INDEX | 9 -
R/callsystemfit.R | 13 +-
R/describe.blogit.R | 3 +-
R/describe.bprobit.R | 3 +-
R/describe.mlogit.R | 3 +-
R/param.mixed.R | 45 +-
R/plot.ci.R | 11 +-
R/qi.mixed.R | 46 +-
R/zelig2sur.R | 3 +-
R/zelig2threesls.R | 2 +-
R/zelig2twosls.R | 3 +-
inst/doc/Rplots.ps | 46782 ++++++-----
inst/doc/Zcommands.tex | 1 -
inst/doc/aov.pdf | Bin 161891 -> 162173 bytes
inst/doc/arima.pdf | Bin 123057 -> 123131 bytes
inst/doc/blogit.pdf | Bin 153114 -> 152249 bytes
inst/doc/bprobit.pdf | Bin 165549 -> 160685 bytes
inst/doc/chopit.pdf | Bin 108509 -> 108508 bytes
inst/doc/cloglog.net.pdf | Bin 105366 -> 105336 bytes
inst/doc/commands/model.end.aux | 20 +-
inst/doc/commands/model.frame.multiple.aux | 22 +-
inst/doc/commands/model.matrix.multiple.aux | 22 +-
inst/doc/commands/parse.formula.aux | 26 +-
inst/doc/commands/parse.par.aux | 22 +-
inst/doc/commands/put.start.aux | 20 +-
inst/doc/commands/set.start.aux | 22 +-
inst/doc/commands/tag.aux | 22 +-
inst/doc/commandsRd/MatchIt.url.pdf | Bin 19853 -> 19852 bytes
inst/doc/commandsRd/PErisk.pdf | Bin 42257 -> 42257 bytes
inst/doc/commandsRd/SupremeCourt.pdf | Bin 32130 -> 32129 bytes
inst/doc/commandsRd/Weimar.pdf | Bin 25053 -> 25054 bytes
inst/doc/commandsRd/Zelig-package.pdf | Bin 49587 -> 47952 bytes
inst/doc/commandsRd/Zelig-package.tex | 74 +-
inst/doc/commandsRd/Zelig.pdf | Bin 41493 -> 41496 bytes
inst/doc/commandsRd/Zelig.url.pdf | Bin 18736 -> 18734 bytes
inst/doc/commandsRd/approval.pdf | Bin 24314 -> 24314 bytes
inst/doc/commandsRd/bivariate.pdf | Bin 31446 -> 31447 bytes
inst/doc/commandsRd/coalition.pdf | Bin 33900 -> 33898 bytes
inst/doc/commandsRd/coalition2.pdf | Bin 34955 -> 34955 bytes
inst/doc/commandsRd/current.packages.pdf | Bin 38488 -> 38487 bytes
inst/doc/commandsRd/dims.pdf | Bin 38330 -> 38332 bytes
inst/doc/commandsRd/eidat.pdf | Bin 20565 -> 20561 bytes
inst/doc/commandsRd/free1.pdf | Bin 40772 -> 40773 bytes
inst/doc/commandsRd/free2.pdf | Bin 40801 -> 40800 bytes
inst/doc/commandsRd/friendship.pdf | Bin 31871 -> 31873 bytes
inst/doc/commandsRd/grunfeld.pdf | Bin 26302 -> 26299 bytes
inst/doc/commandsRd/gsource.pdf | Bin 43874 -> 43875 bytes
inst/doc/commandsRd/help.zelig.pdf | Bin 33300 -> 33301 bytes
inst/doc/commandsRd/hoff.pdf | Bin 33603 -> 33603 bytes
inst/doc/commandsRd/homerun.pdf | Bin 39387 -> 39387 bytes
inst/doc/commandsRd/immigration.pdf | Bin 33057 -> 33052 bytes
inst/doc/commandsRd/klein.pdf | Bin 27453 -> 27454 bytes
inst/doc/commandsRd/kmenta.pdf | Bin 24919 -> 24918 bytes
inst/doc/commandsRd/macro.pdf | Bin 32697 -> 32696 bytes
inst/doc/commandsRd/match.data.pdf | Bin 35998 -> 35999 bytes
inst/doc/commandsRd/mexico.pdf | Bin 33501 -> 33502 bytes
inst/doc/commandsRd/mi.pdf | Bin 38080 -> 38081 bytes
inst/doc/commandsRd/mid.pdf | Bin 37171 -> 37171 bytes
inst/doc/commandsRd/model.end.pdf | Bin 36594 -> 36596 bytes
inst/doc/commandsRd/model.frame.multiple.pdf | Bin 47453 -> 47454 bytes
inst/doc/commandsRd/model.matrix.multiple.pdf | Bin 53328 -> 53332 bytes
inst/doc/commandsRd/network.pdf | Bin 35147 -> 35147 bytes
inst/doc/commandsRd/newpainters.pdf | Bin 34963 -> 34961 bytes
inst/doc/commandsRd/parse.formula.pdf | Bin 50653 -> 50655 bytes
inst/doc/commandsRd/parse.par.pdf | Bin 52101 -> 52104 bytes
inst/doc/commandsRd/plot.ci.pdf | Bin 51973 -> 51972 bytes
inst/doc/commandsRd/plot.zelig.pdf | Bin 40112 -> 40112 bytes
inst/doc/commandsRd/put.start.pdf | Bin 34220 -> 34223 bytes
inst/doc/commandsRd/repl.pdf | Bin 49397 -> 49396 bytes
inst/doc/commandsRd/rocplot.pdf | Bin 50647 -> 50649 bytes
inst/doc/commandsRd/sanction.pdf | Bin 31281 -> 31279 bytes
inst/doc/commandsRd/set.start.pdf | Bin 43071 -> 43070 bytes
inst/doc/commandsRd/setx.pdf | Bin 54740 -> 54742 bytes
inst/doc/commandsRd/sim.pdf | Bin 66015 -> 66025 bytes
inst/doc/commandsRd/sna.ex.pdf | Bin 22893 -> 22893 bytes
inst/doc/commandsRd/summary.zelig.pdf | Bin 40775 -> 40773 bytes
inst/doc/commandsRd/swiss.pdf | Bin 29949 -> 29948 bytes
inst/doc/commandsRd/ternaryplot.pdf | Bin 59266 -> 59266 bytes
inst/doc/commandsRd/ternarypoints.pdf | Bin 35075 -> 35078 bytes
inst/doc/commandsRd/tobin.pdf | Bin 24016 -> 24015 bytes
inst/doc/commandsRd/turnout.pdf | Bin 32994 -> 32997 bytes
inst/doc/commandsRd/user.prompt.pdf | Bin 35228 -> 35231 bytes
inst/doc/commandsRd/voteincome.pdf | Bin 30468 -> 30468 bytes
inst/doc/commandsRd/zeligDepStatus.pdf | Bin 44748 -> 44746 bytes
inst/doc/commandsRd/zeligDepUpdate.pdf | Bin 50257 -> 50252 bytes
inst/doc/commandsRd/zeligVDC.pdf | Bin 46759 -> 46763 bytes
inst/doc/coxph.pdf | Bin 162527 -> 162537 bytes
inst/doc/ei.RxC.pdf | Bin 95153 -> 95157 bytes
inst/doc/ei.dynamic.pdf | Bin 104080 -> 104082 bytes
inst/doc/ei.hier.pdf | Bin 101618 -> 101615 bytes
inst/doc/exp.pdf | Bin 135612 -> 135615 bytes
inst/doc/factor.bayes.pdf | Bin 90097 -> 90091 bytes
inst/doc/factor.mix.pdf | Bin 94647 -> 94647 bytes
inst/doc/factor.ord.pdf | Bin 93057 -> 93058 bytes
inst/doc/gamma.gee.pdf | Bin 132000 -> 131771 bytes
inst/doc/gamma.mixed.Rnw | 31 +-
inst/doc/gamma.mixed.pdf | Bin 104298 -> 104080 bytes
inst/doc/gamma.net.pdf | Bin 130729 -> 130465 bytes
inst/doc/gamma.pdf | Bin 133160 -> 133237 bytes
inst/doc/index.shtml | 9 +-
inst/doc/irt1d.Rnw | 7 +-
inst/doc/irt1d.pdf | Bin 99409 -> 99407 bytes
inst/doc/irtkd.Rnw | 7 +-
inst/doc/irtkd.pdf | Bin 101618 -> 101613 bytes
inst/doc/logit.bayes.pdf | Bin 101709 -> 101706 bytes
inst/doc/logit.gam.pdf | Bin 271911 -> 271908 bytes
inst/doc/logit.gee.pdf | Bin 155220 -> 155218 bytes
inst/doc/logit.mixed.Rnw | 35 +-
inst/doc/logit.mixed.pdf | Bin 104217 -> 104004 bytes
inst/doc/logit.net.pdf | Bin 110328 -> 110326 bytes
inst/doc/logit.pdf | Bin 156315 -> 156315 bytes
inst/doc/lognorm.pdf | Bin 136853 -> 136856 bytes
inst/doc/ls.mixed.Rnw | 35 +-
inst/doc/ls.mixed.pdf | Bin 133693 -> 134684 bytes
inst/doc/ls.net.pdf | Bin 107425 -> 107434 bytes
inst/doc/ls.pdf | Bin 184719 -> 184926 bytes
inst/doc/mlogit.bayes.pdf | Bin 103458 -> 103456 bytes
inst/doc/mlogit.pdf | Bin 159583 -> 159584 bytes
inst/doc/mloglm.pdf | Bin 53351 -> 53350 bytes
inst/doc/negbin.pdf | Bin 114739 -> 114756 bytes
inst/doc/normal.bayes.pdf | Bin 104182 -> 104184 bytes
inst/doc/normal.gam.pdf | Bin 267323 -> 267321 bytes
inst/doc/normal.gee.pdf | Bin 131596 -> 131592 bytes
inst/doc/normal.net.pdf | Bin 100527 -> 100525 bytes
inst/doc/normal.pdf | Bin 132238 -> 132237 bytes
inst/doc/ologit.pdf | Bin 101665 -> 101659 bytes
inst/doc/oprobit.bayes.pdf | Bin 106706 -> 106708 bytes
inst/doc/oprobit.pdf | Bin 169452 -> 169454 bytes
inst/doc/poisson.bayes.pdf | Bin 99808 -> 99806 bytes
inst/doc/poisson.gam.pdf | Bin 267777 -> 267778 bytes
inst/doc/poisson.gee.pdf | Bin 123352 -> 123355 bytes
inst/doc/poisson.mixed.Rnw | 35 +-
inst/doc/poisson.mixed.pdf | Bin 101166 -> 100953 bytes
inst/doc/poisson.net.pdf | Bin 92072 -> 92077 bytes
inst/doc/poisson.pdf | Bin 111002 -> 111001 bytes
inst/doc/probit.bayes.pdf | Bin 102329 -> 102327 bytes
inst/doc/probit.gam.pdf | Bin 267841 -> 267843 bytes
inst/doc/probit.gee.pdf | Bin 155388 -> 155395 bytes
inst/doc/probit.mixed.Rnw | 36 +-
inst/doc/probit.mixed.pdf | Bin 104608 -> 104395 bytes
inst/doc/probit.net.pdf | Bin 105675 -> 105674 bytes
inst/doc/probit.pdf | Bin 99319 -> 99319 bytes
inst/doc/refman.tex | 2 +
inst/doc/relogit.pdf | Bin 146115 -> 146117 bytes
inst/doc/static/Zcommands.pdf | Bin 159938 -> 159935 bytes
inst/doc/static/acknowledgments.pdf | Bin 29857 -> 29857 bytes
inst/doc/static/addingModels.pdf | Bin 87602 -> 87604 bytes
inst/doc/static/advancedR.pdf | Bin 96391 -> 96391 bytes
inst/doc/static/faq.pdf | Bin 104829 -> 104829 bytes
inst/doc/static/graphs.pdf | Bin 173302 -> 173305 bytes
inst/doc/static/install.pdf | Bin 97974 -> 97972 bytes
inst/doc/static/intro.pdf | Bin 69845 -> 69845 bytes
inst/doc/static/newModels.pdf | Bin 135792 -> 135798 bytes
inst/doc/static/syntax.pdf | Bin 93889 -> 93889 bytes
inst/doc/sur.pdf | Bin 99548 -> 99544 bytes
inst/doc/threesls.pdf | Bin 135837 -> 135832 bytes
inst/doc/tobit.bayes.pdf | Bin 103712 -> 103712 bytes
inst/doc/tobit.pdf | Bin 93873 -> 93872 bytes
inst/doc/twosls.pdf | Bin 160232 -> 160232 bytes
inst/doc/vigpics/aov-ExamplesPlot1.eps | 718 +-
inst/doc/vigpics/aov-ExamplesPlot1.pdf | 1048 +-
inst/doc/vigpics/aov-ExamplesPlot2.eps | 1345 +-
inst/doc/vigpics/aov-ExamplesPlot2.pdf | 2032 +-
inst/doc/vigpics/aov-ExamplesPlot3.eps | 770 +-
inst/doc/vigpics/aov-ExamplesPlot3.pdf | 1038 +-
inst/doc/vigpics/arima-Example1Plot.eps | 220 +-
inst/doc/vigpics/arima-Example1Plot.pdf | 252 +-
inst/doc/vigpics/arima-Example2Plot.eps | 761 +-
inst/doc/vigpics/arima-Example2Plot.pdf | 1154 +-
inst/doc/vigpics/arima-Example3Plot.eps | 188 +-
inst/doc/vigpics/arima-Example3Plot.pdf | 208 +-
inst/doc/vigpics/blogit-BasicExamplePlot.eps | 2895 +-
inst/doc/vigpics/blogit-BasicExamplePlot.pdf | 1457 +-
inst/doc/vigpics/blogit-JointExamplePlot.eps | 1804 +-
inst/doc/vigpics/blogit-JointExamplePlot.pdf | 947 +-
inst/doc/vigpics/bprobit-BasicExamplePlot.eps | 3840 +-
inst/doc/vigpics/bprobit-BasicExamplePlot.pdf | 1864 +-
inst/doc/vigpics/bprobit-JointEstimationPlot.eps | 2038 +-
inst/doc/vigpics/bprobit-JointEstimationPlot.pdf | 1041 +-
inst/doc/vigpics/cloglognet-007.eps | 762 +-
inst/doc/vigpics/cloglognet-007.pdf | 1079 +-
inst/doc/vigpics/cloglognet-009.eps | 762 +-
inst/doc/vigpics/cloglognet-009.pdf | 1079 +-
inst/doc/vigpics/coxph-ExamplePlot.eps | 1291 +-
inst/doc/vigpics/coxph-ExamplePlot.pdf | 2204 +-
inst/doc/vigpics/coxph-ExamplePlot2.eps | 811 +-
inst/doc/vigpics/coxph-ExamplePlot2.pdf | 1316 +-
inst/doc/vigpics/exp-ExamplePlot.eps | 2020 +-
inst/doc/vigpics/exp-ExamplePlot.pdf | 2968 +-
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 | 2245 +-
inst/doc/vigpics/gamma-ExamplePlot.pdf | 3106 +-
inst/doc/vigpics/gamma_gee-ExamplePlot.eps | 1495 +-
inst/doc/vigpics/gamma_gee-ExamplePlot.pdf | 2101 +-
inst/doc/vigpics/gammanet-007.eps | 1594 +-
inst/doc/vigpics/gammanet-007.pdf | 2120 +-
inst/doc/vigpics/gammanet-009.eps | 1594 +-
inst/doc/vigpics/gammanet-009.pdf | 2120 +-
inst/doc/vigpics/lmmixed-ExamplesPlot.eps | 651 +-
inst/doc/vigpics/lmmixed-ExamplesPlot.pdf | 1016 +-
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/lognormal-ExamplePlot.pdf | 6 +-
inst/doc/vigpics/ls-DummyPlot.eps | 1367 +-
inst/doc/vigpics/ls-DummyPlot.pdf | 2081 +-
inst/doc/vigpics/ls-Example4Plot.eps | 1429 +-
inst/doc/vigpics/ls-Example4Plot.pdf | 2100 +-
inst/doc/vigpics/ls-ExamplesPlot.eps | 1403 +-
inst/doc/vigpics/ls-ExamplesPlot.pdf | 2045 +-
inst/doc/vigpics/lsnet-ExamplesPlot.eps | 1450 +-
inst/doc/vigpics/lsnet-ExamplesPlot.pdf | 2059 +-
inst/doc/vigpics/mlogit-LevelsTernaryPlot.eps | 2000 +-
inst/doc/vigpics/mlogit-LevelsTernaryPlot.pdf | 2020 +-
inst/doc/vigpics/mlogit-TernaryPlot.eps | 2000 +-
inst/doc/vigpics/mlogit-TernaryPlot.pdf | 2020 +-
inst/doc/vigpics/negbin-Example1Plot.eps | 836 +-
inst/doc/vigpics/negbin-Example1Plot.pdf | 1090 +-
inst/doc/vigpics/normal-ExamplesPlot.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/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/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 137633 -> 137629 bytes
inst/doc/zelig.pdf | 78542 ++++++++++---------
inst/doc/zelig.tex | 13 +-
inst/zideal/zideal.RData | Bin 1656 -> 0 bytes
man/Zelig-package.Rd | 74 +-
246 files changed, 103526 insertions(+), 104500 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 117d776..dd19ead 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,12 +1,12 @@
Package: Zelig
-Version: 3.1-0
-Date: 2007-11-30
+Version: 3.1-1
+Date: 2008-02-12
Title: Everyone's Statistical Software
Author: Kosuke Imai <kimai at Princeton.Edu>,
Gary King <king at harvard.edu>,
Olivia Lau <olau at fas.harvard.edu>
Maintainer: Kosuke Imai <kimai at Princeton.Edu>
-Depends: R (>= 2.5.0), MASS, boot
+Depends: R (>= 2.6.0), MASS, boot
Description: Zelig is an easy-to-use program that can estimate, and
help interpret the results of, an enormous range of
statistical models. It literally is ``everyone's statistical
@@ -24,5 +24,5 @@ License: GPL (>= 2)
URL: http://gking.harvard.edu/zelig
Suggests: VGAM (>= 0.7-1), MCMCpack (>= 0.7-4), mvtnorm, survival,
sandwich (>= 2.0-0), zoo (>= 1.2-1), coda, nnet, sna, gee,
- systemfit, mgcv, lme4, anchors (>= 2.0)
-Packaged: Fri Nov 30 17:03:48 2007; king
+ systemfit, mgcv
+Packaged: Tue Feb 12 10:11:36 2008; king
diff --git a/INDEX b/INDEX
index 4e05d7a..b1b4b50 100644
--- a/INDEX
+++ b/INDEX
@@ -1,15 +1,10 @@
approval U.S. Presidential Approval Data
-bivariate Sample data for bivariate probit regression
coalition Coalition Dissolution in Parliamentary
Democracies
-coalition2 Coalition Dissolution in Parliamentary
- Democracies, Modified Version
current.packages Find all packages in a dependency chain
dims Return Dimensions of Vectors, Arrays, and Data
Frames
eidat Simulation Data for Ecological Inference
-free1 Freedom of Speech Data
-free2 Freedom of Speech Data
friendship Simulated Example of Schoolchildren Friendship
Network
grunfeld Simulation Data for model Seemingly Unrelated
@@ -18,8 +13,6 @@ grunfeld Simulation Data for model Seemingly Unrelated
gsource Read Data As a Space-Delimited Table
help.zelig HTML Help for Zelig Commands and Models
hoff Social Security Expenditure Data
-homerun Sample Data on Home Runs Hit By Mark McGwire
- and Sammy Sosa in 1998.
immigration Individual Preferences Over Immigration Policy
klein Simulation Data for model Two-Stage Least
Square (twosls) that corresponds to method 2SLS
@@ -66,8 +59,6 @@ tobin Tobin's Tobit Data
turnout Turnout Data Set from the National Election
Survey
user.prompt Pause in demo files
-voteincome Sample Turnout and Demographic Data from the
- 2000 Current Population Survey
Weimar 1932 Weimar election data
zelig Estimating a Statistical Model
zeligDepStatus Zelig Dependencies Packages Client Status
diff --git a/R/callsystemfit.R b/R/callsystemfit.R
index e90bc2d..438b194 100644
--- a/R/callsystemfit.R
+++ b/R/callsystemfit.R
@@ -1,9 +1,8 @@
callsystemfit<-function(formula,data,method,inst=NULL,...){
-### t<-terms.multiple(formula)
- t<-terms(formula)
-
- out<-systemfit(data=data,eqns=formula,method=method,inst=inst,...)
- attr(out,"terms")<-t
- class(out)<-c(class(out),"multiple")
- return (out)
+ out<-systemfit(data=data,formula=formula,method=method,inst=inst,...)
+ class(formula)<-c("multiple","list")
+ t<-terms(formula)
+ attr(out,"terms")<-t
+ class(out)<-c("multiple",class(out))
+ return (out)
}
diff --git a/R/describe.blogit.R b/R/describe.blogit.R
index 939424a..104ea80 100644
--- a/R/describe.blogit.R
+++ b/R/describe.blogit.R
@@ -2,8 +2,7 @@ describe.blogit<-function(){
category <- "dichotomous"
description <- "Bivariate Logistic Regression for Dichotomous Dependent Variables"
package <-list( name ="VGAM",
- version ="0.6",
- CRAN ="http://www.stat.auckland.ac.nz/~yee"
+ version ="0.6"
)
parameters<-list(mu="mu",phi="phi")
parameters$mu<-list(equations=c(2,2),
diff --git a/R/describe.bprobit.R b/R/describe.bprobit.R
index 95a003d..a91bc88 100644
--- a/R/describe.bprobit.R
+++ b/R/describe.bprobit.R
@@ -2,8 +2,7 @@ describe.bprobit<-function(){
category <- "dichotomous"
description <- "Bivariate Probit Regression for Dichotomous Dependent Variables"
package <-list( name ="VGAM",
- version ="0.6",
- CRAN ="http://www.stat.auckland.ac.nz/~yee"
+ version ="0.6"
)
parameters<-list(mu="mu", rho="rho")
parameters$mu<-list(equations=c(2,2),
diff --git a/R/describe.mlogit.R b/R/describe.mlogit.R
index 1faafcf..c43f1af 100644
--- a/R/describe.mlogit.R
+++ b/R/describe.mlogit.R
@@ -2,8 +2,7 @@ describe.mlogit<-function(){
category <- "multinomial"
description <- "Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values"
package <-list( name ="VGAM",
- version ="0.6",
- CRAN="http://www.stat.auckland.ac.nz/~yee"
+ version ="0.6"
)
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,Inf),
diff --git a/R/param.mixed.R b/R/param.mixed.R
index 933bcc8..53d11bb 100644
--- a/R/param.mixed.R
+++ b/R/param.mixed.R
@@ -1,45 +1,42 @@
param.lmer <- param.glmer <- function(object, num, bootstrap = FALSE) {
- if (!bootstrap) {
- object <- selectMethod("summary", "lmer") (object)
- #print(class(object))
- fixed <- fixef(object)
- vcov.fix <- vcov(object)
+ fixed <- fixef(object)
+ vars <- object at bVar
+ scale <- attr(VarCorr(object), "sc")
+ gammas <- NULL
+ n.G <- length(vars)
+
+ if (!bootstrap) {
+ object <- selectMethod("summary", "lmer") (object)
+ ## print(class(object))
+ ## *Sample* fixed effects
betasF <- NULL
+ vcov.fix <- vcov(object)
if (length(fixed) > 0){
betasF <- mvrnorm(num, fixed, vcov.fix)
}
- scale <- attr(VarCorr(object), "sc")
- vars <- object at bVar
- gammas <- NULL
- n.G <- length(vars)
-
+ ## *Sample* random effects
for (m in 1:n.G){
V.beta <- VarCorr(object)[[m]]
vars.m <- vars[[m]]
J <- dim(vars.m)[1]
gammas[[m]] <- mvrnorm(num, rep(0, J), V.beta)
}
- names(gammas) <- names(vars)
- betas <- betasF
- scale <- object at sigma
-
} else {
object <- summary(object)
- fixed <- fixef(object)
- scale <- attr (VarCorr(object), "sc")
+ ## *Set* fixed effects
betasF <- fixed
- vars <- object at bVar
- gammas <- NULL
- n.G <- length(vars)
+ ## *Sample* random effects
for (m in 1:n.G){
V.beta <- VarCorr(object)[[m]]
gammas[[m]] <- mvrnorm(1, 0, V.beta)
}
- names(gammas)<- names(vars)
- betas <- betasF
- scale <- object at sigma
}
- res <- list(betas=betas, gammas=gammas, scale=scale)
- res
+
+ names(gammas) <- names(vars)
+ betas <- betasF
+ scale <- object at sigma
+
+ ## Return
+ list(betas=betas, gammas=gammas, scale=scale)
}
diff --git a/R/plot.ci.R b/R/plot.ci.R
index 7ec60e9..08b20d4 100644
--- a/R/plot.ci.R
+++ b/R/plot.ci.R
@@ -4,9 +4,14 @@ plot.ci <- function(x, CI=95, qi = "ev", main = "",
if (class(x) != "zelig")
stop(" plot.ci() works only for sim() output.")
if (!(x$zelig.call$model) %in% c("ls", "logit", "probit", "exp", "gamma", "lognorm",
- "weibull", "normal", "poisson", "relogit",
- "negbin", "MCMCregress", "MCMClogit", "MCMCprobit",
- "MCMCpoisson", "MCMCregress"))
+ "weibull", "normal", "poisson", "tobit", "relogit",
+ "negbin", "logit.bayes", "probit.bayes",
+ "poisson.bayes", "normal.bayes", "tobit.bayes",
+ "ls.mixed", "logit.mixed", "probit.mixed",
+ "gamma.mixed", "poisson.mixed",
+ "logit.gam", "gamma.gee", "normal.gam", "poisson.gam",
+ "probit.gam", "logit.gee", "normal.gee",
+ "poisson.gee", "probit.gee"))
stop("\n plot.ci() is valid only for non-categorical, univariate response models.")
var <- var1 <- list()
idx1 <- idx <- NULL
diff --git a/R/qi.mixed.R b/R/qi.mixed.R
index a9a5d8f..1ab292b 100644
--- a/R/qi.mixed.R
+++ b/R/qi.mixed.R
@@ -1,5 +1,6 @@
## dbailey at wustl.edu
-## modeified by Ferdi 10/30/07
+## modified by Gregor Gorjanc 2008-01-07
+## modified by Ferdi 10/30/07
################################
qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
@@ -20,6 +21,7 @@ qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
fTermsNames <- colnames(model.matrix(parsefml$fixed, data = D))
+ cat(fTermsNames, "\n")
fTerms <- x[,fTermsNames]
rTerms <- list()
@@ -43,12 +45,13 @@ qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
##rTermsNames <- colnames(model.matrix(parsefml$random[[i]],data=D))
##rTerms.x1[[i]] <- x1[, rTermsNames]
}
- names(rTerms.x1) <- names(parsefml$random)
+ names(rTerms.x1) <- names(parsefml$random)
}
## Expected Values and Predicted Values
if (class(object) == "glmer"){
- family <- object at family[[1]] ## is a list, is family name always the first element ? -Ferdi
+ family <- object at family$family
+ link <- object at family$link
eta <- betas %*% t(as.matrix(fTerms))
theta <- matrix(object at family$linkinv(eta), nrow=nrow(betas))
mu <- eta
@@ -60,10 +63,11 @@ qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
ev <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta))
pr <- matrix(NA, nrow=nrow(mut), ncol=ncol(mut))
dimnames(ev) <- dimnames(pr) <- dimnames(theta)
+ n <- length(mut[, 1])
if (family == "binomial"){
ev <- theta
for (i in 1:ncol(mut)){
- pr[,i] <- as.character(rbinom(length(mut[,i]), 1, mut[,i]))
+ pr[,i] <- as.character(rbinom(n, 1, mut[,i]))
}
if (!is.null(y)) {
if (NCOL(y) > 1) {
@@ -73,15 +77,28 @@ qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
else if (family == "Gamma"){
ev <- theta * 1/alpha
+ n <- length(mut[i,])
for (i in 1:nrow(mut)){
- pr[i,] <- rgamma(length(mut[i,]), shape = mut[i,], scale= 1/alpha)
+ pr[i,] <- rgamma(n, shape = mut[i,], scale= 1/alpha)
+ }
+ }
+ else if (family == "gaussian"){
+ ev <- theta
+ if (link == "log"){
+ for (i in 1:ncol(mut)){
+ pr[,i] <- rlnorm(n, meanlog=mut[,i], sdlog=alpha)
+ }
+ } else {
+ stop(sprintf("no method for %s family and %s link", family, link))
}
}
else if (family == "poisson"){
ev <- theta
for (i in 1:ncol(mut)){
- pr[,i] <- rpois(length(mut[,i]), lambda = mut[,i])
+ pr[,i] <- rpois(n, lambda = mut[,i])
}
+ } else {
+ stop(sprintf("no method for %s family", family))
}
}
else if (class(object) == "lmer"){
@@ -91,7 +108,11 @@ qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
for (i in 1:length(rTerms)){
mu <- mu + gammas[[names(rTerms[i])]] %*% t(as.matrix(rTerms[[i]]))
}
- pr <- mu
+ n <- length(mu[,1])
+ pr <- matrix(NA, nrow=nrow(mu), ncol=ncol(mu))
+ for (i in 1:ncol(mu)){
+ pr[,i] <- rnorm(n, mean=mu[,i], sd=alpha)
+ }
}
qi <- list(ev=ev, pr = pr)
@@ -120,10 +141,11 @@ qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
if (!is.null(y)) {
yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
tmp.ev <- yvar - qi$ev
- if (family == "binomial")
- tmp.pr <- yvar - as.integer(qi$pr)
- else
- tmp.pr <- yvar - qi$pr
+ if (family == "binomial"){
+ tmp.pr <- yvar - as.integer(qi$pr)
+ } else {
+ tmp.pr <- yvar - qi$pr
+ }
qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
qi$att.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(simpar))
qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
@@ -133,5 +155,3 @@ qi.lmer <- qi.glmer <- function(object, simpar, x, x1 = NULL, y = NULL) {
}
-
-
diff --git a/R/zelig2sur.R b/R/zelig2sur.R
index 61431dd..811a2a2 100644
--- a/R/zelig2sur.R
+++ b/R/zelig2sur.R
@@ -5,6 +5,7 @@ zelig2sur <- function(formula, model, data, M,...) {
tt<-terms(formula)
mf$method<-"SUR"
mf$model<- mf$M<-NULL
- mf$formula<-formula
+ #mf$formula<-formula
+ mf$formula<-formula[names(attr(tt,"depVars"))]
as.call(mf)
}
diff --git a/R/zelig2threesls.R b/R/zelig2threesls.R
index dbf39f2..c9e3290 100644
--- a/R/zelig2threesls.R
+++ b/R/zelig2threesls.R
@@ -15,6 +15,6 @@ zelig2threesls <- function(formula, model, data, M,...) {
mf$inst<-inst
mf$model<- mf$M<-NULL
mf$formula<-formula[names(attr(tt,"depVars"))]
- class(mf$formula)<-c("multiple","list")
+ #class(mf$formula)<-c("multiple","list")
as.call(mf)
}
diff --git a/R/zelig2twosls.R b/R/zelig2twosls.R
index 4abd046..5595ec5 100644
--- a/R/zelig2twosls.R
+++ b/R/zelig2twosls.R
@@ -18,7 +18,6 @@ zelig2twosls <- function(formula, model, data, M,...) {
mf$inst<-inst
mf$model<- mf$M<-NULL
mf$formula<-formula[names(attr(tt,"depVars"))]
- class(mf$formula)<-c("multiple","list")
-
+
as.call(mf)
}
diff --git a/inst/doc/Rplots.ps b/inst/doc/Rplots.ps
index 43a2d65..1d591e8 100644
--- a/inst/doc/Rplots.ps
+++ b/inst/doc/Rplots.ps
@@ -88,510 +88,509 @@ bp
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diff --git a/inst/doc/Zcommands.tex b/inst/doc/Zcommands.tex
index ef4fd70..ff2e5c2 100644
--- a/inst/doc/Zcommands.tex
+++ b/inst/doc/Zcommands.tex
@@ -6,7 +6,6 @@
For any statistical model, Zelig does its work with a combination of
three commands.
-
\begin{figure}[h!]
\caption{Main Zelig commands (solid arrows) and some options (dashed arrows)}
\label{3steps}
diff --git a/inst/doc/aov.pdf b/inst/doc/aov.pdf
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diff --git a/inst/doc/commands/model.end.aux b/inst/doc/commands/model.end.aux
index cfdd695..7c234a8 100644
--- a/inst/doc/commands/model.end.aux
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\relax
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diff --git a/inst/doc/commands/model.frame.multiple.aux b/inst/doc/commands/model.frame.multiple.aux
index 10d90dc..12b850a 100644
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index 8d40dd9..8ff3eae 100644
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index 820e09a..47b683a 100644
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diff --git a/inst/doc/commandsRd/MatchIt.url.pdf b/inst/doc/commandsRd/MatchIt.url.pdf
index 5df2404..49aedad 100644
Binary files a/inst/doc/commandsRd/MatchIt.url.pdf and b/inst/doc/commandsRd/MatchIt.url.pdf differ
diff --git a/inst/doc/commandsRd/PErisk.pdf b/inst/doc/commandsRd/PErisk.pdf
index 976c3e4..652a998 100644
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diff --git a/inst/doc/commandsRd/SupremeCourt.pdf b/inst/doc/commandsRd/SupremeCourt.pdf
index 6069ccb..7e2ffd5 100644
Binary files a/inst/doc/commandsRd/SupremeCourt.pdf and b/inst/doc/commandsRd/SupremeCourt.pdf differ
diff --git a/inst/doc/commandsRd/Weimar.pdf b/inst/doc/commandsRd/Weimar.pdf
index 8961bdb..af21653 100644
Binary files a/inst/doc/commandsRd/Weimar.pdf and b/inst/doc/commandsRd/Weimar.pdf differ
diff --git a/inst/doc/commandsRd/Zelig-package.pdf b/inst/doc/commandsRd/Zelig-package.pdf
index 40d968b..0faf173 100644
Binary files a/inst/doc/commandsRd/Zelig-package.pdf and b/inst/doc/commandsRd/Zelig-package.pdf differ
diff --git a/inst/doc/commandsRd/Zelig-package.tex b/inst/doc/commandsRd/Zelig-package.tex
index 6f046b7..6a81241 100644
--- a/inst/doc/commandsRd/Zelig-package.tex
+++ b/inst/doc/commandsRd/Zelig-package.tex
@@ -1,4 +1,4 @@
-\include{zinput} \begin{document}\section{{\tt Zelig-package}: Everyone's Statistical Software}\label{ss:Zelig.Rdash.package}
+\include{zinput} \begin{document}\section{{\tt Zelig-package}: Zelig: Everyone's Statistical Software}\label{ss:Zelig.Rdash.package}
\aliasA{Zelig}{Zelig-package}{Zelig}
\keyword{package}{Zelig-package}
\begin{Description}\relax
@@ -19,49 +19,31 @@ quantities of direct interest.
\begin{Details}\relax
\Tabular{ll}{
Package: & Zelig\\
-Version: & 3.1-0\\
+Version: & 2.8-5\\
Date: & 2007-06-12\\
-Depends: & R (>= 2.5.0), MASS, boot\\
+Depends: & R (>= 2.4.0), MASS, boot\\
Suggests: & VGAM (>= 0.7-1), MCMCpack (>= 0.7-4), mvtnorm, survival,
sandwich (>= 2.0-0), zoo (>= 1.2-1), coda, nnet, sna, gee,
-systemfit, mgcv, lme4, anchors (>= 2.0)\\
+systemfit, mgcv\\
License: & GPL version 2 or newer\\
URL: & http://gking.harvard.edu/zelig\\
-Packaged: & Wed May 30 16:14:15 2007; falimadh\\
-Built: & R 2.6.0; ; 2007-11-30 13:37:37; unix\\
}
Index:
\begin{alltt}
approval U.S. Presidential Approval Data
-bivariate Sample data for bivariate probit regression
coalition Coalition Dissolution in Parliamentary
Democracies
-coalition2 Coalition Dissolution in Parliamentary
- Democracies, Modified Version
current.packages Find all packages in a dependency chain
dims Return Dimensions of Vectors, Arrays, and Data
Frames
eidat Simulation Data for Ecological Inference
-free1 Freedom of Speech Data
-free2 Freedom of Speech Data
friendship Simulated Example of Schoolchildren Friendship
Network
-grunfeld Simulation Data for model Seemingly Unrelated
- Regression (sur) that corresponds to method SUR
- of systemfit
gsource Read Data As a Space-Delimited Table
help.zelig HTML Help for Zelig Commands and Models
hoff Social Security Expenditure Data
-homerun Sample Data on Home Runs Hit By Mark McGwire
- and Sammy Sosa in 1998.
immigration Individual Preferences Over Immigration Policy
-klein Simulation Data for model Two-Stage Least
- Square (twosls) that corresponds to method 2SLS
- of systemfit
-kmenta Simulation Data for model Three-Stage Least
- Square (threesls) that corresponds to method
- 3SLS of systemfit
macro Macroeconomic Data
match.data Output matched data sets
MatchIt.url Table of links for Zelig
@@ -101,79 +83,67 @@ tobin Tobin's Tobit Data
turnout Turnout Data Set from the National Election
Survey
user.prompt Pause in demo files
-voteincome Sample Turnout and Demographic Data from the
- 2000 Current Population Survey
Weimar 1932 Weimar election data
zelig Estimating a Statistical Model
zeligDepStatus Zelig Dependencies Packages Client Status
zeligDepUpdate Download Zelig Dependencies Packages
zeligDescribeModelXML Zelig interface functions
-Zelig-package Zelig: Everyone's Statistical Software
+Zelig-package Everyone's Statistical Software
Zelig.url Table of links for Zelig
+zideal Zelig Matrix of Dependencies
\end{alltt}
Further information is available in the following vignettes:
+
\Tabular{ll}{
-\code{aov} & Fit an Analysis of Variance Model (source)\\
\code{arima} & ARIMA Models for Time Series Data (source)\\
\code{blogit} & Bivariate Logistic Regression for Two Dichotomous Dependent Variables (source)\\
\code{bprobit} & Bivariate Probit Regression for Dichotomous Dependent Variables (source)\\
-\code{chopit} & Compound Hierarchical Ordered Probit for Survey Vignettes (source)\\
-\code{cloglog.net} & Social Network Complementary Log Log Regression for Dichotomous Dependent Variables (source)\\
-\code{coxph} & Cox Proportional Hazards Regression for Duration Dependent Variables (source)\\
\code{ei.RxC} & Hierarchical Multinomial-Dirichlet Ecological Inference Model (source)\\
\code{ei.dynamic} & Quinn's Dynamic Ecological Inference (source)\\
-\code{ei.hier} & Hierarchical Ecological Inference Model for 2x2 tables (source)\\
+\code{ei.hier} & Hierarchical Ecological Inference Model (source)\\
\code{exp} & Exponential Regression for Duration Dependent Variables (source)\\
\code{factor.bayes} & Bayesian Factor Analysis (source)\\
\code{factor.mix} & Mixed Data Factor Analysis (source)\\
\code{factor.ord} & Ordinal Data Factor Analysis (source)\\
+\code{gam.logit} & gam.logit: Generalized Additive Model for Dichotomous Dependent Variables (source)\\
+\code{gam.normal} & Generalized Additive Model for Continuous Dependent Variables (source)\\
+\code{gam.poisson} & Generalized Additive Model for Count Dependent Variables (source)\\
+\code{gam.probit} & Generalized Additive Model for Dichotomous Dependent Variables (source)\\
\code{gamma} & Gamma Regression for Continuous, Positive Dependent Variables (source)\\
-\code{gamma.gee} & Generalized Estimating Equation for Gamma Regression (source)\\
-\code{gamma.mixed} & Gamma mixed effects linear regression (source)\\
-\code{gamma.net} & Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables (source)\\
-\code{irt1d} & One Dimensional Item Response Mode (source)\\
+\code{irt1d} & One Dimensional Item Response Mode (source)\\
\code{irtkd} & K-Dimensional Item Response Model (source)\\
\code{logit} & Logistic Regression for Dichotomous Dependent Variables (source)\\
\code{logit.bayes} & Bayesian Logistic Regression for Dichotomous Dependent Variables (source)\\
-\code{logit.gam} & Generalized Additive Model for Dichotomous Dependent Variables (source)\\
\code{logit.gee} & Generalized Estimating Equation for Logistic Regression (source)\\
-\code{logit.mixed} & Mixed effects linear regression (source)\\
-\code{logit.net} & Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables (source)\\
\code{lognorm} & Log-Normal Regression for Duration Dependent Variables (source)\\
\code{ls} & Least Squares Regression for Continuous Dependent Variables (source)\\
-\code{ls.mixed} & Mixed effects linear regression (source)\\
-\code{ls.net} & Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables (source)\\
\code{mlogit} & Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values (source)\\
\code{mlogit.bayes} & Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values (source)\\
\code{mloglm} & Multinomial Log-Linear Regression for Contingency Table Models (source)\\
\code{negbin} & Negative Binomial Regression for Event Count Dependent Variables (source)\\
+\code{netcloglog} & Least Squares Regression for Continuous Dependent Variables (source)\\
+\code{netgamma} & Least Squares Regression for Continuous Dependent Variables (source)\\
+\code{netlogit} & Least Squares Regression for Continuous Dependent Variables (source)\\
+\code{netls} & Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables (source)\\
+\code{netnormal} & Least Squares Regression for Continuous Dependent Variables (source)\\
+\code{netpoisson} & Least Squares Regression for Continuous Dependent Variables (source)\\
+\code{netprobit} & Least Squares Regression for Continuous Dependent Variables (source)\\
\code{normal} & Normal Regression for Continuous Dependent Variables (source)\\
\code{normal.bayes} & Bayesian Normal Linear Regression (source)\\
-\code{normal.gam} & Generalized Additive Model for Continuous Dependent Variables (source)\\
-\code{normal.gee} & Generalized Estimating Equation for Normal Regression (source)\\
-\code{normal.net} & Network Normal Regression for Continuous Proximity Matrix Dependent Variables (source)\\
\code{ologit} & Ordinal Logistic Regression for Ordered Categorical Dependent Variables (source)\\
\code{oprobit} & Ordinal Probit Regression for Ordered Categorical Dependent Variables (source)\\
\code{oprobit.bayes} & Bayesian Ordered Probit Regression (source)\\
\code{poisson} & Poisson Regression for Event Count Dependent Variables (source)\\
\code{poisson.bayes} & Bayesian Poisson Regression (source)\\
-\code{poisson.gam} & Generalized Additive Model for Count Dependent Variables (source)\\
-\code{poisson.gee} & Generalized Estimating Equation for Poisson Regression (source)\\
-\code{poisson.mixed} & Mixed effects poisson regression (source)\\
-\code{poisson.net} & Network Poisson Regression for Event Count Proximity Matrix Dependent Variables (source)\\
\code{probit} & Probit Regression for Dichotomous Dependent Variables (source)\\
\code{probit.bayes} & Bayesian Probit Regression for Dichotomous Dependent Variable (source)\\
-\code{probit.gam} & Generalized Additive Model for Dichotomous Dependent Variables (source)\\
-\code{probit.gee} & Generalized Estimating Equation for Probit Regression (source)\\
-\code{probit.mixed} & Mixed effects probit regression (source)\\
-\code{probit.net} & Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables (source)\\
\code{relogit} & Rare Events Logistic Regression for Dichotomous Dependent Variables (source)\\
\code{sur} & Seemingly Unrelated Regression (source)\\
-\code{threesls} & Three Stage Least Squares (source)\\
+\code{threesls} & Three Stage Least Squares (source)\\
\code{tobit} & Linear regression for Left-Censored Dependet Variable (source)\\
\code{tobit.bayes} & Bayesian Linear Regression for a Censored Dependent Variable (source)\\
-\code{twosls} & Two Stage Least Squares (source)\\
+\code{twosls} & Two Stage Least Squares (source)\\
\code{weibull} & Weibull Regression for Duration Dependent Variables (source)\\
}
\end{Details}
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index 3b11b98..f60fc66 100644
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diff --git a/inst/doc/gamma.mixed.Rnw b/inst/doc/gamma.mixed.Rnw
index cfcd599..67f647e 100644
--- a/inst/doc/gamma.mixed.Rnw
+++ b/inst/doc/gamma.mixed.Rnw
@@ -17,17 +17,17 @@ library(Zelig)
\section{{\tt gamma.mixed}: Mixed effects gamma regression}
\label{gamma.mixed}
-Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. Gamma regression models a continuous, positive dependent variable.
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. Gamma regression models a continuous, positive dependent variable.
While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
\subsubsection{Syntax}
\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
data=mydata, model="gamma.mixed")
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, delta | g),
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, delta | g),
delta= ~ tag(w1 + w2 | g)), data=mydata, model="gamma.mixed")
\end{verbatim}
@@ -45,10 +45,10 @@ In addition, {\tt zelig()} accepts the following additional arguments for model
\begin{itemize}
\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
+\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
\end{itemize}
-Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+Additionally, users may with to refer to {\tt lmer} in the package {\tt lme4} for more information, including control parameters for the estimation algorithm and their defaults.
\subsubsection{Examples}
@@ -58,29 +58,29 @@ Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix}
Attach sample data: \\
<<Examples.data>>=
data(coalition2)
-@
+@
-Estimate model using optional arguments to specify approximation method for the log-likelihood, and the log link function for the Gamma family:
-<<Examples.zelig>>=
+Estimate model using optional arguments to specify approximation method for the log-likelihood, and the log link function for the Gamma family:
+<<Examples.zelig>>=
z.out1 <- zelig(duration ~ invest + fract + polar + numst2 + crisis + tag(1 | country), data=coalition2, model="gamma.mixed", method="PQL",family=Gamma(link=log))
-@
+@
\noindent Summarize regression coefficients and estimated variance of random effects:\\
<<Examples.summary>>=
summary(z.out1)
-@
+@
Set the baseline values (with the ruling coalition in the minority) and the alternative values (with the ruling coalition in the majority) for X:\\
<<Examples.setx>>=
x.high <- setx(z.out1, numst2 = 1)
x.low <- setx(z.out1, numst2 = 0)
@
-
+
Simulate expected values ({\tt qi\$ev}) and first differences({\tt qi\$fd}): \\
<<Examples.sim>>=
s.out1 <- sim(z.out1, x=x.high, x1=x.low)
summary(s.out1)
-@
+@
\end{enumerate}
@@ -102,7 +102,7 @@ for $\alpha, \; \lambda_{ij}, \; y_{ij} \; > 0$.
\begin{equation*}
b_i \sim Normal(0, \Psi)
\end{equation*}
-\item The \emph{systematic component} is
+\item The \emph{systematic component} is
\begin{equation*}
\lambda_{ij} \equiv \frac{1}{X_{ij} \beta + Z_{ij} b_i}
\end{equation*}
@@ -162,7 +162,7 @@ The output of each Zelig command contains useful information which you may view.
\end{itemize}
-\subsection* {How to Cite}
+\subsection* {How to Cite}
To cite the \emph{gamma.mixed} Zelig model:
\begin{verse}
@@ -183,6 +183,3 @@ Mixed effects gamma regression is part of {\tt lme4} package by Douglas M. Bates
detach(pos=match(pkg,search()))
@
\end{document}
-
-
-
diff --git a/inst/doc/gamma.mixed.pdf b/inst/doc/gamma.mixed.pdf
index 27c29ba..fe08d14 100644
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diff --git a/inst/doc/gamma.net.pdf b/inst/doc/gamma.net.pdf
index 67b73c5..8f87b01 100644
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diff --git a/inst/doc/gamma.pdf b/inst/doc/gamma.pdf
index 9db310b..01f1ebb 100644
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diff --git a/inst/doc/index.shtml b/inst/doc/index.shtml
index c7c832a..f2390ef 100644
--- a/inst/doc/index.shtml
+++ b/inst/doc/index.shtml
@@ -12,7 +12,7 @@
<a href="http://www.people.fas.harvard.edu/~olau/">Olivia Lau</a>
<br /><br />
<!-- rbuild: replace 'Version:' '</b>' version -->
-<b>Version:3.1-0</b>
+<b>Version:3.1-1</b>
<table border='0' cellpadding='0' cellspacing='8'><tr><td valign='top' width='200'>
<p !style="float:left; width: 200; margin: 1.5em;"> <a
@@ -58,14 +58,15 @@ Bayesian, robust Bayesian, and nonparametric theories of
inference.</p>
<ul>
-<li>Documentation: <a href="/zelig/docs/index.html">Read on-line</a> or <a
-href="/zelig/docs/zelig.pdf">Download PDF</a></li>
+ <li><a href="/zelig/docs/index.html">Documentation</a> and <a
+href="/zelig/docs/static/install.pdf">Installation</a></li>
+
<li><a href="/zelig/docs/static/install.pdf">Installation</a>, <a
href="/zelig/docs/static/faq.pdf">Frequently Asked
Questions</a></li>
-<li>All questions, bugs and requests: Zelig Mailing List, <a
+<li>Please send <strong>All</strong> questions, bugs and requests: Zelig Mailing List, <a
href="http://lists.hmdc.harvard.edu/index.cgi?info=zelig"
target="gkingext">[Un]Subscribe</a>, or <a
href="http://lists.hmdc.harvard.edu/lists/zelig/"
diff --git a/inst/doc/irt1d.Rnw b/inst/doc/irt1d.Rnw
index 911a9b7..4f8f3fd 100644
--- a/inst/doc/irt1d.Rnw
+++ b/inst/doc/irt1d.Rnw
@@ -266,8 +266,11 @@ Ben Goodrich and Ying Lu. 2007. ``irt1d: One Dimensional Item Response Mode,'' i
\end{verse}
\input{citeZelig}
\subsection*{See also}
-The unidimensional item-response function is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}.
-The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}. Sample date are adapted from \cite{MarQui05}.
+The unidimensional item-response function is part of the MCMCpack
+library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The
+convergence diagnostics are part of the CODA library by Martyn
+Plummer, Nicky Best, Kate Cowles, and Karen Vines
+\citep{PluBesCowVin05}. Sample data are adapted from \cite{MarQui05}.
\bibliographystyle{asa}
\bibliography{gk,gkpubs}
diff --git a/inst/doc/irt1d.pdf b/inst/doc/irt1d.pdf
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diff --git a/inst/doc/irtkd.Rnw b/inst/doc/irtkd.Rnw
index c5ce308..e67e983 100644
--- a/inst/doc/irtkd.Rnw
+++ b/inst/doc/irtkd.Rnw
@@ -269,8 +269,11 @@ Ben Goodrich and Ying Lu. 2007. ``irtkd: K-Dimensional Item Response Model,'' in
\end{verse}
\input{citeZelig}
\subsection*{See also}
-The $k$ dimensional item-response function is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}.
-The convergence diagnostics are part of the CODA library by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines \citep{PluBesCowVin05}. Sample date are adapted from \cite{MarQui05}.
+The $k$ dimensional item-response function is part of the MCMCpack
+library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}. The
+convergence diagnostics are part of the CODA library by Martyn
+Plummer, Nicky Best, Kate Cowles, and Karen Vines
+\citep{PluBesCowVin05}. Sample data are adapted from \cite{MarQui05}.
\bibliographystyle{asa}
diff --git a/inst/doc/irtkd.pdf b/inst/doc/irtkd.pdf
index 5c1c6ea..a9665a8 100644
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index a88d20f..14a60c8 100644
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diff --git a/inst/doc/logit.gam.pdf b/inst/doc/logit.gam.pdf
index 8dfffa7..275b9fd 100644
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diff --git a/inst/doc/logit.gee.pdf b/inst/doc/logit.gee.pdf
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diff --git a/inst/doc/logit.mixed.Rnw b/inst/doc/logit.mixed.Rnw
index 75b73ef..40f0914 100644
--- a/inst/doc/logit.mixed.Rnw
+++ b/inst/doc/logit.mixed.Rnw
@@ -1,9 +1,9 @@
\SweaveOpts{results=hide, prefix.string=vigpics/logitmixed}
\include{zinput}
-%\VignetteIndexEntry{Mixed effects linear regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{mixed,logit regression}
-%\VignettePackage{Zelig}
+%\VignetteIndexEntry{Mixed effects logistic regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed, logistic regression}
+%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
<<beforepkgs, echo=FALSE>>=
@@ -17,17 +17,17 @@ library(Zelig)
\section{{\tt logit.mixed}: Mixed effects logistic Regression}
\label{mixed}
-Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The logit model is appropriate when the dependent variable is dichotomous.
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The logit model is appropriate when the dependent variable is dichotomous.
While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
\subsubsection{Syntax}
\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
data=mydata, model="logit.mixed")
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
gamma= ~ tag(w1 + w2 | g)), data=mydata, model="logit.mixed")
\end{verbatim}
@@ -45,10 +45,10 @@ In addition, {\tt zelig()} accepts the following additional arguments for model
\begin{itemize}
\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
+\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
\end{itemize}
-Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+Additionally, users may with to refer to {\tt lmer} in the package {\tt lme4} for more information, including control parameters for the estimation algorithm and their defaults.
\subsubsection{Examples}
@@ -58,28 +58,28 @@ Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix}
Attach sample data: \\
<<Examples.data>>=
data(voteincome)
-@
-Estimate model:
+@
+Estimate model:
<<Examples.zelig>>=
z.out1 <- zelig(vote ~ education + age + female + tag(1 | state), data=voteincome, model="logit.mixed")
-@
+@
\noindent Summarize regression coefficients and estimated variance of random effects:\\
<<Examples.summary>>=
summary(z.out1)
-@
+@
Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
<<Examples.setx>>=
x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
-@
+@
Generate first differences for the effect of high versus low education on voting: \\
<<Examples.sim>>=
s.out1 <- sim(z.out1, x=x.high, x1=x.low)
summary(s.out1)
-@
+@
\end{enumerate}
@@ -101,7 +101,7 @@ where
\begin{equation*}
b_i \sim Normal(0, \Psi)
\end{equation*}
-\item The \emph{systematic component} is
+\item The \emph{systematic component} is
\begin{equation*}
\pi_{ij} \equiv \frac{1}{1 + \exp(-(X_{ij} \beta + Z_{ij} b_i))}
\end{equation*}
@@ -167,7 +167,7 @@ The output of each Zelig command contains useful information which you may view.
\end{itemize}
-\subsection* {How to Cite}
+\subsection* {How to Cite}
To cite the \emph{logit.mixed} Zelig model:
\begin{verse}
@@ -188,4 +188,3 @@ Mixed effects logistic regression is part of {\tt lme4} package by Douglas M. Ba
detach(pos=match(pkg,search()))
@
\end{document}
-
diff --git a/inst/doc/logit.mixed.pdf b/inst/doc/logit.mixed.pdf
index 8f8bb30..f087ce5 100644
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diff --git a/inst/doc/lognorm.pdf b/inst/doc/lognorm.pdf
index 963910d..c5a1684 100644
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diff --git a/inst/doc/ls.mixed.Rnw b/inst/doc/ls.mixed.Rnw
index 0158ae1..9819f5f 100644
--- a/inst/doc/ls.mixed.Rnw
+++ b/inst/doc/ls.mixed.Rnw
@@ -1,5 +1,6 @@
\SweaveOpts{results=hide, prefix.string=vigpics/lmmixed}
\include{zinput}
+% \usepackage{Sweave}
%\VignetteIndexEntry{Mixed effects linear regression}
%\VignetteDepends{Zelig}
%\VignetteKeyWords{mixed,linear, linear regression}
@@ -24,10 +25,10 @@ While generally called multi-level models in the social sciences, this class of
\subsubsection{Syntax}
\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
data=mydata, model="lm.multi")
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
gamma= ~ tag(w1 + w2 | g)), data=mydata, model="lm.multi")
\end{verbatim}
@@ -45,10 +46,11 @@ In addition, {\tt zelig()} accepts the following additional arguments for model
\begin{itemize}
\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt method:} a character string. For a linear mixed model the default is {\tt "REML"} indicating that the model should be fit by maximizing the restricted log-likelihood. The alternative is {\tt "ML"} indicating that the log-likelihood should be maximized.
+\item {\tt family:} A GLM family, see {\tt glm} and {\tt family} in the {\tt stats} package. If {\tt family} is missing then a linear mixed model is fit; otherwise a generalized linear mixed model is fit. In the later case only {\tt gaussian} family with {\tt "log"} link is supported at the moment.
+\item {\tt method:} A character string. For a linear mixed model the default is {\tt "REML"} indicating that the model should be fit by maximizing the restricted log-likelihood. The alternative is {\tt "ML"} indicating that the log-likelihood should be maximized.
\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
\end{itemize}
-Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+Additionally, users may wish to refer to {\tt lmer} in the package {\tt lme4} for more information, including control parameters for the estimation algorithm and their defaults.
\subsubsection{Examples}
@@ -59,8 +61,8 @@ Attach sample data: \\
<<Examples.data>>=
data(voteincome)
@
-
-Estimate model:
+
+Estimate model:
<<Examples.zelig>>=
z.out1 <- zelig(income ~ education + age + female + tag(1 | state), data=voteincome, model="ls.mixed")
@@ -69,18 +71,18 @@ z.out1 <- zelig(income ~ education + age + female + tag(1 | state), data=voteinc
\noindent Summarize regression coefficients and estimated variance of random effects:\\
<<Examples.summary>>=
summary(z.out1)
-@
+@
Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
<<Examples.setx>>=
x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
-@
+@
Generate first differences for the effect of high versus low education on income: \\
<<Examples.sim>>=
s.out1 <- sim(z.out1, x=x.high, x1=x.low)
summary(s.out1)
-@
+@
\begin{center}
<<label=ExamplesPlot, fig=true, echo=true>>=
plot(s.out1)
@@ -102,7 +104,7 @@ Y_{ij} \sim \mathrm{Normal}(y_{ij} | \mu_{ij}, \sigma^2)
\begin{equation*}
b_i \sim Normal(0, \Psi)
\end{equation*}
-\item The \emph{systematic component} is
+\item The \emph{systematic component} is
\begin{equation*}
\mu_{ij} \equiv X_{ij} \beta + Z_{ij} b_i
\end{equation*}
@@ -112,11 +114,12 @@ where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects exp
\subsubsection{Quantities of Interest}
\begin{itemize}
-\item The predicted values ({\tt qi\$pr}) are calculated as
+
+\item The predicted values ({\tt qi\$pr}) are draws from the normal distribution defined by mean $\mu_{ij}$ and variance $\sigma^2$,
\begin{equation*}
-Y_{ij} = X_{ij} \beta + Z_{ij} b_i
+\mu_{ij} = X_{ij} \beta + Z_{ij} b_{i}
\end{equation*}
-given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+given $X_{ij}$ and $Z_{ij}$ and simulations of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
\item The expected values ({\tt qi\$ev}) are averaged over the stochastic components and are given by
\begin{equation*}
@@ -140,6 +143,8 @@ where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij}
\end{equation*}
where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+\item If {\tt "log"} link is used, expected values are computed as above and then exponentiated, while predicted values are draws from the log-normal distribution whose logarithm has mean and variance equal to $\mu_{ij}$ and $\sigma^2$, respectively.
+
\end{itemize}
\subsubsection{Output Values}
@@ -165,7 +170,7 @@ The output of each Zelig command contains useful information which you may view.
-\subsection* {How to Cite}
+\subsection* {How to Cite}
To cite the \emph{ls.mixed} Zelig model:
\begin{verse}
@@ -186,5 +191,3 @@ Mixed effects linear regression is part of {\tt lme4} package by Douglas M. Bate
detach(pos=match(pkg,search()))
@
\end{document}
-
-
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diff --git a/inst/doc/poisson.mixed.Rnw b/inst/doc/poisson.mixed.Rnw
index 03277c2..9c01da4 100644
--- a/inst/doc/poisson.mixed.Rnw
+++ b/inst/doc/poisson.mixed.Rnw
@@ -1,9 +1,9 @@
\SweaveOpts{results=hide, prefix.string=vigpics/poissonmixed}
\include{zinput}
-%\VignetteIndexEntry{Mixed effects poisson regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{mixed,poisson regression}
-%\VignettePackage{Zelig}
+%\VignetteIndexEntry{Mixed effects poisson regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,poisson regression}
+%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
<<beforepkgs, echo=FALSE>>=
@@ -24,10 +24,10 @@ While generally called multi-level models in the social sciences, this class of
\subsubsection{Syntax}
\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
data=mydata, model="poisson.mixed")
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
gamma= ~ tag(w1 + w2 | g)), data=mydata, model="poisson.mixed")
\end{verbatim}
@@ -45,10 +45,10 @@ In addition, {\tt zelig()} accepts the following additional arguments for model
\begin{itemize}
\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
+\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
\end{itemize}
-Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+Additionally, users may with to refer to {\tt lmer} in the package {\tt lme4} for more information, including control parameters for the estimation algorithm and their defaults.
\subsubsection{Examples}
@@ -58,25 +58,25 @@ Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix}
Attach sample data: \\
<<Examples.data>>=
data(homerun)
-@
-Estimate model:
+@
+Estimate model:
<<Examples.zelig>>=
z.out1 <- zelig(homeruns ~ player + tag(player - 1 | month), data=homerun, model="poisson.mixed")
-@
+@
\noindent Summarize regression coefficients and estimated variance of random effects:\\
<<Examples.summary>>=
summary(z.out1)
-@
+@
Set explanatory variables to their default values:\\
<<Examples.setx>>=
x.out <- setx(z.out1)
-@
+@
Simulate draws using the default bootstrap method and view simulated quantities of interest: \\
<<Examples.sim>>=
s.out1 <- sim(z.out1, x=x.out)
summary(s.out1)
-@
+@
\end{enumerate}
@@ -98,7 +98,7 @@ y_{ij} = 0, 1, \ldots
\begin{equation*}
b_i \sim Normal(0, \Psi)
\end{equation*}
-\item The \emph{systematic component} is
+\item The \emph{systematic component} is
\begin{equation*}
\lambda_{ij} \equiv \exp(X_{ij} \beta + Z_{ij} b_i)
\end{equation*}
@@ -160,7 +160,7 @@ The output of each Zelig command contains useful information which you may view.
-\subsection* {How to Cite}
+\subsection* {How to Cite}
To cite the \emph{poisson.mixed} Zelig model:
\begin{verse}
@@ -181,6 +181,3 @@ Mixed effects poisson regression is part of {\tt lme4} package by Douglas M. Bat
detach(pos=match(pkg,search()))
@
\end{document}
-
-
-
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index 09e1f8b..42d7790 100644
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diff --git a/inst/doc/probit.mixed.Rnw b/inst/doc/probit.mixed.Rnw
index f7be4c3..10e082d 100644
--- a/inst/doc/probit.mixed.Rnw
+++ b/inst/doc/probit.mixed.Rnw
@@ -1,9 +1,9 @@
\SweaveOpts{results=hide, prefix.string=vigpics/probitmixed}
\include{zinput}
-%\VignetteIndexEntry{Mixed effects probit regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{mixed,probit regression}
-%\VignettePackage{Zelig}
+%\VignetteIndexEntry{Mixed effects probit regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,probit regression}
+%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
<<beforepkgs, echo=FALSE>>=
@@ -24,10 +24,10 @@ While generally called multi-level models in the social sciences, this class of
\subsubsection{Syntax}
\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
data=mydata, model="probit.mixed")
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
gamma= ~ tag(w1 + w2 | g)), data=mydata, model="probit.mixed")
\end{verbatim}
@@ -45,10 +45,10 @@ In addition, {\tt zelig()} accepts the following additional arguments for model
\begin{itemize}
\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
+\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
\end{itemize}
-Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix} for more information, including control parameters for the estimation algorithm and their defaults.
+Additionally, users may with to refer to {\tt lmer} in the package {\tt lme4} for more information, including control parameters for the estimation algorithm and their defaults.
\subsubsection{Examples}
@@ -58,24 +58,24 @@ Additionally, users may with to refer to {\tt lmer} in the package {\tt Matrix}
Attach sample data: \\
<<Examples.data>>=
data(voteincome)
-@
-Estimate model:
-<<Examples.zelig>>=
+@
+Estimate model:
+<<Examples.zelig>>=
z.out1 <- zelig(vote ~ education + age + female + tag(1 | state), data=voteincome, model="probit.mixed")
-@
+@
\noindent Summarize regression coefficients and estimated variance of random effects:\\
<<Examples.summary>>=
summary(z.out1)
-@
+@
Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
<<Examples.setx>>=
x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
-@
+@
Generate first differences for the effect of high versus low education on voting: \\
<<Examples.sim>>=
-s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
summary(s.out1)
@
@@ -98,7 +98,7 @@ where
\begin{equation*}
b_i \sim Normal(0, \Psi)
\end{equation*}
-\item The \emph{systematic component} is
+\item The \emph{systematic component} is
\begin{equation*}
\pi_{ij} \equiv \Phi(X_{ij} \beta + Z_{ij} b_i)
\end{equation*}
@@ -167,7 +167,7 @@ The output of each Zelig command contains useful information which you may view.
-\subsection* {How to Cite}
+\subsection* {How to Cite}
To cite the \emph{probit.mixed} Zelig model:
\begin{verse}
@@ -188,5 +188,3 @@ Mixed effects probit regression is part of {\tt lme4} package by Douglas M. Bate
detach(pos=match(pkg,search()))
@
\end{document}
-
-
diff --git a/inst/doc/probit.mixed.pdf b/inst/doc/probit.mixed.pdf
index 78aeffb..6848ae4 100644
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diff --git a/inst/doc/probit.net.pdf b/inst/doc/probit.net.pdf
index 351f485..b342582 100644
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diff --git a/inst/doc/probit.pdf b/inst/doc/probit.pdf
index 01f7d90..ff3c90c 100644
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diff --git a/inst/doc/refman.tex b/inst/doc/refman.tex
index 34c1a14..eb3f8f5 100644
--- a/inst/doc/refman.tex
+++ b/inst/doc/refman.tex
@@ -103,6 +103,8 @@ of simulations increases.
\include{logit.bayes}
\include{logit.gam}
\include{logit.gee}
+\include{logit.gee}
+\include{logit.mixed}
\include{logit.mixed}
\include{logit.net}
\include{lognorm}
diff --git a/inst/doc/relogit.pdf b/inst/doc/relogit.pdf
index 17a74d9..0ae6137 100644
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diff --git a/inst/doc/static/Zcommands.pdf b/inst/doc/static/Zcommands.pdf
index fdb18cf..09d4b65 100644
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diff --git a/inst/doc/static/acknowledgments.pdf b/inst/doc/static/acknowledgments.pdf
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diff --git a/inst/doc/static/addingModels.pdf b/inst/doc/static/addingModels.pdf
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diff --git a/inst/doc/static/advancedR.pdf b/inst/doc/static/advancedR.pdf
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diff --git a/inst/doc/static/faq.pdf b/inst/doc/static/faq.pdf
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diff --git a/inst/doc/static/install.pdf b/inst/doc/static/install.pdf
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diff --git a/inst/doc/static/intro.pdf b/inst/doc/static/intro.pdf
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diff --git a/inst/doc/static/newModels.pdf b/inst/doc/static/newModels.pdf
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diff --git a/inst/doc/static/syntax.pdf b/inst/doc/static/syntax.pdf
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diff --git a/inst/doc/tobit.bayes.pdf b/inst/doc/tobit.bayes.pdf
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diff --git a/inst/doc/vigpics/aov-ExamplesPlot1.eps b/inst/doc/vigpics/aov-ExamplesPlot1.eps
index f0f13cc..4a233d0 100644
--- a/inst/doc/vigpics/aov-ExamplesPlot1.eps
+++ b/inst/doc/vigpics/aov-ExamplesPlot1.eps
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diff --git a/inst/doc/vigpics/gamma-ExamplePlot.eps b/inst/doc/vigpics/gamma-ExamplePlot.eps
index 91a592b..f582042 100644
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trailer
<<
-/Size 5575
-/Root 5573 0 R
-/Info 5574 0 R
+/Size 5678
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+/Info 5677 0 R
>>
startxref
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%%EOF
diff --git a/inst/doc/zelig.tex b/inst/doc/zelig.tex
index 7ce8716..6e89f5a 100644
--- a/inst/doc/zelig.tex
+++ b/inst/doc/zelig.tex
@@ -21,7 +21,7 @@ Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
\texttt{OLau at Fas.Harvard.Edu}).}}
% rbuild: replace 'Version ' '\\' Version
-\date{Version 3.1-0\\ \today}
+\date{Version 3.1-1\\ \today}
\begin{document}
\maketitle
@@ -75,9 +75,16 @@ few days.
\begin{itemize}
+\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
+ 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 \\
diff --git a/inst/zideal/zideal.RData b/inst/zideal/zideal.RData
deleted file mode 100644
index 4b4ecc6..0000000
Binary files a/inst/zideal/zideal.RData and /dev/null differ
diff --git a/man/Zelig-package.Rd b/man/Zelig-package.Rd
index aa1a5c4..67b6382 100644
--- a/man/Zelig-package.Rd
+++ b/man/Zelig-package.Rd
@@ -3,7 +3,7 @@
\alias{Zelig}
\docType{package}
\title{
-Everyone's Statistical Software
+Zelig: Everyone's Statistical Software
}
\description{
Zelig is an easy-to-use program that can estimate, and
@@ -23,49 +23,31 @@ quantities of direct interest.
\details{
\tabular{ll}{
Package: \tab Zelig\cr
-Version: \tab 3.1-0\cr
+Version: \tab 2.8-5\cr
Date: \tab 2007-06-12\cr
-Depends: \tab R (>= 2.5.0), MASS, boot\cr
+Depends: \tab R (>= 2.4.0), MASS, boot\cr
Suggests: \tab VGAM (>= 0.7-1), MCMCpack (>= 0.7-4), mvtnorm, survival,
sandwich (>= 2.0-0), zoo (>= 1.2-1), coda, nnet, sna, gee,
-systemfit, mgcv, lme4, anchors (>= 2.0)\cr
+systemfit, mgcv\cr
License: \tab GPL version 2 or newer\cr
URL: \tab http://gking.harvard.edu/zelig\cr
-Packaged: \tab Wed May 30 16:14:15 2007; falimadh\cr
-Built: \tab R 2.6.0; ; 2007-11-30 13:37:37; unix\cr
}
Index:
\preformatted{
approval U.S. Presidential Approval Data
-bivariate Sample data for bivariate probit regression
coalition Coalition Dissolution in Parliamentary
Democracies
-coalition2 Coalition Dissolution in Parliamentary
- Democracies, Modified Version
current.packages Find all packages in a dependency chain
dims Return Dimensions of Vectors, Arrays, and Data
Frames
eidat Simulation Data for Ecological Inference
-free1 Freedom of Speech Data
-free2 Freedom of Speech Data
friendship Simulated Example of Schoolchildren Friendship
Network
-grunfeld Simulation Data for model Seemingly Unrelated
- Regression (sur) that corresponds to method SUR
- of systemfit
gsource Read Data As a Space-Delimited Table
help.zelig HTML Help for Zelig Commands and Models
hoff Social Security Expenditure Data
-homerun Sample Data on Home Runs Hit By Mark McGwire
- and Sammy Sosa in 1998.
immigration Individual Preferences Over Immigration Policy
-klein Simulation Data for model Two-Stage Least
- Square (twosls) that corresponds to method 2SLS
- of systemfit
-kmenta Simulation Data for model Three-Stage Least
- Square (threesls) that corresponds to method
- 3SLS of systemfit
macro Macroeconomic Data
match.data Output matched data sets
MatchIt.url Table of links for Zelig
@@ -105,79 +87,67 @@ tobin Tobin's Tobit Data
turnout Turnout Data Set from the National Election
Survey
user.prompt Pause in demo files
-voteincome Sample Turnout and Demographic Data from the
- 2000 Current Population Survey
Weimar 1932 Weimar election data
zelig Estimating a Statistical Model
zeligDepStatus Zelig Dependencies Packages Client Status
zeligDepUpdate Download Zelig Dependencies Packages
zeligDescribeModelXML Zelig interface functions
-Zelig-package Zelig: Everyone's Statistical Software
+Zelig-package Everyone's Statistical Software
Zelig.url Table of links for Zelig
+zideal Zelig Matrix of Dependencies
}
Further information is available in the following vignettes:
+
\tabular{ll}{
-\code{aov} \tab Fit an Analysis of Variance Model (source)\cr
\code{arima} \tab ARIMA Models for Time Series Data (source)\cr
\code{blogit} \tab Bivariate Logistic Regression for Two Dichotomous Dependent Variables (source)\cr
\code{bprobit} \tab Bivariate Probit Regression for Dichotomous Dependent Variables (source)\cr
-\code{chopit} \tab Compound Hierarchical Ordered Probit for Survey Vignettes (source)\cr
-\code{cloglog.net} \tab Social Network Complementary Log Log Regression for Dichotomous Dependent Variables (source)\cr
-\code{coxph} \tab Cox Proportional Hazards Regression for Duration Dependent Variables (source)\cr
\code{ei.RxC} \tab Hierarchical Multinomial-Dirichlet Ecological Inference Model (source)\cr
\code{ei.dynamic} \tab Quinn's Dynamic Ecological Inference (source)\cr
-\code{ei.hier} \tab Hierarchical Ecological Inference Model for 2x2 tables (source)\cr
+\code{ei.hier} \tab Hierarchical Ecological Inference Model (source)\cr
\code{exp} \tab Exponential Regression for Duration Dependent Variables (source)\cr
\code{factor.bayes} \tab Bayesian Factor Analysis (source)\cr
\code{factor.mix} \tab Mixed Data Factor Analysis (source)\cr
\code{factor.ord} \tab Ordinal Data Factor Analysis (source)\cr
+\code{gam.logit} \tab gam.logit: Generalized Additive Model for Dichotomous Dependent Variables (source)\cr
+\code{gam.normal} \tab Generalized Additive Model for Continuous Dependent Variables (source)\cr
+\code{gam.poisson} \tab Generalized Additive Model for Count Dependent Variables (source)\cr
+\code{gam.probit} \tab Generalized Additive Model for Dichotomous Dependent Variables (source)\cr
\code{gamma} \tab Gamma Regression for Continuous, Positive Dependent Variables (source)\cr
-\code{gamma.gee} \tab Generalized Estimating Equation for Gamma Regression (source)\cr
-\code{gamma.mixed} \tab Gamma mixed effects linear regression (source)\cr
-\code{gamma.net} \tab Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables (source)\cr
-\code{irt1d} \tab One Dimensional Item Response Mode (source)\cr
+\code{irt1d} \tab One Dimensional Item Response Mode (source)\cr
\code{irtkd} \tab K-Dimensional Item Response Model (source)\cr
\code{logit} \tab Logistic Regression for Dichotomous Dependent Variables (source)\cr
\code{logit.bayes} \tab Bayesian Logistic Regression for Dichotomous Dependent Variables (source)\cr
-\code{logit.gam} \tab Generalized Additive Model for Dichotomous Dependent Variables (source)\cr
\code{logit.gee} \tab Generalized Estimating Equation for Logistic Regression (source)\cr
-\code{logit.mixed} \tab Mixed effects linear regression (source)\cr
-\code{logit.net} \tab Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables (source)\cr
\code{lognorm} \tab Log-Normal Regression for Duration Dependent Variables (source)\cr
\code{ls} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
-\code{ls.mixed} \tab Mixed effects linear regression (source)\cr
-\code{ls.net} \tab Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables (source)\cr
\code{mlogit} \tab Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values (source)\cr
\code{mlogit.bayes} \tab Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values (source)\cr
\code{mloglm} \tab Multinomial Log-Linear Regression for Contingency Table Models (source)\cr
\code{negbin} \tab Negative Binomial Regression for Event Count Dependent Variables (source)\cr
+\code{netcloglog} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
+\code{netgamma} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
+\code{netlogit} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
+\code{netls} \tab Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables (source)\cr
+\code{netnormal} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
+\code{netpoisson} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
+\code{netprobit} \tab Least Squares Regression for Continuous Dependent Variables (source)\cr
\code{normal} \tab Normal Regression for Continuous Dependent Variables (source)\cr
\code{normal.bayes} \tab Bayesian Normal Linear Regression (source)\cr
-\code{normal.gam} \tab Generalized Additive Model for Continuous Dependent Variables (source)\cr
-\code{normal.gee} \tab Generalized Estimating Equation for Normal Regression (source)\cr
-\code{normal.net} \tab Network Normal Regression for Continuous Proximity Matrix Dependent Variables (source)\cr
\code{ologit} \tab Ordinal Logistic Regression for Ordered Categorical Dependent Variables (source)\cr
\code{oprobit} \tab Ordinal Probit Regression for Ordered Categorical Dependent Variables (source)\cr
\code{oprobit.bayes} \tab Bayesian Ordered Probit Regression (source)\cr
\code{poisson} \tab Poisson Regression for Event Count Dependent Variables (source)\cr
\code{poisson.bayes} \tab Bayesian Poisson Regression (source)\cr
-\code{poisson.gam} \tab Generalized Additive Model for Count Dependent Variables (source)\cr
-\code{poisson.gee} \tab Generalized Estimating Equation for Poisson Regression (source)\cr
-\code{poisson.mixed} \tab Mixed effects poisson regression (source)\cr
-\code{poisson.net} \tab Network Poisson Regression for Event Count Proximity Matrix Dependent Variables (source)\cr
\code{probit} \tab Probit Regression for Dichotomous Dependent Variables (source)\cr
\code{probit.bayes} \tab Bayesian Probit Regression for Dichotomous Dependent Variable (source)\cr
-\code{probit.gam} \tab Generalized Additive Model for Dichotomous Dependent Variables (source)\cr
-\code{probit.gee} \tab Generalized Estimating Equation for Probit Regression (source)\cr
-\code{probit.mixed} \tab Mixed effects probit regression (source)\cr
-\code{probit.net} \tab Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables (source)\cr
\code{relogit} \tab Rare Events Logistic Regression for Dichotomous Dependent Variables (source)\cr
\code{sur} \tab Seemingly Unrelated Regression (source)\cr
-\code{threesls} \tab Three Stage Least Squares (source)\cr
+\code{threesls} \tab Three Stage Least Squares (source)\cr
\code{tobit} \tab Linear regression for Left-Censored Dependet Variable (source)\cr
\code{tobit.bayes} \tab Bayesian Linear Regression for a Censored Dependent Variable (source)\cr
-\code{twosls} \tab Two Stage Least Squares (source)\cr
+\code{twosls} \tab Two Stage Least Squares (source)\cr
\code{weibull} \tab Weibull Regression for Duration Dependent Variables (source)\cr
}
}
--
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