[r-cran-zelig] 68/102: Import Upstream version 3.4-5
Andreas Tille
tille at debian.org
Sun Jan 8 16:59:39 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 2cee912a1feb0b5cd1f0e45c123ce7b516bd7781
Author: Andreas Tille <tille at debian.org>
Date: Sun Jan 8 09:40:06 2017 +0100
Import Upstream version 3.4-5
---
DESCRIPTION | 8 +-
R/categories.R | 6 +-
R/describe.aov.R | 4 +-
R/describe.arima.R | 4 +-
R/describe.blogit.R | 4 +-
R/describe.bprobit.R | 4 +-
R/describe.chopit.R | 5 +-
R/describe.coxph.R | 4 +-
R/describe.default.R | 5 +-
R/describe.ei.RxC.R | 5 +-
R/describe.ei.dynamic.R | 4 +-
R/describe.ei.hier.R | 4 +-
R/describe.exp.R | 4 +-
R/describe.factor.bayes.R | 4 +-
R/describe.factor.mix.R | 6 +-
R/describe.factor.ord.R | 5 +-
R/describe.gam.logit.R | 5 +-
R/describe.gam.normal.R | 4 +-
R/describe.gam.poisson.R | 4 +-
R/describe.gam.probit.R | 4 +-
R/describe.gamma.R | 4 +-
R/describe.gamma.gee.R | 4 +-
R/describe.gamma.mixed.R | 5 +-
R/describe.gamma.survey.R | 4 +-
R/describe.irt1d.R | 4 +-
R/describe.irtkd.R | 4 +-
R/describe.lm.mixed.R | 5 +-
R/describe.logit.R | 5 +-
R/describe.logit.bayes.R | 4 +-
R/describe.logit.gee.R | 4 +-
R/describe.logit.mixed.R | 4 +-
R/describe.logit.survey.R | 4 +-
R/describe.lognorm.R | 4 +-
R/describe.ls.R | 4 +-
R/describe.mlogit.R | 4 +-
R/describe.mlogit.bayes.R | 4 +-
R/describe.mloglm.R | 18 +
R/describe.negbin.R | 4 +-
R/describe.netcloglog.R | 4 +-
R/describe.netgamma.R | 5 +-
R/describe.netlogit.R | 4 +-
R/describe.netls.R | 4 +-
R/describe.netnormal.R | 4 +-
R/describe.netpoisson.R | 5 +-
R/describe.netprobit.R | 5 +-
R/describe.nlme.R | 20 -
R/describe.normal.R | 4 +-
R/describe.normal.bayes.R | 4 +-
R/describe.normal.gee.R | 4 +-
R/describe.normal.survey.R | 4 +-
R/describe.ologit.R | 4 +-
R/describe.oprobit.R | 5 +-
R/describe.oprobit.bayes.R | 4 +-
R/describe.poisson.R | 4 +-
R/describe.poisson.bayes.R | 4 +-
R/describe.poisson.gee.R | 4 +-
R/describe.poisson.mixed.R | 4 +-
R/describe.poisson.survey.R | 4 +-
R/describe.probit.R | 5 +-
R/describe.probit.bayes.R | 4 +-
R/describe.probit.gee.R | 4 +-
R/describe.probit.mixed.R | 5 +-
R/describe.probit.survey.R | 6 +-
R/describe.relogit.R | 4 +-
R/describe.sur.R | 5 +-
R/describe.threesls.R | 4 +-
R/describe.tobit.R | 4 +-
R/describe.tobit.bayes.R | 4 +-
R/describe.twosls.R | 4 +-
R/describe.weibull.R | 4 +-
R/get.R | 47 +-
R/load.first.R | 18 +-
R/param.gam.R | 54 +-
R/param.mixed.R | 84 +-
R/plot.ci.R | 63 +-
...ot.zelig.gam.logit.R => plot.zelig.logit.gam.R} | 6 +-
...elig.gam.poisson.R => plot.zelig.poisson.gam.R} | 6 +-
....zelig.gam.probit.R => plot.zelig.probit.gam.R} | 6 +-
R/qi.gam.R | 51 +-
R/qi.mixed.R | 328 +-
R/setx.gam.R | 472 +-
R/zelig.R | 3 +-
R/zelig.citation.R | 68 +
R/zelig.default.R | 8 +-
R/zelig2gamma.mixed.R | 34 +-
R/zelig2lm.mixed.R | 32 +-
R/zelig2negbin.R | 2 +
R/zelig2ologit.R | 2 +
R/zelig2oprobit.R | 2 +
R/zelig3mixed.R | 14 +-
inst/doc/Rplots.pdf | 204134 +++++++++---------
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inst/doc/vigpics/lsnet-ExamplesPlot.eps | 1564 +-
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inst/doc/vigpics/mlogit-LevelsTernaryPlot.eps | 2006 +-
inst/doc/vigpics/mlogit-LevelsTernaryPlot.pdf | 2081 +-
inst/doc/vigpics/mlogit-TernaryPlot.eps | 2006 +-
inst/doc/vigpics/mlogit-TernaryPlot.pdf | 2081 +-
inst/doc/vigpics/negbin-Example1Plot.eps | 885 +-
inst/doc/vigpics/negbin-Example1Plot.pdf | 1224 +-
inst/doc/vigpics/normal-ExamplesPlot.eps | 2144 +-
inst/doc/vigpics/normal-ExamplesPlot.pdf | 3112 +-
inst/doc/vigpics/normal-ExistingPlot.eps | 2195 +-
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inst/doc/vigpics/normal-ReplicatePlot.eps | 1519 +-
inst/doc/vigpics/normal-ReplicatePlot.pdf | 2266 +-
inst/doc/vigpics/normal_gee-ExamplePlot.eps | 1589 +-
inst/doc/vigpics/normal_gee-ExamplePlot.pdf | 2177 +-
inst/doc/vigpics/normalnet-007.eps | 1535 +-
inst/doc/vigpics/normalnet-007.pdf | 2163 +-
inst/doc/vigpics/oprobit-FirstDifferencesPlot.eps | 7130 +-
inst/doc/vigpics/oprobit-FirstDifferencesPlot.pdf | 8391 +-
inst/doc/vigpics/poisson-ExamplePlot.eps | 859 +-
inst/doc/vigpics/poisson-ExamplePlot.pdf | 1219 +-
inst/doc/vigpics/poisson_gee-ExamplePlot.eps | 874 +-
inst/doc/vigpics/poisson_gee-ExamplePlot.pdf | 1155 +-
inst/doc/vigpics/poissonnet-007.eps | 816 -
inst/doc/vigpics/poissonnet-007.pdf | 755 -
inst/doc/vigpics/poissonsurvey-ExistingPlot.eps | 1586 +-
inst/doc/vigpics/poissonsurvey-ExistingPlot.pdf | 2214 +-
inst/doc/vigpics/poissonsurvey-ReplicatePlot.eps | 1498 +-
inst/doc/vigpics/poissonsurvey-ReplicatePlot.pdf | 2248 +-
inst/doc/vigpics/probit_gee-ExamplePlot.eps | 848 +-
inst/doc/vigpics/probit_gee-ExamplePlot.pdf | 1151 +-
.../doc/vigpics/probit_gee-FirstDifferencePlot.eps | 2208 +-
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inst/doc/vigpics/probitnet-007.eps | 786 +-
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inst/doc/vigpics/probitnet-009.eps | 786 +-
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inst/doc/vigpics/probitsurvey-ExistingPlot.eps | 1986 +-
inst/doc/vigpics/probitsurvey-ExistingPlot.pdf | 3194 +-
inst/doc/vigpics/probitsurvey-ReplicatePlot.pdf | 119 +-
inst/doc/vigpics/relogit-Example1Plot.eps | 700 +-
inst/doc/vigpics/relogit-Example1Plot.pdf | 1151 +-
inst/doc/vigpics/relogit-Example3Plot.eps | 730 +-
inst/doc/vigpics/relogit-Example3Plot.pdf | 1129 +-
inst/doc/vigpics/threesls-Examplesthreesls.eps | 3300 +-
inst/doc/vigpics/threesls-Examplesthreesls.pdf | 4261 +-
inst/doc/vigpics/twosls-Examplestwosls.eps | 4704 +-
inst/doc/vigpics/twosls-Examplestwosls.pdf | 6357 +-
inst/doc/vigpics/weibull-ExamplePlot.eps | 2069 +-
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inst/doc/weibull.Rnw | 6 +-
inst/doc/weibull.pdf | Bin 137672 -> 136497 bytes
inst/doc/whatsnew.tex | 29 +-
inst/doc/zelig.html | 11 +-
inst/doc/zelig.pdf | 175155 +++++++--------
inst/doc/zelig.tex | 3 +-
inst/doc/zeligheader.html | 2 +-
inst/doc/zeligmodels.html | 9 +-
inst/doc/zinput.tex | 4 +-
man/Weimar.Rd | 26 +-
man/Zelig.Rd | 12 +-
man/homerun.Rd | 6 +-
man/model.end.Rd | 3 +-
man/plot.ci.Rd | 2 +-
man/plot.surv.Rd | 2 +-
man/rocplot.Rd | 3 +-
man/setx.Rd | 2 +
man/sim.Rd | 4 +-
man/summary.zelig.Rd | 2 +
487 files changed, 311449 insertions(+), 313312 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index ff88872..d634c9a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: Zelig
-Version: 3.3-1
-Date: 2008-06-13
+Version: 3.4-5
+Date: 2009-03-13
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: Affero GPL
+License: GPL (>=2)
URL: http://gking.harvard.edu/zelig
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 Jun 13 18:24:07 2008; king
+Packaged: Fri Mar 13 10:20:49 2009; king
diff --git a/R/categories.R b/R/categories.R
index f4f53ff..a339d99 100644
--- a/R/categories.R
+++ b/R/categories.R
@@ -1,10 +1,10 @@
categories <-function(){
-list(continuous="Models for Continous Dependent Variables",
+list(continuous="Models for Continuous Dependent Variables",
dichotomous="Models for Dichotomous Dependent Variables",
ordinal="Models for Ordinal Dependent Variables",
- bounded="Models for Continous Bounded Dependent Variables",
+ bounded="Models for Continous Bounded Dependent Variables",
multinomial="Multinomial Choice Models",
count="Event Count Models",
mixed="Models for Mixed Dependent Variables",
- ei="Ecological inverence models")
+ ei="Ecological Inference Models")
}
diff --git a/R/describe.aov.R b/R/describe.aov.R
index 6cd69d5..1210381 100644
--- a/R/describe.aov.R
+++ b/R/describe.aov.R
@@ -1,6 +1,8 @@
describe.aov<-function(){
category <- "continuous"
description <- "Fit an Analysis of Variance Model"
+authors <- c()
+year <- 2007
package <-list( name ="stats",
version ="2.5.0"
)
@@ -13,5 +15,5 @@ parameters$mu<-list(equations=c(1,1),
##varInSpecialFunction= c(2,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.arima.R b/R/describe.arima.R
index e5dcb28..efff81d 100644
--- a/R/describe.arima.R
+++ b/R/describe.arima.R
@@ -1,6 +1,8 @@
describe.arima<- function(){
category<- "continuous"
description <- "Arima models for Time Series Data"
+authors <- c("Justin Grimmer")
+year <- 2007
package <-list( name ="stats",
version ="0.1"
)
@@ -15,5 +17,5 @@ sigma2<- list(equations=c(1,1),
depVar=FALSE,
expVar=FALSE)
pars<- list(mu=mu, sigma2=sigma2)
-model<- list(category=category,description=description,package=package, parameters=pars)
+model<- list(category = category, authors = authors, year = year,description=description,package=package, parameters=pars)
}
diff --git a/R/describe.blogit.R b/R/describe.blogit.R
index 104ea80..47fccc7 100644
--- a/R/describe.blogit.R
+++ b/R/describe.blogit.R
@@ -1,6 +1,8 @@
describe.blogit<-function(){
category <- "dichotomous"
description <- "Bivariate Logistic Regression for Dichotomous Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="VGAM",
version ="0.6"
)
@@ -14,5 +16,5 @@ parameters$phi<-list(equations=c(1,1),
tagsAllowed=FALSE,
depVar=FALSE,
expVar=TRUE)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.bprobit.R b/R/describe.bprobit.R
index a91bc88..db9076c 100644
--- a/R/describe.bprobit.R
+++ b/R/describe.bprobit.R
@@ -1,6 +1,8 @@
describe.bprobit<-function(){
category <- "dichotomous"
description <- "Bivariate Probit Regression for Dichotomous Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="VGAM",
version ="0.6"
)
@@ -14,5 +16,5 @@ parameters$rho<-list(equations=c(1,1),
tagsAllowed=FALSE,
depVar=FALSE,
expVar=TRUE)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.chopit.R b/R/describe.chopit.R
index c5ba39b..b03b9b0 100644
--- a/R/describe.chopit.R
+++ b/R/describe.chopit.R
@@ -1,6 +1,9 @@
describe.chopit <- function(){
category <- "ordinal"
description <- "Compound Hierarchical Ordinal Probit Regression for Survey Vignettes"
+ authors <- c()
+ year <- 2007
+
package <-list(name = "anchors",
version = "2.0",
CRAN = "http://wand.stanford.edu/R/CRAN")
@@ -21,7 +24,7 @@ describe.chopit <- function(){
depVar = FALSE,
expVar = TRUE)
- list(category = category,
+ list(category = category, authors = authors, year = year,
description = description,
package = package,
parameters = parameters)
diff --git a/R/describe.coxph.R b/R/describe.coxph.R
index a9e31cb..675252a 100644
--- a/R/describe.coxph.R
+++ b/R/describe.coxph.R
@@ -1,6 +1,8 @@
describe.coxph<-function(){
category <- "bounded"
description <- "Cox Proportional Hazard Regression for Duration Dependent Variables"
+ authors <- c("Patrick Lam")
+ year <- 2007
package <- list(name ="survival",
version ="2.34"
)
@@ -13,5 +15,5 @@ describe.coxph<-function(){
varInSpecialFunction=c(2,2)
)
- list(category=category,description=description,package=package,parameters=parameters)
+ list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.default.R b/R/describe.default.R
index a4d0ca1..f279123 100644
--- a/R/describe.default.R
+++ b/R/describe.default.R
@@ -2,11 +2,14 @@ describe.default<-function(){
category <- "Dichotomous"
description <- "A statistical model"
+authors <- c("an author")
+year <- "a year"
+
parameters<-list(mu="mu")
parameters$mu<-list(equations=c(1,1),
tagsAllowed=FALSE,
depVar=TRUE,
expVar=TRUE)
-list(category=category,description=description,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,parameters=parameters)
}
diff --git a/R/describe.ei.RxC.R b/R/describe.ei.RxC.R
index ef2074a..c92dac1 100644
--- a/R/describe.ei.RxC.R
+++ b/R/describe.ei.RxC.R
@@ -1,6 +1,9 @@
describe.ei.RxC <- function(){
category <- "ei"
description <- "Hierarchical Multinomial-Dirichlet Ecological Inference Model for R x C Tables"
+authors <- c("Jason Wittenberg", "Ferdinand Alimadhi","Badri Narayan Bhaskar","Olivia Lau")
+year <- 2007
+
package <-list( name ="stats",
version ="0.1"
)
@@ -13,5 +16,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(2,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.ei.dynamic.R b/R/describe.ei.dynamic.R
index cef56d9..2d1d36d 100644
--- a/R/describe.ei.dynamic.R
+++ b/R/describe.ei.dynamic.R
@@ -1,6 +1,8 @@
describe.ei.dynamic <- function(){
category <- "ei"
description <- "Quinn's Dynamic Ecological Inference Model"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <- list (
name="MCMCpack",
version="0.8-2"
@@ -14,5 +16,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(2,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.ei.hier.R b/R/describe.ei.hier.R
index adf69c6..2ffcedf 100644
--- a/R/describe.ei.hier.R
+++ b/R/describe.ei.hier.R
@@ -1,6 +1,8 @@
describe.ei.hier <- function(){
category <- "ei"
description <- "Hierarchical Ecological Inference Model for 2 x 2 Tables"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <- list (
name="MCMCpack",
version="0.8-2"
@@ -14,5 +16,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(2,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.exp.R b/R/describe.exp.R
index 8ce2f6f..e66ba75 100644
--- a/R/describe.exp.R
+++ b/R/describe.exp.R
@@ -1,6 +1,8 @@
describe.exp<-function(){
category <- "bounded"
description <- "Exponential Regression for Duration Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="survival",
version ="2.0"
)
@@ -12,5 +14,5 @@ parameters$mu<-list(equations=c(1,1),
specialFunction="Surv",
varInSpecialFunction=c(2,2))
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.factor.bayes.R b/R/describe.factor.bayes.R
index 44aa637..63024e1 100644
--- a/R/describe.factor.bayes.R
+++ b/R/describe.factor.bayes.R
@@ -1,6 +1,8 @@
describe.factor.bayes<-function(){
category <- "continuous"
description <- "Bayesian Factor Analysis"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -13,5 +15,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(3,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.factor.mix.R b/R/describe.factor.mix.R
index 5d1d13c..12fd76d 100644
--- a/R/describe.factor.mix.R
+++ b/R/describe.factor.mix.R
@@ -1,6 +1,10 @@
describe.factor.mix<-function(){
category <- "mixed"
description <- "Mixed Data Factor Analysis"
+
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
+
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -13,5 +17,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(2,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.factor.ord.R b/R/describe.factor.ord.R
index fcfacf1..dd4b0df 100644
--- a/R/describe.factor.ord.R
+++ b/R/describe.factor.ord.R
@@ -1,6 +1,9 @@
describe.factor.ord<-function(){
category <- "ordinal"
description <- "Ordinal Data Factor Analysis"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
+
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -13,5 +16,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(2,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.gam.logit.R b/R/describe.gam.logit.R
index bda22f6..1e747f4 100644
--- a/R/describe.gam.logit.R
+++ b/R/describe.gam.logit.R
@@ -1,6 +1,9 @@
describe.logit.gam<-function(){
category <- "dichotomous"
description <- "Generalized Additive Model for Dichotomous Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
+
package <-list( name ="mgcv",
version ="0.1"
)
@@ -11,5 +14,5 @@ parameters$pi<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.gam.normal.R b/R/describe.gam.normal.R
index 99e5973..8313b08 100644
--- a/R/describe.gam.normal.R
+++ b/R/describe.gam.normal.R
@@ -1,6 +1,8 @@
describe.normal.gam<-function(){
category <- "continuous"
description <- "Generalized Additive Model for Continuous Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
package <-list( name ="mgcv",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.gam.poisson.R b/R/describe.gam.poisson.R
index 9f1d104..d68608e 100644
--- a/R/describe.gam.poisson.R
+++ b/R/describe.gam.poisson.R
@@ -1,6 +1,8 @@
describe.poisson.gam<-function(){
category <- "count"
description <- "Generalized Additive Model for Event Count Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
package <-list( name ="mgcv",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$lambda<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.gam.probit.R b/R/describe.gam.probit.R
index e3368ea..b53480b 100644
--- a/R/describe.gam.probit.R
+++ b/R/describe.gam.probit.R
@@ -1,6 +1,8 @@
describe.probit.gam<-function(){
category <- "dichotomous"
description <- "Generalized Additive Model for Dichotomous Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
package <-list( name ="mgcv",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.gamma.R b/R/describe.gamma.R
index da3814b..729b56a 100644
--- a/R/describe.gamma.R
+++ b/R/describe.gamma.R
@@ -1,6 +1,8 @@
describe.gamma<-function(){
category <- "bounded"
description <- "Gamma Regression for Continuous, Positive Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="stats",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$lambda<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.gamma.gee.R b/R/describe.gamma.gee.R
index 8293a25..a5badcf 100644
--- a/R/describe.gamma.gee.R
+++ b/R/describe.gamma.gee.R
@@ -1,6 +1,8 @@
describe.gamma.gee<-function(){
category <- "bounded"
description <- "General Estimating Equation for Gamma Regression"
+ authors <- c("Patrick Lam")
+ year <- 2007
package <- list(name ="gee",
version ="4.13-12"
)
@@ -10,6 +12,6 @@ describe.gamma.gee<-function(){
expVar=TRUE
)
- list(category=category,description=description,package=package,parameters=list(lambda=lambda))
+ list(category = category, authors = authors, year = year,description=description,package=package,parameters=list(lambda=lambda))
}
diff --git a/R/describe.gamma.mixed.R b/R/describe.gamma.mixed.R
index b063e89..7600178 100644
--- a/R/describe.gamma.mixed.R
+++ b/R/describe.gamma.mixed.R
@@ -1,6 +1,9 @@
describe.gamma.mixed <- function(){
category <- "bounded"
description <- "Mixed effects gamma model"
+ authors <- c("Delia Bailey","Ferdinand Alimadhi")
+ year <- 2007
+
parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
parameters$mu <- list(equations=c(1,1),
@@ -23,7 +26,7 @@ describe.gamma.mixed <- function(){
package <- list(name="lme4", version="0.99875-9")
- list(category=category, description = description, package=package, parameters=parameters)
+ list(category = category, authors = authors, year = year, description = description, package=package, parameters=parameters)
}
diff --git a/R/describe.gamma.survey.R b/R/describe.gamma.survey.R
index 00601d2..0689f22 100644
--- a/R/describe.gamma.survey.R
+++ b/R/describe.gamma.survey.R
@@ -1,6 +1,8 @@
describe.gamma.survey<-function(){
category <- "bounded"
description <- "Survey-Weighted Gamma Regression for Continuous, Positive Dependent Variables"
+authors <- c("Nicholas Carnes")
+year <- 2008
package <-list( name ="survey",
version ="3.6-13"
)
@@ -11,5 +13,5 @@ parameters$lambda<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.irt1d.R b/R/describe.irt1d.R
index 3957983..bd8917d 100644
--- a/R/describe.irt1d.R
+++ b/R/describe.irt1d.R
@@ -1,6 +1,8 @@
describe.irt1d<-function(){
category <- "dichotomous"
description <- "One Dimensional Item Response Model"
+authors <- c("Ben Goodrich","Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -13,5 +15,5 @@ parameters$pi<-list(equations=c(1,1),
varInSpecialFunction=c(1,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.irtkd.R b/R/describe.irtkd.R
index 30769ef..690e3c6 100644
--- a/R/describe.irtkd.R
+++ b/R/describe.irtkd.R
@@ -1,6 +1,8 @@
describe.irtkd<-function(){
category <- "dichotomous"
description <- "K-Dimensional Item Response Model"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -13,5 +15,5 @@ parameters$pi<-list(equations=c(1,1),
varInSpecialFunction=c(2,Inf)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.lm.mixed.R b/R/describe.lm.mixed.R
index f17539c..a29d760 100644
--- a/R/describe.lm.mixed.R
+++ b/R/describe.lm.mixed.R
@@ -1,6 +1,9 @@
describe.ls.mixed <- function(){
category <- "continuous"
description <- "Mixed effects linear model"
+
+ authors <- c("Delia Bailey","Ferdinand Alimadhi")
+ year <- 2007
parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
parameters$mu <- list(equations=c(1,1),
@@ -23,7 +26,7 @@ describe.ls.mixed <- function(){
package <- list(name="lme4", version="0.99875-9")
- list(category=category, package=package, description = description, parameters=parameters)
+ list(category = category, authors = authors, year = year, package=package, description = description, parameters=parameters)
}
diff --git a/R/describe.logit.R b/R/describe.logit.R
index 9a88102..f911762 100644
--- a/R/describe.logit.R
+++ b/R/describe.logit.R
@@ -1,6 +1,9 @@
describe.logit<-function(){
category <- "dichotomous"
description <- "Logistic Regression for Dichotomous Dependent Variables"
+authors <- c()
+year <- 2008
+
package <-list( name ="stats",
version ="0.1"
)
@@ -11,5 +14,5 @@ parameters$pi<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.logit.bayes.R b/R/describe.logit.bayes.R
index e259a66..380ba33 100644
--- a/R/describe.logit.bayes.R
+++ b/R/describe.logit.bayes.R
@@ -1,6 +1,8 @@
describe.logit.bayes<-function(){
category <- "dichotomous"
description <- "Bayesian Logistic Regression for Dichotomous Dependent Variables"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -11,5 +13,5 @@ parameters$pi<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.logit.gee.R b/R/describe.logit.gee.R
index 7222bf2..f085590 100644
--- a/R/describe.logit.gee.R
+++ b/R/describe.logit.gee.R
@@ -1,6 +1,8 @@
describe.logit.gee<-function(){
category <- "dichotomous"
description <- "General Estimating Equation for Logistic Regression"
+ authors <- c("Patrick Lam")
+ year <- 2007
package <- list(name ="gee",
version ="4.13-12"
)
@@ -10,6 +12,6 @@ describe.logit.gee<-function(){
expVar=TRUE
)
- list(category=category,description=description,package=package,parameters=list(pi=pi))
+ list(category = category, authors = authors, year = year,description=description,package=package,parameters=list(pi=pi))
}
diff --git a/R/describe.logit.mixed.R b/R/describe.logit.mixed.R
index e7341e9..08bc760 100644
--- a/R/describe.logit.mixed.R
+++ b/R/describe.logit.mixed.R
@@ -1,6 +1,8 @@
describe.logit.mixed <- function(){
category <- "dichotomous"
description <- "Mixed effects logistic model"
+ authors <- c("Delia Bailey", "Ferdinand Alimadhi")
+ year <- 2007
parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
parameters$mu <- list(equations=c(1,1),
@@ -23,7 +25,7 @@ describe.logit.mixed <- function(){
package <- list(name="lme4", version="0.99875-9")
- list(category=category, package=package,description = description, parameters=parameters)
+ list(category = category, authors = authors, year = year, package=package,description = description, parameters=parameters)
}
diff --git a/R/describe.logit.survey.R b/R/describe.logit.survey.R
index d06642f..5d04a7b 100644
--- a/R/describe.logit.survey.R
+++ b/R/describe.logit.survey.R
@@ -1,6 +1,8 @@
describe.logit.survey<-function(){
category <- "dichotomous"
description <- "Survey-Weighted Logistic Regression for Dichotomous Dependent Variables"
+authors <- c("Nicholas Carnes")
+year <- 2007
package <-list( name ="survey",
version ="3.6-13"
)
@@ -11,5 +13,5 @@ parameters$pi<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.lognorm.R b/R/describe.lognorm.R
index 2690c02..25466e9 100644
--- a/R/describe.lognorm.R
+++ b/R/describe.lognorm.R
@@ -1,6 +1,8 @@
describe.lognorm<-function(){
category <- "bounded"
description <- "Log-Normal Regression for Duration Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="survival",
version ="2.2"
)
@@ -13,5 +15,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(2,2)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.ls.R b/R/describe.ls.R
index 67c65db..73f4972 100644
--- a/R/describe.ls.R
+++ b/R/describe.ls.R
@@ -1,6 +1,8 @@
describe.ls<-function(){
category <- "continuous"
description <- "Least Squares Regression for Continuous Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="stats",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.mlogit.R b/R/describe.mlogit.R
index c43f1af..71255ad 100644
--- a/R/describe.mlogit.R
+++ b/R/describe.mlogit.R
@@ -1,6 +1,8 @@
describe.mlogit<-function(){
category <- "multinomial"
description <- "Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values"
+authors <- c()
+year <- 2007
package <-list( name ="VGAM",
version ="0.6"
)
@@ -13,5 +15,5 @@ parameters$mu<-list(equations=c(1,Inf),
varInSpecialFunction=c(1,1)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.mlogit.bayes.R b/R/describe.mlogit.bayes.R
index 383681c..46d0247 100644
--- a/R/describe.mlogit.bayes.R
+++ b/R/describe.mlogit.bayes.R
@@ -1,6 +1,8 @@
describe.mlogit.bayes<-function(){
category <- "multinomial"
description <- "Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -13,5 +15,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(1,1)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.mloglm.R b/R/describe.mloglm.R
new file mode 100644
index 0000000..a003123
--- /dev/null
+++ b/R/describe.mloglm.R
@@ -0,0 +1,18 @@
+describe.mloglm<-function(){
+category <- "multinomial"
+description <- "Multinomial Log-Linear Regression for Contingency Table Models"
+authors <- c()
+year <- 2007
+package <-list( name ="nnet",
+ version ="0.6"
+ )
+
+parameters<-list(mu="mu")
+parameters$mu<-list(equations=c(1,1),
+ tagsAllowed=FALSE,
+ depVar=TRUE,
+ expVar=TRUE
+ )
+
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
+}
diff --git a/R/describe.negbin.R b/R/describe.negbin.R
index 6d5308d..1b21708 100644
--- a/R/describe.negbin.R
+++ b/R/describe.negbin.R
@@ -1,6 +1,8 @@
describe.negbin<-function(){
category <- "count"
description <- "Negative Binomial Regression for Event Count Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="MASS",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.netcloglog.R b/R/describe.netcloglog.R
index 12ec288..d7b21f7 100644
--- a/R/describe.netcloglog.R
+++ b/R/describe.netcloglog.R
@@ -1,6 +1,8 @@
describe.cloglog.net<-function(){
category <- "dichotomous"
description <- "Social Network Complementary Log Log Regression for Dichotomous Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
package <-list( name ="sna",
version ="1.4"
)
@@ -11,5 +13,5 @@ parameters$pi<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.netgamma.R b/R/describe.netgamma.R
index 18ceb31..1da6fb9 100644
--- a/R/describe.netgamma.R
+++ b/R/describe.netgamma.R
@@ -1,6 +1,9 @@
describe.gamma.net<-function(){
category <- "bounded"
description <- "Social Network Gamma Regression for Continuous, Positive Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
+
package <-list( name ="sna",
version ="1.4"
)
@@ -11,5 +14,5 @@ parameters$lambda<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.netlogit.R b/R/describe.netlogit.R
index c5c55f3..d4051bf 100644
--- a/R/describe.netlogit.R
+++ b/R/describe.netlogit.R
@@ -1,6 +1,8 @@
describe.logit.net<-function(){
category <- "dichotomous"
description <- "Social Network Logistic Regression for Dichotomous Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
package <-list( name ="sna",
version ="1.4"
)
@@ -11,5 +13,5 @@ parameters$pi<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.netls.R b/R/describe.netls.R
index 74c34ca..e50d8d7 100644
--- a/R/describe.netls.R
+++ b/R/describe.netls.R
@@ -1,6 +1,8 @@
describe.ls.net<-function(){
category <- "continuous"
description <- "Social Network Least Squares Regression for Continuous Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
package <-list( name ="sna",
version ="1.4"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.netnormal.R b/R/describe.netnormal.R
index 90abad9..2058d63 100644
--- a/R/describe.netnormal.R
+++ b/R/describe.netnormal.R
@@ -1,6 +1,8 @@
describe.normal.net<-function(){
category <- "continuous"
description <- "Social Network Normal Regression for Continuous Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
package <-list( name ="sna",
version ="1.4"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.netpoisson.R b/R/describe.netpoisson.R
index 9a817fc..9dc7f22 100644
--- a/R/describe.netpoisson.R
+++ b/R/describe.netpoisson.R
@@ -1,6 +1,9 @@
describe.poisson.net<-function(){
category <- "count"
description <- "Social Network Poisson Regression for Event Count Dependent Variables"
+authors <- c("Skyler J. Cranmer")
+year <- 2007
+
package <-list( name ="sna",
version ="1.4"
)
@@ -11,5 +14,5 @@ parameters$lambda<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.netprobit.R b/R/describe.netprobit.R
index 47cfb76..8903402 100644
--- a/R/describe.netprobit.R
+++ b/R/describe.netprobit.R
@@ -1,6 +1,9 @@
describe.probit.net<-function(){
category <- "dichotomous"
description <- "Social Network Probit Regression for Dichotomous Dependent Variables"
+
+authors <- c("Skyler J. Cranmer")
+year <- 2007
package <-list( name ="stats",
version ="0.1"
)
@@ -11,5 +14,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.nlme.R b/R/describe.nlme.R
deleted file mode 100644
index 815a8d7..0000000
--- a/R/describe.nlme.R
+++ /dev/null
@@ -1,20 +0,0 @@
-describe.nlme<-function(){
- category <- "continuous"
-description <- "Non linear regression model"
-
-package <-list( name ="nlme",
- version ="3.1-75"
- )
-parameters<-list(mu="mu",rho="rho")
-parameters$mu<-list(equations=c(1,1),
- tagsAllowed=TRUE,
- depVar=TRUE,
- expVar=TRUE)
-
-parameters$rho<-list(equations=c(1,1),
- tagsAllowed=TRUE,
- depVar=FALSE,
- expVar=TRUE)
-
-list(category=category,description=description,package=package,parameters=parameters)
-}
diff --git a/R/describe.normal.R b/R/describe.normal.R
index 5a3fede..5e52b57 100644
--- a/R/describe.normal.R
+++ b/R/describe.normal.R
@@ -1,6 +1,8 @@
describe.normal<-function(){
category <- "continuous"
description <- "Normal Regression for Continuous Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="stats",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.normal.bayes.R b/R/describe.normal.bayes.R
index 576b9de..ab6670f 100644
--- a/R/describe.normal.bayes.R
+++ b/R/describe.normal.bayes.R
@@ -1,6 +1,8 @@
describe.normal.bayes<-function(){
category <- "continuous"
description <- "Bayesian Normal Linear Regression"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.normal.gee.R b/R/describe.normal.gee.R
index ec452ab..51b74e1 100644
--- a/R/describe.normal.gee.R
+++ b/R/describe.normal.gee.R
@@ -1,6 +1,8 @@
describe.normal.gee<-function(){
category <- "continuous"
description <- "General Estimating Equation for Normal Regression"
+ authors <- c("Patrick Lam")
+ year <- 2007
package <- list(name ="gee",
version ="4.13-12"
)
@@ -10,6 +12,6 @@ describe.normal.gee<-function(){
expVar=TRUE
)
- list(category=category,description=description,package=package,parameters=list(mu=mu))
+ list(category = category, authors = authors, year = year,description=description,package=package,parameters=list(mu=mu))
}
diff --git a/R/describe.normal.survey.R b/R/describe.normal.survey.R
index 4fa1197..713c9e6 100644
--- a/R/describe.normal.survey.R
+++ b/R/describe.normal.survey.R
@@ -1,6 +1,8 @@
describe.normal.survey<-function(){
category <- "continuous"
description <- "Survey-Weighted Normal Regression for Continuous Dependent Variables"
+authors <- c("Nicholas Carnes")
+year <- 2008
package <-list(name ="survey",
version ="3.6-13"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.ologit.R b/R/describe.ologit.R
index 195f4da..37b86b6 100644
--- a/R/describe.ologit.R
+++ b/R/describe.ologit.R
@@ -1,6 +1,8 @@
describe.ologit<-function(){
category <- "ordinal"
description <- "Ordinal Logistic Regression for Ordered Categorical Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="MASS",
version ="0.1"
)
@@ -13,5 +15,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(1,1)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.oprobit.R b/R/describe.oprobit.R
index 5319bed..8808d6e 100644
--- a/R/describe.oprobit.R
+++ b/R/describe.oprobit.R
@@ -2,6 +2,9 @@ describe.oprobit<-function(){
category <- "ordinal"
description <- "Ordinal Probit Regression for Ordered Categorical Dependent Variables"
+authors <- c()
+year <- 2007
+
package <-list( name ="MASS",
version ="0.1"
)
@@ -14,6 +17,6 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(1,1)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.oprobit.bayes.R b/R/describe.oprobit.bayes.R
index e54ba0e..0707879 100644
--- a/R/describe.oprobit.bayes.R
+++ b/R/describe.oprobit.bayes.R
@@ -1,6 +1,8 @@
describe.oprobit.bayes<-function(){
category <- "ordinal"
description <- "Bayesian Ordered Probit Regression"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -13,5 +15,5 @@ parameters$mu<-list(equations=c(1,1),
varInSpecialFunction=c(1,1)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.poisson.R b/R/describe.poisson.R
index 3f1a5ec..3cc0c98 100644
--- a/R/describe.poisson.R
+++ b/R/describe.poisson.R
@@ -1,6 +1,8 @@
describe.poisson<-function(){
category <- "count"
description <- "Poisson Regression for Event Count Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="stats",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$lambda<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.poisson.bayes.R b/R/describe.poisson.bayes.R
index 6676e9e..e4ee362 100644
--- a/R/describe.poisson.bayes.R
+++ b/R/describe.poisson.bayes.R
@@ -1,6 +1,8 @@
describe.poisson.bayes<-function(){
category <- "count"
description <- "Bayesian Poisson Regression"
+authors <- c("Ben Goodrich", "Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -11,5 +13,5 @@ parameters$lambda<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.poisson.gee.R b/R/describe.poisson.gee.R
index 6a3fdd5..f140a74 100644
--- a/R/describe.poisson.gee.R
+++ b/R/describe.poisson.gee.R
@@ -1,6 +1,8 @@
describe.poisson.gee<-function(){
category <- "count"
description <- "General Estimating Equation for Poisson Regression"
+ authors <- c("Patrick Lam")
+ year <- 2007
package <- list(name ="gee",
version ="4.13-12"
)
@@ -10,6 +12,6 @@ describe.poisson.gee<-function(){
expVar=TRUE
)
- list(category=category,description=description,package=package,parameters=list(lambda=lambda))
+ list(category = category, authors = authors, year = year,description=description,package=package,parameters=list(lambda=lambda))
}
diff --git a/R/describe.poisson.mixed.R b/R/describe.poisson.mixed.R
index b1cfbf8..fe0f7f4 100644
--- a/R/describe.poisson.mixed.R
+++ b/R/describe.poisson.mixed.R
@@ -1,6 +1,8 @@
describe.poisson.mixed <- function(){
category <- "count"
description <- "Mixed effects poisson model"
+ authors <- c("Delia Bailey", "Ferdinand Alimadhi")
+ year <- 2007
parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
parameters$mu <- list(equations=c(1,1),
@@ -23,7 +25,7 @@ describe.poisson.mixed <- function(){
package <- list(name="lme4", version="0.99875-9")
- list(category=category, package=package, description = description, parameters=parameters)
+ list(category = category, authors = authors, year = year, package=package, description = description, parameters=parameters)
}
diff --git a/R/describe.poisson.survey.R b/R/describe.poisson.survey.R
index 9e9566d..d6c549a 100644
--- a/R/describe.poisson.survey.R
+++ b/R/describe.poisson.survey.R
@@ -1,6 +1,8 @@
describe.poisson.survey<-function(){
category <- "count"
description <- "Survey-Weighted Poisson Regression for Event Count Dependent Variables"
+authors <- c("Nicholas Carnes")
+year <- 2008
package <-list( name ="survey",
version ="3.6-13"
)
@@ -11,5 +13,5 @@ parameters$lambda<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.probit.R b/R/describe.probit.R
index 85aae9f..2d90e25 100644
--- a/R/describe.probit.R
+++ b/R/describe.probit.R
@@ -1,6 +1,9 @@
describe.probit<-function(){
category <- "dichotomous"
description <- "Probit Regression for Dichotomous Dependent Variables"
+authors <- c()
+year <- 2007
+
package <-list( name ="stats",
version ="0.1"
)
@@ -11,5 +14,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.probit.bayes.R b/R/describe.probit.bayes.R
index a17dd50..aa09909 100644
--- a/R/describe.probit.bayes.R
+++ b/R/describe.probit.bayes.R
@@ -1,6 +1,8 @@
describe.probit.bayes<-function(){
category <- "dichotomous"
description <- "Bayesian Probit Regression for Dichotomous Dependent Variables"
+authors <- c("Ben Goodrich","Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.probit.gee.R b/R/describe.probit.gee.R
index 183f3cd..ceafd9c 100644
--- a/R/describe.probit.gee.R
+++ b/R/describe.probit.gee.R
@@ -1,6 +1,8 @@
describe.probit.gee<-function(){
category <- "dichotomous"
description <- "General Estimating Equation for Probit Regression"
+ authors <- c("Patrick Lam")
+ year <- 2007
package <- list(name ="gee",
version ="4.13-12"
)
@@ -10,6 +12,6 @@ describe.probit.gee<-function(){
expVar=TRUE
)
- list(category=category,description=description,package=package,parameters=list(pi=pi))
+ list(category = category, authors = authors, year = year,description=description,package=package,parameters=list(pi=pi))
}
diff --git a/R/describe.probit.mixed.R b/R/describe.probit.mixed.R
index e8155f8..c3cb44c 100644
--- a/R/describe.probit.mixed.R
+++ b/R/describe.probit.mixed.R
@@ -1,6 +1,9 @@
describe.probit.mixed <- function(){
category <- "dichotomous"
description <- "Mixed effects probit model"
+ authors <- c("Delia Bailey", "Ferdinand Alimadhi")
+ year <- 2007
+
parameters <- list(mu="mu", delta="delta", sigma2="sigma2")
parameters$mu <- list(equations=c(1,1),
@@ -23,7 +26,7 @@ describe.probit.mixed <- function(){
package <- list(name="lme4", version="0.99875-9")
- list(category=category, package=package, description = description, parameters=parameters)
+ list(category = category, authors = authors, year = year, package=package, description = description, parameters=parameters)
}
diff --git a/R/describe.probit.survey.R b/R/describe.probit.survey.R
index b3a12eb..17a1fa6 100644
--- a/R/describe.probit.survey.R
+++ b/R/describe.probit.survey.R
@@ -1,6 +1,8 @@
describe.probit.survey<-function(){
category <- "dichotomous"
-description <- "Probit Regression for Dichotomous Dependent Variables"
+description <- "Survey-Weighted Probit Regression for Dichotomous Dependent Variables"
+authors <- c("Nicholas Carnes")
+year <- 2008
package <-list( name ="survey",
version ="3.6-13"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.relogit.R b/R/describe.relogit.R
index 014cd6a..2481797 100644
--- a/R/describe.relogit.R
+++ b/R/describe.relogit.R
@@ -1,6 +1,8 @@
describe.relogit<-function(){
category <- "dichotomous"
description <- "Rare Events Logistic Regression for Dichotomous Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="stats",
version ="0.1"
)
@@ -11,5 +13,5 @@ parameters$pi<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.sur.R b/R/describe.sur.R
index 17a2135..5e08b0f 100644
--- a/R/describe.sur.R
+++ b/R/describe.sur.R
@@ -1,7 +1,8 @@
describe.sur<-function(){
category <- "continuous"
description <- "Seemingly Unrelated Regression"
-
+authors <- c("Ferdinand Alimadhi","Ying Lu", "Elena Villalon")
+year <- 2007
package <-list( name ="systemfit",
version ="0.8"
)
@@ -11,5 +12,5 @@ parameters$mu<-list(equations=c(2,Inf),
depVar=TRUE,
expVar=TRUE)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.threesls.R b/R/describe.threesls.R
index bac6261..481c01f 100644
--- a/R/describe.threesls.R
+++ b/R/describe.threesls.R
@@ -1,6 +1,8 @@
describe.threesls<-function(){
category <- "continuous"
description <- "Three Stage Least Squares"
+authors <- c("Ferdinand Alimadhi","Ying Lu", "Elena Villalon")
+year <- 2007
package <-list( name ="systemfit",
version ="0.8"
)
@@ -14,5 +16,5 @@ parameters$inst<-list(equations=c(1,1),
tagsAllowed=FALSE,
depVar=FALSE,
expVar=TRUE)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.tobit.R b/R/describe.tobit.R
index 68046c1..3d18ae9 100644
--- a/R/describe.tobit.R
+++ b/R/describe.tobit.R
@@ -1,6 +1,8 @@
describe.tobit<-function(){
category <- "continuous"
description <- "Linear regression for Left-Censored Dependent Variable"
+authors <- c()
+year <- 2007
package <-list( name ="survival",
version ="2.2"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.tobit.bayes.R b/R/describe.tobit.bayes.R
index f135fbe..17ef27b 100644
--- a/R/describe.tobit.bayes.R
+++ b/R/describe.tobit.bayes.R
@@ -1,6 +1,8 @@
describe.tobit.bayes<-function(){
category <- "continuous"
description <- "Bayesian Linear Regression for a Censored Dependent Variable"
+authors <- c("Ben Goodrich","Ying Lu")
+year <- 2007
package <-list( name ="MCMCpack",
version ="0.6"
)
@@ -11,5 +13,5 @@ parameters$mu<-list(equations=c(1,1),
expVar=TRUE
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.twosls.R b/R/describe.twosls.R
index b2a6d29..3c73a52 100644
--- a/R/describe.twosls.R
+++ b/R/describe.twosls.R
@@ -1,6 +1,8 @@
describe.twosls<-function(){
category <- "continuous"
description <- "Two Stage Least Squares"
+authors <- c("Ferdinand Alimadhi","Ying Lu", "Elena Villalon")
+year <- 2007
package <-list( name ="systemfit",
version ="0.8"
)
@@ -14,5 +16,5 @@ parameters$inst<-list(equations=c(1,1),
tagsAllowed=FALSE,
depVar=FALSE,
expVar=TRUE)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/describe.weibull.R b/R/describe.weibull.R
index f6bd8ca..7c6e760 100644
--- a/R/describe.weibull.R
+++ b/R/describe.weibull.R
@@ -1,6 +1,8 @@
describe.weibull<-function(){
category <- "bounded"
description <- "Weibull Regression for Duration Dependent Variables"
+authors <- c()
+year <- 2007
package <-list( name ="survival",
version ="2.2"
)
@@ -13,5 +15,5 @@ parameters$lambda<-list(equations=c(1,1),
varInSpecialFunction=c(2,2)
)
-list(category=category,description=description,package=package,parameters=parameters)
+list(category = category, authors = authors, year = year,description=description,package=package,parameters=parameters)
}
diff --git a/R/get.R b/R/get.R
index f9bf842..7a2f96e 100644
--- a/R/get.R
+++ b/R/get.R
@@ -1,30 +1,43 @@
getzelig <- function(x) {
- check <- slotNames(x)
- if (length(check) > 0) return(x at call$model)
- else return(x$call$model)
+ if (zeligS4check(x)) return(eval(x at call$model))
+ else return(eval(x$call$model))
}
getcall <- function(x) {
- check <- slotNames(x)
- if (length(check) > 0) return(x at call)
- else return(x$call)
+ if (zeligS4check(x)) {
+ return(x at call)
+ } else {
+ return(x$call)
+ }
}
getcoef <- function(x) {
- check <- slotNames(x)
- if (length(check) > 0) {
- if ("coef3" %in% check) return(x at coef3)
- else return(x at coef)
+ if (zeligS4check(x)) {
+ if ("coef3" %in% slotNames(x)) {
+ return(x at coef3)
+ } else {
+ return(x at coef)
+ }
+ } else {
+ return(x$coef)
}
- else return(x$coef)
}
getdata <- function(x) {
- check <- slotNames(x)
- if (length(check) > 0) {
- if ("data" %in% check) return(x at data)
- else if ("model" %in% check) return(x at model)
- else return(NULL)
+ if (zeligS4check(x)) {
+ if ("data" %in% slotNames(x)) {
+ return(x at data)
+ } else if ("model" %in% slotNames(x)){
+ return(x at model)
+ } else {
+ return(NULL)
+ }
+ } else {
+ return(x$zelig.data)
}
- else return(x$zelig.data)
+}
+
+
+zeligS4check <- function(obj){
+ return(isS4(obj))
}
diff --git a/R/load.first.R b/R/load.first.R
index 223073b..a6b1bbf 100644
--- a/R/load.first.R
+++ b/R/load.first.R
@@ -3,9 +3,21 @@
ver <- packageDescription("Zelig", lib = mylib)$Version
builddate <- packageDescription("Zelig", lib = mylib)$Date
cat(paste("## \n## Zelig (Version ", ver, ", built: ", builddate, ")\n", sep = ""))
- cat("## Please refer to http://gking.harvard.edu/zelig for full documentation \n",
- "## or help.zelig() for help with commands and models supported by Zelig.\n##\n",
- sep="")
+ cat("## Please refer to http://gking.harvard.edu/zelig for full\n",
+ "## documentation or help.zelig() for help with commands and\n",
+ "## models supported by Zelig.\n##\n\n", sep="")
+ cat("## Zelig project citations:\n",
+ "## Kosuke Imai, Gary King, and Olivia Lau. (2009).\n",
+ "## ``Zelig: Everyone's Statistical Software,''\n",
+ "## http://gking.harvard.edu/zelig.\n",
+ "## and\n",
+ "## Kosuke Imai, Gary King, and Olivia Lau. (2008).\n",
+ "## ``Toward A Common Framework for Statistical Analysis\n",
+ "## and Development,'' Journal of Computational and\n",
+ "## Graphical Statistics, Vol. 17, No. 4 (December)\n",
+ "## pp. 892-913. \n\n",
+ "## To cite individual Zelig models, please use the citation format printed with\n",
+ "## each model run and in the documentation.\n##\n", sep="")
if(!any(search()=="package:MASS"))
require(MASS)
if(!any(search()=="package:boot"))
diff --git a/R/param.gam.R b/R/param.gam.R
index a9d38c8..9953fd3 100644
--- a/R/param.gam.R
+++ b/R/param.gam.R
@@ -1,27 +1,27 @@
-param.gam <- function(object, num = NULL, bootstrap = FALSE) {
- if (!bootstrap) {
- coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
- if (getzelig(object) == "normal.gam") {
- df <- object$df.residual
- sig2 <- summary(object)$dispersion
- alpha <- sqrt(df*sig2/rchisq(num, df=df))
- res <- cbind(coef, alpha)
- }
- else if (getzelig(object) == "gam.gamma") {
- rate <- gamma.shape(object)
- alpha <- rnorm(num, mean = rate$alpha, sd = rate$SE)
- res <- cbind(coef, alpha)
- }
- else if (getzelig(object) == "gam.negbin") {
- alpha <- object$theta
- res <- cbind(coef, c(alpha))
- }
- else
- res <- coef
- }
- res
-}
-
-
-
-
+param.gam <- function(object, num = NULL, bootstrap = FALSE) {
+ if (!bootstrap) {
+ coef <- mvrnorm(num, mu=coef(object), Sigma=vcov(object))
+ if (getzelig(object) == "normal.gam") {
+ df <- object$df.residual
+ sig2 <- summary(object)$dispersion
+ alpha <- sqrt(df*sig2/rchisq(num, df=df))
+ res <- cbind(coef, alpha)
+ }
+ else if (getzelig(object) == "gamma.gam") {
+ rate <- gamma.shape(object)
+ alpha <- rnorm(num, mean = rate$alpha, sd = rate$SE)
+ res <- cbind(coef, alpha)
+ }
+ else if (getzelig(object) == "negbin.gam") {
+ alpha <- object$theta
+ res <- cbind(coef, c(alpha))
+ }
+ else
+ res <- coef
+ }
+ res
+}
+
+
+
+
diff --git a/R/param.mixed.R b/R/param.mixed.R
index 53d11bb..6ef4275 100644
--- a/R/param.mixed.R
+++ b/R/param.mixed.R
@@ -1,42 +1,42 @@
-param.lmer <- param.glmer <- function(object, num, bootstrap = FALSE) {
-
- 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)
- }
- ## *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)
- }
- } else {
- object <- summary(object)
- ## *Set* fixed effects
- betasF <- fixed
- ## *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
-
- ## Return
- list(betas=betas, gammas=gammas, scale=scale)
-}
+## modified by delia 09/22/08
+################################
+
+param.mer <- function(object, num, bootstrap=FALSE){
+ fixed <- fixef(object)
+ vars <- ranef(object, postVar=TRUE)
+ gammas <- NULL
+ n.G <- length(vars)
+
+ if (!bootstrap){
+ object <- selectMethod("summary", "mer")(object)
+ # sample fixed effects
+ betasF <- NULL
+ vcov.fix <- vcov(object)
+ if (length(fixed) > 0){
+ betasF <- mvrnorm(num, fixed, vcov.fix)
+ }
+ # sample random effects
+ for (m in 1:n.G){
+ vars.m <- attr(vars[[m]], "postVar")
+ V.beta <- VarCorr(object)[[m]]
+ J <- dim(vars.m)[1]
+ gammas[[m]] <- mvrnorm(num, rep(0, J), V.beta)
+ }
+ }
+ else {
+ object <- summary(object)
+ # set fixed effects
+ betasF <- fixed
+ # 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
+
+ list(betas=betas, gammas=gammas, scale=scale)
+}
\ No newline at end of file
diff --git a/R/plot.ci.R b/R/plot.ci.R
index 10604cf..45a9d23 100644
--- a/R/plot.ci.R
+++ b/R/plot.ci.R
@@ -1,6 +1,7 @@
plot.ci <- function(x, CI=95, qi = "ev", main = "",
ylab = NULL, xlab = NULL, xlim = NULL,
ylim = NULL, col = c("red", "blue"), ...) {
+ "%w/o%" <- function(x,y) x[!x %in% y] #-- x without y
if (class(x) != "zelig")
stop(" plot.ci() works only for sim() output.")
if (!(x$zelig.call$model) %in%
@@ -14,8 +15,6 @@ plot.ci <- function(x, CI=95, qi = "ev", main = "",
"probit.gam", "logit.gee", "normal.gee",
"poisson.gee", "probit.gee"))
stop("\n plot.ci() is valid only for non-categorical, univariate response models.")
- var <- var1 <- list()
- idx1 <- idx <- NULL
cip <- c((100-CI)/200, 1-(100-CI)/200)
summarize <- function(z, cip){
res <- NULL
@@ -23,29 +22,29 @@ plot.ci <- function(x, CI=95, qi = "ev", main = "",
res <- cbind(res, apply(z, 2, quantile, prob=cip[2]))
res
}
- for (i in 1:ncol(x$x)) {
- var[[i]] <- unique(x$x[,i])
- if (length(var[[i]]) > 1)
- idx[i] <- i
- else
- idx[i] <- NA
- }
- if (!is.null(x$x1)) {
- idx1 <- NULL
- for (i in 1:ncol(x$x1)) {
- var1[[i]] <- unique(x$x1[,i])
- if (length(var1[[i]]) > 1)
- idx1[i] <- i
- else
- idx1[i] <- NA
+ vv <- apply(x$x, 2, unique)
+ idx <- sapply(vv, length)
+ cidx <- which(idx > 1)
+ if (!is.null(x$x1)) {
+ vv1 <- apply(x$x1, 2, unique)
+ idx1 <- sapply(vv1, length)
+ cidx1 <- which(idx1 > 1)
+ if (!identical(names(idx), names(idx1)))
+ stop("variables in x and x1 do not match.")
+ ## Checking for one dimension of variation, including interaction terms
+ if (length(cidx) > length(cidx1)) {
+ tmp <- names(idx)[cidx %w/o% cidx1]
+ tmp1 <- names(idx)[cidx[cidx %in% cidx1]]
+ }
+ else {
+ tmp <- names(idx1)[cidx1 %w/o% cidx]
+ tmp1 <- names(idx1)[cidx1[cidx1 %in% cidx]]
}
- idx1 <- na.omit(idx1)
+ check <- grep(tmp1, tmp)
+ if (length(check) != length(tmp))
+ stop("x and x1 vary on more than one dimension.")
}
- idx <- na.omit(idx)
- if (!is.null(idx1))
- if (!identical(idx, idx1))
- stop(" x and x1 in vary on different dimensions.")
- var <- var[[idx[1]]]
+ var <- vv[[cidx[1]]]
q <- pmatch(qi, names(x$qi))
qofi <- x$qi[[q]]
sum.qi <- summarize(qofi, cip)
@@ -54,20 +53,14 @@ plot.ci <- function(x, CI=95, qi = "ev", main = "",
ev1 <- fd + qofi
sum.qi1 <- summarize(ev1, cip)
}
- else
- sum.qi1 <- NULL
- if (is.null(ylab))
- ylab <- x$qi.name[[q]]
- if (is.null(xlab))
- xlab <- paste("Range of", colnames(x$x)[idx[1]])
+ else sum.qi1 <- NULL
+ if (is.null(ylab)) ylab <- x$qi.name[[q]]
+ if (is.null(xlab)) xlab <- paste("Range of", colnames(x$x)[cidx[1]])
if (is.null(ylim)) {
- if (is.null(sum.qi1))
- ylim <- c(min(sum.qi), max(sum.qi))
- else
- ylim <- c(min(sum.qi, sum.qi1), max(sum.qi, sum.qi1))
+ if (is.null(sum.qi1)) ylim <- c(min(sum.qi), max(sum.qi))
+ else ylim <- c(min(sum.qi, sum.qi1), max(sum.qi, sum.qi1))
}
- if (is.null(xlim))
- xlim <- c(min(var), max(var))
+ if (is.null(xlim)) xlim <- c(min(var), max(var))
plot.default(var, type = "n", ylab = ylab, main = main, xlab = xlab,
xlim = xlim, ylim = ylim)
for (i in 1:length(var)) {
diff --git a/R/plot.zelig.gam.logit.R b/R/plot.zelig.logit.gam.R
similarity index 75%
rename from R/plot.zelig.gam.logit.R
rename to R/plot.zelig.logit.gam.R
index 03239bb..b83dc11 100644
--- a/R/plot.zelig.gam.logit.R
+++ b/R/plot.zelig.logit.gam.R
@@ -3,14 +3,16 @@ plot.zelig.logit.gam <- function(x, xlab = "", user.par = FALSE, alt.col = "red"
op <- par(no.readonly = TRUE)
if (!user.par)
par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, 1))
+ if(length(unique(x$qi$pr)) > 1){par(mfrow = c(k, 1))}
+ if(length(unique(x$qi$pr)) == 1){par(mfrow = c(k-1, 1))}
pr <- x$qi$pr
y0 <- 100 * sum(pr == 0)/length(pr)
y1 <- 100 * sum(pr == 1)/length(pr)
+ if(length(unique(x$qi$pr)) > 1){
barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1,
names.arg = c("Y = 0", "Y = 1"),
xlab = "Percentage of Simulations",
- main = x$qi.name$pr, xlim = c(0, 100))
+ main = x$qi.name$pr, xlim = c(0, 100))}
x$qi$pr <- x$qi.name$pr <- NULL
for (i in 1:(k-1)) {
qi <- as.vector(x$qi[[i]])
diff --git a/R/plot.zelig.gam.poisson.R b/R/plot.zelig.poisson.gam.R
similarity index 69%
rename from R/plot.zelig.gam.poisson.R
rename to R/plot.zelig.poisson.gam.R
index 8399f33..0b51f92 100644
--- a/R/plot.zelig.gam.poisson.R
+++ b/R/plot.zelig.poisson.gam.R
@@ -3,9 +3,11 @@ plot.zelig.poisson.gam <- function(x, xlab = "", user.par = FALSE, ...){
op <- par(no.readonly = TRUE)
if (!user.par)
par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, 1))
+ if(length(unique(x$qi$pr)) > 1){par(mfrow = c(k, 1))}
+ if(length(unique(x$qi$pr)) == 1){par(mfrow = c(k-1, 1))}
+ if(length(unique(x$qi$pr)) > 1){
truehist(x$qi$pr, main = x$qi.name$pr, x0 = -0.25,
- xlab = xlab, ylab = "Probability", ...)
+ xlab = xlab, ylab = "Probability", ...)}
plot(density(x$qi$ev), main = x$qi.name$ev, xlab = xlab, ...)
if (k > 2) {
for (i in 3:k)
diff --git a/R/plot.zelig.gam.probit.R b/R/plot.zelig.probit.gam.R
similarity index 75%
rename from R/plot.zelig.gam.probit.R
rename to R/plot.zelig.probit.gam.R
index 8c74603..2aafc0f 100644
--- a/R/plot.zelig.gam.probit.R
+++ b/R/plot.zelig.probit.gam.R
@@ -3,14 +3,16 @@ plot.zelig.probit.gam <- function(x, xlab = "", user.par = FALSE, alt.col = "red
op <- par(no.readonly = TRUE)
if (!user.par)
par(mar = c(4,4,2,1), tcl = -0.25, mgp = c(2, 0.6, 0))
- par(mfrow = c(k, 1))
+ if(length(unique(x$qi$pr)) > 1){par(mfrow = c(k, 1))}
+ if(length(unique(x$qi$pr)) == 1){par(mfrow = c(k-1, 1))}
pr <- x$qi$pr
y0 <- 100 * sum(pr == 0)/length(pr)
y1 <- 100 * sum(pr == 1)/length(pr)
+ if(length(unique(x$qi$pr)) > 1){
barplot(c(y0, y1), horiz = TRUE, col = alt.col, las = 1,
names.arg = c("Y = 0", "Y = 1"),
xlab = "Percentage of Simulations",
- main = x$qi.name$pr, xlim = c(0, 100))
+ main = x$qi.name$pr, xlim = c(0, 100))}
x$qi$pr <- x$qi.name$pr <- NULL
for (i in 1:(k-1)) {
qi <- as.vector(x$qi[[i]])
diff --git a/R/qi.gam.R b/R/qi.gam.R
index fc789a1..383951c 100644
--- a/R/qi.gam.R
+++ b/R/qi.gam.R
@@ -1,4 +1,7 @@
-qi.gam <- function(object, simpar, x, x1 = NULL, y = NULL) {
+qi.gam <- function(object, simpar, x, x1 = NULL, y = NULL, num = NULL) {
+num=nrow(simpar)
+x <- as.data.frame(x)
+if(!is.null(x1)){x1 <- as.data.frame(x1)}
check <- FALSE
model <- getzelig(object)
k <- length(object$coef)
@@ -7,48 +10,42 @@ qi.gam <- function(object, simpar, x, x1 = NULL, y = NULL) {
alpha <- simpar[,(k+1):ncol(simpar)]
eta <- coef %*% t(x) ## Here is the nonconformability problem
theta <- matrix(object$family$linkinv(eta), nrow = nrow(coef))
- pr <- ev <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta))
+ pr <- ev <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta)) ## 1000 by 1 matrix
dimnames(pr) <- dimnames(ev) <- dimnames(theta)
if (model %in% c("logit.gam", "probit.gam")) {
check <- TRUE
- ev <- theta
- for (i in 1:ncol(theta))
- pr[,i] <- as.character(rbinom(length(ev[,i]), 1, ev[,i]))
+ evfit <- predict.gam(object, x, se.fit=TRUE, type="link")$fit
+ evse <- predict.gam(object, x, se.fit=TRUE, type="link")$se.fit
+ ev <- rnorm(num, mean=object$family$linkinv(evfit), sd=evse)
+ prfit <- predict.gam(object, x, se.fit=TRUE, type="response")$fit
+ pr <- rbinom(num, 1, prfit)
if (!is.null(y)) {
if (NCOL(y) > 1)
y <- y[,1]
}
}
else if (model == "normal.gam") {
- ev <- theta
- for (i in 1:nrow(ev))
- pr[i,] <- rnorm(length(ev[i,]), mean = ev[i,], sd = alpha[i])
- }
- else if (model == "gam.gamma") {
- ev <- theta * 1/alpha
- for (i in 1:nrow(ev))
- pr[i,] <- rgamma(length(ev[i,]), shape = theta[i,], scale = 1/alpha[i])
+ evfit <- predict.gam(object, x, se.fit=TRUE, type="link")$fit
+ evse <- predict.gam(object, x, se.fit=TRUE, type="link")$se.fit
+ ev <- rnorm(num, mean=object$family$linkinv(evfit), sd=evse)
+ prfit <- predict.gam(object, x, se.fit=TRUE, type="response")$fit
+ prse <- predict.gam(object, x, se.fit=TRUE, type="response")$se.fit
+ pr <- rnorm(num, mean=prfit, sd=prse)
}
else if (model == "poisson.gam") {
- ev <- theta
- for (i in 1:ncol(ev))
- pr[,i] <- rpois(length(ev[,i]), lambda = ev[,i])
- }
- else if (model == "gam.negbin") {
- ev <- theta
- for (i in 1:nrow(ev))
- pr[i,] <- rnegbin(length(ev[i,]), mu = ev[i,], theta = alpha[i])
+ evfit <- predict.gam(object, x, se.fit=TRUE, type="link")$fit
+ evse <- predict.gam(object, x, se.fit=TRUE, type="link")$se.fit
+ ev <- rnorm(num, mean=object$family$linkinv(evfit), sd=evse)
+ prfit <- predict.gam(object, x, se.fit=TRUE, type="response")$fit
+ pr <- rpois(num, prfit)
}
qi <- list(ev = ev, pr = pr)
qi.name <- list(ev = "Expected Values: E(Y|X)",
pr = "Predicted Values: Y|X")
if (!is.null(x1)){
- theta1 <- matrix(object$family$linkinv(coef %*% t(as.matrix(x1))),
- nrow = nrow(coef))
- if (model == "gamma")
- ev1 <- theta1 * 1/alpha
- else
- ev1 <- theta1
+ evfit1 <- predict.gam(object, x1, se.fit=TRUE, type="link")$fit
+ evse1 <- predict.gam(object, x1, se.fit=TRUE, type="link")$se.fit
+ ev1 <- rnorm(num, mean=object$family$linkinv(evfit), sd=evse)
qi$fd <- ev1-ev
qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
if (model %in% c("logit.gam", "probit.gam")) {
diff --git a/R/qi.mixed.R b/R/qi.mixed.R
index 1ab292b..b017272 100644
--- a/R/qi.mixed.R
+++ b/R/qi.mixed.R
@@ -1,157 +1,171 @@
-## dbailey at wustl.edu
-## 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) {
-
- x <- as.data.frame(x)
- if (!is.null(x1))
- x1 <- as.data.frame(x1)
-
-
- ## original dataset
- D <- eval(getcall(object)$data, envir = parent.frame())
- fml <- eval(object at call$formula)
- parsefml <- .getRandAndFixedTerms(fml)
-
- betas <- simpar[["betas"]]
- gammas <- simpar[["gammas"]]
- alpha <- simpar[["scale"]]
-
-
- fTermsNames <- colnames(model.matrix(parsefml$fixed, data = D))
- cat(fTermsNames, "\n")
-
- fTerms <- x[,fTermsNames]
- rTerms <- list()
- for (i in 1:length(parsefml$random)){
- ## for now, intercept is always present
- tt <- terms(parsefml$random[[i]])
- attr(tt,"intercept") <- 1
- rTermsNames <- colnames(model.matrix(tt,data=D))
- rTerms[[i]] <- x[, rTermsNames]
- }
- names(rTerms) <- names(parsefml$random)
- if (!is.null(x1)){
- fTermsNames <- colnames(model.matrix(parsefml$fixed, data = D))
- fTerms.x1 <- x1[,fTermsNames]
- rTerms.x1 <- list()
- for (i in 1:length(parsefml$random)){
- tt <- terms(parsefml$random[[i]])
- attr(tt,"intercept") <- 1
- rTermsNames <- colnames(model.matrix(tt,data=D))
- rTerms.x1[[i]] <- x1[, rTermsNames]
- ##rTermsNames <- colnames(model.matrix(parsefml$random[[i]],data=D))
- ##rTerms.x1[[i]] <- x1[, rTermsNames]
- }
- names(rTerms.x1) <- names(parsefml$random)
- }
-
- ## Expected Values and Predicted Values
- if (class(object) == "glmer"){
- family <- object at family$family
- link <- object at family$link
- eta <- betas %*% t(as.matrix(fTerms))
- theta <- matrix(object at family$linkinv(eta), nrow=nrow(betas))
- mu <- eta
- ## For predicted values, add in random effects draws
- for (i in 1:length(rTerms)){
- mu <- mu + gammas[[names(rTerms[i])]] %*% t(as.matrix(rTerms[[i]]))
- }
- mut <- matrix(object at family$linkinv(mu), nrow=nrow(betas))
- ev <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta))
- pr <- matrix(NA, nrow=nrow(mut), ncol=ncol(mut))
- dimnames(ev) <- dimnames(pr) <- dimnames(theta)
- n <- length(mut[, 1])
- if (family == "binomial"){
- ev <- theta
- for (i in 1:ncol(mut)){
- pr[,i] <- as.character(rbinom(n, 1, mut[,i]))
- }
- if (!is.null(y)) {
- if (NCOL(y) > 1) {
- y <- y[,1]
- }
- }
- }
- else if (family == "Gamma"){
- ev <- theta * 1/alpha
- n <- length(mut[i,])
- for (i in 1:nrow(mut)){
- pr[i,] <- rgamma(n, shape = mut[i,], scale= 1/alpha)
- }
- }
- else if (family == "gaussian"){
- ev <- theta
- if (link == "log"){
- for (i in 1:ncol(mut)){
- pr[,i] <- rlnorm(n, meanlog=mut[,i], sdlog=alpha)
- }
- } else {
- stop(sprintf("no method for %s family and %s link", family, link))
- }
- }
- else if (family == "poisson"){
- ev <- theta
- for (i in 1:ncol(mut)){
- pr[,i] <- rpois(n, lambda = mut[,i])
- }
- } else {
- stop(sprintf("no method for %s family", family))
- }
- }
- else if (class(object) == "lmer"){
- ev <- betas %*% t(as.matrix(fTerms))
- mu <- ev
- ## For predicted values, add in random effects draws
- for (i in 1:length(rTerms)){
- mu <- mu + gammas[[names(rTerms[i])]] %*% t(as.matrix(rTerms[[i]]))
- }
- n <- length(mu[,1])
- pr <- matrix(NA, nrow=nrow(mu), ncol=ncol(mu))
- for (i in 1:ncol(mu)){
- pr[,i] <- rnorm(n, mean=mu[,i], sd=alpha)
- }
- }
-
- qi <- list(ev=ev, pr = pr)
- qi.name <- list(ev="Expected Values: E(Y|X)", pr="Predicted Values: Y|X")
- if (!is.null(x1)){
- if (class(object) == "glmer"){
- theta1 <- matrix(object at family$linkinv(betas %*% t(as.matrix(fTerms.x1))),
- nrow = nrow(betas))
- if (family == "Gamma")
- ev1 <- theta1 * 1/alpha
- else
- ev1 <- theta1
- qi$fd <- ev1-ev
- qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
- if (family == "binomial") {
- qi$rr <- ev1/ev
- qi.name$rr <- "Risk Ratios: P(Y=1|X1)/P(Y=1|X)"
- }
- }
- else if (class(object) == "lmer"){
- ev1 <- betas %*% t(as.matrix(fTerms.x1))
- qi$fd <- ev1-ev
- qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
- }
- }
- if (!is.null(y)) {
- yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
- tmp.ev <- yvar - qi$ev
- if (family == "binomial"){
- tmp.pr <- yvar - as.integer(qi$pr)
- } else {
- tmp.pr <- yvar - qi$pr
- }
- qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
- qi$att.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(simpar))
- qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
- qi.name$att.pr <- "Average Treatment Effect for the Treated: Y - PR"
- }
- list(qi=qi, qi.name=qi.name)
-}
-
-
+## dbailey at wustl.edu
+## modified by Gregor Gorjanc 2008-01-07
+## modified by Ferdi 10/30/07
+## modified by delia 09/22/08
+################################
+
+qi.mer <- function(object, simpar, x, x1 = NULL, y = NULL) {
+
+ x <- as.data.frame(x)
+ if (!is.null(x1))
+ x1 <- as.data.frame(x1)
+
+
+ ## original dataset
+ D <- eval(object at call$data, envir = parent.frame())
+ fml <- eval(object at call$formula)
+ parsefml <- .getRandAndFixedTerms(fml)
+
+ betas <- simpar[["betas"]]
+ gammas <- simpar[["gammas"]]
+ alpha <- simpar[["scale"]]
+
+
+ fTermsNames <- colnames(model.matrix(parsefml$fixed, data = D))
+ #cat(fTermsNames, "\n")
+
+ fTerms <- x[,fTermsNames]
+ rTerms <- list()
+ for (i in 1:length(parsefml$random)){
+ ## for now, intercept is always present
+ tt <- terms(parsefml$random[[i]])
+ attr(tt,"intercept") <- 1
+ rTermsNames <- colnames(model.matrix(tt,data=D))
+ rTerms[[i]] <- x[, rTermsNames]
+ }
+ names(rTerms) <- names(parsefml$random)
+ if (!is.null(x1)){
+ fTermsNames <- colnames(model.matrix(parsefml$fixed, data = D))
+ fTerms.x1 <- x1[,fTermsNames]
+ rTerms.x1 <- list()
+ for (i in 1:length(parsefml$random)){
+ tt <- terms(parsefml$random[[i]])
+ attr(tt,"intercept") <- 1
+ rTermsNames <- colnames(model.matrix(tt,data=D))
+ rTerms.x1[[i]] <- x1[, rTermsNames]
+ ##rTermsNames <- colnames(model.matrix(parsefml$random[[i]],data=D))
+ ##rTerms.x1[[i]] <- x1[, rTermsNames]
+ }
+ names(rTerms.x1) <- names(parsefml$random)
+ }
+
+ ## Expected Values and Predicted Values
+ if (class(object) == "mer"){
+
+
+ family <- try(object at nlmodel$family$family ,silent=TRUE)
+ if (inherits(family,"try-error")) {
+ family=NULL
+ }
+
+ link <- try(object at nlmodel$family$link ,silent=TRUE)
+ if (inherits(link,"try-error")) {
+ link=NULL
+ }
+
+ eta <- betas %*% t(as.matrix(fTerms))
+ mu <- eta
+ ## For predicted values, add in random effects draws
+ for (i in 1:length(rTerms)){
+ mu <- mu + gammas[[names(rTerms[i])]] %*% t(as.matrix(rTerms[[i]]))
+ }
+ if (is.null(family)){
+ ev <- eta
+ 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)
+ }
+ } else {
+ theta <- matrix(object at nlmodel$family$linkinv(eta), nrow=nrow(betas))
+ mut <- matrix(object at nlmodel$family$linkinv(mu), nrow=nrow(betas))
+ ev <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta))
+ pr <- matrix(NA, nrow=nrow(mut), ncol=ncol(mut))
+ dimnames(ev) <- dimnames(pr) <- dimnames(theta)
+ n <- length(mut[, 1])
+ if (family == "binomial"){
+ ev <- theta
+ for (i in 1:ncol(mut)){
+ pr[,i] <- as.character(rbinom(n, 1, mut[,i]))
+ }
+ if (!is.null(y)) {
+ if (NCOL(y) > 1) {
+ y <- y[,1]
+ }
+ }
+ }
+ else if (family == "Gamma"){
+ ev <- theta * 1/alpha
+ n <- length(mut[i,])
+ for (i in 1:nrow(mut)){
+ 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(n, lambda = mut[,i])
+ }
+ }
+ else {
+ stop(sprintf("no method for %s family", family))
+ }
+ }
+ }
+ qi <- list(ev=ev, pr=pr)
+ qi.name <- list(ev="Expected Values: E(Y|X)", pr="Predicted Values: Y|X")
+ if (!is.null(x1)){
+ if (class(object) == "mer"){
+ if (is.null(family)){
+ ev1 <- betas %*% t(as.matrix(fTerms.x1))
+ }
+ else {
+ theta1 <-
+ matrix(object at nlmodel$family$linkinv(betas %*% t(as.matrix(fTerms.x1))),
+ nrow = nrow(betas))
+ if (family == "Gamma") {
+ ev1 <- theta1 * 1/alpha
+ }
+ else {
+ ev1 <- theta1
+ }
+ if (family == "binomial") {
+ qi$rr <- ev1/ev
+ qi.name$rr <- "Risk Ratios: P(Y=1|X1)/P(Y=1|X)"
+ }
+ }
+ qi$fd <- ev1-ev
+ qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+ }
+ }
+ if (!is.null(y)) {
+ yvar <- matrix(rep(y, nrow(simpar)), nrow = nrow(simpar), byrow = TRUE)
+ tmp.ev <- yvar - qi$ev
+ if (!is.null(family)){
+ if (family == "binomial"){
+ tmp.pr <- yvar - as.integer(qi$pr)
+ } else {
+ tmp.pr <- yvar - qi$pr
+ }
+ }
+ else {
+ tmp.pr <- yvar - qi$pr
+ }
+ qi$att.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(simpar))
+ qi$att.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(simpar))
+ qi.name$att.ev <- "Average Treatment Effect for the Treated: Y - EV"
+ qi.name$att.pr <- "Average Treatment Effect for the Treated: Y - PR"
+ }
+ list(qi=qi, qi.name=qi.name)
+}
+
diff --git a/R/setx.gam.R b/R/setx.gam.R
index e12dac1..340cf92 100644
--- a/R/setx.gam.R
+++ b/R/setx.gam.R
@@ -1,236 +1,236 @@
-setx.gam <- function(object, fn = list(numeric = mean, ordered =
- median, other = mode), data = NULL,
- cond = FALSE, counter = NULL, ...){
- mc <- match.call()
- if (class(object)[1]=="MI")
- object <- object[[1]]
- mode <- function(x){
- tb <- tapply(x, x, length)
- if(is.factor(x))
- value <- factor(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])),
- levels=levels(x))
- else if (is.logical(x))
- value <- as.logical(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
- else if (is.character(x))
- value <- as.character(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
- else
- stop(paste(vars[i], "is not a supported variable type."))
- if (length(value)>1) {
- warning("There is more than one mode. The first level is selected.")
- value <- sort(value)[1]
- }
- return(value)
- }
- median.default <- median
- median <- function(x) {
- if(is.numeric(x))
- value <- median.default(x)
- else if (is.ordered(x)) {
- value <- factor(levels(x)[quantile(as.integer(x), type = 1, prob = 0.5)],
- levels=levels(x))
- } else
- stop("median cannot be calculated for this data type")
- return(value)
- }
- max.default <- max
- max <- function(x, na.rm=FALSE) {
- if(is.numeric(x))
- value <- max.default(x, na.rm=na.rm)
- else if (is.ordered(x))
- value <- factor(levels(x)[length(levels(x))], levels=levels(x))
- else
- stop("max cannot be calculated for this data type")
- return(value)
- }
- min.default <- min
- min <- function(x, na.rm=FALSE) {
- if(is.numeric(x))
- value <- min.default(x, na.rm = na.rm)
- else if (is.ordered(x))
- value <- factor(levels(x)[1], levels=levels(x))
- else
- stop("min cannot be calculated for this data type")
- return(value)
- }
-
-
- # Testing From Here
-
-
- tt <- terms(object)
- tt.attr <- attributes(tt)
- env <- tt.attr$.Environment
- if (is.null(env))
- env <- parent.frame()
- ## original data
- if (is.null(data))
- if (nrow(as.data.frame(object$zelig.data)) > 0)
- dta <- object$zelig.data
- else
- dta <- eval(object$call$data, envir = env)
- else
- dta <- as.data.frame(data)
- ## extract variables we need
- mf <- model.frame(tt, data = dta, na.action = na.pass)
- if(any(class(tt)=="multiple"))
- vars<-unlist(c(attr(tt,"depVars"),attr(tt,"indVars")),use.names=FALSE)
- else
- vars <- all.vars(tt)
- if (!is.null(tt.attr$response) && tt.attr$response)
- resvars <- all.vars(tt.attr$variables[[1+tt.attr$response]])
- else
- resvars <- NULL
- data <- dta[complete.cases(mf), names(dta)%in%vars, drop=FALSE]
- if (!is.null(counter)) {
- if (!any(counter == vars))
- stop("the variable specified for counter is not used in the model")
- treat <- data[, names(data)==counter]
- if(is.numeric(treat)) {
- data[treat==1, names(data)==counter] <- 0
- data[treat==0, names(data)==counter] <- 1
- }
- else if(is.factor(treat)) {
- lev <- levels(treat)
- if(length(lev)==2) {
- treat <- as.numeric(treat) - 1
- data[treat==1, names(data)==counter] <- lev[1]
- data[treat==0, names(data)==counter] <- lev[2]
- }
- else
- stop("counter only takes a binary variable")
- }
- else if(is.logical(treat)) {
- treat <- as.numeric(treat)
- data[treat==1, names(data)==counter] <- FALSE
- data[treat==0, names(data)==counter] <- TRUE
- }
- else
- stop("not supported variable type for counter")
- if(!cond)
- stop("if counter is specified, cond must be TRUE")
- }
- if (cond) {
- if (is.null(data))
- stop("if cond = TRUE, you must specify the data frame.")
- if (is.null(mc$fn))
- fn <- NULL
- if (!is.null(fn)) {
- warning("when cond = TRUE, fn is coerced to NULL")
- fn <- NULL
- }
- maxl <- nrow(data)
- }
- else if (!is.null(fn)) {
- if (is.null(fn$numeric) || !is.function(fn$numeric)) {
- warning("fn$numeric coerced to mean().")
- fn$numeric <- mean
- }
- if (is.null(fn$ordered) || !is.function(fn$ordered) ||
- identical(mean, fn$ordered)) {
- warning("fn$ordered coreced to median().")
- fn$ordered <- median
- }
- else if (identical(min.default, fn$ordered))
- fn$ordered <- min
- else if (identical(max.default, fn$ordered))
- fn$ordered <- max
- else if (identical(median.default, fn$ordered))
- fn$ordered <- median
- if (is.null(fn$other) || !is.function(fn$other)) {
- warning("the only available fn for other is mode.")
- fn$other <- mode
- }
- for (i in 1:ncol(data)) {
- if (!(colnames(data)[i] %in% resvars)) {
- if (is.numeric(data[,i]))
- value <- lapply(list(data[,i]), fn$numeric)[[1]]
- else if (is.ordered(data[,i]))
- value <- lapply(list(data[,i]), fn$ordered)[[1]]
- else
- value <- lapply(list(data[,i]), fn$other)[[1]]
- data[,i] <- value
- }
- }
- maxl <- 1
- } else {
- maxl <- nrow(data)
- }
- opt <- vars[na.omit(pmatch(names(mc), vars))]
- if (length(opt) > 0)
- for (i in 1:length(opt)) {
- value <- eval(mc[[opt[i]]], envir = env)
- lv <- length(value)
- if (lv>1)
- if (maxl==1 || maxl==lv) {
- maxl <- lv
- data <- data[1:lv,,drop = FALSE]
- }
- else
- stop("vector inputs should have the same length.")
- if (is.factor(data[,opt[i]]))
- data[,opt[i]] <- list(factor(value, levels=levels(data[,opt[i]])))
- else if (is.numeric(data[,opt[i]]))
- data[,opt[i]] <- list(as.numeric(value))
- else if (is.logical(data[,opt[i]]))
- data[,opt[i]] <- list(as.logical(value))
- else
- data[,opt[i]] <- list(value)
- }
- data <- data[1:maxl,,drop = FALSE]
-
- if (cond) {
- X <- model.frame(tt, data = dta)
- if (!is.null(counter)) {
- X <- list(treat=X[treat==1,,drop=FALSE],
- control=X[treat==0,,drop=FALSE])
- class(X$treat) <- class(X$control) <- c("data.frame", "cond")
- class(X) <- "setx.counter"
- }
- else
- class(X) <- c("data.frame", "cond")
- }
- else {
- X <- as.data.frame(model.matrix(tt, data = data))
- }
-
- ## Now X has the values I want
- ## Introduce the repetitions
-
- pterms <- attr(object$pterms, "term.labels")
- sterms <- list()
- for(i in 1: length(object$smooth)){
- sterms[[i]] <- object$smooth[[i]]$term }
-
-
- df.vec <- vector()
- for(i in 1: length(object$smooth)){
- df.vec[[i]] <- object$smooth[[i]]$df}
-
- if(attr(object$terms, "intercept") > 0){
- newX <- data.frame(X[[1]])
- names(newX) <- names(X)[[1]] }
- if(attr(object$terms, "intercept") == 0){
- stop("So far, the model must have an intercept")}
-
-
- for(i in 2:ncol(X)){
- if( names(X)[[i]] %in% pterms){
- temp <- as.data.frame(X[[i]])
- names(temp) <- names(X)[[i]]
- newX <- cbind(newX, temp) }
-
- if( names(X)[[i]] %in% sterms){
- for(j in 1: length(object$smooth)){
- if(names(X)[[i]] == object$smooth[[j]]$term)
- repnum <- object$smooth[[j]]$df}
- temp <- as.data.frame(t(rep(X[[i]], repnum)))
- names(temp) <- rep(names(X)[[i]] , repnum)
- newX <- cbind(newX, temp)
- }
-
-
- }
-
-
- return(newX)
-}
+setx.gam <- function(object, fn = list(numeric = mean, ordered =
+ median, other = mode), data = NULL,
+ cond = FALSE, counter = NULL, ...){
+ mc <- match.call()
+ if (class(object)[1]=="MI")
+ object <- object[[1]]
+ mode <- function(x){
+ tb <- tapply(x, x, length)
+ if(is.factor(x))
+ value <- factor(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])),
+ levels=levels(x))
+ else if (is.logical(x))
+ value <- as.logical(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
+ else if (is.character(x))
+ value <- as.character(unlist(labels(tb[seq(along=tb)[tb==max(tb)]])))
+ else
+ stop(paste(vars[i], "is not a supported variable type."))
+ if (length(value)>1) {
+ warning("There is more than one mode. The first level is selected.")
+ value <- sort(value)[1]
+ }
+ return(value)
+ }
+ median.default <- median
+ median <- function(x) {
+ if(is.numeric(x))
+ value <- median.default(x)
+ else if (is.ordered(x)) {
+ value <- factor(levels(x)[quantile(as.integer(x), type = 1, prob = 0.5)],
+ levels=levels(x))
+ } else
+ stop("median cannot be calculated for this data type")
+ return(value)
+ }
+ max.default <- max
+ max <- function(x, na.rm=FALSE) {
+ if(is.numeric(x))
+ value <- max.default(x, na.rm=na.rm)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[length(levels(x))], levels=levels(x))
+ else
+ stop("max cannot be calculated for this data type")
+ return(value)
+ }
+ min.default <- min
+ min <- function(x, na.rm=FALSE) {
+ if(is.numeric(x))
+ value <- min.default(x, na.rm = na.rm)
+ else if (is.ordered(x))
+ value <- factor(levels(x)[1], levels=levels(x))
+ else
+ stop("min cannot be calculated for this data type")
+ return(value)
+ }
+
+
+ # Testing From Here
+
+
+ tt <- terms(object)
+ tt.attr <- attributes(tt)
+ env <- tt.attr$.Environment
+ if (is.null(env))
+ env <- parent.frame()
+ ## original data
+ if (is.null(data))
+ if (nrow(as.data.frame(object$zelig.data)) > 0)
+ dta <- object$zelig.data
+ else
+ dta <- eval(object$call$data, envir = env)
+ else
+ dta <- as.data.frame(data)
+ ## extract variables we need
+ mf <- model.frame(tt, data = dta, na.action = na.pass)
+ if(any(class(tt)=="multiple"))
+ vars<-unlist(c(attr(tt,"depVars"),attr(tt,"indVars")),use.names=FALSE)
+ else
+ vars <- all.vars(tt)
+ if (!is.null(tt.attr$response) && tt.attr$response)
+ resvars <- all.vars(tt.attr$variables[[1+tt.attr$response]])
+ else
+ resvars <- NULL
+ data <- dta[complete.cases(mf), names(dta)%in%vars, drop=FALSE]
+ if (!is.null(counter)) {
+ if (!any(counter == vars))
+ stop("the variable specified for counter is not used in the model")
+ treat <- data[, names(data)==counter]
+ if(is.numeric(treat)) {
+ data[treat==1, names(data)==counter] <- 0
+ data[treat==0, names(data)==counter] <- 1
+ }
+ else if(is.factor(treat)) {
+ lev <- levels(treat)
+ if(length(lev)==2) {
+ treat <- as.numeric(treat) - 1
+ data[treat==1, names(data)==counter] <- lev[1]
+ data[treat==0, names(data)==counter] <- lev[2]
+ }
+ else
+ stop("counter only takes a binary variable")
+ }
+ else if(is.logical(treat)) {
+ treat <- as.numeric(treat)
+ data[treat==1, names(data)==counter] <- FALSE
+ data[treat==0, names(data)==counter] <- TRUE
+ }
+ else
+ stop("not supported variable type for counter")
+ if(!cond)
+ stop("if counter is specified, cond must be TRUE")
+ }
+ if (cond) {
+ if (is.null(data))
+ stop("if cond = TRUE, you must specify the data frame.")
+ if (is.null(mc$fn))
+ fn <- NULL
+ if (!is.null(fn)) {
+ warning("when cond = TRUE, fn is coerced to NULL")
+ fn <- NULL
+ }
+ maxl <- nrow(data)
+ }
+ else if (!is.null(fn)) {
+ if (is.null(fn$numeric) || !is.function(fn$numeric)) {
+ warning("fn$numeric coerced to mean().")
+ fn$numeric <- mean
+ }
+ if (is.null(fn$ordered) || !is.function(fn$ordered) ||
+ identical(mean, fn$ordered)) {
+ warning("fn$ordered coreced to median().")
+ fn$ordered <- median
+ }
+ else if (identical(min.default, fn$ordered))
+ fn$ordered <- min
+ else if (identical(max.default, fn$ordered))
+ fn$ordered <- max
+ else if (identical(median.default, fn$ordered))
+ fn$ordered <- median
+ if (is.null(fn$other) || !is.function(fn$other)) {
+ warning("the only available fn for other is mode.")
+ fn$other <- mode
+ }
+ for (i in 1:ncol(data)) {
+ if (!(colnames(data)[i] %in% resvars)) {
+ if (is.numeric(data[,i]))
+ value <- lapply(list(data[,i]), fn$numeric)[[1]]
+ else if (is.ordered(data[,i]))
+ value <- lapply(list(data[,i]), fn$ordered)[[1]]
+ else
+ value <- lapply(list(data[,i]), fn$other)[[1]]
+ data[,i] <- value
+ }
+ }
+ maxl <- 1
+ } else {
+ maxl <- nrow(data)
+ }
+ opt <- vars[na.omit(pmatch(names(mc), vars))]
+ if (length(opt) > 0)
+ for (i in 1:length(opt)) {
+ value <- eval(mc[[opt[i]]], envir = env)
+ lv <- length(value)
+ if (lv>1)
+ if (maxl==1 || maxl==lv) {
+ maxl <- lv
+ data <- data[1:lv,,drop = FALSE]
+ }
+ else
+ stop("vector inputs should have the same length.")
+ if (is.factor(data[,opt[i]]))
+ data[,opt[i]] <- list(factor(value, levels=levels(data[,opt[i]])))
+ else if (is.numeric(data[,opt[i]]))
+ data[,opt[i]] <- list(as.numeric(value))
+ else if (is.logical(data[,opt[i]]))
+ data[,opt[i]] <- list(as.logical(value))
+ else
+ data[,opt[i]] <- list(value)
+ }
+ data <- data[1:maxl,,drop = FALSE]
+
+ if (cond) {
+ X <- model.frame(tt, data = dta)
+ if (!is.null(counter)) {
+ X <- list(treat=X[treat==1,,drop=FALSE],
+ control=X[treat==0,,drop=FALSE])
+ class(X$treat) <- class(X$control) <- c("data.frame", "cond")
+ class(X) <- "setx.counter"
+ }
+ else
+ class(X) <- c("data.frame", "cond")
+ }
+ else {
+ X <- as.data.frame(model.matrix(tt, data = data))
+ }
+
+ ## Now X has the values I want
+ ## Introduce the repetitions
+
+ pterms <- attr(object$pterms, "term.labels")
+ sterms <- list()
+ for(i in 1: length(object$smooth)){
+ sterms[[i]] <- object$smooth[[i]]$term }
+
+
+ df.vec <- vector()
+ for(i in 1: length(object$smooth)){
+ df.vec[[i]] <- object$smooth[[i]]$df}
+
+ if(attr(object$terms, "intercept") > 0){
+ newX <- data.frame(X[[1]])
+ names(newX) <- names(X)[[1]] }
+ if(attr(object$terms, "intercept") == 0){
+ stop("So far, the model must have an intercept")}
+
+
+ for(i in 2:ncol(X)){
+ if( names(X)[[i]] %in% pterms){
+ temp <- as.data.frame(X[[i]])
+ names(temp) <- names(X)[[i]]
+ newX <- cbind(newX, temp) }
+
+ if( names(X)[[i]] %in% sterms){
+ for(j in 1: length(object$smooth)){
+ if(names(X)[[i]] == object$smooth[[j]]$term)
+ repnum <- object$smooth[[j]]$df}
+ temp <- as.data.frame(t(rep(X[[i]], repnum)))
+ names(temp) <- rep(names(X)[[i]] , repnum)
+ newX <- cbind(newX, temp)
+ }
+
+
+ }
+
+
+ return(newX)
+}
diff --git a/R/zelig.R b/R/zelig.R
index e88b142..19ad096 100644
--- a/R/zelig.R
+++ b/R/zelig.R
@@ -1,2 +1,3 @@
-zelig <- function(formula, ...)
+zelig <- function(formula, model, data, by = NULL, save.data =
+ FALSE, cite = TRUE, ...)
UseMethod("zelig")
diff --git a/R/zelig.citation.R b/R/zelig.citation.R
new file mode 100644
index 0000000..e89893e
--- /dev/null
+++ b/R/zelig.citation.R
@@ -0,0 +1,68 @@
+getModelCitation <- function (modelName){
+ citeobj <- .getModelCitation(modelName)
+ authors <- citeobj$mauthors
+ year <- citeobj$myear
+ title <- citeobj$mtitle
+ const <- citeobj$zconst
+ url <- citeobj$zurl
+ res <- paste("How to cite this model in Zelig:\n", authorsToText(authors),". ", year ,". ", descToText(title,modelName), " ", const, " ", url, "\n" , sep="")
+ res
+
+}
+
+authorsToText <- function(auths){
+ howmany <- length(auths)
+
+ ## if empty it's Kosuke, Gary, Olivia
+ if (howmany == 0) {
+ return ("Kosuke Imai, Gary King, and Oliva Lau")
+ }
+
+ ## if aurhor return it
+ if (howmany == 1){
+ return(auths[[1]])
+ }
+
+ ## if 2 just an "and" in between
+ if (howmany == 2) {
+ return (paste(auths[[1]]," and ", auths[[2]], sep = ""))
+ }
+
+ ## separate by comma and an "and" before the last author
+ res <- paste(auths[1:(howmany - 1)], collapse = ", ", sep="")
+ res <- paste(res, ", and ", auths[[howmany]], sep = "")
+ return(res)
+}
+
+.getModelCitation <- function(modelName){
+
+ zconst <- "in Kosuke Imai, Gary King, and Olivia Lau, \"Zelig: Everyone's Statistical Software,\""
+ zurl <- "http://gking.harvard.edu/zelig"
+ descObject <- Zelig:::zeligDescribeModel(modelName)
+ if (!is.null(descObject)){
+ mauthors <- descObject$authors
+ mtitle <- descObject$description
+ myear <- descObject$year
+
+ }
+ list(mauthors=mauthors, mtitle=mtitle, myear=myear, zconst=zconst, zurl=zurl)
+
+}
+
+descToText <- function(desc, modelName){
+ paste('"',modelName,": ",desc,'"',sep="")
+}
+
+
+
+.getModelCitationTex <- function (modelName){
+ citeobj <- .getModelCitation(modelName)
+ authors <- citeobj$mauthors
+ year <- citeobj$myear
+ title <- citeobj$mtitle
+ const <- citeobj$zconst
+ url <- citeobj$zurl
+ res <- paste(authorsToText(authors),". ", year ,". ", descToText(title,modelName), " ", const, "\\url{", url, '}' , sep="")
+ res
+
+}
diff --git a/R/zelig.default.R b/R/zelig.default.R
index 7251988..4541c8b 100644
--- a/R/zelig.default.R
+++ b/R/zelig.default.R
@@ -1,5 +1,5 @@
zelig.default <- function(formula, model, data, by = NULL, save.data =
- FALSE, ...) {
+ FALSE, cite = TRUE, ...) {
fn1 <- paste("zelig2", model, sep = "")
fn2 <- paste("zelig3", model, sep = "")
@@ -71,7 +71,8 @@ zelig.default <- function(formula, model, data, by = NULL, save.data =
zcall = as.list(zelig.call)))
## check the class of the object (S3/S4)
- check <- length(slotNames(res)) > 0
+ ##check <- length(slotNames(res)) > 0
+ check <- isS4(res)
if (check) { #S4
if ("call" %in% slotNames(res))
res at call <- as.call(zelig.call)
@@ -107,6 +108,9 @@ zelig.default <- function(formula, model, data, by = NULL, save.data =
class(object) <- "strata"
names(object) <- lev
}
+ if (cite){
+ cat(getModelCitation(model))
+ }
return(object)
}
diff --git a/R/zelig2gamma.mixed.R b/R/zelig2gamma.mixed.R
index e122e85..765f8c0 100644
--- a/R/zelig2gamma.mixed.R
+++ b/R/zelig2gamma.mixed.R
@@ -1,17 +1,17 @@
-zelig2gamma.mixed <- function(formula, model, data, M, ...){
- mf <- match.call(expand.dots=TRUE)
- mf[[1]]<- as.name("lmer")
-
- mf$formula <- tolmerFormat(reduceMI(formula))
-
- mf$model <- mf$M <- NULL
-
- if (is.null(mf$family)){
- mf$family <- Gamma
- }
- return(as.call(mf))
-}
-
-model.frame.lmer <- function(obj){
- obj at frame
-}
+zelig2gamma.mixed <- function(formula, model, data, M, ...){
+ mf <- match.call(expand.dots=TRUE)
+ mf[[1]]<- as.name("lmer")
+
+ mf$formula <- tolmerFormat(reduceMI(formula))
+
+ mf$model <- mf$M <- NULL
+
+ if (is.null(mf$family)){
+ mf$family <- Gamma(link="identity")
+ }
+ return(as.call(mf))
+}
+
+model.frame.lmer <- function(obj){
+ obj at frame
+}
\ No newline at end of file
diff --git a/R/zelig2lm.mixed.R b/R/zelig2lm.mixed.R
index f9596a5..294cd2d 100644
--- a/R/zelig2lm.mixed.R
+++ b/R/zelig2lm.mixed.R
@@ -1,15 +1,17 @@
-zelig2ls.mixed <- function(formula, model, data, M, ...){
- mf <- match.call(expand.dots=TRUE)
-
- mf[[1]]<- as.name("lmer")
-
- mf$formula <- tolmerFormat(reduceMI(formula))
-
- mf$model <- mf$M <- NULL
-
- return (as.call(mf))
-}
-
-model.frame.lmer<-function(obj){
-obj at frame
-}
+zelig2ls.mixed <- function(formula, model, data, M, ...){
+ mf <- match.call(expand.dots=TRUE)
+
+ mf[[1]]<- as.name("lmer")
+
+ mf$formula <- tolmerFormat(reduceMI(formula))
+
+ mf$model <- mf$M <- NULL
+
+ mf$family <- NULL
+
+ return (as.call(mf))
+}
+
+model.frame.lmer<-function(obj){
+obj at frame
+}
\ No newline at end of file
diff --git a/R/zelig2negbin.R b/R/zelig2negbin.R
index 7c64930..9c04282 100644
--- a/R/zelig2negbin.R
+++ b/R/zelig2negbin.R
@@ -2,5 +2,7 @@ zelig2negbin <- function(formula, model, data, M, ...) {
mf <- match.call(expand.dots = TRUE)
mf$model <- mf$M <- mf$robust <- NULL
mf[[1]] <- MASS::glm.nb
+ if (is.character(mf$weights))
+ mf$weights <- as.name(mf$weights)
as.call(mf)
}
diff --git a/R/zelig2ologit.R b/R/zelig2ologit.R
index 82509d3..206d048 100644
--- a/R/zelig2ologit.R
+++ b/R/zelig2ologit.R
@@ -4,5 +4,7 @@ zelig2ologit <- function(formula, model, data, M, ...) {
mf[[1]] <- MASS::polr
mf$Hess <- TRUE
mf$method <- "logistic"
+ if (is.character(mf$weights))
+ mf$weights <- as.name(mf$weights)
as.call(mf)
}
diff --git a/R/zelig2oprobit.R b/R/zelig2oprobit.R
index 410f472..e93f01a 100644
--- a/R/zelig2oprobit.R
+++ b/R/zelig2oprobit.R
@@ -4,5 +4,7 @@ zelig2oprobit <- function(formula, model, data, M, ...) {
mf[[1]] <- MASS::polr
mf$Hess <- TRUE
mf$method <- "probit"
+ if (is.character(mf$weights))
+ mf$weights <- as.name(mf$weights)
as.call(mf)
}
diff --git a/R/zelig3mixed.R b/R/zelig3mixed.R
index 3ef7013..0510905 100644
--- a/R/zelig3mixed.R
+++ b/R/zelig3mixed.R
@@ -1,6 +1,8 @@
-zelig3ls.mixed <- zelig3gamma.mixed <- zelig3poisson.mixed <- zelig3probit.mixed <- zelig3logit.mixed <- function (res, fcall = NULL,zcall=NULL) {
- tt <- attr(res at frame, "terms")
- attr(tt, "intercept") <- 1
- res at terms <- tt
- return (res)
-}
+## modified by delia/ferdi 09/22/08
+############################
+
+zelig3ls.mixed <- zelig3gamma.mixed <- zelig3poisson.mixed <- zelig3probit.mixed <-
+ zelig3logit.mixed <- function(res, fcall = NULL, zcall=NULL){
+ res at nlmodel <- fcall at call
+ return (res)
+}
\ No newline at end of file
diff --git a/inst/doc/Rplots.pdf b/inst/doc/Rplots.pdf
index d8d28c0..7c81222 100644
--- a/inst/doc/Rplots.pdf
+++ b/inst/doc/Rplots.pdf
@@ -1,11 +1,11 @@
-%PDF-1.1
+%PDF-1.4
%���ρ�\r
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<<
-/CreationDate (D:20080613180226)
-/ModDate (D:20080613180226)
+/CreationDate (D:20090313100648)
+/ModDate (D:20090313100648)
/Title (R Graphics Output)
-/Producer (R 2.7.0)
+/Producer (R 2.8.1)
/Creator (R)
>>
endobj
@@ -17,23 +17,15 @@ endobj
endobj
5 0 obj
<<
-/Type /Font
-/Subtype /Type1
-/Name /F1
-/BaseFont /ZapfDingbats
->>
-endobj
-6 0 obj
-<<
/Type /Page
/Parent 3 0 R
-/Contents 7 0 R
+/Contents 6 0 R
/Resources 4 0 R
>>
endobj
-7 0 obj
+6 0 obj
<<
-/Length 8 0 R
+/Length 7 0 R
>>
stream
q
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S
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trailer
<<
-/Size 345
+/Size 327
/Info 1 0 R
/Root 2 0 R
>>
startxref
-4203853
+4096546
%%EOF
diff --git a/inst/doc/Sweave.sty b/inst/doc/Sweave.sty
new file mode 100644
index 0000000..a76e1bd
--- /dev/null
+++ b/inst/doc/Sweave.sty
@@ -0,0 +1,33 @@
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesPackage{Sweave}{}
+
+\RequirePackage{ifthen}
+\newboolean{Sweave at gin}
+\setboolean{Sweave at gin}{true}
+\newboolean{Sweave at ae}
+\setboolean{Sweave at ae}{true}
+
+\DeclareOption{nogin}{\setboolean{Sweave at gin}{false}}
+\DeclareOption{noae}{\setboolean{Sweave at ae}{false}}
+\ProcessOptions
+
+\RequirePackage{graphicx,fancyvrb}
+\IfFileExists{upquote.sty}{\RequirePackage{upquote}}{}
+
+\ifthenelse{\boolean{Sweave at gin}}{\setkeys{Gin}{width=0.8\textwidth}}{}%
+\ifthenelse{\boolean{Sweave at ae}}{%
+ \RequirePackage[T1]{fontenc}
+ \RequirePackage{ae}
+}{}%
+
+\DefineVerbatimEnvironment{Sinput}{Verbatim}{fontshape=sl}
+\DefineVerbatimEnvironment{Soutput}{Verbatim}{}
+\DefineVerbatimEnvironment{Scode}{Verbatim}{fontshape=sl}
+
+\newenvironment{Schunk}{}{}
+
+\newcommand{\Sconcordance}[1]{%
+ \ifx\pdfoutput\undefined%
+ \csname newcount\endcsname\pdfoutput\fi%
+ \ifcase\pdfoutput\special{#1}%
+ \else\immediate\pdfobj{#1}\fi}
diff --git a/inst/doc/aov.Rnw b/inst/doc/aov.Rnw
index 0ff8ef3..0583cce 100644
--- a/inst/doc/aov.Rnw
+++ b/inst/doc/aov.Rnw
@@ -232,11 +232,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{aov} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``aov: Analysis of Variance for Continuous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-Elena Villalon implemented the software.
-\end{verse}
+\input{cites/aov}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/aov.pdf b/inst/doc/aov.pdf
index dcbd9ac..6198411 100644
Binary files a/inst/doc/aov.pdf and b/inst/doc/aov.pdf differ
diff --git a/inst/doc/arima.Rnw b/inst/doc/arima.Rnw
index 2955a6d..7c388ef 100644
--- a/inst/doc/arima.Rnw
+++ b/inst/doc/arima.Rnw
@@ -302,12 +302,7 @@ counterfactual values specified in {\tt x}.
\subsection* {How to Cite}
-To cite the ARIMA Zelig module:
-\begin{verse}
-Justin Grimmer. 2007. ``ARIMA: Models for Time Series Data,'' in Kosuke Imai,
-Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/arima}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/arima.pdf b/inst/doc/arima.pdf
index 49fd2fd..d6b4010 100644
Binary files a/inst/doc/arima.pdf and b/inst/doc/arima.pdf differ
diff --git a/inst/doc/blogit.Rnw b/inst/doc/blogit.Rnw
index 21d5f8f..100321e 100644
--- a/inst/doc/blogit.Rnw
+++ b/inst/doc/blogit.Rnw
@@ -321,10 +321,7 @@ below.
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{blogit} Zelig model use:
-\begin{verse}
-Kosuke Imai,Gary King, and Olivia Lau. 2007. ``blogit: Bivariate Logistic Regression for Two Dichotomous Dependent Variable,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/blogit}
\input{citeZelig}
\subsection*{See also}
The bivariate logit function is part of the VGAM package by Thomas Yee \citep{YeeHas03}. In addition, advanced users may wish to refer to \texttt{help(vglm)}
diff --git a/inst/doc/blogit.pdf b/inst/doc/blogit.pdf
index 1bb822e..427d1b8 100644
Binary files a/inst/doc/blogit.pdf and b/inst/doc/blogit.pdf differ
diff --git a/inst/doc/bprobit.Rnw b/inst/doc/bprobit.Rnw
index ced513d..c36f642 100644
--- a/inst/doc/bprobit.Rnw
+++ b/inst/doc/bprobit.Rnw
@@ -357,10 +357,7 @@ obtain a default summary of information through
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{bprobit} Zelig model use:
-\begin{verse}
-Kosuke Imai,Gary King, and Olivia Lau. 2007. ``bprobit: Bivariate Probit Regression for Two Dichotomous Dependent Variable,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/bprobit}
\input{citeZelig}
\subsection*{See also}
The bivariate probit function is part of the VGAM package by Thomas Yee \citep{YeeHas03}. In addition, advanced users may wish to refer to \texttt{help(vglm)}
diff --git a/inst/doc/bprobit.pdf b/inst/doc/bprobit.pdf
index 2c3bceb..edbfee2 100644
Binary files a/inst/doc/bprobit.pdf and b/inst/doc/bprobit.pdf differ
diff --git a/inst/doc/chopit.Rnw b/inst/doc/chopit.Rnw
index ac82cec..74b0e78 100644
--- a/inst/doc/chopit.Rnw
+++ b/inst/doc/chopit.Rnw
@@ -1,7 +1,7 @@
\SweaveOpts{eval=false, results=hide, prefix.string=vigpics/chopit}
\include{zinput}
%\VignetteIndexEntry{Compound Hierarchical Ordered Probit for Survey Vignettes}
-%\VignetteDepends{Zelig, stats, anchors}
+%\VignetteDepends{Zelig, stats}
%\VignetteKeyWords{model,chopit,anchors,regression}
%\VignettePackage{Zelig}
\begin{document}
@@ -352,12 +352,7 @@ output from {\tt zelig()}, but no items can be extracted.
\subsection* {How to Cite}
-To cite the \emph{chopit} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``chopit: Compound Hierarchical
-Ordered Probit for Survey Vignettes'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/chopit}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/chopit.pdf b/inst/doc/chopit.pdf
index 0f7b4e3..c0f5217 100644
Binary files a/inst/doc/chopit.pdf and b/inst/doc/chopit.pdf differ
diff --git a/inst/doc/citeZelig.tex b/inst/doc/citeZelig.tex
index ca77527..b6c5b25 100644
--- a/inst/doc/citeZelig.tex
+++ b/inst/doc/citeZelig.tex
@@ -4,8 +4,5 @@ To cite Zelig as a whole, please reference these two sources:
Statistical Software,'' \url{http://GKing.harvard.edu/zelig}.
\end{verse}
\begin{verse}
- Kosuke Imai, Gary King, and Olivia Lau. 2008. ``Toward A Common
- Framework for Statistical Analysis and Development,'' \emph{Journal
- of Computational and Graphical Statistics}, forthcoming,
- \url{http://gking.harvard.edu/files/abs/z-abs.shtml}.
+Imai, Kosuke, Gary King, and Olivia Lau. (2008). ``Toward A Common Framework for Statistical Analysis and Development.'' Journal of Computational and Graphical Statistics, Vol. 17, No. 4 (December), pp. 892-913.
\end{verse}
diff --git a/inst/doc/cloglog.net.Rnw b/inst/doc/cloglog.net.Rnw
index df10c6a..20d77b1 100644
--- a/inst/doc/cloglog.net.Rnw
+++ b/inst/doc/cloglog.net.Rnw
@@ -162,11 +162,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{cloglog.net} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``cloglog.net: Social Network Complementary Log Log Regression for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/cloglog.net}
\input{citeZelig}
\subsection* {See also}
The network complementary log log regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netbinom)}. Sample data are fictional.
diff --git a/inst/doc/cloglog.net.pdf b/inst/doc/cloglog.net.pdf
index 8dd4ede..10cc298 100644
Binary files a/inst/doc/cloglog.net.pdf and b/inst/doc/cloglog.net.pdf differ
diff --git a/inst/doc/commands/matchit.aux b/inst/doc/commands/matchit.aux
new file mode 100644
index 0000000..de51f5a
--- /dev/null
+++ b/inst/doc/commands/matchit.aux
@@ -0,0 +1,70 @@
+\relax
+\citation{HoImaKin05b}
+\@writefile{toc}{\contentsline {section}{\numberline {11.1}{\tt matchit}: Create matched data}{101}{section.11.1}}
+\newlabel{matchit}{{11.1}{101}{{\tt matchit}: Create matched data\relax }{section.11.1}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{101}{section*.65}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{101}{section*.66}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{101}{section*.67}}
+\@writefile{toc}{\contentsline {paragraph}{Arguments for All Matching Methods}{101}{section*.68}}
+\newlabel{subsubsec:inputs-all}{{11.1}{102}{Additional Arguments for Specification of Distance Measures\relax }{section*.69}{}}
+\@writefile{toc}{\contentsline {paragraph}{Additional Arguments for Specification of Distance Measures}{102}{section*.69}}
+\citation{Imai05}
+\newlabel{subsubsec:inputs-subclass}{{11.1}{104}{Additional Arguments for Subclassification\relax }{section*.70}{}}
+\@writefile{toc}{\contentsline {paragraph}{Additional Arguments for Subclassification}{104}{section*.70}}
+\newlabel{subsubsec:inputs-nearest}{{11.1}{104}{Additional Arguments for Nearest Neighbor Matching\relax }{section*.71}{}}
+\@writefile{toc}{\contentsline {paragraph}{Additional Arguments for Nearest Neighbor Matching}{104}{section*.71}}
+\newlabel{subsubsec:inputs-optimal}{{11.1}{105}{Additional Arguments for Optimal Matching\relax }{section*.72}{}}
+\@writefile{toc}{\contentsline {paragraph}{Additional Arguments for Optimal Matching}{105}{section*.72}}
+\newlabel{subsubsec:inputs-full}{{11.1}{105}{Additional Arguments for Full Matching\relax }{section*.73}{}}
+\@writefile{toc}{\contentsline {paragraph}{Additional Arguments for Full Matching}{105}{section*.73}}
+\newlabel{subsubsec:inputs-genetic}{{11.1}{105}{Additional Arguments for Genetic Matching\relax }{section*.74}{}}
+\@writefile{toc}{\contentsline {paragraph}{Additional Arguments for Genetic Matching}{105}{section*.74}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{105}{section*.75}}
+\newlabel{sec:outputs}{{11.1}{105}{Output Values\relax }{section*.75}{}}
+\citation{HoImaKin07}
+\citation{HoImaKin05b}
+\citation{StoKinZen06}
+\citation{KinZen06a}
+\citation{KinZen06b}
+\citation{VenRip02}
+\citation{HasTib90}
+\citation{Ripley96}
+\citation{BreFriOls84}
+\citation{Hansen04}
+\citation{DiaSek05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{106}{section*.76}}
+\@setckpt{commands/matchit}{
+\setcounter{page}{108}
+\setcounter{equation}{0}
+\setcounter{enumi}{4}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{2}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{11}
+\setcounter{section}{1}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{LT at tables}{0}
+\setcounter{LT at chunks}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{216}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{FancyVerbLine}{0}
+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/commands/matchit.tex b/inst/doc/commands/matchit.tex
index d8d38f2..207c6c2 100644
--- a/inst/doc/commands/matchit.tex
+++ b/inst/doc/commands/matchit.tex
@@ -21,9 +21,359 @@ documentation (available at
\hlink{\url{http://gking.harvard.edu/matchit/}}{http://gking.harvard.edu/matchit/})
for more details.
-\input{commands/matchitref}
+\subsubsection{Syntax}
+\begin{verbatim}
+> m.out <- matchit(formula, data, method = "nearest", verbose = FALSE, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\paragraph{Arguments for All Matching Methods}
+
+\begin{itemize}
+
+\item \texttt{formula}: formula used to calculate the distance measure
+ for matching. It takes the usual syntax of R formulas, {\tt treat
+ \~\ x1 + x2}, where {\tt treat} is a binary treatment indicator,
+ and {\tt x1} and {\tt x2} are the pre-treatment covariates. Both the
+ treatment indicator and pre-treatment covariates must be contained
+ in the same data frame, which is specified as {\tt data} (see
+ below). All of the usual R syntax for formulas work here. For
+ example, {\tt x1:x2} represents the first order interaction term
+ between {\tt x1} and {\tt x2}, and {\tt I(x1 \^\ 2)} represents the
+ square term of {\tt x1}. See {\tt help(formula)} for details.
+
+\item \texttt{data}: the data frame containing the variables called in
+ {\tt formula}.
+
+\item \texttt{method}: the matching method (default =
+ \texttt{"nearest"}, nearest neighbor matching). Currently,
+ \texttt{"exact"} (exact matching), \texttt{"full"} (full matching),
+ \texttt{"nearest"} (nearest neighbor matching), \texttt{"optimal"}
+ (optimal matching), \texttt{"subclass"} (subclassification), and
+ \texttt{"genetic"} (genetic matching) are available. Note that
+ within each of these matching methods, \MatchIt\ offers a variety of
+ options. See below for more details.
+
+\item \texttt{verbose}: a logical value indicating whether to print
+ the status of the matching algorithm (default = \texttt{FALSE}).
+\end{itemize}
+
+
+\paragraph{Additional Arguments for Specification of
+ Distance Measures}
+\label{subsubsec:inputs-all}
+
+The following arguments specify distance measures that are used for
+matching methods. These arguments apply to all matching methods {\it
+ except exact matching}.
+
+\begin{itemize}
+
+\item \texttt{distance}: the method used to estimate the distance
+ measure (default = {\tt "logit"}, logistic regression) or a
+ numerical vector of user's own distance measure. Before using any
+ of these techniques, it is best to understand the theoretical
+ groundings of these techniques and to evaluate the results. Most of
+ these methods (such as logistic or probit regression) estimate the
+ propensity score, defined as the probability of receiving treatment,
+ conditional on the covariates. Available methods include:
+ \begin{itemize}
+ \item {\tt "mahalanobis"}: the Mahalanobis distance measure.
+ \item binomial generalized linear models with one of the following
+ link functions:
+ \begin{itemize}
+ \item \texttt{"logit"}: logistic link
+ \item {\tt "linear.logit"}: logistic link with linear propensity
+ score)\footnote{The linear propensity scores are obtained by
+ transforming back onto a linear scale.}
+ \item \texttt{"probit"}: probit link
+ \item {\tt "linear.probit"}: probit link with linear propensity
+ score
+ \item {\tt "cloglog"}: complementary log-log link
+ \item {\tt "linear.cloglog"}: complementary log-log link with linear
+ propensity score
+ \item {\tt "log"}: log link
+ \item {\tt "linear.log"}: log link with linear propensity score
+ \item {\tt "cauchit"} Cauchy CDF link
+ \item {\tt "linear.cauchit"} Cauchy CDF link with linear propensity
+ score
+ \end{itemize}
+ \item Choose one of the following generalized additive models (see
+ {\tt help(gam)} for more options).
+ \begin{itemize}
+ \item \texttt{"GAMlogit"}: logistic link
+ \item {\tt "GAMlinear.logit"}: logistic link with linear propensity
+ score
+ \item \texttt{"GAMprobit"}: probit link
+ \item {\tt "GAMlinear.probit"}: probit link with linear propensity
+ score
+ \item {\tt "GAMcloglog"}: complementary log-log link
+ \item {\tt "GAMlinear.cloglog"}: complementary log-log link with
+ linear propensity score
+ \item {\tt "GAMlog"}: log link
+ \item {\tt "GAMlinear.log"}: log link with linear propensity score,
+ \item {\tt "GAMcauchit"}: Cauchy CDF link
+ \item {\tt "GAMlinear.cauchit"}: Cauchy CDF link with linear
+ propensity score
+\end{itemize}
+\item \texttt{"nnet"}: neural network model. See {\tt help(nnet)} for
+ more options.
+\item \texttt{"rpart"}: classification trees. See {\tt help(rpart)}
+ for more options.
+ \end{itemize}
+
+\item \texttt{distance.options}: optional arguments for estimating the
+ distance measure. The input to this argument should be a list. For
+ example, if the distance measure is estimated with a logistic
+ regression, users can increase the maximum IWLS iterations by
+ \texttt{distance.options = list(maxit = 5000)}. Find additional
+ options for general linear models using {\tt help(glm)} or {\tt
+ help(family)}, for general additive models using {\tt help(gam)},
+ for neutral network models {\tt help(nnet)}, and for classification
+ trees {\tt help(rpart)}.
+
+\item \texttt{discard}: specifies whether to discard units that fall
+ outside some measure of support of the distance measure (default =
+ \texttt{"none"}, discard no units). Discarding units may change the
+ quantity of interest being estimated. Enter a logical vector
+ indicating which unit should be discarded or choose from the
+ following options:
+ \begin{itemize}
+ \item \texttt{"none"}: no units will be discarded before matching.
+ Use this option when the units to be matched are substantially
+ similar, such as in the case of matching treatment and control
+ units from a field experiment that was close to (but not fully)
+ randomized (e.g., \citealt{Imai05}), when caliper matching will
+ restrict the donor pool, or when you do not wish to change the
+ quantity of interest and the parametric methods to be used
+ post-matching can be trusted to extrapolate.
+ \item \texttt{"hull.both"}: all units that are not within the convex
+ hull will be discarded. We recommend that this option be used
+ with observational data sets.
+ \item \texttt{"both"}: all units (treated and control) that are
+ outside the support of the distance measure will be discarded.
+ \item \texttt{"hull.control"}: only control units that are not
+ within the convex hull of the treated units will be discarded.
+ \item \texttt{"control"}: only control units outside the support of
+ the distance measure of the treated units will be discarded. Use
+ this option when the average treatment effect on the treated is of
+ most interest and when you are unwilling to discard
+ non-overlapping treatment units (which would change the quantity
+ of interest).
+ \item \texttt{"hull.treat"}: only treated units that are not within
+ the convex hull of the control units will be discarded.
+ \item \texttt{"treat"}: only treated units outside the support of
+ the distance measure of the control units will be discarded. Use
+ this option when the average treatment effect on the control units
+ is of most interest and when unwilling to discard control units.
+ \end{itemize}
+
+\item \texttt{reestimate}: If {\tt FALSE} (default), the model for the
+ distance measure will not be re-estimated after units are discarded.
+ The input must be a logical value. Re-estimation may be desirable
+ for efficiency reasons, especially if many units were discarded and
+ so the post-discard samples are quite different from the original
+ samples.
+
+\end{itemize}
+
+\paragraph{Additional Arguments for Subclassification}
+\label{subsubsec:inputs-subclass}
+
+\begin{itemize}
+\item \texttt{sub.by}: criteria for subclassification. Choose from:
+ \texttt{"treat"} (default), the number of treatment units;
+ \texttt{"control"}, the number of control units; or \texttt{"all"},
+ the total number of units.
+\item \texttt{subclass}: either a scalar specifying the number of
+ subclasses, or a vector of probabilities bounded between 0 and 1,
+ which create quantiles of the distance measure using the units in
+ the group specified by \texttt{sub.by} (default = \texttt{subclass =
+ 6}).
+\end{itemize}
+
+\paragraph{Additional Arguments for Nearest Neighbor Matching}
+\label{subsubsec:inputs-nearest}
+
+\begin{itemize}
+\item \texttt{m.order}: the order in which to match treatment units
+ with control units.
+ \begin{itemize}
+ \item {\tt "largest"} (default): matches from the largest value of
+ the distance measure to the smallest.
+ \item {\tt "smallest"}: matches from the smallest value of the
+ distance measure to the largest.
+ \item {\tt "random"}: matches in random order.
+ \end{itemize}
+\item \texttt{replace}: logical value indicating whether each control
+ unit can be matched to more than one treated unit (default = {\tt
+ replace = FALSE}, each control unit is used at most once -- i.e.,
+ sampling without replacement). For matching with replacement, use
+ \texttt{replace = TRUE}.
+\item \texttt{ratio}: the number of control units to match to each
+ treated unit (default = {\tt 1}). If matching is done without
+ replacement and there are fewer control units than {\tt ratio} times
+ the number of eligible treated units (i.e., there are not enough
+ control units for the specified method), then the higher ratios will
+ have \texttt{NA} in place of the matching unit number in
+ \texttt{match.matrix}.
+\item \texttt{exact}: variables on which to perform exact matching
+ within the nearest neighbor matching (default = {\tt NULL}, no exact
+ matching). If \texttt{exact} is specified, only matches that
+ exactly match on the covariates in \texttt{exact} will be allowed.
+ Within the matches that match on the variables in \texttt{exact},
+ the match with the closest distance measure will be chosen.
+ \texttt{exact} should be entered as a vector of variable names
+ (e.g., \texttt{exact = c("X1", "X2")}).
+\item \texttt{caliper}: the number of standard deviations of the
+ distance measure within which to draw control units (default = {\tt
+ 0}, no caliper matching). If a caliper is specified, a control
+ unit within the caliper for a treated unit is randomly selected as
+ the match for that treated unit. If \texttt{caliper != 0}, there
+ are two additional options:
+ \begin{itemize}
+ \item \texttt{calclosest}: whether to take the nearest available
+ match if no matches are available within the \texttt{caliper}
+ (default = {\tt FALSE}).
+ \item \texttt{mahvars}: variables on which to perform
+ Mahalanobis-metric matching within each caliper (default = {\tt
+ NULL}). Variables should be entered as a vector of variable
+ names (e.g., \texttt{mahvars = c("X1", "X2")}). If
+ \texttt{mahvars} is specified without \texttt{caliper}, the
+ caliper is set to 0.25.
+ \end{itemize}
+\item \texttt{subclass} and \texttt{sub.by}: See the options for
+ subclassification for more details on these options. If a
+ \texttt{subclass} is specified within \texttt{method = "nearest"},
+ the matched units will be placed into subclasses after the nearest
+ neighbor matching is completed.
+\end{itemize}
+
+\paragraph{Additional Arguments for Optimal Matching}
+\label{subsubsec:inputs-optimal}
+
+\begin{itemize}
+\item {\tt ratio}: the number of control units to be matched to each
+ treatment unit (default = {\tt 1}).
+\item {\tt ...}: additional inputs that can be passed to the {\tt
+ fullmatch()} function in the {\tt optmatch} package. See {\tt
+ help(fullmatch)} or
+ \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html}
+ for details.
+\end{itemize}
+
+\paragraph{Additional Arguments for Full Matching}
+\label{subsubsec:inputs-full}
+
+\begin{itemize}
+\item {\tt ...}: additional inputs that can be passed to the {\tt
+ fullmatch()} function in the {\tt optmatch} package. See {\tt
+ help(fullmatch)} or
+ \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html} for details.
+\end{itemize}
+
+\paragraph{Additional Arguments for Genetic Matching}
+\label{subsubsec:inputs-genetic}
+
+The available options are listed below.
+\begin{itemize}
+\item {\tt ratio}: the number of control units to be matched to each
+ treatment unit (default = {\tt 1}).
+\item {\tt ...}: additional minor inputs that can be passed to the
+ {\tt GenMatch()} function in the {\tt Matching} package. See {\tt
+ help(GenMatch)} or
+ \hlink{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}
+ for details.
+\end{itemize}
+
+\subsubsection{Output Values}
+\label{sec:outputs}
+
+Regardless of the type of matching performed, the \texttt{matchit}
+output object contains the following elements:\footnote{When
+ inapplicable or unnecessary, these elements may equal {\tt NULL}.
+ For example, when exact matching, {\tt match.matrix = NULL}.}
+
+\begin{itemize}
+\item \texttt{call}: the original {\tt matchit()} call.
+
+\item \texttt{formula}: the formula used to specify the model for
+ estimating the distance measure.
+
+\item \texttt{model}: the output of the model used to estimate
+ the distance measure. \texttt{summary(m.out\$model)} will give the
+ summary of the model where \texttt{m.out} is the output object from
+ \texttt{matchit()}.
+
+\item \texttt{match.matrix}: an $n_1 \times$ \texttt{ratio} matrix
+ where:
+ \begin{itemize}
+ \item the row names represent the names of the treatment units
+ (which match the row names of the data frame specified in
+ \texttt{data}).
+ \item each column stores the name(s) of the control unit(s) matched
+ to the treatment unit of that row. For example, when the
+ \texttt{ratio} input for nearest neighbor or optimal matching is
+ specified as 3, the three columns of \texttt{match.matrix}
+ represent the three control units matched to one treatment unit).
+ \item \texttt{NA} indicates that the treatment unit was not matched.
+ \end{itemize}
+
+\item \texttt{discarded}: a vector of length $n$ that displays whether
+ the units were ineligible for matching due to common support
+ restrictions. It equals \texttt{TRUE} if unit $i$ was discarded,
+ and it is set to \texttt{FALSE} otherwise.
+
+\item \texttt{distance}: a vector of length $n$ with the estimated
+ distance measure for each unit.
+
+\item \texttt{weights}: a vector of length $n$ with the weights
+ assigned to each unit in the matching process. Unmatched units have
+ weights equal to $0$. Matched treated units have weight $1$. Each
+ matched control unit has weight proportional to the number of
+ treatment units to which it was matched, and the sum of the control
+ weights is equal to the number of uniquely matched control units.
+
+\item \texttt{subclass}: the subclass index in an ordinal scale from 1
+ to the total number of subclasses as specified in \texttt{subclass}
+ (or the total number of subclasses from full or exact matching).
+ Unmatched units have \texttt{NA}.
+
+\item \texttt{q.cut}: the subclass cut-points that classify the
+ distance measure.
+
+\item \texttt{treat}: the treatment indicator from \texttt{data} (the
+ left-hand side of \texttt{formula}).
+
+\item \texttt{X}: the covariates used for estimating the distance
+ measure (the right-hand side of \texttt{formula}). When applicable,
+ \texttt{X} is augmented by covariates contained in \texttt{mahvars}
+ and \texttt{exact}.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+If you use \MatchIt, please cite
+\begin{verse}
+\bibentry{HoImaKin07}
+and
+\bibentry{HoImaKin05b}
+\end{verse}
+
+The {\tt convex.hull} discard option is implemented via the {\tt
+ WhatIf} package \citep{StoKinZen06,KinZen06a,KinZen06b}.
+Generalized linear distance measures are implemented via the {\tt
+ 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 \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
+\citep{Hansen04}. Genetic matching is implemented via the {\tt
+ Matching} package \citep{DiaSek05}.
%%% Local Variables:
%%% mode: latex
-%%% TeX-master: "zelig"
+%%% TeX-master: "matchit"
%%% End:
diff --git a/inst/doc/commands/matchitref.tex b/inst/doc/commands/matchitref.tex
deleted file mode 100644
index ace8f81..0000000
--- a/inst/doc/commands/matchitref.tex
+++ /dev/null
@@ -1,356 +0,0 @@
-\subsubsection{Syntax}
-\begin{verbatim}
-> m.out <- matchit(formula, data, method = "nearest", verbose = FALSE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\paragraph{Arguments for All Matching Methods}
-
-\begin{itemize}
-
-\item \texttt{formula}: formula used to calculate the distance measure
- for matching. It takes the usual syntax of R formulas, {\tt treat
- \~\ x1 + x2}, where {\tt treat} is a binary treatment indicator,
- and {\tt x1} and {\tt x2} are the pre-treatment covariates. Both the
- treatment indicator and pre-treatment covariates must be contained
- in the same data frame, which is specified as {\tt data} (see
- below). All of the usual R syntax for formulas work here. For
- example, {\tt x1:x2} represents the first order interaction term
- between {\tt x1} and {\tt x2}, and {\tt I(x1 \^\ 2)} represents the
- square term of {\tt x1}. See {\tt help(formula)} for details.
-
-\item \texttt{data}: the data frame containing the variables called in
- {\tt formula}.
-
-\item \texttt{method}: the matching method (default =
- \texttt{"nearest"}, nearest neighbor matching). Currently,
- \texttt{"exact"} (exact matching), \texttt{"full"} (full matching),
- \texttt{"nearest"} (nearest neighbor matching), \texttt{"optimal"}
- (optimal matching), \texttt{"subclass"} (subclassification), and
- \texttt{"genetic"} (genetic matching) are available. Note that
- within each of these matching methods, \MatchIt\ offers a variety of
- options. See below for more details.
-
-\item \texttt{verbose}: a logical value indicating whether to print
- the status of the matching algorithm (default = \texttt{FALSE}).
-\end{itemize}
-
-
-\paragraph{Additional Arguments for Specification of
- Distance Measures}
-\label{subsubsec:inputs-all}
-
-The following arguments specify distance measures that are used for
-matching methods. These arguments apply to all matching methods {\it
- except exact matching}.
-
-\begin{itemize}
-
-\item \texttt{distance}: the method used to estimate the distance
- measure (default = {\tt "logit"}, logistic regression) or a
- numerical vector of user's own distance measure. Before using any
- of these techniques, it is best to understand the theoretical
- groundings of these techniques and to evaluate the results. Most of
- these methods (such as logistic or probit regression) estimate the
- propensity score, defined as the probability of receiving treatment,
- conditional on the covariates. Available methods include:
- \begin{itemize}
- \item {\tt "mahalanobis"}: the Mahalanobis distance measure.
- \item binomial generalized linear models with one of the following
- link functions:
- \begin{itemize}
- \item \texttt{"logit"}: logistic link
- \item {\tt "linear.logit"}: logistic link with linear propensity
- score)\footnote{The linear propensity scores are obtained by
- transforming back onto a linear scale.}
- \item \texttt{"probit"}: probit link
- \item {\tt "linear.probit"}: probit link with linear propensity
- score
- \item {\tt "cloglog"}: complementary log-log link
- \item {\tt "linear.cloglog"}: complementary log-log link with linear
- propensity score
- \item {\tt "log"}: log link
- \item {\tt "linear.log"}: log link with linear propensity score
- \item {\tt "cauchit"} Cauchy CDF link
- \item {\tt "linear.cauchit"} Cauchy CDF link with linear propensity
- score
- \end{itemize}
- \item Choose one of the following generalized additive models (see
- {\tt help(gam)} for more options).
- \begin{itemize}
- \item \texttt{"GAMlogit"}: logistic link
- \item {\tt "GAMlinear.logit"}: logistic link with linear propensity
- score
- \item \texttt{"GAMprobit"}: probit link
- \item {\tt "GAMlinear.probit"}: probit link with linear propensity
- score
- \item {\tt "GAMcloglog"}: complementary log-log link
- \item {\tt "GAMlinear.cloglog"}: complementary log-log link with
- linear propensity score
- \item {\tt "GAMlog"}: log link
- \item {\tt "GAMlinear.log"}: log link with linear propensity score,
- \item {\tt "GAMcauchit"}: Cauchy CDF link
- \item {\tt "GAMlinear.cauchit"}: Cauchy CDF link with linear
- propensity score
-\end{itemize}
-\item \texttt{"nnet"}: neural network model. See {\tt help(nnet)} for
- more options.
-\item \texttt{"rpart"}: classification trees. See {\tt help(rpart)}
- for more options.
- \end{itemize}
-
-\item \texttt{distance.options}: optional arguments for estimating the
- distance measure. The input to this argument should be a list. For
- example, if the distance measure is estimated with a logistic
- regression, users can increase the maximum IWLS iterations by
- \texttt{distance.options = list(maxit = 5000)}. Find additional
- options for general linear models using {\tt help(glm)} or {\tt
- help(family)}, for general additive models using {\tt help(gam)},
- for neutral network models {\tt help(nnet)}, and for classification
- trees {\tt help(rpart)}.
-
-\item \texttt{discard}: specifies whether to discard units that fall
- outside some measure of support of the distance measure (default =
- \texttt{"none"}, discard no units). Discarding units may change the
- quantity of interest being estimated. Enter a logical vector
- indicating which unit should be discarded or choose from the
- following options:
- \begin{itemize}
- \item \texttt{"none"}: no units will be discarded before matching.
- Use this option when the units to be matched are substantially
- similar, such as in the case of matching treatment and control
- units from a field experiment that was close to (but not fully)
- randomized (e.g., \citealt{Imai05}), when caliper matching will
- restrict the donor pool, or when you do not wish to change the
- quantity of interest and the parametric methods to be used
- post-matching can be trusted to extrapolate.
- \item \texttt{"hull.both"}: all units that are not within the convex
- hull will be discarded. We recommend that this option be used
- with observational data sets.
- \item \texttt{"both"}: all units (treated and control) that are
- outside the support of the distance measure will be discarded.
- \item \texttt{"hull.control"}: only control units that are not
- within the convex hull of the treated units will be discarded.
- \item \texttt{"control"}: only control units outside the support of
- the distance measure of the treated units will be discarded. Use
- this option when the average treatment effect on the treated is of
- most interest and when you are unwilling to discard
- non-overlapping treatment units (which would change the quantity
- of interest).
- \item \texttt{"hull.treat"}: only treated units that are not within
- the convex hull of the control units will be discarded.
- \item \texttt{"treat"}: only treated units outside the support of
- the distance measure of the control units will be discarded. Use
- this option when the average treatment effect on the control units
- is of most interest and when unwilling to discard control units.
- \end{itemize}
-
-\item \texttt{reestimate}: If {\tt FALSE} (default), the model for the
- distance measure will not be re-estimated after units are discarded.
- The input must be a logical value. Re-estimation may be desirable
- for efficiency reasons, especially if many units were discarded and
- so the post-discard samples are quite different from the original
- samples.
-
-\end{itemize}
-
-\paragraph{Additional Arguments for Subclassification}
-\label{subsubsec:inputs-subclass}
-
-\begin{itemize}
-\item \texttt{sub.by}: criteria for subclassification. Choose from:
- \texttt{"treat"} (default), the number of treatment units;
- \texttt{"control"}, the number of control units; or \texttt{"all"},
- the total number of units.
-\item \texttt{subclass}: either a scalar specifying the number of
- subclasses, or a vector of probabilities bounded between 0 and 1,
- which create quantiles of the distance measure using the units in
- the group specified by \texttt{sub.by} (default = \texttt{subclass =
- 6}).
-\end{itemize}
-
-\paragraph{Additional Arguments for Nearest Neighbor Matching}
-\label{subsubsec:inputs-nearest}
-
-\begin{itemize}
-\item \texttt{m.order}: the order in which to match treatment units
- with control units.
- \begin{itemize}
- \item {\tt "largest"} (default): matches from the largest value of
- the distance measure to the smallest.
- \item {\tt "smallest"}: matches from the smallest value of the
- distance measure to the largest.
- \item {\tt "random"}: matches in random order.
- \end{itemize}
-\item \texttt{replace}: logical value indicating whether each control
- unit can be matched to more than one treated unit (default = {\tt
- replace = FALSE}, each control unit is used at most once -- i.e.,
- sampling without replacement). For matching with replacement, use
- \texttt{replace = TRUE}.
-\item \texttt{ratio}: the number of control units to match to each
- treated unit (default = {\tt 1}). If matching is done without
- replacement and there are fewer control units than {\tt ratio} times
- the number of eligible treated units (i.e., there are not enough
- control units for the specified method), then the higher ratios will
- have \texttt{NA} in place of the matching unit number in
- \texttt{match.matrix}.
-\item \texttt{exact}: variables on which to perform exact matching
- within the nearest neighbor matching (default = {\tt NULL}, no exact
- matching). If \texttt{exact} is specified, only matches that
- exactly match on the covariates in \texttt{exact} will be allowed.
- Within the matches that match on the variables in \texttt{exact},
- the match with the closest distance measure will be chosen.
- \texttt{exact} should be entered as a vector of variable names
- (e.g., \texttt{exact = c("X1", "X2")}).
-\item \texttt{caliper}: the number of standard deviations of the
- distance measure within which to draw control units (default = {\tt
- 0}, no caliper matching). If a caliper is specified, a control
- unit within the caliper for a treated unit is randomly selected as
- the match for that treated unit. If \texttt{caliper != 0}, there
- are two additional options:
- \begin{itemize}
- \item \texttt{calclosest}: whether to take the nearest available
- match if no matches are available within the \texttt{caliper}
- (default = {\tt FALSE}).
- \item \texttt{mahvars}: variables on which to perform
- Mahalanobis-metric matching within each caliper (default = {\tt
- NULL}). Variables should be entered as a vector of variable
- names (e.g., \texttt{mahvars = c("X1", "X2")}). If
- \texttt{mahvars} is specified without \texttt{caliper}, the
- caliper is set to 0.25.
- \end{itemize}
-\item \texttt{subclass} and \texttt{sub.by}: See the options for
- subclassification for more details on these options. If a
- \texttt{subclass} is specified within \texttt{method = "nearest"},
- the matched units will be placed into subclasses after the nearest
- neighbor matching is completed.
-\end{itemize}
-
-\paragraph{Additional Arguments for Optimal Matching}
-\label{subsubsec:inputs-optimal}
-
-\begin{itemize}
-\item {\tt ratio}: the number of control units to be matched to each
- treatment unit (default = {\tt 1}).
-\item {\tt ...}: additional inputs that can be passed to the {\tt
- fullmatch()} function in the {\tt optmatch} package. See {\tt
- help(fullmatch)} or
- \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html}
- for details.
-\end{itemize}
-
-\paragraph{Additional Arguments for Full Matching}
-\label{subsubsec:inputs-full}
-
-\begin{itemize}
-\item {\tt ...}: additional inputs that can be passed to the {\tt
- fullmatch()} function in the {\tt optmatch} package. See {\tt
- help(fullmatch)} or
- \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html} for details.
-\end{itemize}
-
-\paragraph{Additional Arguments for Genetic Matching}
-\label{subsubsec:inputs-genetic}
-
-The available options are listed below.
-\begin{itemize}
-\item {\tt ratio}: the number of control units to be matched to each
- treatment unit (default = {\tt 1}).
-\item {\tt ...}: additional minor inputs that can be passed to the
- {\tt GenMatch()} function in the {\tt Matching} package. See {\tt
- help(GenMatch)} or
- \hlink{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}
- for details.
-\end{itemize}
-
-\subsubsection{Output Values}
-\label{sec:outputs}
-
-Regardless of the type of matching performed, the \texttt{matchit}
-output object contains the following elements:\footnote{When
- inapplicable or unnecessary, these elements may equal {\tt NULL}.
- For example, when exact matching, {\tt match.matrix = NULL}.}
-
-\begin{itemize}
-\item \texttt{call}: the original {\tt matchit()} call.
-
-\item \texttt{formula}: the formula used to specify the model for
- estimating the distance measure.
-
-\item \texttt{model}: the output of the model used to estimate
- the distance measure. \texttt{summary(m.out\$model)} will give the
- summary of the model where \texttt{m.out} is the output object from
- \texttt{matchit()}.
-
-\item \texttt{match.matrix}: an $n_1 \times$ \texttt{ratio} matrix
- where:
- \begin{itemize}
- \item the row names represent the names of the treatment units
- (which match the row names of the data frame specified in
- \texttt{data}).
- \item each column stores the name(s) of the control unit(s) matched
- to the treatment unit of that row. For example, when the
- \texttt{ratio} input for nearest neighbor or optimal matching is
- specified as 3, the three columns of \texttt{match.matrix}
- represent the three control units matched to one treatment unit).
- \item \texttt{NA} indicates that the treatment unit was not matched.
- \end{itemize}
-
-\item \texttt{discarded}: a vector of length $n$ that displays whether
- the units were ineligible for matching due to common support
- restrictions. It equals \texttt{TRUE} if unit $i$ was discarded,
- and it is set to \texttt{FALSE} otherwise.
-
-\item \texttt{distance}: a vector of length $n$ with the estimated
- distance measure for each unit.
-
-\item \texttt{weights}: a vector of length $n$ with the weights
- assigned to each unit in the matching process. Unmatched units have
- weights equal to $0$. Matched treated units have weight $1$. Each
- matched control unit has weight proportional to the number of
- treatment units to which it was matched, and the sum of the control
- weights is equal to the number of uniquely matched control units.
-
-\item \texttt{subclass}: the subclass index in an ordinal scale from 1
- to the total number of subclasses as specified in \texttt{subclass}
- (or the total number of subclasses from full or exact matching).
- Unmatched units have \texttt{NA}.
-
-\item \texttt{q.cut}: the subclass cut-points that classify the
- distance measure.
-
-\item \texttt{treat}: the treatment indicator from \texttt{data} (the
- left-hand side of \texttt{formula}).
-
-\item \texttt{X}: the covariates used for estimating the distance
- measure (the right-hand side of \texttt{formula}). When applicable,
- \texttt{X} is augmented by covariates contained in \texttt{mahvars}
- and \texttt{exact}.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-If you use \MatchIt, please cite
-\begin{verse}
-\bibentry{HoImaKin07}
-and
-\bibentry{HoImaKin05b}
-\end{verse}
-
-The {\tt convex.hull} discard option is implemented via the {\tt
- WhatIf} package \citep{StoKinZen06,KinZen06a,KinZen06b}.
-Generalized linear distance measures are implemented via the {\tt
- 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 \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
-%%% TeX-master: "matchit"
-%%% End:
diff --git a/inst/doc/commands/mi.tex b/inst/doc/commands/mi.tex
deleted file mode 100644
index 7d2ecbc..0000000
--- a/inst/doc/commands/mi.tex
+++ /dev/null
@@ -1,36 +0,0 @@
-\section{{\tt mi}: Create a list of multiply imputed data frames}
-\label{mi.command}
-
-\subsubsection{Description}
-
-Use {\tt mi()} to create a list of multiply imputed data frames for
-use with {\tt zelig()}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-mi(data1, data2, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt data1}: the first multiply imputed data frame.
-\item {\tt data2}: the second multiply imputed data frame.
-\item {\tt \dots}: additional multiply imputed data frames.
-\end{itemize}
-
-\subsubsection{Output Values}
-A list of data frames with class {\tt "mi"}
-\subsubsection{See Also}
-\begin{itemize}
-\item See \Sref{mi.ex} to use {\tt mi()} with {\tt zelig()}.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/model.end.aux b/inst/doc/commands/model.end.aux
index f5be021..b44e52a 100644
--- a/inst/doc/commands/model.end.aux
+++ b/inst/doc/commands/model.end.aux
@@ -1,14 +1,6 @@
\relax
-\@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}}
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-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{554}{section*.691}}
-\@writefile{toc}{\contentsline {subsubsection}{See Also}{554}{section*.692}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{554}{section*.693}}
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diff --git a/inst/doc/commands/model.end.tex b/inst/doc/commands/model.end.tex
deleted file mode 100644
index 3433a60..0000000
--- a/inst/doc/commands/model.end.tex
+++ /dev/null
@@ -1,49 +0,0 @@
-\section{{\tt model.end}: Cleaning up after optimization}
-\label{model.end}
-
-\subsubsection{Description}
-
-The {\tt model.end()} function creates a list of regression output from
-{\tt optim()} output. The list includes coefficients (from the {\tt optim()
-par} output), a variance-covariance matrix (from the {\tt optim()} Hessian output),
-and any terms, contrasts, or xlevels (from the model frame). Use {\tt
-model.end()} after calling {\tt optim()}, but before assigning a class to the
-regression output.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-model.end(res, mf)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt res}: the output from {\tt optim()} or another fitting-algorithm.
-\item {\tt mf}: the model frame output by {\tt model.frame()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-A list of regression output, including:
-\begin{itemize}
-\item {\tt coefficients}: the optimized parameters.
-\item {\tt variance}: the variance-covariance matrix (the negative
- inverse of the Hessian matrix returned from the optimization
- procedure).
-\item {\tt terms}: the terms object. See {\tt help(terms.object)}
- for more information.
-\item {\tt \dots}: additional elements passed from {\tt res}.
-\end{itemize}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{s:new} for an overview of how to write a new model.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/model.frame.multiple.aux b/inst/doc/commands/model.frame.multiple.aux
index ab47701..aaa4bc1 100644
--- a/inst/doc/commands/model.frame.multiple.aux
+++ b/inst/doc/commands/model.frame.multiple.aux
@@ -1,15 +1,6 @@
\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.2}{\tt model.frame.multiple}: Extracting the ``environment'' of a model formula}{555}{section.13.2}}
-\newlabel{model.frame.multiple}{{13.2}{555}{{\tt model.frame.multiple}: Extracting the ``environment'' of a model formula\relax }{section.13.2}{}}
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-\@writefile{toc}{\contentsline {subsubsection}{See Also}{556}{section*.699}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{556}{section*.700}}
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diff --git a/inst/doc/commands/model.frame.multiple.tex b/inst/doc/commands/model.frame.multiple.tex
deleted file mode 100644
index b6016aa..0000000
--- a/inst/doc/commands/model.frame.multiple.tex
+++ /dev/null
@@ -1,80 +0,0 @@
-\section{{\tt model.frame.multiple}: Extracting the ``environment'' of
-a model formula}
-\label{model.frame.multiple}
-
-\subsubsection{Description}
-Use {\tt model.frame.multiple()} after {\tt parse.par()} to create a
-data frame of the unique variables identified in the formula (or list
-of formulas).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-model.frame.multiple(formula, data, eqn = NULL, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
- \item {\tt formula}: a list of formulas of class {\tt "multiple"},
-returned from {\tt parse.par()}.
- \item {\tt data}: a data frame containing all the variables used in
-{\tt formula}.
-\item {\tt eqn}: an optional character string or vector of character strings
-specifying the equations for which you would like to extract variables. Defaults
-to {\tt NULL}, which pulls out all the variables for all equations in {\tt
-formula}.
- \item {\tt \dots}: additional arguments passed to {\tt
-model.frame.default()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output is a data frame (with a terms attribute) containing all the
-unique explanatory and response variables identified in the list of
-formulas. By default, missing ({\tt NA}) values are listwise deleted.
-
-If {\tt as.factor()} appears on the left-hand side, the response
-variables will be returned as an indicator (0/1) matrix with columns
-corresponding to the unique levels in the factor variable.
-
-If any formula contains a {\tt tag()}, {\tt model.frame.multiple()}
-will return the original variable in the data frame and use the {\tt
-tag()} information in the terms attribute only.
-
-\subsubsection{Examples}
-\begin{verbatim}
-formulae <- list(import ~ coop + cost + target,
- export ~ coop + cost + target)
-fml <- parse.formula(formulae, model = "bivariate.logit")
-D <- model.frame(fml, data = mydata)
-\end{verbatim}
-Since the output from {\tt parse.formula()} is of class {\tt
-"multiple"}, you do not need to call {\tt model.frame.multiple()}
-explicitly, but can use the generic {\tt model.frame()} instead.
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{parse.formula} for {\tt parse.formula()}
-\item \Sref{ui} for an overview of the user-interface.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/model.matrix.multiple.aux b/inst/doc/commands/model.matrix.multiple.aux
index 95b3c90..b21d3ef 100644
--- a/inst/doc/commands/model.matrix.multiple.aux
+++ b/inst/doc/commands/model.matrix.multiple.aux
@@ -1,15 +1,6 @@
\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.3}{\tt model.matrix.multiple}: Design matrix for multivariate models}{557}{section.13.3}}
-\newlabel{model.matrix.multiple}{{13.3}{557}{{\tt model.matrix.multiple}: Design matrix for multivariate models\relax }{section.13.3}{}}
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diff --git a/inst/doc/commands/model.matrix.multiple.tex b/inst/doc/commands/model.matrix.multiple.tex
deleted file mode 100644
index ec6b9d6..0000000
--- a/inst/doc/commands/model.matrix.multiple.tex
+++ /dev/null
@@ -1,111 +0,0 @@
-\section{{\tt model.matrix.multiple}: Design matrix for multivariate models}
-\label{model.matrix.multiple}
-
-\subsubsection{Description}
-Use {\tt model.matrix.multiple()} after {\tt parse.formula()} to
-create a design matrix for multiple-equation models.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-model.matrix(object, data, shape = "compact", eqn = NULL, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
- \item {\tt object}: the list of formulas output from \texttt{parse.formula()}.
- \item {\tt data}: a data frame created with {\tt model.frame.multiple()}.
- \item {\tt shape}: a character string specifying the shape of the
- outputed matrix. Available options are:
-
-\begin{itemize}
-
-\item {\tt "compact"}: (default) the output matrix will be an $n \times v$,
-where $v$ is the number of unique variables in all of the equations
-(including the intercept term).
-
-\item {\tt "array"}: the output is an $n \times K \times J$ array where $J$ is the
-total number of equations and $K$ is the total number of parameters
-across all the equations. If a variable is not in a certain equation,
-it is observed as a vector of 0s.
-
-\item {\tt "stacked"}: the output will be a $2n \times K$ matrix where $K$ is the total number of parameters across all the equations.
-
-\end{itemize}
-
- \item {\tt eqn}: a character string or a vector of character
- strings identifying the equations from which to construct the design
- matrix. The defaults to {\tt NULL}, which only uses the systematic
- parameters (for which {\tt DepVar = TRUE} in the appropriate {\tt
-describe.model()}) .
-\item {\tt \dots}: additional arguments passed to {\tt model.matrix.default()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-A design matrix or array, depending on the options chosen in {\tt
-shape}, with appropriate terms attributes.
-
-\subsubsection{Examples}
-Let's say that the name of the model is {\tt "bivariate.probit"}, and
-the corresponding describe function is {\tt
-describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
-as systematic components, and an ancillary parameter {\tt rho}, which
-may be parameterized, but is estimated as a scalar by default. Let
-{\tt par} be the parameter vector (including parameters for {\tt
-rho}), {\tt formulae} a user-specified formula given in one of the
-formats in Table \ref{good.formulas}, and {\tt mydata} the user
-specified data frame.
-
-Acceptable combinations of {\tt parse.par()} and {\tt model.matrix()}
-are as follows:
-\begin{verbatim}
-## Setting up
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = mydata)
-terms <- terms(D)
-
-## Intuitive option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
-eta <- X %*% Beta
-
-## Memory-efficient (compact) option (default)
-Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
-eta <- X %*% Beta
-
-## Computationally-efficient (array) option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
-eta <- apply(X, 3, '%*%', Beta)
-\end{verbatim}
-In each case, {\tt eta} is an $n \times 2$ matrix with columns
-corresponding to the linear predictors for {\tt mu1} and {\tt mu2},
-respectively.
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{parse.par} for selecting and shaping parameter vectors.
-\item \Sref{s:new} for examples of how to write new models.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/network.tex b/inst/doc/commands/network.tex
deleted file mode 100644
index 477748f..0000000
--- a/inst/doc/commands/network.tex
+++ /dev/null
@@ -1,49 +0,0 @@
-\subsection{ \texttt{network}: Format Individual Matricies into a Data Frame for Social Network Analysis }\label{network}
-
-\subsubsection{Description}
-This function accepts individual matricies as its inputs, combining
-the input matricies into a single data frame which can then
-be used in the {\tt data} argument for social network analysis (models
-\verb|"netlm"| and \verb|"netlogit"|) in Zelig.\\
-
-\subsubsection{Usage}
-\begin{verbatim}
-network(...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{enumerate}
-\item[...] Sociomatricies. These can be given as named arguments and should be given in the order the in which the user wishes them to appear in the dataframe.
-\end{enumerate}
-
-\subsubsection{Details}
-The \verb|network()| function creates a dataframe which contains
-matricies instead of vectors as its variables. Inputs to the function
-should all be square matricies and can be given as named arguments.
-
-\noindent A matrix within the data frame can be called as a named
-argument with
-\begin{verbatim}
-> data.name$matrix.name
-\end{verbatim} %$
-or numerically with
-\begin{verbatim}
-> data.name[i]
-\end{verbatim}
-
-
-\subsubsection{Example}
-
-Attach sample data (individual matricies from the {\tt sna.ex} dataset):
-\begin{verbatim}
-data(Var1, Var2, Var3, Var4, Var5)
-\end{verbatim}
-
-To make a dataframe formated for social network analysis:
-\begin{verbatim}
-> friendship <- network(Var1, Var2, Var3, Var4, Var5)
-\end{verbatim}
-
-\subsubsection{Contributors}
-This function was written by Skyler J. Cranmer.
diff --git a/inst/doc/commands/parse.formula.aux b/inst/doc/commands/parse.formula.aux
index a92644d..01ae0be 100644
--- a/inst/doc/commands/parse.formula.aux
+++ b/inst/doc/commands/parse.formula.aux
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diff --git a/inst/doc/commands/parse.formula.tex b/inst/doc/commands/parse.formula.tex
deleted file mode 100644
index 968b1ad..0000000
--- a/inst/doc/commands/parse.formula.tex
+++ /dev/null
@@ -1,113 +0,0 @@
-\section{{\tt parse.formula}: Parsing the inputs}
-\label{parse.formula}
-
-\subsubsection{Description}
-Parse the input formula (or list of formulas) into the standard format
-described below. Since labels for this format will vary by model,
-{\tt parse.formula()} will evaluate a function {\tt describe.model()},
-where {\tt model} is given as an input to {\tt parse.formula()}.
-
-If the {\tt check.model()} function has more than one parameter for
-which {\tt ExpVar = TRUE} and {\tt DepVar = TRUE}, then the
-user-specified equations must have labels to match those parameters,
-else {\tt parse.formula()} should return an error. In addition, if the
-formula entries are not unambiguous, then {\tt parse.formula()} should
-return an error.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> fml <- parse.formula(formula, model, data = NULL)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
- \item {\tt formula}: either a single formula or a list of formula
-objects.
- \item {\tt model}: a character string specifying the name of the model.
- \item {\tt data}: an optional data frame for models that require a
-factor response variable.
-\end{itemize}
-
-\subsubsection{Output Values}
-The output is a list of formula objects with class {\tt ("multiple",
-"list")}. Let's say that the name of the model is {\tt
-"bivariate.probit"}, and the corresponding describe function is {\tt
-describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
-as systematic components, and an ancillary parameter {\tt rho}, which
-may be parameterized, but is estimated as a scalar by default. Given
-this model, Table \ref{good.formulas} gives acceptable user inputs.
-
-\subsubsection{Examples}
-\begin{verbatim}
-formulae <- list(cbind(import, export) ~ coop + cost + target)
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = mydata)
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{ui} for commented examples of how {\tt parse.formula()}
-and {\tt describe.model()} work together.
- \item \Sref{tag} for constraints between coefficients in a multiple
-equation context.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-\clearpage
-\begin{landscape}
-\begin{table}[h!]
-\caption{Examples of acceptable short-hand for user-specified
-formulas, using bivariate probit as an example}
-\label{good.formulas}
-\begin{center}
-\begin{tabular}{lll}
-\\
- & User Input & Output from {\tt parse.formula()} \\
-\hline
-\\
-Same covariates, & \verb|cbind(y1, y2) ~ x1 + x2 * x3| & \verb|list(mu1 = y1 ~ x1 + x2 * x3,|\\
-separate effects & & \verb| mu2 = y2 ~ x1 + x2 * x3,| \\
-& & \verb| rho = ~ 1)| \\
-\\
-With $\rho$ as a & \verb|list(cbind(y1, y2) ~ x1 + x2,| &
-\verb|list(mu1 = y1 ~ x1 + x2,|\\
-systematic equation & \verb| rho = ~ x4 + x5)| & \verb| mu2 = y2 ~ x1 + x2,|\\
-& & \verb| rho = ~ x4 + x5)| \\
-\\
-With constraints & \verb|list(mu1 = y1 ~ x1 + tag(x2, "x2"),| &
-\verb|list(mu1 = y1 ~ x1 + tag(x2, "x2"),| \\
-(same variable) & \verb| mu2 = y2 ~ x3 + tag(x2, "x2"))| &
-\verb| mu2 = y2 ~ x3 + tag(x2, "x2"),|\\
-& & \verb| rho = ~ 1)|\\
-\\
-With constraints & \verb|list(mu1 = y1 ~ x1 + tag(x2, "z1"),| &
-\verb|list(mu1 = y1 ~ x1 + tag(x2, "z1"),| \\
-(different variables) & \verb| mu2 = y2 ~ x3 + tag(x4, "z1"))| &
-\verb| mu2 = y2 ~ x3 + tag(x4, "z1"),|\\
-& & \verb| rho = ~ 1)|\\
-\end{tabular}
-\end{center}
-\end{table}
-\end{landscape}
-
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/parse.par.aux b/inst/doc/commands/parse.par.aux
index 4ca0bb4..421ca06 100644
--- a/inst/doc/commands/parse.par.aux
+++ b/inst/doc/commands/parse.par.aux
@@ -1,15 +1,6 @@
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-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{562}{section*.716}}
-\@writefile{toc}{\contentsline {subsubsection}{Arguments}{562}{section*.717}}
-\@writefile{toc}{\contentsline {subsubsection}{Output Values}{562}{section*.718}}
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-\@writefile{toc}{\contentsline {subsubsection}{See Also}{563}{section*.720}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{563}{section*.721}}
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diff --git a/inst/doc/commands/parse.par.tex b/inst/doc/commands/parse.par.tex
deleted file mode 100644
index a806b9d..0000000
--- a/inst/doc/commands/parse.par.tex
+++ /dev/null
@@ -1,95 +0,0 @@
-\section{{\tt parse.par}: Select and reshape parameter vectors}\label{parse.par}
-
-\subsubsection{Description}
-
-The {\tt parse.par()} function reshapes parameter vectors for
-compatability with the output matrix from {\tt
-model.matrix.multiple()}. (See\Sref{model.matrix.multiple}.) Use {\tt
-parse.par()} to identify sets of parameters; for example, within
-optimization functions that require vector input, or within {\tt qi()}
-functions that take matrix input of all parameters as a lump.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-parse.par(par, terms, shape = "matrix", eqn = NULL)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt par}: the vector (or matrix) of parameters.
-\item {\tt terms}: the terms from either {\tt model.frame.*()} or
- {\tt model.matrix.*()}.
-\item {\tt shape}: a character string (either {\tt "matrix"} or {\tt "vector"})
-that identifies the type of output structure.
-\item {\tt eqn}: a character string (or strings) that identify the
-parameters that you would like to subset from the larger {\tt par}
-structure.
-\end{itemize}
-
-\subsubsection{Output Values}
-A matrix or vector of the sub-setted (and reshaped) parameters for the specified
-parameters given in {\tt eqn}. By default, {\tt eqn = NULL}, such that all systematic
-components are selected. (Systematic components have {\tt ExpVar =
-TRUE} in the appropriate {\tt describe.model()} function.)
-
-If an ancillary parameter (for which {\tt ExpVar = FALSE} in
-{\tt describe.model()}) is specified in {\tt eqn}, it is
-always returned as a vector (ignoring {\tt shape}). (Ancillary
-parameters are all parameters that have intercept only formulas.)
-
-\subsubsection{Examples}
-Let's say that the name of the model is {\tt "bivariate.probit"}, and
-the corresponding describe function is {\tt
-describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
-as systematic components, and an ancillary parameter {\tt rho}, which
-may be parameterized, but is estimated as a scalar by default. Let
-{\tt par} be the parameter vector (including parameters for {\tt
-rho}), {\tt formulae} a user-specified formula given in one of the
-formats in Table \ref{good.formulas}, and {\tt mydata} the user
-specified data frame.
-
-Acceptable combinations of {\tt parse.par()} and {\tt model.matrix()}
-are as follows:
-\begin{verbatim}
-## Setting up
-fml <- parse.formula(formulae, model = "bivariate.probit")
-D <- model.frame(fml, data = mydata)
-terms <- terms(D)
-
-## Intuitive option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
-eta <- X %*% Beta
-
-## Memory-efficient (compact) option (default)
-Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
-eta <- X %*% Beta
-
-## Computationally-efficient (array) option
-Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
-X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
-eta <- apply(X, 3, '%*%', Beta)
-\end{verbatim}
-In each case, {\tt eta} is an $n \times 2$ matrix with columns
-corresponding to the linear predictors for {\tt mu1} and {\tt mu2},
-respectively.
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{model.matrix.multiple} for a description of how multiple
-equation models work with {\tt model.matrix()}.
-\item \Sref{constraints} for more detail on the three combinations
-(intuitive, memory-efficient, and computationally-efficient) methods
-of multiplying matrices of parameters and variables.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/plot.ci.tex b/inst/doc/commands/plot.ci.tex
deleted file mode 100644
index 99ebfd0..0000000
--- a/inst/doc/commands/plot.ci.tex
+++ /dev/null
@@ -1,86 +0,0 @@
-\section{{\tt plot.ci}: Plotting Vertical confidence Intervals}\label{plot.ci}
-
-\subsubsection{Description}
-The \texttt{plot.ci()} command generates vertical confidence intervals
-for linear or generalized linear univariate response models.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> plot.ci(object, CI = 95, qi = "ev", col = c("red", "blue"), ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt object}: stored output from {\tt sim()}. The {\tt x} and optional
- {\tt x1} values used to generate the {\tt sim()} output object must
- have more than one observation.
-\item {\tt CI}: the selected confidence interval. Defaults to 95
- percent.
-\item {\tt qi}: the selected quantity of interest. Defaults to
- expected values.
-\item {\tt col}: a vector of at most two colors for plotting the
- expected value given by {\tt x} and the alternative set of expected
- values given by {\tt x1} in {\tt sim()}. If the quantity of
- interest selected is not the expected value, or {\tt x1 = NULL},
- only the first color will be used.
-\item {\tt \dots}: Additional parameters, such as {\tt xlab}, {\tt
- ylab}, and {\tt main}, passed to {\tt plot()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-For all univariate response models, {\tt plot.ci()} returns vertical
-confidence intervals over a specified range of one explanatory
-variable. You may save this plot using the commands described in
-\Sref{ss:output}.
-
-\subsubsection{Example}
-
-This demo is available within R as {\tt demo(vertci)}. Load the
-sample data and estimate the model.
-\begin{verbatim}
-data(turnout)
-z.out <- zelig(vote ~ race + educate + age + I(age^2) + income,
- model = "logit", data = turnout)
-\end{verbatim}
-Establish a range for {\tt `age'}, the key explanatory variable, and
-create two {\tt setx} objects that hold the other explanatory
-variables to fixed, while allowing age to range from 18 to 95, inclusive.
-\begin{verbatim}
-age.range <- 18:95
-x.low <- setx(z.out, educate = 12, age = age.range)
-x.high <- setx(z.out, educate = 16, age = age.range)
-\end{verbatim}
-Simulate quantities of interest.
-\begin{verbatim}
-s.out <- sim(z.out, x = x.low, x1 = x.high)
-\end{verbatim}
-Plot these results using the {\tt plot.ci()} function:
-\begin{verbatim}
-plot.ci(s.out, xlab = "Age in Years",
- ylab = "Predicted Probability of Voting",
- main = "Effect of Education and Age on Voting Behavior")
-legend(45, 0.52, legend = c("College Education (16 years)",
- "High School Education (12 years)"), col = c("blue","red"),
- lty = c("solid"))
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item The {\tt help(plot)} and {\tt help(lines)} reference pages.
- \item \Sref{s:plot} for an overview of plotting procedures
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau created the R procedure to
-generate vertical confidence intervals for simulated quantities of
-interest.
-
-Sample data are a selection of $2,000$ observations from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/plot.zelig.tex b/inst/doc/commands/plot.zelig.tex
deleted file mode 100644
index 90d7ff5..0000000
--- a/inst/doc/commands/plot.zelig.tex
+++ /dev/null
@@ -1,68 +0,0 @@
-\section{{\tt plot}: Graphing Quantities of Interest}\label{ss:plot.zelig}
-
-\subsubsection{Description}
-The \texttt{plot()} command generates default plots for {\tt
- sim()} objects with one-observation values in {\tt x} and {\tt x1}.
- Additional default plotting methods for other data structures are
- described in {\tt help(plot)}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> plot(s.out, alt.col = "red", user.par = FALSE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item{\tt s.out}: stored output from {\tt sim()}. If the {\tt x} or
- {\tt x1} values stored in the object contain more than one
- observation, {\tt plot.zelig()} will return an error. For linear or
- generalized linear models with more than one observation in {\tt x}
- and optionally {\tt x1}, you may use {\tt plot.ci()} (see \Sref{plot.ci}).
-\item{\tt alt.col}: alternative color used for contrast in the plots.
- The primary color is black. Type {\tt colors()} to see a list of
- available alternative colors.
-\item{\tt user.par}: a logical value indicating whether to use the
- default Zelig plotting parameters ({\tt user.par = FALSE}) or
- user-defined parameters ({\tt user.par = TRUE}), set using the {\tt
- par()} function prior to plotting.
-\item{\tt \dots}: Additional parameters passed to {\tt plot()}.
- Because {\tt plot.zelig()} primarily produces diagnostic plots, many
- of these parameters are hard-coded for convenience and
- presentation. If you wish to produce plots using different
- parameters, you may follow the directions in \Sref{s:plot}.
-\end{itemize}
-
-\subsubsection{Output Values}
-Depending on the class of model selected, \texttt{plot.zelig()} will
-return an on-screen window with graphs of the various quantities of
-interest. You may save these plots using the commands described in
-\Sref{ss:output}.
-
-\subsubsection{Examples}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "logit", data = mydata)
-> x.out <- setx(z.out, x1 = 10)
-> x.alt <- setx(z.out, x1 = 0)
-> s.out <- sim(z.out, x = x.out, x1 = x.alt)
-> plot(s.out)
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item The {\tt help(plot)} and {\tt help(lines)} reference pages for
- optional arguments passed to {\tt \dots}.
-\item The {\tt help(par)} reference page for user-specified plotting
- parameters.
-\item \Sref{s:plot} for an overview of plotting procedures
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau created the plots generated by
-{\tt plot.zelig()}. These plots are individually available in the
-base, MASS, and vcd libraries.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/print.aux b/inst/doc/commands/print.aux
index 9e58496..a958b58 100644
--- a/inst/doc/commands/print.aux
+++ b/inst/doc/commands/print.aux
@@ -27,7 +27,6 @@
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\setcounter{Item}{216}
@@ -40,5 +39,6 @@
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diff --git a/inst/doc/commands/put.start.aux b/inst/doc/commands/put.start.aux
index e0b8270..0afb276 100644
--- a/inst/doc/commands/put.start.aux
+++ b/inst/doc/commands/put.start.aux
@@ -1,14 +1,6 @@
\relax
-\@writefile{toc}{\contentsline {section}{\numberline {13.6}{\tt put.start}: Set specific starting values for certain parameters}{564}{section.13.6}}
-\newlabel{put.start}{{13.6}{564}{{\tt put.start}: Set specific starting values for certain parameters\relax }{section.13.6}{}}
-\@writefile{toc}{\contentsline {subsubsection}{Description}{564}{section*.722}}
-\@writefile{toc}{\contentsline {subsubsection}{Syntax}{564}{section*.723}}
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-\@writefile{toc}{\contentsline {subsubsection}{See Also}{564}{section*.726}}
-\@writefile{toc}{\contentsline {subsubsection}{Contributors}{564}{section*.727}}
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diff --git a/inst/doc/commands/put.start.tex b/inst/doc/commands/put.start.tex
deleted file mode 100644
index 1d7d9af..0000000
--- a/inst/doc/commands/put.start.tex
+++ /dev/null
@@ -1,44 +0,0 @@
-\section{{\tt put.start}: Set specific starting values for certain parameters}
-\label{put.start}
-
-\subsubsection{Description}
-
-After calling {\tt set.start()} to create default starting values, use
-{\tt put.start()} to change starting values for specific parameters or
-parameter sets.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-put.start(start.val, value, terms, eqn)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt start.val}: the vector of starting values created by {\tt
-set.start()}.
-\item {\tt value}: the scalar or vector of replacement starting
-values.
-\item {\tt terms}: the terms output from {\tt model.frame.multiple()}.
-\item {\tt eqn}: the parameters for which you would like to replace
-the default values with {\tt value}.
-\end{itemize}
-
-\subsubsection{Output Values}
-A vector of starting values (of the same length as {\tt start.val}).
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{set.start} to set default starting values.
-\item \Sref{s:new} for an overview of the procedure to add
- models to Zelig.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/replicate.tex b/inst/doc/commands/replicate.tex
deleted file mode 100644
index 78c3f3e..0000000
--- a/inst/doc/commands/replicate.tex
+++ /dev/null
@@ -1,82 +0,0 @@
-\section{{\tt repl}: Replicating Analyses}\label{ss:replicate}
-
-\subsubsection{Description}
-The {\tt repl()} command included with Zelig takes {\tt zelig()}
-or {\tt sim()} output objects and replicates (literally, re-runs) the
-entire analysis. The results should be an output object identical to
-the original input object in the case of {\tt zelig()} output. In the
-case of {\tt sim()} output, the replicated analyses may differ
-slightly due to stochastic randomness in the simulation procedure.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> repl(z.out, data = NULL)
-> repl(s.out, data = NULL, prev = NULL, x = NULL, x1 = NULL,
- bootfn = NULL)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt z.out}: Stored output from {\tt zelig()}.
-\item {\tt s.out}: Stored output from {\tt sim()}
-\item {\tt data}: You may manually input the data frame name rather
- than allowing replicate to draw the data frame name from the object
- to be replicated.
-\item {\tt prev}: When replicating {\tt sim()} output, you may
- optionally use the previously simulated parameters to calculate the
- quantities of interest rather than simulating a new set of
- parameters. For all models, this should produce identical
- quantities of interest. If the parameters were
- bootstrapped in the original analysis, this will save a considerable
- amount of time.
-\item {\tt x}: When replicating {\tt sim()} output, you may
- optionally use an alternative {\tt setx()} value for the {\tt x}
- input.
-\item {\tt x1}: When replicating {\tt sim()} output, you may
- optionally use an alternative {\tt setx()} object for the {\tt x1}
- input to replicating the {\tt sim()} object.
-\item {\tt bootfn}: When replicating {\tt sim()} output with
- bootstrapped parameters, you should manually specify the {\tt
- bootfn} if a non-default option was used.
-\end{itemize}
-
-\subsubsection{Output Values}
-For {\tt zelig()} output, {\tt repl()} will create output that is in
-every way identical to the original input. You may check to see
-whether they are identical by using the {\tt identical()} command.
-
-For {\tt sim()} output, {\tt repl()} output will be will be
-identical to the original object if you choose not to simulate new
-parameters, and instead choose to calculate quantities of interest
-using the previously simulated parameters (using the {\tt prev}
-option. If you choose to simulate new parameters, the summary
-statistics for each quantity of interest should be identical, up to a
-random approximation error. As the number of simulations increases,
-this error decreases.
-
-\subsubsection{Examples}
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout[1:1000,])
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-> z.rep <- repl(z.out)
-> identical(z.out$coef, z.rep$coef)
-> z.alt <- repl(z.out, data = turnout[1001:2000,])
-> s.rep <- repl(s.out, prev = s.out$par)
-> identical(s.out$ev, s.rep$ev)
-\end{verbatim} %$
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{load.data} for loading data from disk.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/rocplot.tex b/inst/doc/commands/rocplot.tex
deleted file mode 100644
index b21ab71..0000000
--- a/inst/doc/commands/rocplot.tex
+++ /dev/null
@@ -1,91 +0,0 @@
-\section{{\tt rocplot}: Receiver Operator Characteristic Plots}\label{ss:rocplot}
-
-\subsubsection{Description}
-The \texttt{rocplot()} command generates a receiver operator
-characteristic plot to compare the in-sample (default) or
-out-of-sample fit for two logit or probit regressions.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-rocplot(y1, y2, fitted1, fitted2, cutoff = seq(from=0, to=1, length=100),
- lty1 = "solid", lty2 = "dashed", lwd1 = par("lwd"), lwd2 = par("lwd"),
- col1 = par("col"), col2 = par("col"), plot = TRUE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt y1}: Response variable for the first model.
-\item {\tt y2}: Response variable for the second model.
-\item {\tt fitted1}: Fitted values for the first model. These values
- may represent either the in-sample or out-of-sample fitted values.
-\item {\tt fitted2}: Fitted values for the second model.
-\item {\tt cutoff}: A vector of cut-off values between 0 and 1, at
- which to evaluate the proportion of 0s and 1s correctly predicted by
- the first and second model. By default, this is 100 increments
- between 0 and 1, inclusive.
-\item {\tt lty1}, {\tt lty2}: The line type for the first model ({\tt
- lty1}) and the second model ({\tt lty2}), defaulting to solid and
- dashed, respectively.
-\item {\tt lwd1}, {\tt lwd2}: The width of the line for the first
- model ({\tt lwd1}) and the second model ({\tt lwd2}), defaulting to
- 1 for both.
-\item {\tt col1}, {\tt col2}: The colors of the line for the first
- model ({\tt col1}) and the second model ({\tt col2}), defaulting to
- black for both.
-\item{\tt plot}: By default, {\tt plot = TRUE}, which generates a plot to the
- selected device. If {\tt FALSE}, {\tt rocplot()} returns a list of output (see below).
-\item {\tt \dots}: Additional parameters passed to plot, including
- {\tt xlab}, {\tt ylab}, and {\tt main}.
-\end{itemize}
-
-\subsubsection{Output Values}
-
-If {\tt plot = TRUE}, {\tt rocplot()} generates an ROC plot for two
-logit or probit models. You may save this plot using the commands
-described in \Sref{ss:output}.
-
-If {\tt plot = FALSE}, {\tt rocplot()} returns a list with the following
-elements:
-\begin{itemize}
-\item {\tt roc1}: A matrix containing a vector of x-coordinates and
- y-coordinates corresponding to the number of ones and zeros correctly
- predicted for the first model.
-\item {\tt roc2}: A matrix containing a vector of x-coordinates and
- y-coordinates corresponding to the number of ones and zeros correctly
- predicted for the second model.
-\item {\tt area1}: The area under the first ROC curve, calculated using
- Reimann sums.
-\item {\tt area2}: The area under the second ROC curve, calculated using
- Reimann sums.
-\end{itemize}
-
-\subsubsection{Example}
-You may view this example using {\tt demo(roc)}.
-\begin{verbatim}
-> data(turnout)
-> z.out1 <- zelig(vote ~ race + educate + age, model = "logit",
- data = turnout)
-> z.out2 <- zelig(vote ~ race + educate, model = "logit",
- data = turnout)
-> rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item The {\tt help(plot)} and {\tt help(lines)} reference pages.
- \item \Sref{s:plot} for an overview of plotting procedures.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau.
-
-Sample data are a selection of $2,000$ observations from
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
diff --git a/inst/doc/commands/set.start.aux b/inst/doc/commands/set.start.aux
index d246f78..153fb89 100644
--- a/inst/doc/commands/set.start.aux
+++ b/inst/doc/commands/set.start.aux
@@ -1,15 +1,6 @@
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diff --git a/inst/doc/commands/set.start.tex b/inst/doc/commands/set.start.tex
deleted file mode 100644
index 70c68f9..0000000
--- a/inst/doc/commands/set.start.tex
+++ /dev/null
@@ -1,53 +0,0 @@
-\section{{\tt set.start}: Set starting values for all parameters}
-\label{set.start}
-
-\subsubsection{Description}
-
-After using {\tt parse.par()} and {\tt model.matrix()}, use {\tt
-set.start()} to set starting values for all parameters. By default,
-starting values are set to 0. If you wish to select alternative
-starting values for certain parameters, use {\tt put.start()} after
-{\tt set.start()}.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-set.start(start.val = NULL, terms)
-\end{verbatim}
-
-\subsubsection{Arguments}
-
-\begin{itemize}
-\item {\tt start.val}: user-specified starting values. If {\tt NULL}
-(default), the default starting values for all parameters are set to
-0.
-\item {\tt terms}: the terms output from {\tt
-model.frame.multiple()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-A named vector of starting values for all parameters specified in {\tt
-terms}, defaulting to 0.
-
-\subsubsection{Example}
-\begin{verbatim}
-fml <- parse.formula(formula, model = "bivariate.probit")
-D <- model.frame(fml, data = data)
-terms <- terms(D)
-start.val <- set.start(start.val = NULL, terms)
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{put.start} to change starting values for specific
-parameter sets.
-\item \Sref{s:new} for detailed examples of writing new models.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, Olivia Lau and Ferdinand Alimadhi.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: "~/zelig/docs/zelig"
-%%% End:
diff --git a/inst/doc/commands/setx.tex b/inst/doc/commands/setx.tex
deleted file mode 100644
index 9b066ef..0000000
--- a/inst/doc/commands/setx.tex
+++ /dev/null
@@ -1,149 +0,0 @@
-\section{{\tt setx}: Setting Explanatory Variable Values}\label{ss:setx}
-
-\subsubsection{Description}
-The \texttt{setx()} command uses the variables identified in the
-\texttt{formula} generated by \texttt{zelig()} and sets the values of
-the explanatory variables to the selected values. Use \texttt{setx()}
-after {\tt zelig()} and before \texttt{sim()} to simulate quantities
-of interest.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> x.out <- setx(z.out, fn = list(numeric = mean, ordered = median,
- other = mode), data = NULL, cond = FALSE, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-
-\item {\tt z.out}: the saved output from \texttt{zelig()}.
-
-\item {\tt fn}: a list of three functions to apply to three types of
- variables
- \begin{itemize}
- \item {\tt numeric} variables are set to their mean by default, but
- you may select any other mathematical function.
- \item {\tt ordered} factors are set to their median by default, and
- most mathematical operations will work on them. (If you select
- {\tt ordered = mean}, however, {\tt setx()} will default to median
- with a warning.)
- \item {\tt other} variables may consist of unordered factors,
- character strings, or logical variables. The {\tt other}
- variables may only be set to their mode. (If you wish to set one
- of the other variables to a specific value, you may do so using
- {\tt \dots} or by making them numeric and using \texttt{fn}.)
- \end{itemize}
- In the special case {\tt fn = NULL}, {\tt setx()} will return all of
- the observations without applying any function to the data.
-\item {\tt data}: a new data frame used to set the values of
- explanatory variables. If {\tt data = NULL} (the default), the data
- frame called in \texttt{zelig()} is used.
-\item {\tt cond}: a logical value indicating whether the usual
- unconditional prediction is used (the default) or whether the optional
- conditional prediction should be performed (choose {\tt cond =
- TRUE}). If you choose {\tt cond = TRUE}, {\tt setx()} will coerce
- {\tt fn = NULL} and ignore the arguments in {\tt \dots}.
-\item {\tt \dots}: user-defined values of specific variables that
- overwrite the default values set by the function. For example,
- adding {\tt var1 = mean(data\$var1)} sets the value of \texttt{var1}
- to the mean of {\tt data\$var1}; {\tt x1 = 12} explicitly sets the value
- of {\tt x1} to 12. In addition, you may specify one explanatory
- variable as a range of values, creating one observation for every
- unique value in the range of values. The other variables are set to
- the values given by {\tt fn}.
-\end{itemize}
-
-\subsubsection{Output Values}
-\begin{itemize}
-\item For unconditional prediction, {\tt x.out} is a model matrix
- based on the specified values for the explanatory variables. For
- multiple analyses (i.e., when choosing the {\tt by} option in {\tt
- zelig()}, {\tt setx()} returns the selected values calculated over
- the entire data frame. If you wish to calculate values over just
- one subset of the data frame, the 5th subset for example, you may
- use:
-\begin{verbatim}
-> x.out <- setx(z.out[[5]])
-\end{verbatim}
-
-\item For conditional prediction, {\tt x.out} includes the model matrix
- and the dependent variable(s). For multiple analyses (when choosing
- the {\tt by} option in {\tt zelig()}), {\tt setx()} returns the
- observed explanatory variables in each subset.
-\end{itemize}
-
-\subsubsection{Example: Unconditional Prediction}
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
-> x.out <- setx(z.out)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-Using all observations:
-\begin{verbatim}
-> x.out <- setx(z.out, fn = NULL)
-\end{verbatim}
-Defining a user defined function for {\tt fn}:
-\begin{verbatim}
-> quants <- function(x) {
-+ quantile(x, 0.25)
-+ }
-> x.out <- setx(z.out, fn = list(numeric = quants))
-\end{verbatim}
-
-\subsubsection{Example: Conditional Prediction With MatchIt Data}
-Use {\tt demo(match)} to view this example.
-\begin{verbatim}
-> data(lalonde)
-> match.out <- matchit(treat ~ age + educ + black + hispan + married +
- nodegree + re74 + re75, data = lalonde)
-> z.out <- zelig(re78 ~ pscore, data = match.data(match, "control"),
- model = "ls")
-> x.out <- setx(z.out, data = match.data(match, "treat"), fn = NULL,
- cond = TRUE)
-> s.out <- sim(z.out, x = x.out)
-\end{verbatim}
-
-\subsubsection{Example: Conditional Prediction With Multiple Analyses}
-Use {\tt demo(conditional)} to view this example:
-\begin{verbatim}
-> data(turnout)
-> z.out <- zelig(vote ~ age + educate + income, by = "race",
- data = turnout, model = "probit")
-
-# Calculate the ATE (average treatment effect) for race == "white":
-> x.white <- setx(z.out$others, data = turnout[turnout$race == "white",],
- fn = NULL, cond = TRUE)
-> s.others <- sim(z.out$others, x = x.white)
-
-# Calculate the ATE for race == "others":
-> x.others <- setx(z.out$white, data = turnout[turnout$race == "others",],
- fn = NULL, cond = TRUE)
-> s.others <- sim(z.out$white, x = x.others)
-\end{verbatim} %$
-
-\subsubsection{See Also}
- \begin{itemize}
- \item \Sref{s:commands} for an overview of the Zelig simulation
- procedure.
- \item \Sref{s:models} for an overview of supported models.
- \item \Sref{s:syntax} for an overview of R syntax.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau ported {\tt setx()} to R, following
-the logic of Clarify (for Stata). See King, Tomz, and Wittenberg,
-2000.\nocite{KinTomWit00}
-
-Sample data are a selection of $2,000$ observations from
-
-\begin{verse}
-\bibentry{KinTomWit00}.
-\end{verse}
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
diff --git a/inst/doc/commands/sim.tex b/inst/doc/commands/sim.tex
deleted file mode 100644
index 0f46809..0000000
--- a/inst/doc/commands/sim.tex
+++ /dev/null
@@ -1,152 +0,0 @@
-\section{{\tt sim}: Simulating Quantities of Interest}\label{ss:sim}
-
-\subsubsection{Description}
-Simulate quantities of interest from the estimated model output from
-\texttt{zelig()} given specified values of explanatory variables
-established in \texttt{setx()}. For classical \emph{maximum
- likelihood} models, {\tt sim()} uses asymptotic normal approximation
-to the log-likelihood. For \emph{Bayesian models}, Zelig simulates
-quantities of interest from the posterior density, whenever possible.
-For \emph{robust Bayesian models}, simulations are drawn from the
-identified class of Bayesian posteriors. Alternatively, you may
-generate quantities of interest using bootstrapped parameters.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> s.out <- sim(z.out, x, x1 = NULL, num = c(1000, 100), prev = NULL,
- bootstrap = FALSE, bootfn = NULL, cond.data = NULL, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
- \item {\tt z.out}: the output object from \texttt{zelig()}.
- \item {\tt x}: values of explanatory variables used
- for simulation, generated by {\tt setx()}.
- \item {\tt x1}: optional values of explanatory variables (generated
- by a second call of {\tt setx()}), used to simulate first
- differences and risk ratios. (Not available for conditional prediction.)
- \item {\tt num}: the number of simulations, i.e., posterior draws.
- If the \texttt{num} argument is omitted, \texttt{sim()} draws 1,000
- simulations if {\tt bootstrap = FALSE} (the default), or
- 100 simulations if {\tt bootstrap = TRUE}. You may increase this
- value to improve accuracy. (Not available for conditional prediction.)
- \item {\tt bootstrap:} a logical value indicating if parameters
- should be generated by re-fitting the model for bootstrapped
- data, rather than from the likelihood or posterior. (Not
- available for conditional prediction.)
- \item {\tt bootfn:} a function which governs how the data is
- sampled, re-fits the model, and returns the bootstrapped model
- parameters. If {\tt bootstrap = TRUE} and {\tt bootfn = NULL},
- {\tt sim()} will sample observations from the original data (with
- replacement) until it creates a sampled dataset with the same
- number of observations as the original data. Alternative
- bootstrap methods (for which users have to specify their own {\tt
-bootfn}) include sampling the residuals rather than the
- observations, weighted sampling, and parametric bootstrapping.
- (Not available for conditional prediction.)
- \item {\tt \dots}: additional optional arguments passed to
- \texttt{boot()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-The output stored in {\tt s.out} varies by model. Use the {\tt
- names()} command to view the output stored in {\tt s.out}. Common
-elements include:
-\begin{itemize}
- \item {\tt x}: the {\tt setx()} values for the explanatory variables,
- used to calculate the quantities of interest (expected values,
- predicted values, etc.).
- \item {\tt x1}: the optional {\tt setx()} object used to simulate
- first differences, and other model-specific quantities of
- interest, such as risk-ratios.
- \item {\tt call}: the options selected for {\tt sim()}, used to
- replicate quantities of interest.
- \item {\tt zelig.call}: the original command and options for {\tt
- zelig()}, used to replicate analyses.
- \item {\tt num}: the number of simulations requested.
- \item {\tt par}: the parameters (coefficients, and additional
- model-specific parameters). You may wish to use the same set of
- simulated parameters to calculate quantities of interest rather
- than simulating another set.
- \item {\tt qi\$ev}: simulations of the expected values given the
- model and {\tt x}.
- \item {\tt qi\$pr}: simulations of the predicted values given by the
- fitted values.
- \item {\tt qi\$fd}: simulations of the first differences (or risk
- difference for binary models) for the given {\tt x} and {\tt x1}.
- The difference is calculated by subtracting the expected values
- given {\tt x} from the expected values given {\tt x1}. (If do not
- specify {\tt x1}, you will not get first differences or risk
- ratios.)
- \item {\tt qi\$rr}: simulations of the risk ratios for binary and
- multinomial models. See specific models for details.
- \item {\tt qi\$ate.ev}: simulations of the average expected
- treatment effect for the treatment group, using conditional
- prediction. Let $t_i$ be a binary explanatory variable defining
- the treatment ($t_i=1$) and control ($t_i=0$) groups. Then the
- average expected treatment effect for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
- E[Y_i(t_i=0)] \},
- \end{equation*}
- where $Y_i(t_i=1)$ is the value of the dependent variable for
- observation $i$ in the treatment group. Variation in the
- simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
- the counterfactual expected value of $Y_i$ for observations in the
- treatment group, under the assumption that everything stays the
- same except that the treatment indicator is switched to $t_i=0$.
- \item {\tt qi\$ate.pr}: simulations of the average predicted
- treatment effect for the treatment group, using conditional
- prediction. Let $t_i$ be a binary explanatory variable defining
- the treatment ($t_i=1$) and control ($t_i=0$) groups. Then the
- average predicted treatment effect for the treatment group is
- \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
- \widehat{Y_i(t_i=0)} \},
- \end{equation*}
- where $Y_i(t_i=1)$ is the value of the dependent variable for
- observation $i$ in the treatment group. Variation in the
- simulations are due to uncertainty in simulating
- $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
- $Y_i$ for observations in the treatment group, under the
- assumption that everything stays the same except that the
- treatment indicator is switched to $t_i=0$.
-\end{itemize}
-
-In the case of censored $Y$ in the exponential, Weibull, and lognormal
-models, {\tt sim()} first imputes the uncensored values for $Y$ before
-calculating the ATE.
-
-You may use the {\tt \$} operator to extract any of the
-above from {\tt s.out}. For example, {\tt s.out\$qi\$ev} extracts the
-simulated expected values.
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{s:commands} for an overview of the Zelig simulation procedure.
- \item \Sref{s:models} for an overview of supported models.
- \item \Sref{s:syntax} for an overview of R syntax.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau generalized {\tt sim()} from a
-similar procedure used in Clarify (for Stata, see King, Tomz, and
-Wittenberg, 2000) and added procedures to bootstrap quantities of
-interest, and perform conditional prediction.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/tag.aux b/inst/doc/commands/tag.aux
index d6e5378..69bc237 100644
--- a/inst/doc/commands/tag.aux
+++ b/inst/doc/commands/tag.aux
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diff --git a/inst/doc/commands/ternaryplot.tex b/inst/doc/commands/ternaryplot.tex
deleted file mode 100644
index 7be782d..0000000
--- a/inst/doc/commands/ternaryplot.tex
+++ /dev/null
@@ -1,81 +0,0 @@
-\section{{\tt ternaryplot}: Ternary Diagram for 3D Data}
-\label{ternaryplot}
-
-\subsubsection{Description}
-Visualizes compositional, 3-dimensional data in an equilateral
-triangle. A point's coordinates are found by computing the gravity center
-of mass points using the data entries as weights. Thus, the coordinates
-of a point $P(a,b,c)$ for $a + b + c = 1$ are: $P(b + \frac{c}{2}, \frac{c
-\sqrt{3}}{2})$.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-ternaryplot(x, scale = 1, dimnames = NULL,
- dimnames.position = c("corner","edge","none"),
- dimnames.color = "black", id = NULL, id.color = "black",
- coordinates = FALSE, grid = TRUE, grid.color = "gray",
- labels = c("inside", "outside", "none"),
- labels.color = "darkgray", border = "black", bg = "white",
- pch = 19, cex = 1, prop.size = FALSE, col = "red",
- main = "ternary plot", ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt x}: a matrix with three columns.
-\item {\tt scale}: row sums scale to be used.
-\item {\tt dimnames}: dimension labels (defaults to the column names
-of {\tt x}).
-\item {\tt dimnames.position, dimnames.color}: position and color of dimension labels.
-\item {\tt id}: optional labels to be plotted below the plot
-symbols. {\tt coordinates} and {\tt id} are mutual exclusive.
-\item {\tt id.color}: color of these labels.
-\item {\tt coordinates}: if {\tt TRUE}, the coordinates of the points are
- plotted below them. {\tt coordinates} and {\tt id} are mutual exclusive.
-\item {\tt grid}: if {\tt TRUE}, a grid is plotted. May optionally
- be a string indicating the line type (default: {\tt "dotted"}).
-\item {\tt grid.color}: grid color.
-\item {\tt labels, labels.color}: position and color of the grid labels.
-\item {\tt border}: color of the triangle border.
-\item {\tt bg}: triangle background.
-\item {\tt pch}: plotting character. Defaults to filled dots.
-\item {\tt cex}: a numerical value giving the amount by which plotting text
- and symbols should be scaled relative to the default. Ignored for
- the symbol size if {\tt prop.size} is not {\tt FALSE}.
-\item {\tt prop.size}: if {\tt TRUE}, the symbol size is plotted
- proportional to the row sum of the three variables, i.e. represents
- the weight of the observation.
-\item {\tt col}: plotting color.
- \item {\tt main}: main title.
-\item {\tt \dots}: additional graphics parameters (see {\tt par}).
-\end{itemize}
-
-\subsubsection{Examples}
-\begin{verbatim}
-data(mexico)
-z.out <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok,
- model = "mlogit", data = mexico)
-x.out <- setx(z.out)
-s.out <- sim(z.out, x = x.out)
-
-ternaryplot(s.out$qi$ev, pch = ".", col = "blue",
- main = "1988 Mexican Presidential Election")
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
-\item \Sref{ternarypoints} for a way to add points to an existing
-ternary diagram.
-\item \Sref{s:plot} for an overview of plotting procedures.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-This function was originally written by David Meyer for the vcd
-library, version 0.1, which uses plot graphics, and differs from
-the current implementation in vcd, which uses grid graphics.
-For additional information on vcd, please consult
-\begin{verse}
-\bibentry{Friendly00}.
-\end{verse}
-
diff --git a/inst/doc/commands/ternarypoints.tex b/inst/doc/commands/ternarypoints.tex
deleted file mode 100644
index 13e1167..0000000
--- a/inst/doc/commands/ternarypoints.tex
+++ /dev/null
@@ -1,63 +0,0 @@
-\section{{\tt ternarypoints}: Adding Points to Ternary Diagrams}
-\label{ternarypoints}
-
-\subsubsection{Description}
-Use {\tt ternarypoints()} to add points to a ternary diagram generated
-using the {\tt ternaryplot()} function in the vcd library. Use
-ternary diagrams to plot expected values for multinomial choice models
-with three categories in the dependent variable.
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> ternarypoints(object, pch = 19, col = "blue", ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item {\tt object}: The input object must be a matrix with three
- columns.
-\item {\tt pch}: The selected type of point. By default, {\tt pch =
- 19}, solid disks.
-\item {\tt col}: The color of the points. By default, {\tt col =
- "blue"}.
-\item {\tt \dots}: Additional parameters passed to {\tt points()}.
-\end{itemize}
-
-\subsubsection{Output Values}
-The {\tt ternarypoints()} command adds points to a previously existing
-ternary diagram. Use {\tt ternaryplot()} to generate the main ternary diagram.
-
-\subsubsection{Examples}
-\begin{verbatim}
-> ternaryplot(s.out$qi$ev)
-> ternarypoints((s.out$qi$ev + s.out$qi$fd))
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{ternaryplot} to create a ternary diagram, to which you
-may add points.
- \item \Sref{s:plot} for an overview of plotting procedures
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau created the {\tt
- ternarypoints()} function to work with {\tt ternaryplot()}.
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commands/zelig.tex b/inst/doc/commands/zelig.tex
deleted file mode 100644
index 723fd94..0000000
--- a/inst/doc/commands/zelig.tex
+++ /dev/null
@@ -1,193 +0,0 @@
-\section{{\tt zelig}: Estimating a Statistical Model}\label{ss:zelig}
-
-\subsubsection{Description}
-The \texttt{zelig()} command estimates a variety of statistical models. Use
-{\tt zelig()} output with {\tt setx()} and {\tt sim()} to compute
-quantities of interest, such as predicted probabilities, expected
-values, and first differences, along with the associated measures of
-uncertainty (standard errors and confidence intervals).
-
-\subsubsection{Syntax}
-\begin{verbatim}
-> z.out <- zelig(formula, model, data, by = NULL, ...)
-\end{verbatim}
-
-\subsubsection{Arguments}
-\begin{itemize}
-\item{\tt formula}: a symbolic representation of the model to be
- estimated, in the form {\tt y \~\, x1 + x2}, where {\tt y} is the
- dependent variable and {\tt x1} and {\tt x2} are the explanatory
- variables, and {\tt y}, {\tt x1}, and {\tt x2} are contained in the
- same dataset. (You may include more than two explanatory variables,
- of course.) The {\tt +} symbol means ``inclusion'' not
- ``addition.'' You may also include interaction terms and main
- effects in the form {\tt x1*x2} without computing them in prior
- steps; {\tt I(x1*x2)} to include only the interaction term and
- exclude the main effects; and quadratic terms in the form {\tt
- I(x1\^{}2)}.
-\item{\tt model}: the name of a statistical model, enclosed in {\tt ""}.
- Currently, the following models are supported:
- \begin{itemize}
- \item {\tt "arima"}: ARIMA regression (see \Sref{arima})
- \item {\tt "blogit"}: Bivariate logistic regression. (see
- \Sref{blogit})
- \item {\tt "bprobit"}: Bivariate probit regression. (see
- \Sref{bprobit})
- \item {\tt "exp"}: Exponential duration regression. (see
- \Sref{exp})
- \item {\tt "factor.bayes"}: Bayesian factor analysis. (see
-\Sref{factor.bayes})
- \item {\tt "factor.mix"}: Mixed data Bayesian factor analysis.
-(see \Sref{factor.mix})
- \item {\tt "factor.ord"}: Bayesian factor analysis for ordinal
-variables. (see \Sref{factor.ord})
- \item {\tt "ei.dynamic"}: Quinn's dyanamic ecological inference
-model for $2 \times 2$ tables. (see \Sref{ei.dynamic})
- \item {\tt "ei.hier"}: Bayesian hierarchical ecological inference
-model for a cross-section of $2 \times 2$ tables. (see
-\Sref{ei.hier})
- \item {\tt "ei.RxC"}: Non-linear least squares approximation to
-the hierarchical Multinomial-Dirichlet ecological inference model in
-contingency tables with more than 2 rows or columns. (see \Sref{eiRxC})
- \item {\tt "gamma"}: Gamma regression. (see
- \Sref{gamma})
- \item {\tt "irt1d"}: Bayesian unidimensional item response theory model.
-(see \Sref{irt1d})
- \item {\tt "irtkd"}: Bayesian $k$-dimensional item response theory
-model. (see \Sref{irtkd})
- \item {\tt "logit"}: Logistic regression. (see
- \Sref{logit})
- \item {\tt "logit.bayes"}: Bayesian logistic regression. (see
-\Sref{logit.bayes})
- \item {\tt "lognorm"}: Log-normal duration regression. (see
- \Sref{lognorm})
- \item {\tt "ls"}: Linear least squares regression. (see
- \Sref{ls})
- \item {\tt "mlogit"}: Multinomial logistic regression. (see
- \Sref{mlogit})
- \item {\tt "mlogit.bayes"}: Bayesian multinomial logistic
-regression. (see \Sref{mlogit.bayes})
-% \item {\tt "mloglm"}: Multinomial log-linear regression. (see
-% \Sref{mloglm})
-% \item {\tt "mprobit"}: Multinomial probit regression. (see
-% \Sref{mprobit})
- \item {\tt "negbin"}: Negative binomial event count regression.
- (see \Sref{negbin})
- \item {\tt "netls"}: Network least squares regression (see
-\Sref{netls})
-\item {\tt "netlogit"}: Network logistic regression (see
-\Sref{netlogit})
-
- \item {\tt "negbin"}: Negative binomial event count regression.
- (see \Sref{negbin})
- \item {\tt "normal"}: Normal linear regression. (see
- \Sref{normal})
- \item {\tt "normal.bayes"}: Bayesian Normal regression (see \Sref{normal.bayes})
- \item {\tt "ologit"}: Ordinal logistic regression. (see
- \Sref{ologit})
- \item {\tt "oprobit"}: Ordinal probit regression. (see
- \Sref{oprobit})
- \item {\tt "oprobit.bayes"}: Bayesian ordinal probit regression.
-(see \Sref{oprobit.bayes})
- \item {\tt "poisson"}: Poisson event count regression. (see
- \Sref{poisson})
- \item {\tt "poisson.bayes"}: Bayesian Poisson regression. (see \Sref{poisson.bayes})
- \item {\tt "probit"}: Probit regression. (see
- \Sref{probit})
- \item {\tt "probit.bayes"}: Bayesian Probit regression. (see \Sref{probit.bayes})
- \item {\tt "relogit"}: Rare events logistic regression. (see
- \Sref{relogit})
- \item {\tt "tobit"}: Tobit regression. (see \Sref{tobit})
- \item {\tt "tobit.bayes"}: Bayesian tobit regression. (see \Sref{tobit.bayes})
- \item {\tt "weibull"}: Weibull regression. (see
- \Sref{weibull})
- \end{itemize}
-\item {\tt data}: the name of a data frame containing the variables
- referenced in the formula, or a list of multiply imputed data frames
- each having the same variable names and number of rows (created by
- {\tt mi()}).
-\item {\tt by}: \label{by} A factor variable contained in {\tt
- data}. Zelig will subset the data frame based on the levels in
- the {\tt by} variable, and estimate a model for each subset. This a
- particularly powerful option which will allow you to save a
- considerable amount of effort. For example, to run the same model
- on all fifty states, you could type:
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "state")
-\end{verbatim}
- You may also use {\tt by} to run models by
- \hlink{MatchIt}{http://gking.harvard.edu/matchit/} subclass.
-
-\item{\tt save.data}: the logical variable indicating whether the
- original data frame should be saved within the output of
- \texttt{zelig()}. If {\tt FALSE} (default), only the name of the
- data frame will be stored.
-
-\item{\tt \dots}: additional arguments passed to \texttt{zelig()},
- depending on the model to be estimated.
-\end{itemize}
-
-\subsubsection{Output Values}
-Depending on the class of model selected, \texttt{zelig()} will return
-an object with elements including \texttt{coefficients}, \texttt{residuals},
-and \texttt{formula} which may be summarized using
-\texttt{summary(z.out)} or individually extracted using, for example,
-\texttt{z.out\$coefficients}. See the specific models listed above
-for additional output values, or simply type {\tt names(z.out)}.
-
-\subsubsection{Examples}
-\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "logit", data = mydata)
-> z.out <- zelig(log(y) ~ x1 + x2, model = "ls", data = mydata)
-> z.out <- zelig(y ~ x1 + I(x2^2), model = "ologit", data = mydata)
-> z.out <- zelig(y ~ x1 * x2, model = "probit", data = mydata)
-> z.out <- zelig(y ~ x1 * x2, model = "probit",
- data = mi(mydata1, mydata2, mydata3, mydata4, mydata5))
-\end{verbatim}
-
-\subsubsection{See Also}
-\begin{itemize}
- \item \Sref{s:commands} for an overview of the Zelig simulation procedure.
- \item \Sref{s:models} for an overview of supported models.
- \item \Sref{s:syntax} for an overview of R syntax.
- \item \Sref{factors} for how to create factor variables.
-\end{itemize}
-
-\subsubsection{Contributors}
-
-Kosuke Imai, Gary King, and Olivia Lau created the {\tt zelig()}
-framework for handling a selection of the statistical models contained
-in the base, survreg, VGAM, and nnet packages for R. For additional
-details, please refer to the appropriate model reference page.
-
-
-%%% Local Variables:
-%%% mode: latex
-%%% TeX-master: t
-%%% End:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/inst/doc/commandsRd/MatchIt.url.pdf b/inst/doc/commandsRd/MatchIt.url.pdf
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diff --git a/inst/doc/commandsRd/Weimar.pdf b/inst/doc/commandsRd/Weimar.pdf
index 91a5f60..ce7ad61 100644
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diff --git a/inst/doc/commandsRd/Weimar.tex b/inst/doc/commandsRd/Weimar.tex
index 13e14d3..2c3fffd 100644
--- a/inst/doc/commandsRd/Weimar.tex
+++ b/inst/doc/commandsRd/Weimar.tex
@@ -7,17 +7,18 @@ This data set contains election results for 10 kreise (equivalent to precincts)
\begin{verbatim}data(Weimar)\end{verbatim}
\end{Usage}
\begin{Format}\relax
-A table containing 11 variables and 10 observations. The variables are {
-{Nazi}{Number of votes for the Nazi party}
-{Government}{Number of votes for the Government}
-{Communists}{Number of votes for the Communist party}
-{FarRight}{Number of votes for far right parties}
-{Other}{Number of votes for other parties, and non-voters}
-{shareunemployed}{Proportion unemployed}
-{shareblue}{Proportion working class}
-{sharewhite}{Proportion white-collar workers}
-{sharedomestic}{Proportion domestic servants}
-{shareprotestants}{Proportion Protestant}
+A table containing 11 variables and 10 observations. The variables are
+\describe{
+\item[Nazi] Number of votes for the Nazi party
+\item[Government] Number of votes for the Government
+\item[Communists] Number of votes for the Communist party
+\item[FarRight] Number of votes for far right parties
+\item[Other] Number of votes for other parties, and non-voters
+\item[shareunemployed] Proportion unemployed
+\item[shareblue] Proportion working class
+\item[sharewhite] Proportion white-collar workers
+\item[sharedomestic] Proportion domestic servants
+\item[shareprotestants] Proportion Protestant
}
\end{Format}
\begin{Source}\relax
diff --git a/inst/doc/commandsRd/Zelig-package.pdf b/inst/doc/commandsRd/Zelig-package.pdf
index c3dda41..88d73ed 100644
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diff --git a/inst/doc/commandsRd/Zelig.pdf b/inst/doc/commandsRd/Zelig.pdf
index 77f0336..2f48b32 100644
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diff --git a/inst/doc/commandsRd/Zelig.tex b/inst/doc/commandsRd/Zelig.tex
index 45dfc55..1507a00 100644
--- a/inst/doc/commandsRd/Zelig.tex
+++ b/inst/doc/commandsRd/Zelig.tex
@@ -9,7 +9,7 @@ first differences, along with the associated measures of uncertainty
\end{Description}
\begin{Usage}
\begin{verbatim}
-z.out <- zelig(formula, model, data, by, ...)
+z.out <- zelig(formula, model, data, by, save.data, cite, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
@@ -30,7 +30,12 @@ Type \code{help.zelig("models")} to see a list of currently supported
models.
\item[\code{data}] the name of a data frame containing the variables
referenced in the formula, or a list of multiply imputed data frames
-each having the same variable names and row numbers (created by \code{mi}).
+each having the same variable names and row numbers (created by
+\code{mi}).
+\item[\code{save.data}] If is set to "TRUE", the input dataframe will be saved
+as an attribute ("zelig.data") of the zelig output object.
+\item[\code{cite}] If is set to "TRUE" (default), the model citation will be
+will be printed out when this function is called.
\item[\code{by}] a factor variable contained in \code{data}. Zelig will subset
the data frame based on the levels in the \code{by} variable, and
estimate a model for each subset. This a particularly powerful option
diff --git a/inst/doc/commandsRd/Zelig.url.pdf b/inst/doc/commandsRd/Zelig.url.pdf
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diff --git a/inst/doc/commandsRd/homerun.tex b/inst/doc/commandsRd/homerun.tex
index 404b876..5969cdc 100644
--- a/inst/doc/commandsRd/homerun.tex
+++ b/inst/doc/commandsRd/homerun.tex
@@ -20,7 +20,7 @@ A data frame containing 5 variables ("gameno", "month", "homeruns", "playerstatu
\url{http://www.amstat.org}
\end{Source}
\begin{References}\relax
-Simonoff, Jeffrey S. 1998. ``Move Over, Roger Maris: Breaking Baseball's Most Famous Record.'' {\it Journal of Statistics Education} 6(3). Data used are a subset of the data in the article. \bsl{}
+Simonoff, Jeffrey S. 1998. ``Move Over, Roger Maris: Breaking Baseball's Most Famous Record.'' \emph{Journal of Statistics Education} 6(3). Data used are a subset of the data in the article.
\end{References}
\end{document}
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diff --git a/inst/doc/commandsRd/model.end.tex b/inst/doc/commandsRd/model.end.tex
index 0de8439..5bff709 100644
--- a/inst/doc/commandsRd/model.end.tex
+++ b/inst/doc/commandsRd/model.end.tex
@@ -30,8 +30,6 @@ procedure)
for more information
\item[\code{...}] additional elements passed from \code{res}
\end{ldescription}
-
-normal-bracket37bracket-normal
\end{Value}
\begin{Author}\relax
Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
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Binary files a/inst/doc/commandsRd/parse.par.pdf and b/inst/doc/commandsRd/parse.par.pdf differ
diff --git a/inst/doc/commandsRd/plot.ci.pdf b/inst/doc/commandsRd/plot.ci.pdf
index aa7d434..829a6bd 100644
Binary files a/inst/doc/commandsRd/plot.ci.pdf and b/inst/doc/commandsRd/plot.ci.pdf differ
diff --git a/inst/doc/commandsRd/plot.ci.tex b/inst/doc/commandsRd/plot.ci.tex
index fe90436..56f6915 100644
--- a/inst/doc/commandsRd/plot.ci.tex
+++ b/inst/doc/commandsRd/plot.ci.tex
@@ -34,7 +34,7 @@ only the first color will be used.
\end{ldescription}
\end{Arguments}
\begin{Value}
-For all univariate response models, {\tt plot.ci()} returns vertical
+For all univariate response models, \code{plot.ci()} returns vertical
confidence intervals over a specified range of one explanatory
variable. You may save this plot using the commands described in the
Zelig manual (\url{http://gking.harvard.edu/zelig}).
diff --git a/inst/doc/commandsRd/plot.surv.pdf b/inst/doc/commandsRd/plot.surv.pdf
index 0550a62..357afb7 100644
Binary files a/inst/doc/commandsRd/plot.surv.pdf and b/inst/doc/commandsRd/plot.surv.pdf differ
diff --git a/inst/doc/commandsRd/plot.surv.tex b/inst/doc/commandsRd/plot.surv.tex
index e1a4aa7..3f44334 100644
--- a/inst/doc/commandsRd/plot.surv.tex
+++ b/inst/doc/commandsRd/plot.surv.tex
@@ -22,7 +22,7 @@ The \code{plot.surv} command generates confidence intervals for Kaplan-Meier sur
\end{ldescription}
\end{Arguments}
\begin{Value}
-For survival models, {\tt plot.surv()} returns vertical
+For survival models, \code{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}
diff --git a/inst/doc/commandsRd/plot.zelig.pdf b/inst/doc/commandsRd/plot.zelig.pdf
index 03860f1..71d46d4 100644
Binary files a/inst/doc/commandsRd/plot.zelig.pdf and b/inst/doc/commandsRd/plot.zelig.pdf differ
diff --git a/inst/doc/commandsRd/put.start.pdf b/inst/doc/commandsRd/put.start.pdf
index be8f213..e04d0f7 100644
Binary files a/inst/doc/commandsRd/put.start.pdf and b/inst/doc/commandsRd/put.start.pdf differ
diff --git a/inst/doc/commandsRd/repl.pdf b/inst/doc/commandsRd/repl.pdf
index 9eeea5e..b2d8d8d 100644
Binary files a/inst/doc/commandsRd/repl.pdf and b/inst/doc/commandsRd/repl.pdf differ
diff --git a/inst/doc/commandsRd/rocplot.pdf b/inst/doc/commandsRd/rocplot.pdf
index 7f4fb06..41deb9b 100644
Binary files a/inst/doc/commandsRd/rocplot.pdf and b/inst/doc/commandsRd/rocplot.pdf differ
diff --git a/inst/doc/commandsRd/rocplot.tex b/inst/doc/commandsRd/rocplot.tex
index e680a56..7fa25b6 100644
--- a/inst/doc/commandsRd/rocplot.tex
+++ b/inst/doc/commandsRd/rocplot.tex
@@ -50,7 +50,7 @@ items (see below).
\begin{Value}
If \code{plot = TRUE}, \code{rocplot} generates an ROC plot for
two logit or probit models. If \code{plot = FALSE}, \code{rocplot}
-returns a list with the following elements: normal-bracket63bracket-normal
+returns a list with the following elements:
\begin{ldescription}
\item[\code{roc1}] a matrix containing a vector of x-coordinates and
y-coordinates corresponding to the number of ones and zeros correctly
@@ -63,8 +63,6 @@ Reimann sums.
\item[\code{area2}] the area under the second ROC curve, calculated using
Reimann sums.
\end{ldescription}
-
-normal-bracket63bracket-normal
\end{Value}
\begin{Author}\relax
Kosuke Imai <\email{kimai at princeton.edu}>; Gary King
diff --git a/inst/doc/commandsRd/sanction.pdf b/inst/doc/commandsRd/sanction.pdf
index 1514333..2a4f1bd 100644
Binary files a/inst/doc/commandsRd/sanction.pdf and b/inst/doc/commandsRd/sanction.pdf differ
diff --git a/inst/doc/commandsRd/set.start.pdf b/inst/doc/commandsRd/set.start.pdf
index f89ebe2..26f242f 100644
Binary files a/inst/doc/commandsRd/set.start.pdf and b/inst/doc/commandsRd/set.start.pdf differ
diff --git a/inst/doc/commandsRd/setx.pdf b/inst/doc/commandsRd/setx.pdf
index 2d9de6a..21b2e31 100644
Binary files a/inst/doc/commandsRd/setx.pdf and b/inst/doc/commandsRd/setx.pdf differ
diff --git a/inst/doc/commandsRd/setx.tex b/inst/doc/commandsRd/setx.tex
index 39ee580..c9917d9 100644
--- a/inst/doc/commandsRd/setx.tex
+++ b/inst/doc/commandsRd/setx.tex
@@ -18,6 +18,7 @@ x.out <- setx(object, fn = list(numeric = mean, ordered = median,
\begin{ldescription}
\item[\code{object}] the saved output from \code{\LinkA{zelig}{zelig}}.
\item[\code{fn}] a list of functions to apply to three types of variables:
+\Itemize{
\item[numeric] \code{numeric} variables are set to their mean by
default, but you may select any mathematical function to apply to
numeric variables.
@@ -30,6 +31,7 @@ strings, or logical variables. The \code{other} variables may
only be set to their mode. If you wish to set one of the other
variables to a specific value, you may do so using \code{...}
below.
+}
In the special case \code{fn = NULL}, \code{setx} will return all
of the observations without applying any function to the data.
\item[\code{data}] a new data frame used to set the values of
diff --git a/inst/doc/commandsRd/sim.pdf b/inst/doc/commandsRd/sim.pdf
index e58214e..9194764 100644
Binary files a/inst/doc/commandsRd/sim.pdf and b/inst/doc/commandsRd/sim.pdf differ
diff --git a/inst/doc/commandsRd/sim.tex b/inst/doc/commandsRd/sim.tex
index 4dae93f..6c6050b 100644
--- a/inst/doc/commandsRd/sim.tex
+++ b/inst/doc/commandsRd/sim.tex
@@ -54,7 +54,7 @@ observations, weighted sampling, and parametric bootstrapping.
\begin{Value}
The output stored in \code{s.out} varies by model. Use the
\code{names} command to view the output stored in \code{s.out}.
-Common elements include: normal-bracket109bracket-normal
+Common elements include:
\begin{ldescription}
\item[\code{x}] the \code{\LinkA{setx}{setx}} values for the explanatory variables,
used to calculate the quantities of interest (expected values,
@@ -110,9 +110,9 @@ simulations are due to uncertainty in simulating
\eqn{Y_i}{} for observations in the treatment group, under the
assumption that everything stays the same except that the
treatment indicator is switched to \eqn{t_i=0}{}.
+
\end{ldescription}
-normal-bracket109bracket-normal
In the case of censored $Y$ in the exponential, Weibull, and lognormal
models, \code{sim} first imputes the uncensored values for $Y$ before
diff --git a/inst/doc/commandsRd/sna.ex.pdf b/inst/doc/commandsRd/sna.ex.pdf
index dcd012a..bd76ba9 100644
Binary files a/inst/doc/commandsRd/sna.ex.pdf and b/inst/doc/commandsRd/sna.ex.pdf differ
diff --git a/inst/doc/commandsRd/summary.zelig.pdf b/inst/doc/commandsRd/summary.zelig.pdf
index 9827da4..cffd7d0 100644
Binary files a/inst/doc/commandsRd/summary.zelig.pdf and b/inst/doc/commandsRd/summary.zelig.pdf differ
diff --git a/inst/doc/commandsRd/summary.zelig.tex b/inst/doc/commandsRd/summary.zelig.tex
index 065f753..5120638 100644
--- a/inst/doc/commandsRd/summary.zelig.tex
+++ b/inst/doc/commandsRd/summary.zelig.tex
@@ -17,11 +17,13 @@ summary(object, subset = NULL, CI = 95, stats = c("mean", "sd"), ...)
\item[\code{object}] output object from \code{\LinkA{sim}{sim}} (of class
\code{"zelig"}).
\item[\code{subset}] takes one of three values:
+\Itemize{
\item[NULL] (default) for more than one observation, summarizes all the
observations at once for each quantity of interest.
\item[a numeric vector] indicates which observations to summarize,
and summarizes each one independently.
\item[all] summarizes all the observations independently.
+}
\item[\code{stats}] summary statistics to be calculated.
\item[\code{CI}] a confidence interval to be calculated.
diff --git a/inst/doc/commandsRd/swiss.pdf b/inst/doc/commandsRd/swiss.pdf
index 4d72c32..aada4b2 100644
Binary files a/inst/doc/commandsRd/swiss.pdf and b/inst/doc/commandsRd/swiss.pdf differ
diff --git a/inst/doc/commandsRd/ternaryplot.pdf b/inst/doc/commandsRd/ternaryplot.pdf
index bee2c0e..bbf61c5 100644
Binary files a/inst/doc/commandsRd/ternaryplot.pdf and b/inst/doc/commandsRd/ternaryplot.pdf differ
diff --git a/inst/doc/commandsRd/ternarypoints.pdf b/inst/doc/commandsRd/ternarypoints.pdf
index d2327bd..7d052c8 100644
Binary files a/inst/doc/commandsRd/ternarypoints.pdf and b/inst/doc/commandsRd/ternarypoints.pdf differ
diff --git a/inst/doc/commandsRd/tobin.pdf b/inst/doc/commandsRd/tobin.pdf
index 387fd17..9135975 100644
Binary files a/inst/doc/commandsRd/tobin.pdf and b/inst/doc/commandsRd/tobin.pdf differ
diff --git a/inst/doc/commandsRd/turnout.pdf b/inst/doc/commandsRd/turnout.pdf
index eab8535..cb635b4 100644
Binary files a/inst/doc/commandsRd/turnout.pdf and b/inst/doc/commandsRd/turnout.pdf differ
diff --git a/inst/doc/commandsRd/user.prompt.pdf b/inst/doc/commandsRd/user.prompt.pdf
index 3c4d1cd..0cae981 100644
Binary files a/inst/doc/commandsRd/user.prompt.pdf and b/inst/doc/commandsRd/user.prompt.pdf differ
diff --git a/inst/doc/commandsRd/voteincome.pdf b/inst/doc/commandsRd/voteincome.pdf
index d80af23..0cfec99 100644
Binary files a/inst/doc/commandsRd/voteincome.pdf and b/inst/doc/commandsRd/voteincome.pdf differ
diff --git a/inst/doc/commandsRd/zeligDepStatus.pdf b/inst/doc/commandsRd/zeligDepStatus.pdf
index 91c9f12..dbcce49 100644
Binary files a/inst/doc/commandsRd/zeligDepStatus.pdf and b/inst/doc/commandsRd/zeligDepStatus.pdf differ
diff --git a/inst/doc/commandsRd/zeligDepUpdate.pdf b/inst/doc/commandsRd/zeligDepUpdate.pdf
index 91a8c2f..6c6630e 100644
Binary files a/inst/doc/commandsRd/zeligDepUpdate.pdf and b/inst/doc/commandsRd/zeligDepUpdate.pdf differ
diff --git a/inst/doc/commandsRd/zeligVDC.pdf b/inst/doc/commandsRd/zeligVDC.pdf
index 123a014..937bee8 100644
Binary files a/inst/doc/commandsRd/zeligVDC.pdf and b/inst/doc/commandsRd/zeligVDC.pdf differ
diff --git a/inst/doc/commandsRd/zinput.tex b/inst/doc/commandsRd/zinput.tex
index 477c1b4..528bba1 100644
--- a/inst/doc/commandsRd/zinput.tex
+++ b/inst/doc/commandsRd/zinput.tex
@@ -1,6 +1,8 @@
\documentclass[oneside,letterpaper,12pt]{book}
\usepackage{Rd}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
+%\usepackage{Sweave}
+%\usepackage{/usr/lib64/R/share/texmf/Sweave}
+%\usepackage{/usr/share/R/texmf/Sweave}
\usepackage{bibentry}
\usepackage{upquote}
\usepackage{graphicx}
diff --git a/inst/doc/coxph.Rnw b/inst/doc/coxph.Rnw
index 7f6796c..7c601fd 100644
--- a/inst/doc/coxph.Rnw
+++ b/inst/doc/coxph.Rnw
@@ -393,12 +393,8 @@ below.
\subsection*{How To Cite}
-To cite the \emph{coxph} Zelig model:
-\begin{verse}
-Patrick Lam. 2007. ``coxph: Cox Proportional Hazards Regression for Duration Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-%\input{citeZelig}
+%\input{cites/coxph}
+\input{citeZelig}
\subsection*{See also}
diff --git a/inst/doc/coxph.pdf b/inst/doc/coxph.pdf
index ecb68c5..344cdbf 100644
Binary files a/inst/doc/coxph.pdf and b/inst/doc/coxph.pdf differ
diff --git a/inst/doc/ei.RxC.Rnw b/inst/doc/ei.RxC.Rnw
index 2f98993..0fcef11 100644
--- a/inst/doc/ei.RxC.Rnw
+++ b/inst/doc/ei.RxC.Rnw
@@ -208,10 +208,7 @@ view. For example, if you run
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{ei.RxC} Zelig model use:
-\begin{verse}
-Jason Wittenberg, Ferdinand Alimadhi, Badri Narayan Bhaskar, and Olivia Lau. 2007. ``ei.RxC:Hierarchical Multinomial-Dirichlet Ecological Inference Model,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/ei.RxC}
\input{citeZelig}
\subsection*{See also}
For more information please see \cite{RosJiaKin01}
diff --git a/inst/doc/ei.RxC.pdf b/inst/doc/ei.RxC.pdf
index 860861d..923035d 100644
Binary files a/inst/doc/ei.RxC.pdf and b/inst/doc/ei.RxC.pdf differ
diff --git a/inst/doc/ei.dynamic.Rnw b/inst/doc/ei.dynamic.Rnw
index 5d11f5d..57d372e 100644
--- a/inst/doc/ei.dynamic.Rnw
+++ b/inst/doc/ei.dynamic.Rnw
@@ -283,10 +283,7 @@ cell given the observed marginals.
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{ei.dynamic} Zelig model use:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``ei.dynamic: Quinn's Dynamic Ecological Inference,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/ei.dynamic}
\input{citeZelig}
\subsection*{See also}
\emph{ei.dynamic} function is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}.
diff --git a/inst/doc/ei.dynamic.pdf b/inst/doc/ei.dynamic.pdf
index 9ae1cb6..ab0f789 100644
Binary files a/inst/doc/ei.dynamic.pdf and b/inst/doc/ei.dynamic.pdf differ
diff --git a/inst/doc/ei.hier.Rnw b/inst/doc/ei.hier.Rnw
index 5fc8a62..4cc772a 100644
--- a/inst/doc/ei.hier.Rnw
+++ b/inst/doc/ei.hier.Rnw
@@ -290,10 +290,7 @@ given the observed marginals.
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{ei.hier} Zelig model use:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``ei.hier: Hierarchical Ecological Inference Model for 2x2 tables'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/ei.hier}
\input{citeZelig}
\subsection*{See also}
\emph{ei.hier} function is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}.
diff --git a/inst/doc/ei.hier.pdf b/inst/doc/ei.hier.pdf
index 180d629..2d6ccd9 100644
Binary files a/inst/doc/ei.hier.pdf and b/inst/doc/ei.hier.pdf differ
diff --git a/inst/doc/exp.Rnw b/inst/doc/exp.Rnw
index fee4710..9a2527a 100644
--- a/inst/doc/exp.Rnw
+++ b/inst/doc/exp.Rnw
@@ -267,11 +267,7 @@ the {\tt \$} operator are listed below.
\subsection* {How to Cite}
-To cite the \emph{exp} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``exp: Exponential 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{cites/exp}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/exp.pdf b/inst/doc/exp.pdf
index 50ac356..636e9b2 100644
Binary files a/inst/doc/exp.pdf and b/inst/doc/exp.pdf differ
diff --git a/inst/doc/factor.bayes.Rnw b/inst/doc/factor.bayes.Rnw
index 864d2c6..7f64b52 100644
--- a/inst/doc/factor.bayes.Rnw
+++ b/inst/doc/factor.bayes.Rnw
@@ -274,11 +274,7 @@ procedure is not applicable for factor analysis models.
\subsection* {How to Cite}
-To cite the \emph{factor.bayes} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``factor.bayes: Bayesian Factor Analysis,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/factor.bayes}
\input{citeZelig}
<<afterpkgs, echo=FALSE>>=
diff --git a/inst/doc/factor.bayes.pdf b/inst/doc/factor.bayes.pdf
index aecdc4f..cff1b5e 100644
Binary files a/inst/doc/factor.bayes.pdf and b/inst/doc/factor.bayes.pdf differ
diff --git a/inst/doc/factor.mix.Rnw b/inst/doc/factor.mix.Rnw
index 24c6136..ff957c8 100644
--- a/inst/doc/factor.mix.Rnw
+++ b/inst/doc/factor.mix.Rnw
@@ -282,11 +282,7 @@ not applicable for factor analysis models.
\subsection* {How to Cite}
-To cite the \emph{factor.mix} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``factor.mix: Mixed Data Factor Analysis ,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/factor.mix}
\input{citeZelig}
<<afterpkgs, echo=FALSE>>=
diff --git a/inst/doc/factor.mix.pdf b/inst/doc/factor.mix.pdf
index f31eb15..e2caf2c 100644
Binary files a/inst/doc/factor.mix.pdf and b/inst/doc/factor.mix.pdf differ
diff --git a/inst/doc/factor.ord.Rnw b/inst/doc/factor.ord.Rnw
index 2ffbcc5..1de5ad8 100644
--- a/inst/doc/factor.ord.Rnw
+++ b/inst/doc/factor.ord.Rnw
@@ -259,11 +259,7 @@ not applicable for factor analysis models.
\subsection* {How to Cite}
-To cite the \emph{factor.ord} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``factor.ord: Ordinal Data Factor Analysis,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/factor.ord}
\input{citeZelig}
<<afterpkgs, echo=FALSE>>=
diff --git a/inst/doc/factor.ord.pdf b/inst/doc/factor.ord.pdf
index d169633..83aa9c3 100644
Binary files a/inst/doc/factor.ord.pdf and b/inst/doc/factor.ord.pdf differ
diff --git a/inst/doc/gamma.Rnw b/inst/doc/gamma.Rnw
index 67b736c..2ffbb0e 100644
--- a/inst/doc/gamma.Rnw
+++ b/inst/doc/gamma.Rnw
@@ -215,11 +215,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{gamma} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``gamma: Gamma Regression for Continuous, Positive Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/gamma}
\input{citeZelig}
diff --git a/inst/doc/gamma.gee.Rnw b/inst/doc/gamma.gee.Rnw
index a367f58..494adaa 100644
--- a/inst/doc/gamma.gee.Rnw
+++ b/inst/doc/gamma.gee.Rnw
@@ -282,12 +282,8 @@ below.
\subsection*{How To Cite}
-To cite the \emph{gamma.gee} Zelig model:
-\begin{verse}
-Patrick Lam. 2007. ``gamma.gee: Generalized Estimating Equation for Gamma Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-%\input{citeZelig}
+\input{cites/gamma.gee}
+\input{citeZelig}
\subsection*{See also}
The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinAltBur90}.
diff --git a/inst/doc/gamma.gee.pdf b/inst/doc/gamma.gee.pdf
index 2d116d9..3cea3f5 100644
Binary files a/inst/doc/gamma.gee.pdf and b/inst/doc/gamma.gee.pdf differ
diff --git a/inst/doc/gamma.mixed.Rnw b/inst/doc/gamma.mixed.Rnw
index 67f647e..d94e7e5 100644
--- a/inst/doc/gamma.mixed.Rnw
+++ b/inst/doc/gamma.mixed.Rnw
@@ -1,4 +1,4 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/gammamixed}
+\SweaveOpts{eval=false, results=hide, prefix.string=vigpics/gammamixed}
\include{zinput}
%\VignetteIndexEntry{Gamma mixed effects linear regression}
%\VignetteDepends{Zelig}
@@ -164,11 +164,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{gamma.mixed} Zelig model:
-\begin{verse}
-Delia Bailey, Ferdinand Alimadhi. 2007. ``gamma.mixed: Mixed effects gamma regression'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/gamma.mixed}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/gamma.mixed.pdf b/inst/doc/gamma.mixed.pdf
index be4c169..6afd44e 100644
Binary files a/inst/doc/gamma.mixed.pdf and b/inst/doc/gamma.mixed.pdf differ
diff --git a/inst/doc/gamma.net.Rnw b/inst/doc/gamma.net.Rnw
index 420c463..7066c5a 100644
--- a/inst/doc/gamma.net.Rnw
+++ b/inst/doc/gamma.net.Rnw
@@ -166,11 +166,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{gamma.net} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``gamma.net: Network Gamma Regression for Continuous, Positive Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/gamma.net}
\input{citeZelig}
\subsection* {See also}
The network gamma regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(gamma.net)}. Sample data are fictional.
diff --git a/inst/doc/gamma.net.pdf b/inst/doc/gamma.net.pdf
index eb962e7..b589554 100644
Binary files a/inst/doc/gamma.net.pdf and b/inst/doc/gamma.net.pdf differ
diff --git a/inst/doc/gamma.pdf b/inst/doc/gamma.pdf
index 75c09a2..057825b 100644
Binary files a/inst/doc/gamma.pdf and b/inst/doc/gamma.pdf differ
diff --git a/inst/doc/gamma.survey.Rnw b/inst/doc/gamma.survey.Rnw
index 560ed83..d19973a 100644
--- a/inst/doc/gamma.survey.Rnw
+++ b/inst/doc/gamma.survey.Rnw
@@ -474,38 +474,31 @@ object called {\tt .survey.prob.weights} is created in the global environment.
are therefore advised to re-name any object called {\tt .survey.prob.weights} before using {\tt gamma.survey} models in {\tt Zelig}.
\subsection* {How to Cite}
-
-To cite the \emph{gamma.survey} Zelig model: \begin{verse} Nicholas
-Carnes. 2008. ``gamma.survey: Survey-Weighted Gamma Regression for
-Continuous, Positive Dependent Variables,'' in Kosuke Imai, Gary
-King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,''
-\url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-
-Survey-weighted linear models and the sample data used in the
-examples above are a part of the {\tt survey} package by Thomas
-Lumley. Users may wish to refer to the help files for the three
-functions that Zelig draws upon when estimating survey-weighted
-models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
-help(svrepdesign)}. The Gamma model is part of the stats package
-by \citet{VenRip02}. Advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\cite{McCNel89}.
-
-
-
-
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
+\input{cites/gamma.survey}
+ \input{citeZelig}
+
+ \subsection* {See also}
+
+ Survey-weighted linear models and the sample data used in the
+ examples above are a part of the {\tt survey} package by Thomas
+ Lumley. Users may wish to refer to the help files for the three
+ functions that Zelig draws upon when estimating survey-weighted
+ models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+ help(svrepdesign)}. The Gamma model is part of the stats package
+ by \citet{VenRip02}. Advanced users may wish to refer to
+ \texttt{help(glm)} and \texttt{help(family)}, as well as
+ \cite{McCNel89}.
+
+
+
+
+
+ \bibliographystyle{asa}
+ \bibliography{gk,gkpubs}
+ <<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+ @
+ \end{document}
diff --git a/inst/doc/gamma.survey.pdf b/inst/doc/gamma.survey.pdf
index 403ebc3..525d0bc 100644
Binary files a/inst/doc/gamma.survey.pdf and b/inst/doc/gamma.survey.pdf differ
diff --git a/inst/doc/index.shtml b/inst/doc/index.shtml
index 39e2407..84bad68 100644
--- a/inst/doc/index.shtml
+++ b/inst/doc/index.shtml
@@ -12,7 +12,9 @@
<a href="http://www.people.fas.harvard.edu/~olau/">Olivia Lau</a>
<br /><br />
<!-- rbuild: replace 'Version:' '</b>' version -->
-<b>Version:3.3-1</b>
+<b>Version:3.4-5</b>
+<!-- rbuild: replace 'Zelig_' '.tar.gz' version -->
+<a href="/src/contrib/Zelig_3.4-5.tar.gz">(Download)</a>
<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
@@ -76,7 +78,7 @@ target="gkingext">Browse/Search Archives</a></li>
Kosuke Imai, Gary King, and Olivia Lau. <span class="bg">"Toward A
Common Framework for Statistical Analysis and Development"</span>
<em>Journal of Computational and Graphical Statistics</em>,
- forthcoming (<span class="bg">Abstract:</span> <a
+ Vol. 17, No. 4 (December), pp. 892-913 (<span class="bg">Abstract:</span> <a
href="/files/abs/z-abs.shtml">HTML</a> | <span
class="bg">Paper:</span> <a href="/files/z.pdf">PDF</a>)
diff --git a/inst/doc/intro.tex b/inst/doc/intro.tex
index 502bbd6..97c23ad 100644
--- a/inst/doc/intro.tex
+++ b/inst/doc/intro.tex
@@ -133,17 +133,7 @@ previous messages before posting your query.
\section{How to Cite Zelig}
-Please cite Zelig with reference to these two sources:
-\begin{quote}
- Kosuke Imai, Gary King, and Olivia Lau. 2007. ``Zelig: Everyone's
- Statistical Software,'' \url{http://GKing.harvard.edu/zelig}.
-\end{quote}
-\begin{quote}
- Kosuke Imai, Gary King, and Olivia Lau. 2008. ``Toward A Common
- Framework for Statistical Analysis and Development,'' \emph{Journal
- of Computational and Graphical Statistics}, forthcoming,
- \url{http://gking.harvard.edu/files/abs/z-abs.shtml}.
-\end{quote}
+\input{citeZelig}
To refer to a particular Zelig model, please refer to the ``how to
cite'' portion at the end of each model documentation section.
diff --git a/inst/doc/irt1d.Rnw b/inst/doc/irt1d.Rnw
index 4f8f3fd..657caa4 100644
--- a/inst/doc/irt1d.Rnw
+++ b/inst/doc/irt1d.Rnw
@@ -260,10 +260,8 @@ procedure is not applicable for item response models.
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{irt1d} Zelig model use:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``irt1d: One Dimensional Item Response Mode,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+
+\input{cites/irt1d}
\input{citeZelig}
\subsection*{See also}
The unidimensional item-response function is part of the MCMCpack
diff --git a/inst/doc/irt1d.pdf b/inst/doc/irt1d.pdf
index 700a780..326dfb9 100644
Binary files a/inst/doc/irt1d.pdf and b/inst/doc/irt1d.pdf differ
diff --git a/inst/doc/irtkd.Rnw b/inst/doc/irtkd.Rnw
index e67e983..2a55d34 100644
--- a/inst/doc/irtkd.Rnw
+++ b/inst/doc/irtkd.Rnw
@@ -263,10 +263,7 @@ procedure is not applicable for item response models.
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{irtkd} Zelig model use:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``irtkd: K-Dimensional Item Response Model,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/irtkd}
\input{citeZelig}
\subsection*{See also}
The $k$ dimensional item-response function is part of the MCMCpack
diff --git a/inst/doc/irtkd.pdf b/inst/doc/irtkd.pdf
index 9edfa5a..aab2da5 100644
Binary files a/inst/doc/irtkd.pdf and b/inst/doc/irtkd.pdf differ
diff --git a/inst/doc/logit.Rnw b/inst/doc/logit.Rnw
index e5951e1..9495918 100644
--- a/inst/doc/logit.Rnw
+++ b/inst/doc/logit.Rnw
@@ -262,11 +262,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{logit} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``logit: Logistic Regression for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/logit}
\input{citeZelig}
diff --git a/inst/doc/logit.bayes.Rnw b/inst/doc/logit.bayes.Rnw
index 1daccdd..9a802eb 100644
--- a/inst/doc/logit.bayes.Rnw
+++ b/inst/doc/logit.bayes.Rnw
@@ -259,11 +259,7 @@ for the treated from conditional prediction models.
\subsection* {How to Cite}
-To cite the \emph{logit.bayes} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``logit.bayes: Bayesian Logistic Regression for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/logit.bayes}
\input{citeZelig}
\subsection*{See also}
Bayesian logistic regression 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}.
diff --git a/inst/doc/logit.bayes.pdf b/inst/doc/logit.bayes.pdf
index 12674e3..44586a7 100644
Binary files a/inst/doc/logit.bayes.pdf and b/inst/doc/logit.bayes.pdf differ
diff --git a/inst/doc/logit.gam.Rnw b/inst/doc/logit.gam.Rnw
index c359de5..201313c 100644
--- a/inst/doc/logit.gam.Rnw
+++ b/inst/doc/logit.gam.Rnw
@@ -272,11 +272,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{logit.gam} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``logit.gam: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/logit.gam}
\input{citeZelig}
\subsection* {See also}
The {\tt logit.gam} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
diff --git a/inst/doc/logit.gam.pdf b/inst/doc/logit.gam.pdf
index a2765e9..541d667 100644
Binary files a/inst/doc/logit.gam.pdf and b/inst/doc/logit.gam.pdf differ
diff --git a/inst/doc/logit.gee.Rnw b/inst/doc/logit.gee.Rnw
index e160a99..2b1c433 100644
--- a/inst/doc/logit.gee.Rnw
+++ b/inst/doc/logit.gee.Rnw
@@ -328,12 +328,8 @@ below.
\subsection*{How To Cite}
-To cite the \emph{logit.gee} Zelig model:
-\begin{verse}
-Patrick Lam. 2007. ``logit.gee: Generalized Estimating Equation for Logit Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-%\input{citeZelig}
+\input{cites/logit.gee}
+\input{citeZelig}
\subsection*{See also}
The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
diff --git a/inst/doc/logit.gee.pdf b/inst/doc/logit.gee.pdf
index 8c837c9..b42be8e 100644
Binary files a/inst/doc/logit.gee.pdf and b/inst/doc/logit.gee.pdf differ
diff --git a/inst/doc/logit.mixed.Rnw b/inst/doc/logit.mixed.Rnw
index 40f0914..16bf04d 100644
--- a/inst/doc/logit.mixed.Rnw
+++ b/inst/doc/logit.mixed.Rnw
@@ -1,190 +1,185 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/logitmixed}
-\include{zinput}
-%\VignetteIndexEntry{Mixed effects logistic regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{mixed, logistic regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt logit.mixed}: Mixed effects logistic Regression}
-\label{mixed}
-
-Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The logit model is appropriate when the dependent variable is dichotomous.
-
-While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
- data=mydata, model="logit.mixed")
-
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
- gamma= ~ tag(w1 + w2 | g)), data=mydata, model="logit.mixed")
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
-\begin{itemize}
-\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
-Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional arguments for model specification:
-
-\begin{itemize}
-\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
-\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
-\end{itemize}
-Additionally, users may with to refer to {\tt lmer} in the package {\tt lme4} for more information, including control parameters for the estimation algorithm and their defaults.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item Basic Example with First Differences \\
-\\
-Attach sample data: \\
-<<Examples.data>>=
-data(voteincome)
-@
-Estimate model:
-<<Examples.zelig>>=
-z.out1 <- zelig(vote ~ education + age + female + tag(1 | state), data=voteincome, model="logit.mixed")
-@
-
-\noindent Summarize regression coefficients and estimated variance of random effects:\\
-<<Examples.summary>>=
-summary(z.out1)
-@
-Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
-
-<<Examples.setx>>=
-x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
-x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
-@
-Generate first differences for the effect of high versus low education on voting: \\
-
-<<Examples.sim>>=
-s.out1 <- sim(z.out1, x=x.high, x1=x.low)
-summary(s.out1)
-@
-
-\end{enumerate}
-
-
-\subsubsection{Mixed effects Logistic Regression Model}
-
-Let $Y_{ij}$ be the binary dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$ which takes the value of either 0 or 1, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by a Bernoulli distribution with mean vector $\pi_{ij}$.
-\begin{equation*}
-Y_{ij} \sim \mathrm{Bernoulli}(y_{ij} | \pi_{ij}) = \pi_{ij}^{y_{ij}} (1 - \pi_{ij})^{1 - y_{ij}}
-\end{equation*}
-where
-\begin{equation*}
-\pi_{ij} = \mathrm{Pr}(Y_{ij} = 1)
-\end{equation*}
-\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
-\begin{equation*}
-b_i \sim Normal(0, \Psi)
-\end{equation*}
-\item The \emph{systematic component} is
-\begin{equation*}
-\pi_{ij} \equiv \frac{1}{1 + \exp(-(X_{ij} \beta + Z_{ij} b_i))}
-\end{equation*}
-where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value, $\pi_{ij}$ for
-\begin{equation*}
-\pi_{ij} = \frac{1}{1 + \exp(-(X_{ij} \beta + Z_{ij} b_i))}
-\end{equation*}
-given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
-
-\item The expected values ({\tt qi\$ev}) are simulations of the predicted probability of a success given draws of $\beta$ from its posterior:
-\begin{equation*}
-E(Y_{ij} | X_{ij}) = \pi_{ij} = \frac{1}{1 + exp(- X_{ij} \beta)}.
-\end{equation*}
-
-\item The first difference ({\tt qi\$fd}) is given by the difference in predicted probabilities, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
-\begin{equation*}
-FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = Pr(Y_{ij} = 1 | X_{ij}) - Pr(Y_{ij} = 1 | X_{ij}^\prime)
-\end{equation*}
-
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-RR(Y_{ij} | X_{ij}, X_{ij}^{\prime}) = \frac{Pr(Y_{ij} = 1 | X_{ij})}{Pr(Y_{ij} = 1 | X_{ij}^{\prime})}
-\end{equation*}
-
-\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
-\begin{equation*}
-\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
-\end{equation*}
-where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
-
-\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
-\begin{equation*}
-\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
-\end{equation*}
-where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
-\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
-\item[--] {\tt frame}: the model frame for the model.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
-\begin{itemize}
-\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
-\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
-\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
-\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
-\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-
-\subsection* {How to Cite}
-
-To cite the \emph{logit.mixed} Zelig model:
-\begin{verse}
-Delia Bailey, Ferdinand Alimadhi. 2007. ``logit.mixed: Mixed effects logistic regression'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-Mixed effects logistic regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after <- search()
- torm <- setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
+\SweaveOpts{results=hide, prefix.string=vigpics/logitmixed}
+\include{zinput}
+%\VignetteIndexEntry{Mixed effects logistic regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed, logistic regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt logit.mixed}: Mixed effects logistic Regression}
+\label{mixed}
+
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The logit model is appropriate when the dependent variable is dichotomous.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="logit.mixed")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+ gamma= ~ tag(w1 + w2 | g)), data=mydata, model="logit.mixed")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt 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 lme4} for more information, including control parameters for the estimation algorithm and their defaults.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(voteincome)
+@
+Estimate model:
+<<Examples.zelig>>=
+z.out1 <- zelig(vote ~ education + age + female + tag(1 | state), data=voteincome, model="logit.mixed")
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
+
+<<Examples.setx>>=
+x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
+x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
+@
+Generate first differences for the effect of high versus low education on voting: \\
+
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+@
+
+\end{enumerate}
+
+
+\subsubsection{Mixed effects Logistic Regression Model}
+
+Let $Y_{ij}$ be the binary dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$ which takes the value of either 0 or 1, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a Bernoulli distribution with mean vector $\pi_{ij}$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Bernoulli}(y_{ij} | \pi_{ij}) = \pi_{ij}^{y_{ij}} (1 - \pi_{ij})^{1 - y_{ij}}
+\end{equation*}
+where
+\begin{equation*}
+\pi_{ij} = \mathrm{Pr}(Y_{ij} = 1)
+\end{equation*}
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\pi_{ij} \equiv \frac{1}{1 + \exp(-(X_{ij} \beta + Z_{ij} b_i))}
+\end{equation*}
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value, $\pi_{ij}$ for
+\begin{equation*}
+\pi_{ij} = \frac{1}{1 + \exp(-(X_{ij} \beta + Z_{ij} b_i))}
+\end{equation*}
+given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+
+\item The expected values ({\tt qi\$ev}) are simulations of the predicted probability of a success given draws of $\beta$ from its posterior:
+\begin{equation*}
+E(Y_{ij} | X_{ij}) = \pi_{ij} = \frac{1}{1 + exp(- X_{ij} \beta)}.
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in predicted probabilities, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = Pr(Y_{ij} = 1 | X_{ij}) - Pr(Y_{ij} = 1 | X_{ij}^\prime)
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+RR(Y_{ij} | X_{ij}, X_{ij}^{\prime}) = \frac{Pr(Y_{ij} = 1 | X_{ij})}{Pr(Y_{ij} = 1 | X_{ij}^{\prime})}
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+\subsection* {How to Cite}
+
+\input{cites/logit.mixed}
+\input{citeZelig}
+
+\subsection* {See also}
+Mixed effects logistic regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+<<afterpkgs, echo=FALSE>>=
+ after <- search()
+ torm <- setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+\end{document}
diff --git a/inst/doc/logit.mixed.pdf b/inst/doc/logit.mixed.pdf
index f375b7c..80d9dd0 100644
Binary files a/inst/doc/logit.mixed.pdf and b/inst/doc/logit.mixed.pdf differ
diff --git a/inst/doc/logit.net.Rnw b/inst/doc/logit.net.Rnw
index f795e64..e50c295 100644
--- a/inst/doc/logit.net.Rnw
+++ b/inst/doc/logit.net.Rnw
@@ -167,11 +167,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{logit.net} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``logit.net: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/logit.net}
\input{citeZelig}
\subsection* {See also}
The network logistic regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netgamma)}. Sample data are fictional.
diff --git a/inst/doc/logit.net.pdf b/inst/doc/logit.net.pdf
index 8ff15b7..7550695 100644
Binary files a/inst/doc/logit.net.pdf and b/inst/doc/logit.net.pdf differ
diff --git a/inst/doc/logit.pdf b/inst/doc/logit.pdf
index 3940470..1c263fb 100644
Binary files a/inst/doc/logit.pdf and b/inst/doc/logit.pdf differ
diff --git a/inst/doc/logit.survey.Rnw b/inst/doc/logit.survey.Rnw
index 9d1b518..897e82b 100644
--- a/inst/doc/logit.survey.Rnw
+++ b/inst/doc/logit.survey.Rnw
@@ -493,38 +493,31 @@ are therefore advised to re-name any object called {\tt .survey.prob.weights} be
\subsection* {How to Cite}
-To cite the \emph{logit.survey} Zelig model:
-\begin{verse}
-Nicholas Carnes. 2008. ``logit.survey: Survey-weighted Logistic
-Regression for Dichotomous Dependent Variables,'' in Kosuke Imai,
-Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical
-Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-
-Survey-weighted linear models and the sample data used in the
-examples above are a part of the {\tt survey} package by Thomas
-Lumley. Users may wish to refer to the help files for the three
-functions that Zelig draws upon when estimating survey-weighted
-models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
-help(svrepdesign)}. The logit model is part of the stats package
-by \citet{VenRip02}. Advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\cite{McCNel89}.
-
-
-
-
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
+\input{cites/logit.survey}
+ \input{citeZelig}
+
+ \subsection* {See also}
+
+ Survey-weighted linear models and the sample data used in the
+ examples above are a part of the {\tt survey} package by Thomas
+ Lumley. Users may wish to refer to the help files for the three
+ functions that Zelig draws upon when estimating survey-weighted
+ models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+ help(svrepdesign)}. The Gamma model is part of the stats package
+ by \citet{VenRip02}. Advanced users may wish to refer to
+ \texttt{help(glm)} and \texttt{help(family)}, as well as
+ \cite{McCNel89}.
+
+
+
+
+
+ \bibliographystyle{asa}
+ \bibliography{gk,gkpubs}
+ <<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+ @
+ \end{document}
diff --git a/inst/doc/logit.survey.pdf b/inst/doc/logit.survey.pdf
index 9f31cf9..9e0264b 100644
Binary files a/inst/doc/logit.survey.pdf and b/inst/doc/logit.survey.pdf differ
diff --git a/inst/doc/lognorm.Rnw b/inst/doc/lognorm.Rnw
index 51d8b06..0d7cd4d 100644
--- a/inst/doc/lognorm.Rnw
+++ b/inst/doc/lognorm.Rnw
@@ -257,11 +257,7 @@ the {\tt \$} operator are listed below.
\subsection* {How to Cite}
-To cite the \emph{lognorm} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``lognorm: Log-Normal Regression for Duration Dependent Variable,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/lognorm}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/lognorm.pdf b/inst/doc/lognorm.pdf
index 3b4635d..3fcd2bf 100644
Binary files a/inst/doc/lognorm.pdf and b/inst/doc/lognorm.pdf differ
diff --git a/inst/doc/ls.Rnw b/inst/doc/ls.Rnw
index 1973dc8..649124c 100644
--- a/inst/doc/ls.Rnw
+++ b/inst/doc/ls.Rnw
@@ -264,11 +264,7 @@ below.
\end{itemize}
\subsection* {How to Cite}
-To cite the \emph{ls} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``ls: Least Squares Regression for Continuous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/ls}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/ls.mixed.Rnw b/inst/doc/ls.mixed.Rnw
index 9819f5f..efdbd77 100644
--- a/inst/doc/ls.mixed.Rnw
+++ b/inst/doc/ls.mixed.Rnw
@@ -1,193 +1,187 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/lmmixed}
-\include{zinput}
-% \usepackage{Sweave}
-%\VignetteIndexEntry{Mixed effects linear regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{mixed,linear, linear regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt ls.mixed}: Mixed effects Linear Regression}
-\label{mixed}
-
-Use multi-level linear regression if you have covariates that are grouped according to one or more classification factors and a continuous dependent variable.
-
-While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
- data=mydata, model="lm.multi")
-
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
- gamma= ~ tag(w1 + w2 | g)), data=mydata, model="lm.multi")
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\noindent {\tt zelig()} takes the following arguments for {\tt multi}:
-\begin{itemize}
-\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
-Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional arguments for model specification:
-
-\begin{itemize}
-\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt 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 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}
-
-\begin{enumerate}
-\item Basic Example with First Differences \\
-\\
-Attach sample data: \\
-<<Examples.data>>=
-data(voteincome)
-@
-
-Estimate model:
-
-<<Examples.zelig>>=
-z.out1 <- zelig(income ~ education + age + female + tag(1 | state), data=voteincome, model="ls.mixed")
-@
-
-\noindent Summarize regression coefficients and estimated variance of random effects:\\
-<<Examples.summary>>=
-summary(z.out1)
-@
-Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
-<<Examples.setx>>=
-x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
-x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
-@
-
-Generate first differences for the effect of high versus low education on income: \\
-<<Examples.sim>>=
-s.out1 <- sim(z.out1, x=x.high, x1=x.low)
-summary(s.out1)
-@
-\begin{center}
-<<label=ExamplesPlot, fig=true, echo=true>>=
-plot(s.out1)
-@
-\end{center}
-
-\end{enumerate}
-
-\subsubsection{Mixed effects linear regression model}
-
-Let $Y_{ij}$ be the continuous dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by a univariate normal model with a vector of means $\mu_{ij}$ and scalar variance $\sigma^2$.
-\begin{equation*}
-Y_{ij} \sim \mathrm{Normal}(y_{ij} | \mu_{ij}, \sigma^2)
-\end{equation*}
-\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
-\begin{equation*}
-b_i \sim Normal(0, \Psi)
-\end{equation*}
-\item The \emph{systematic component} is
-\begin{equation*}
-\mu_{ij} \equiv X_{ij} \beta + Z_{ij} b_i
-\end{equation*}
-where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-
-\item The predicted values ({\tt qi\$pr}) are draws from the normal distribution defined by mean $\mu_{ij}$ and variance $\sigma^2$,
-\begin{equation*}
-\mu_{ij} = X_{ij} \beta + Z_{ij} b_{i}
-\end{equation*}
-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*}
-E(Y_{ij} | X_{ij}) = X_{ij} \beta.
-\end{equation*}
-
-\item The first difference ({\tt qi\$fd}) is given by the difference in expected values, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
-\begin{equation*}
-FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = E(Y_{ij} | X_{ij}) - E(Y_{ij} | X_{ij}^\prime)
-\end{equation*}
-
-\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
-\begin{equation*}
-\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
-\end{equation*}
-where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
-
-\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
-\begin{equation*}
-\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
-\end{equation*}
-where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
-
-\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}
-
-The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
-\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
-\item[--] {\tt frame}: the model frame for the model.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
-\begin{itemize}
-\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
-\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
-\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
-\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
-\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-
-
-
-\subsection* {How to Cite}
-
-To cite the \emph{ls.mixed} Zelig model:
-\begin{verse}
-Delia Bailey, Ferdinand Alimadhi. 2007. ``ls.mixed: Mixed effects linear regression'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-Mixed effects linear regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after <- search()
- torm <- setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
+\SweaveOpts{results=hide, prefix.string=vigpics/lmmixed}
+\include{zinput}
+%\VignetteIndexEntry{Mixed effects linear regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,linear, linear regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt ls.mixed}: Mixed effects Linear Regression}
+\label{mixed}
+
+Use multi-level linear regression if you have covariates that are grouped according to one or more classification factors and a continuous dependent variable.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="lm.multi")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+ gamma= ~ tag(w1 + w2 | g)), data=mydata, model="lm.multi")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt multi}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt 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 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 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}
+
+\begin{enumerate}
+\item Basic Example with First Differences \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(voteincome)
+@
+
+Estimate model:
+
+<<Examples.zelig>>=
+z.out1 <- zelig(income ~ education + age + female + tag(1 | state), data=voteincome, model="ls.mixed")
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
+<<Examples.setx>>=
+x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
+x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
+@
+
+Generate first differences for the effect of high versus low education on income: \\
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+@
+\begin{center}
+<<label=ExamplesPlot, fig=true, echo=true>>=
+plot(s.out1)
+@
+\end{center}
+
+\end{enumerate}
+
+\subsubsection{Mixed effects linear regression model}
+
+Let $Y_{ij}$ be the continuous dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a univariate normal model with a vector of means $\mu_{ij}$ and scalar variance $\sigma^2$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Normal}(y_{ij} | \mu_{ij}, \sigma^2)
+\end{equation*}
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\mu_{ij} \equiv X_{ij} \beta + Z_{ij} b_i
+\end{equation*}
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The predicted values ({\tt qi\$pr}) are draws from the normal distribution defined by mean $\mu_{ij}$ and variance $\sigma^2$,
+\begin{equation*}
+\mu_{ij} = X_{ij} \beta + Z_{ij} b_{i}
+\end{equation*}
+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*}
+E(Y_{ij} | X_{ij}) = X_{ij} \beta.
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in expected values, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = E(Y_{ij} | X_{ij}) - E(Y_{ij} | X_{ij}^\prime)
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\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}
+
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+
+
+\subsection* {How to Cite}
+
+\input{cites/ls.mixed}
+\input{citeZelig}
+
+\subsection* {See also}
+Mixed effects linear regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+<<afterpkgs, echo=FALSE>>=
+ after <- search()
+ torm <- setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+\end{document}
diff --git a/inst/doc/ls.mixed.pdf b/inst/doc/ls.mixed.pdf
index c7bdb6f..c71e3f0 100644
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diff --git a/inst/doc/ls.net.Rnw b/inst/doc/ls.net.Rnw
index 9a87c70..15392a5 100644
--- a/inst/doc/ls.net.Rnw
+++ b/inst/doc/ls.net.Rnw
@@ -176,11 +176,7 @@ expected values) for the specified values of {\tt x} and {\tt x1}.
\subsection* {How to Cite}
-To cite the \emph{ls.net} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``ls.net: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/ls.net}
\input{citeZelig}
\subsection* {See also}
The network least squares regression is part of the sna package by
diff --git a/inst/doc/ls.net.pdf b/inst/doc/ls.net.pdf
index 0bbced6..f4faaa5 100644
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diff --git a/inst/doc/ls.pdf b/inst/doc/ls.pdf
index 87c128a..a3d15ed 100644
Binary files a/inst/doc/ls.pdf and b/inst/doc/ls.pdf differ
diff --git a/inst/doc/makeDocs b/inst/doc/makeDocs
index 7d9afd3..6605b71 100755
--- a/inst/doc/makeDocs
+++ b/inst/doc/makeDocs
@@ -27,9 +27,11 @@ for f in `cat models.txt`
if [ $# -eq 0 ]
then
echo "Sweave(\"$f.Rnw\")" | R --slave
+ echo "sweaving $f"
fi
## create pdfs for all the tex file produced by sweave
+ echo "pdflatexing $f"
pdflatex $f
bibtex $f
pdflatex $f
@@ -181,6 +183,10 @@ for f in `cat models.txt`
do
rm -f $f.tex
done
+
+## remove cites dir (created by a007.Rnw
+rm -rf cites
+
###rm models.txt
#### here is how u delete the \end{document}
diff --git a/inst/doc/mlogit.Rnw b/inst/doc/mlogit.Rnw
index ea6a086..c310a0c 100644
--- a/inst/doc/mlogit.Rnw
+++ b/inst/doc/mlogit.Rnw
@@ -253,10 +253,7 @@ below.
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{mlogit} Zelig model use:
-\begin{verse}
-Kosuke Imai,Gary King, and Olivia Lau. 2007. ``mlogit: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/mlogit}
\input{citeZelig}
\subsection*{See also}
The multinomial logit function is part of the VGAM package by Thomas Yee \citep{YeeHas03}. In addition, advanced users may wish to refer to \texttt{help(vglm)}
diff --git a/inst/doc/mlogit.bayes.Rnw b/inst/doc/mlogit.bayes.Rnw
index 66f37b9..ea999c2 100644
--- a/inst/doc/mlogit.bayes.Rnw
+++ b/inst/doc/mlogit.bayes.Rnw
@@ -291,11 +291,7 @@ for the treated from conditional prediction models.
\subsection* {How to Cite}
-To cite the \emph{mlogit.bayes} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``mlogit.bayes: Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values ,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/mlogit.bayes}
\input{citeZelig}
\subsection*{See also}
Bayesian logistic regression 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}.
diff --git a/inst/doc/mlogit.bayes.pdf b/inst/doc/mlogit.bayes.pdf
index dc85d43..0b16d72 100644
Binary files a/inst/doc/mlogit.bayes.pdf and b/inst/doc/mlogit.bayes.pdf differ
diff --git a/inst/doc/mlogit.pdf b/inst/doc/mlogit.pdf
index e69482e..e8834d7 100644
Binary files a/inst/doc/mlogit.pdf and b/inst/doc/mlogit.pdf differ
diff --git a/inst/doc/mloglm.Rnw b/inst/doc/mloglm.Rnw
index 8ef77c9..af5297b 100644
--- a/inst/doc/mloglm.Rnw
+++ b/inst/doc/mloglm.Rnw
@@ -85,10 +85,7 @@ below.
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{mloglm} Zelig model use:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``mloglm: Multinomial Log-Linear Regression for Contingency Table Models,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/mloglm}
\input{citeZelig}
\subsection*{See also}
The multinomial logit model is part of the nnet library by Brian D.
diff --git a/inst/doc/mloglm.pdf b/inst/doc/mloglm.pdf
index d1f9fbe..a27ba7f 100644
Binary files a/inst/doc/mloglm.pdf and b/inst/doc/mloglm.pdf differ
diff --git a/inst/doc/negbin.Rnw b/inst/doc/negbin.Rnw
index fc8a7b3..ff361b2 100644
--- a/inst/doc/negbin.Rnw
+++ b/inst/doc/negbin.Rnw
@@ -227,11 +227,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{negbin} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``negbin: Negative Binomial Regression for Event Count Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/negbin}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/negbin.pdf b/inst/doc/negbin.pdf
index 94c37d5..f5f2898 100644
Binary files a/inst/doc/negbin.pdf and b/inst/doc/negbin.pdf differ
diff --git a/inst/doc/normal.Rnw b/inst/doc/normal.Rnw
index 5e2c85e..d8aae12 100644
--- a/inst/doc/normal.Rnw
+++ b/inst/doc/normal.Rnw
@@ -259,11 +259,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{normal} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``normal: Normal Regression for Continuous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/normal}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/normal.bayes.Rnw b/inst/doc/normal.bayes.Rnw
index a0c8abf..a5d84cc 100644
--- a/inst/doc/normal.bayes.Rnw
+++ b/inst/doc/normal.bayes.Rnw
@@ -249,11 +249,7 @@ for the treated from conditional prediction models.
\subsection* {How to Cite}
-To cite the \emph{normal.bayes} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``normal.bayes: Bayesian Normal Linear Regression ,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/normal.bayes}
\input{citeZelig}
\subsection*{See also}
Bayesian normal regression 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}.
diff --git a/inst/doc/normal.bayes.pdf b/inst/doc/normal.bayes.pdf
index e376124..fc7f57d 100644
Binary files a/inst/doc/normal.bayes.pdf and b/inst/doc/normal.bayes.pdf differ
diff --git a/inst/doc/normal.gam.Rnw b/inst/doc/normal.gam.Rnw
index 9173642..2fe1e18 100644
--- a/inst/doc/normal.gam.Rnw
+++ b/inst/doc/normal.gam.Rnw
@@ -266,11 +266,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{normal.gam} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``normal.gam: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/normal.gam}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/normal.gam.pdf b/inst/doc/normal.gam.pdf
index 60b45cb..2375ad4 100644
Binary files a/inst/doc/normal.gam.pdf and b/inst/doc/normal.gam.pdf differ
diff --git a/inst/doc/normal.gee.Rnw b/inst/doc/normal.gee.Rnw
index 884c013..8c1224d 100644
--- a/inst/doc/normal.gee.Rnw
+++ b/inst/doc/normal.gee.Rnw
@@ -275,12 +275,8 @@ below.
\subsection*{How To Cite}
-To cite the \emph{normal.gee} Zelig model:
-\begin{verse}
-Patrick Lam. 2007. ``normal.gee: Generalized Estimating Equation for Normal Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-%\input{citeZelig}
+\input{cites/normal.gee}
+\input{citeZelig}
\subsection*{See also}
The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
diff --git a/inst/doc/normal.gee.pdf b/inst/doc/normal.gee.pdf
index 76e2c55..a186f9b 100644
Binary files a/inst/doc/normal.gee.pdf and b/inst/doc/normal.gee.pdf differ
diff --git a/inst/doc/normal.net.Rnw b/inst/doc/normal.net.Rnw
index 086b3ef..a558345 100644
--- a/inst/doc/normal.net.Rnw
+++ b/inst/doc/normal.net.Rnw
@@ -146,11 +146,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{normal.net} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``normal.net: Network Normal Regression for Continuous Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/normal.net}
\input{citeZelig}
\subsection* {See also}
The network normal regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(normal.net)}. Sample data are fictional.
diff --git a/inst/doc/normal.net.pdf b/inst/doc/normal.net.pdf
index c06c2a7..b8d49b8 100644
Binary files a/inst/doc/normal.net.pdf and b/inst/doc/normal.net.pdf differ
diff --git a/inst/doc/normal.pdf b/inst/doc/normal.pdf
index b888f1c..275ee8c 100644
Binary files a/inst/doc/normal.pdf and b/inst/doc/normal.pdf differ
diff --git a/inst/doc/normal.survey.Rnw b/inst/doc/normal.survey.Rnw
index 6fe93fe..a8da597 100644
--- a/inst/doc/normal.survey.Rnw
+++ b/inst/doc/normal.survey.Rnw
@@ -483,39 +483,31 @@ object called {\tt .survey.prob.weights} is created in the global environment.
are therefore advised to re-name any object called {\tt .survey.prob.weights} before using {\tt normal.survey} models in {\tt Zelig}.
\subsection* {How to Cite}
-
-To cite the \emph{normal.survey} Zelig model:
-\begin{verse}
-Nicholas Carnes. 2008. ``normal.survey: Survey-weighted Normal
-Regression for Continuous Dependent Variables,'' in Kosuke Imai,
-Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical
-Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-
-Survey-weighted linear models and the sample data used in the
-examples above are a part of the {\tt survey} package by Thomas
-Lumley. Users may wish to refer to the help files for the three
-functions that Zelig draws upon when estimating survey-weighted
-models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
-help(svrepdesign)}. The normal model is part of the stats package
-by \citet{VenRip02}. Advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\cite{McCNel89}.
-
-
-
-
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
+\input{cites/normal.survey}
+ \input{citeZelig}
+
+ \subsection* {See also}
+
+ Survey-weighted linear models and the sample data used in the
+ examples above are a part of the {\tt survey} package by Thomas
+ Lumley. Users may wish to refer to the help files for the three
+ functions that Zelig draws upon when estimating survey-weighted
+ models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+ help(svrepdesign)}. The Gamma model is part of the stats package
+ by \citet{VenRip02}. Advanced users may wish to refer to
+ \texttt{help(glm)} and \texttt{help(family)}, as well as
+ \cite{McCNel89}.
+
+
+
+
+
+ \bibliographystyle{asa}
+ \bibliography{gk,gkpubs}
+ <<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+ @
+ \end{document}
diff --git a/inst/doc/normal.survey.pdf b/inst/doc/normal.survey.pdf
index 387a268..7b478d5 100644
Binary files a/inst/doc/normal.survey.pdf and b/inst/doc/normal.survey.pdf differ
diff --git a/inst/doc/ologit.Rnw b/inst/doc/ologit.Rnw
index 96fc66b..55d8ac7 100644
--- a/inst/doc/ologit.Rnw
+++ b/inst/doc/ologit.Rnw
@@ -234,11 +234,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{ologit} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``ologit: Ordinal Logistic Regression for Ordered Categorical Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/ologit}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/ologit.pdf b/inst/doc/ologit.pdf
index 060f1ad..fe2c57d 100644
Binary files a/inst/doc/ologit.pdf and b/inst/doc/ologit.pdf differ
diff --git a/inst/doc/oprobit.Rnw b/inst/doc/oprobit.Rnw
index 9f9f8a3..a468e7a 100644
--- a/inst/doc/oprobit.Rnw
+++ b/inst/doc/oprobit.Rnw
@@ -244,10 +244,7 @@ below.
\end{itemize}
\subsection*{How to Cite}
-To cite the \emph{oprobit} Zelig model use:
-\begin{verse}
-Kosuke Imai,Gary King, and Olivia Lau. 2007. ``oprobit: Ordinal Probit Regression for Ordered Categorical Dependent Variables,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/oprobit}
\input{citeZelig}
\subsection*{See also}
The ordinal probit function is part of the VGAM package by Thomas Yee \citep{YeeHas03}. In addition, advanced users may wish to refer to \texttt{help(vglm)}
diff --git a/inst/doc/oprobit.bayes.Rnw b/inst/doc/oprobit.bayes.Rnw
index a9c8f7e..990b3f9 100644
--- a/inst/doc/oprobit.bayes.Rnw
+++ b/inst/doc/oprobit.bayes.Rnw
@@ -285,10 +285,7 @@ for the treated from conditional prediction models.
\subsection*{How to Cite}
-To cite the \emph{oprobit.bayes} Zelig model use:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``oprobit.bayes: Bayesian Ordered Probit Regression,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/oprobit.bayes}
\input{citeZelig}
\subsection*{See also}
Bayesian ordinal probit regression is part of the MCMCpack library by Andrew D. Martin and Kevin M. Quinn \citep{MarQui05}.
diff --git a/inst/doc/oprobit.bayes.pdf b/inst/doc/oprobit.bayes.pdf
index 6724bba..415973b 100644
Binary files a/inst/doc/oprobit.bayes.pdf and b/inst/doc/oprobit.bayes.pdf differ
diff --git a/inst/doc/oprobit.pdf b/inst/doc/oprobit.pdf
index 43ce6c2..04d6b8c 100644
Binary files a/inst/doc/oprobit.pdf and b/inst/doc/oprobit.pdf differ
diff --git a/inst/doc/poisson.Rnw b/inst/doc/poisson.Rnw
index b617582..2f3ba5c 100644
--- a/inst/doc/poisson.Rnw
+++ b/inst/doc/poisson.Rnw
@@ -214,11 +214,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{poisson} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``poisson: Poisson Regression for Event Count Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/poisson}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/poisson.bayes.Rnw b/inst/doc/poisson.bayes.Rnw
index 13012ad..bcd0b7b 100644
--- a/inst/doc/poisson.bayes.Rnw
+++ b/inst/doc/poisson.bayes.Rnw
@@ -246,11 +246,7 @@ for the treated from conditional prediction models.
\subsection* {How to Cite}
-To cite the \emph{poisson.bayes} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``poisson.bayes: Bayesian Poisson Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/poisson.bayes}
\input{citeZelig}
\subsection*{See also}
Bayesian poisson regression 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}.
diff --git a/inst/doc/poisson.bayes.pdf b/inst/doc/poisson.bayes.pdf
index 6328fcc..fe0e207 100644
Binary files a/inst/doc/poisson.bayes.pdf and b/inst/doc/poisson.bayes.pdf differ
diff --git a/inst/doc/poisson.gam.Rnw b/inst/doc/poisson.gam.Rnw
index dd7087b..010a439 100644
--- a/inst/doc/poisson.gam.Rnw
+++ b/inst/doc/poisson.gam.Rnw
@@ -270,11 +270,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{poisson.gam} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``poisson.gam: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/poisson.gam}
\input{citeZelig}
\subsection* {See also}
The {\tt gam.logit} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
diff --git a/inst/doc/poisson.gam.pdf b/inst/doc/poisson.gam.pdf
index 2733125..ad22913 100644
Binary files a/inst/doc/poisson.gam.pdf and b/inst/doc/poisson.gam.pdf differ
diff --git a/inst/doc/poisson.gee.Rnw b/inst/doc/poisson.gee.Rnw
index 74bfff7..66789da 100644
--- a/inst/doc/poisson.gee.Rnw
+++ b/inst/doc/poisson.gee.Rnw
@@ -283,12 +283,8 @@ below.
\subsection*{How To Cite}
-To cite the \emph{poisson.gee} Zelig model:
-\begin{verse}
-Patrick Lam. 2007. ``poisson.gee: Generalized Estimating Equation for Poisson Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-%\input{citeZelig}
+\input{cites/poisson.gee}
+\input{citeZelig}
\subsection*{See also}
The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{Martin92}. Please inquire with Lisa Martin before publishing
diff --git a/inst/doc/poisson.gee.pdf b/inst/doc/poisson.gee.pdf
index ab9c21c..494d3ac 100644
Binary files a/inst/doc/poisson.gee.pdf and b/inst/doc/poisson.gee.pdf differ
diff --git a/inst/doc/poisson.mixed.Rnw b/inst/doc/poisson.mixed.Rnw
index 9c01da4..dea7289 100644
--- a/inst/doc/poisson.mixed.Rnw
+++ b/inst/doc/poisson.mixed.Rnw
@@ -1,183 +1,178 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/poissonmixed}
-\include{zinput}
-%\VignetteIndexEntry{Mixed effects poisson regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{mixed,poisson regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt poisson.mixed}: Mixed effects poisson Regression}
-\label{mixed}
-
-Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. Poisson regression applies to dependent variables that represent the number of independent events that occur during a fixed period of time.
-
-While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
- data=mydata, model="poisson.mixed")
-
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
- gamma= ~ tag(w1 + w2 | g)), data=mydata, model="poisson.mixed")
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
-\begin{itemize}
-\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
-Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional arguments for model specification:
-
-\begin{itemize}
-\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
-\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
-\end{itemize}
-Additionally, users may with to refer to {\tt lmer} in the package {\tt lme4} for more information, including control parameters for the estimation algorithm and their defaults.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item Basic Example \\
-\\
-Attach sample data: \\
-<<Examples.data>>=
-data(homerun)
-@
-Estimate model:
-<<Examples.zelig>>=
-z.out1 <- zelig(homeruns ~ player + tag(player - 1 | month), data=homerun, model="poisson.mixed")
-@
-
-\noindent Summarize regression coefficients and estimated variance of random effects:\\
-<<Examples.summary>>=
-summary(z.out1)
-@
-Set explanatory variables to their default values:\\
-<<Examples.setx>>=
-x.out <- setx(z.out1)
-@
-Simulate draws using the default bootstrap method and view simulated quantities of interest: \\
-<<Examples.sim>>=
-s.out1 <- sim(z.out1, x=x.out)
-summary(s.out1)
-@
-
-\end{enumerate}
-
-
-\subsubsection{Mixed effects Poisson Regression Model}
-
-Let $Y_{ij}$ be the number of independent events that occur during a fixed time period, realized for observation $j$ in group $i$ as $y_{ij}$, which takes any non-negative integer as its value, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by a Poisson distribution with mean and variance parameter $\lambda_{ij}$.
-\begin{equation*}
-Y_{ij} \sim \mathrm{Poisson}(y_{ij} | \lambda_{ij}) = \frac{\exp(-\lambda_{ij}) \lambda_{ij}^{y_{ij}}}{y_{ij}!}
-\end{equation*}
-where
-\begin{equation*}
-y_{ij} = 0, 1, \ldots
-\end{equation*}
-\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
-\begin{equation*}
-b_i \sim Normal(0, \Psi)
-\end{equation*}
-\item The \emph{systematic component} is
-\begin{equation*}
-\lambda_{ij} \equiv \exp(X_{ij} \beta + Z_{ij} b_i)
-\end{equation*}
-where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
-\end{itemize}
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The predicted values ({\tt qi\$pr}) are draws from the poisson distribution defined by mean $ \lambda_{ij} $, for
-\begin{equation*}
-\lambda_{ij} = \exp(X_{ij} \beta + Z_{ij} b_i)
-\end{equation*}
-given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
-
-\item The expected values ({\tt qi\$ev}) is the mean of simulations of the stochastic component given draws of $\beta$ from its posterior:
-\begin{equation*}
-E(Y_{ij} | X_{ij}) = \lambda_{ij} = \exp(X_{ij} \beta).
-\end{equation*}
-
-\item The first difference ({\tt qi\$fd}) is given by the difference in expected values, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
-\begin{equation*}
-FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = E(Y_{ij} | X_{ij}) - E(Y_{ij} | X_{ij}^\prime)
-\end{equation*}
-
-\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
-\begin{equation*}
-\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
-\end{equation*}
-where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
-
-\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
-\begin{equation*}
-\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
-\end{equation*}
-where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
-\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
-\item[--] {\tt frame}: the model frame for the model.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
-\begin{itemize}
-\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
-\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
-\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
-\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
-\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-
-
-\subsection* {How to Cite}
-
-To cite the \emph{poisson.mixed} Zelig model:
-\begin{verse}
-Delia Bailey, Ferdinand Alimadhi. 2007. ``poisson.mixed: Mixed effects poisson regression'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-Mixed effects poisson regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after <- search()
- torm <- setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
+\SweaveOpts{results=hide, prefix.string=vigpics/poissonmixed}
+\include{zinput}
+%\VignetteIndexEntry{Mixed effects poisson regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,poisson regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt poisson.mixed}: Mixed effects poisson Regression}
+\label{mixed}
+
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. Poisson regression applies to dependent variables that represent the number of independent events that occur during a fixed period of time.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="poisson.mixed")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+ gamma= ~ tag(w1 + w2 | g)), data=mydata, model="poisson.mixed")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt 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 lme4} for more information, including control parameters for the estimation algorithm and their defaults.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(homerun)
+@
+Estimate model:
+<<Examples.zelig>>=
+z.out1 <- zelig(homeruns ~ player + tag(player - 1 | month), data=homerun, model="poisson.mixed")
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+Set explanatory variables to their default values:\\
+<<Examples.setx>>=
+x.out <- setx(z.out1)
+@
+Simulate draws using the default bootstrap method and view simulated quantities of interest: \\
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.out)
+summary(s.out1)
+@
+
+\end{enumerate}
+
+
+\subsubsection{Mixed effects Poisson Regression Model}
+
+Let $Y_{ij}$ be the number of independent events that occur during a fixed time period, realized for observation $j$ in group $i$ as $y_{ij}$, which takes any non-negative integer as its value, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a Poisson distribution with mean and variance parameter $\lambda_{ij}$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Poisson}(y_{ij} | \lambda_{ij}) = \frac{\exp(-\lambda_{ij}) \lambda_{ij}^{y_{ij}}}{y_{ij}!}
+\end{equation*}
+where
+\begin{equation*}
+y_{ij} = 0, 1, \ldots
+\end{equation*}
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\lambda_{ij} \equiv \exp(X_{ij} \beta + Z_{ij} b_i)
+\end{equation*}
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The predicted values ({\tt qi\$pr}) are draws from the poisson distribution defined by mean $ \lambda_{ij} $, for
+\begin{equation*}
+\lambda_{ij} = \exp(X_{ij} \beta + Z_{ij} b_i)
+\end{equation*}
+given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+
+\item The expected values ({\tt qi\$ev}) is the mean of simulations of the stochastic component given draws of $\beta$ from its posterior:
+\begin{equation*}
+E(Y_{ij} | X_{ij}) = \lambda_{ij} = \exp(X_{ij} \beta).
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in expected values, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = E(Y_{ij} | X_{ij}) - E(Y_{ij} | X_{ij}^\prime)
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+
+\subsection* {How to Cite}
+
+\input{cites/logit.mixed}
+\input{citeZelig}
+
+\subsection* {See also}
+Mixed effects poisson regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+<<afterpkgs, echo=FALSE>>=
+ after <- search()
+ torm <- setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+\end{document}
diff --git a/inst/doc/poisson.mixed.pdf b/inst/doc/poisson.mixed.pdf
index 593805f..7087d37 100644
Binary files a/inst/doc/poisson.mixed.pdf and b/inst/doc/poisson.mixed.pdf differ
diff --git a/inst/doc/poisson.net.Rnw b/inst/doc/poisson.net.Rnw
index d3d18bf..5e08fd0 100644
--- a/inst/doc/poisson.net.Rnw
+++ b/inst/doc/poisson.net.Rnw
@@ -1,157 +1,240 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/poissonnet}
+\SweaveOpts{results=hide, prefix.string=vigpics/ologit}
\include{zinput}
-%\VignetteIndexEntry{Network Poisson Regression for Event Count Proximity Matrix Dependent Variables}
-%\VignetteDepends{Zelig, stats}
-%\VignetteKeyWords{model,least squares,continuous, regression}
+%\VignetteIndexEntry{Ordinal Logistic Regression for Ordered Categorical Dependent Variables}
+%\VignetteDepends{Zelig, MASS}
+%\VignetteKeyWords{model, logistic,regression,ordinal, ordered,categorical}
%\VignettePackage{Zelig}
\begin{document}
\nobibliography*
<<beforepkgs, echo=FALSE>>=
before=search()
@
+
<<loadLibrary, echo=F,results=hide>>=
+pkg <- search()
+if(!length(grep("package:Zelig",pkg)))
library(Zelig)
-@
+@
-\section{{\tt poisson.net}: Network Poisson Regression for Event Count Proximity Matrix Dependent Variables}
+\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
+Categorical Dependent Variables}\label{ologit}
-Use network Poisson regression analysis for a dependent variable that represents the number of events that occur during a fixed period of time as a proximity matrix (a.k.a. sociomatricies, adjacency matrices, or matrix representations of directed graphs).
+Use the ordinal logit regression model if your dependent variable is
+ordered and categorical, either in the form of integer values or character strings.
\subsubsection{Syntax}
+
\begin{verbatim}
-> z.out <- zelig(y ~ x1 + x2, model = "poisson.net", data = mydata)
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
> x.out <- setx(z.out)
> s.out <- sim(z.out, x = x.out)
\end{verbatim}
+If {\tt Y} takes discrete integer values, the {\tt as.factor()}
+command will order automatically order the values. If {\tt Y} takes
+on values composed of character strings, such as ``strongly agree'',
+``agree'', and ``disagree'', {\tt as.factor()} will order the values
+in the order in which they appear in {\tt Y}. You will need to
+replace your dependent variable with a factored variable prior to
+estimating the model through {\tt zelig()}. See Section \ref{factors}
+for more information on creating ordered factors and Example
+\ref{ord.fact} below.
+
+\subsubsection{Example}
-\subsubsection{Additional Inputs}
-
-In addition to the standard inputs, {\tt zelig()} takes the following additional options for network poisson regression:
-
-\begin{itemize}
-\item {\tt LF}: specifies the link function to be used for the network poisson regression. Default is {\tt LF="log"}, but {\tt LF} can also be set to {\tt "sqrt"} by the user.
-\end{itemize}
-
-\subsubsection{Examples}
\begin{enumerate}
-\item Basic Example
-
-Load the sample data (see {\tt ?friendship} for details on the structure of the network dataframe):
-<<echo=TRUE, results=hide, fig=FALSE>>=
-data(friendship)
+\item {Creating An Ordered Dependent Variable} \label{ord.fact}
-
-
-@
-Estimate model:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-z.out <- zelig(count ~ advice + prestige + perpower, model = "poisson.net", data = friendship)
-summary(z.out)
-
-@
-Setting values for the explanatory variables to their default values:
-
-<<echo=TRUE, results=hide, fig=FALSE>>=
-x.out <- setx(z.out)
-
-@
-Simulate fitted values.
-<<echo=TRUE, results=hide, fig=FALSE>>=
-s.out <- sim(z.out, x = x.out)
-summary(s.out)
-plot(s.out)
-
-@
-
-\begin{figure}[here]
-\centering
-<<fig=TRUE,echo=false, width=12, height=6>>=
-plot(s.out)
+Load the sample data:
+<<Example.data>>=
+ data(sanction)
+@
+Create an ordered dependent variable:
+<<Example.factor>>=
+ sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+ levels = c("net gain", "little effect",
+ "modest loss", "major loss"))
+@
+Estimate the model:
+<<Example.zelig>>=
+ z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
+@
+Set the explanatory variables to their observed values:
+<<Example.setx>>=
+ x.out <- setx(z.out, fn = NULL)
+@
+Simulate fitted values given {\tt x.out} and view the results:
+<<Example.sim>>=
+ s.out <- sim(z.out, x = x.out)
@
-\label{fig:plotgam}
-\end{figure}
+<<Example.summary>>=
+ summary(s.out)
+@
+\item {First Differences}
+Using the sample data \texttt{sanction}, estimate the empirical model and returning the coefficients:
+<<FirstDifferences.zelig>>=
+ z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit",
+ data = sanction)
+@
+<<FirstDifferences.summary>>=
+summary(z.out)
+@
+Set the explanatory variables to their means, with {\tt mil} set
+to 0 (no military action in addition to sanctions) in the baseline
+case and set to 1 (military action in addition to sanctions) in the
+alternative case:
+<<FirstDifferences.setx>>=
+ x.low <- setx(z.out, mil = 0)
+ x.high <- setx(z.out, mil = 1)
+@
+Generate simulated fitted values and first differences, and view the results:
+<<FirstDifferences.sim>>=
+ s.out <- sim(z.out, x = x.low, x1 = x.high)
+ summary(s.out)
+@
\end{enumerate}
-
-
\subsubsection{Model}
-The {\tt poisson.net} model performs a Poisson regression of the proximity matrix $\mathbf{Y}$, a $m \times m$ matrix representing network ties, on a set of proximity matrices $\mathbf{X}$. This network regression model is directly analogous to standard Poisson regression element-wise on the appropriately vectorized matrices. Proximity matrices are vectorized by creating $Y$, a $m^2 \times 1$ vector to represent the proximity matrix. The vectorization which produces the $Y$ vector from t [...]
-Let $Y_{i}$ be the dependent variable, produced by vectorizing an event count proximity matrix, for observation $i$. $Y_i$ is thus the number of independent events that occur during a fixed time period. This variable can take any non-negative integer.
+Let $Y_i$ be the ordered categorical dependent variable for
+observation $i$ that takes one of the integer values from $1$ to $J$
+where $J$ is the total number of categories.
+
\begin{itemize}
-\item The Poisson distribution has \emph{stochastic component}
-\begin{equation*}
-Y_{i} \sim \text{Poisson} ( \lambda_{i}),\\
-\end{equation*}
-where $\lambda_{i}$ is the mean and variance parameter.
-\item The \emph{systematic component} is given by:
-\begin{equation*}
-\lambda_i = \exp(x_i\beta).
-\end{equation*}
-where $ x_i$ is the vector of explanatory variables and $\beta$ is the vector of coefficients.
+\item The \emph{stochastic component} begins with an unobserved continuous
+ variable, $Y^*_i$, which follows the standard logistic distribution
+ with a parameter $\mu_i$,
+ \begin{equation*}
+ Y_i^* \; \sim \; \textrm{Logit}(y_i^* \mid \mu_i),
+ \end{equation*}
+ to which we add an observation mechanism
+ \begin{equation*}
+ Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
+ \quad {\rm for} \quad j=1,\dots,J.
+ \end{equation*}
+ where $\tau_l$ (for $l=0,\dots,J$) are the threshold parameters with
+ $\tau_l < \tau_m$ for all $l<m$ and $\tau_0=-\infty$ and
+ $\tau_J=\infty$.
+
+\item The \emph{systematic component} has the following form, given
+ the parameters $\tau_j$ and $\beta$, and the explanatory variables $x_i$:
+ \begin{equation*}
+ \Pr(Y \le j) \; = \; \Pr(Y^* \le \tau_j) \; = \frac{\exp(\tau_j -
+ x_i \beta)}{1+\exp(\tau_j -x_i \beta)},
+ \end{equation*}
+ which implies:
+ \begin{equation*}
+ \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}{1 + \exp(\tau_j -
+ x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
+ \exp(\tau_{j-1} - x_i \beta)}.
+ \end{equation*}
\end{itemize}
+\subsubsection{Quantities of Interest}
-\subsubsection{Quantities of Interest}
-The quantities of interest for the network Poisson regression are the same as those for the standard Poisson regression.
\begin{itemize}
-\item The expected value ({\tt qi\$ev}) for the {\tt poisson.net} model is the mean of simulations from the stochastic component,
+\item The expected values ({\tt qi\$ev}) for the ordinal logit model
+ are simulations of the predicted probabilities for each category:
\begin{equation*}
-E(Y) = \lambda_{i} = \exp(x_{i}\beta),
+E(Y = j) \; = \; \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}
+{1 + \exp(\tau_j - x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
+ \exp(\tau_{j-1} - x_i \beta)},
\end{equation*}
-given draws of $\beta$ from its sampling distribution.
-
-\item The predicted value ({\tt qi\$pr}) is a random draw from the Poisson distribution defined by mean $\lambda_i$.
+given a draw of $\beta$ from its sampling distribution.
+
+\item The predicted value ({\tt qi\$pr}) is drawn from the logit
+ distribution described by $\mu_i$, and observed as one of $J$
+ discrete outcomes.
+
+\item The difference in each of the predicted probabilities ({\tt
+ qi\$fd}) is given by
+ \begin{equation*}
+ \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
+ j=1,\dots,J.
+ \end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+ effect ({\tt att.ev}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ E[Y_i(t_i=0)] \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment
+ effect ({\tt att.pr}) for the treatment group is
+ \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+ \widehat{Y_i(t_i=0)} \right\},
+ \end{equation*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the
+number of treated observations in category $j$.
-\item The first difference ({\tt qi\$fd}) for the network Poisson model is defined as
-\begin{equation*}
-FD = \text{Pr}(Y| x_{1}) - \text{Pr}(Y| x)
-\end{equation*}
\end{itemize}
-
\subsubsection{Output Values}
-The output of each Zelig command contains useful information which you may view. For example, you run \verb{ z.out <- zelig(y ~ x, model = "poisson.net", data){, then you may examine the available information in {\tt z.out} by using {\tt names(z.out)}, see the coefficients by using {\tt z.out\$coefficients}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \$} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt z.out}, you may extract:
-\begin{itemize}
-\item {\tt coefficients}: parameter estimates for the explanatory variables.
-\item {\tt fitted.values}: the vector of fitted values for the systemic component $\lambda$.
-\item {\tt residuals}: the working residuals in the final iteration of the IWLS fit.
-\item {\tt linear.predictors}: the vector of $x_{i}\beta$.
-\item {\tt aic}: Akaike\'s Information Criterion (minus twice the maximized log-likelihood plus twice the number of coefficients).
-\item {\tt bic}: the Bayesian Information Criterion (minus twice the maximized log-likelihood plus the number of coefficients times log $n$).
-\item {\tt df.residual}: the residual degrees of freedom.
-\item {\tt df.null}: the residual degrees of freedom for the null model.
-\item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}
+The output of each Zelig command contains useful information which you
+may view. For example, if you run \texttt{z.out <- zelig(y \~\,
+ x, model = "ologit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
-\end{itemize}
-\item From {\tt summary(z.out)}(as well as from {\tt zelig()}), you may extract:
-\begin{itemize}
-\item {\tt mod.coefficients}: the parameter estimates with their associated standard errors, $p$-values, and $t$ statistics.
-\item {\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
-\item {\tt cov.unscaled}: a $k \times k$ matrix of unscaled covariances.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract:
\begin{itemize}
-\item {\tt qi\$ev}: the simulated expected probabilities for the specified values of {\tt x}.
-\item {\tt qi\$pr}: the simulated predicted values for the specified values of {\tt x}.
-\item {\tt qi\$fd}: the simulated first differences in the expected probabilities simulated from {\tt x} and {\tt x1}.
-\end{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+ extract:
+ \begin{itemize}
+ \item {\tt coefficients}: parameter estimates for the explanatory
+ variables.
+ \item {\tt zeta}: a vector containing the estimated class
+ boundaries $\tau_j$.
+ \item {\tt deviance}: the residual deviance.
+ \item {\tt fitted.values}: the $n \times J$ matrix of in-sample
+ fitted values.
+ \item {\tt df.residual}: the residual degrees of freedom.
+ \item {\tt edf}: the effective degrees of freedom.
+ \item {\tt Hessian}: the Hessian matrix.
+ \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.
+ \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+ \begin{itemize}
+ \item {\tt coefficients}: the parameter estimates with their
+ associated standard errors, and $t$-statistics.
+ \end{itemize}
+
+ \item From the {\tt sim()} output object {\tt s.out}, you may extract
+ quantities of interest arranged as arrays. Available quantities
+ are:
+
+ \begin{itemize}
+ \item {\tt qi\$ev}: the simulated expected probabilities for the
+ specified values of {\tt x}, indexed by simulation $\times$
+ quantity $\times$ {\tt x}-observation (for more than one {\tt
+ x}-observation).
+ \item {\tt qi\$pr}: the simulated predicted values drawn from the
+ distribution defined by the expected probabilities, indexed by
+ simulation $\times$ {\tt x}-observation.
+ \item {\tt qi\$fd}: the simulated first difference in the predicted
+ probabilities for the values specified in {\tt x} and {\tt x1},
+ indexed by simulation $\times$ quantity $\times$ {\tt
+ x}-observation (for more than one {\tt x}-observation).
+ \item {\tt qi\$att.ev}: the simulated average expected treatment
+ effect for the treated from conditional prediction models.
+ \item {\tt qi\$att.pr}: the simulated average predicted treatment
+ effect for the treated from conditional prediction models.
+ \end{itemize}
\end{itemize}
- \subsection* {How to Cite}
-
-To cite the \emph{poisson.net} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``poisson.net: Network Poisson Regression for Event Count Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
+\subsection* {How to Cite}
-\end{verse}
+\input{cites/poisson.net}
\input{citeZelig}
\subsection* {See also}
The network normal regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(poisson.net)}. Sample data are fictional.
diff --git a/inst/doc/poisson.net.pdf b/inst/doc/poisson.net.pdf
index da43f6e..3aa5293 100644
Binary files a/inst/doc/poisson.net.pdf and b/inst/doc/poisson.net.pdf differ
diff --git a/inst/doc/poisson.pdf b/inst/doc/poisson.pdf
index f22f139..113c829 100644
Binary files a/inst/doc/poisson.pdf and b/inst/doc/poisson.pdf differ
diff --git a/inst/doc/poisson.survey.Rnw b/inst/doc/poisson.survey.Rnw
index 45de621..12cd45f 100644
--- a/inst/doc/poisson.survey.Rnw
+++ b/inst/doc/poisson.survey.Rnw
@@ -486,37 +486,31 @@ are therefore advised to re-name any object called {\tt .survey.prob.weights} be
\subsection* {How to Cite}
-To cite the \emph{poisson.survey} Zelig model: \begin{verse}
-Nicholas Carnes. 2008. ``poisson.survey: Survey-Weighted Poisson
-Regression for Event Count Dependent Variables,'' in Kosuke Imai,
-Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical
-Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-
-Survey-weighted linear models and the sample data used in the
-examples above are a part of the {\tt survey} package by Thomas
-Lumley. Users may wish to refer to the help files for the three
-functions that Zelig draws upon when estimating survey-weighted
-models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
-help(svrepdesign)}. The poisson model is part of the stats package
-by \citet{VenRip02}. Advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\cite{McCNel89}.
-
-
-
-
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
+\input{cites/poisson.survey}
+ \input{citeZelig}
+
+ \subsection* {See also}
+
+ Survey-weighted linear models and the sample data used in the
+ examples above are a part of the {\tt survey} package by Thomas
+ Lumley. Users may wish to refer to the help files for the three
+ functions that Zelig draws upon when estimating survey-weighted
+ models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+ help(svrepdesign)}. The Gamma model is part of the stats package
+ by \citet{VenRip02}. Advanced users may wish to refer to
+ \texttt{help(glm)} and \texttt{help(family)}, as well as
+ \cite{McCNel89}.
+
+
+
+
+
+ \bibliographystyle{asa}
+ \bibliography{gk,gkpubs}
+ <<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+ @
+ \end{document}
diff --git a/inst/doc/poisson.survey.pdf b/inst/doc/poisson.survey.pdf
index b26b12d..c1eaecc 100644
Binary files a/inst/doc/poisson.survey.pdf and b/inst/doc/poisson.survey.pdf differ
diff --git a/inst/doc/probit.Rnw b/inst/doc/probit.Rnw
index a6fe696..71f7c2a 100644
--- a/inst/doc/probit.Rnw
+++ b/inst/doc/probit.Rnw
@@ -214,11 +214,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{probit} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``probit: Probit Regression for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/probit}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/probit.bayes.Rnw b/inst/doc/probit.bayes.Rnw
index 0b50e4d..91ae461 100644
--- a/inst/doc/probit.bayes.Rnw
+++ b/inst/doc/probit.bayes.Rnw
@@ -271,11 +271,7 @@ for the treated from conditional prediction models.
\subsection* {How to Cite}
-To cite the \emph{probit.bayes} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``probit.bayes: Bayesian Probit Regression for Dichotomous Dependent Variable,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/probit.bayes}
\input{citeZelig}
\subsection*{See also}
Bayesian probit regression 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}.
diff --git a/inst/doc/probit.bayes.pdf b/inst/doc/probit.bayes.pdf
index 5d5144b..e9d4ddc 100644
Binary files a/inst/doc/probit.bayes.pdf and b/inst/doc/probit.bayes.pdf differ
diff --git a/inst/doc/probit.gam.Rnw b/inst/doc/probit.gam.Rnw
index 8a0b7a3..9a38925 100644
--- a/inst/doc/probit.gam.Rnw
+++ b/inst/doc/probit.gam.Rnw
@@ -266,11 +266,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{probit.gam} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``probit.gam: Generalized Additive Model for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/probit.gam}
\input{citeZelig}
\subsection* {See also}
The {\tt gam.logit} model is adapted from the {\tt mgcv} package by Simon N. Wood \citep{Wood06}. Advanced users may wish to refer to {\tt help(gam)}, \cite{Wood04}, \cite{Wood00}, and other documentation accompanying the {\tt mgcv} package. All examples are reproduced and extended from {\tt mgcv}'s {\tt gam()} help pages.
diff --git a/inst/doc/probit.gam.pdf b/inst/doc/probit.gam.pdf
index 97e1d2b..2d2ee98 100644
Binary files a/inst/doc/probit.gam.pdf and b/inst/doc/probit.gam.pdf differ
diff --git a/inst/doc/probit.gee.Rnw b/inst/doc/probit.gee.Rnw
index a0b1769..8ad0223 100644
--- a/inst/doc/probit.gee.Rnw
+++ b/inst/doc/probit.gee.Rnw
@@ -337,12 +337,8 @@ below.
\subsection*{How To Cite}
-To cite the \emph{probit.gee} Zelig model:
-\begin{verse}
-Patrick Lam. 2007. ``probit.gee: Generalized Estimating Equation for Probit Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-%\input{citeZelig}
+\input{cites/probit.gee}
+\input{citeZelig}
\subsection*{See also}
The gee function is part of the gee package by Vincent J. Carey, ported to R by Thomas Lumley and Brian Ripley. Advanced users may wish to refer to \texttt{help(gee)} and \texttt{help(family)}. Sample data are from \cite{KinTomWit00}.
diff --git a/inst/doc/probit.gee.pdf b/inst/doc/probit.gee.pdf
index cca4ba1..735da6e 100644
Binary files a/inst/doc/probit.gee.pdf and b/inst/doc/probit.gee.pdf differ
diff --git a/inst/doc/probit.mixed.Rnw b/inst/doc/probit.mixed.Rnw
index 10e082d..e2469be 100644
--- a/inst/doc/probit.mixed.Rnw
+++ b/inst/doc/probit.mixed.Rnw
@@ -1,190 +1,185 @@
-\SweaveOpts{results=hide, prefix.string=vigpics/probitmixed}
-\include{zinput}
-%\VignetteIndexEntry{Mixed effects probit regression}
-%\VignetteDepends{Zelig}
-%\VignetteKeyWords{mixed,probit regression}
-%\VignettePackage{Zelig}
-\begin{document}
-\nobibliography*
-<<beforepkgs, echo=FALSE>>=
- before=search()
-@
-
-<<loadLibrary, echo=F,results=hide>>=
-library(Zelig)
-@
-
-\section{{\tt probit.mixed}: Mixed effects probit Regression}
-\label{mixed}
-
-Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The probit model is appropriate when the dependent variable is dichotomous.
-
-While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
-
-\subsubsection{Syntax}
-
-\begin{verbatim}
-z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
- data=mydata, model="probit.mixed")
-
-z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
- gamma= ~ tag(w1 + w2 | g)), data=mydata, model="probit.mixed")
-\end{verbatim}
-
-\subsubsection{Inputs}
-
-\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
-\begin{itemize}
-\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
-Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
-\end{itemize}
-
-\subsubsection{Additional Inputs}
-
-In addition, {\tt zelig()} accepts the following additional arguments for model specification:
-
-\begin{itemize}
-\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
-\item {\tt method:} a character string. The criterion is always the log-likelihood but this criterion does not have a closed form expression and must be approximated. The default approximation is {\tt "PQL"} or penalized quasi-likelihood. Alternatives are {\tt "Laplace"} or {\tt "AGQ"} indicating the Laplacian and adaptive Gaussian quadrature approximations respectively.
-\item {\tt na.action:} A function that indicates what should happen when the data contain {\tt NAs}. The default action ({\tt na.fail}) causes {\tt zelig()} to print an error message and terminate if there are any incomplete observations.
-\end{itemize}
-Additionally, users may with to refer to {\tt lmer} in the package {\tt lme4} for more information, including control parameters for the estimation algorithm and their defaults.
-
-\subsubsection{Examples}
-
-\begin{enumerate}
-\item Basic Example with First Differences \\
-\\
-Attach sample data: \\
-<<Examples.data>>=
-data(voteincome)
-@
-Estimate model:
-<<Examples.zelig>>=
-z.out1 <- zelig(vote ~ education + age + female + tag(1 | state), data=voteincome, model="probit.mixed")
-@
-
-\noindent Summarize regression coefficients and estimated variance of random effects:\\
-<<Examples.summary>>=
-summary(z.out1)
-@
-Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
-<<Examples.setx>>=
-x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
-x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
-@
-Generate first differences for the effect of high versus low education on voting: \\
-<<Examples.sim>>=
-s.out1 <- sim(z.out1, x=x.high, x1=x.low)
-summary(s.out1)
-@
-
-\end{enumerate}
-
-\subsubsection{Mixed effects probit Regression Model}
-
-Let $Y_{ij}$ be the binary dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$ which takes the value of either 0 or 1, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
-
-\begin{itemize}
-\item The \emph{stochastic component} is described by a Bernoulli distribution with mean vector $\pi_{ij}$.
-\begin{equation*}
-Y_{ij} \sim \mathrm{Bernoulli}(y_{ij} | \pi_{ij}) = \pi_{ij}^{y_{ij}} (1 - \pi_{ij})^{1 - y_{ij}}
-\end{equation*}
-where
-\begin{equation*}
-\pi_{ij} = \mathrm{Pr}(Y_{ij} = 1)
-\end{equation*}
-\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
-\begin{equation*}
-b_i \sim Normal(0, \Psi)
-\end{equation*}
-\item The \emph{systematic component} is
-\begin{equation*}
-\pi_{ij} \equiv \Phi(X_{ij} \beta + Z_{ij} b_i)
-\end{equation*}
-where $\Phi(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance, and \\
-where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
-\end{itemize}
-
-
-\subsubsection{Quantities of Interest}
-
-\begin{itemize}
-\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value, $\pi_{ij}$ for
-\begin{equation*}
-\pi_{ij} = \Phi(X_{ij} \beta + Z_{ij} b_i)
-\end{equation*}
-given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
-
-\item The expected values ({\tt qi\$ev}) are simulations of the predicted probability of a success given draws of $\beta$ from its posterior:
-\begin{equation*}
-E(Y_{ij} | X_{ij}) = \pi_{ij} = \Phi(X_{ij} \beta).
-\end{equation*}
-
-\item The first difference ({\tt qi\$fd}) is given by the difference in predicted probabilities, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
-\begin{equation*}
-FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = Pr(Y_{ij} = 1 | X_{ij}) - Pr(Y_{ij} = 1 | X_{ij}^\prime)
-\end{equation*}
-
-\item The risk ratio ({\tt qi\$rr}) is defined as
-\begin{equation*}
-RR(Y_{ij} | X_{ij}, X_{ij}^{\prime}) = \frac{Pr(Y_{ij} = 1 | X_{ij})}{Pr(Y_{ij} = 1 | X_{ij}^{\prime})}
-\end{equation*}
-
-\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
-\begin{equation*}
-\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
-\end{equation*}
-where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
-
-\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
-\begin{equation*}
-\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
-\end{equation*}
-where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
-
-\end{itemize}
-
-\subsubsection{Output Values}
-
-The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
-\begin{itemize}
-\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
-\begin{itemize}
-\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
-\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
-\item[--] {\tt frame}: the model frame for the model.
-\end{itemize}
-\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
-\begin{itemize}
-\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
-\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
-\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
-\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
-\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
-\end{itemize}
-\end{itemize}
-
-
-
-\subsection* {How to Cite}
-
-To cite the \emph{probit.mixed} Zelig model:
-\begin{verse}
-Delia Bailey, Ferdinand Alimadhi. 2007. ``probit.mixed: Mixed effects probit regression'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-Mixed effects probit regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after <- search()
- torm <- setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
-\end{document}
+\SweaveOpts{results=hide, prefix.string=vigpics/probitmixed}
+\include{zinput}
+%\VignetteIndexEntry{Mixed effects probit regression}
+%\VignetteDepends{Zelig}
+%\VignetteKeyWords{mixed,probit regression}
+%\VignettePackage{Zelig}
+\begin{document}
+\nobibliography*
+<<beforepkgs, echo=FALSE>>=
+ before=search()
+@
+
+<<loadLibrary, echo=F,results=hide>>=
+library(Zelig)
+@
+
+\section{{\tt probit.mixed}: Mixed effects probit Regression}
+\label{mixed}
+
+Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The probit model is appropriate when the dependent variable is dichotomous.
+
+While generally called multi-level models in the social sciences, this class of models is often referred to as mixed-effects models in the statistics literature and as hierarchical models in a Bayesian setting. This general class of models consists of linear models that are expressed as a function of both \emph{fixed effects}, parameters corresponding to an entire population or certain repeatable levels of experimental factors, and \emph{random effects}, parameters corresponding to indiv [...]
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+z.out <- zelig(formula= y ~ x1 + x2 + tag(z1 + z2 | g),
+ data=mydata, model="probit.mixed")
+
+z.out <- zelig(formula= list(mu=y ~ xl + x2 + tag(z1, gamma | g),
+ gamma= ~ tag(w1 + w2 | g)), data=mydata, model="probit.mixed")
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\noindent {\tt zelig()} takes the following arguments for {\tt mixed}:
+\begin{itemize}
+\item {\tt formula:} a two-sided linear formula object describing the systematic component of the model, with the response on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1 + ... + zn | g)} with {\tt z1 + ... + zn} specifying the model for the random effects and {\tt g} the grouping structure. Random intercept terms are included with the notation {\tt ta [...]
+Alternatively, {\tt formula} may be a list where the first entry, {\tt mu}, is a two-sided linear formula object describing the systematic component of the model, with the repsonse on the left of a {\tt $\tilde{}$} operator and the fixed effects terms, separated by {\tt +} operators, on the right. Any random effects terms are included with the notation {\tt tag(z1, gamma | g)} with {\tt z1} specifying the individual level model for the random effects, {\tt g} the grouping structure and { [...]
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item {\tt data:} An optional data frame containing the variables named in {\tt formula}. By default, the variables are taken from the environment from which {\tt zelig()} is called.
+\item {\tt 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 lme4} for more information, including control parameters for the estimation algorithm and their defaults.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences \\
+\\
+Attach sample data: \\
+<<Examples.data>>=
+data(voteincome)
+@
+Estimate model:
+<<Examples.zelig>>=
+z.out1 <- zelig(vote ~ education + age + female + tag(1 | state), data=voteincome, model="probit.mixed")
+@
+
+\noindent Summarize regression coefficients and estimated variance of random effects:\\
+<<Examples.summary>>=
+summary(z.out1)
+@
+Set explanatory variables to their default values, with high (80th percentile) and low (20th percentile) values for education:\\
+<<Examples.setx>>=
+x.high <- setx(z.out1, education=quantile(voteincome$education, 0.8))
+x.low <- setx(z.out1, education=quantile(voteincome$education, 0.2))
+@
+Generate first differences for the effect of high versus low education on voting: \\
+<<Examples.sim>>=
+s.out1 <- sim(z.out1, x=x.high, x1=x.low)
+summary(s.out1)
+@
+
+\end{enumerate}
+
+\subsubsection{Mixed effects probit Regression Model}
+
+Let $Y_{ij}$ be the binary dependent variable, realized for observation $j$ in group $i$ as $y_{ij}$ which takes the value of either 0 or 1, for $i = 1, \ldots, M$, $j = 1, \ldots, n_i$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by a Bernoulli distribution with mean vector $\pi_{ij}$.
+\begin{equation*}
+Y_{ij} \sim \mathrm{Bernoulli}(y_{ij} | \pi_{ij}) = \pi_{ij}^{y_{ij}} (1 - \pi_{ij})^{1 - y_{ij}}
+\end{equation*}
+where
+\begin{equation*}
+\pi_{ij} = \mathrm{Pr}(Y_{ij} = 1)
+\end{equation*}
+\item The $q$-dimensional vector of \emph{random effects}, $b_i$, is restricted to be mean zero, and therefore is completely characterized by the variance covarance matrix $\Psi$, a $(q \times q)$ symmetric positive semi-definite matrix.
+\begin{equation*}
+b_i \sim Normal(0, \Psi)
+\end{equation*}
+\item The \emph{systematic component} is
+\begin{equation*}
+\pi_{ij} \equiv \Phi(X_{ij} \beta + Z_{ij} b_i)
+\end{equation*}
+where $\Phi(\mu)$ is the cumulative distribution function of the Normal distribution with mean 0 and unit variance, and \\
+where $X_{ij}$ is the $(n_i \times p \times M)$ array of known fixed effects explanatory variables, $\beta$ is the $p$-dimensional vector of fixed effects coefficients, $Z_{ij}$ is the $(n_i \times q \times M)$ array of known random effects explanatory variables and $b_i$ is the $q$-dimensional vector of random effects.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial distribution with mean equal to the simulated expected value, $\pi_{ij}$ for
+\begin{equation*}
+\pi_{ij} = \Phi(X_{ij} \beta + Z_{ij} b_i)
+\end{equation*}
+given $X_{ij}$ and $Z_{ij}$ and simulations of of $\beta$ and $b_i$ from their posterior distributions. The estimated variance covariance matrices are taken as correct and are themselves not simulated.
+
+\item The expected values ({\tt qi\$ev}) are simulations of the predicted probability of a success given draws of $\beta$ from its posterior:
+\begin{equation*}
+E(Y_{ij} | X_{ij}) = \pi_{ij} = \Phi(X_{ij} \beta).
+\end{equation*}
+
+\item The first difference ({\tt qi\$fd}) is given by the difference in predicted probabilities, conditional on $X_{ij}$ and $X_{ij}^\prime$, representing different values of the explanatory variables.
+\begin{equation*}
+FD(Y_{ij} | X_{ij}, X_{ij}^\prime) = Pr(Y_{ij} = 1 | X_{ij}) - Pr(Y_{ij} = 1 | X_{ij}^\prime)
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+RR(Y_{ij} | X_{ij}, X_{ij}^{\prime}) = \frac{Pr(Y_{ij} = 1 | X_{ij})}{Pr(Y_{ij} = 1 | X_{ij}^{\prime})}
+\end{equation*}
+
+\item In conditional prediction models, the average predicted treatment effect ({\tt qi\$att.pr}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - \widehat{Y_{ij}(t_{ij} = 0)} \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $Y_{ij}(t_{ij} = 0)$, the counterfactual predicted value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\item In conditional prediction models, the average expected treatment effect ({\tt qi\$att.ev}) for the treatment group is given by
+\begin{equation*}
+\frac{1}{\sum_{i = 1}^M \sum_{j = 1}^{n_i} t_{ij}} \sum_{i = 1}^M \sum_{j:t_{ij} = 1}^{n_i} \{ Y_{ij} (t_{ij} = 1) - E[Y_{ij}(t_{ij} = 0)] \},
+\end{equation*}
+where $t_{ij}$ is a binary explanatory variable defining the treatment $(t_{ij} = 1)$ and control $(t_{ij} = 0)$ groups. Variation in the simulations is due to uncertainty in simulating $E[Y_{ij}(t_{ij} = 0)]$, the counterfactual expected value of $Y_{ij}$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_{ij} = 0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may view. You may examine the available information in {\tt z.out} by using {\tt slotNames(z.out)}, see the fixed effect coefficients by using {\tt summary(z.out)@coefs}, and a default summary of information through {\tt summary(z.out)}. Other elements available through the {\tt \@} operator are listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+\item[--] {\tt fixef}: numeric vector containing the conditional estimates of the fixed effects.
+\item[--] {\tt ranef}: numeric vector containing the conditional modes of the random effects.
+\item[--] {\tt frame}: the model frame for the model.
+\end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}, you may extract quantities of interest stored in a data frame:
+\begin{itemize}
+\item {\tt qi\$pr}: the simulated predicted values drawn from the distributions defined by the expected values.
+\item {\tt qi\$ev}: the simulated expected values for the specified values of x.
+\item {\tt qi\$fd}: the simulated first differences in the expected values for the values specified in x and x1.
+\item {\tt qi\$ate.pr}: the simulated average predicted treatment effect for the treated from conditional prediction models.
+\item {\tt qi\$ate.ev}: the simulated average expected treatment effect for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+
+
+\subsection* {How to Cite}
+
+\input{cites/probit.mixed}
+\input{citeZelig}
+
+\subsection* {See also}
+Mixed effects probit regression is part of {\tt lme4} package by Douglas M. Bates \citep{Bates07}. For a detailed discussion of mixed-effects models, please see \cite{JosBat00}
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+<<afterpkgs, echo=FALSE>>=
+ after <- search()
+ torm <- setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+@
+\end{document}
diff --git a/inst/doc/probit.mixed.pdf b/inst/doc/probit.mixed.pdf
index 78a0cf7..05542f9 100644
Binary files a/inst/doc/probit.mixed.pdf and b/inst/doc/probit.mixed.pdf differ
diff --git a/inst/doc/probit.net.Rnw b/inst/doc/probit.net.Rnw
index b95a3f8..8745344 100644
--- a/inst/doc/probit.net.Rnw
+++ b/inst/doc/probit.net.Rnw
@@ -163,11 +163,7 @@ The output of each Zelig command contains useful information which you may view.
\subsection* {How to Cite}
-To cite the \emph{probit.net} Zelig model:
-\begin{verse}
-Skyler J. Cranmer. 2007. ``probit.net: Network Probit Regression for Dichotomous Proximity Matrix Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/probit.net}
\input{citeZelig}
\subsection* {See also}
The network probit regression is part of the {\tt netglm} package by Skyler J. Cranmer and is built using some of the functionality of the {\tt sna} package by Carter T. Butts \citep{ButCar01}.In addition, advanced users may wish to refer to {\tt help(netpoisson)}. Sample data are fictional.
diff --git a/inst/doc/probit.net.pdf b/inst/doc/probit.net.pdf
index a38482a..9052f72 100644
Binary files a/inst/doc/probit.net.pdf and b/inst/doc/probit.net.pdf differ
diff --git a/inst/doc/probit.pdf b/inst/doc/probit.pdf
index 414bf4c..fc1a959 100644
Binary files a/inst/doc/probit.pdf and b/inst/doc/probit.pdf differ
diff --git a/inst/doc/probit.survey.Rnw b/inst/doc/probit.survey.Rnw
index 50641a7..66fdff3 100644
--- a/inst/doc/probit.survey.Rnw
+++ b/inst/doc/probit.survey.Rnw
@@ -493,38 +493,31 @@ are therefore advised to re-name any object called {\tt .survey.prob.weights} be
\subsection* {How to Cite}
-To cite the \emph{probit.survey} Zelig model:
-\begin{verse}
-Nicholas Carnes. 2008. ``probit.survey: Survey-weighted Probit
-Regression for Dichotomous Dependent Variables,'' in Kosuke Imai,
-Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical
-Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
-\input{citeZelig}
-
-\subsection* {See also}
-
-Survey-weighted linear models and the sample data used in the
-examples above are a part of the {\tt survey} package by Thomas
-Lumley. Users may wish to refer to the help files for the three
-functions that Zelig draws upon when estimating survey-weighted
-models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
-help(svrepdesign)}. The probit model is part of the stats package
-by \citet{VenRip02}. Advanced users may wish to refer to
-\texttt{help(glm)} and \texttt{help(family)}, as well as
-\cite{McCNel89}.
-
-
-
-
-
-\bibliographystyle{asa}
-\bibliography{gk,gkpubs}
-<<afterpkgs, echo=FALSE>>=
- after<-search()
- torm<-setdiff(after,before)
- for (pkg in torm)
- detach(pos=match(pkg,search()))
-@
- \end{document}
+\input{cites/probit.survey}
+ \input{citeZelig}
+
+ \subsection* {See also}
+
+ Survey-weighted linear models and the sample data used in the
+ examples above are a part of the {\tt survey} package by Thomas
+ Lumley. Users may wish to refer to the help files for the three
+ functions that Zelig draws upon when estimating survey-weighted
+ models, namely, {\tt help(svyglm)}, {\tt help(svydesign)}, and {\tt
+ help(svrepdesign)}. The Gamma model is part of the stats package
+ by \citet{VenRip02}. Advanced users may wish to refer to
+ \texttt{help(glm)} and \texttt{help(family)}, as well as
+ \cite{McCNel89}.
+
+
+
+
+
+ \bibliographystyle{asa}
+ \bibliography{gk,gkpubs}
+ <<afterpkgs, echo=FALSE>>=
+ after<-search()
+ torm<-setdiff(after,before)
+ for (pkg in torm)
+ detach(pos=match(pkg,search()))
+ @
+ \end{document}
diff --git a/inst/doc/probit.survey.pdf b/inst/doc/probit.survey.pdf
index 65e41c2..7eb4e36 100644
Binary files a/inst/doc/probit.survey.pdf and b/inst/doc/probit.survey.pdf differ
diff --git a/inst/doc/refman.tex b/inst/doc/refman.tex
index ffb40fc..a906abd 100644
--- a/inst/doc/refman.tex
+++ b/inst/doc/refman.tex
@@ -17,7 +17,7 @@ the {\tt setx()} command. (Occasionally, you may need to use, for example, {\tt
\chapter{Supplementary Commands}
-\include{matchit}
+\include{commands/matchit}
\include{commandsRd/mi}
\include{commandsRd/network}
\include{commandsRd/plot.ci}
diff --git a/inst/doc/relogit.Rnw b/inst/doc/relogit.Rnw
index 74dea8f..9076da4 100644
--- a/inst/doc/relogit.Rnw
+++ b/inst/doc/relogit.Rnw
@@ -402,10 +402,7 @@ standard matrix calculations.
\subsection* {How to Cite}
-To cite the \emph{relogit} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/relogit}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/relogit.pdf b/inst/doc/relogit.pdf
index 774ce55..5366d71 100644
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diff --git a/inst/doc/static/Zcommands.pdf b/inst/doc/static/Zcommands.pdf
index b285a07..c01c2d2 100644
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diff --git a/inst/doc/static/acknowledgments.pdf b/inst/doc/static/acknowledgments.pdf
index 8f582e7..6561cd0 100644
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diff --git a/inst/doc/static/addingModels.pdf b/inst/doc/static/addingModels.pdf
index 9e22c7c..1634b2e 100644
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diff --git a/inst/doc/static/describemymodel.pdf b/inst/doc/static/describemymodel.pdf
index aeef980..14ece08 100644
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diff --git a/inst/doc/static/faq.pdf b/inst/doc/static/faq.pdf
index 5755fa9..e76bf03 100644
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diff --git a/inst/doc/static/graphs.pdf b/inst/doc/static/graphs.pdf
index f7985c0..0eef946 100644
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diff --git a/inst/doc/static/install.pdf b/inst/doc/static/install.pdf
index 9403c06..aa9f435 100644
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diff --git a/inst/doc/static/intro.pdf b/inst/doc/static/intro.pdf
index 57dafca..2c40b82 100644
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diff --git a/inst/doc/static/matchitref.pdf b/inst/doc/static/matchitref.pdf
index c8dbfe3..f27b467 100644
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diff --git a/inst/doc/static/newModels.pdf b/inst/doc/static/newModels.pdf
index 05fd18a..3b81bbe 100644
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diff --git a/inst/doc/static/programmingstatements.pdf b/inst/doc/static/programmingstatements.pdf
index 32f88f0..bc743fd 100644
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diff --git a/inst/doc/static/robjects.pdf b/inst/doc/static/robjects.pdf
index f560abd..d78589e 100644
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diff --git a/inst/doc/static/syntax.pdf b/inst/doc/static/syntax.pdf
index 7bddde1..a6cce60 100644
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diff --git a/inst/doc/static/whatsnew.pdf b/inst/doc/static/whatsnew.pdf
index c926aab..3469672 100644
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diff --git a/inst/doc/sur.Rnw b/inst/doc/sur.Rnw
index 7576745..967f666 100644
--- a/inst/doc/sur.Rnw
+++ b/inst/doc/sur.Rnw
@@ -195,11 +195,7 @@ available through the \texttt{\$} operator are listed below:
\subsection* {How to Cite}
-To cite the \emph{sur} Zelig model:
-\begin{verse}
-Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. ``sur: Seemingly Unrelated Regression,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/sur}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/sur.pdf b/inst/doc/sur.pdf
index 834d475..010a141 100644
Binary files a/inst/doc/sur.pdf and b/inst/doc/sur.pdf differ
diff --git a/inst/doc/threesls.Rnw b/inst/doc/threesls.Rnw
index 9ad3af2..5acb3a5 100644
--- a/inst/doc/threesls.Rnw
+++ b/inst/doc/threesls.Rnw
@@ -192,11 +192,7 @@ available through the \texttt{\$} operator are listed below:
\subsection* {How to Cite}
-To cite the \emph{threesls} Zelig model:
-\begin{verse}
-Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. ``threesls: Three Stage Least Squares,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/threesls}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/threesls.pdf b/inst/doc/threesls.pdf
index 8919c75..3d1332c 100644
Binary files a/inst/doc/threesls.pdf and b/inst/doc/threesls.pdf differ
diff --git a/inst/doc/tobit.Rnw b/inst/doc/tobit.Rnw
index 2841122..4f77ccd 100644
--- a/inst/doc/tobit.Rnw
+++ b/inst/doc/tobit.Rnw
@@ -205,10 +205,7 @@ of the variance $\sigma^2$.
\subsection*{How to Cite}
-To cite the \emph{oprobit.bayes} Zelig model use:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``oprobit.bayes: Bayesian Ordered Probit Regression,'' in Kosuke Imai,Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-\end{verse}
+\input{cites/tobit}
\input{citeZelig}
\subsection*{See also}
The tobit function is part of the survival library by Terry
diff --git a/inst/doc/tobit.bayes.Rnw b/inst/doc/tobit.bayes.Rnw
index 86e253a..b34357b 100644
--- a/inst/doc/tobit.bayes.Rnw
+++ b/inst/doc/tobit.bayes.Rnw
@@ -275,11 +275,7 @@ for the treated from conditional prediction models.
\subsection* {How to Cite}
-To cite the \emph{tobit.bayes} Zelig model:
-\begin{verse}
-Ben Goodrich and Ying Lu. 2007. ``tobit.bayes: Bayesian Linear Regression for a Censored Dependent Variable,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/tobit.bayes}
\input{citeZelig}
\subsection*{See also}
Bayesian tobit regression 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}.
diff --git a/inst/doc/tobit.bayes.pdf b/inst/doc/tobit.bayes.pdf
index 3cb44db..d035abc 100644
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diff --git a/inst/doc/tobit.pdf b/inst/doc/tobit.pdf
index b2b1189..fc0bb7b 100644
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diff --git a/inst/doc/twosls.Rnw b/inst/doc/twosls.Rnw
index dca3580..5ee11cd 100644
--- a/inst/doc/twosls.Rnw
+++ b/inst/doc/twosls.Rnw
@@ -218,11 +218,7 @@ available through the \texttt{\$} operator are listed below:
\subsection* {How to Cite}
-To cite the \emph{twosls} Zelig model:
-\begin{verse}
-Ferdinand Alimadhi, Ying Lu, and Elena Villalon. 2007. ``twosls: Two Stage Least Squares,'' in Kosuke Imai, Gary King, and Olivia Lau, ``Zelig: Everyone's Statistical Software,'' \url{http://gking.harvard.edu/zelig}.
-
-\end{verse}
+\input{cites/twosls}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/twosls.pdf b/inst/doc/twosls.pdf
index 0085a68..aa423bb 100644
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diff --git a/inst/doc/vigpics/aov-ExamplesPlot1.eps b/inst/doc/vigpics/aov-ExamplesPlot1.eps
index cb8a176..ea28123 100644
--- a/inst/doc/vigpics/aov-ExamplesPlot1.eps
+++ b/inst/doc/vigpics/aov-ExamplesPlot1.eps
@@ -99,20 +99,25 @@ np
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diff --git a/inst/doc/weibull.Rnw b/inst/doc/weibull.Rnw
index b736cee..5f7b1ed 100644
--- a/inst/doc/weibull.Rnw
+++ b/inst/doc/weibull.Rnw
@@ -258,11 +258,7 @@ below.
\subsection* {How to Cite}
-To cite the \emph{weibull} Zelig model:
-\begin{verse}
-Kosuke Imai, Gary King, and Olivia Lau. 2007. ``weibull: Weibull 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{cites/weibull}
\input{citeZelig}
\subsection* {See also}
diff --git a/inst/doc/weibull.pdf b/inst/doc/weibull.pdf
index b54ecff..2cb21ae 100644
Binary files a/inst/doc/weibull.pdf and b/inst/doc/weibull.pdf differ
diff --git a/inst/doc/whatsnew.tex b/inst/doc/whatsnew.tex
index ec86332..1119c26 100644
--- a/inst/doc/whatsnew.tex
+++ b/inst/doc/whatsnew.tex
@@ -7,8 +7,35 @@ may contain bugs, but are usually replaced by stable releases within a
few days.
\begin{itemize}
+
+\item {3.4-5} (Mar 13, 2009); A bug fixed in {\tt plot.ci()} (thanks to Ken Benoit) \\
+
+\item {3.4-4} (Mar 4, 2009); weights are incorporated into {\tt ologit}, {\tt oprobit}, and {\tt negbin} models (thanks to Casey Klofstad) \\
+
+\item {3.4-2} (Feb 10, 2009): Small fixes in the Rd files as required
+by new check in CRAN \\
+
+\item {3.4-0} (Jan 2, 2009): Bug-fix release for R 2.8.0
+Changed the Zelig citation \\
+Fixed zelig() signature to ensure that the formals() work properly
+and all arguments remain documented. "save.data" and "cite" were not
+documented (thanks to Micah Altman)\\
+Fixed some typos in model family names (thanks to Kevin Condon)\\
+Fixed the predicted values in gam.* models \\
+Fixed the plot functions in gam.* models \\
+
+\item {3.4-0} (Oct 27, 2008): Stable release for R 2.8.0.
+zelig() now takes a "citation" argument. If "citation" is "true" (default)
+the model citation is printed in each zelig run \\
+Introduced two new elemetns on the describe.mymodel function: authors and year \\
+Fixed the problems with lme4 package. Note that there is still a problem with
+simulation step of "gamma.mixed" model. We are still working on that. \\
+Fixed the bug with "gam" models (wrong predicted values) \\
+Fixed the bug with when zelig model name was provided from a variable (reported from
+Jeroen)
+
-\item {\bf 3.3-1} (June 13, 2008): Bug-fix release for R.2.7.0. A bug
+\item {\bf 3.3-1} (June 12, 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).
diff --git a/inst/doc/zelig.html b/inst/doc/zelig.html
index b288a00..bcbb80b 100644
--- a/inst/doc/zelig.html
+++ b/inst/doc/zelig.html
@@ -217,7 +217,7 @@ 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="static/matchit.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>
@@ -235,7 +235,7 @@ left: 100px;
<ul id="models">
-<li class="mainindx"><a href="">Models for Continous Dependent Variables</a>
+<li class="mainindx"><a href="">Models for Continuous Dependent Variables</a>
<ul>
<li><a href="aov.pdf">aov : Fit an Analysis of Variance Model</a></li>
<li><a href="arima.pdf">arima : Arima models for Time Series Data</a></li>
@@ -276,7 +276,7 @@ left: 100px;
<li><a href="probit.gee.pdf">probit.gee : General Estimating Equation for Probit Regression</a></li>
<li><a href="probit.mixed.pdf">probit.mixed : Mixed effects probit model</a></li>
<li><a href="probit.net.pdf">probit.net : Social Network Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.survey.pdf">probit.survey : Probit Regression for Dichotomous Dependent Variables</a></li>
+ <li><a href="probit.survey.pdf">probit.survey : Survey-Weighted Probit Regression for Dichotomous Dependent Variables</a></li>
<li><a href="relogit.pdf">relogit : Rare Events Logistic Regression for Dichotomous Dependent Variables</a></li>
</ul>
</li>
@@ -289,7 +289,7 @@ left: 100px;
<li><a href="oprobit.bayes.pdf">oprobit.bayes : Bayesian Ordered Probit Regression</a></li>
</ul>
</li>
-<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
+<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
<ul>
<li><a href="coxph.pdf">coxph : Cox Proportional Hazard Regression for Duration Dependent Variables</a></li>
<li><a href="exp.pdf">exp : Exponential Regression for Duration Dependent Variables</a></li>
@@ -306,6 +306,7 @@ left: 100px;
<ul>
<li><a href="mlogit.pdf">mlogit : Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
<li><a href="mlogit.bayes.pdf">mlogit.bayes : Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
+ <li><a href="mloglm.pdf">mloglm : Multinomial Log-Linear Regression for Contingency Table Models</a></li>
</ul>
</li>
<li class="mainindx"><a href="">Event Count Models</a>
@@ -325,7 +326,7 @@ left: 100px;
<li><a href="factor.mix.pdf">factor.mix : Mixed Data Factor Analysis</a></li>
</ul>
</li>
-<li class="mainindx"><a href="">Ecological inverence models</a>
+<li class="mainindx"><a href="">Ecological Inference Models</a>
<ul>
<li><a href="ei.dynamic.pdf">ei.dynamic : Quinn's Dynamic Ecological Inference Model</a></li>
<li><a href="ei.hier.pdf">ei.hier : Hierarchical Ecological Inference Model for 2 x 2 Tables</a></li>
diff --git a/inst/doc/zelig.pdf b/inst/doc/zelig.pdf
index 912ce47..115b1dc 100644
--- a/inst/doc/zelig.pdf
+++ b/inst/doc/zelig.pdf
@@ -435,589 +435,547 @@ endobj
<< /S /GoTo /D (section.11.1) >>
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+(matchit: Create matched data)
endobj
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endobj
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endobj
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endobj
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+(plot.ci: Plotting Vertical confidence Intervals)
endobj
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<< /S /GoTo /D (section.11.5) >>
endobj
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+(plot.surv: Plotting Confidence Intervals for Survival Curves)
endobj
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endobj
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-(ternaryplot: Ternary diagram)
+(rocplot: Receiver Operator Characteristic Plots)
endobj
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<< /S /GoTo /D (section.11.7) >>
endobj
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-(ternarypoints: Adding Points to Ternary Diagrams)
+(ternaryplot: Ternary diagram)
endobj
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-<< /S /GoTo /D (chapter.12) >>
+<< /S /GoTo /D (section.11.8) >>
endobj
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+(ternarypoints: Adding Points to Ternary Diagrams)
endobj
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+<< /S /GoTo /D (chapter.12) >>
endobj
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+(Models Zelig Can Run)
endobj
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-<< /S /GoTo /D (section.12.2) >>
+<< /S /GoTo /D (section.12.1) >>
endobj
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+(aov: Analysis of Variance for Continuous Dependent Variables)
endobj
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-<< /S /GoTo /D (section.12.3) >>
+<< /S /GoTo /D (section.12.2) >>
endobj
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-(blogit: Bivariate Logistic Regression for Two Dichotomous Dependent Variables)
+(ARIMA: ARIMA Models for Time Series Data)
endobj
337 0 obj
-<< /S /GoTo /D (section.12.4) >>
+<< /S /GoTo /D (section.12.3) >>
endobj
340 0 obj
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+(blogit: Bivariate Logistic Regression for Two Dichotomous Dependent Variables)
endobj
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-<< /S /GoTo /D (section.12.5) >>
+<< /S /GoTo /D (section.12.4) >>
endobj
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-(chopit: Compound Hierarchical Ordered Probit for Survey Vignettes)
+(bprobit: Bivariate Logistic Regression for Two Dichotomous Dependent Variables)
endobj
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-<< /S /GoTo /D (section.12.6) >>
+<< /S /GoTo /D (section.12.5) >>
endobj
348 0 obj
-(cloglog.net: Network Complementary Log Log Regression for Dichotomous Proximity Matrix Dependent Variables)
+(chopit: Compound Hierarchical Ordered Probit for Survey Vignettes)
endobj
349 0 obj
-<< /S /GoTo /D (section.12.7) >>
+<< /S /GoTo /D (section.12.6) >>
endobj
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+(cloglog.net: Network Complementary Log Log Regression for Dichotomous Proximity Matrix Dependent Variables)
endobj
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-<< /S /GoTo /D (section.12.8) >>
+<< /S /GoTo /D (section.12.7) >>
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+(coxph: Cox Proportional Hazards Regression for Duration Dependent Variables)
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trailer
<<
-/Size 6129
-/Root 6127 0 R
-/Info 6128 0 R
+/Size 5875
+/Root 5873 0 R
+/Info 5874 0 R
>>
startxref
-3619025
+3581346
%%EOF
diff --git a/inst/doc/zelig.tex b/inst/doc/zelig.tex
index b6d578c..ffaf697 100644
--- a/inst/doc/zelig.tex
+++ b/inst/doc/zelig.tex
@@ -1,4 +1,5 @@
\include{zinput}
+\usepackage{Sweave}
\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
@@ -21,7 +22,7 @@ Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
\texttt{OLau at Fas.Harvard.Edu}).}}
% rbuild: replace 'Version ' '\\' Version
-\date{Version 3.3-1\\ \today}
+\date{Version 3.4-5\\ \today}
\begin{document}
\maketitle
diff --git a/inst/doc/zeligheader.html b/inst/doc/zeligheader.html
index 272f6c0..2cf26d5 100644
--- a/inst/doc/zeligheader.html
+++ b/inst/doc/zeligheader.html
@@ -217,7 +217,7 @@ 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="static/matchit.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>
diff --git a/inst/doc/zeligmodels.html b/inst/doc/zeligmodels.html
index 96b8f8a..6836aec 100644
--- a/inst/doc/zeligmodels.html
+++ b/inst/doc/zeligmodels.html
@@ -1,5 +1,5 @@
<ul id="models">
-<li class="mainindx"><a href="">Models for Continous Dependent Variables</a>
+<li class="mainindx"><a href="">Models for Continuous Dependent Variables</a>
<ul>
<li><a href="aov.pdf">aov : Fit an Analysis of Variance Model</a></li>
<li><a href="arima.pdf">arima : Arima models for Time Series Data</a></li>
@@ -40,7 +40,7 @@
<li><a href="probit.gee.pdf">probit.gee : General Estimating Equation for Probit Regression</a></li>
<li><a href="probit.mixed.pdf">probit.mixed : Mixed effects probit model</a></li>
<li><a href="probit.net.pdf">probit.net : Social Network Probit Regression for Dichotomous Dependent Variables</a></li>
- <li><a href="probit.survey.pdf">probit.survey : Probit Regression for Dichotomous Dependent Variables</a></li>
+ <li><a href="probit.survey.pdf">probit.survey : Survey-Weighted Probit Regression for Dichotomous Dependent Variables</a></li>
<li><a href="relogit.pdf">relogit : Rare Events Logistic Regression for Dichotomous Dependent Variables</a></li>
</ul>
</li>
@@ -53,7 +53,7 @@
<li><a href="oprobit.bayes.pdf">oprobit.bayes : Bayesian Ordered Probit Regression</a></li>
</ul>
</li>
-<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
+<li class="mainindx"><a href="">Models for Continous Bounded Dependent Variables</a>
<ul>
<li><a href="coxph.pdf">coxph : Cox Proportional Hazard Regression for Duration Dependent Variables</a></li>
<li><a href="exp.pdf">exp : Exponential Regression for Duration Dependent Variables</a></li>
@@ -70,6 +70,7 @@
<ul>
<li><a href="mlogit.pdf">mlogit : Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
<li><a href="mlogit.bayes.pdf">mlogit.bayes : Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values</a></li>
+ <li><a href="mloglm.pdf">mloglm : Multinomial Log-Linear Regression for Contingency Table Models</a></li>
</ul>
</li>
<li class="mainindx"><a href="">Event Count Models</a>
@@ -89,7 +90,7 @@
<li><a href="factor.mix.pdf">factor.mix : Mixed Data Factor Analysis</a></li>
</ul>
</li>
-<li class="mainindx"><a href="">Ecological inverence models</a>
+<li class="mainindx"><a href="">Ecological Inference Models</a>
<ul>
<li><a href="ei.dynamic.pdf">ei.dynamic : Quinn's Dynamic Ecological Inference Model</a></li>
<li><a href="ei.hier.pdf">ei.hier : Hierarchical Ecological Inference Model for 2 x 2 Tables</a></li>
diff --git a/inst/doc/zinput.tex b/inst/doc/zinput.tex
index 477c1b4..528bba1 100644
--- a/inst/doc/zinput.tex
+++ b/inst/doc/zinput.tex
@@ -1,6 +1,8 @@
\documentclass[oneside,letterpaper,12pt]{book}
\usepackage{Rd}
-\usepackage{/usr/lib64/R/share/texmf/Sweave}
+%\usepackage{Sweave}
+%\usepackage{/usr/lib64/R/share/texmf/Sweave}
+%\usepackage{/usr/share/R/texmf/Sweave}
\usepackage{bibentry}
\usepackage{upquote}
\usepackage{graphicx}
diff --git a/man/Weimar.Rd b/man/Weimar.Rd
index f6f729d..6f4d191 100644
--- a/man/Weimar.Rd
+++ b/man/Weimar.Rd
@@ -8,18 +8,20 @@
\usage{data(Weimar)}
-\format{A table containing 11 variables and 10 observations. The variables are {
-{Nazi}{Number of votes for the Nazi party}
-{Government}{Number of votes for the Government}
-{Communists}{Number of votes for the Communist party}
-{FarRight}{Number of votes for far right parties}
-{Other}{Number of votes for other parties, and non-voters}
-{shareunemployed}{Proportion unemployed}
-{shareblue}{Proportion working class}
-{sharewhite}{Proportion white-collar workers}
-{sharedomestic}{Proportion domestic servants}
-{shareprotestants}{Proportion Protestant}
-}}
+\format{A table containing 11 variables and 10 observations. The variables are
+\describe{
+\item{Nazi}{Number of votes for the Nazi party}
+\item{Government}{Number of votes for the Government}
+\item{Communists}{Number of votes for the Communist party}
+\item{FarRight}{Number of votes for far right parties}
+\item{Other}{Number of votes for other parties, and non-voters}
+\item{shareunemployed}{Proportion unemployed}
+\item{shareblue}{Proportion working class}
+\item{sharewhite}{Proportion white-collar workers}
+\item{sharedomestic}{Proportion domestic servants}
+\item{shareprotestants}{Proportion Protestant}
+}
+}
\source{ICPSR}
diff --git a/man/Zelig.Rd b/man/Zelig.Rd
index f180fbb..66c4a18 100644
--- a/man/Zelig.Rd
+++ b/man/Zelig.Rd
@@ -11,7 +11,7 @@ first differences, along with the associated measures of uncertainty
(standard errors and confidence intervals). }
\usage{
-z.out <- zelig(formula, model, data, by, \dots)
+z.out <- zelig(formula, model, data, by, save.data, cite, \dots)
}
\arguments{
@@ -31,7 +31,15 @@ z.out <- zelig(formula, model, data, by, \dots)
models. }
\item{data}{the name of a data frame containing the variables
referenced in the formula, or a list of multiply imputed data frames
- each having the same variable names and row numbers (created by \code{mi}). }
+ each having the same variable names and row numbers (created by
+ \code{mi}). }
+
+\item{save.data}{If is set to "TRUE", the input dataframe will be saved
+ as an attribute ("zelig.data") of the zelig output object. }
+
+\item{cite}{If is set to "TRUE" (default), the model citation will be
+ will be printed out when this function is called. }
+
\item{by}{a factor variable contained in \code{data}. Zelig will subset
the data frame based on the levels in the \code{by} variable, and
estimate a model for each subset. This a particularly powerful option
diff --git a/man/homerun.Rd b/man/homerun.Rd
index 08059d6..ddd05ad 100644
--- a/man/homerun.Rd
+++ b/man/homerun.Rd
@@ -15,11 +15,11 @@
\item{\code{homeruns}}{an integer vector denoting the number of homeruns hit in that game for that player}
\item{\code{playerstatus}}{an integer vector equal to "0" if the player played in the game, and "1" if they did not.}
\item{\code{player}}{an integer vector equal to "0" (McGwire) or "1" (Sosa)}
-}
+ }
}
\source{\url{http://www.amstat.org}}
-\references{Simonoff, Jeffrey S. 1998. ``Move Over, Roger Maris: Breaking Baseball's Most Famous Record.'' {\it Journal of Statistics Education} 6(3). Data used are a subset of the data in the article. \\}
+\references{Simonoff, Jeffrey S. 1998. ``Move Over, Roger Maris: Breaking Baseball's Most Famous Record.'' \emph{Journal of Statistics Education} 6(3). Data used are a subset of the data in the article.}
-\keyword{datasets}
\ No newline at end of file
+\keyword{datasets}
diff --git a/man/model.end.Rd b/man/model.end.Rd
index c004d88..a30b3fb 100644
--- a/man/model.end.Rd
+++ b/man/model.end.Rd
@@ -20,7 +20,6 @@ model.end(res, mf)
\value{
A list of regression output, including:
-\itemize{
\item{coefficients}{the optimized parameters}
\item{variance}{the variance-covariance matrix (the negative
inverse of the Hessian matrix returned from the optimization
@@ -28,7 +27,7 @@ A list of regression output, including:
\item{terms}{the terms object. See \code{\link{terms.object}}
for more information}
\item{\dots}{additional elements passed from \code{res}}
-}}
+}
\seealso{The full Zelig manual at \url{http://gking.harvard.edu/zelig} for examples.}
diff --git a/man/plot.ci.Rd b/man/plot.ci.Rd
index 204af5e..1fe414a 100644
--- a/man/plot.ci.Rd
+++ b/man/plot.ci.Rd
@@ -35,7 +35,7 @@ plot.ci(x, CI = 95, qi = "ev", main = "", ylab = NULL, xlab = NULL,
}
\value{
-For all univariate response models, {\tt plot.ci()} returns vertical
+For all univariate response models, \code{plot.ci()} returns vertical
confidence intervals over a specified range of one explanatory
variable. You may save this plot using the commands described in the
Zelig manual (\url{http://gking.harvard.edu/zelig}).
diff --git a/man/plot.surv.Rd b/man/plot.surv.Rd
index da65e7d..11b1cc1 100644
--- a/man/plot.surv.Rd
+++ b/man/plot.surv.Rd
@@ -23,7 +23,7 @@
}
\value{
-For survival models, {\tt plot.surv()} returns vertical
+For survival models, \code{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}).
}
diff --git a/man/rocplot.Rd b/man/rocplot.Rd
index ddafe80..86ede2b 100644
--- a/man/rocplot.Rd
+++ b/man/rocplot.Rd
@@ -50,7 +50,7 @@ items (see below).}
\value{ If \code{plot = TRUE}, \code{rocplot} generates an ROC plot for
two logit or probit models. If \code{plot = FALSE}, \code{rocplot}
-returns a list with the following elements: {
+returns a list with the following elements:
\item{roc1}{a matrix containing a vector of x-coordinates and
y-coordinates corresponding to the number of ones and zeros correctly
predicted for the first model.}
@@ -61,7 +61,6 @@ returns a list with the following elements: {
Reimann sums.}
\item{area2}{the area under the second ROC curve, calculated using
Reimann sums.}
-}
}
\examples{
diff --git a/man/setx.Rd b/man/setx.Rd
index b4c0e22..dc30e75 100644
--- a/man/setx.Rd
+++ b/man/setx.Rd
@@ -19,6 +19,7 @@ x.out <- setx(object, fn = list(numeric = mean, ordered = median,
\arguments{
\item{object}{the saved output from \code{\link{zelig}}. }
\item{fn}{a list of functions to apply to three types of variables:
+ \itemize{
\item{numeric}{\code{numeric} variables are set to their mean by
default, but you may select any mathematical function to apply to
numeric variables.}
@@ -31,6 +32,7 @@ x.out <- setx(object, fn = list(numeric = mean, ordered = median,
only be set to their mode. If you wish to set one of the other
variables to a specific value, you may do so using \code{\dots}
below. }
+ }
In the special case \code{fn = NULL}, \code{setx} will return all
of the observations without applying any function to the data. }
\item{data}{a new data frame used to set the values of
diff --git a/man/sim.Rd b/man/sim.Rd
index 7d28814..8c0b8c3 100644
--- a/man/sim.Rd
+++ b/man/sim.Rd
@@ -54,7 +54,7 @@ s.out <- sim(object, x, x1 = NULL, num = c(1000, 100), prev = NULL,
\value{ The output stored in \code{s.out} varies by model. Use the
\code{names} command to view the output stored in \code{s.out}.
- Common elements include: {
+ Common elements include:
\item{x}{the \code{\link{setx}} values for the explanatory variables,
used to calculate the quantities of interest (expected values,
predicted values, etc.). }
@@ -108,7 +108,7 @@ s.out <- sim(object, x, x1 = NULL, num = c(1000, 100), prev = NULL,
\eqn{\widehat{Y_i(t_i=0)}}, the counterfactual predicted value of
\eqn{Y_i} for observations in the treatment group, under the
assumption that everything stays the same except that the
- treatment indicator is switched to \eqn{t_i=0}. }
+ treatment indicator is switched to \eqn{t_i=0}.
}
In the case of censored $Y$ in the exponential, Weibull, and lognormal
diff --git a/man/summary.zelig.Rd b/man/summary.zelig.Rd
index 532b6f2..8dd4e48 100644
--- a/man/summary.zelig.Rd
+++ b/man/summary.zelig.Rd
@@ -17,12 +17,14 @@
\item{object}{output object from \code{\link{sim}} (of class
\code{"zelig"}).}
\item{subset}{takes one of three values:
+ \itemize{
\item{NULL}{(default) for more than one observation, summarizes all the
observations at once for each quantity of interest.}
\item{a numeric vector}{indicates which observations to summarize,
and summarizes each one independently.}
\item{all}{summarizes all the observations independently.}
}
+ }
\item{stats}{summary statistics to be calculated.}
\item{CI}{a confidence interval to be calculated.}
\item{\ldots}{further arguments passed to or from other methods.}
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/r-cran-zelig.git
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