Zelig/0000755000176200001440000000000013006420075011321 5ustar liggesusersZelig/inst/0000755000176200001440000000000012722061503012277 5ustar liggesusersZelig/inst/JSON/0000755000176200001440000000000012722061503013050 5ustar liggesusersZelig/inst/JSON/zelig5models.json0000644000176200001440000003746612722061503016366 0ustar liggesusers{ "zelig5models": { "ls": { "name": ["ls"], "description": ["Least Squares Regression for Continuous Dependent Variables"], "outcome": { "modelingType": ["continous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-ls.html"], "wrapper": ["ls"], "tree": ["Zelig-ls"] }, "logit": { "name": ["logit"], "description": ["Logistic Regression for Dichotomous Dependent Variables"], "outcome": { "modelingType": ["binary"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-logit.html"], "wrapper": ["logit"], "tree": ["Zelig-logit", "Zelig-binchoice", "Zelig-glm"] }, "probit": { "name": ["probit"], "description": ["Probit Regression for Dichotomous Dependent Variables"], "outcome": { "modelingType": ["binary"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-probit.html"], "wrapper": ["probit"], "tree": ["Zelig-probit", "Zelig-binchoice", "Zelig-glm"] }, "poisson": { "name": ["poisson"], "description": ["Poisson Regression for Event Count Dependent Variables"], "outcome": { "modelingType": ["discrete"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-poisson.html"], "wrapper": ["poisson"], "tree": ["Zelig-poisson", "Zelig-glm"] }, "normal": { "name": ["normal"], "description": ["Normal Regression for Continuous Dependent Variables"], "outcome": { "modelingType": ["continuous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-normal.html"], "wrapper": ["normal"], "tree": ["Zelig-normal", "Zelig-glm"] }, "gamma": { "name": ["gamma"], "description": ["Gamma Regression for Continuous, Positive Dependent Variables"], "outcome": { "modelingType": ["continous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-gamma.html"], "wrapper": ["gamma"], "tree": ["Zelig-gamma", "Zelig-glm"] }, "negbin": { "name": ["negbin"], "description": ["Negative Binomial Regression for Event Count Dependent Variables"], "outcome": { "modelingType": ["discrete"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-negbin.html"], "wrapper": ["negbin"], "tree": ["Zelig-negbin"] }, "exp": { "name": ["exp"], "description": ["Exponential Regression for Duration Dependent Variables"], "outcome": { "modelingType": ["continous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-exp.html"], "wrapper": ["exp"], "tree": ["Zelig-exp"] }, "lognorm": { "name": ["lognorm"], "description": ["Log-Normal Regression for Duration Dependent Variables"], "outcome": { "modelingType": ["discrete"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-lognorm.html"], "wrapper": ["lognorm"], "tree": ["Zelig-lognorm"] }, "tobit": { "name": ["tobit"], "description": ["Linear regression for Left-Censored Dependent Variable"], "outcome": { "modelingType": ["continous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-tobit.html"], "wrapper": ["tobit"], "tree": ["Zelig-tobit"] }, "quantile": { "name": ["quantile"], "description": ["Quantile Regression for Continuous Dependent Variables"], "outcome": { "modelingType": ["continuous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-quantile.html"], "wrapper": ["rq"], "tree": ["Zelig-quantile"] }, "relogit": { "name": ["relogit"], "description": ["Rare Events Logistic Regression for Dichotomous Dependent Variables"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-relogit.html"], "wrapper": ["relogit"], "tree": ["Zelig-relogit"] }, "logitgee": { "name": ["logit-gee"], "description": ["General Estimating Equation for Logistic Regression"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-logitgee.html"], "wrapper": ["logit.gee"], "tree": ["Zelig-logit-gee", "Zelig-binchoice-gee", "Zelig-gee"] }, "probitgee": { "name": ["probit-gee"], "description": ["General Estimating Equation for Probit Regression"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-probitgee.html"], "wrapper": ["probit.gee"], "tree": ["Zelig-probit-gee", "Zelig-binchoice-gee", "Zelig-gee"] }, "gammagee": { "name": ["gamma-gee"], "description": ["General Estimating Equation for Gamma Regression"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-gammagee.html"], "wrapper": ["gamma.gee"], "tree": ["Zelig-gamma-gee", "Zelig-gee"] }, "normalgee": { "name": ["normal-gee"], "description": ["General Estimating Equation for Normal Regression"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-normalgee.html"], "wrapper": ["normal.gee"], "tree": ["Zelig-normal-gee", "Zelig-gee"] }, "poissongee": { "name": ["poisson-gee"], "description": ["General Estimating Equation for Poisson Regression"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-poissongee.html"], "wrapper": ["poisson.gee"], "tree": ["Zelig-poisson-gee", "Zelig-gee"] }, "factorbayes": { "name": ["factor-bayes"], "description": ["Bayesian Factor Analysis"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-factorbayes.html"], "wrapper": ["factor.bayes"], "tree": ["Zelig-factor-bayes"] }, "logitbayes": { "name": ["logit-bayes"], "description": ["Bayesian Logistic Regression for Dichotomous Dependent Variables"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-logitbayes.html"], "wrapper": ["logit.bayes"], "tree": ["Zelig-logit-bayes", "Zelig-bayes"] }, "mlogitbayes": { "name": ["mlogit-bayes"], "description": ["Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-mlogitbayes.html"], "wrapper": ["mlogit.bayes"], "tree": ["Zelig-mlogit-bayes", "Zelig-bayes"] }, "normalbayes": { "name": ["normal-bayes"], "description": ["Bayesian Normal Linear Regression"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-normalbayes.html"], "wrapper": ["normal.bayes"], "tree": ["Zelig-normal-bayes", "Zelig-bayes"] }, "oprobitbayes": { "name": ["oprobit-bayes"], "description": ["Bayesian Probit Regression for Dichotomous Dependent Variables"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-oprobitbayes.html"], "wrapper": ["oprobit.bayes"], "tree": ["Zelig-oprobit-bayes", "Zelig-bayes"] }, "poissonbayes": { "name": ["poisson-bayes"], "description": ["Bayesian Poisson Regression"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-poissonbayes.html"], "wrapper": ["poisson.bayes"], "tree": ["Zelig-poisson-bayes", "Zelig-bayes"] }, "probitbayes": { "name": ["probit-bayes"], "description": ["Bayesian Probit Regression for Dichotomous Dependent Variables"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-probitbayes.html"], "wrapper": ["probit.bayes"], "tree": ["Zelig-probit-bayes", "Zelig-bayes"] }, "tobitbayes": { "name": ["tobit-bayes"], "description": ["Bayesian Tobit Regression for a Censored Dependent Variable"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-tobitbayes.html"], "wrapper": ["tobit.bayes"], "tree": ["Zelig-tobit-bayes", "Zelig-bayes"] }, "weibull": { "name": ["weibull"], "description": ["Weibull Regression for Duration Dependent Variables"], "outcome": { "modelingType": ["bounded"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-weibull.html"], "wrapper": ["weibull"], "tree": ["Zelig-weibull"] }, "logitsurvey": { "name": ["logit-survey"], "description": ["Logistic Regression with Survey Weights"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-logitsurvey.html"], "wrapper": ["logit.survey"], "tree": ["Zelig-logit-survey", "Zelig-binchoice-survey", "Zelig-survey"] }, "probitsurvey": { "name": ["probit-survey"], "description": ["Probit Regression with Survey Weights"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-probitsurvey.html"], "wrapper": ["probit.survey"], "tree": ["Zelig-probit-survey", "Zelig-binchoice-survey", "Zelig-survey"] }, "normalsurvey": { "name": ["normal-survey"], "description": ["Normal Regression for Continuous Dependent Variables with Survey Weights"], "outcome": { "modelingType": ["continuous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-normalsurvey.html"], "wrapper": ["normal.survey"], "tree": ["Zelig-normal-survey", "Zelig-survey"] }, "gammasurvey": { "name": ["gamma-survey"], "description": ["Gamma Regression with Survey Weights"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-gammasurvey.html"], "wrapper": ["gamma.survey"], "tree": ["Zelig-gamma-survey", "Zelig-survey"] }, "poissonsurvey": { "name": ["poisson-survey"], "description": ["Poisson Regression with Survey Weights"], "outcome": { "modelingType": [""] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-poissonsurvey.html"], "wrapper": ["poisson.survey"], "tree": ["Zelig-poisson-survey", "Zelig-survey"] }, "arima": { "name": ["arima"], "description": ["Autoregressive Moving-Average Models for Time-Series Data"], "outcome": { "modelingType": ["continuous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-arima.html"], "wrapper": ["arima"], "tree": ["Zelig-arima", "Zelig-timeseries"] }, "ma": { "name": ["ma"], "description": ["Time-Series Model with Moving Average"], "outcome": { "modelingType": ["continuous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-ma.html"], "wrapper": ["ma"], "tree": ["Zelig-ma", "Zelig-timeseries"] }, "ar": { "name": ["ar"], "description": ["Time-Series Model with Autoregressive Disturbance"], "outcome": { "modelingType": ["continuous"] }, "explanatory": { "modelingType": ["continuous", "discrete", "nominal", "ordinal", "binary"] }, "vignette.url": ["http://docs.zeligproject.org/en/latest/zelig-ar.html"], "wrapper": ["ar"], "tree": ["Zelig-ar", "Zelig-timeseries"] } } } Zelig/inst/CITATION0000644000176200001440000000245412722061503013441 0ustar liggesuserscitHeader("To cite Zelig in publications please use:") if(!exists("meta") || is.null(meta)) meta <- packageDescription("Zelig") year <- sub(".*(2[[:digit:]]{3})-.*", "\\1", meta$Date) vers <- paste("Version", meta$Version) bibentry( bibtype="Manual", title = "Zelig: Everyone's Statistical Software", author = c( person("Christine", "Choirat", email="cchoirat@iq.harvard.edu", role = "aut"), person("James", "Honaker", email="jhonaker@iq.harvard.edu", role = "aut"), person("Kosuke", "Imai", role = "aut"), person("Gary", "King", role = "aut"), person("Olivia", "Lau", role = "aut") ), year = year, note = vers, url = "http://zeligproject.org/") bibentry( bibtype="Article", title = "Toward A Common Framework for Statistical Analysis and Development", author = c( person("Kosuke", "Imai"), person("Gary", "King"), person("Olivia", "Lau") ), journal = "Journal of Computational Graphics and Statistics", volume = 17, number = 4, year = 2008, pages = "892-913", url = "http://j.mp/msE15c") Zelig/tests/0000755000176200001440000000000013006221765012470 5ustar liggesusersZelig/tests/testthat.R0000755000176200001440000000010512722061504014447 0ustar liggesuserslibrary(testthat) library(Zelig) set.seed("123") test_check("Zelig")Zelig/tests/testthat/0000755000176200001440000000000013006420075014323 5ustar liggesusersZelig/tests/testthat/test-logit.R0000755000176200001440000000012212722061504016541 0ustar liggesusersz <- zlogit$new() test <- z$mcunit(minx=-2, maxx=2, plot=FALSE) expect_true(test) Zelig/tests/testthat/test-negbin.R0000644000176200001440000000010112722061504016657 0ustar liggesusersz <- znegbin$new() test <- z$mcunit(plot=FALSE) expect_true(test)Zelig/tests/testthat/test-probit.R0000644000176200001440000000010112722061504016714 0ustar liggesusersz <- zprobit$new() test <- z$mcunit(plot=FALSE) expect_true(test)Zelig/tests/testthat/test-lognom.R0000755000176200001440000000010212722061504016714 0ustar liggesusersz <- zlognorm$new() test <- z$mcunit(plot=FALSE) expect_true(test)Zelig/tests/testthat/test-poisson.R0000644000176200001440000000011212722061504017111 0ustar liggesusersz <- zpoisson$new() test <- z$mcunit(minx=0, plot=FALSE) expect_true(test)Zelig/tests/testthat/test-ls.R0000755000176200001440000000007512722061504016050 0ustar liggesusersz <- zls$new() test <- z$mcunit(plot=FALSE) expect_true(test)Zelig/NAMESPACE0000755000176200001440000000325013006174071012545 0ustar liggesusersimport(sandwich, methods, survival, jsonlite, dplyr, geepack, MCMCpack, coda, Amelia, MatchIt, maxLik, survey) importFrom("plyr", "llply") importFrom("MASS", "glm.nb", "rnegbin", "mvrnorm", "gamma.shape") importFrom("VGAM", "vglm") importFrom("AER", "tobit") importFrom("quantreg", "rq", "summary.rq", "bandwidth.rq") importFrom("grDevices", "col2rgb", "heat.colors", "rgb") importFrom("graphics", "abline", "axis", "barplot", "box", "image", "layout", "lines", "par", "polygon", "text") importFrom("stats", "binomial", "complete.cases", "density", "glm", "lm", "lm.influence", "median", "model.frame", "model.matrix", "model.response", "na.omit", "quantile", "sd", "terms", "update", "ARMAacf", "rnorm") importClassesFrom("VGAM", "vglm") importMethodsFrom("VGAM", "coef", "fitted", "predict", "vcov") S3method(summary, Arima) exportPattern("^[[:alpha:]]+") exportClasses( "Zelig", "Zelig-ls", "Zelig-glm", "Zelig-binchoice", "Zelig-logit", "Zelig-probit", "Zelig-gamma", "Zelig-exp", "Zelig-negbin", "Zelig-normal", "Zelig-poisson", "Zelig-lognorm", "Zelig-tobit", "Zelig-gee", "Zelig-binchoice-gee", "Zelig-logit-gee", "Zelig-probit-gee", "Zelig-gamma-gee", "Zelig-normal-gee", "Zelig-poisson-gee", "Zelig-bayes", "Zelig-factor-bayes", "Zelig-logit-bayes", "Zelig-mlogit-bayes", "Zelig-normal-bayes", "Zelig-oprobit-bayes", "Zelig-poisson-bayes", "Zelig-probit-bayes", "Zelig-tobit-bayes", "Zelig-weibull", "Zelig-timeseries", "Zelig-arima", "Zelig-ar", "Zelig-ma" ) Zelig/demo/0000755000176200001440000000000013006221765012252 5ustar liggesusersZelig/demo/demo-data-table.R0000644000176200001440000000054712722061503015317 0ustar liggesuserslibrary(data.table) data(cars) Cars <- data.table(cars) z5 <- zls$new() z5$zelig(dist ~ speed, data = Cars) z5$setx1(speed = 30) z5$setx() set.seed(42) z5$sim() z5$summarize() z5$cite() library(Amelia) data(africa) Africa <- data.table(africa) imp <- amelia(x = Africa , cs = "country", m = 5) z5 <- zls$new() z5$zelig(infl ~ trade + civlib, data = imp) Zelig/demo/demo-setx-fn.R0000644000176200001440000000173013006170425014700 0ustar liggesusers##----- See thread: https://groups.google.com/forum/#!topic/zelig-statistical-software/1ohCNA5S_0A library(Zelig) z5 <- zls$new() z5$zelig(Fertility ~ Agriculture + Education, data = swiss) z5$setx(Education = 5, fn = list(numeric = Mode)) # setx: # (Intercept) Agriculture Education # 1 1 84.6 5 z5$setx(Education = 5, fn = list(numeric = mode)) # same: function 'mode' added for backward compatibility z5$setx(Education = 5, fn = list(numeric = median)) # setx: # (Intercept) Agriculture Education # 1 1 54.1 5 z5$setx(Education = 5, fn = list(numeric = function(x) 1, other = function(x) 2)) # setx: # (Intercept) Agriculture Education # 1 1 1 5 z5$setx1(Education = 10, fn = list(numeric = mode)) # setx1: # (Intercept) Agriculture Education # 1 1 50.66 10 z5$setx(Education = 5) z5$setx1(Education = 10) # model summary summary(z5) z5$sim() # model summary summary(z5) Zelig/demo/demo-signif-stars.R0000644000176200001440000000015712722061503015727 0ustar liggesusersdata(cars) z.out <- zelig(dist ~ speed, cars, model = "ls") summary(z.out) summary(z.out, signif.stars = TRUE) Zelig/demo/demo-setx.R0000755000176200001440000000043112722061503014277 0ustar liggesusersdata(cars) z5 <- zls$new() z5$zelig(dist ~ speed, data = cars) z5 z5$setx(speed = 30) z5$setx.out$x z5$setx1(speed = 40) z5$setx.out$x1 z5$setx1(xssx = 0, jbcjdhsb = 987) z5$setx.out$x1 z5$setx1(scs =98457, speed = 50, dcksj = 34) z5$setx.out$x1 z5$sim(num=10) z5$summarize() Zelig/demo/demo-mlogit-bayes.R0000644000176200001440000000076712722061503015721 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(mexico) z.out <- Zelig4::zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes", data = mexico) summary(z.out) x.out <- Zelig4::setx(z.out) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.out, num = 1000) summary(s.out) # Zelig 5 code: z5 <- zmlogitbayes$new() z5$zelig(vote88 ~ pristr + othcok + othsocok, data = mexico, verbose = FALSE) z5 z5$zelig.out z5$setx() set.seed(42) z5$sim(num = 1000) z5$summarize() z5$cite() Zelig/demo/demo-oprobit-bayes.R0000644000176200001440000000127312722061503016075 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(sanction) sanction$ncost <- factor(sanction$ncost, ordered = TRUE, levels = c("net gain", "little effect", "modest loss", "major loss")) z.out <- Zelig4:: zelig(ncost ~ mil + coop, model = "oprobit.bayes", data = sanction, verbose = TRUE) summary(z.out) x.out <- Zelig4::setx(z.out) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.out, num = 1000) summary(s.out) # Zelig 5 code: z5 <- zoprobitbayes$new() z5$zelig(ncost ~ mil + coop, data = sanction, verbose = FALSE) z5 z5$zelig.out z5$setx() set.seed(42) z5$sim(num = 1000) z5$summarize() z5$cite() Zelig/demo/demo-relogit.R0000755000176200001440000000433512722061503014770 0ustar liggesusers## Example 1 # Zelig 4 code: library(Zelig4) data(mid) z.out1 <- Zelig4::zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, model = "relogit", tau = 1042 / 303772) summary(z.out1) x.out1 <- Zelig4::setx(z.out1) set.seed(42) s.out1 <- Zelig4::sim(z.out1, x = x.out1, num = 1000) summary(s.out1) # Zelig 5 code: z5 <- zrelogit$new() z5$zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, tau = 1042/303772) z5 z5$setx() z5$setx.out set.seed(42) z5$sim(num=1000) z5$summarize() z5$cite() plot(z5) z.out <- zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, tau = 1042/303772, model = "relogit") ## Example 2 # Zelig 4 code: z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, model = "relogit", tau = 1042/303772, case.control = "weighting", robust = TRUE) summary(z.out2) x.out2 <- setx(z.out2) set.seed(42) s.out2 <- sim(z.out2, x = x.out2, num=1000) summary(s.out2) # Zelig 5 code: z5 <- zrelogit$new() z5$zelig(conflict ~ major + contig + power + maxdem + mindem + years, data = mid, tau = 1042/303772, case.control = "weighting") z5 z5$setx() set.seed(42) z5$sim(num=1000) z5$summarize() z5$cite() ## Example 3: broken in Zelig 4 # # Zelig 4 code: # # z.out2 <- zelig(conflict ~ major + contig + power + maxdem # + mindem + years, data = mid, model = "relogit", tau = c(0.002, 0.005)) # summary(z.out2) # x.out2 <- setx(z.out2) # s.out <- sim(z.out2, x = x.out2) # summary(s.out2) # # # z.out3 <- zelig(conflict ~ major + contig + power + maxdem # + mindem + years, data = mid, model = "relogit", tau = c(0.002, 0.005)) # summary(z.out3) # x.out3 <- setx(z.out3) # s.out3 <- sim(z.out3, x = x.out3) # summary(s.out3) # # # Zelig 5 code: # z5 <- zrelogit$new() # z5$zelig(conflict ~ major + contig + power + maxdem # + mindem + years, data = mid, tau = c(0.002, 0.005)) # z5 # z5$setx() # set.seed(42) # z5$sim(num=1000) # z5$summarize() # z5$cite() # r <- relogit(conflict ~ major + contig + power + maxdem + mindem + years, # data = mid, tau = 1042/303772) # rs Zelig/demo/demo-zip.R0000755000176200001440000000247112722061503014124 0ustar liggesuserslibrary(pscl) source(file.path("..", "R", "utils.R")) source(file.path("..", "R", "model-zelig.R")) source(file.path("..", "R", "model-zip.R")) data("bioChemists", package = "pscl") ## without inflation ## ("art ~ ." is "art ~ fem + mar + kid5 + phd + ment") fm_pois <- glm(art ~ ., data = bioChemists, family = poisson) fm_qpois <- glm(art ~ ., data = bioChemists, family = quasipoisson) fm_nb <- glm.nb(art ~ ., data = bioChemists) ## with simple inflation (no regressors for zero component) fm_zip <- zeroinfl(art ~ . | 1, data = bioChemists) fm_zip2 <- zeroinfl(art ~ . | ., data = bioChemists) summary(fm_zip2) z5 <- zzip$new() z5$zelig(art ~ phd + ment | ., data = bioChemists) z5$zelig(art ~ . - phd - ment, data = bioChemists) z5 .self <- z5 z5$setx(phd = 3, ment = 8) z5$setx.out set.seed(42) z5$sim(num=100) z5$summarize() library(Zelig4) z.out <- zelig(art ~ . - phd - ment, data = bioChemists, model = "ls") summary(z.out) x.out <- setx(z.out, phd = 3, ment = 8) x.out set.seed(42) s.out <- sim(z.out, x.out, num = 100) summary(s.out) x.out <- setx(z.out) x.out set.seed(42) s.out <- sim(z.out, x.out, num = 100) s.out # z5$sim() library(Zelig4) z.out <- zelig(art ~ phd * ment , bioChemists, model = "ls") summary(z.out) x.out <- setx(z.out) x.out set.seed(42) s.out <- sim(z.out, x.out, num = 100) summary(s.out) Zelig/demo/demo-weibull.R0000644000176200001440000000071112722061503014755 0ustar liggesusersdata(coalition) z5 <- zweibull$new() z5 z5$zelig(Surv(duration, ciep12) ~ fract + numst2, data = coalition) z5 z5$setx() # works z5$setx(numst2 = 0) z.out <- z5$zelig.out$z.out[[1]] z.out # z5$setx(numst2 = 0) # fails # Error in terms(lm(formula, data), "predvars") : # error in evaluating the argument 'x' in selecting a method for function 'terms': # Error in Ops.Surv(y, z$residuals) : Invalid operation on a survival time z5$sim() z5 z5$graph() Zelig/demo/demo-factor-bayes.R0000644000176200001440000000422212722061503015672 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(swiss) names(swiss) <- c("Fert", "Agr", "Exam", "Educ", "Cath", "InfMort") z.out <- Zelig4::zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL, model = "factor.bayes", data = swiss, factors = 2, verbose = TRUE, a0 = 1, b0 = 0.15, burnin = 500, mcmc = 5000) summary(z.out) x.out <- Zelig4::setx(z.out, math = 30) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.out, num = 1000) summary(s.out) # Zelig 5 code: z5 <- zfactorbayes$new() # z5$zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL, # data = swiss, factors = 2, verbose = TRUE, # a0 = 1, b0 = 0.15, burnin = 500, mcmc = 5000) z5$zelig(~ Agr + Exam + Educ + Cath + InfMort, data = swiss, factors = 2, verbose = FALSE, a0 = 1, b0 = 0.15, burnin = 500, mcmc = 5000) # z5$zelig(~Agriculture+Examination+Education+Catholic # +Infant.Mortality, factors=2, # lambda.constraints=list(Examination=list(1,"+"), # Examination=list(2,"-"), Education=c(2,0), # Infant.Mortality=c(1,0)), # verbose=0, store.scores=FALSE, a0=1, b0=0.15, # data=swiss, burnin=500, mcmc=500, thin=20) z5 z.out <- zelig(~ Agr + Exam + Educ + Cath + InfMort, model = "factor.bayes", data = swiss, factors = 2, verbose = FALSE, a0 = 1, b0 = 0.15, burnin = 500, mcmc = 5000) z.out <- zelig(~ Agr + Exam + Educ + Cath + InfMort, model = "factor.bayes", data = swiss, factors = 2, lambda.constraints = list(Exam = list(1,"+"), Exam = list(2,"-"), Educ = c(2, 0), InfMort = c(1, 0)), verbose = FALSE, a0 = 1, b0 = 0.15, burnin = 500, mcmc = 5000) lapply(z5$zelig.out$z.out, geweke.diag) z5$zelig.call z5$model.call z5$zelig.out$z.out[[1]] z5$setx() # z5$setx(math = 30) set.seed(42) # z5$sim(num = 1000) # z5$summarize() z5$cite() geweke.diag(z5$zelig.out$z.out[[1]]) heidel.diag(z5$zelig.out$z.out[[1]]) raftery.diag(z5$zelig.out$z.out[[1]]) summary(z5) Zelig/demo/demo-strata.R0000755000176200001440000000210612722061503014613 0ustar liggesuserslibrary(Zelig4) data(turnout) turnout$dedu <- ifelse(turnout$educate > 15, 1, 0) z.out <- zelig(vote ~ age , data = turnout, model = "ls", by = c("race", "dedu")) summary(z.out) x.out <- setx(z.out, age = 18) x1.out <- setx(z.out, age = 20) set.seed(42) s.out <- sim(z.out, x.out, x1.out) summary(s.out) z5 <- zls$new() z5$zelig(vote ~ age , data = turnout, by = c("race", "dedu")) z5$zelig.out z5 z5$setx(age = 18) z5$setx1(age=20) z5$setrange(age = 18:20) z5$setx.out z5$sim(10) z5$sim.out z5$summarize() z5 <- zls$new() z5$zelig(vote ~ age + income , data = turnout) z5$zelig.out z5 z5$setx(age = 18) z5$setx1(age = 20) z5$setrange(age = 18:20) z5$setx.out z5$sim(10) z5$sim.out z5$summarize() by(turnout, factor(turnout$race), function(x) lm(vote ~ age , data = x)) by(turnout, factor(turnout$race), function(x) Zelig::zelig(vote ~ age , data = x, model = "ls")) by(turnout, factor(turnout$race), function(x) { z.out <<- zelig(vote ~ age , data = x, model = "ls") x.out <<- setx(z.out, age = 30) s.out <<- sim(z.out, x.out, age = 30) }) Zelig/demo/demo-gamma.R0000755000176200001440000000073712722061503014407 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(coalition) z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition) summary(z.out) x.low <- setx(z.out, numst2 = 0) set.seed(42) s.out <- sim(z.out, x = x.low, n=100000) summary(s.out) # Zelig 5 code: data(coalition) z5 <- zgamma$new() z5$zelig(duration ~ fract + numst2, data = coalition) z5 z5$setx(numst2 = 0) set.seed(42) z5$sim(num=100000) statmat(z5$sim.out$x$ev[[1]]) statmat(z5$sim.out$x$pv[[1]]) z5$summarize() Zelig/demo/demo-ls.R0000755000176200001440000000121012722061503013726 0ustar liggesuserslibrary(MASS) data(cars) # Zelig 4 code: library(Zelig4) z.out <- zelig(dist ~ speed, cars, model = "ls") summary(z.out) x.out <- setx(z.out, speed = 30) set.seed(42) s.out <- sim(z.out, x.out, num = 100) summary(s.out) # Zelig 5 code: z5 <- zls$new() z5$zelig(dist ~ speed, data = cars) z5 z5$zelig.out z5$model.call z5$zelig.call z5$setx(sascxasx = 9879, speed = 30, sdjchbsdc = 87, kcsbc = 8787) z5$setx(sascxasx = 9879) z5$setx1(speed = 30) z5$setx() z5$setx.out z5 set.seed(42) z5$sim(num=3) z5$sim.out z5 z5$summarize() z5$cite() z5$setrange(speed = 30:32,speed = 19:23) z5$setx.out set.seed(42) z5$sim(num=3) z5$sim.out z5$summarize() z5 Zelig/demo/demo-gamma-gee.R0000755000176200001440000000204612722061503015140 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(coalition) coalition$cluster <- c(rep(c(1:62),5),rep(c(63),4)) sorted.coalition <- coalition[order(coalition$cluster),] z.out <- Zelig4::zelig(duration ~ fract + numst2, model = "gamma.gee", id = "cluster", data = sorted.coalition) x.low <- Zelig4::setx(z.out, numst2 = 0) x.high <- Zelig4::setx(z.out, numst2 = 1) s.out <- Zelig4::sim(z.out, x = x.low, x1 = x.high) summary(s.out) # Zelig 5 code: set.seed(42) z5 <- zgammagee$new() z5$zelig(duration ~ fract + numst2, id = coalition$cluster, data = coalition, corstr = "exchangeable") z5 z5$zelig.call z5$model.call z5$setx(numst2 = 0) z5$setx1(numst2 = 1) set.seed(42) z5$sim(num=10) z5$summarize() z5$cite() # geepack::geeglm(formula = duration ~ fract + numst2, family = Gamma("inverse"), # data = coalition, id = coalition$cluster, corstr = "exchangeable") # # geepack::geeglm(formula = duration ~ fract + numst2, family = Gamma, # data = coalition, id = coalition$cluster, corstr = "exchangeable") Zelig/demo/demo-tobit.R0000755000176200001440000000112112722061503014432 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(tobin) z.out <- zelig(durable ~ age + quant, model = "tobit", data = tobin) summary(z.out) x.out <- setx(z.out) set.seed(42) s.out1 <- sim(z.out, x = x.out) summary(s.out1) # Zelig 5 code: data(tobin) z5 <- ztobit$new() z5$zelig(durable ~ age + quant, data = tobin, below = 1, above = 20) z5 z5$setx() set.seed(42) z5$sim(num=10000) statmat(z5$sim.out$x$ev[[1]]) statmat(z5$sim.out$x$pv[[1]]) z5$summarize() z5$cite() # library(AER) # fit <- tobit(durable ~ age + quant, data = tobin, left = 1, right = 2) # summary(fit) # fit$coefficients # coef(fit) Zelig/demo/demo-probit-bayes.R0000644000176200001440000000126212722061503015714 0ustar liggesuserslibrary(MASS) mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") mydata$rank <- factor(mydata$rank) # Zelig 4 code: library(Zelig4) z.out <- Zelig4::zelig(admit ~ gre + gpa, data = mydata, model="probit.bayes") summary(z.out) x.out <- Zelig4::setx(z.out, gpa = 1.3, gre = 4500, speed = 12) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.out, num = 1000) summary(s.out) # Zelig 5 code: z5 <- zprobitbayes$new() z5$zelig(admit ~ gre + gpa, data = mydata) z5 z5$zelig.out z5$setx(gpa = 1.3, gre = 4500, speed = 12) set.seed(42) z5$sim(num = 1000) z5$summarize() z5$cite() z.out <- zelig(admit ~ gre + gpa, data = mydata, model = "probit.bayes") z.out Zelig/demo/demo-tobitbayes.R0000644000176200001440000000025512722061503015462 0ustar liggesusersdata(tobin) z5 <- ztobitbayes$new() z5$zelig(durable ~ age + quant, data = tobin, below = 1, above = 20, verbose = FALSE) z5 z5$setx() set.seed(42) z5$sim() z5$summarize() Zelig/demo/demo-json.R0000755000176200001440000000047712722061503014277 0ustar liggesuserslibrary(MASS) library(jsonlite) data(cars) source(file.path("..", "R", "utils.R")) source(file.path("..", "R", "model-zelig.R")) source(file.path("..", "R", "model-ls.R")) z5 <- zls$new() z5$zelig(dist ~ speed, data = cars) z5 z5$setx(speed = 30) set.seed(42) z5$sim(num=100) z5$summarize() z5$toJSON() cat(z5$json) Zelig/demo/demo-setx-2.R0000755000176200001440000000163412722061503014444 0ustar liggesusersdata(macro) ## Zelig 4 z.out <- zelig(unem ~ gdp + trade + capmob + country, model = "ls", data = macro) summary(z.out) x.US <- setx(z.out, country = "United States") x.US x.Japan <- setx(z.out, country = "Japan") x.Japan s.out <- sim(z.out, x = x.US, x1 = x.Japan) summary(s.out) ## Zelig 5 z5 <- zls$new() z5$zelig(unem ~ gdp + trade + capmob + country, data = macro) z5 z5$setx(country = "United States", unem = 2) z5$setx.out z5$setx() z5$setx.out z5$setx1(country = "Japan") z5$setx.out$x1 z5$sim() z5$summarize() z5$zelig(unem ~ ., data = macro) z5 z5 <- zls$new() z5$zelig(unem ~ gdp + exp(trade) + capmob + as.factor(country), data = macro) z5$zelig.out # model.matrix(z5$zelig.out) z5$setx(country = "United States", unem = 2) z5$setx.out fit <- lm(unem ~ gdp + exp(trade) + capmob + as.factor(country), data = macro) fit model.matrix(fit) Zelig/demo/demo-probit-gee.R0000755000176200001440000000120212722061503015346 0ustar liggesusers# Zelig 4 code: data(turnout) turnout$cluster <- rep(c(1:200), 10) sorted.turnout <- turnout[order(turnout$cluster), ] z.out1 <- Zelig4::zelig(vote ~ race + educate, model = "probit.gee", id = "cluster", data = sorted.turnout, corstr = "stat_M_dep", Mv = 3) x.out1 <- Zelig4::setx(z.out1) set.seed(42) s.out1 <- Zelig4::sim(z.out1, x = x.out1) summary(s.out1) # Zelig 5 code: z5 <- zprobitgee$new() z5$zelig(vote ~ race + educate, id = "cluster", data = sorted.turnout, corstr = "unstructured") z5 z5$zelig.call z5$model.call z5$setx() set.seed(42) z5$sim() z5$summarize() z5$cite() Zelig/demo/demo-dplyr.R0000755000176200001440000000131712722061503014452 0ustar liggesuserslibrary(Amelia) data(freetrade) df <- freetrade imp <- amelia(x = df , cs = "country", m = 10) z5 <- zls$new() z5 z5$zelig(gdp.pc ~ tariff + intresmi, data = imp) z5 z5$zelig.out z5$setx(tariff = 0.4) z5$setx.out # z5$setx1(tariff = 0.6) z5$setx.out z5$zelig.out # z5$setrange(tariff = 1:3) z5$setx.out z5$zelig.out z5 # TODO: Fix when re-running sim z5$sim(10) # z5$simx1(10) z5$sim.out # z5$zelig.out z5 <- zls$new() z5$zelig(gdp.pc ~ tariff + intresmi, data = dat, by = c("country", "year")) z5$zelig.out z5$setx(tariff = 0.4) z5$setx1(tariff = 0.6) z5$setrange(tariff = seq(0.2, 1.5, 0.1)) z5$zelig.out z5$setx.out .self <- z5 # TODO: Fix when re-running sim z5$sim(10) z5$zelig.out z5$sim.out z5$sim.out$range Zelig/demo/demo-roc.R0000644000176200001440000000031412722061503014074 0ustar liggesusersdata(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, z.out2) Zelig/demo/demo-quantile.R0000755000176200001440000000437412722061503015150 0ustar liggesusersdata(CPS1988) z5 <- zquantile$new() z5$zelig(log(wage) ~ experience + I(experience^2) + education, data = CPS1988, tau = 0.75) z5 z5$zelig.out z5$setx(education = 15) z5$setx1(education = 10) z5$sim(num = 10) z5$summarize() z5$graph() data(stackloss) z.out1 <- zquantile$new() z.out1$zelig(stack.loss ~ Air.Flow + Water.Temp + Acid.Conc., data = stackloss, tau = 0.5) z.out1 <- zelig(stack.loss ~ Air.Flow + Water.Temp + Acid.Conc., model = "rq", data = stackloss, tau = 0.5) quantreg::rq(formula = log(wage) ~ experience + I(experience^2) + education, data = CPS1988, tau = 0.75) z.out1 summary(z.out1$zelig.out$z.out[[1]]) x.high <- z.out1$setx(Water.Temp = quantile(stackloss$Water.Temp, 0.8)) x.low <- z.out1$setx1(Water.Temp = quantile(stackloss$Water.Temp, 0.2)) z.out1$sim(num = 10000) z.out1$summarize() data(macro) z.out2 <- zquantile$new() z.out2$zelig(unem ~ gdp + trade + capmob + as.factor(country), tau = 0.5, data = macro) z.out2$setx(country = "United States") z.out2$setx1(country = "Japan") z.out2$sim() z.out1$summarize() data(engel) z.out3 <- zquantile$new() z.out3$zelig(foodexp ~ income, tau = seq(0.1, 0.3, by = 0.1), data = engel) z.out3$setx() z.out3$sim() # r <- .self$data %>% # group_by(tau) %>% # do(model = rq(foodexp ~ income, data = ., tau = .$tau[1])) library(Zelig4) data(engel) z.out <- zelig(foodexp ~ income, tau = seq(0.1, 0.9, by = 0.1), data = engel, model = "rq") z.out3$setx() z.out3$sim() data(engel) z.out4 <- zquantile$new() z.out4$zelig(foodexp ~ income, data = engel, by = ) z.out4$setx() z.out4$sim() summary(z.out3$zelig.out[[1]]) plot(summary(z.out3$zelig.out[[1]])) z.out3$setx(income = quantile(engel$income, 0.25)) z.out3$setx1(income = quantile(engel$income, 0.75)) z.out3$sim() fit <- rq(foodexp ~ income, tau = seq(0.1, 0.9, by = 0.1), data = engel) plot(summary(fit)) ##### # data(engel) # z.out3 <- zelig(foodexp ~ income, model = "quantile", # tau = seq(0.1,0.9,by=0.1), data = engel) # summary(z.out3) # plot(summary(z.out3)) # plot(z.out3) # # x.bottom <- setx(z.out3, income=quantile(engel$income, 0.25)) # x.top <- setx(z.out3, income=quantile(engel$income, 0.75)) # # s.out3 <- sim(z.out3, x = x.bottom, x1 = x.top) # # summary(s.out3) Zelig/demo/demo-scope.R0000755000176200001440000000165712722061503014440 0ustar liggesusersz5 <- zls$new() z5$zelig(dist ~ speed, data = cars) z5 z.out <- Zelig::zelig(dist ~ speed, model = "ls", data = cars) print(z.out) z.out$zelig.call ## As expected f <- function() { z5 <- zls$new() z5$zelig(dist ~ speed, data = cars) print(z5) z5$setx(speed = 3) z5$sim() z5$summarize() return(z5) } Z <- f() print(Z) Z$summarize() g <- function() { z.out <- Zelig::zelig(dist ~ speed, model = "ls", data = cars) print(z5) Zelig::setx(z.out, speed = 3) Zelig::sim(z.out) Zelig::summary(z.out) return(z.out) } ZZ <- g() ZZ g()$summarize() data(cars) x <- cars zelig(dist ~ speed, data = x, model = "ls") rm(list = ls()) test_function <- function() { data(cars) x <- cars zelig(dist ~ speed, data = x, model = "ls") } test_function() test_function_refclasses <- function() { data(cars) x <- cars zref <- zls$new() zref$zelig(dist ~ speed, data = x) return(zref) } test_function_refclasses() Zelig/demo/demo-show.R0000755000176200001440000000015612722061503014300 0ustar liggesusersz5 <- zls$new() print(z5) z5$zelig(dist ~ speed, data = cars) print(z5) z5$setx() print(z5) z5$sim() print(z5)Zelig/demo/00Index0000644000176200001440000000473313006172056013411 0ustar liggesusersdemo-amelia use of output objects from Amelia package demo-data-table use of data tables demo-dplyr use of dplyr package demo-exp use of exponential model demo-factor-bayes use of Bayesian factor model demo-feedback use of feedback function demo-gamma-gee example of gamma model in generalized estimating equations demo-gamma example of gamma regression model demo-json example of construction of json file of model inheriance demo-logit-bayes example of Bayesian logistic model demo-logit-gee example of logisitic model with generalized estimating equations demo-logit example of GLM logistic model demo-lognorm example of lognormal survival model demo-ls example of least squares regression demo-mlogit-bayes example of multinomial logistic model demo-negbinom example of negative binomial regression demo-normal-bayes example of Bayesian normally distributed outcomes demo-normal-gee example of normally distributed outcomes with generalized estimating equations demo-normal example of model of normally distributed outcomes demo-oprobit-bayes example of ordered probit model demo-poisson-bayes example of Bayesian Poisson regression demo-poisson-gee example of Poisson regression with generalized estimating equations demo-poisson example of Poisson regression demo-probit-bayes example of Bayesian Probit regression demo-probit-gee example of Probit model with generalized estimating equations demo-probit example of Probit model for binary choice outcomes demo-quantile example of quantile regression model demo-range example of use of range utility for estimating quantities of interest demo-relogit example of Rare Events Logisitc regression demo-roc example of ROC graph for comparing binary choice outcome models demo-scope example of scoping behavior demo-setx-2 additional example of setx function for estimating quantities of interest demo-setx example of setx function for estimating quantities of interest demo-setx-fn example of setx function using the fn argument demo-show example of printing model coefficients demo-signif-stars example of modifying tables of model coefficients demo-strata example of estimatation with strata demo-tobit example of Tobit censored regression model demo-tobitbayes example of Bayesian Tobit censored regression model demo-weibull example of Weibull survival model demo-wrappers example of wrappers to emulate prior zelig call structure demo-zip example of zero inflated poisson mixture model Zelig/demo/demo-logit.R0000755000176200001440000000215012722061503014432 0ustar liggesuserslibrary(MASS) mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") mydata$rank <- factor(mydata$rank) # Zelig 4 code: library(Zelig4) # epsilon does not seem to be taken into account z.out <- zelig(admit ~ gre + gpa, data = mydata, model="logit", epsilon=.0001) summary(z.out) x.out <- setx(z.out, gpa=1.3, gre=4500, speed=12) set.seed(42) s.out <- sim(z.out, x = x.out, num=1000) summary(s.out) # Zelig 5 code: z5 <- zlogit$new() z5$zelig(admit ~ gre + gpa, data = mydata, epsilon=1) z5 z5$zelig.out # removed epsilon to compare with Zelig 4 z5$zelig(admit ~ gre + gpa, data = mydata) z5 z5$setx(gpa=1.3, gre=4500, speed=12) set.seed(42) z5$sim(num=1000) z5$summarize() z5$cite() # Example 2 data(turnout) z.out <- zelig(vote ~ race + educate, data = turnout, model = "logit") summary(z.out) x.out <- setx(z.out, educate = 12) set.seed(42) s.out <- sim(z.out, x = x.out, num = 1000) summary(s.out) z5 <- zlogit$new() z5$zelig(vote ~ race + educate, data = turnout) z5$show() z5$setx(educate = 12) set.seed(42) z5$sim(num = 1000) z5$summarize() Zelig/demo/demo-poisson-bayes.R0000644000176200001440000000141512722061503016107 0ustar liggesusersp <- read.csv("http://www.ats.ucla.edu/stat/data/poisson_sim.csv") p <- within(p, { prog <- factor(prog, levels = 1:3, labels = c("General", "Academic", "Vocational")) id <- factor(id) }) head(p) set.seed(42) w <- runif(200) # Zelig 4 code: library(Zelig4) z.out <- Zelig4::zelig(num_awards ~ prog + math, data=p, model="poisson.bayes", weights=w) summary(z.out) x.out <- Zelig4::setx(z.out, math = 30) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.out, num = 1000) summary(s.out) # Zelig 5 code: z5 <- zpoissonbayes$new() z5$zelig(num_awards ~ prog + math, data = p) z5 z5$zelig.call z5$model.call z5$zelig.out$z.out[[1]] z5$setx() z5$setx(math = 30) set.seed(42) z5$sim(num = 1000) z5$summarize() z5$cite() Zelig/demo/demo-amelia.R0000755000176200001440000000163712722061503014555 0ustar liggesuserslibrary(Amelia) data(africa) df <- head(africa, 100) imp <- amelia(x = df , cs = "country", m = 5) imp2 <- imp$imputations # Zelig 4 code: library(Zelig4) z.out <- zelig(infl ~ trade + civlib, data = imp, model = "ls") summary(z.out) x.out <- setx(z.out, civlib = .5) set.seed(42) s.out <- sim(z.out, x.out, num = 100) summary(s.out) # Zelig 5 code: # library(data.table) library(Amelia) library(Zelig) z5 <- zls$new() z5$zelig(infl ~ trade + civlib, data = imp) z5$zelig(infl ~ trade + civlib, data = imp, by = "country") z5 z5$zelig.out z5$model.call z5$zelig.call z5$setx(civlib = .5) z5$setrange(civlib = c(0.3, .5)) z5$setx.out set.seed(42) z5$sim(num=1000) z5$sim.out z5$summarize() z5$cite() z5$setrange(speed = 30:32,speed = 30:32) z5$setx.out set.seed(42) z5$sim(num=3) z5$sim.out z5$summarize() data(freetrade) df <- head(freetrade, 1000) imp <- amelia(x = df, cs = "country", m = 10) imp2 <- imp$imputations Zelig/demo/demo-negbinom.R0000755000176200001440000000071712722061503015121 0ustar liggesuserslibrary(MASS) # Zelig 4 code: library(Zelig4) data(sanction) z.out <- zelig(num ~ target + coop, model = "negbinom", data = sanction) summary(z.out) x.out <- setx(z.out) set.seed(42) s.out <- sim(z.out, x = x.out, num=100) summary(s.out) # Zelig 5 code: data(sanction) z5 <- znegbin$new() z5$zelig(num ~ target + coop, data = sanction) z5 z5$setx() set.seed(42) z5$sim(num=100) statmat(z5$sim.out$x$ev[[1]]) statlevel(z5$sim.out$x$pv[[1]], 100) z5$summarize() Zelig/demo/demo-wrappers.R0000755000176200001440000000103312722061503015156 0ustar liggesusers## Zelig 5 library(Zelig) z.out5 <- zelig(dist ~ speed, model = "ls", data = cars) print(z.out5) summary(z.out5) x.out5 <- Zelig::setx(z.out5, speed=30) x1.out5 <- Zelig::setx(z.out5, speed = 50) s.out5 <- Zelig::sim(z.out5, x.out5, x1.out5, num = 1000) print(s.out5) summary(s.out5) ## Zelig 4 library(Zelig4) z.out4 <- Zelig4::zelig(dist ~ speed, model = "ls", data = cars) x.out4 <- Zelig4::setx(z.out4, speed = 30) x1.out4 <- Zelig4::setx(z.out4, speed = 50) s.out4 <- Zelig4::sim(z.out4, x.out4, x1.out4, num = 1000) summary(s.out4) Zelig/demo/demo-logit-gee.R0000755000176200001440000000106212722061503015171 0ustar liggesusers# Zelig 4 code: data(turnout) turnout$cluster <- rep(c(1:200), 10) sorted.turnout <- turnout[order(turnout$cluster), ] z.out1 <- Zelig4::zelig(vote ~ race + educate, model = "logit.gee", id = "cluster", data = sorted.turnout) x.out1 <- Zelig4::setx(z.out1) set.seed(42) s.out1 <- Zelig4::sim(z.out1, x = x.out1) summary(s.out1) # Zelig 5 code: z5 <- zlogitgee$new() z5$zelig(vote ~ race + educate, id = "cluster", data = sorted.turnout) z5 z5$zelig.call z5$model.call z5$setx() set.seed(42) z5$sim(500) z5$summarize() z5$cite() Zelig/demo/demo-normal-gee.R0000755000176200001440000000133112722061503015342 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(coalition) coalition$cluster <- c(rep(c(1:62), 5), rep(c(63), 4)) sorted.coalition <- coalition[order(coalition$cluster),] z.out <- Zelig4::zelig(duration ~ fract + numst2, model = "normal.gee", id = "cluster", data = coalition) x.low <- Zelig4::setx(z.out, numst2 = 0) x.high <- Zelig4::setx(z.out, numst2 = 1) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.low, x1 = x.high) summary(s.out) # Zelig 5 code: set.seed(42) z5 <- znormalgee$new() z5$zelig(duration ~ fract + numst2, id = "cluster", data = coalition, corstr = "exchangeable") z5 z5$zelig.call z5$model.call z5$setx(numst2 = 0) z5$setx1(numst2 = 1) set.seed(42) z5$sim() z5$summarize() z5$cite() Zelig/demo/demo-poisson-gee.R0000755000176200001440000000125112722061503015545 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(sanction) sanction$cluster <- c(rep(c(1:15), 5), rep(c(16), 3)) sorted.sanction <- sanction[order(sanction$cluster), ] z.out <- Zelig4::zelig(num ~ target + coop, model = "poisson.gee", id = "cluster", data = sorted.sanction, robust = FALSE, corstr = "exchangeable") summary(z.out) x.out <- Zelig4::setx(z.out) set.seed(42) s.out <- Zelig4::sim(z.out, x.out) summary(s.out) # Zelig 5 code: set.seed(42) z5 <- zpoissongee$new() z5$zelig(num ~ target + coop, id = "cluster", data = sorted.sanction, corstr = "exchangeable") z5 z5$zelig.call z5$model.call z5$setx() set.seed(42) z5$sim() z5$summarize() z5$cite() Zelig/demo/demo-range.R0000755000176200001440000000260712722061503014417 0ustar liggesuserslibrary(Zelig4) data(turnout) z.out <- zelig(vote ~ race + educate + age + I(age^2) + income, model = "ls", data = turnout) summary(z.out) x.low <- setx(z.out, educate = 12, age = 18, race = "others") x.low x.high <- setx(z.out, educate = 13, age = 18, race = "others") x.high set.seed(42) s.out <- sim(z.out, x = x.low, x1 = x.high, num = 5) summary(s.out) plot(s.out) z.out <- zelig(vote ~ race + educate + age + I(age^2) + income, model = "ls", data = turnout) summary(z.out) x.low <- setx(z.out, educate = 12:14, age = 18:22) x.low x.high <- setx(z.out, educate = 12:14, age = 18:22) x.high set.seed(42) s.out <- sim(z.out, x = x.low, x1 = x.high, num = 5) summary(s.out) ## Zelig 5 z5 <- zls$new() z5$zelig(vote ~ race + educate + age + I(age^2) + income, data = turnout) z5$zelig.out z5$setx(educate = 12, age = 18, race = "others") z5$setx.out z5$setx1(educate = 13, age = 18, race = "others") set.seed(42) z5$sim(num = 5) z5$summarize() s <- list(educate = 12, age = 18, race = "others") z5 <- zls$new() z5$zelig(vote ~ race + educate + age + I(age^2) + income, data = turnout) z5 z5$setrange(educate = 12:13, age = 18) z5$setx.out set.seed(42) z5$sim(num = 5) z5$summarize() z5$setrange(educate = 12:13) z5$setx.out s <- list(educate = c(10, 15)) expand.grid(s) z5$setrange(educate = c(10, 15), age = c(10, 18)) s <- list(educate = 12:13, age = 18) expand.grid(s) Zelig/demo/demo-logit-bayes.R0000644000176200001440000000133312722061503015532 0ustar liggesuserslibrary(MASS) mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") mydata$rank <- factor(mydata$rank) # Zelig 4 code: library(Zelig4) # epsilon does not seem to be taken into account z.out <- Zelig4::zelig(admit ~ gre + gpa, data = mydata, model="logit.bayes") summary(z.out) x.out <- Zelig4::setx(z.out, gpa = 1.3, gre = 4500, speed = 12) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.out, num = 1000) summary(s.out) # Zelig 5 code: z5 <- zlogitbayes$new() z5$zelig(admit ~ gre + gpa, data = mydata) z5 z5$zelig.out z5$setx(gpa = 1.3, gre = 4500, speed = 12) set.seed(42) z5$sim(num = 1000) z5$summarize() z5$cite() z.out <- zelig(admit ~ gre + gpa, data = mydata, model = "logit.bayes") Zelig/demo/demo-poisson.R0000755000176200001440000000172612722061503015016 0ustar liggesusersp <- read.csv("http://www.ats.ucla.edu/stat/data/poisson_sim.csv") p <- within(p, { prog <- factor(prog, levels = 1:3, labels = c("General", "Academic", "Vocational")) id <- factor(id) }) head(p) set.seed(42) w <- runif(200) # Zelig 4 code: library(Zelig4) z.out <- Zelig4::zelig(num_awards ~ prog + math, data=p, model="poisson", weights=w) summary(z.out) x.out <- Zelig4::setx(z.out, math = 40) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.out, num = 1000) summary(s.out) # Zelig 5 code: z5 <- zpoisson$new() z5$zelig(num_awards ~ prog + math, data = p, weights = w) z5 z5$zelig.call z5$model.call z5$zelig.out$z.out z5$setx(math = 40) set.seed(42) z5$sim(num = 1000) z5$summarize() z5$cite() z5 <- zpoisson$new() z5$zelig(num_awards ~ + math, data = p, by = c("prog")) z5 z5$zelig.call z5$model.call z5$zelig.out z5$zelig.out$z.out z5$setx(math = 40) set.seed(42) .self <- z5 z5$sim(num = 1000) z5$summarize() z5$cite() Zelig/demo/demo-feedback.R0000644000176200001440000000026612722061503015043 0ustar liggesuserslibrary(Zelig) z5 <- zlogit$new() z5$feedback() # z5$finalize() # Not clear how to trigger it with q() # quitting R triggers call to finalize method z6 <- zls$new() z6$feedback() Zelig/demo/demo-probit.R0000755000176200001440000000127212722061503014617 0ustar liggesusersmydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") mydata$rank <- factor(mydata$rank) # Zelig 4 code: library(Zelig4) # epsilon does not seem to be taken into account z.out <- zelig(admit ~ gre + gpa, data = mydata, model="probit", epsilon=.0001) summary(z.out) x.out <- setx(z.out, gpa=1.3, gre=4500, speed=12) set.seed(42) s.out <- sim(z.out, x = x.out, num=1000) summary(s.out) # Zelig 5 code: z5 <- zprobit$new() z5$zelig(admit ~ gre + gpa, data = mydata, epsilon=.0001) # removed epsilon to compare with Zelig 4 z5$zelig(admit ~ gre + gpa, data = mydata) z5 z5$setx(gpa=1.3, gre=4500, speed=12) set.seed(42) z5$sim(num=1000) z5$summarize() z5$cite() Zelig/demo/demo-exp.R0000755000176200001440000000100512722061503014106 0ustar liggesuserslibrary(survival) # Zelig 4 code: library(Zelig4) data(coalition) z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp", data = coalition) summary(z.out) x.low <- setx(z.out, numst2 = 0) set.seed(42) s.out <- sim(z.out, x = x.low, num=10) summary(s.out) # Zelig 5 code: data(coalition) z5 <- zexp$new() z5$zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp", data = coalition) z5 z5$zelig.out z5$setx(numst2 = 0) set.seed(42) z5$sim(num=10) z5$summarize() z5$cite() Zelig/demo/demo-normal.R0000755000176200001440000000067412722061503014615 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(macro) z.out1 <- Zelig4::zelig(unem ~ gdp + capmob + trade, model = "normal", data = macro) summary(z.out1) x.high <- Zelig4::setx(z.out1, trade = 50) set.seed(42) s.out1 <- Zelig4::sim(z.out1, x = x.high) summary(s.out1) # Zelig 5 code: data(macro) z5 <- znormal$new() z5$zelig(unem ~ gdp + capmob + trade, data=macro) z5 z5$setx(trade = 50) set.seed(42) z5$sim() z5$summarize() z5$cite() Zelig/demo/demo-normal-bayes.R0000644000176200001440000000076412722061503015713 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(macro) z.out <- Zelig4::zelig(unem ~ gdp + capmob + trade, model = "normal.bayes", data = macro, verbose = TRUE) summary(z.out) x.out <- Zelig4::setx(z.out) set.seed(42) s.out <- Zelig4::sim(z.out, x = x.out, num = 1000) summary(s.out) # Zelig 5 code: z5 <- znormalbayes$new() z5$zelig(unem ~ gdp + capmob + trade, data = macro, verbose = FALSE) z5 z5$zelig.out z5$setx() set.seed(42) z5$sim(num = 1000) z5$summarize() z5$cite() Zelig/demo/demo-lognorm.R0000755000176200001440000000104412722061503014772 0ustar liggesusers# Zelig 4 code: library(Zelig4) data(coalition) z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm", data = coalition) summary(z.out) x.low <- setx(z.out, numst2 = 0) set.seed(42) s.out <- sim(z.out, x = x.low, num=10) summary(s.out) # Zelig 5 code: data(coalition) z5 <- zlognorm$new() z5$zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm", data = coalition) z5 z5$zelig.out z5$setx(numst2 = 0) set.seed(42) z5$sim(num=10) z5$sim.out 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ÃäßÞ3Åöq\rƒê²4z§„ë‹ú¨+Ú$‘„kP¨ŒÍ,Î9¾Ý#°kýÌD¿Ûxé/c$ËœÜ1ñ×6f™“CàË“Õþ %=«Š˜ýJÎþ¸@_=nSl\H·!/¿í äK:¼wäh6@ œ·®&Õ(–ÇVIØa¨z”F~ŽÙ3e7ÿW²E^yˆ¶Ñ;yýŠ»åeÉ!‹ë™+±Î%+„s£1F 1«]‹ÏBÝ Í ‰ S©qŸä=Mïò\¡g™‹ÝêlÝ[lËÝþp'u›ßýå¿ÒPŒ7‡rs†4›á‚Â/æŸeB¾mjÝ ¢±®¯¬úUˆH*%³œê1¥?{ãÖM<‚3ÖÏÚnÇ`~´q„ßôD1„ìç!ÑNà•¥¯S–†•:Ë%±ÝX/q„?\JKg¬w‚Hb5¬ÆJs¼§Í½7™5xÄœ&V#s¸ì.A 1î¬ÒÒ Âø]x‡Lõ ý,‘ëta¶D{+·û×~Á ÈÊé’ìåç’.®Ü¬ºµÈñ¯–½I]’€mP”ØÈ•„ìÍZw6ø!ëÃ_åÊl|Ñ9îРÐÁ°ú ØøKµ† ”v,-£„tIBjbƒ‚PÚµS[ƒ²o‰ðƒÑ„&fyþôî2²ßY£„;Ñ5^”'œ:h²›;c,6>žmðï.53Ê>«þŸ”»í‰ìϽû¡=/g¶|À%9‰<ŠÎÑÊBo¼ÈŽ“Ô vÛaËÀ|¾©ÌÙÛàÀ Éf6,“ºÙ§Ìû¢-ÀÒ¹5f<öÉãƃ҄eùÍÃÞ5FGýê‰1JïWOŒ­°_‹~ JêåÅNi?A¬$Ì5ÀÛˆ¦Yßø`ÏMVqlîÿàÿö7R/ô9dZelig/R/0000755000176200001440000000000013006221765011527 5ustar liggesusersZelig/R/model-exp.R0000755000176200001440000000463412722061503013552 0ustar liggesusers#' Exponential Regression for Duration Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-exp.html} #' @import methods #' @export Zelig-exp #' @exportClass Zelig-exp #' #' @include model-zelig.R zexp <- setRefClass("Zelig-exp", contains = "Zelig", fields = list(simalpha = "list", linkinv = "function")) zexp$methods( initialize = function() { callSuper() .self$name <- "exp" .self$authors <- "Olivia Lau, Kosuke Imai, Gary King" .self$packageauthors <- "Terry M. Therneau, and Thomas Lumley" .self$year <- 2011 .self$description <- "Exponential Regression for Duration Dependent Variables" .self$fn <- quote(survival::survreg) .self$linkinv <- survreg.distributions[["exponential"]]$itrans # JSON .self$outcome <- "continous" .self$wrapper <- "exp" .self$acceptweights <- TRUE } ) zexp$methods( zelig = function(formula, ..., robust = FALSE, cluster = NULL, data, weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call if (!(is.null(cluster) || robust)) stop("If cluster is specified, then `robust` must be TRUE") # Add cluster term if (robust || !is.null(cluster)) formula <- cluster.formula(formula, cluster) .self$model.call$dist <- "exponential" .self$model.call$model <- FALSE callSuper(formula = formula, data = data, ..., robust = robust, cluster = cluster, weights = weights, by = by, bootstrap = bootstrap) rse<-plyr::llply(.self$zelig.out$z.out, (function(x) vcovHC(x,type="HC0"))) .self$test.statistics<- list(robust.se = rse) } ) zexp$methods( qi = function(simparam, mm) { eta <- simparam %*% t(mm) ev <- as.matrix(apply(eta, 2, linkinv)) pv <- as.matrix(rexp(length(ev), rate = 1 / ev)) return(list(ev = ev, pv = pv)) } ) zexp$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ .self$mcformula <- as.formula("Surv(y.sim, event) ~ x.sim") lambda <-exp(b0 + b1 * x) event <- rep(1, length(x)) y.sim <- rexp(n=length(x), rate=lambda) y.hat <- 1/lambda if(sim){ data <- data.frame(y.sim=y.sim, event=event, x.sim=x) return(data) }else{ data <- data.frame(y.hat=y.hat, event=event, x.seq=x) return(data) } } ) Zelig/R/model-probit-bayes.R0000644000176200001440000000222312722061503015343 0ustar liggesusers#' Bayesian Probit Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-probitbayes.html} #' @import methods #' @export Zelig-probit-bayes #' @exportClass Zelig-probit-bayes #' #' @include model-zelig.R #' @include model-probit.R zprobitbayes <- setRefClass("Zelig-probit-bayes", contains = c("Zelig-bayes", "Zelig-probit")) zprobitbayes$methods( initialize = function() { callSuper() .self$name <- "probit-bayes" .self$family <- "binomial" .self$link <- "probit" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$year <- 2013 .self$category <- "dichotomous" .self$authors <- "Ben Goodrich, Ying Lu" .self$description = "Bayesian Probit Regression for Dichotomous Dependent Variables" .self$fn <- quote(MCMCpack::MCMCprobit) # JSON from parent .self$wrapper <- "probit.bayes" } ) zprobitbayes$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ mu <- pnorm(b0 + b1 * x) if(sim){ y <- rbinom(n=length(x), size=1, prob=mu) return(y) }else{ return(mu) } } )Zelig/R/model-gamma-gee.R0000755000176200001440000000156612722061503014577 0ustar liggesusers#' Generalized Estimating Equation for Gamma Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-gammagee.html} #' @import methods #' @export Zelig-gamma #' @exportClass Zelig-gamma #' #' @include model-zelig.R #' @include model-gee.R #' @include model-gamma.R zgammagee <- setRefClass("Zelig-gamma-gee", contains = c("Zelig-gee", "Zelig-gamma")) zgammagee$methods( initialize = function() { callSuper() .self$name <- "gamma-gee" .self$family <- "Gamma" .self$link <- "inverse" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$year <- 2011 .self$category <- "continuous" .self$authors <- "Patrick Lam" .self$description = "General Estimating Equation for Gamma Regression" .self$fn <- quote(geepack::geeglm) # JSON from parent .self$wrapper <- "gamma.gee" } ) Zelig/R/model-ar.R0000755000176200001440000000125312722061503013352 0ustar liggesusers#' Time-Series Model with Autoregressive Disturbance #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-ar.html} #' @import methods #' @export Zelig-ar #' @exportClass Zelig-ar #' #' @include model-zelig.R #' @include model-timeseries.R zar <- setRefClass("Zelig-ar", contains = "Zelig-timeseries") zar$methods( initialize = function() { callSuper() .self$name <- "ar" .self$link <- "identity" .self$fn <- quote(zeligArimaWrapper) .self$description = "Time-Series Model with Autoregressive Disturbance" .self$packageauthors <- "R Core Team" .self$outcome <- "continuous" .self$wrapper <- "timeseries" } ) Zelig/R/utils.R0000755000176200001440000001506213006174522013017 0ustar liggesusers#' Compute the Statistical Mode of a Vector #' @aliases Mode mode #' @param x a vector of numeric, factor, or ordered values #' @return the statistical mode of the vector. If two modes exist, one is #' randomly selected (by design) #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} Mode <- function (x) { # build a table of values of x tab <- table(as.factor(x)) # find the mode, then if there's more than one, select one randomly v <- sample(names(which(tab == max(tab))), size = 1) # if it came in as a factor, we need to re-cast it # as a factor, with the same exact levels if (is.factor(x)) return(factor(v, levels = levels(x))) # re-cast as any other data-type as(v, class(x)) } ## Zelig 3 and 4 backward compatibility mode <- Mode #' Compute the Statistical Median of a Vector #' @param x a vector of numeric or ordered values #' @param na.rm ignored #' @return the median of the vector #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu} Median <- function (x, na.rm=NULL) { v <- ifelse(is.numeric(x), median(v), levels(x)[ceiling(median(as.numeric(x)))] ) if (is.ordered(x)) v <- factor(v, levels(x)) v } #' Create a table, but ensure that the correct #' columns exist. In particular, this allows for #' entires with zero as a value, which is not #' the default for standard tables #' @param x a vector #' @param levels a vector of levels #' @param ... parameters for table #' @return a table #' @author Matt Owen \email{mowen@@iq.harvard.edu} table.levels <- function (x, levels, ...) { # if levels are not explicitly set, then # search inside of x if (missing(levels)) { levels <- attr(x, 'levels') table(factor(x, levels=levels), ...) } # otherwise just do the normal thing else { table(factor(x, levels=levels), ...) } } #' Compute central tendancy as approrpriate to data type #' @param val a vector of values #' @return a mean (if numeric) or a median (if ordered) or mode (otherwise) #' @export avg <- function(val) { if (is.numeric(val)) mean(val) else if (is.ordered(val)) Median(val) else Mode(val) } #' Set new value of a factor variable, checking for existing levels #' @param fv factor variable #' @param v value #' @return a factor variable with a value \code{val} and the same levels #' @keywords internal setfactor <- function (fv, v) { lev <- levels(fv) if (!v %in% lev) stop("Wrong factor") return(factor(v, levels = lev)) } #' Set new value of a variable as approrpriate to data type #' @param val old value #' @param newval new value #' @return a variable of the same type with a value \code{val} #' @keywords internal setval <- function(val, newval) { if (is.numeric(val)) newval else if (is.ordered(val)) newval else { lev <- levels(val) if (!newval %in% lev) stop("Wrong factor") return(factor(newval, levels = lev)) } } #' Calculate the reduced dataset to be used in code{\link{setx}} #' #' #' This method is used internally #' #' @param dataset Zelig object data, possibly split to deal with \code{by} argument #' @param s list of variables and their tentative \code{setx} values #' @param formula a simplified version of the Zelig object formula (typically with 1 on the lhs) #' @param data Zelig object data #' @param avg function of data transformations #' @return a list of all the model variables either at their central tendancy or their \code{setx} value #' @export #' @keywords internal #' @author Christine Choirat reduce = function(dataset, s, formula, data, avg = avg) { pred <- try(terms(fit <- lm(formula, data), "predvars"), silent = TRUE) if ("try-error" %in% class(pred)) # exp and weibull pred <- try(terms(fit <- survreg(formula, data), "predvars"), silent = TRUE) dataset <- model.frame(fit) ldata <- lapply(dataset, avg) if (length(s) > 0) { n <- union(as.character(attr(pred, "predvars"))[-1], names(dataset)) if (is.list(s[[1]])) s <- s[[1]] m <- match(names(s), n) ma <- m[!is.na(m)] if (!all(complete.cases(m))) { w <- paste("Variable '", names(s[is.na(m)]), "' not in data set.\n", sep = "") warning(w) } for (i in seq(n[ma])) ldata[n[ma]][i][[1]] <- setval(dataset[n[ma]][i][[1]], s[n[ma]][i][[1]]) } return(ldata) } #' Describe Here #' @param qi quantity of interest in the discrete case #' @return a formatted qi #' @keywords internal #' @author Christine Choirat statmat <- function(qi) { m <- t(apply(qi, 2, quantile, c(.5, .025, .975), na.rm = TRUE)) n <- matrix(apply(qi, 2, mean, na.rm = TRUE)) colnames(n) <- "mean" o <- matrix(apply(qi, 2, sd, na.rm = TRUE)) colnames(o) <- "sd" p <- cbind(n, o, m) return(p) } #' Describe Here #' @param qi quantity of interest in the discrete case #' @param num number of simulations #' @return a formatted qi #' @keywords internal #' @author Christine Choirat statlevel <- function(qi, num) { if (is.matrix(qi)){ #m <- t(apply(qi, 2, table)) / num all.levels <- levels(qi) m <- t(apply(qi, 2, function(x) table(factor(x, levels=all.levels)))) / num }else{ m <- table(qi) / num } return(m) } #' Pass Quantities of Interest to Appropriate Summary Function #' #' @param qi quantity of interest (e.g., estimated value or predicted value) #' @param num number of simulations #' @return a formatted qi #' @keywords internal #' @author Christine Choirat stat <- function(qi, num) { if (is.null(attr(qi, "levels"))) return(statmat(qi)) else return(statlevel(qi, num)) } #' Generate Formulae that Consider Clustering #' #' This method is used internally by the "Zelig" Package to interpret #' clustering in GEE models. #' @param formula a formula object #' @param cluster a vector #' @return a formula object describing clustering cluster.formula <- function (formula, cluster) { # Convert LHS of formula to a string lhs <- deparse(formula[[2]]) cluster.part <- if (is.null(cluster)) # NULL values require sprintf("cluster(1:nrow(%s))", lhs) else # Otherwise we trust user input sprintf("cluster(%s)", cluster) update(formula, paste(". ~ .", cluster.part, sep = " + ")) } #' Zelig Copy of plyr::mutate to avoid namespace conflict with dplyr #' @keywords internal zeligPlyrMutate<-function (.data, ...) { stopifnot(is.data.frame(.data) || is.list(.data) || is.environment(.data)) cols <- as.list(substitute(list(...))[-1]) cols <- cols[names(cols) != ""] for (col in names(cols)) { .data[[col]] <- eval(cols[[col]], .data, parent.frame()) } .data } Zelig/R/model-poisson.R0000755000176200001440000000274312722061503014447 0ustar liggesusers#' Poisson Regression for Event Count Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-poisson.html} #' @import methods #' @export Zelig-poisson #' @exportClass Zelig-poisson #' #' @include model-zelig.R #' @include model-glm.R zpoisson <- setRefClass("Zelig-poisson", contains = "Zelig-glm", fields = list(theta = "ANY")) zpoisson$methods( initialize = function() { callSuper() .self$name <- "poisson" .self$family <- "poisson" .self$link <- "log" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$authors <- "Kosuke Imai, Gary King, Olivia Lau" .self$year <- 2007 .self$category <- "count" .self$description <- "Poisson Regression for Event Count Dependent Variables" # JSON .self$outcome <- "discrete" .self$wrapper <- "poisson" } ) zpoisson$methods( qi = function(simparam, mm) { eta <- simparam %*% t(mm) theta.local <- matrix(.self$linkinv(eta), nrow = nrow(simparam)) ev <- theta.local pv <- matrix(NA, nrow = nrow(theta.local), ncol = ncol(theta.local)) for (i in 1:ncol(theta.local)) pv[, i] <- rpois(nrow(theta.local), lambda = theta.local[, i]) return(list(ev = ev, pv = pv)) } ) zpoisson$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ lambda <- exp(b0 + b1 * x) if(sim){ y <- rpois(n=length(x), lambda=lambda) return(y) }else{ return(lambda) } } ) Zelig/R/model-timeseries.R0000755000176200001440000001415312722061503015124 0ustar liggesusers#' Time-series models in Zelig #' #' @import methods #' @export Zelig-timeseries #' @exportClass Zelig-timeseries #' #' @include model-zelig.R ztimeseries <- setRefClass("Zelig-timeseries", contains = "Zelig", fields = list(link = "character", linkinv = "function")) ztimeseries$methods( initialize = function() { callSuper() .self$packageauthors <- "R Core Team" .self$modelauthors <- "James Honaker" .self$acceptweights <- FALSE # Need to deal with block bootstrap .self$category <- "timeseries" .self$setx.labels <- list(ev = "Expected Values: E(Y|X)", ev1 = "Expected Values: E(Y|X1)", pv = "Predicted Values: Y|X", pv1 = "Predicted Values: Y|X1", fd = "First Differences: E(Y|X1) - E(Y|X)", acf = "Autocorrelation Function", ev.shortrun = "Expected Values Immediately Resulting from Shock", ev.longrun = "Long Run Expected Values after Innovation", pv.shortrun = "Predicted Values Immediately Resulting from Shock", pv.longrun = "Long Run Predicted Values after Innovation", evseries.shock = "Expected Values Over Time from Shock", evseries.innovation ="Expected Values Over Time from Innovation", pvseries.shock = "Predicted Values Over Time from Shock", pvseries.innovation ="Predicted Values Over Time from Innovation") } ) ztimeseries$methods( zelig = function(formula, data, order=c(1,0,0), ts=NULL, cs=NULL, ..., weights=NULL, by=NULL, bootstrap = FALSE){ if(!identical(bootstrap,FALSE)){ stop("Error: The bootstrap is not implemented for time-series models") } .self$zelig.call <- match.call(expand.dots = TRUE) if(identical(.self$name,"ar")){ order<-c(1,0,0) .self$zelig.call$order <- order } else if(identical(.self$name,"ma")){ order<-c(0,0,1) .self$zelig.call$order <- order } .self$model.call <- .self$zelig.call ## Sort dataset by time and cross-section ## Should add checks that ts, cs, are valid, and consider how to interact with by. ## This follows handling from Amelia::prep.r, which also has code to deal with lags, should we add those. if(!identical(ts,NULL)){ .self$model.call$ts <- NULL if (!identical(cs,NULL)) { .self$model.call$cs <- NULL tsarg<-list(data[,cs],data[,ts]) by <- cs # Use by architecture to deal with cross-sections in time-series models that do not support such. Currently overrides. } else { tsarg<-list(data[,ts]) } tssort<-do.call("order",tsarg) data<-data[tssort,] } ## ts and cs are used to reorganize dataset, and do not get further passed on to Super callSuper(formula = formula, data = data, order=order, ..., weights = weights, by = by, bootstrap = FALSE) } ) # replace packagename method as stats::arima() has a second layer of wrapping in zeligArimaWrapper(). ztimeseries$methods( packagename = function() { "Automatically retrieve wrapped package name" return("stats") } ) # replace simx method to add ACF as QI. ztimeseries$methods( simx = function() { d <- zeligPlyrMutate(.self$zelig.out, simparam = .self$simparam$simparam) d <- zeligPlyrMutate(d, mm = .self$setx.out$x$mm) .self$sim.out$x <- d %>% do(qi = .self$qi(.$simparam, .$mm)) %>% do(acf = .$qi$acf, ev = .$qi$ev, pv = .$qi$pv) } ) ztimeseries$methods( simx1 = function() { d <- zeligPlyrMutate(.self$zelig.out, simparam = .self$simparam$simparam) d <- zeligPlyrMutate(d, mm = .self$setx.out$x$mm) d <- zeligPlyrMutate(d, mm1 = .self$setx.out$x1$mm) # return(list(acf = acf, ev = ev, pv = pv, pv.shortrun=pv.shortrun, pv.longrun=pv.longrun, ev.shortrun=ev.shortrun, ev.longrun=ev.longrun, # pvseries.shock=yseries$y.shock, pvseries.innovation=yseries$y.innovation, # evseries.shock=yseries$ev.shock, evseries.innovation=yseries$ev.innovation)) .self$sim.out$x1 <- d %>% do(qi = .self$qi(.$simparam, .$mm, .$mm1)) %>% do(acf = .$qi$acf, ev = .$qi$ev, pv = .$qi$pv, ev.shortrun = .$qi$ev.shortrun, pv.shortrun = .$qi$pv.shortrun, ev.longrun = .$qi$ev.longrun, pv.longrun = .$qi$pv.longrun, pvseries.shock = .$qi$pvseries.shock, evseries.shock = .$qi$evseries.shock, pvseries.innovation = .$qi$pvseries.innovation, evseries.innovation = .$qi$evseries.innovation) # Will eventually have to then move acf, ev, and pv from .self$setx.out$x1 to .self$setx.out$x # This will also effect next line: d <- zeligPlyrMutate(.self$sim.out$x1, ev0 = .self$sim.out$x1$ev) # Eventually, when ev moves, then this path for ev0 changes. (Or make movement happen after fd calculation.) d <- d %>% do(fd = .$ev.longrun - .$ev0) .self$sim.out$x1 <- zeligPlyrMutate(.self$sim.out$x1, fd = d$fd) #JH } ) # replace sim method to skip {simx, simx1, simrange, simrange1} methods as they are not separable # instead go directly to qi method ztimeseries$methods( sim = function(num = 1000) { "Timeseries Method for Computing and Organizing Simulated Quantities of Interest" if (length(.self$num) == 0) .self$num <- num .self$simparam <- .self$zelig.out %>% do(simparam = .self$param(.$z.out)) # NOTE difference here from standard Zelig approach. # Normally these are done in sequence, but now we do one or the other. if (.self$bsetx1){ .self$simx1() }else{ .self$simx() } } ) # There is no fitting summary function for objects of class Arima. # So this passes the object through to print, and z$summary() is essentially print(summary(x)). #' Summary of an object of class Arima #' @method summary Arima #' @param object An object of class Arima #' @param ... Additional parameters #' @return The original object #' @export summary.Arima = function(object, ...) object Zelig/R/model-gamma.R0000755000176200001440000000375412722061503014042 0ustar liggesusers#' Gamma Regression for Continuous, Positive Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-gamma.html} #' @import methods #' @export Zelig-gamma #' @exportClass Zelig-gamma #' #' @include model-zelig.R #' @include model-glm.R zgamma <- setRefClass("Zelig-gamma", contains = "Zelig-glm") zgamma$methods( initialize = function() { callSuper() .self$name <- "gamma" .self$family <- "Gamma" .self$link <- "inverse" .self$authors <- "Kosuke Imai, Gary King, Olivia Lau" .self$year <- 2007 .self$category <- "bounded" .self$description <- "Gamma Regression for Continuous, Positive Dependent Variables" # JSON .self$outcome <- "continous" .self$wrapper <- "gamma" } ) zgamma$methods( param = function(z.out, method="mvn") { shape <- MASS::gamma.shape(z.out) if(identical(method, "mvn")){ simalpha <- rnorm(n = .self$num, mean = shape$alpha, sd = shape$SE) simparam.local <- mvrnorm(n = .self$num, mu = coef(z.out), Sigma = vcov(z.out)) simparam.local <- list(simparam = simparam.local, simalpha = simalpha) return(simparam.local) } else if(identical(method,"point")){ return(list(simparam = t(as.matrix(coef(z.out))), simalpha = shape$alpha )) } } ) zgamma$methods( qi = function(simparam, mm) { coeff <- simparam$simparam eta <- coeff %*% t(mm) theta <- matrix(1 / eta, nrow = nrow(coeff)) ev <- theta pv <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta)) for (ii in 1:nrow(ev)) pv[ii, ] <- rgamma(ncol(ev), shape = simparam$simalpha[ii], scale = theta[ii] / simparam$simalpha[ii]) return(list(ev = ev, pv = pv)) } ) zgamma$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ lambda <- 1/(b0 + b1 * x) if(sim){ y <- rgamma(n=length(x), shape=lambda, scale = alpha) return(y) }else{ return(alpha * lambda) } } ) Zelig/R/model-oprobit-bayes.R0000644000176200001440000000550012722061503015523 0ustar liggesusers#' Bayesian Ordered Probit Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-oprobitbayes.html} #' @import methods #' @export Zelig-oprobit-bayes #' @exportClass Zelig-oprobit-bayes #' #' @include model-zelig.R #' @include model-bayes.R zoprobitbayes <- setRefClass("Zelig-oprobit-bayes", contains = c("Zelig-bayes")) zoprobitbayes$methods( initialize = function() { callSuper() .self$name <- "oprobit-bayes" .self$year <- 2013 .self$category <- "discrete" .self$authors <- "Ben Goodrich, Ying Lu" .self$description = "Bayesian Probit Regression for Dichotomous Dependent Variables" .self$fn <- quote(MCMCpack::MCMCoprobit) # JSON from parent .self$wrapper <- "oprobit.bayes" } ) zoprobitbayes$methods( param = function(z.out) { simparam <- callSuper(z.out) # Produce the model matrix in order to get all terms (explicit and implicit) # from the regression model. mat <- model.matrix(.self$formula, data = .self$data) # Response Terms p <- ncol(mat) # All coefficients coefficients <- simparam # Coefficients for predictor variables beta <- coefficients[, 1:p] # Middle values of "gamma" matrix mid.gamma <- coefficients[, -(1:p)] # ... level <- ncol(coefficients) - p + 2 # Initialize the "gamma" parameters gamma <- matrix(NA, nrow(coefficients), level + 1) # The first, second and last values are fixed gamma[, 1] <- -Inf gamma[, 2] <- 0 gamma[, ncol(gamma)] <- Inf # All others are determined by the coef-matrix (now stored in mid.gamma) if (ncol(gamma) > 3) gamma[, 3:(ncol(gamma) - 1)] <- mid.gamma # return simparam <- list(simparam = beta, simalpha = gamma) return(simparam) } ) zoprobitbayes$methods( qi = function(simparam, mm) { beta <- simparam$simparam gamma <- simparam$simalpha labels <- levels(model.response(model.frame(.self$formula, data = .self$data))) # x is implicitly cast into a matrix eta <- beta %*% t(mm) # **TODO: Sort out sizes of matrices for these things. ev <- array(NA, c(nrow(eta), ncol(gamma) - 1, ncol(eta))) pv <- matrix(NA, nrow(eta), ncol(eta)) # Compute Expected Values # *********************** # Note that the inverse link function is: # pnorm(gamma[, j+1]-eta) - pnorm(gamma[, j]-eta) for (j in 1:(ncol(gamma) - 1)) { ev[, j, ] <- pnorm(gamma[, j + 1] - eta) - pnorm(gamma[, j] - eta) } colnames(ev) <- labels # Compute Predicted Values # ************************ for (j in 1:nrow(pv)) { mu <- eta[j, ] pv[j, ] <- as.character(cut(mu, gamma[j, ], labels = labels)) } pv <- as.factor(pv) # **TODO: Update summarize to work with at most 3-dimensional arrays ev <- ev[, , 1] return(list(ev = ev, pv = pv)) } ) Zelig/R/create-json.R0000755000176200001440000001234412722061503014067 0ustar liggesusers#' @include utils.R #' @include model-zelig.R #' @include model-ls.R #' @include model-glm.R #' @include model-binchoice.R #' @include model-logit.R #' @include model-probit.R #' @include model-poisson.R #' @include model-normal.R #' @include model-gamma.R #' @include model-negbinom.R #' @include model-exp.R #' @include model-lognorm.R #' @include model-tobit.R #' @include model-quantile.R #' @include model-relogit.R #' @include model-gee.R #' @include model-binchoice-gee.R #' @include model-logit-gee.R #' @include model-probit-gee.R #' @include model-gamma-gee.R #' @include model-normal-gee.R #' @include model-poisson-gee.R #' @include model-bayes.R #' @include model-factor-bayes.R #' @include model-logit-bayes.R #' @include model-mlogit-bayes.R #' @include model-normal-bayes.R #' @include model-oprobit-bayes.R #' @include model-poisson-bayes.R #' @include model-probit-bayes.R #' @include model-tobit-bayes.R #' @include model-weibull.R #' @include model-timeseries.R #' @include model-arima.R #' @include model-ar.R #' @include model-ma.R #library(jsonlite) createJSON <- function(){ z5ls <- zls$new() z5ls$toJSON() z5logit <- zlogit$new() z5logit$toJSON() z5probit <- zprobit$new() z5probit$toJSON() z5poisson <- zpoisson$new() z5poisson$toJSON() z5normal <- znormal$new() z5normal$toJSON() z5gamma <- zgamma$new() z5gamma$toJSON() z5negbin <- znegbin$new() z5negbin$toJSON() z5exp <- zexp$new() z5exp$toJSON() z5lognorm <- zlognorm$new() z5lognorm$toJSON() z5tobit <- ztobit$new() z5tobit$toJSON() z5quantile <- zquantile$new() z5quantile$toJSON() z5relogit <- zrelogit$new() z5relogit$toJSON() z5logitgee <- zlogitgee$new() z5logitgee$toJSON() z5probitgee <- zprobitgee$new() z5probitgee$toJSON() z5gammagee <- zgammagee$new() z5gammagee$toJSON() z5normalgee <- znormalgee$new() z5normalgee$toJSON() z5poissongee <- zpoissongee$new() z5poissongee$toJSON() z5factorbayes <- zfactorbayes$new() z5factorbayes$toJSON() z5logitbayes <- zlogitbayes$new() z5logitbayes$toJSON() z5mlogitbayes <- zmlogitbayes$new() z5mlogitbayes$toJSON() z5normalbayes <- znormalbayes$new() z5normalbayes$toJSON() z5oprobitbayes <- zoprobitbayes$new() z5oprobitbayes$toJSON() z5poissonbayes <- zpoissonbayes$new() z5poissonbayes$toJSON() z5probitbayes <- zprobitbayes$new() z5probitbayes$toJSON() z5tobitbayes <- ztobitbayes$new() z5tobitbayes$toJSON() z5weibull <- zweibull$new() z5weibull$toJSON() z5logitsurvey <- zlogitsurvey$new() z5logitsurvey$toJSON() z5probitsurvey <- zprobitsurvey$new() z5probitsurvey$toJSON() z5gammasurvey <- zgammasurvey$new() z5gammasurvey$toJSON() z5normalsurvey <- znormalsurvey$new() z5normalsurvey$toJSON() z5poissonsurvey <- zpoissonsurvey$new() z5poissonsurvey$toJSON() z5arima <- zarima$new() z5arima$toJSON() z5ar <- zar$new() z5ar$toJSON() z5ma <- zma$new() z5ma$toJSON() zeligmodels <- list(zelig5models = list("ls" = z5ls$ljson, "logit" = z5logit$ljson, "probit" = z5probit$ljson, "poisson" = z5poisson$ljson, "normal" = z5normal$ljson, "gamma" = z5gamma$ljson, "negbin" = z5negbin$ljson, "exp" = z5exp$ljson, "lognorm" = z5lognorm$ljson, "tobit" = z5tobit$ljson, "quantile" = z5quantile$ljson, "relogit" = z5relogit$ljson, "logitgee" = z5logitgee$ljson, "probitgee" = z5probitgee$ljson, "gammagee" = z5gammagee$ljson, "normalgee" = z5normalgee$ljson, "poissongee" = z5poissongee$ljson, "factorbayes" = z5factorbayes$ljson, "logitbayes" = z5logitbayes$ljson, "mlogitbayes" = z5mlogitbayes$ljson, "normalbayes" = z5normalbayes$ljson, "oprobitbayes" = z5oprobitbayes$ljson, "poissonbayes" = z5poissonbayes$ljson, "probitbayes" = z5probitbayes$ljson, "tobitbayes" = z5tobitbayes$ljson, "weibull" = z5weibull$ljson, "logitsurvey" = z5logitsurvey$ljson, "probitsurvey" = z5probitsurvey$ljson, "normalsurvey" = z5normalsurvey$ljson, "gammasurvey" = z5gammasurvey$ljson, "poissonsurvey" = z5poissonsurvey$ljson, "arima" = z5arima$ljson, "ma" = z5ma$ljson, "ar" = z5ar$ljson)) # cat(toJSON(zeligmodels, pretty = TRUE), file = file.path("tools", "zelig5models.json")) # file.copy(from = file.path("tools", "zelig5models.json"), to = file.path("inst", "JSON", "zelig5models.json")) cat(toJSON(zeligmodels, pretty = TRUE), "\n", file = file.path("zelig5models.json")) file.rename(from = file.path("zelig5models.json"), to = file.path("inst", "JSON", "zelig5models.json")) file.remove(file.path("zelig5models.json")) # cat(toJSON(zeligmodels, pretty = TRUE)) # j <- jsonlite::fromJSON(txt = readLines(file.path("..", "/JSON", "/zelig5models.json"))) return(TRUE) } Zelig/R/model-probit-survey.R0000755000176200001440000000153312722061503015603 0ustar liggesusers#' Probit Regression with Survey Weights #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-probitsurvey.html} #' @import methods #' @export Zelig-probit-survey #' @exportClass Zelig-probit-survey #' #' @include model-zelig.R #' @include model-binchoice-survey.R zprobitsurvey <- setRefClass("Zelig-probit-survey", contains = c("Zelig-binchoice-survey")) zprobitsurvey$methods( initialize = function() { callSuper() .self$name <- "probit-survey" .self$link <- "probit" .self$description <- "Probit Regression with Survey Weights" .self$wrapper <- "probit.survey" } ) zprobitsurvey$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ mu <- pnorm(b0 + b1 * x) if(sim){ y <- rbinom(n=length(x), size=1, prob=mu) return(y) }else{ return(mu) } } )Zelig/R/model-poisson-bayes.R0000644000176200001440000000222712722061503015542 0ustar liggesusers#' Bayesian Poisson Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-poissonbayes.html} #' @import methods #' @export Zelig-poisson-bayes #' @exportClass Zelig-poisson-bayes #' #' @include model-zelig.R #' @include model-bayes.R #' @include model-poisson.R zpoissonbayes <- setRefClass("Zelig-poisson-bayes", contains = c("Zelig-bayes", "Zelig-poisson")) zpoissonbayes$methods( initialize = function() { callSuper() .self$name <- "poisson-bayes" .self$family <- "poisson" .self$link <- "log" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$year <- 2013 .self$category <- "continuous" .self$authors <- "Ben Goodrich, Ying Lu" .self$description = "Bayesian Poisson Regression" .self$fn <- quote(MCMCpack::MCMCpoisson) # JSON from parent .self$wrapper <- "poisson.bayes" } ) zpoissonbayes$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ lambda <- exp(b0 + b1 * x) if(sim){ y <- rpois(n=length(x), lambda=lambda) return(y) }else{ return(lambda) } } ) Zelig/R/model-zelig.R0000755000176200001440000013601413006170425014066 0ustar liggesusers#' Zelig reference class #' #' Zelig website: \url{http://zeligproject.org/} #' #' @import methods #' @export Zelig #' @exportClass Zelig #' #' @field fn R function to call to wrap #' @field formula Zelig formula #' @field weights forthcoming #' @field name name of the Zelig model #' @field data data frame or matrix #' @field by split the data by factors #' @field mi work with imputed dataset #' @field idx model index #' @field zelig.call Zelig function call #' @field model.call wrapped function call #' @field zelig.out estimated zelig model(s) #' @field setx.out set values #' @field setx.labels pretty-print qi #' @field bsetx is x set? #' @field bsetx1 is x1 set? #' @field bsetrange is range set? #' @field bsetrange1 is range1 set? #' @field range range #' @field range1 range1 #' @field test.statistics list of test statistics #' @field sim.out simulated qi's #' @field simparam simulated parameters #' @field num number of simulations #' @field authors Zelig model authors #' @field zeligauthors Zelig authors #' @field modelauthors wrapped model authors #' @field packageauthors wrapped package authors #' @field refs citation information #' @field year model is released #' @field description model description #' @field url model URL #' @field url.docs model documentation URL #' @field category model category #' @field vignette.url vignette URL #' @field json JSON export #' @field ljson JSON export #' @field outcome JSON export #' @field wrapper JSON export #' @field explanatory JSON export #' @field mcunit.test unit testing #' @field with.feedback Feedback z <- setRefClass("Zelig", fields = list(fn = "ANY", # R function to call to wrap formula = "ANY", # Zelig formula weights = "ANY", acceptweights = "logical", name = "character", # name of the Zelig model data = "ANY", # data frame or matrix, originaldata = "ANY", # data frame or matrix, originalweights = "ANY", # ddata = "ANY", # data.by = "ANY", # data frame or matrix by = "ANY", mi = "logical", matched = "logical", avg = "ANY", idx = "ANY", # model index zelig.call = "call", # Zelig function call model.call = "call", # wrapped function call zelig.out = "ANY", # estimated zelig model(s) signif.stars = "logical", signif.stars.default = "logical", # significance stars default setx.out = "ANY", # set values setx.labels = "list", # pretty-print qi, bsetx = "logical", bsetx1 = "logical", bsetrange = "logical", bsetrange1 = "logical", range = "ANY", range1 = "ANY", setforeveryby = "logical", test.statistics = "ANY", sim.out = "list", # simulated qi's simparam = "ANY", # simulated parameters num = "numeric", # nb of simulations bootstrap = "logical", # use bootstrap bootstrap.num = "numeric", # number of bootstraps to use authors = "character", # Zelig model description zeligauthors = "character", modelauthors = "character", packageauthors = "character", refs = "ANY", # is there a way to recognize class "bibentry"?, year = "numeric", description = "character", url = "character", url.docs = "character", category = "character", vignette.url = "character", json = "ANY", # JSON export ljson = "ANY", outcome = "ANY", wrapper = "character", explanatory = "ANY", #Unit Testing mcunit.test = "ANY", mcformula = "ANY", # Feedback with.feedback = "logical")) z$methods( initialize = function() { .self$authors <- "Kosuke Imai, Gary King, and Olivia Lau" .self$zeligauthors <- "Christine Choirat, James Honaker, Kosuke Imai, Gary King, and Olivia Lau" .self$refs <- bibentry() .self$year <- as.numeric(format(Sys.Date(), "%Y")) .self$url <- "http://zeligproject.org/" .self$url.docs <- "http://docs.zeligproject.org/en/latest/" .self$setx.out <- list() .self$setx.labels <- list(ev = "Expected Values: E(Y|X)", ev1 = "Expected Values: E(Y|X1)", pv = "Predicted Values: Y|X", pv1 = "Predicted Values: Y|X1", fd = "First Differences: E(Y|X1) - E(Y|X)") .self$bsetx <- FALSE .self$bsetx1 <- FALSE .self$bsetrange <- FALSE .self$bsetrange1 <- FALSE .self$acceptweights <- FALSE .self$bootstrap <- FALSE .self$bootstrap.num <- 100 # JSON .self$vignette.url <- paste(.self$url.docs, tolower(class(.self)[1]), ".html", sep = "") .self$vignette.url <- sub("-gee", "gee", .self$vignette.url) .self$vignette.url <- sub("-bayes", "bayes", .self$vignette.url) # .self$vignette.url <- paste(.self$url.docs, "zelig-", sub("-", "", .self$name), ".html", sep = "") .self$category <- "undefined" .self$explanatory <- c("continuous", "discrete", "nominal", "ordinal", "binary") .self$outcome <- "" .self$wrapper <- "wrapper" # Is 'ZeligFeedback' package installed? .self$with.feedback <- "ZeligFeedback" %in% installed.packages() .self$setforeveryby <- TRUE .self$avg <- function(val) { if (is.numeric(val)) mean(val) else if (is.ordered(val)) Median(val) else Mode(val) } } ) z$methods( packagename = function() { "Automatically retrieve wrapped package name" # If this becomes "quote(mypackage::myfunction) then # regmatches(.self$fn,regexpr("(?<=\\()(.*?)(?=\\::)",.self$fn, perl=TRUE)) # would extract "mypackage" return(as.character(.self$fn)[2]) } ) z$methods( cite = function() { "Provide citation information about Zelig and Zelig model, and about wrapped package and wrapped model" title <- paste(.self$name, ": ", .self$description, sep="") localauthors <- "" if (length(.self$modelauthors) & (!identical(.self$modelauthors,""))){ # covers both empty styles: character(0) and "" --the latter being length 1. localauthors<-.self$modelauthors }else if (length(.self$packageauthors) & (!identical(.self$packageauthors,""))){ localauthors<-.self$packageauthors }else{ localauthors<-.self$zeligauthors } cat("How to cite this model in Zelig:\n ", localauthors, ". ", .self$year, ".\n ", title, "\n in ", .self$zeligauthors, ",\n \"Zelig: Everyone's Statistical Software,\" ", .self$url, "\n", sep = "") } ) # Construct a reference list specific to a Zelig model # Styles available from the bibentry print method: "text", "Bibtex", "citation", "html", "latex", "R", "textVersion" # The "sphinx" style reformats "text" style with some markdown substitutions z$methods( references = function(style="sphinx") { "Construct a reference list specific to a Zelig model." mystyle <- style if (mystyle=="sphinx"){ mystyle <- "text" } mycites<-.self$refs if(!is.na(.self$packagename() )){ mycites<-c(mycites, citation(.self$packagename())) # Concatentate model specific Zelig references with package references } mycites<-mycites[!duplicated(mycites)] # Remove duplicates (many packages have duplicate references in their lists) s<-capture.output(print(mycites, style = mystyle)) if(style == "sphinx"){ # format the "text" style conventions for sphinx markdown for building docs for zeligproject.org s<-gsub("\\*","\\*\\*",s, perl=TRUE) s<-gsub("_","\\*",s, perl=TRUE) s<-gsub("\\*\\(","\\* \\(",s, perl=TRUE) } cat(s, sep="\n") } ) z$methods( zelig = function(formula, data, model=NULL, ..., weights=NULL, by, bootstrap=FALSE) { "The zelig command estimates a variety of statistical models" fn2 <- function(fc, data) { fc$data <- data return(fc) } .self$formula <- formula # Overwrite formula with mc unit test formula into correct environment, if it exists # Requires fixing R scoping issue if("formula" %in% class(.self$mcformula)){ .self$formula <- as.formula( deparse(.self$mcformula), env=environment(.self$formula) ) .self$model.call$formula <- as.formula( deparse(.self$mcformula), env=globalenv() ) }else if(is.character(.self$mcformula)) { .self$formula <- as.formula( .self$mcformula, env=environment(.self$formula) ) .self$model.call$formula <- as.formula( .self$mcformula, env=globalenv() ) } if(!is.null(model)){ cat("Argument model is only valid for the Zelig wrapper, but not the Zelig method, and will be ignored.\n") flag <- !(names(.self$model.call)=="model") .self$model.call <- .self$model.call[flag] flag <- !(names(.self$zelig.call)=="model") .self$zelig.call <- .self$zelig.call[flag] } .self$by <- by .self$originaldata <- data .self$originalweights <- weights datareformed <- FALSE if(is.numeric(bootstrap)){ .self$bootstrap <- TRUE .self$bootstrap.num <- bootstrap }else if(is.logical(bootstrap)){ .self$bootstrap <- bootstrap } # Remove bootstrap argument from model call .self$model.call$bootstrap <- NULL # Check if bootstrap possible by checking whether param method has method argument available if(.self$bootstrap){ if(!("method" %in% names(formals(.self$param)))){ stop("The bootstrap does not appear to be implemented for this Zelig model. Check that the param() method allows point predictions.") } .self$setforeveryby <- FALSE # compute covariates in set() at the dataset-level } # Matched datasets from MatchIt if ("matchit" %in% class(data)){ idata <- MatchIt::match.data(data) iweights <- idata$weights .self$matched <- TRUE .self$data <- idata datareformed <- TRUE # Check if noninteger valued weights exist and are incompatible with zelig model validweights <- TRUE if(!.self$acceptweights){ # This is a convoluted way to do this, but avoids the costly "any()" calculation if not necessary if(any(iweights != ceiling(iweights))){ # any(y != ceiling(y)) tests slightly faster than all(y == ceiling(y)) validweights <- FALSE } } if(!validweights){ # could also be if((!acceptweights) & (any(iweights != ceiling(iweights)) but avoid the long any for big datasets cat("The weights created by matching for this dataset have noninteger values,\n", "however, the statistical model you have chosen is only compatible with integer weights.\n", "Either change the matching method (such as to `optimal' matching with a 1:1 ratio)\n", "or change the statistical model in Zelig.\n", "We will round matching weights up to integers to proceed.\n\n") .self$weights <- ceiling(iweights) }else{ .self$weights <- iweights } # Set references appropriate to matching methods used .self$refs <- c(.self$refs, citation("MatchIt")) if(m.out$call$method=="cem" & ("cem" %in% installed.packages())) .self$refs <- c(.self$refs, citation("cem")) #if(m.out$call$method=="exact") .self$refs <- c(.self$refs, citation("")) if((m.out$call$method=="full") & ("optmatch" %in% installed.packages())) .self$refs <- c(.self$refs, citation("optmatch")) if(m.out$call$method=="genetic" & ("Matching" %in% installed.packages())) .self$refs <- c(.self$refs, citation("Matching")) #if(m.out$call$method=="nearest") .self$refs <- c(.self$refs, citation("")) if(m.out$call$method=="optimal" & ("optmatch" %in% installed.packages())) .self$refs <- c(.self$refs, citation("optmatch")) #if(m.out$call$method=="subclass") .self$refs <- c(.self$refs, citation("")) } else { .self$matched <- FALSE } # Multiply Imputed datasets from Amelia # Notice Amelia objects ignore weights currently, which is reasonable as the Amelia package ignores weights if ("amelia" %in% class(data)){ idata <- data$imputations .self$data <- rbind_all(lapply(seq(length(idata)), function(imputationNumber) cbind(imputationNumber, idata[[imputationNumber]]))) .self$weights <- NULL # This should be considered or addressed datareformed <- TRUE .self$by <- c("imputationNumber", by) .self$mi <- TRUE .self$setforeveryby <- FALSE # compute covariates in set() at on the entire stacked dataset .self$refs<-c(.self$refs, citation("Amelia")) } else { .self$mi <- FALSE } if (!datareformed){ .self$data <- data # If none of the above package integrations have already reformed the data from another object, use the supplied data # Run some checking on weights argument, and see if is valid string or vector if(!is.null(weights)){ if(is.character(weights)){ if(weights %in% names(.self$data)){ .self$weights <- .self$data[[weights]] # This is a way to convert data.frame portion to type numeric (as data.frames are lists) }else{ cat("Variable name given for weights not found in dataset, so will be ignored.\n\n") .self$weights <- NULL # No valid weights .self$model.call$weights <- NULL } }else if(is.vector(weights)){ if(length(weights)==nrow(.self$data) & is.vector(weights)){ if(min(weights)<0){ weights[weights < 0] <- 0 cat("Negative valued weights were supplied and will be replaced with zeros.") } .self$weights <- weights # Weights }else{ cat("Length of vector given for weights is not equal to number of observations in dataset, and will be ignored.\n\n") .self$weights <- NULL # No valid weights .self$model.call$weights <- NULL } }else{ cat("Supplied weights argument is not a vector or a variable name in the dataset, and will be ignored.\n\n") .self$weights <- NULL # No valid weights .self$model.call$weights <- NULL } }else{ .self$weights <- NULL # No weights set, so weights are NULL .self$model.call$weights <- NULL } } # If the Zelig model does not not accept weights, but weights are provided, we rebuild the data # by bootstrapping using the weights as probabilities # or by duplicating rows proportional to the ceiling of their weight # Otherwise we pass the weights to the model call if(!is.null(.self$weights)){ if ((!.self$acceptweights)){ .self$buildDataByWeights2() # Could use alternative method $buildDataByWeights() for duplication approach. Maybe set as argument?\ .self$model.call$weights <- NULL } else { .self$model.call$weights <- .self$weights # NEED TO CHECK THIS IS THE NAME FOR ALL MODELS, or add more generic field containing the name for the weights argument } } if(.self$bootstrap){ .self$buildDataByBootstrap() } .self$model.call[[1]] <- .self$fn .self$model.call$by <- NULL if (is.null(.self$by)) { .self$data <- cbind(1, .self$data) names(.self$data)[1] <- "by" .self$by <- "by" } #cat("zelig.call:\n") #print(.self$zelig.call) #cat("model.call:\n") #print(.self$model.call) .self$data <- tbl_df(.self$data) #.self$zelig.out <- eval(fn2(.self$model.call, quote(as.data.frame(.)))) # shortened test version that bypasses "by" .self$zelig.out <- .self$data %>% group_by_(.self$by) %>% do(z.out = eval(fn2(.self$model.call, quote(as.data.frame(.))))) } ) z$methods( set = function(..., fn = list(numeric = mean, ordered = Median, other = Mode)) { "Setting Explanatory Variable Values" .self$avg <- function(val) { if (is.numeric(val)) ifelse(is.null(fn$numeric), mean(val), fn$numeric(val)) else if (is.ordered(val)) ifelse(is.null(fn$ordered), Median(val), fn$ordered(val)) else ifelse(is.null(fn$other), Mode(val), fn$other(val)) } s <-list(...) # This eliminates warning messages when factor rhs passed to lm() model in reduce() utility function if(.self$category=="multinomial"){ # Perhaps find more robust way to test if dep.var. is factor f2 <- update(.self$formula, as.numeric(.) ~ .) }else{ f2 <- .self$formula } f <- update(.self$formula, 1 ~ .) # update <- na.omit(.self$data) %>% # remove missing values # compute on each slice of the dataset defined by "by" if(.self$setforeveryby){ update <- .self$data %>% group_by_(.self$by) %>% do(mm = model.matrix(f, reduce(dataset = "MEANINGLESS ARGUMENT", s, formula = f2, data = ., avg = .self$avg))) # fix in last argument from data=.self$data to data=. (JH) # compute over the entire dataset - currently used for mi and bootstrap. Should be opened up to user. } else { if(.self$bootstrap){ flag <- .self$data$bootstrapIndex == (.self$bootstrap.num + 1) # These are the original observations tempdata <- .self$data[flag,] }else{ tempdata <- .self$data # presently this is for mi. And this is then the entire stacked dataset. } allreduce <- reduce(dataset = "MEANINGLESS ARGUMENT", s, formula = f2, data = tempdata, avg = .self$avg) allmm <- model.matrix(f, allreduce) update <- .self$data %>% group_by_(.self$by) %>% do(mm = allmm) } return(update) } ) z$methods( setx = function(..., fn = list(numeric = mean, ordered = Median, other = Mode)) { .self$bsetx <- TRUE .self$setx.out$x <- .self$set(..., fn = fn) } ) z$methods( setx1 = function(..., fn = list(numeric = mean, ordered = Median, other = Mode)) { .self$bsetx1 <- TRUE .self$setx.out$x1 <- .self$set(...) } ) z$methods( setrange = function(..., fn = list(numeric = mean, ordered = Median, other = Mode)) { .self$bsetrange <- TRUE rng <- list() s <- list(...) m <- expand.grid(s) .self$range <- m .self$setx.out$range <- list() for (i in 1:nrow(m)) { l <- as.list(as.list(m[i, ])) names(l) <- names(m) .self$setx.out$range[[i]] <- .self$set(l) } } ) z$methods( setrange1 = function(..., fn = list(numeric = mean, ordered = Median, other = Mode)) { .self$bsetrange1 <- TRUE rng <- list() s <- list(...) m <- expand.grid(s) .self$range1 <- m .self$setx.out$range1 <- list() for (i in 1:nrow(m)) { l <- as.list(as.list(m[i, ])) names(l) <- names(m) .self$setx.out$range1[[i]] <- .self$set(l) } } ) z$methods( param = function(z.out, method="mvn") { if(identical(method,"mvn")){ return(mvrnorm(.self$num, coef(z.out), vcov(z.out))) } else if(identical(method,"point")){ return(t(as.matrix(coef(z.out)))) } else { stop("param called with method argument of undefined type.") } } ) z$methods( sim = function(num = NULL) { "Generic Method for Computing and Organizing Simulated Quantities of Interest" ## If num is defined by user, it overrides the value stored in the .self$num field. ## If num is not defined by user, but is also not yet defined in .self$num, then it defaults to 1000. if (length(.self$num) == 0){ if(is.null(num)){ num <- 1000 } } if(!is.null(num)){ .self$num <- num } # This was previous version, that assumed sim only called once, or only method to access/write .self$num field: #if (length(.self$num) == 0) # .self$num <- num # Divide simulations among imputed datasets if(.self$mi){ am.m<-length(.self$getcoef()) .self$num <- ceiling(.self$num/am.m) } # If bootstrapped, use distribution of estimated parameters, # otherwise use $param() method for parametric bootstrap. if (.self$bootstrap & ! .self$mi){ .self$num <- 1 .self$simparam <- .self$zelig.out %>% do(simparam = .self$param(.$z.out, method="point")) } else { .self$simparam <- .self$zelig.out %>% do(simparam = .self$param(.$z.out)) } if (.self$bsetx) .self$simx() if (.self$bsetx1) .self$simx1() if (.self$bsetrange) .self$simrange() if (.self$bsetrange1) .self$simrange1() } ) z$methods( simx = function() { d <- zeligPlyrMutate(.self$zelig.out, simparam = .self$simparam$simparam) d <- zeligPlyrMutate(d, mm = .self$setx.out$x$mm) .self$sim.out$x <- d %>% do(qi = .self$qi(.$simparam, .$mm)) %>% do(ev = .$qi$ev, pv = .$qi$pv) } ) z$methods( simx1 = function() { d <- zeligPlyrMutate(.self$zelig.out, simparam = .self$simparam$simparam) d <- zeligPlyrMutate(d, mm = .self$setx.out$x1$mm) .self$sim.out$x1 <- d %>% do(qi = .self$qi(.$simparam, .$mm)) %>% do(ev = .$qi$ev, pv = .$qi$pv) d <- zeligPlyrMutate(.self$sim.out$x1, ev0 = .self$sim.out$x$ev) d <- d %>% do(fd = .$ev - .$ev0) .self$sim.out$x1 <- zeligPlyrMutate(.self$sim.out$x1, fd = d$fd) #JH } ) z$methods( simrange = function() { .self$sim.out$range <- list() for (i in 1:nrow(.self$range)) { d <- zeligPlyrMutate(.self$zelig.out, simparam = .self$simparam$simparam) d <- zeligPlyrMutate(d, mm = .self$setx.out$range[[i]]$mm) .self$sim.out$range[[i]] <- d %>% do(qi = .self$qi(.$simparam, .$mm)) %>% do(ev = .$qi$ev, pv = .$qi$pv) } } ) z$methods( simrange1 = function() { .self$sim.out$range1 <- list() for (i in 1:nrow(.self$range1)) { d <- zeligPlyrMutate(.self$zelig.out, simparam = .self$simparam$simparam) d <- zeligPlyrMutate(d, mm = .self$setx.out$range1[[i]]$mm) .self$sim.out$range1[[i]] <- d %>% do(qi = .self$qi(.$simparam, .$mm)) %>% do(ev = .$qi$ev, pv = .$qi$pv) } } ) z$methods( simx = function() { d <- zeligPlyrMutate(.self$zelig.out, simparam = .self$simparam$simparam) d <- zeligPlyrMutate(d, mm = .self$setx.out$x$mm) .self$sim.out$x <- d %>% do(qi = .self$qi(.$simparam, .$mm)) %>% do(ev = .$qi$ev, pv = .$qi$pv) } ) z$methods( ATT = function(treatment, treated=1, quietly=TRUE, num=NULL) { "Generic Method for Computing Simulated (Sample) Average Treatment Effects on the Treated" ## Checks on user provided arguments if(!is.character(treatment)){ stop("Argument treatment should be the name of the treatment variable in the dataset.") } if(!(treatment %in% names(.self$data))){ stop(cat("Specified treatment variable", treatment, "is not in the dataset.")) } # Check treatment variable included in model. # Check treatment variable is 0 or 1 (or generalize to dichotomous). # Check argument "treated" is 0 or 1 (or generalize to values of "treatment"). # Check "ev" is available QI. # Check if multiple equation model (which will need method overwrite). ## If num is defined by user, it overrides the value stored in the .self$num field. ## If num is not defined by user, but is also not yet defined in .self$num, then it defaults to 1000. if (length(.self$num) == 0){ if(is.null(num)){ num <- 1000 } } if(!is.null(num)){ if(!identical(num,.self$num)){ # .self$num changed, so regenerate simparam .self$num <- num .self$simparam <- .self$zelig.out %>% do(simparam = .self$param(.$z.out)) } } ## Extract name of dependent variable, treated units depvar <- as.character(.self$zelig.call[[2]][2]) ## Use dplyr to cycle over all splits of dataset ## NOTE: THIS IS GOING TO USE THE SAME simparam SET FOR EVERY SPLIT .self$sim.out$TE <- .self$data %>% group_by_(.self$by) %>% do(ATT = .self$simATT(simparam=.self$simparam$simparam[[1]], data=. , depvar=depvar, treatment=treatment, treated=treated) ) # z.out = eval(fn2(.self$model.call, quote(as.data.frame(.))))) if(!quietly){ return(.self$sim.out$TE) # The $getqi() method may generalize, otherwise, write a $getter. } } ) # Has calls to .self, so constructed as method rather than function internal to $ATT() # Function to simulate ATT z$methods( simATT = function(simparam, data, depvar, treatment, treated){ "Simulate an Average Treatment on the Treated" flag <- data[[treatment]]==treated data[[treatment]] <- 1-treated cf.mm <- model.matrix(.self$formula, data) # Counterfactual model matrix cf.mm <- cf.mm[flag,] y1 <- data[flag, depvar] y1.n <- sum(flag) ATT <- matrix(NA, nrow=y1.n, ncol= .self$num) for(i in 1:y1.n){ # Maybe $qi() generally works for all mm? Of all dimensions? If so, loop not needed. ATT[i,] <- as.numeric(y1[i,1]) - .self$qi(simparam=simparam, mm=cf.mm[i, , drop=FALSE])$ev } ATT <- apply(ATT, 2, mean) return(ATT) } ) z$methods( show = function(signif.stars = FALSE, subset = NULL, bagging = FALSE) { "Display a Zelig object" .self$signif.stars <- signif.stars .self$signif.stars.default <- getOption("show.signif.stars") options(show.signif.stars = .self$signif.stars) if ("uninitializedField" %in% class(.self$zelig.out)) cat("Next step: Use 'zelig' method") else if (length(.self$setx.out) == 0) { ############################################################################# # Current workaround to display call as $zelig.call rather than $model.call # This is becoming a more complex workaround than revising the summary method # should improve this approach in future: for(jj in 1:length(.self$zelig.out$z.out)){ if("S4" %in% typeof(.self$zelig.out$z.out[[jj]]) ){ slot(.self$zelig.out$z.out[[jj]],"call") <- .self$zelig.call } else { if("call" %in% names(.self$zelig.out$z.out[[jj]])){ .self$zelig.out$z.out[[jj]]$call <- .self$zelig.call } else if ("call" %in% names(attributes(.self$zelig.out$z.out[[1]])) ){ attr(.self$zelig.out$z.out[[1]],"call")<- .self$zelig.call } } } ############################################################################# if((.self$mi) & is.null(subset)){ cat("Model: Combined Imputations \n") vcovlist <-.self$getvcov() coeflist <-.self$getcoef() am.m<-length(coeflist) am.k<-length(coeflist[[1]]) q <- matrix(unlist(coeflist), nrow=am.m, ncol=am.k, byrow=TRUE) se <- matrix(NA, nrow=am.m, ncol=am.k) for(i in 1:am.m){ se[i,]<-sqrt(diag(vcovlist[[i]])) } ones <- matrix(1, nrow = 1, ncol = am.m) imp.q <- (ones %*% q)/am.m # Slightly faster than "apply(b,2,mean)" ave.se2 <- (ones %*% (se^2))/am.m # Similarly, faster than "apply(se^2,2,mean)" diff <- q - matrix(1, nrow = am.m, ncol = 1) %*% imp.q sq2 <- (ones %*% (diff^2))/(am.m - 1) imp.se <- sqrt(ave.se2 + sq2 * (1 + 1/am.m)) Estimate<-as.vector(imp.q) Std.Error<-as.vector(imp.se) zvalue<-Estimate/Std.Error Pr.z<-2*(1-pnorm(abs(zvalue))) stars<-rep("",am.k) stars[Pr.z<.05]<-"." stars[Pr.z<.01]<-"*" stars[Pr.z<.001]<-"**" stars[Pr.z<.0001]<-"***" results<-data.frame(Estimate,Std.Error,zvalue,Pr.z,stars,row.names=names(coeflist[[1]])) names(results)<-c("Estimate","Std.Error","z value","Pr(>|z|)","") print(results, digits=max(3, getOption("digits") - 3)) cat("---\nSignif. codes: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n") cat("\n") cat("For results from individual imputed datasets, use summary(x, subset = i:j)\n") }else if ((.self$mi) & !is.null(subset)) { for(i in subset){ cat("Imputed Dataset ",i,sep="") print(base::summary(.self$zelig.out$z.out[[i]])) } }else if ((.self$bootstrap) & is.null(subset)) { # Much reuse of Rubin's Rules from above. Probably able to better generalize across these two cases: cat("Model: Combined Bootstraps \n") vcovlist <-.self$getvcov() coeflist <-.self$getcoef() am.m<-length(coeflist) - 1 am.k<-length(coeflist[[1]]) q <- matrix(unlist(coeflist[-(am.m+1)]), nrow=am.m, ncol=am.k, byrow=TRUE) #se <- matrix(NA, nrow=am.m, ncol=am.k) #for(i in 1:am.m){ # se[i,]<-sqrt(diag(vcovlist[[i]])) #} ones <- matrix(1, nrow = 1, ncol = am.m) imp.q <- (ones %*% q)/am.m # Slightly faster than "apply(b,2,mean)" #ave.se2 <- (ones %*% (se^2))/am.m # Similarly, faster than "apply(se^2,2,mean)" diff <- q - matrix(1, nrow = am.m, ncol = 1) %*% imp.q sq2 <- (ones %*% (diff^2))/(am.m - 1) #imp.se <- sqrt(ave.se2 + sq2 * (1 + 1/am.m)) imp.se <- sqrt(sq2 * (1 + 1/am.m)) # Note departure from Rubin's rules here. if(bagging){ Estimate<-as.vector(imp.q) }else{ Estimate<-coeflist[[am.m+1]] } Std.Error<-as.vector(imp.se) zvalue<-Estimate/Std.Error Pr.z<-2*(1-pnorm(abs(zvalue))) stars<-rep("",am.k) stars[Pr.z<.05]<-"." stars[Pr.z<.01]<-"*" stars[Pr.z<.001]<-"**" stars[Pr.z<.0001]<-"***" results<-data.frame(Estimate,Std.Error,zvalue,Pr.z,stars,row.names=names(coeflist[[1]])) names(results)<-c("Estimate","Std.Error","z value","Pr(>|z|)","") print(results, digits=max(3, getOption("digits") - 3)) cat("---\nSignif. codes: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n") cat("\n") cat("For results from individual bootstrapped datasets, use summary(x, subset = i:j)\n") }else if ((.self$bootstrap) & !is.null(subset)) { for(i in subset){ cat("Bootstrapped Dataset ",i,sep="") print(base::summary(.self$zelig.out$z.out[[i]])) } }else{ summ <- .self$zelig.out %>% do(summ = {cat("Model: \n") if (length(.self$by) == 1) { if (.self$by == "by") { cat() } else { print(.[.self$by]) } } else { print(.[.self$by]) } if("S4" %in% typeof(.$z.out)){ # Need to change summary method here for some classes print(summary(.$z.out)) }else{ print(base::summary(.$z.out)) } }) } if("gim.criteria" %in% names(.self$test.statistics)){ if(.self$test.statistics$gim.criteria){ # cat("According to the GIM-rule-of-thumb, your model probably has some type of specification error.\n", # "We suggest you run model diagnostics and seek to fix the problem.\n", # "You may also wish to run the full GIM test (which takes more time) to be sure.\n", # "See http://.... for more information.\n \n") cat("Statistical Warning: The GIM test suggests this model is misspecified\n", "(based on comparisons between classical and robust SE's; see http://j.mp/GIMtest).\n", "We suggest you run diagnostics to ascertain the cause, respecify the model\n", "and run it again.\n\n") } } cat("Next step: Use 'setx' method\n") } else if (length(.self$setx.out) != 0 & length(.self$sim.out) == 0) { niceprint<-function(obj, name){ if(!is.null(obj[[1]])){ cat(name,":\n", sep="") screenoutput<-obj[[1]] attr(screenoutput,"assign")<-NULL print(screenoutput, digits=max(3, getOption("digits") - 3)) } } niceprint(obj=.self$setx.out$x$mm, name="setx") niceprint(obj=.self$setx.out$x1$mm, name="setx1") niceprint(obj=.self$setx.out$range[[1]]$mm, name="range") niceprint(obj=.self$setx.out$range1[[1]]$mm, name="range1") cat("\nNext step: Use 'sim' method\n") } else { # sim.out pstat <- function(s.out, what = "sim x") { simu <- s.out %>% do(simu = {cat("\n", what, ":\n") cat(" -----\n") cat("ev\n") print(stat(.$ev, .self$num)) cat("pv\n") print(stat(.$pv, .self$num)) if (!is.null(.$fd)) { cat("fd\n") print(stat(.$fd, .self$num))} } ) } pstat(.self$sim.out$x) pstat(.self$sim.out$x1, "sim x1") if (!is.null(.self$setx.out$range)) { for (i in seq(.self$sim.out$range)) { cat("\n") print(.self$range[i, ]) cat("\n") pstat(.self$sim.out$range[[i]], "sim range") cat("\n") } } if (!is.null(.self$setx.out$range1)) { for (i in seq(.self$sim.out$range1)) { cat("\n") print(.self$range1[i, ]) cat("\n") pstat(.self$sim.out$range1[[i]], "sim range") cat("\n") } } } options(show.signif.stars = .self$signif.stars.default) } ) z$methods( graph = function() { "Plot the quantities of interest" qi.plot(.self) } ) z$methods( summarize = function(...) { "Display a Zelig object" show(...) } ) z$methods( summarise = function(...) { "Display a Zelig object" show(...) } ) z$methods( help = function() { "Open the model vignette from http://zeligproject.org/" # vignette(class(.self)[1]) browseURL(.self$vignette.url) } ) z$methods( getcoef = function() { "Get estimated model coefficients" result <- try(lapply(.self$zelig.out$z.out, coef), silent = TRUE) if ("try-error" %in% class(result)) stop("'coef' method' not implemented for model '", .self$name, "'") else return(result) } ) z$methods( getvcov = function() { "Get estimated model variance-covariance matrix" result <- lapply(.self$zelig.out$z.out, vcov) if ("try-error" %in% class(result)) stop("'vcov' method' not implemented for model '", .self$name, "'") else return(result) } ) z$methods( getfitted = function() { result <- lapply(.self$zelig.out$z.out, fitted) if ("try-error" %in% class(result)) stop("'predict' method' not implemented for model '", .self$name, "'") else return(result) } ) z$methods( getpredict = function() { "Get predicted values" result <- lapply(.self$zelig.out$z.out, predict) if ("try-error" %in% class(result)) stop("'predict' method' not implemented for model '", .self$name, "'") else return(result) } ) z$methods( getqi = function(qi="ev", xvalue="x", subset=NULL){ "Get quantities of interest" possiblexvalues <- names(.self$sim.out) if(!(xvalue %in% possiblexvalues)){ stop(paste("xvalue must be ", paste(possiblexvalues, collapse=" or ") , ".", sep="")) } possibleqivalues <- c(names(.self$sim.out[[xvalue]]), names(.self$sim.out[[xvalue]][[1]])) if(!(qi %in% possibleqivalues)){ stop(paste("qi must be ", paste(possibleqivalues, collapse=" or ") , ".", sep="")) } if(.self$mi){ if(is.null(subset)){ am.m<-length(.self$getcoef()) subset <- 1:am.m } tempqi <- do.call(rbind, .self$sim.out[[xvalue]][[qi]][subset]) } else if(.self$bootstrap){ if(is.null(subset)){ subset <- 1:.self$bootstrap.num } tempqi <- do.call(rbind, .self$sim.out[[xvalue]][[qi]][subset]) } else if(xvalue %in% c("range", "range1")) { tempqi <- do.call(rbind, .self$sim.out[[xvalue]])[[qi]] } else { tempqi<- .self$sim.out[[xvalue]][[qi]][[1]] # also works: tempqi <- do.call(rbind, .self$sim.out[[xvalue]][[qi]]) } return(tempqi) } ) z$methods( toJSON = function() { "Convert Zelig object to JSON format" if (!is.list(.self$json)) .self$json <- list() .self$json$"name" <- .self$name .self$json$"description" <- .self$description .self$json$"outcome" <- list(modelingType = .self$outcome) .self$json$"explanatory" <- list(modelingType = .self$explanatory) .self$json$"vignette.url" <- .self$vignette.url .self$json$"wrapper" <- .self$wrapper tree <- c(class(.self)[1], .self$.refClassDef@refSuperClasses) .self$json$tree <- head(tree, match("Zelig", tree) - 1) .self$ljson <- .self$json .self$json <- jsonlite::toJSON(json, pretty = TRUE) return(.self$json) } ) # empty default data generating process to avoid error if not created as model specific method z$methods( mcfun = function(x, ...){ return( rep(1,length(x)) ) } ) # Monte Carlo unit test z$methods( mcunit = function(nsim=500, minx=-2, maxx=2, b0=0, b1=1, alpha=1, ci=0.95, plot = TRUE, ...){ passes <- TRUE n.short <- 10 # number of p alpha.ci <- 1-ci # alpha values for ci bounds, not speed parameter x.sim <- runif(n=nsim, min=minx, max=maxx) x.seq <- seq(from=minx, to=maxx, length = nsim) data.hat <- .self$mcfun(x=x.seq, b0=b0, b1=b1, alpha=alpha, ..., sim=FALSE) if(!is.data.frame(data.hat)){ data.hat<-data.frame(x.seq=x.seq, y.hat=data.hat) } data.sim <- .self$mcfun(x=x.sim, b0=b0, b1=b1, alpha=alpha, ..., sim=TRUE) if(!is.data.frame(data.sim)){ data.sim<-data.frame(x.sim=x.sim, y.sim=data.sim) } ## Estimate Zelig model and create numerical bounds on expected values # This should be the solution, but requires fixing R scoping issue: #.self$zelig(y.sim~x.sim, data=data.sim) # formula will be overwritten in zelig() if .self$mcformula has been set ## Instead, remove formula field and set by hard code .self$mcformula <- NULL if(.self$name %in% c("exp","weibull","lognorm")){ .self$zelig(Surv(y.sim,event)~x.sim, data=data.sim) }else{ .self$zelig(y.sim~x.sim, data=data.sim) } x.short.seq<-seq(from=minx, to=maxx, length=n.short) .self$setrange(x.sim=x.short.seq) .self$sim() data.short.hat <- .self$mcfun(x=x.short.seq, b0=b0, b1=b1, alpha=alpha, ..., sim=FALSE) if(!is.data.frame(data.short.hat)){ data.short.hat<-data.frame(x.seq=x.short.seq, y.hat=data.short.hat) } history.ev <- history.pv <- matrix(NA, nrow=n.short, ncol=2) for(i in 1:n.short){ xtemp<-x.short.seq[i] .self$setx(x.sim=xtemp) .self$sim() #temp<-sort( .self$sim.out$x$ev[[1]] ) temp<-.self$sim.out$range[[i]]$ev[[1]] # This is for ev's that are a probability distribution across outcomes, like ordered logit/probit if(ncol(temp)>1){ temp <- temp %*% as.numeric(sort(unique(data.sim$y.sim))) #as.numeric(colnames(temp)) } temp <- sort(temp) #calculate bounds of expected values history.ev[i,1]<-temp[max(round(length(temp)*(alpha.ci/2)),1) ] # Lower ci bound history.ev[i,2]<-temp[round(length(temp)*(1 - (alpha.ci/2)))] # Upper ci bound #temp<-sort( .self$sim.out$x$pv[[1]] ) temp<-sort( .self$sim.out$range[[i]]$pv[[1]] ) #check that ci contains true value passes <- passes & (min(history.ev[i,]) <= data.short.hat$y.hat[i] ) & (max(history.ev[i,]) >= data.short.hat$y.hat[i] ) #calculate bounds of predicted values history.pv[i,1]<-temp[max(round(length(temp)*(alpha.ci/2)),1) ] # Lower ci bound history.pv[i,2]<-temp[round(length(temp)*(1 - (alpha.ci/2)))] # Upper ci bound } ## Plot Monte Carlo Data if(plot){ all.main = substitute( paste(modelname, "(", beta[0], "=", b0, ", ", beta[1], "=", b1,",", alpha, "=", a0, ")"), list(modelname = .self$name, b0 = b0, b1=b1, a0 = alpha) ) all.ylim<-c( min(c(data.sim$y.sim, data.hat$y.hat)) , max(c(data.sim$y.sim, data.hat$y.hat)) ) plot(data.sim$x.sim, data.sim$y.sim, main=all.main, ylim=all.ylim, xlab="x", ylab="y", col="steelblue") par(new=TRUE) plot(data.hat$x.seq, data.hat$y.hat, main="", ylim=all.ylim, xlab="", ylab="", xaxt="n", yaxt="n", type="l", col="green", lwd=2) for(i in 1:n.short){ lines(x=rep(x.short.seq[i],2), y=c(history.pv[i,1],history.pv[i,2]), col="lightpink", lwd=1.6) lines(x=rep(x.short.seq[i],2), y=c(history.ev[i,1],history.ev[i,2]), col="firebrick", lwd=1.6) } } return(passes) } ) # rebuild dataset by duplicating observations by (rounded) weights z$methods( buildDataByWeights = function() { if(!.self$acceptweights){ idata <- .self$data iweights <- .self$weights ceilweights <- ceiling(iweights) n.obs <- nrow(idata) windex <- rep(1:n.obs, ceilweights) idata <- idata[windex,] .self$data <- idata if(any(iweights != ceiling(iweights))){ cat("Noninteger weights were set, but the model in Zelig is only able to use integer valued weights.\n", "Each weight has been rounded up to the nearest integer.\n\n") } } } ) # rebuild dataset by bootstrapping using weights as probabilities z$methods( buildDataByWeights2 = function() { if(!.self$acceptweights){ iweights <- .self$weights if(any(iweights != ceiling(iweights))){ cat("Noninteger weights were set, but the model in Zelig is only able to use integer valued weights.\n", "A bootstrapped version of the dataset was constructed using the weights as sample probabilities.\n\n") idata <- .self$data n.obs <- nrow(idata) n.w <- sum(iweights) iweights <- iweights/n.w windex <- sample(x=1:n.obs, size=n.w, replace=TRUE, prob=iweights) # Should size be n.w or n.obs? Relatedly, n.w might not be integer. idata <- idata[windex,] .self$data <- idata }else{ .self$buildDataByWeights() # If all weights are integers, just use duplication to rebuild dataset. } } } ) # rebuild dataset by bootstrapping using weights as probabilities # might possibly combine this method with $buildDataByWeights2() z$methods( buildDataByBootstrap = function() { idata <- .self$data n.boot <- .self$bootstrap.num n.obs <- nrow(idata) if(!is.null(.self$weights)){ iweights <- .self$weights n.w <- sum(iweights) iweights <- iweights/n.w }else{ iweights <- NULL } windex <- bootstrapIndex <- NULL for(i in 1:n.boot){ windex <- c(windex, sample(x=1:n.obs, size=n.obs, replace=TRUE, prob=iweights)) bootstrapIndex <- c(bootstrapIndex, rep(i,n.obs)) } # Last dataset is original data idata <- rbind(idata[windex,], idata) bootstrapIndex <- c(bootstrapIndex, rep(n.boot+1,n.obs)) idata$bootstrapIndex <- bootstrapIndex .self$data <- idata .self$by <- c("bootstrapIndex", .self$by) } ) z$methods( feedback = function() { "Send feedback to the Zelig team" if (!.self$with.feedback) return("ZeligFeedback package not installed") # If ZeligFeedback is installed print("ZeligFeedback package installed") print(ZeligFeedback::feedback(.self)) } ) # z$methods( # finalize = function() { # if (!.self$with.feedback) # return("ZeligFeedback package not installed") # # If ZeligFeedback is installed # print("Thanks for providing Zelig usage information") # # print(ZeligFeedback::feedback(.self)) # write(paste("feedback", ZeligFeedback::feedback(.self)), # file = paste0("test-zelig-finalize-", date(), ".txt")) # } # ) #' Summary method for Zelig objects" #' @param object An Object of Class Zelig #' @param ... Additional parameters to be passed to summary setMethod("summary", "Zelig", function(object, ...) { object$summarize(...) } ) #' Plot method for Zelig objects #' @param x An Object of Class Zelig #' @param y unused #' @param ... Additional parameters to be passed to plot setMethod("plot", "Zelig", function(x, ...) { x$graph() } ) #' Variance-covariance method for Zelig objects #' @param object An Object of Class Zelig #' @param ... Additional parameters to be passed to vcov setMethod("vcov", "Zelig", function(object, ...) { object$getvcov() } ) #' Method for extracting estimated coefficients from Zelig objects #' @param object An Object of Class Zelig #' @param ... Additional parameters to be passed to coef setMethod("coef", "Zelig", function(object, ...) { object$getcoef() } ) #' Method for extracting estimated fitted values from Zelig objects #' @param object An Object of Class Zelig #' @param ... Additional parameters to be passed to fitted setMethod("fitted", "Zelig", function(object, ...) { object$getfitted() } ) #' Method for getting predicted values from Zelig objects #' @param object An Object of Class Zelig #' @param ... Additional parameters to be passed to predict setMethod("predict", "Zelig", function(object, ...) { object$getpredict() } ) Zelig/R/model-lognorm.R0000755000176200001440000000705612722061503014434 0ustar liggesusers#' Log-Normal Regression for Duration Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-lognorm.html} #' @import methods #' @export Zelig-lognorm #' @exportClass Zelig-lognorm #' #' @include model-zelig.R zlognorm <- setRefClass("Zelig-lognorm", contains ="Zelig", fields = list(linkinv = "function")) zlognorm$methods( initialize = function() { callSuper() .self$name <- "lognorm" .self$authors <- "Matthew Owen, Olivia Lau, Kosuke Imai, Gary King" .self$packageauthors <- "Terry M Therneau, and Thomas Lumley" .self$year <- 2007 .self$description <- "Log-Normal Regression for Duration Dependent Variables" .self$fn <- quote(survival::survreg) .self$linkinv <- survreg.distributions[["lognormal"]]$itrans # JSON .self$outcome <- "discrete" .self$wrapper <- "lognorm" .self$acceptweights <- TRUE } ) zlognorm$methods( zelig = function(formula, ..., robust = FALSE, cluster = NULL, data, weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call if (!(is.null(cluster) || robust)) stop("If cluster is specified, then `robust` must be TRUE") # Add cluster term if (robust || !is.null(cluster)) formula <- cluster.formula(formula, cluster) .self$model.call$dist <- "lognormal" .self$model.call$model <- FALSE callSuper(formula = formula, data = data, ..., robust = robust, cluster = cluster, weights = weights, by = by, bootstrap = bootstrap) if(!robust){ fn2 <- function(fc, data) { fc$data <- data return(fc) } robust.model.call <- .self$model.call robust.model.call$robust <- TRUE robust.zelig.out <- .self$data %>% group_by_(.self$by) %>% do(z.out = eval(fn2(robust.model.call, quote(as.data.frame(.))))$var ) .self$test.statistics<- list(robust.se = robust.zelig.out$z.out) } } ) zlognorm$methods( param = function(z.out, method="mvn") { if(identical(method,"mvn")){ coeff <- coef(z.out) mu <- c(coeff, log(z.out$scale)) cov <- vcov(z.out) simulations <- mvrnorm(.self$num, mu = mu, Sigma = cov) simparam.local <- as.matrix(simulations[, 1:length(coeff)]) simalpha <- as.matrix(simulations[, -(1:length(coeff))]) simparam.local <- list(simparam = simparam.local, simalpha = simalpha) return(simparam.local) } else if(identical(method,"point")){ return(list(simparam = t(as.matrix(coef(z.out))), simalpha = log(z.out$scale) )) } } ) zlognorm$methods( qi = function(simparam, mm) { alpha <- simparam$simalpha beta <- simparam$simparam coeff <- simparam$simparam eta <- coeff %*% t(mm) theta <- as.matrix(apply(eta, 2, linkinv)) ev <- exp(log(theta) + 0.5 * (exp(alpha))^2) pv <- as.matrix(rlnorm(n=length(ev), meanlog=log(theta), sdlog=exp(alpha)), nrow=length(ev), ncol=1) dimnames(ev) <- dimnames(theta) return(list(ev = ev, pv = pv)) } ) zlognorm$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ .self$mcformula <- as.formula("Surv(y.sim, event) ~ x.sim") mu <- b0 + b1 * x event <- rep(1, length(x)) y.sim <- rlnorm(n=length(x), meanlog=mu, sdlog=alpha) y.hat <- exp(mu + 0.5*alpha^2) if(sim){ data <- data.frame(y.sim=y.sim, event=event, x.sim=x) return(data) }else{ data <- data.frame(y.hat=y.hat, event=event, x.seq=x) return(data) } } ) Zelig/R/model-mlogit-bayes.R0000644000176200001440000000362612722061503015347 0ustar liggesusers#' Bayesian Multinomial Logistic Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-mlogitbayes.html} #' @import methods #' @export Zelig-mlogit-bayes #' @exportClass Zelig-mlogit-bayes #' #' @include model-zelig.R #' @include model-bayes.R zmlogitbayes <- setRefClass("Zelig-mlogit-bayes", contains = c("Zelig-bayes")) zmlogitbayes$methods( initialize = function() { callSuper() .self$name <- "mlogit-bayes" .self$year <- 2013 .self$category <- "discrete" .self$authors <- "Ben Goodrich, Ying Lu" .self$description = "Bayesian Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values" .self$fn <- quote(MCMCpack::MCMCmnl) # JSON from parent .self$wrapper <- "mlogit.bayes" } ) zmlogitbayes$methods( qi = function(simparam, mm) { resp <- model.response(model.frame(.self$formula, data = .self$data)) level <- length(table(resp)) p <- dim(model.matrix(eval(.self$formula), data = .self$data))[2] coef <- simparam eta <- array(NA, c(nrow(coef), level, nrow(mm))) eta[, 1, ] <- matrix(0, nrow(coef), nrow(mm)) for (j in 2:level) { ind <- (1:p) * (level - 1) - (level - j) eta[, j, ]<- coef[, ind] %*% t(mm) } eta <- exp(eta) ev <- array(NA, c(nrow(coef), level, nrow(mm))) pv <- matrix(NA, nrow(coef), nrow(mm)) colnames(ev) <- rep(NA, level) for (k in 1:nrow(mm)) { for (j in 1:level) ev[, j, k] <- eta[, j, k] / rowSums(eta[, , k]) } for (j in 1:level) { colnames(ev)[j] <- paste("P(Y=", j, ")", sep="") } for (k in 1:nrow(mm)) { probs <- as.matrix(ev[, , k]) temp <- apply(probs, 1, FUN = rmultinom, n = 1, size = 1) temp <- as.matrix(t(temp) %*% (1:nrow(temp))) pv <- apply(temp, 2, as.character) pv <- as.factor(pv) } return(list(ev = ev, pv = pv)) } ) Zelig/R/model-normal-survey.R0000755000176200001440000000452012722061503015573 0ustar liggesusers#' Normal Regression for Continuous Dependent Variables with Survey Weights #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-normalsurvey.html} #' @import methods #' @export Zelig-normal #' @exportClass Zelig-normal #' #' @include model-zelig.R #' @include model-survey.R #' @include model-normal.R znormalsurvey <- setRefClass("Zelig-normal-survey", contains = c("Zelig-survey"), fields = list(family = "character", link = "character", linkinv = "function")) #, "Zelig-normal")) znormalsurvey$methods( initialize = function() { callSuper() .self$name <- "normal-survey" .self$family <- "gaussian" .self$link <- "identity" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$category <- "continuous" .self$description <- "Normal Regression for Continuous Dependent Variables with Survey Weights" .self$outcome <- "continuous" # JSON .self$wrapper <- "normal.survey" } ) znormalsurvey$methods( param = function(z.out, method="mvn") { degrees.freedom <- z.out$df.residual sig2 <- base::summary(z.out)$dispersion # not to call class summary method simalpha <- sqrt(degrees.freedom * sig2 / rchisq(.self$num, degrees.freedom)) if(identical(method,"mvn")){ simparam.local <- mvrnorm(n = .self$num, mu = coef(z.out), Sigma = vcov(z.out)) simparam.local <- list(simparam = simparam.local, simalpha = simalpha) return(simparam.local) } else if(identical(method,"point")){ return(list(simparam = t(as.matrix(coef(z.out))), simalpha = simalpha)) } } ) znormalsurvey$methods( qi = function(simparam, mm) { theta <- matrix(simparam$simparam %*% t(mm), nrow = nrow(simparam$simparam)) ev <- theta pv <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta)) for (j in 1:nrow(ev)) pv[j, ] <- rnorm(ncol(ev), mean = ev[j, ], sd = simparam$simalpha[j]) return(list(ev = ev, pv = pv)) } ) znormalsurvey$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ y <- b0 + b1*x + sim * rnorm(n=length(x), sd=alpha) return(y) } ) Zelig/R/model-logit-survey.R0000755000176200001440000000153412722061503015423 0ustar liggesusers#' Logit Regression with Survey Weights #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-logitsurvey.html} #' @import methods #' @export Zelig-logit-survey #' @exportClass Zelig-logit-survey #' #' @include model-zelig.R #' @include model-binchoice-survey.R zlogitsurvey <- setRefClass("Zelig-logit-survey", contains = c("Zelig-binchoice-survey")) zlogitsurvey$methods( initialize = function() { callSuper() .self$name <- "logit-survey" .self$link <- "logit" .self$description <- "Logistic Regression with Survey Weights" .self$wrapper <- "logit.survey" } ) zlogitsurvey$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ mu <- 1/(1 + exp(-b0 - b1 * x)) if(sim){ y <- rbinom(n=length(x), size=1, prob=mu) return(y) }else{ return(mu) } } )Zelig/R/model-weibull.R0000644000176200001440000000703112722061503014410 0ustar liggesusers#' Weibull Regression for Duration Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-weibull.html} #' @import methods #' @export Zelig-tobit-bayes #' @exportClass Zelig-tobit-bayes #' #' @include model-zelig.R zweibull <- setRefClass("Zelig-weibull", contains = "Zelig", fields = list(simalpha = "list", linkinv = "function", lambda = "ANY")) zweibull$methods( initialize = function() { callSuper() .self$name <- "weibull" .self$authors <- "Olivia Lau, Kosuke Imai, Gary King" .self$packageauthors <- "Terry M Therneau, and Thomas Lumley" .self$year <- 2007 .self$description <- "Weibull Regression for Duration Dependent Variables" .self$fn <- quote(survival::survreg) .self$linkinv <- survreg.distributions[["weibull"]]$itrans # JSON .self$outcome <- "bounded" .self$wrapper <- "weibull" .self$acceptweights <- TRUE } ) zweibull$methods( zelig = function(formula, ..., robust = FALSE, cluster = NULL, data, weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call if (!(is.null(cluster) || robust)) stop("If cluster is specified, then `robust` must be TRUE") # Add cluster term if (robust || !is.null(cluster)) formula <- cluster.formula(formula, cluster) .self$model.call$dist <- "weibull" .self$model.call$model <- FALSE callSuper(formula = formula, data = data, ..., robust = robust, cluster = cluster, weights = weights, by = by, bootstrap = bootstrap) if(!robust){ fn2 <- function(fc, data) { fc$data <- data return(fc) } robust.model.call <- .self$model.call robust.model.call$robust <- TRUE robust.zelig.out <- .self$data %>% group_by_(.self$by) %>% do(z.out = eval(fn2(robust.model.call, quote(as.data.frame(.))))$var ) .self$test.statistics<- list(robust.se = robust.zelig.out$z.out) } } ) zweibull$methods( param = function(z.out, method="mvn") { if(identical(method,"mvn")){ coeff <- coef(z.out) mu <- c(coeff, z.out$scale) cov <- vcov(z.out) simulations <- mvrnorm(.self$num, mu = mu, Sigma = cov) simparam.local <- as.matrix(simulations[, 1:length(coeff)]) simalpha.local <- as.matrix(simulations[, -(1:length(coeff))]) simparam.local <- list(simparam = simparam.local, simalpha = simalpha.local) return(simparam.local) } else if(identical(method,"point")){ return(list(simparam = t(as.matrix(coef(z.out))), simalpha = z.out$scale)) } } ) zweibull$methods( qi = function(simparam, mm) { eta <- simparam$simparam %*% t(mm) theta <- as.matrix(apply(eta, 2, linkinv)) ev <- theta * gamma(1 + 1/exp(simparam$simalpha)) pv <- as.matrix(rweibull(length(ev), shape = exp(simparam$simalpha), scale = theta)) return(list(ev = ev, pv = pv)) } ) zweibull$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ .self$mcformula <- as.formula("Surv(y.sim, event) ~ x.sim") lambda <-exp(b0 + b1 * x) event <- rep(1, length(x)) y.sim <- rweibull(n=length(x), shape=alpha, scale=lambda) y.hat <- lambda * gamma(1 + (1/alpha)) if(sim){ data <- data.frame(y.sim=y.sim, event=event, x.sim=x) return(data) }else{ data <- data.frame(y.hat=y.hat, event=event, x.seq=x) return(data) } } ) Zelig/R/model-binchoice.R0000755000176200001440000000210512722061503014670 0ustar liggesusers#' Binary Choice object for inheritance across models in Zelig #' #' @import methods #' @export Zelig-binchoice #' @exportClass Zelig-binchoice #' #' @include model-zelig.R #' @include model-glm.R zbinchoice <- setRefClass("Zelig-binchoice", contains = "Zelig-glm") zbinchoice$methods( initialize = function() { callSuper() .self$authors <- "Kosuke Imai, Gary King, Olivia Lau" .self$year <- 2007 .self$category <- "dichotomous" .self$family <- "binomial" # JSON .self$outcome <- "binary" } ) zbinchoice$methods( qi = function(simparam, mm) { .self$linkinv <- eval(call(.self$family, .self$link))$linkinv coeff <- simparam eta <- simparam %*% t(mm) eta <- Filter(function (y) !is.na(y), eta) theta <- matrix(.self$linkinv(eta), nrow = nrow(coeff)) ev <- matrix(.self$linkinv(eta), ncol = ncol(theta)) pv <- matrix(nrow = nrow(ev), ncol = ncol(ev)) for (j in 1:ncol(ev)) pv[, j] <- rbinom(length(ev[, j]), 1, prob = ev[, j]) levels(pv) <- c(0, 1) return(list(ev = ev, pv = pv)) } ) Zelig/R/model-tobit.R0000755000176200001440000000664512722061503014103 0ustar liggesusers#' Linear Regression for a Left-Censored Dependent Variable #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-tobit.html} #' @import methods #' @export Zelig-tobit #' @exportClass Zelig-tobit #' #' @include model-zelig.R ztobit <- setRefClass("Zelig-tobit", contains = "Zelig", fields = list(above = "numeric", below = "numeric")) ztobit$methods( initialize = function() { callSuper() .self$name <- "tobit" .self$authors <- "Kosuke Imai, Gary King, Olivia Lau" .self$packageauthors <- "Christian Kleiber, and Achim Zeileis" .self$year <- 2011 .self$description = "Linear regression for Left-Censored Dependent Variable" .self$fn <- quote(AER::tobit) # JSON .self$outcome <- "continous" .self$wrapper <- "tobit" .self$acceptweights <- TRUE } ) ztobit$methods( zelig = function(formula, ..., below = 0, above = Inf, robust = FALSE, data, weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call .self$below <- below .self$above <- above .self$model.call$below <- NULL .self$model.call$above <- NULL .self$model.call$left <- below .self$model.call$right <- above callSuper(formula = formula, data = data, ..., weights = weights, by = by, bootstrap = bootstrap) if(!robust){ fn2 <- function(fc, data) { fc$data <- data return(fc) } robust.model.call <- .self$model.call robust.model.call$robust <- TRUE robust.zelig.out <- .self$data %>% group_by_(.self$by) %>% do(z.out = eval(fn2(robust.model.call, quote(as.data.frame(.))))$var ) .self$test.statistics<- list(robust.se = robust.zelig.out$z.out) } } ) ztobit$methods( param = function(z.out, method="mvn") { if(identical(method,"mvn")){ mu <- c(coef(z.out), log(z.out$scale)) simfull <- mvrnorm(n = .self$num, mu = mu, Sigma = vcov(z.out)) simparam.local <- as.matrix(simfull[, -ncol(simfull)]) simalpha <- exp(as.matrix(simfull[, ncol(simfull)])) simparam.local <- list(simparam = simparam.local, simalpha = simalpha) return(simparam.local) } else if(identical(method,"point")){ return(list(simparam = t(as.matrix(coef(z.out))), simalpha = log(z.out$scale) )) } } ) ztobit$methods( qi = function(simparam, mm) { Coeff <- simparam$simparam %*% t(mm) SD <- simparam$simalpha alpha <- simparam$simalpha lambda <- dnorm(Coeff / SD) / (pnorm(Coeff / SD)) ev <- pnorm(Coeff / SD) * (Coeff + SD * lambda) pv <- ev pv <- matrix(nrow = nrow(ev), ncol = ncol(ev)) for (j in 1:ncol(ev)) { pv[, j] <- rnorm(nrow(ev), mean = ev[, j], sd = SD) pv[, j] <- pmin(pmax(pv[, j], .self$below), .self$above) } return(list(ev = ev, pv = pv)) } ) ztobit$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ mu <- b0 + b1 * x ystar <- rnorm(n=length(x), mean=mu, sd=alpha) if(sim){ y <- (ystar>0) * ystar # censoring from below at zero return(y) }else{ y.uncensored.hat.tobit<- mu + dnorm(mu, mean=0, sd=alpha)/pnorm(mu, mean=0, sd=alpha) y.hat.tobit<- y.uncensored.hat.tobit * (1- pnorm(0, mean=mu, sd=alpha) ) # expected value of censored outcome return(y.hat.tobit) } } ) Zelig/R/wrappers.R0000755000176200001440000003151713006170425013523 0ustar liggesusers#' Estimating a Statistical Model #' #' The zelig command estimates a variety of statistical #' models. Use \code{zelig} output with \code{setx} and \code{sim} to compute #' quantities of interest, such as predicted probabilities, expected values, and #' first differences, along with the associated measures of uncertainty #' (standard errors and confidence intervals). #' #' @param formula a symbolic representation of the model to be #' estimated, in the form \code{y \~\, x1 + x2}, where \code{y} is the #' dependent variable and \code{x1} and \code{x2} are the explanatory #' variables, and \code{y}, \code{x1}, and \code{x2} are contained in the #' same dataset. (You may include more than two explanatory variables, #' of course.) The \code{+} symbol means ``inclusion'' not #' ``addition.'' You may also include interaction terms and main #' effects in the form \code{x1*x2} without computing them in prior #' steps; \code{I(x1*x2)} to include only the interaction term and #' exclude the main effects; and quadratic terms in the form #' \code{I(x1^2)} #' @param model the name of a statistical model. #' Type \code{help.zelig("models")} to see a list of currently supported #' models #' @param 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}) #' @param ... additional arguments passed to \code{zelig}, #' depending on the model to be estimated #' @param by a factor variable contained in \code{data}. Zelig will subset #' the data frame based on the levels in the \code{by} variable, and #' estimate a model for each subset. This a particularly powerful option #' which will allow you to save a considerable amount of effort. For #' example, to run the same model on all fifty states, you could type: #' \code{z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "state")} #' You may also use \code{by} to run models using MatchIt subclass #' @param cite If is set to "TRUE" (default), the model citation will be #' @return Depending on the class of model selected, \code{zelig} will return #' an object with elements including \code{coefficients}, \code{residuals}, #' and \code{formula} which may be summarized using #' \code{summary(z.out)} or individually extracted using, for example, #' \code{z.out\$coefficients}. See the specific models listed above #' for additional output values, or simply type \code{names(z.out)}. #' #' @name zelig #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu}, Kosuke Imai, Olivia Lau, and #' Gary King #' Maintainer: Matt Owen \email{mowen@@iq.harvard.edu} #' @keywords package zelig <- function(formula, model, data, ..., by = NULL, cite = TRUE) { # .Deprecated("\nz$new() \nz$zelig(...)") # Zelig Core zeligmodels <- system.file(file.path("JSON", "zelig5models.json"), package = "Zelig") models <- jsonlite::fromJSON(txt = readLines(zeligmodels))$zelig5models # Zelig Choice zeligchoicemodels <- system.file(file.path("JSON", "zelig5choicemodels.json"), package = "ZeligChoice") if (zeligchoicemodels != "") models <- c(models, jsonlite::fromJSON(txt = readLines(zeligchoicemodels))$zelig5choicemodels) # Zelig Panel zeligpanelmodels <- system.file(file.path("JSON", "zelig5panelmodels.json"), package = "ZeligPanel") if (zeligpanelmodels != "") models <- c(models, jsonlite::fromJSON(txt = readLines(zeligpanelmodels))$zelig5panelmodels) # Zelig GAM zeligammodels <- system.file(file.path("JSON", "zelig5gammodels.json"), package = "ZeligGAM") if (zeligammodels != "") models <- c(models, jsonlite::fromJSON(txt = readLines(zeligammodels))$zelig5gammodels) # Zelig Multilevel zeligmixedmodels <- system.file(file.path("JSON", "zelig5mixedmodels.json"), package = "ZeligMultilevel") if (zeligmixedmodels != "") models <- c(models, jsonlite::fromJSON(txt = readLines(zeligmixedmodels))$zelig5mixedmodels) # Aggregating all available models models4 <- list() for (i in seq(models)) { models4[[models[[i]]$wrapper]] <- names(models)[i] } model.init <- paste0("z", models4[[model]], "$new()") z5 <- try(eval(parse(text = model.init)), silent = TRUE) if ("try-error" %in% class(z5)) stop("Model '", model,"' not found") ## End: Zelig 5 models mf <- match.call() mf$model <- NULL mf$cite <- NULL mf[[1]] <- quote(z5$zelig) mf <- try(eval(mf, environment()), silent = TRUE) if ("try-error" %in% class(mf)) z5$zelig(formula = formula, data = data, ..., by = by) if (cite) z5$cite() return(z5) } #' Setting Explanatory Variable Values #' #' The \code{setx} command uses the variables identified in #' the \code{formula} generated by \code{zelig} and sets the values of #' the explanatory variables to the selected values. Use \code{setx} #' after \code{zelig} and before \code{sim} to simulate quantities of #' interest. #' @param obj the saved output from zelig #' @param fn a list of functions to apply to the data frame #' @param data a new data frame used to set the values of #' explanatory variables. If data = NULL (the default), the #' data frame called in zelig is used #' @param cond a logical value indicating whether unconditional #' (default) or conditional (choose \code{cond = TRUE}) prediction #' should be performed. If you choose \code{cond = TRUE}, \code{setx} #' will coerce \code{fn = NULL} and ignore the additional arguments in #' \code{\dots}. If \code{cond = TRUE} and \code{data = NULL}, #' \code{setx} will prompt you for a data frame. #' @param ... user-defined values of specific variables for overwriting the #' default values set by the function \code{fn}. For example, adding #' \code{var1 = mean(data\$var1)} or \code{x1 = 12} explicitly sets the value #' of \code{x1} to 12. In addition, you may specify one explanatory variable #' as a range of values, creating one observation for every unique value in #' the range of values #' @return For unconditional prediction, \code{x.out} is a model matrix based #' on the specified values for the explanatory variables. For multiple #' analyses (i.e., when choosing the \code{by} option in \code{\link{zelig}}, #' \code{setx} returns the selected values calculated over the entire #' data frame. If you wish to calculate values over just one subset of #' the data frame, the 5th subset for example, you may use: #' \code{x.out <- setx(z.out[[5]])} #' @export #' @examples #' #' # Unconditional prediction: #' data(turnout) #' z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout) #' x.out <- setx(z.out) #' s.out <- sim(z.out, x = x.out) #' #' @author Matt Owen \email{mowen@@iq.harvard.edu}, Olivia Lau and Kosuke Imai #' @seealso The full Zelig manual may be accessed online at #' \url{http://gking.harvard.edu/zelig} #' @keywords file setx <- function(obj, fn = NULL, data = NULL, cond = FALSE, ...) { # .Deprecated("\nz$new() \nz$zelig(...) \nz$setx() or z$setx1 or z$setrange") x5 <- obj$copy() # This is the length of each argument in '...'s s <- list(...) if (length(s) > 0) { hold <- rep(1, length(s)) for(i in 1:length(s)) { hold[i] <- length(s[i][[1]]) } } else { hold <- 1 } if (max(hold) > 1) { x5$setrange(...) } else { x5$setx(...) } return(x5) } #' Generic Method for Computing and Organizing Simulated Quantities of Interest #' #' Simulate quantities of interest from the estimated model #' output from \code{zelig()} given specified values of explanatory #' variables established in \code{setx()}. For classical \emph{maximum #' likelihood} models, \code{sim()} uses asymptotic normal #' approximation to the log-likelihood. For \emph{Bayesian models}, #' Zelig simulates quantities of interest from the posterior density, #' whenever possible. For \emph{robust Bayesian models}, simulations #' are drawn from the identified class of Bayesian posteriors. #' Alternatively, you may generate quantities of interest using #' bootstrapped parameters. #' @param obj the output object from zelig #' @param x values of explanatory variables used for simulation, #' generated by setx #' @param x1 optional values of explanatory variables (generated by a #' second call of setx) #' particular computations of quantities of interest #' @param y a parameter reserved for the computation of particular #' quantities of interest (average treatment effects). Few #' models currently support this parameter #' @param num an integer specifying the number of simulations to compute #' @param bootstrap currently unsupported #' @param bootfn currently unsupported #' @param cond.data currently unsupported #' @param ... arguments reserved future versions of Zelig #' @return 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: #' \item{x}{the \code{\link{setx}} values for the explanatory variables, #' used to calculate the quantities of interest (expected values, #' predicted values, etc.). } #' \item{x1}{the optional \code{\link{setx}} object used to simulate #' first differences, and other model-specific quantities of #' interest, such as risk-ratios.} #' \item{call}{the options selected for \code{\link{sim}}, used to #' replicate quantities of interest. } #' \item{zelig.call}{the original command and options for #' \code{\link{zelig}}, used to replicate analyses. } #' \item{num}{the number of simulations requested. } #' \item{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{qi\$ev}{simulations of the expected values given the #' model and \code{x}. } #' \item{qi\$pr}{simulations of the predicted values given by the #' fitted values. } #' \item{qi\$fd}{simulations of the first differences (or risk #' difference for binary models) for the given \code{x} and \code{x1}. #' The difference is calculated by subtracting the expected values #' given \code{x} from the expected values given \code{x1}. (If do not #' specify \code{x1}, you will not get first differences or risk #' ratios.) } #' \item{qi\$rr}{simulations of the risk ratios for binary and #' multinomial models. See specific models for details.} #' \item{qi\$ate.ev}{simulations of the average expected #' treatment effect for the treatment group, using conditional #' prediction. Let \eqn{t_i} be a binary explanatory variable defining #' the treatment (\eqn{t_i=1}) and control (\eqn{t_i=0}) groups. Then the #' average expected treatment effect for the treatment group is #' \deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - #' E[Y_i(t_i=0)] \mid t_i=1 \,],} #' where \eqn{Y_i(t_i=1)} is the value of the dependent variable for #' observation \eqn{i} in the treatment group. Variation in the #' simulations are due to uncertainty in simulating \eqn{E[Y_i(t_i=0)]}, #' the counterfactual expected value of \eqn{Y_i} for observations in the #' treatment group, under the assumption that everything stays the #' same except that the treatment indicator is switched to \eqn{t_i=0}. } #' \item{qi\$ate.pr}{simulations of the average predicted #' treatment effect for the treatment group, using conditional #' prediction. Let \eqn{t_i} be a binary explanatory variable defining #' the treatment (\eqn{t_i=1}) and control (\eqn{t_i=0}) groups. Then the #' average predicted treatment effect for the treatment group is #' \deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - #' \widehat{Y_i(t_i=0)} \mid t_i=1 \,],} #' where \eqn{Y_i(t_i=1)} is the value of the dependent variable for #' observation \eqn{i} in the treatment group. Variation in the #' simulations are due to uncertainty in simulating #' \eqn{\widehat{Y_i(t_i=0)}}, the counterfactual predicted value of #' \eqn{Y_i} for observations in the treatment group, under the #' assumption that everything stays the same except that the #' treatment indicator is switched to \eqn{t_i=0}.} #' @export #' @author Matt Owen \email{mowen@@iq.harvard.edu}, Olivia Lau and Kosuke Imai sim <- function(obj, x = NULL, x1 = NULL, y = NULL, num = 1000, bootstrap = F, bootfn = NULL, cond.data = NULL, ...) { # .Deprecated("\nz$new() \n[...] \nz$sim(...)") s5 <- x$copy() if (!is.null(x1)) { s15 <- x1$copy() if (!is.null(s15$setx.out$x)) { s5$setx.out$x1 <- s15$setx.out$x s5$bsetx1 <- TRUE } if (!is.null(s15$setx.out$range)) { s5$range1<-s15$range s5$setx.out$range1 <- s15$setx.out$range s5$bsetrange1 <- TRUE } } s5$sim(num = num) return(s5) } Zelig/R/model-logit-gee.R0000755000176200001440000000116112722061503014622 0ustar liggesusers#' Generalized Estimating Equation for Logit Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-logitgee.html} #' @import methods #' @export Zelig-logit-gee #' @exportClass Zelig-logit-gee #' #' @include model-zelig.R #' @include model-binchoice-gee.R zlogitgee <- setRefClass("Zelig-logit-gee", contains = c("Zelig-binchoice-gee")) zlogitgee$methods( initialize = function() { callSuper() .self$name <- "logit-gee" .self$link <- "logit" .self$description <- "General Estimating Equation for Logistic Regression" .self$wrapper <- "logit.gee" } )Zelig/R/model-probit.R0000755000176200001440000000156312722061503014253 0ustar liggesusers#' Probit Regression for Dichotomous Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-probit.html} #' @import methods #' @export Zelig-probit #' @exportClass Zelig-probit #' #' @include model-zelig.R #' @include model-glm.R #' @include model-binchoice.R zprobit <- setRefClass("Zelig-probit", contains = "Zelig-binchoice") zprobit$methods( initialize = function() { callSuper() .self$name <- "probit" .self$link <- "probit" .self$description = "Probit Regression for Dichotomous Dependent Variables" .self$packageauthors <- "R Core Team" .self$wrapper <- "probit" } ) zprobit$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ mu <- pnorm(b0 + b1 * x) if(sim){ y <- rbinom(n=length(x), size=1, prob=mu) return(y) }else{ return(mu) } } ) Zelig/R/model-tobit-bayes.R0000644000176200001440000000322212722061503015165 0ustar liggesusers#' Bayesian Tobit Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-tobitbayes.html} #' @import methods #' @export Zelig-tobit-bayes #' @exportClass Zelig-tobit-bayes #' #' @include model-zelig.R #' @include model-bayes.R #' @include model-tobit.R ztobitbayes <- setRefClass("Zelig-tobit-bayes", contains = c("Zelig-bayes", "Zelig-tobit")) ztobitbayes$methods( initialize = function() { callSuper() .self$name <- "tobit-bayes" .self$year <- 2013 .self$category <- "dichotomous" .self$authors <- "Ben Goodrich, Ying Lu" .self$description = "Bayesian Tobit Regression for a Censored Dependent Variable" .self$fn <- quote(MCMCpack::MCMCtobit) # JSON from parent .self$wrapper <- "tobit.bayes" } ) ztobitbayes$methods( param = function(z.out) { if (length(.self$below) == 0) .self$below <- 0 if (length(.self$above) == 0) .self$above <- Inf simparam.local <- list() simparam.local$simparam <- z.out[, 1:(ncol(z.out) - 1)] simparam.local$simalpha <- sqrt(z.out[, ncol(z.out)]) return(simparam.local) } ) ztobitbayes$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ mu <- b0 + b1 * x ystar <- rnorm(n=length(x), mean=mu, sd=alpha) if(sim){ y <- (ystar>0) * ystar # censoring from below at zero return(y) }else{ y.uncensored.hat.tobit<- mu + dnorm(mu, mean=0, sd=alpha)/pnorm(mu, mean=0, sd=alpha) y.hat.tobit<- y.uncensored.hat.tobit * (1- pnorm(0, mean=mu, sd=alpha) ) # expected value of censored outcome return(y.hat.tobit) } } )Zelig/R/plots.R0000755000176200001440000011252012722061503013013 0ustar liggesusers#' Plot Quantities of Interest in a Zelig-fashion #' #' Various graph generation for different common types of simulated results from #' Zelig #' @usage simulations.plot(y, y1=NULL, xlab="", ylab="", main="", col=NULL, line.col=NULL, #' axisnames=TRUE) #' @param y A matrix or vector of simulated results generated by Zelig, to be #' graphed. #' @param y1 For comparison of two sets of simulated results at different #' choices of covariates, this should be an object of the same type and #' dimension as y. If no comparison is to be made, this should be NULL. #' @param xlab Label for the x-axis. #' @param ylab Label for the y-axis. #' @param main Main plot title. #' @param col A vector of colors. Colors will be used in turn as the graph is #' built for main plot objects. For nominal/categorical data, this colors #' renders as the bar color, while for numeric data it renders as the background #' color. #' @param line.col A vector of colors. Colors will be used in turn as the graph is #' built for line color shading of plot objects. #' @param axisnames a character-vector, specifying the names of the axes #' @return nothing #' @author James Honaker simulations.plot <-function(y, y1=NULL, xlab="", ylab="", main="", col=NULL, line.col=NULL, axisnames=TRUE) { binarytest <- function(j){ if(!is.null(attr(j,"levels"))) return(identical( sort(levels(j)),c(0,1))) return(FALSE) } ## Univariate Plots ## if(is.null(y1)){ if (is.null(col)) col <- rgb(100,149,237,maxColorValue=255) if (is.null(line.col)) line.col <- "black" # Integer Values if ((length(unique(y))<11 & all(as.integer(y) == y)) | is.factor(y) | is.character(y)) { if(is.factor(y) | is.character(y)){ y <- as.numeric(y) } # Create a sequence of names nameseq <- paste("Y=", min(y):max(y), sep="") # Set the heights of the barplots. # Note that tablar requires that all out values are greater than zero. # So, we subtract the min value (ensuring everything is at least zero) # then add 1 bar.heights <- tabulate(y - min(y) + 1) / length(y) # Barplot with (potentially) some zero columns output <- barplot(bar.heights, xlab=xlab, ylab=ylab, main=main, col=col[1], axisnames=axisnames, names.arg=nameseq) # Vector of 1's and 0's } else if(ncol(as.matrix(y))>1 & binarytest(y) ){ n.y <- nrow(y) # Precedence is names > colnames > 1:n if(is.null(names(y))){ if(is.null(colnames(y) )){ all.names <- 1:n.y }else{ all.names <- colnames(y) } }else{ all.names <- names(y) } # Barplot with (potentially) some zero columns output <- barplot( apply(y,2,sum)/n.y, xlab=xlab, ylab=ylab, main=main, col=col[1], axisnames=axisnames, names.arg=all.names) # Continuous Values } else if(is.numeric(y)){ if(ncol(as.matrix(y))>1){ ncoly <- ncol(y) hold.dens <- list() ymax <- xmax <- xmin <- rep(0,ncol(y)) for(i in 1:ncoly){ hold.dens[[i]] <- density(y[,i]) ymax[i] <- max(hold.dens[[i]]$y) xmax[i] <- max(hold.dens[[i]]$x) xmin[i] <- min(hold.dens[[i]]$x) } shift <- 0:ncoly all.xlim <- c(min(xmin), max(xmax)) all.ylim <- c(0,ncoly) # Precedence is names > colnames > 1:n if(is.null(names(y))){ if(is.null(colnames(y) )){ all.names <- 1:ncoly }else{ all.names <- colnames(y) } }else{ all.names <- names(y) } shrink <- 0.9 for(i in 1:ncoly ){ if(i ((max(comp)-min(comp))/2) )+1]) } } axis(side=1,labels=nameseq, at=seq(0,1,length=n.y), cex.axis=1, las=1) axis(side=2,labels=nameseq, at=seq(0,1,length=n.y), cex.axis=1, las=3) box() par(pty=old.pty,mai=old.mai) ## Two Vectors of 1's and 0's }else if( ncol(as.matrix(y))>1 & binarytest(y) & ncol(as.matrix(y1))>1 & binarytest(y1) ) { # Everything in this section assumes ncol(y)==ncol(y1) # Precedence is names > colnames > 1:n if(is.null(names(y))){ if(is.null(colnames(y) )){ nameseq <- 1:n.y }else{ nameseq <- colnames(y) } }else{ nameseq <- names(y) } n.y <- ncol(y) yseq <- 1:n.y y <- y %*% yseq y1 <- y1 %*% yseq ## FROM HERE ON -- Replicates above. Should address more generically colors<-rev(heat.colors(n.y^2)) lab.colors<-c("black","white") comp<-matrix(NA,nrow=n.y,ncol=n.y) for(i in 1:n.y){ for(j in 1:n.y){ flag<- y==yseq[i] & y1==yseq[j] comp[i,j]<-mean(flag) } } old.pty<-par()$pty old.mai<-par()$mai par(pty="s") par(mai=c(0.3,0.3,0.3,0.1)) image(z=comp, axes=FALSE, col=colors, zlim=c(min(comp),max(comp)),main=main ) locations.x<-seq(from=0,to=1,length=nrow(comp)) locations.y<-locations.x for(m in 1:n.y){ for(n in 1:n.y){ text(x=locations.x[m],y=locations.y[n],labels=paste(round(100*comp[m,n])), col=lab.colors[(comp[m,n]> ((max(comp)-min(comp))/2) )+1]) } } axis(side=1,labels=nameseq, at=seq(0,1,length=n.y), cex.axis=1, las=1) axis(side=2,labels=nameseq, at=seq(0,1,length=n.y), cex.axis=1, las=3) box() par(pty=old.pty,mai=old.mai) ## Numeric - Plot two densities on top of each other }else if(is.numeric(y) & is.numeric(y1)){ if(is.null(col)){ semi.col.x <-rgb(142,229,238,150,maxColorValue=255) semi.col.x1<-rgb(255,114,86,150,maxColorValue=255) col<-c(semi.col.x,semi.col.x1) }else if(length(col)<2){ col<-c(col,col) } if(ncol(as.matrix(y))>1){ shrink <- 0.9 ncoly <- ncol(y) # Assumes columns of y match cols y1. Should check or enforce. # Precedence is names > colnames > 1:n if(is.null(names(y))){ if(is.null(colnames(y) )){ all.names <- 1:ncoly }else{ all.names <- colnames(y) } }else{ all.names <- names(y) } hold.dens.y <- hold.dens.y1 <- list() ymax <- xmax <- xmin <- rep(0,ncoly) for(i in 1:ncoly){ hold.dens.y[[i]] <- density(y[,i]) hold.dens.y1[[i]] <- density(y1[,i], bw=hold.dens.y[[i]]$bw) ymax[i] <- max(hold.dens.y[[i]]$y, hold.dens.y1[[i]]$y) xmax[i] <- max(hold.dens.y[[i]]$x, hold.dens.y1[[i]]$x) xmin[i] <- min(hold.dens.y[[i]]$x, hold.dens.y1[[i]]$x) } all.xlim <- c(min(xmin), max(xmax)) all.ylim <- c(0,ncoly) shift <- 0:ncoly for(i in 1:ncoly ){ if(i0) & (length(obj$sim.out$x1$ev)>0) # Determine whether two "Predicted Values" qi's exist both.pv.exist <- (length(obj$sim.out$x$pv)>0) & (length(obj$sim.out$x1$pv)>0) color.x <- rgb(242, 122, 94, maxColorValue=255) color.x1 <- rgb(100, 149, 237, maxColorValue=255) # Interpolation of the above colors in rgb color space: color.mixed <- rgb(t(round((col2rgb(color.x) + col2rgb(color.x1))/2)), maxColorValue=255) if (! ("x" %in% names(obj$sim.out))) { return(par(old.par)) } else if (! ("x1" %in% names(obj$sim.out))) { panels <- matrix(1:2, 2, 1) # The plotting device: # # +-----------+ # | 1 | # +-----------+ # | 2 | # +-----------+ } else { panels <- matrix(c(1:5, 5), ncol=2, nrow=3, byrow = TRUE) # the plotting device: # # +-----+-----+ # | 1 | 2 | # +-----+-----+ # | 3 | 4 | # +-----+-----+ # | 5 | # +-----------+ panels <- if (xor(both.ev.exist, both.pv.exist)) rbind(panels, c(6, 6)) # the plotting device: # # +-----+-----+ # | 1 | 2 | # +-----+-----+ # | 3 | 4 | # +-----+-----+ # | 5 | # +-----------+ # | 6 | # +-----------+ else if (both.ev.exist && both.pv.exist) rbind(panels, c(6, 7)) else panels # the plotting device: # # +-----+-----+ # | 1 | 2 | # +-----+-----+ # | 3 | 4 | # +-----+-----+ # | 5 | # +-----+-----+ # | 6 | 7 | # +-----+-----+ } layout(panels) titles <- obj$setx.labels # Plot each simulation if(length(obj$sim.out$x$pv)>0) simulations.plot(obj$getqi(qi="pv", xvalue="x"), main = titles$pv, col = color.x, line.col = "black") if(length(obj$sim.out$x1$pv)>0) simulations.plot(obj$getqi(qi="pv", xvalue="x1"), main = titles$pv1, col = color.x1, line.col = "black") if(length(obj$sim.out$x$ev)>0) simulations.plot(obj$getqi(qi="ev", xvalue="x"), main = titles$ev, col = color.x, line.col = "black") if(length(obj$sim.out$x1$ev)>0) simulations.plot(obj$getqi(qi="ev", xvalue="x1"), main = titles$ev1, col = color.x1, line.col = "black") if(length(obj$sim.out$x1$fd)>0) simulations.plot(obj$getqi(qi="fd", xvalue="x1"), main = titles$fd, col = color.mixed, line.col = "black") if(both.pv.exist) simulations.plot(y=obj$getqi(qi="pv", xvalue="x"), y1=obj$getqi(qi="pv", xvalue="x1"), main = "Comparison of Y|X and Y|X1", col = paste(c(color.x, color.x1), "80", sep=""), line.col = "black") if(both.ev.exist) simulations.plot(y=obj$getqi(qi="ev", xvalue="x"), y1=obj$getqi(qi="ev", xvalue="x1"), main = "Comparison of E(Y|X) and E(Y|X1)", col = paste(c(color.x, color.x1), "80", sep=""), line.col = "black") # Restore old state par(old.par) # Return old parameter invisibly invisible(old.par) } #' Method for plotting qi simulations across a range within a variable, with confidence intervals #' #' @param obj A reference class zelig5 object #' @param qi a character-string specifying the quantity of interest to plot #' @param var The variable to be used on the x-axis. Default is the variable #' across all the chosen values with smallest nonzero variance #' @param ... Parameters to be passed to the `truehist' function which is #' implicitly called for numeric simulations #' @param main a character-string specifying the main heading of the plot #' @param sub a character-string specifying the sub heading of the plot #' @param xlab a character-string specifying the label for the x-axis #' @param ylab a character-string specifying the label for the y-axis #' @param xlim Limits to the x-axis #' @param ylim Limits to the y-axis #' @param legcol ``legend color'', an valid color used for plotting the line #' colors in the legend #' @param col a valid vector of colors of at least length 3 to use to color the #' confidence intervals #' @param leg ``legend position'', an integer from 1 to 4, specifying the #' position of the legend. 1 to 4 correspond to ``SE'', ``SW'', ``NW'', and #' ``NE'' respectively #' @param legpos ``legend type'', exact coordinates and sizes for legend. #' Overrides argment ``leg.type'' #' @param ci vector of length three of confidence interval levels to draw. #' @param discont optional point of discontinuity along the x-axis at which #' to interupt the graph #' @return the current graphical parameters. This is subject to change in future #' implementations of Zelig #' @author James Honaker #' @export plot.ci #' @usage ci.plot(obj, qi="ev", var=NULL, ..., main = NULL, sub = #' NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = #' NULL, legcol="gray20", col=NULL, leg=1, legpos= #' NULL, ci = c(80, 95, 99.9), discont=NULL) ci.plot <- function(obj, qi="ev", var=NULL, ..., main = NULL, sub = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, legcol="gray20", col=NULL, leg=1, legpos=NULL, ci=c(80,95,99.9), discont=NULL) { ########################### #### Utility Functions #### # Define function to cycle over range list and extract correct qi's ## CAN THESE NOW BE REPLACED WITH THE GETTER METHODS? extract.sims<-function(obj,qi){ d<-length(obj$sim.out$range) k<-length(obj$sim.out$range[[1]][qi][[1]][[1]]) # THAT IS A LONG PATH THAT MAYBE SHOULD BE CHANGED hold<-matrix(NA,nrow=k, ncol=d) for(i in 1:d){ hold[,i]<-obj$sim.out$range[[i]][qi][[1]][[1]] # THAT IS A LONG PATH THAT MAYBE SHOULD BE CHANGED } return(hold) } extract.sims1<-function(obj,qi){ #Should find better architecture for alternate range sims d<-length(obj$sim.out$range1) k<-length(obj$sim.out$range1[[1]][qi][[1]][[1]]) # THAT IS A LONG PATH THAT MAYBE SHOULD BE CHANGED hold<-matrix(NA,nrow=k, ncol=d) for(i in 1:d){ hold[,i]<-obj$sim.out$range1[[i]][qi][[1]][[1]] # THAT IS A LONG PATH THAT MAYBE SHOULD BE CHANGED } return(hold) } # Define functions to compute confidence intervals ## CAN WE MERGE THESE TOGETHER SO AS NOT TO HAVE TO SORT TWICE? ci.upper <- function (x, alpha) { pos <- max(round((1-(alpha/100))*length(x)), 1) return(sort(x)[pos]) } ci.lower <- function (x, alpha) { pos<-max(round((alpha/100)*length(x)), 1) return(sort(x)[pos]) } ########################### if(length(ci)<3){ ci<-rep(ci,3) } if(length(ci)>3){ ci<-ci[1:3] } ci<-sort(ci) ## Timeseries: if("timeseries" %in% obj$category){ #xmatrix<- ## Do we need to know the x in which the shock/innovation occcured? For secondary graphs, titles, legends? xname <- "Time" qiseries <- c("pvseries.shock","pvseries.innovation","evseries.shock","evseries.innovation") if (!qi %in% qiseries){ cat(paste("Error: For Timeseries models, argument qi must be one of ", paste(qiseries, collapse=" or ") ,".\n", sep="") ) return() } ev<-t( obj$getqi(qi=qi, xvalue="x1") ) # NOTE THE NECESSARY TRANSPOSE. Should we more clearly standardize this? d<-ncol(ev) xseq<-1:d ev1 <- NULL # Maybe want to add ability to overlay another graph? # Define xlabel if (is.null(xlab)) xlab <- xname if (is.null(ylab)){ if(qi %in% c("pvseries.shock", "pvseries.innovation")) ylab<- as.character(obj$setx.labels["pv"]) if(qi %in% c("evseries.shock", "evseries.innovation")) ylab<- as.character(obj$setx.labels["ev"]) } if (is.null(main)) main <- as.character(obj$setx.labels[qi]) if (is.null(discont)) discont <- 22.5 # NEED TO SET AUTOMATICALLY ## Everything Else: }else{ d<-length(obj$sim.out$range) if (d<1) { return() # Should add warning } xmatrix<-matrix(NA,nrow=d, ncol=length( obj$setx.out$range[[1]]$mm[[1]] )) # THAT IS A LONG PATH THAT MAYBE SHOULD BE CHANGED for(i in 1:d){ xmatrix[i,]<-as.matrix( obj$setx.out$range[[i]]$mm[[1]] ) # THAT IS A LONG PATH THAT MAYBE SHOULD BE CHANGED } if (d == 1 && is.null(var)) { warning("Must specify the `var` parameter when plotting the confidence interval of an unvarying model. Plotting nothing.") return(invisible(FALSE)) } xvarnames<-names(as.data.frame( obj$setx.out$range[[1]]$mm[[1]])) # MUST BE A BETTER WAY/PATH TO GET NAMES if(is.character(var)){ if( !(var %in% xvarnames ) ){ warning("Specified variable for confidence interval plot is not in estimated model. Plotting nothing.") return(invisible(FALSE)) } } if (is.null(var)) { each.var <- apply(xmatrix,2,sd) flag <- each.var>0 min.var<-min(each.var[flag]) var.seq<-1:ncol(xmatrix) position<-var.seq[each.var==min.var] } else { if(is.numeric(var)){ position<-var }else if(is.character(var)){ position<-grep(var,xvarnames ) } } position<-min(position) xseq<-xmatrix[,position] xname<-xvarnames[position] # Define xlabel if (is.null(xlab)) xlab <- paste("Range of",xname) # Use "qi" argument to select quantities of interest and set labels ev1<-NULL if(!is.null(obj$sim.out$range1)){ ev1<-extract.sims1(obj,qi=qi) } ev<-extract.sims(obj,qi=qi) if (is.null(ylab)){ ylab <- as.character(obj$setx.labels[qi]) } } # k<-ncol(ev) n<-nrow(ev) # if(is.null(col)){ myblue1<-rgb( 100, 149, 237, alpha=50, maxColorValue=255) myblue2<-rgb( 152, 245, 255, alpha=50, maxColorValue=255) myblue3<-rgb( 191, 239, 255, alpha=70, maxColorValue=255) myred1 <-rgb( 237, 149, 100, alpha=50, maxColorValue=255) myred2 <-rgb( 255, 245, 152, alpha=50, maxColorValue=255) myred3 <-rgb( 255, 239, 191, alpha=70, maxColorValue=255) col<-c(myblue1,myblue2,myblue3,myred1,myred2,myred3) }else{ if(length(col)<6){ col<-rep(col,6)[1:6] } } # Define function to numerically extract summaries of distributions from set of all simulated qi's form.history <- function (k,xseq,results,ci=c(80,95,99.9)){ history<-matrix(NA, nrow=k,ncol=8) for (i in 1:k) { v <- c( xseq[i], median(results[,i]), ci.upper(results[,i],ci[1]), ci.lower(results[,i],ci[1]), ci.upper(results[,i],ci[2]), ci.lower(results[,i],ci[2]), ci.upper(results[,i],ci[3]), ci.lower(results[,i],ci[3]) ) history[i, ] <- v } if (k == 1) { left <- c( xseq[1]-.5, median(results[,1]), ci.upper(results[,1],ci[1]), ci.lower(results[,1],ci[1]), ci.upper(results[,1],ci[2]), ci.lower(results[,1],ci[2]), ci.upper(results[,1],ci[3]), ci.lower(results[,1],ci[3]) ) right <- c( xseq[1]+.5, median(results[,1]), ci.upper(results[,1],ci[1]), ci.lower(results[,1],ci[1]), ci.upper(results[,1],ci[2]), ci.lower(results[,1],ci[2]), ci.upper(results[,1],ci[3]), ci.lower(results[,1],ci[3]) ) v <- c( xseq[1], median(results[,1]), ci.upper(results[,1],ci[1]), ci.lower(results[,1],ci[1]), ci.upper(results[,1],ci[2]), ci.lower(results[,1],ci[2]), ci.upper(results[,1],ci[3]), ci.lower(results[,1],ci[3]) ) history <- rbind(left, v, right) } return(history) } history<- form.history(k,xseq,ev,ci) if(!is.null(ev1)){ history1<- form.history(k,xseq,ev1,ci) }else{ history1<-NULL } # This is for small sets that have been duplicated so as to have observable volume if(k==1){ k<-3 } # Specify x-axis length all.xlim <- if (is.null(xlim)) c(min(c(history[, 1],history1[, 1])),max(c(history[, 1],history1[, 1]))) else xlim # Specify y-axis length all.ylim <-if (is.null(ylim)) c(min(c(history[, -1],history1[, -1])),max(c(history[, -1],history1[, -1]))) else ylim # Define y label if (is.null(ylab)) ylab <- "Expected Values: E(Y|X)" ## This is the plot par(bty="n") centralx<-history[,1] centraly<-history[,2] if(is.null(discont)){ gotok <- k }else{ gotok <- sum(xseq < discont) if((gotok<2) | (gotok>(k-2))){ cat("Warning: Discontinuity is located at edge or outside the range of x-axis.\n") gotok<-k discont<-NULL } if(gotok= cutoff[i]) roc2[i,1] <- mean(fitted2[y2==1] >= cutoff[i]) roc1[i,2] <- mean(fitted1[y1==0] < cutoff[i]) roc2[i,2] <- mean(fitted2[y2==0] < cutoff[i]) } if (plot) { plot(0:1, 0:1, type = "n", xaxs = "i", yaxs = "i", main=main, xlab=xlab, ylab=ylab, ...) lines(roc1, lty = lty1, lwd = lwd1, col=col1) lines(roc2, lty = lty2, lwd = lwd2, col=col2) abline(1, -1, lty = "dotted") } else { area1 <- area2 <- array() for (i in 2:length(cutoff)) { area1[i-1] <- (roc1[i,2] - roc1[(i-1),2]) * roc1[i,1] area2[i-1] <- (roc2[i,2] - roc2[(i-1),2]) * roc2[i,1] } return(list(roc1 = roc1, roc2 = roc2, area1 = sum(na.omit(area1)), area2 = sum(na.omit(area2)))) } } #' Plot Autocorrelation Function from Zelig QI object #' @keywords internal zeligACFplot <- function(z, omitzero=FALSE, barcol="black", epsilon=0.1, col=NULL, main="Autocorrelation Function", xlab="Period", ylab="Correlation of Present Shock with Future Outcomes", ylim=NULL, ...){ x <- z$expected.acf ci.x <- z$ci.acf if(omitzero){ x<-x[2:length(x)] ci.x$ci.upper <- ci.x$ci.upper[2:length(ci.x$ci.upper)] ci.x$ci.lower <- ci.x$ci.lower[2:length(ci.x$ci.lower)] } if(is.null(ylim)){ ylim<-c(min( c(ci.x$ci.lower, 0, x) ), max( c(ci.x$ci.upper, 0 , x) )) } if(is.null(col)){ col <- rgb(100,149,237,maxColorValue=255) } bout <- barplot(x, col=col, main=main, xlab=xlab, ylab=ylab, ylim=ylim, ...) n <- length(x) xseq <- as.vector(bout) NAseq <- rep(NA, n) xtemp <- cbind( xseq-epsilon, xseq+epsilon, NAseq) xtemp <- as.vector(t(xtemp)) ytemp <- cbind(ci.x$ci.upper, ci.x$ci.upper, NAseq) ytemp <- as.vector(t(ytemp)) lines(x=xtemp ,y=ytemp, col=barcol) ytemp <- cbind(ci.x$ci.lower, ci.x$ci.lower, NAseq) ytemp <- as.vector(t(ytemp)) lines(x=xtemp ,y=ytemp, col=barcol) xtemp <- cbind( xseq, xseq, NAseq) xtemp <- as.vector(t(xtemp)) ytemp <- cbind(ci.x$ci.upper, ci.x$ci.lower, NAseq) ytemp <- as.vector(t(ytemp)) lines(x=xtemp ,y=ytemp, col=barcol) } Zelig/R/model-gee.R0000755000176200001440000000456512722061503013521 0ustar liggesusers#' Generalized Estimating Equations Model object for inheritance across models in Zelig #' #' @import methods #' @export Zelig-gee #' @exportClass Zelig-gee #' #' @include model-zelig.R zgee <- setRefClass("Zelig-gee", contains = "Zelig") zgee$methods( initialize = function() { callSuper() .self$packageauthors <- "Soren Hojsgaard, Ulrich Halekoh, and Jun Yan" .self$modelauthors <- "Patrick Lam" .self$acceptweights <- TRUE } ) zgee$methods( zelig = function(formula, id, ..., zcor = NULL, corstr = "independence", data, weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call if (corstr == "fixed" && is.null(zcor)) stop("R must be defined") # if id is a valid column-name in data, then we just need to extract the # column and re-order the data.frame and cluster information if (is.character(id) && length(id) == 1 && id %in% colnames(data)) { id <- data[, id] data <- data[order(id), ] id <- sort(id) } .self$model.call$family <- call(.self$family, .self$link) .self$model.call$id <- id .self$model.call$zcor <- zcor .self$model.call$corstr <- corstr callSuper(formula = formula, data = data, ..., weights = weights, by = by, bootstrap = bootstrap) # Prettify summary display without modifying .self$model.call for (i in length(.self$zelig.out$z.out)) { .self$zelig.out$z.out[[i]]$call$id <- .self$zelig.call$id .self$zelig.out$z.out[[i]]$call$zcor <- "zcor" } } ) zgee$methods( param = function(z.out, method="mvn") { so <- summary(z.out) shape <- so$dispersion if(identical(method,"point")){ return( list(simparam = t(as.matrix(coef(z.out))), simalpha = shape[1][1] )) }else if(identical(method,"mvn")){ simalpha <- rnorm(n = .self$num, mean = shape[1][[1]], sd = shape[2][[1]]) simparam.local <- mvrnorm(n = .self$num, mu = coef(z.out), Sigma = so$cov.unscaled) simparam.local <- list(simparam = simparam.local, simalpha = simalpha) return(simparam.local) } } ) # zgee$methods( # show = function() { # for (i in length(.self$zelig.out$z.out)) { # .self$zelig.out$z.out[[i]]$call$id <- "id" # } # callSuper() # } # ) Zelig/R/model-binchoice-survey.R0000644000176200001440000000115512722061503016224 0ustar liggesusers#' Object for Binary Choice outcomes with Survey Weights #' for inheritance across models in Zelig #' #' @import methods #' @export Zelig-binchoice-survey #' @exportClass Zelig-binchoice-survey #' #' @include model-zelig.R #' @include model-binchoice.R #' @include model-survey.R zbinchoicesurvey <- setRefClass("Zelig-binchoice-survey", contains = c("Zelig-survey", "Zelig-binchoice")) zbinchoicesurvey$methods( initialize = function() { callSuper() .self$family <- "binomial" .self$category <- "continuous" # JSON from parent } ) Zelig/R/model-arima.R0000755000176200001440000003235212722061503014045 0ustar liggesusers#' Autoregressive and Moving-Average Models with Integration for Time-Series Data #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-arima.html} #' @import methods #' @export Zelig-arima #' @exportClass Zelig-arima #' #' @include model-zelig.R #' @include model-timeseries.R zarima <- setRefClass("Zelig-arima", contains = "Zelig-timeseries") zarima$methods( initialize = function() { callSuper() .self$name <- "arima" .self$link <- "identity" #.self$family <- "gaussian" .self$fn <- quote(zeligArimaWrapper) #.self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$description <- "Autoregressive Moving-Average Models for Time-Series Data" # JSON .self$outcome <- "continuous" .self$wrapper <- "timeseries" } ) zarima$methods( qi = function(simparam, mm, mm1=NULL){ myorder <- eval(.self$zelig.call$order) mycoef <- coef(.self$zelig.out$z.out[[1]]) sd <- sqrt(.self$zelig.out$z.out[[1]]$sigma2) ## Check mm and mm1. Particularly for issues surrounding intercept. rebuildMM <- function(simparam, x){ xnames <- colnames(x) snames <- colnames(simparam) ## parameter "intercept" can be spelt "(Intercept)"" in model matrix if("(Intercept)" %in% xnames){ flag <- xnames == "(Intercept)" xnames[flag] <- "intercept" colnames(x)[flag]<- "intercept" # this is equivalent to: colnames(x) <- xnames } ## "intercept" can be included in model matrix when not an estimated parameter (for example in models with integration) xnamesflag <- xnames %in% snames x <- x[, xnamesflag, drop=FALSE] return(x) } mm <- rebuildMM(simparam, mm) if(!is.null(mm1)){ mm1 <- rebuildMM(simparam, mm1) } ## Make ACF acf <- simacf(coef=mycoef, order=myorder, params=simparam, alpha=0.05) acf.length <- length(acf$expected.acf) t1 <- 2*acf.length t2 <- 2*acf.length if(.self$bsetx1){ # could also check if mm1 is NULL # zeligARMAbreakforecaster() calls zeligARMAlongrun() internally # return(y.shock = yseries, y.innovation = y.innov, ev.shock = evseries, ev.innovation = ev.innov) yseries <- zeligARMAbreakforecaster(y.init=NULL, x=mm, x1=mm1, simparam=simparam, order=myorder, sd=sd, t1=t1, t2=t2) # maybe check nrow(yseries)=t1 + t2 ? pv <- yseries$y.innovation[t1,] # could use either $innovation or $shock here pv.shortrun <- yseries$y.innovation[t1+1,] # could use either $innovation or $shock here pv.longrun <- yseries$y.innovation[t1+t2,] # must use $innovation here # Remember, these are expectations using the same simparam in each expectation. ev <- yseries$ev.innovation[t1,] ev.shortrun <- yseries$ev.innovation[t1+1,] ev.longrun <- yseries$ev.innovation[t1+t2,] return(list(acf = acf, ev = ev, pv = pv, pv.shortrun=pv.shortrun, pv.longrun=pv.longrun, ev.shortrun=ev.shortrun, ev.longrun=ev.longrun, pvseries.shock=yseries$y.shock, pvseries.innovation=yseries$y.innovation, evseries.shock=yseries$ev.shock, evseries.innovation=yseries$ev.innovation)) }else{ # just call zeligARMAlongrun() yseries <- zeligARMAlongrun(y.init=NULL, x=mm, simparam=simparam, order=myorder, sd=sd) pv <- yseries$y[1,] # zeligARMAlongrun returns the series in reverse order to zeligARMAbreakforecaster # Remember, these are expectations using the same simparam in each expectation: ev <- yseries$ev[1,] return(list(acf = acf, ev = ev, pv = pv)) } } ) zarima$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ mu <- exp(b0 + b1 * x) if(sim){ y <- rnorm(n=length(x), mean=mu) return(y) }else{ return(mu) } } ) #' Estimation wrapper function for arima models, to easily fit with Zelig architecture #' @keywords internal zeligArimaWrapper <- function(formula, order=c(1,0,0), ... , include.mean=TRUE, data){ # Using with(): # myArimaCall <- quote( arima(x=, order =, xreg= ) ) # output <- with(data, myArimaCall ) # Using arima() directly: mf <- model.frame(formula, data) acf3 <- as.character(formula[[3]]) yflag <- names(mf) %in% all.vars(formula[-3]) xflag <- names(mf) %in% all.vars(formula[-2]) myx <- as.matrix(mf[,yflag, drop=FALSE]) # could use get_all_vars() myxreg <- as.matrix(mf[,xflag, drop=FALSE]) if (("1" %in% acf3 ) & ("-" %in% acf3 )){ include.mean <- FALSE } output <- stats::arima(x=myx, order=order, xreg=myxreg, include.mean=include.mean, ...) } #' Construct Autocorrelation Function from Zelig object and simulated parameters #' @keywords internal simacf <- function(coef, order, params, alpha = 0.5){ #order <- eval(.self$zelig.call$order) myar <- myma <- myar.seq <- myma.seq <- NULL if(order[1]>0){ arnames <- paste("ar", 1:order[1], sep="") myar <- coef[arnames] myar.seq <- params[, arnames, drop=FALSE] } if(order[3]>0){ manames <- paste("ma", 1:order[3], sep="") myma <- coef[manames] myma.seq <- params[, manames, drop=FALSE] } mylag.max<-10 # Need to set automatically. n.sims<-nrow(params) expected.acf <- ARMAacf(ar=myar, ma=myma, lag.max=mylag.max) acf.history<-matrix(NA, nrow=n.sims, ncol=length(expected.acf)) # length(expected.acf) = mylag.max +1 for(i in 1:n.sims){ acf.history[i,] <- ARMAacf(ar=myar.seq[i,], ma=myma.seq[i,], lag.max=mylag.max) } # Define functions to compute confidence intervals for each column in a matrix ci.matrix <- function(x, alpha) { pos.hi <- max(round((1-(alpha/2))*nrow(x)), 1) pos.low <-max(round((alpha/2)*nrow(x)), 1) ci.lower <- ci.upper <- rep(NA, ncol(x)) for(i in 1:ncol(x)){ temp<-sort(x[,i]) ci.lower[i]<-temp[pos.low] ci.upper[i]<-temp[pos.hi] } return(list(ci.lower=ci.lower, ci.upper=ci.upper)) } ci.acf <- ci.matrix(x=acf.history, alpha=0.05) return(list(expected.acf=expected.acf, ci.acf=ci.acf, sims.acf=acf.history)) } #' Construct Simulated Next Step in Dynamic Series #' @keywords internal zeligARMAnextstep <- function(yseries=NULL, xseries, wseries=NULL, beta, ar=NULL, i=NULL, ma=NULL, sd){ ## Check inputs # t is obs across time # s is sims # k is covariates # order is (p,q,r) # assume yseries (t x sims), xseries (t x k), wseries (t x s), beta (s x k), ar (s x p), ma (s x r) are matrix # assume sd is scalar ## Could construct these by using known order more deliberatively if(is.vector(yseries)){ #print("warning: yseries is vector") yseries <- matrix(yseries, nrow=1) # Assume if y is a vector, that we are only running one simulation chain of y, so y is (t x 1) } if(is.vector(xseries)){ #print("warning: xseries is vector") xseries <- matrix(xseries, nrow=1) # Assume if x is a vector, that there are no lagged terms, so x is (1 x k) } if(is.vector(wseries)){ #print("warning: wseries is vector") wseries <- matrix(wseries, nrow=1) # Assume if w is a vector, that we are only running one simulation chain of y, so w is (t x 1) } if(is.vector(beta)){ #print("warning: beta is vector") beta <- matrix(beta, ncol=1) } if(is.vector(ar)){ #print("warning: ar is vector") ar <- matrix(ar, ncol=1) } if(is.vector(ma)){ #print("warning: ma is vector") ma <- matrix(ma, ncol=1) } ar.term <- function(yseries, ar, n){ yshort <- yseries[1:ncol(ar), , drop=FALSE] # because we only need the diagonal of a square matrix, we can avoid full matrix multiplication return( rowSums( ar * t(yshort) ) ) # diag[(s x p) . (p x s)] = diag[(s x s)] = (s x 1) } xt.term <- function(xseries, beta){ return( as.vector(beta %*% t(xseries)) ) # (s x k) . t(1 x k) = (s x 1) } ma.term <- function(wseries, ma){ wshort <- wseries[1:ncol(ma), , drop=FALSE] return( rowSums( ma * t(wshort)) ) # diag[(s x r) . (r x s)] = diag[(s x s)] = (s x 1) } n.sims <- ncol(yseries) w <- rnorm(n=n.sims, mean=0, sd=sd) y <- xt.term(xseries,beta) + w # conformable if xt is vector and w vector if(!is.null(ar)){ y <- y + ar.term(yseries,ar) # conformable if y vector and ar vector } if(!is.null(ma)){ y <- y + ma.term(wseries,ma) # conformable if y vector and ma vector } exp.y <- y - w # one interpretation of an EV QI: E(y| l(w), l(y)) return(list(y=y, w=w, exp.y=exp.y)) } #' Calculate the Long Run Exquilibrium for Fixed X #' @keywords internal zeligARMAlongrun <- function(y.init=NULL, x, simparam, order, sd, tol=NULL, burnin=20){ if(is.null(tol)){ tol<-0.01 } ar <- i <- ma <- NULL ## Ensure parameter simulations in same order as model matrix xnames <- colnames(x) beta <- simparam[,xnames] ## Extract AR and MA terms if(order[1]>0){ arnames <- paste("ar", 1:order[1], sep="") ar <- simparam[,arnames] } if(order[3]>0){ manames <- paste("ma", 1:order[3], sep="") ma <- simparam[,manames] } timepast <- max(order[1],order[3]) n.sims <- nrow(simparam) if(is.vector(x)){ x<-matrix(x,nrow=1, ncol=length(x)) } if(is.null(y.init)){ betabar <- t(apply(beta,2, mean)) y.init <- x %*% t(beta) } yseries <- matrix(y.init, nrow=timepast, ncol=n.sims, byrow=TRUE) wseries <- matrix(rnorm(n=timepast*n.sims), nrow=timepast, ncol=n.sims) evseries <- matrix(NA, nrow=timepast, ncol=n.sims) finished <- FALSE count <- 0 while(!finished){ y <- zeligARMAnextstep(yseries=yseries[1:timepast, ], xseries=x, wseries=wseries[1:timepast, ], beta=beta, ar=ar, i=i, ma=ma, sd=sd) yseries <- rbind(y$y, yseries) wseries <- rbind(y$w, wseries) evseries<- rbind(y$exp.y, evseries) #diff <- mean(abs(y.1 - y.0)) # Eventually need to determine some automated stopping rule count <- count+1 finished <- count>burnin #| (diff < tol) } return(list(y.longrun=yseries, w.longrun=wseries, ev.longrun=evseries)) } #' Construct Simulated Series with Internal Discontinuity in X #' @keywords internal zeligARMAbreakforecaster <- function(y.init=NULL, x, x1, simparam, order, sd, t1=5, t2=10){ longrun.out <- zeligARMAlongrun(y.init=y.init, x=x, simparam=simparam, order=order, sd=sd) yseries <- longrun.out$y.longrun wseries <- longrun.out$w.longrun evseries <- longrun.out$ev.longrun ## Ensure parameter simulations in same order as model matrix xnames <- colnames(x) beta <- simparam[,xnames] ## Extract AR and MA terms ar <- i <- ma <- NULL if(order[1]>0){ arnames <- paste("ar", 1:order[1], sep="") ar <- simparam[,arnames] } if(order[3]>0){ manames <- paste("ma", 1:order[3], sep="") ma <- simparam[,manames] } timepast <- max(order[1],order[3]) # How many steps backward are needed in the series -- could we be more precise? # Take a step at covariates x for(i in 2:t1){ nextstep <- zeligARMAnextstep(yseries=yseries[1:timepast, ], xseries=x, wseries=wseries[1:timepast, ], beta=beta, ar=ar, i=i, ma=ma, sd=sd) yseries <- rbind(nextstep$y, yseries) # Could just change arguments so nextstep(nextstep) doesn't need to copy elsewhere. wseries <- rbind(nextstep$w, wseries) evseries <- rbind(nextstep$exp.y, evseries) } # Introduce shock nextstep <- zeligARMAnextstep(yseries=yseries[1:timepast, ], xseries=x1, wseries=wseries[1:timepast, ], beta=beta, ar=ar, i=i, ma=ma, sd=sd) yseries <- rbind(nextstep$y, yseries) # Could just change arguments so nextstep(nextstep) doesn't need to copy elsewhere. wseries <- rbind(nextstep$w, wseries) evseries <- rbind(nextstep$exp.y, evseries) y.innov <- yseries w.innov <- wseries # Note: sequence of stocastic terms are going to depart now ev.innov <- evseries for(i in 2:t2){ # Take further steps at covariates x1 (an introduction of an innovation) nextstep <- zeligARMAnextstep(yseries=y.innov[1:timepast, ], xseries=x1, wseries=w.innov[1:timepast, ], beta=beta, ar=ar, i=i, ma=ma, sd=sd) y.innov <- rbind(nextstep$y, y.innov) # Could just change arguments so nextstep(nextstep) doesn't need to copy elsewhere. w.innov <- rbind(nextstep$w, w.innov) ev.innov <- rbind(nextstep$exp.y, ev.innov) # And take steps returning to old covariates (an introduction of a shock) nextstep <- zeligARMAnextstep(yseries=yseries[1:timepast, ], xseries=x, wseries=wseries[1:timepast, ], beta=beta, ar=ar, i=i, ma=ma, sd=sd) yseries <- rbind(nextstep$y, yseries) # Could just change arguments so nextstep(nextstep) doesn't need to copy elsewhere. wseries <- rbind(nextstep$w, wseries) evseries <- rbind(nextstep$exp.y, evseries) } yseries <- yseries[1:(t1 + t2), ] # Truncate series to last periods, removing burn-in to equilibrium y.innov <- y.innov[1:(t1 + t2), ] evseries <- evseries[1:(t1 + t2), ] ev.innov <- ev.innov[1:(t1 + t2), ] yseries <- yseries[nrow(yseries):1,] # Change y to conventional row ordering by time before returning y.innov <- y.innov[nrow(y.innov):1,] evseries <- evseries[nrow(evseries):1, ] ev.innov <- ev.innov[nrow(ev.innov):1, ] return(list(y.shock = yseries, y.innovation = y.innov, ev.shock = evseries, ev.innovation = ev.innov)) } Zelig/R/model-normal.R0000755000176200001440000000407312722061503014243 0ustar liggesusers#' Normal Regression for Continuous Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-normal.html} #' @import methods #' @export Zelig-normal #' @exportClass Zelig-normal #' #' @include model-zelig.R #' @include model-glm.R znormal <- setRefClass("Zelig-normal", contains = "Zelig-glm") znormal$methods( initialize = function() { callSuper() .self$name <- "normal" .self$family <- "gaussian" .self$link <- "identity" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$authors <- "Kosuke Imai, Gary King, Olivia Lau" .self$year <- 2008 .self$category <- "continuous" .self$description <- "Normal Regression for Continuous Dependent Variables" # JSON .self$outcome <- "continuous" .self$wrapper <- "normal" } ) znormal$methods( param = function(z.out, method="mvn") { degrees.freedom <- z.out$df.residual sig2 <- base::summary(z.out)$dispersion # not to call class summary method simalpha <- sqrt(degrees.freedom * sig2 / rchisq(.self$num, degrees.freedom)) if(identical(method,"mvn")){ simparam.local <- mvrnorm(n = .self$num, mu = coef(z.out), Sigma = vcov(z.out)) simparam.local <- list(simparam = simparam.local, simalpha = simalpha) return(simparam.local) } else if(identical(method,"point")){ return(list(simparam = t(as.matrix(coef(z.out))), simalpha = simalpha)) } } ) znormal$methods( qi = function(simparam, mm) { theta <- matrix(simparam$simparam %*% t(mm), nrow = nrow(simparam$simparam)) ev <- theta pv <- matrix(NA, nrow = nrow(theta), ncol = ncol(theta)) for (j in 1:nrow(ev)) pv[j, ] <- rnorm(ncol(ev), mean = ev[j, ], sd = simparam$simalpha[j]) return(list(ev = ev, pv = pv)) } ) znormal$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ y <- b0 + b1*x + sim * rnorm(n=length(x), sd=alpha) return(y) } ) Zelig/R/model-logit.R0000755000176200001440000000167712722061503014100 0ustar liggesusers#' Logistic Regression for Dichotomous Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-logit.html} #' @import methods #' @export Zelig-logit #' @exportClass Zelig-logit #' #' @include model-zelig.R #' @include model-gee.R #' @include model-gamma.R #' @include model-zelig.R #' @include model-glm.R #' @include model-binchoice.R zlogit <- setRefClass("Zelig-logit", contains = "Zelig-binchoice") zlogit$methods( initialize = function() { callSuper() .self$name <- "logit" .self$link <- "logit" .self$description = "Logistic Regression for Dichotomous Dependent Variables" .self$packageauthors <- "R Core Team" .self$wrapper <- "logit" } ) zlogit$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ mu <- 1/(1 + exp(-b0 - b1 * x)) if(sim){ y <- rbinom(n=length(x), size=1, prob=mu) return(y) }else{ return(mu) } } ) Zelig/R/model-logit-bayes.R0000644000176200001440000000225212722061503015164 0ustar liggesusers#' Bayesian Logit Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-logitbayes.html} #' @import methods #' @export Zelig-logit-bayes #' @exportClass Zelig-logit-bayes #' #' @include model-zelig.R #' @include model-bayes.R #' @include model-logit.R zlogitbayes <- setRefClass("Zelig-logit-bayes", contains = c("Zelig-bayes", "Zelig-logit")) zlogitbayes$methods( initialize = function() { callSuper() .self$name <- "logit-bayes" .self$family <- "binomial" .self$link <- "logit" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$year <- 2013 .self$category <- "dichotomous" .self$authors <- "Ben Goodrich, Ying Lu" .self$description = "Bayesian Logistic Regression for Dichotomous Dependent Variables" .self$fn <- quote(MCMCpack::MCMClogit) # JSON from parent .self$wrapper <- "logit.bayes" } ) zlogitbayes$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ mu <- 1/(1 + exp(-b0 - b1 * x)) if(sim){ y <- rbinom(n=length(x), size=1, prob=mu) return(y) }else{ return(mu) } } ) Zelig/R/model-normal-bayes.R0000644000176200001440000000302712722061503015337 0ustar liggesusers#' Bayesian Normal Linear Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-normalbayes.html} #' @import methods #' @export Zelig-normal-bayes #' @exportClass Zelig-normal-bayes #' #' @include model-zelig.R #' @include model-bayes.R #' @include model-normal.R znormalbayes <- setRefClass("Zelig-normal-bayes", contains = c("Zelig-bayes", "Zelig-normal")) znormalbayes$methods( initialize = function() { callSuper() .self$name <- "normal-bayes" # CC: should't it be lsbayes? .self$year <- 2013 .self$category <- "continuous" .self$authors <- "Ben Goodrich, Ying Lu" .self$description = "Bayesian Normal Linear Regression" .self$fn <- quote(MCMCpack::MCMCregress) # JSON from parent .self$wrapper <- "normal.bayes" } ) znormalbayes$methods( qi = function(simparam, mm) { # Extract simulated parameters and get column names coef <- simparam cols <- colnames(coef) # Place the simulated variances in their own vector sigma2 <- coef[, ncol(coef)] # Remove the "sigma2" (variance) parameter # which should already be placed # in the simulated parameters cols <- cols[ ! "sigma2" == cols ] coef <- coef[, cols] ev <- coef %*% t(mm) pv <- matrix(rnorm(nrow(ev), ev, sqrt(sigma2))) return(list(ev = ev, pv = pv)) } ) znormalbayes$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ y <- b0 + b1*x + sim * rnorm(n=length(x), sd=alpha) return(y) } )Zelig/R/model-survey.R0000755000176200001440000000550012722061503014304 0ustar liggesusers#' Survey models in Zelig for weights for complex sampling designs #' #' @import methods #' @export Zelig-survey #' @exportClass Zelig-survey #' #' @include model-zelig.R zsurvey <- setRefClass("Zelig-survey", contains = "Zelig") zsurvey$methods( initialize = function() { callSuper() .self$fn <- quote(survey::svyglm) .self$packageauthors <- "Thomas Lumley" .self$modelauthors <- "Nicholas Carnes" .self$acceptweights <- TRUE } ) zsurvey$methods( zelig = function(formula, data, ids = ~1, probs = NULL, strata = NULL, fpc = NULL, nest = FALSE, check.strata = !nest, repweights = NULL, type = NULL, combined.weights = FALSE, rho = NULL, bootstrap.average = NULL, scale = NULL, rscales = NULL, fpctype = "fraction", ... , weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) recastString2Formula <- function(a){ if(is.character(a)){ a <- as.formula(paste("~",a)) } return(a) } checkLogical <- function(a, name=""){ if(!("logical" %in% class(a))){ cat(paste("Warning: argument ",name," is a logical and should be set to TRUE for FALSE.", sep="")) return(FALSE) }else{ return(TRUE) } } ## Check arguments: ## Zelig generally accepts formula names of variables present in dataset, ## but survey package looks for formula expressions or data frames, ## so make conversion of any character arguments. ids<-recastString2Formula(ids) probs<-recastString2Formula(probs) weights<-recastString2Formula(weights) strata<-recastString2Formula(strata) fpc<-recastString2Formula(fpc) checkforerror <- checkLogical(nest, "nest") checkforerror <- checkLogical(check.strata, "check.strata") repweights<-recastString2Formula(repweights) # type should be a string checkforerror <- checkLogical(combined.weights, "combined.weights") # rho is shrinkage factor # scale is scaling constant # rscales is scaling constant if(is.null(repweights)){ design <- survey::svydesign(data=data, ids=~1, probs=probs, strata=strata, fpc=fpc, nest=nest, check.strata=check.strata, weights=weights) }else{ design <- survey::svrepdesign(data=data, repweights=repweights, type=type, weights=weights, combined.weights=combined.weights, rho=rho, bootstrap.average=bootstrap.average, scale=scale, rscales=rscales, fpctype=fpctype, fpc=fpc) } .self$model.call <- as.call(list(.self$fn, formula=.self$zelig.call$formula, design=design)) # fn will be set again by super, but initialized here for clarity .self$model.call$family <- call(.self$family, .self$link) callSuper(formula = formula, data = data, ..., by = by, bootstrap = bootstrap) } ) Zelig/R/model-quantile.R0000755000176200001440000000652012722061503014574 0ustar liggesusers#' Quantile Regression for Continuous Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-quantile.html} #' @import methods #' @export Zelig-quantile #' @exportClass Zelig-quantile #' #' @include model-zelig.R zquantile <- setRefClass("Zelig-quantile", contains = "Zelig", field = list(tau = "ANY" )) zquantile$methods( initialize = function() { callSuper() .self$fn <- quote(quantreg::rq) .self$name <- "quantile" .self$authors <- "Alexander D'Amour" .self$packageauthors <- "Roger Koenker" .self$modelauthors <- "Alexander D'Amour" .self$year <- 2008 .self$category <- "continuous" .self$description <- "Quantile Regression for Continuous Dependent Variables" # JSON .self$outcome <- "continuous" .self$wrapper <- "rq" .self$acceptweights <- TRUE } ) zquantile$methods( zelig = function(formula, data, ..., weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- match.call(expand.dots = TRUE) if (!is.null(.self$model.call$tau)) { .self$tau <- eval(.self$model.call$tau) if (length(.self$tau)) { data <- rbind_all(lapply(eval(.self$tau), function(tau) cbind(tau, data))) by <- cbind("tau", by) } } else .self$tau <- 0.5 callSuper(formula = formula, data = data, ..., weights = weights, by = by, bootstrap = bootstrap) rse<-plyr::llply(.self$zelig.out$z.out, (function(x) quantreg::summary.rq(x,se="nid", cov=TRUE)$cov)) .self$test.statistics<- list(robust.se = rse) } ) zquantile$methods( param = function(z.out, method="mvn") { object <- z.out if(identical(method,"mvn")){ rq.sum <- summary.rq(object, cov = TRUE, se = object$se) return(mvrnorm(n = .self$num, mu = object$coef, Sigma = rq.sum$cov)) }else if(identical(method,"point")){ return(t(as.matrix(object$coef))) } } ) zquantile$methods( qi = function(simparam, mm) { object <- mm coeff <- simparam eps <- .Machine$double.eps^(2/3) ev <- coeff %*% t(object) pv <- ev n <- nrow(.self$data) h <- bandwidth.rq(.self$tau, n) # estimate optimal bandwidth for sparsity if (.self$tau + h > 1) stop("tau + h > 1. Sparsity estimate failed. Please specify a tau closer to 0.5") if (.self$tau - h < 0) stop("tau - h < 0. Sparsity estimate failed. Please specify a tau closer to 0.5") beta_high <- rq(.self$formula, data = .self$data, tau = .self$tau + h )$coef beta_low <- rq(.self$formula, data = .self$data, tau = .self$tau - h)$coef F_diff <- mm %*% (beta_high - beta_low) if (any(F_diff <= 0)) warning(paste(sum(F_diff <= 0), "density estimates were non-positive. Predicted values will likely be non-sensical.")) # Includes machine error correction as per summary.rq for nid case f <- pmax(0, (2 * h) / (F_diff - eps)) # Use asymptotic approximation of Q(tau|X,beta) distribution for(ii in 1:nrow(ev)) # Asymptotic distribution as per Koenker 2005 _Quantile Regression_ p. 72 pv[ii, ] <- rnorm(length(ev[ii, ]), mean = ev[ii, ], sqrt((.self$tau * (1 - .self$tau))) / (f * sqrt(n))) return(list(ev = ev, pv = pv)) } ) Zelig/R/model-poisson-survey.R0000755000176200001440000000264012722061503015776 0ustar liggesusers#' Poisson Regression with Survey Weights #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-poissonsurvey.html} #' @import methods #' @export Zelig-poisson-gee #' @exportClass Zelig-poisson-gee #' #' @include model-zelig.R #' @include model-survey.R #' @include model-poisson.R zpoissonsurvey <- setRefClass("Zelig-poisson-survey", contains = c("Zelig-survey", "Zelig-poisson")) zpoissonsurvey$methods( initialize = function() { callSuper() .self$name <- "poisson-survey" .self$family <- "poisson" .self$link <- "log" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$category <- "continuous" .self$description = "Poisson Regression with Survey Weights" # JSON from parent .self$wrapper <- "poisson.survey" } ) zpoissonsurvey$methods( qi = function(simparam, mm) { eta <- simparam %*% t(mm) theta.local <- matrix(.self$linkinv(eta), nrow = nrow(simparam)) ev <- theta.local pv <- matrix(NA, nrow = nrow(theta.local), ncol = ncol(theta.local)) for (i in 1:ncol(theta.local)) pv[, i] <- rpois(nrow(theta.local), lambda = theta.local[, i]) return(list(ev = ev, pv = pv)) } ) zpoissonsurvey$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ lambda <- exp(b0 + b1 * x) if(sim){ y <- rpois(n=length(x), lambda=lambda) return(y) }else{ return(lambda) } } ) Zelig/R/model-probit-gee.R0000755000176200001440000000116712722061503015011 0ustar liggesusers#' Generalized Estimating Equation for Probit Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-probitgee.html} #' @import methods #' @export Zelig-probit-gee #' @exportClass Zelig-probit-gee #' #' @include model-zelig.R #' @include model-binchoice-gee.R zprobitgee <- setRefClass("Zelig-probit-gee", contains = c("Zelig-binchoice-gee")) zprobitgee$methods( initialize = function() { callSuper() .self$name <- "probit-gee" .self$link <- "probit" .self$description <- "General Estimating Equation for Probit Regression" .self$wrapper <- "probit.gee" } )Zelig/R/model-relogit.R0000755000176200001440000001441112722061503014415 0ustar liggesusers#' Rare Events Logistic Regression for Dichotomous Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-relogit.html} #' @import methods #' @export Zelig-relogit #' @exportClass Zelig-relogit #' #' @include model-zelig.R #' @include model-glm.R #' @include model-binchoice.R #' @include model-logit.R zrelogit <- setRefClass("Zelig-relogit", contains = "Zelig", fields = list(family = "character", link = "character", linkinv = "function")) zrelogit$methods( initialize = function() { callSuper() .self$name <- "relogit" .self$description <- "Rare Events Logistic Regression for Dichotomous Dependent Variables" .self$fn <- quote(relogit) .self$family <- "binomial" .self$link <- "logit" .self$wrapper <- "relogit" ref1<-bibentry( bibtype="Article", title = "Logistic Regression in Rare Events Data", author = c( person("Gary", "King"), person("Langche", "Zeng") ), journal = "Political Analysis", volume = 9, number = 2, year = 2001, pages = "137--163") ref2<-bibentry( bibtype="Article", title = "Explaining Rare Events in International Relations", author = c( person("Gary", "King"), person("Langche", "Zeng") ), journal = "International Organization", volume = 55, number = 3, year = 2001, pages = "693--715") .self$refs<-c(.self$refs,ref1,ref2) } ) zrelogit$methods( zelig = function(formula, ..., tau = NULL, bias.correct = NULL, case.control = NULL, data, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call # Catch NULL case.control if (is.null(case.control)) case.control <- "prior" # Catch NULL bias.correct if (is.null(bias.correct)) bias.correct = TRUE # Construct formula. Relogit models have the structure: # cbind(y, 1-y) ~ x1 + x2 + x3 + ... + xN # Where y is the response. form <- update(formula, cbind(., 1 - .) ~ .) .self$model.call$formula <- form .self$model.call$case.control <- case.control .self$model.call$bias.correct <- bias.correct .self$model.call$tau <- tau callSuper(formula = formula, data = data, ..., weights = NULL, by = by, bootstrap = bootstrap) } ) zrelogit$methods( qi = function(simparam, mm) { .self$linkinv <- eval(call(.self$family, .self$link))$linkinv coeff <- simparam eta <- simparam %*% t(mm) eta <- Filter(function (y) !is.na(y), eta) theta <- matrix(.self$linkinv(eta), nrow = nrow(coeff)) ev <- matrix(.self$linkinv(eta), ncol = ncol(theta)) pv <- matrix(nrow = nrow(ev), ncol = ncol(ev)) for (j in 1:ncol(ev)) pv[, j] <- rbinom(length(ev[, j]), 1, prob = ev[, j]) levels(pv) <- c(0, 1) return(list(ev = ev, pv = pv)) } ) #' Estimation function for rare events logit models #' @keywords internal relogit <- function(formula, data = sys.parent(), tau = NULL, bias.correct = TRUE, case.control = "prior", ...){ mf <- match.call() mf$tau <- mf$bias.correct <- mf$case.control <- NULL if (!is.null(tau)) { tau <- unique(tau) if (length(case.control) > 1) stop("You can only choose one option for case control correction.") ck1 <- grep("p", case.control) ck2 <- grep("w", case.control) if (length(ck1) == 0 & length(ck2) == 0) stop("choose either case.control = \"prior\" ", "or case.control = \"weighting\"") if (length(ck2) == 0) weighting <- FALSE else weighting <- TRUE } else weighting <- FALSE if (length(tau) > 2) stop("tau must be a vector of length less than or equal to 2") else if (length(tau) == 2) { mf[[1]] <- relogit res <- list() mf$tau <- min(tau) res$lower.estimate <- eval(as.call(mf), parent.frame()) mf$tau <- max(tau) res$upper.estimate <- eval(as.call(mf), parent.frame()) res$formula <- formula class(res) <- c("Relogit2", "Relogit") return(res) } else { mf[[1]] <- glm mf$family <- binomial(link = "logit") y2 <- model.response(model.frame(mf$formula, data)) if (is.matrix(y2)) y <- y2[,1] else y <- y2 ybar <- mean(y) if (weighting) { w1 <- tau / ybar w0 <- (1-tau) / (1-ybar) wi <- w1 * y + w0 * (1 - y) mf$weights <- wi } res <- eval(as.call(mf), parent.frame()) res$call <- match.call(expand.dots = TRUE) res$tau <- tau X <- model.matrix(res) ## bias correction if (bias.correct){ pihat <- fitted(res) if (is.null(tau)) # w_i = 1 wi <- rep(1, length(y)) else if (weighting) res$weighting <- TRUE else { w1 <- tau/ybar w0 <- (1 - tau) / (1 - ybar) wi <- w1 * y + w0 * (1 - y) res$weighting <- FALSE } W <- pihat * (1 - pihat) * wi ##Qdiag <- diag(X%*%solve(t(X)%*%diag(W)%*%X)%*%t(X)) Qdiag <- lm.influence(lm(y ~ X - 1, weights = W))$hat / W if (is.null(tau)) # w_1=1 since tau=ybar xi <- 0.5 * Qdiag * (2 * pihat - 1) else xi <- 0.5 * Qdiag * ((1 + w0) * pihat - w0) res$coefficients <- res$coefficients - lm(xi ~ X - 1, weights = W)$coefficients res$bias.correct <- TRUE } else res$bias.correct <- FALSE ## prior correction if (!is.null(tau) & !weighting){ if (tau <= 0 || tau >= 1) stop("\ntau needs to be between 0 and 1.\n") res$coefficients["(Intercept)"] <- res$coefficients["(Intercept)"] - log(((1 - tau) / tau) * (ybar / (1 - ybar))) res$prior.correct <- TRUE res$weighting <- FALSE } else res$prior.correct <- FALSE if (is.null(res$weighting)) res$weighting <- FALSE res$linear.predictors <- t(res$coefficients) %*% t(X) res$fitted.values <- 1 / (1 + exp(-res$linear.predictors)) res$zelig <- "Relogit" class(res) <- c("Relogit", "glm") return(res) } } Zelig/R/model-poisson-gee.R0000755000176200001440000000211612722061503015177 0ustar liggesusers#' Generalized Estimating Equation for Poisson Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-poissongee.html} #' @import methods #' @export Zelig-poisson-gee #' @exportClass Zelig-poisson-gee #' #' @include model-zelig.R #' @include model-gee.R #' @include model-poisson.R zpoissongee <- setRefClass("Zelig-poisson-gee", contains = c("Zelig-gee", "Zelig-poisson")) zpoissongee$methods( initialize = function() { callSuper() .self$name <- "poisson-gee" .self$family <- "poisson" .self$link <- "log" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$year <- 2011 .self$category <- "continuous" .self$authors <- "Patrick Lam" .self$description = "General Estimating Equation for Poisson Regression" .self$fn <- quote(geepack::geeglm) # JSON from parent .self$wrapper <- "poisson.gee" } ) zpoissongee$methods( param = function(z.out, method="mvn") { simparam.local <- callSuper(z.out, method=method) return(simparam.local$simparam) # no ancillary parameter } ) Zelig/R/model-normal-gee.R0000755000176200001440000000161712722061503015002 0ustar liggesusers#' Generalized Estimating Equation for Normal Regression #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-normalgee.html} #' @import methods #' @export Zelig-normal-gee #' @exportClass Zelig-normal-gee #' #' @include model-zelig.R #' @include model-gee.R #' @include model-normal.R znormalgee <- setRefClass("Zelig-normal-gee", contains = c("Zelig-gee", "Zelig-normal")) znormalgee$methods( initialize = function() { callSuper() .self$name <- "normal-gee" .self$family <- "gaussian" .self$link <- "identity" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$year <- 2011 .self$category <- "continuous" .self$authors <- "Patrick Lam" .self$description = "General Estimating Equation for Normal Regression" .self$fn <- quote(geepack::geeglm) # JSON from parent .self$wrapper <- "normal.gee" } ) Zelig/R/model-bayes.R0000644000176200001440000001035012722061503014046 0ustar liggesusers#' Bayes Model object for inheritance across models in Zelig #' #' @import methods #' @export Zelig-bayes #' @exportClass Zelig-bayes #' #' @include model-zelig.R zbayes <- setRefClass("Zelig-bayes", contains = "Zelig") zbayes$methods( initialize = function() { callSuper() .self$packageauthors <- "Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park" .self$modelauthors <- "Ben Goodrich, and Ying Lu" } ) zbayes$methods( zelig = function(formula, burnin = 1000, mcmc = 10000, verbose = 0, ..., data, by = NULL, bootstrap = FALSE) { if(!identical(bootstrap,FALSE)){ stop("Error: The bootstrap is not available for Markov chain Monte Carlo (MCMC) models.") } .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call if (missing(verbose)) verbose <- round((mcmc + burnin) / 10) # .self$model.call$family <- call(.self$family, .self$link) .self$model.call$verbose <- verbose .self$num <- mcmc # CC: check callSuper(formula = formula, data = data, ..., by = by, bootstrap = FALSE) } ) zbayes$methods( param = function(z.out) { return(z.out) } ) zbayes$methods( getcoef = function() { "Get estimated model coefficients" return(.self$zelig.out$z.out[[1]]) } ) zbayes$methods( geweke.diag = function() { diag <- lapply(.self$zelig.out$z.out, coda::geweke.diag) # Collapse if only one list element for prettier printing if(length(diag)==1){ diag<-diag[[1]] } if(!citation("coda") %in% .self$refs){ .self$refs<-c(.self$refs,citation("coda")) } ref1<-bibentry( bibtype="InCollection", title = "Evaluating the accuracy of sampling-based approaches to calculating posterior moments.", booktitle = "Bayesian Statistics 4", author = person("John", "Geweke"), year = 1992, publisher = "Clarendon Press", address = "Oxford, UK", editor = c(person("JM", "Bernado"), person("JO", "Berger"), person("AP", "Dawid"), person("AFM", "Smith")) ) .self$refs<-c(.self$refs,ref1) return(diag) } ) zbayes$methods( heidel.diag = function() { diag <- lapply(.self$zelig.out$z.out, coda::heidel.diag) # Collapse if only one list element for prettier printing if(length(diag)==1){ diag<-diag[[1]] } if(!citation("coda") %in% .self$refs){ .self$refs<-c(.self$refs,citation("coda")) } ref1<-bibentry( bibtype="Article", title = "Simulation run length control in the presence of an initial transient.", author = c(person("P", "Heidelberger"), person("PD", "Welch")), journal = "Operations Research", volume = 31, year = 1983, pages = "1109--44") .self$refs<-c(.self$refs,ref1) return(diag) } ) zbayes$methods( raftery.diag = function() { diag <- lapply(.self$zelig.out$z.out, coda::raftery.diag) # Collapse if only one list element for prettier printing if(length(diag)==1){ diag<-diag[[1]] } if(!citation("coda") %in% .self$refs){ .self$refs<-c(.self$refs,citation("coda")) } ref1<-bibentry( bibtype="Article", title = "One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo.", author = c(person("Adrian E", "Raftery"), person("Steven M", "Lewis")), journal = "Statistical Science", volume = 31, year = 1992, pages = "1109--44") ref2<-bibentry( bibtype="InCollection", title = "The number of iterations, convergence diagnostics and generic Metropolis algorithms.", booktitle = "Practical Markov Chain Monte Carlo", author = c(person("Adrian E", "Raftery"), person("Steven M", "Lewis")), year = 1995, publisher = "Chapman and Hall", address = "London, UK", editor = c(person("WR", "Gilks"), person("DJ", "Spiegelhalter"), person("S", "Richardson")) ) .self$refs<-c(.self$refs,ref1,ref2) return(diag) } ) Zelig/R/model-negbinom.R0000755000176200001440000000476012722061503014554 0ustar liggesusers#' Negative Binomial Regression for Event Count Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-negbin.html} #' @import methods #' @export Zelig-negbin #' @exportClass Zelig-negbin #' #' @include model-zelig.R znegbin <- setRefClass("Zelig-negbin", contains = "Zelig", field = list(simalpha = "list" # ancillary parameters )) znegbin$methods( initialize = function() { callSuper() .self$fn <- quote(MASS::glm.nb) .self$name <- "negbin" .self$authors <- "Kosuke Imai, Gary King, Olivia Lau" .self$packageauthors <- "William N. Venables, and Brian D. Ripley" .self$year <- 2008 .self$category <- "count" .self$description <- "Negative Binomial Regression for Event Count Dependent Variables" # JSON .self$outcome <- "discrete" .self$wrapper <- "negbin" .self$acceptweights <- TRUE } ) znegbin$methods( zelig = function(formula, data, ..., weights=NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call callSuper(formula=formula, data=data, ..., weights=weights, by = by, bootstrap = bootstrap) rse<-plyr::llply(.self$zelig.out$z.out, (function(x) vcovHC(x,type="HC0"))) .self$test.statistics<- list(robust.se = rse) } ) znegbin$methods( param = function(z.out, method="mvn") { simalpha.local <- z.out$theta if(identical(method,"mvn")){ simparam.local <- mvrnorm(n = .self$num, mu = coef(z.out), Sigma = vcov(z.out)) simparam.local <- list(simparam = simparam.local, simalpha = simalpha.local) return(simparam.local) } else if(identical(method,"point")){ return(list(simparam = t(as.matrix(coef(z.out))), simalpha = simalpha.local)) } } ) znegbin$methods( qi = function(simparam, mm) { coeff <- simparam$simparam alpha <- simparam$simalpha inverse <- family(.self$zelig.out$z.out[[1]])$linkinv eta <- coeff %*% t(mm) theta <- matrix(inverse(eta), nrow=nrow(coeff)) ev <- theta pv <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta)) # for (i in 1:ncol(ev)) pv[, i] <- rnegbin(nrow(ev), mu = ev[i, ], theta = alpha[i]) return(list(ev = ev, pv = pv)) } ) znegbin$methods( mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){ mu <- exp(b0 + b1 * x) if(sim){ y <- rnbinom(n=length(x), 1, mu=mu) return(y) }else{ return(mu) } } ) Zelig/R/model-ls.R0000755000176200001440000001262212722061503013370 0ustar liggesusers#' Least Squares Regression for Continuous Dependent Variables #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-ls.html} #' @import methods #' @export Zelig-ls #' @exportClass Zelig-ls #' #' @include model-zelig.R zls <- setRefClass("Zelig-ls", contains = "Zelig") zls$methods( initialize = function() { callSuper() .self$name <- "ls" .self$year <- 2007 .self$category <- "continuous" .self$description <- "Least Squares Regression for Continuous Dependent Variables" .self$packageauthors <- "R Core Team" .self$fn <- quote(stats::lm) # JSON .self$outcome <- "continous" .self$wrapper <- "ls" .self$acceptweights <- TRUE } ) zls$methods( zelig = function(formula, data, ..., weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call callSuper(formula = formula, data = data, ..., weights = weights, by = by, bootstrap = bootstrap) # Automated Background Test Statistics and Criteria rse<-plyr::llply(.self$zelig.out$z.out, (function(x) vcovHC(x,type="HC0"))) rse.se <- sqrt(diag(rse[[1]])) # Needs to work with "by" argument est.se <- sqrt(diag(.self$getvcov()[[1]])) quickGim <- any( est.se > 1.5*rse.se | rse.se > 1.5*est.se ) .self$test.statistics<- list(robust.se = rse, gim.criteria = quickGim) } ) zls$methods( param = function(z.out, method="mvn") { if(identical(method,"mvn")){ return(list(simparam=mvrnorm(.self$num, coef(z.out), vcov(z.out)), simalpha=rep( summary(z.out)$sigma, .self$num) ) ) } else if(identical(method,"point")){ return(list(simparam=t(as.matrix(coef(z.out))), simalpha=summary(z.out)$sigma)) } else { stop("param called with method argument of undefined type.") } } ) zls$methods( qi = function(simparam, mm) { ev <- simparam$simparam %*% t(mm) pv <- as.matrix(rnorm(n=length(ev), mean=ev, sd=simparam$simalpha), nrow=length(ev), ncol=1) return(list(ev = ev, pv = pv)) } ) zls$methods( gim = function(B=50, B2=50) { ll.normal.bsIM <- function(par,y,X,sigma){ beta <- par[1:length(X)] sigma2 <- sigma -1/2 * (sum(log(sigma2) + (y -(X%*%beta))^2/sigma2)) } getVb<-function(Dboot){ Dbar <- matrix(apply(Dboot,2,mean),nrow=B, ncol=length(Dhat), byrow=TRUE) Diff <- Dboot - Dbar Vb <- (t(Diff) %*% Diff) / (nrow(Dboot)-1) return(Vb) } getSigma<-function(lm.obj){ return(sum(lm.obj$residuals^2)/(nrow(model.matrix(lm.obj))-ncol(model.matrix(lm.obj)))) } D.est<-function(formula,data){ lm1 <- lm(formula,data, y=TRUE) mm <- model.matrix(lm1) y <- lm1$y sigma <- getSigma(lm1) grad <- apply(cbind(y,mm),1,function(x) numericGradient(ll.normal.bsIM, lm1$coefficients, y=x[1], X=x[2:length(x)], sigma=sigma)) meat <- grad%*%t(grad) bread <- -solve(vcov(lm1)) Dhat <- nrow(mm)^(-1/2)* as.vector(diag(meat + bread)) return(Dhat) } D.est.vb<-function(formula,data){ lm1 <- lm(formula,data, y=TRUE) mm <- model.matrix(lm1) y <- lm1$y sigma <- getSigma(lm1) grad <- apply(cbind(y,mm),1,function(x) numericGradient(ll.normal.bsIM, lm1$coefficients, y=x[1], X=x[2:length(x)], sigma=sigma)) meat <- grad%*%t(grad) bread <- -solve(vcov(lm1)) Dhat <- nrow(mm)^(-1/2)* as.vector(diag(meat + bread)) muB<-lm1$fitted.values DB <- matrix(NA, nrow=B2, ncol=length(Dhat)) for(j in 1:B2){ yB2 <- rnorm(nrow(data), muB, sqrt(sigma)) lm1B2 <- lm(yB2 ~ mm-1) sigmaB2 <- getSigma(lm1B2) grad <- apply(cbind(yB2,model.matrix(lm1B2)),1,function(x) numericGradient(ll.normal.bsIM, lm1B2$coefficients, y=x[1], X=x[2:length(x)], sigma=sigmaB2)) meat <- grad%*%t(grad) bread <- -solve(vcov(lm1B2)) DB[j,] <- nrow(mm)^(-1/2)*diag((meat + bread)) } Vb <- getVb(DB) T<- t(Dhat)%*%solve(Vb)%*%Dhat return(list(Dhat=Dhat,T=T)) } Dhat <- D.est(formula=.self$formula, data=.self$data) lm1 <- lm(formula=.self$formula, data=.self$data) mu <- lm1$fitted.values sigma <- getSigma(lm1) n <- length(mu) yname <- all.vars(.self$formula[[2]]) Dboot <- matrix(NA, nrow=B, ncol=length(Dhat)) bootdata<-data for(i in 1:B){ yB <- rnorm(n, mu, sqrt(sigma)) bootdata[yname] <- yB result <- D.est.vb(formula=.self$formula, data=bootdata) Dboot[i,] <- result$Dhat T[i] <- result$T } Vb <- getVb(Dboot) omega <- t(Dhat) %*% solve(Vb) %*% Dhat pb = (B+1-sum(T< as.numeric(omega)))/(B+1) .self$test.statistics$gim <- list(stat=omega, pval=pb) # When method used, add to references gimreference <- bibentry( bibtype="Article", title = "How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It", author = c( person("Gary", "King"), person("Margret E.", "Roberts") ), journal = "Political Analysis", year = 2014, pages = "1-21", url = "http://j.mp/InK5jU") .self$refs <- c(.self$refs, gimreference) } ) zls$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ y <- b0 + b1*x + sim * rnorm(n=length(x), sd=alpha) return(y) } ) Zelig/R/model-factor-bayes.R0000644000176200001440000001116112722061503015323 0ustar liggesusers#' Bayesian Factor Analysis #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-factorbayes.html} #' @import methods #' @export Zelig-factor-bayes #' @exportClass Zelig-factor-bayes #' #' @include model-zelig.R zfactorbayes <- setRefClass("Zelig-factor-bayes", contains = c("Zelig")) zfactorbayes$methods( initialize = function() { callSuper() .self$name <- "factor-bayes" .self$year <- 2013 .self$authors <- "Ben Goodrich, Ying Lu" .self$packageauthors <- "Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park" .self$description = "Bayesian Factor Analysis" .self$fn <- quote(MCMCpack::MCMCfactanal) # JSON from parent .self$wrapper <- "factor.bayes" } ) zfactorbayes$methods( zelig = function(formula, factors = 2, burnin = 1000, mcmc = 20000, verbose = 0, ..., data, by = NULL, bootstrap = FALSE) { if(!identical(bootstrap,FALSE)){ stop("Error: The bootstrap is not available for Markov chain Monte Carlo (MCMC) models.") } .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call if (missing(verbose)) verbose <- round((mcmc + burnin) / 10) if (factors < 2) stop("Number of factors needs to be at least 2") .self$model.call$verbose <- verbose .self$model.call$x <- formula .self$model.call$factors <- factors callSuper(formula = formula, data = data,..., by = by, bootstrap = FALSE) } ) zfactorbayes$methods( qi = function() { return(NULL) } ) # The following diagnostics are also in Zelig-bayes, which unfortunately Zelig-factor-bayes does not currently inherit. zfactorbayes$methods( geweke.diag = function() { diag <- lapply(.self$zelig.out$z.out, coda::geweke.diag) # Collapse if only one list element for prettier printing if(length(diag)==1){ diag<-diag[[1]] } if(!citation("coda") %in% .self$refs){ .self$refs<-c(.self$refs,citation("coda")) } ref1<-bibentry( bibtype="InCollection", title = "Evaluating the accuracy of sampling-based approaches to calculating posterior moments.", booktitle = "Bayesian Statistics 4", author = person("John", "Geweke"), year = 1992, publisher = "Clarendon Press", address = "Oxford, UK", editor = c(person("JM", "Bernado"), person("JO", "Berger"), person("AP", "Dawid"), person("AFM", "Smith")) ) .self$refs<-c(.self$refs,ref1) return(diag) } ) zfactorbayes$methods( heidel.diag = function() { diag <- lapply(.self$zelig.out$z.out, coda::heidel.diag) # Collapse if only one list element for prettier printing if(length(diag)==1){ diag<-diag[[1]] } if(!citation("coda") %in% .self$refs){ .self$refs<-c(.self$refs,citation("coda")) } ref1<-bibentry( bibtype="Article", title = "Simulation run length control in the presence of an initial transient.", author = c(person("P", "Heidelberger"), person("PD", "Welch")), journal = "Operations Research", volume = 31, year = 1983, pages = "1109--44") .self$refs<-c(.self$refs,ref1) return(diag) } ) zfactorbayes$methods( raftery.diag = function() { diag <- lapply(.self$zelig.out$z.out, coda::raftery.diag) # Collapse if only one list element for prettier printing if(length(diag)==1){ diag<-diag[[1]] } if(!citation("coda") %in% .self$refs){ .self$refs<-c(.self$refs,citation("coda")) } ref1<-bibentry( bibtype="Article", title = "One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo.", author = c(person("Adrian E", "Raftery"), person("Steven M", "Lewis")), journal = "Statistical Science", volume = 31, year = 1992, pages = "1109--44") ref2<-bibentry( bibtype="InCollection", title = "The number of iterations, convergence diagnostics and generic Metropolis algorithms.", booktitle = "Practical Markov Chain Monte Carlo", author = c(person("Adrian E", "Raftery"), person("Steven M", "Lewis")), year = 1995, publisher = "Chapman and Hall", address = "London, UK", editor = c(person("WR", "Gilks"), person("DJ", "Spiegelhalter"), person("S", "Richardson")) ) .self$refs<-c(.self$refs,ref1,ref2) return(diag) } ) Zelig/R/model-binchoice-gee.R0000644000176200001440000000160412722061503015426 0ustar liggesusers#' Object for Binary Choice outcomes in Generalized Estimating Equations #' for inheritance across models in Zelig #' #' @import methods #' @export Zelig-binchoice-gee #' @exportClass Zelig-binchoice-gee #' #' @include model-zelig.R #' @include model-binchoice.R #' @include model-gee.R zbinchoicegee <- setRefClass("Zelig-binchoice-gee", contains = c("Zelig-gee", "Zelig-binchoice")) zbinchoicegee$methods( initialize = function() { callSuper() .self$family <- "binomial" .self$year <- 2011 .self$category <- "continuous" .self$authors <- "Patrick Lam" .self$fn <- quote(geepack::geeglm) # JSON from parent } ) zbinchoicegee$methods( param = function(z.out, method="mvn") { simparam.local <- callSuper(z.out, method=method) return(simparam.local$simparam) # no ancillary parameter } ) Zelig/R/model-gamma-survey.R0000755000176200001440000000311212722061503015361 0ustar liggesusers#' Gamma Regression with Survey Weights #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-gammasurvey.html} #' @import methods #' @export Zelig-gamma #' @exportClass Zelig-gamma #' #' @include model-zelig.R #' @include model-survey.R #' @include model-gamma.R zgammasurvey <- setRefClass("Zelig-gamma-survey", contains = c("Zelig-survey", "Zelig-gamma")) zgammasurvey$methods( initialize = function() { callSuper() .self$name <- "gamma-survey" .self$family <- "Gamma" .self$link <- "inverse" .self$linkinv <- eval(call(.self$family, .self$link))$linkinv .self$category <- "continuous" .self$description = "Gamma Regression with Survey Weights" # JSON from parent .self$wrapper <- "gamma.survey" } ) zgammasurvey$methods( param = function(z.out, method="mvn") { shape <- MASS::gamma.shape(z.out) if(identical(method,"mvn")){ simalpha <- rnorm(n = .self$num, mean = shape$alpha, sd = shape$SE) simparam.local <- mvrnorm(n = .self$num, mu = coef(z.out), Sigma = vcov(z.out)) simparam.local <- list(simparam = simparam.local, simalpha = simalpha) return(simparam.local) } else if(identical(method,"point")){ return(list(simparam = t(as.matrix(coef(z.out))), simalpha = shape$alpha)) } } ) zgammasurvey$methods( mcfun = function(x, b0=0, b1=1, alpha=1, sim=TRUE){ lambda <- 1/(b0 + b1 * x) if(sim){ y <- rgamma(n=length(x), shape=lambda, scale = alpha) return(y) }else{ return(alpha * lambda) } } )Zelig/R/model-glm.R0000755000176200001440000000225512722061503013532 0ustar liggesusers#' Generalized Linear Model object for inheritance across models in Zelig #' #' @import methods #' @export Zelig-glm #' @exportClass Zelig-glm #' #' @include model-zelig.R zglm <- setRefClass("Zelig-glm", contains = "Zelig", fields = list(family = "character", link = "character", linkinv = "function")) zglm$methods( initialize = function() { callSuper() .self$fn <- quote(stats::glm) .self$packageauthors <- "R Core Team" .self$acceptweights <- FALSE # "Why glm refers to the number of trials as weight is a trick question to the developers' conscience." } ) zglm$methods( zelig = function(formula, data, ..., weights = NULL, by = NULL, bootstrap = FALSE) { .self$zelig.call <- match.call(expand.dots = TRUE) .self$model.call <- .self$zelig.call .self$model.call$family <- call(.self$family, .self$link) callSuper(formula = formula, data = data, ..., weights = weights, by = by, bootstrap = bootstrap) rse <- plyr::llply(.self$zelig.out$z.out, (function(x) vcovHC(x, type = "HC0"))) .self$test.statistics <- list(robust.se = rse) } ) Zelig/R/model-ma.R0000755000176200001440000000122312722061503013342 0ustar liggesusers#' Time-Series Model with Moving Average #' #' Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-ma.html} #' @import methods #' @export Zelig-ma #' @exportClass Zelig-ma #' #' @include model-zelig.R #' @include model-timeseries.R zma <- setRefClass("Zelig-ma", contains = "Zelig-timeseries") zma$methods( initialize = function() { callSuper() .self$name <- "ma" .self$link <- "identity" .self$fn <- quote(zeligArimaWrapper) .self$description = "Time-Series Model with Moving Average" .self$packageauthors <- "R Core Team" .self$outcome <- "continuous" .self$wrapper <- "timeseries" } ) Zelig/MD50000644000176200001440000003176413006420075011644 0ustar liggesusers2eec4c44bb7644239e0318f840972f97 *DESCRIPTION 5342f221cbe89af530fac3f4edd2771c *NAMESPACE 051a11b0783a6a6a0f4c57abd50259a0 *R/create-json.R c7d9a1d3b894bb729a56108024f4d594 *R/model-ar.R 046159268b1fc4b3a6e6e8ddab697494 *R/model-arima.R 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*tests/testthat/test-ls.R 9a5ac5e2f69f4d34a7daf5e5e8d88a16 *tests/testthat/test-negbin.R 9255d9ae3f8d7056e090cc97409a0653 *tests/testthat/test-poisson.R b19ab8b7e3f1e2090c19996a006f7065 *tests/testthat/test-probit.R Zelig/DESCRIPTION0000644000176200001440000000513413006420075013032 0ustar liggesusersPackage: Zelig Maintainer: James Honaker License: GPL (>= 2) Title: Everyone's Statistical Software Author: Christine Choirat, James Honaker, Kosuke Imai, Gary King, Olivia Lau Description: A framework that brings together an abundance of common statistical models found across packages into a unified interface, and provides a common architecture for estimation and interpretation, as well as bridging functions to absorb increasingly more models into the collective library. Zelig allows each individual package, for each statistical model, to be accessed by a common uniformly structured call and set of arguments. Moreover, Zelig automates all the surrounding building blocks of a statistical work-flow --procedures and algorithms that may be essential to one user's application but which the original package developer did not use in their own research and might not themselves support. These include bootstrapping, jackknifing, and re-weighting of data. In particular, Zelig automatically generates predicted and simulated quantities of interest (such as relative risk ratios, average treatment effects, first differences and predicted and expected values) to interpret and visualize complex models. Version: 5.0-13 Date: 2016-11-01 Imports: sandwich, methods, MASS, survival, VGAM, jsonlite, AER, plyr, dplyr (>= 0.3.0.2), quantreg, geepack, MCMCpack, coda, Amelia, MatchIt, maxLik, survey Suggests: testthat, knitr Collate: 'utils.R' 'model-zelig.R' 'model-weibull.R' 'model-tobit.R' 'model-bayes.R' 'model-tobit-bayes.R' 'model-glm.R' 'model-gee.R' 'model-gamma.R' 'model-binchoice.R' 'model-logit.R' 'model-relogit.R' 'model-quantile.R' 'model-probit.R' 'model-binchoice-gee.R' 'model-probit-gee.R' 'model-probit-bayes.R' 'model-poisson.R' 'model-poisson-gee.R' 'model-poisson-bayes.R' 'model-oprobit-bayes.R' 'model-normal.R' 'model-normal-gee.R' 'model-normal-bayes.R' 'model-negbinom.R' 'model-mlogit-bayes.R' 'model-ls.R' 'model-lognorm.R' 'model-logit-gee.R' 'model-logit-bayes.R' 'model-gamma-gee.R' 'model-factor-bayes.R' 'model-exp.R' 'create-json.R' 'model-survey.R' 'model-binchoice-survey.R' 'model-gamma-survey.R' 'model-logit-survey.R' 'model-normal-survey.R' 'model-poisson-survey.R' 'model-probit-survey.R' 'model-timeseries.R' 'model-arima.R' 'model-ar.R' 'model-ma.R' 'plots.R' 'wrappers.R' NeedsCompilation: no Packaged: 2016-11-01 23:23:33 UTC; tercer Repository: CRAN Date/Publication: 2016-11-02 18:19:57 Zelig/man/0000755000176200001440000000000013006221765012101 5ustar liggesusersZelig/man/sna.ex.Rd0000644000176200001440000000063112722061504013561 0ustar liggesusers\name{sna.ex} \alias{sna.ex} \title{Simulated Example of Social Network Data} \description{ This data set contains five sociomatrices of simulated data social network data.} \usage{data(sna.ex)} \format{ Each variable in the dataset is a 25 by 25 matrix of simulated social network data. The matrices are labeled "Var1", "Var2", "Var3", "Var4", and "Var5". } \source{fictitious} \keyword{datasets} Zelig/man/Zelig-poisson-class.Rd0000644000176200001440000000052012722061503016226 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-poisson.R \docType{class} \name{Zelig-poisson-class} \alias{Zelig-poisson-class} \alias{zpoisson} \title{Poisson Regression for Event Count Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-poisson.html} } Zelig/man/Zelig-glm-class.Rd0000644000176200001440000000101312722061503015311 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-glm.R \docType{class} \name{Zelig-glm-class} \alias{Zelig-glm-class} \alias{zglm} \title{Generalized Linear Model object for inheritance across models in Zelig} \description{ Generalized Linear Model object for inheritance across models in Zelig } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/seatshare.Rd0000644000176200001440000000147712722061504014355 0ustar liggesusers\name{seatshare} \alias{seatshare} \title{Left Party Seat Share in 11 OECD Countries} \description{ This data set contains time-series data of the seat shares in the lower legislative house of left leaning parties over time, as well as the level of unemployment. Data follows the style used in Hibbs (1977).} \usage{data(seatshare)} \format{A table containing N variables ("country","year","unemp","leftseat") and 384 observations split across 11 countries.} \source{OECD data and Mackie and Rose (1991), extended to further years.} \references{ Douglas A. Hibbs. (1977). \emph{Political Parties and Macroeconomic Policy}. American Political Science Review 71(4):1467-1487. Thomas T. Mackie and Richard Rose. (1991). \emph{The International Almanac of Electoral History} Macmillan: London. } \keyword{datasets} Zelig/man/zeligPlyrMutate.Rd0000644000176200001440000000053312722061504015527 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{zeligPlyrMutate} \alias{zeligPlyrMutate} \title{Zelig Copy of plyr::mutate to avoid namespace conflict with dplyr} \usage{ zeligPlyrMutate(.data, ...) } \description{ Zelig Copy of plyr::mutate to avoid namespace conflict with dplyr } \keyword{internal} Zelig/man/homerun.Rd0000644000176200001440000000241413006172377014052 0ustar liggesusers\name{homerun} \alias{homerun} \docType{data} \title{Sample Data on Home Runs Hit By Mark McGwire and Sammy Sosa in 1998.} \description{ Game-by-game information for the 1998 season for Mark McGwire and Sammy Sosa. Data are a subset of the dataset provided in Simonoff (1998). } \usage{data(homerun)} \format{ A data frame containing 5 variables ("gameno", "month", "homeruns", "playerstatus", "player") and 326 observations. \describe{ \item{\code{gameno}}{an integer variable denoting the game number} \item{\code{month}}{a factor variable taking with levels "March" through "September" denoting the month of the game} \item{\code{homeruns}}{an integer vector denoting the number of homeruns hit in that game for that player} \item{\code{playerstatus}}{an integer vector equal to "0" if the player played in the game, and "1" if they did not.} \item{\code{player}}{an integer vector equal to "0" (McGwire) or "1" (Sosa)} } } \source{\url{https://ww2.amstat.org/publications/jse/v6n3/datasets.simonoff.html}} \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} Zelig/man/free2.Rd0000644000176200001440000000776512722061504013407 0ustar liggesusers\name{free2} \alias{free2} \title{Freedom of Speech Data} \usage{data(free2)} \description{ Selection of individual-level survey data for freedom of speech. } \details{ A table with 150 observations and 12 variables. \itemize{ \item{sex}{1 for men and 0 for women} \item{age}{Age of respondent in years} \item{educ}{Levels of education, coded as a numeric variable with \itemize{ \item{1}{No formal education} \item{2}{Less than primary school education} \item{3}{Completed primary school} \item{4}{Completed secondary school} \item{5}{Completed high school} \item{6}{Completed college} \item{7}{Completed post-graduate degree} } } \item{country}{Character strings consisting of "Oceana", "Eurasia", and "Eastasia", after Orwell's \emph{1984}.} \item{y}{Self assessment (see below).} \item{v1-v6}{Response to vignettes (see below).} } Survey respondents were asked in almost the same language for a self-assessment and for an assessment of several hypothetical persons described by written vignettes. The self assessment (\code{self}, in the data set), "How free do you think [name/you] [is/are] to express [him-her/your]self without fear of government reprisal?" was first asked of the survey respondent with respect to him or herself, and then after each of vignette. The possible response categories are: \itemize{ \item{1}{Completely free} \item{2}{Very free} \item{3}{Moderately free} \item{4}{Slightly free} \item{5}{Not free at all} } The vignettes, ordered from most free to least free, are: \itemize{ \item{vign1}{[Kay] does not like many of the government's policies. She frequently publishes her opinion in newspapers, criticizing decisions by officials and calling for change. She sees little reason these actions could lead to government reprisal.} \item{vign2}{[Michael] disagrees with many of the government's policies. Though he knows criticism is frowned upon, he doesn't believe the government would punish someone for expressing critical views. He makes his opinion known on most issues without regard to who is listening.} \item{vign3}{[Bob] has political views at odds with the government. He has heard of people occasionally being arrested for speaking out against the government, and government leaders sometimes make political speeches condemning those who criticize. He sometimes writes letters to newspapers about politics, but he is careful not to use his real name.} \item{vign4}{[Connie] does not like the government's stance on many issues. She has a friend who was arrested for being too openly critical of governmental leaders, and so she avoids voicing her opinions in public places.} \item{vign5}{[Vito] disagrees with many of the government's policies, and is very careful about whom he says this to, reserving his real opinions for family and close friends only. He knows several men who have been taken away by government officials for saying negative things in public.} \item{vign6}{[Sonny] lives in fear of being harassed for his political views. Everyone he knows who has spoken out against the government has been arrested or taken away. He never says a word about anything the government does, not even when he is at home alone with his family. } } } \references{ \emph{WHO's World Health Survey} by Lydia Bendib, Somnath Chatterji, Alena Petrakova, Ritu Sadana, Joshua A. Salomon, Margie Schneider, Bedirhan Ustun, Maria Villanueva Jonathan Wand, Gary King and Olivia Lau. (2007) ``Anchors: Software for Anchoring Vignettes''. \emph{Journal of Statistical Software}. Forthcoming. copy at http://wand.stanford.edu/research/anchors-jss.pdf Gary King and Jonathan Wand. "Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes," Political Analysis, 15, 1 (Winter, 2007): Pp. 46-66, copy at http://gking.harvard.edu/files/abs/c-abs.shtml. } \keyword{datasets} Zelig/man/Median.Rd0000644000176200001440000000065612722061503013570 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{Median} \alias{Median} \title{Compute the Statistical Median of a Vector} \usage{ Median(x, na.rm = NULL) } \arguments{ \item{x}{a vector of numeric or ordered values} \item{na.rm}{ignored} } \value{ the median of the vector } \description{ Compute the Statistical Median of a Vector } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/simulations.plot.Rd0000644000176200001440000000241212722061504015710 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{simulations.plot} \alias{simulations.plot} \title{Plot Quantities of Interest in a Zelig-fashion} \usage{ simulations.plot(y, y1=NULL, xlab="", ylab="", main="", col=NULL, line.col=NULL, axisnames=TRUE) } \arguments{ \item{y}{A matrix or vector of simulated results generated by Zelig, to be graphed.} \item{y1}{For comparison of two sets of simulated results at different choices of covariates, this should be an object of the same type and dimension as y. If no comparison is to be made, this should be NULL.} \item{xlab}{Label for the x-axis.} \item{ylab}{Label for the y-axis.} \item{main}{Main plot title.} \item{col}{A vector of colors. Colors will be used in turn as the graph is built for main plot objects. For nominal/categorical data, this colors renders as the bar color, while for numeric data it renders as the background color.} \item{line.col}{A vector of colors. Colors will be used in turn as the graph is built for line color shading of plot objects.} \item{axisnames}{a character-vector, specifying the names of the axes} } \value{ nothing } \description{ Various graph generation for different common types of simulated results from Zelig } \author{ James Honaker } Zelig/man/Zelig-mlogit-bayes-class.Rd0000644000176200001440000000053112722061503017132 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-mlogit-bayes.R \docType{class} \name{Zelig-mlogit-bayes-class} \alias{Zelig-mlogit-bayes-class} \alias{zmlogitbayes} \title{Bayesian Multinomial Logistic Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-mlogitbayes.html} } Zelig/man/Zelig-binchoice-class.Rd0000644000176200001440000000051712722061503016465 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-binchoice.R \docType{class} \name{Zelig-binchoice-class} \alias{Zelig-binchoice-class} \alias{zbinchoice} \title{Binary Choice object for inheritance across models in Zelig} \description{ Binary Choice object for inheritance across models in Zelig } Zelig/man/Zelig-bayes-class.Rd0000644000176200001440000000106412722061503015643 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-bayes.R \docType{class} \name{Zelig-bayes-class} \alias{Zelig-bayes-class} \alias{zbayes} \title{Bayes Model object for inheritance across models in Zelig} \description{ Bayes Model object for inheritance across models in Zelig } \section{Methods}{ \describe{ \item{\code{getcoef()}}{Get estimated model coefficients} \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/Zelig-arima-class.Rd0000644000176200001440000000053612722061503015634 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-arima.R \docType{class} \name{Zelig-arima-class} \alias{Zelig-arima-class} \alias{zarima} \title{Autoregressive and Moving-Average Models with Integration for Time-Series Data} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-arima.html} } Zelig/man/mexico.Rd0000644000176200001440000000116512722061504013654 0ustar liggesusers\name{mexico} \alias{mexico} \title{Voting Data from the 1988 Mexican Presidental Election} \description{ This dataset contains voting data for the 1988 Mexican presidential election. } \usage{data(mexico)} \format{A table containing 33 variables and 1,359 observations.} \source{ICPSR} \references{ King, Gary, Michael Tomz and Jason Wittenberg (2000). ``Making the Most of Statistical Analyses: Improving Interpretation and Presentation,'' \emph{American Journal of Political Science}, vol. 44, pp. 341-355. King, Tomz and Wittenberg. ICPSR Publication Related Archive, 1255. } \keyword{datasets} Zelig/man/Zelig-probit-survey-class.Rd0000644000176200001440000000053312722061504017373 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-probit-survey.R \docType{class} \name{Zelig-probit-survey-class} \alias{Zelig-probit-survey-class} \alias{zprobitsurvey} \title{Probit Regression with Survey Weights} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-probitsurvey.html} } Zelig/man/kmenta.Rd0000644000176200001440000000117212722061504013645 0ustar liggesusers\name{kmenta} \alias{kmenta} \title{Simulation Data for model Three-Stage Least Square (threesls) that corresponds to method 3SLS of systemfit} \description{ Dataframe contains 20 annual observations of a supply/demand model with 5 variables. Columns are q=Food consumption per capita, p=Ratio of food price to general consumer prices, d=Disposable income in contstant dollars, f=Ratio of preceding year's prices received by farmers to general consumer prices, a=Time index. } \usage{data(kmenta)} \format{ A table containing 5 variables ("q", "p", "d", "f","a") and 20 observations. } \keyword{datasets} Zelig/man/zeligACFplot.Rd0000644000176200001440000000076212722061504014715 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{zeligACFplot} \alias{zeligACFplot} \title{Plot Autocorrelation Function from Zelig QI object} \usage{ zeligACFplot(z, omitzero = FALSE, barcol = "black", epsilon = 0.1, col = NULL, main = "Autocorrelation Function", xlab = "Period", ylab = "Correlation of Present Shock with Future Outcomes", ylim = NULL, ...) } \description{ Plot Autocorrelation Function from Zelig QI object } \keyword{internal} Zelig/man/klein.Rd0000644000176200001440000000142212722061504013466 0ustar liggesusers\name{klein} \alias{klein} \title{Simulation Data for model Two-Stage Least Square (twosls) that corresponds to method 2SLS of systemfit} \description{ Dataframe contains annual observations of US economy from 1920 to 1940. The columns are, Year, C=Consumption, P=Corporate profits, P1=Previous year corporate profit,Wtot=Total wage, Wp=Private wage bill, Wg=Government wage bill,I=Investment, K1=Previous year capital stock,X=GNP,G=Government spending, T=Taxes, X1=Previous year GNP, Tm=Year-1931. } \usage{data(klein)} \format{ A table containing 14 variables ("year", "C", "P", "P1","Wtot", "Wp", "Wg", "I", "K1","X", "G", "T", "X1", "Tm") and 21 observations. } \source{http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm} \keyword{datasets} Zelig/man/SupremeCourt.Rd0000644000176200001440000000200712722061503015020 0ustar liggesusers\name{SupremeCourt} \alias{SupremeCourt} \title{U.S. Supreme Court Vote Matrix} \description{ This dataframe contains a matrix votes cast by U.S. Supreme Court justices in all cases in the 2000 term. } \usage{data(SupremeCourt)} \format{ The dataframe has contains data for justices Rehnquist, Stevens, O'Connor, Scalia, Kennedy, Souter, Thomas, Ginsburg, and Breyer for the 2000 term of the U.S. Supreme Court. It contains data from 43 non-unanimous cases. The votes are coded liberal (1) and conservative (0) using the protocol of Spaeth (2003). The unit of analysis is the case citation (ANALU=0). We are concerned with formally decided cases issued with written opinions, after full oral argument and cases decided by an equally divided vote (DECTYPE=1,5,6,7).} \source{ Harold J. Spaeth (2005). ``Original United States Supreme Court Database: 1953-2004 Terms.'' . } \keyword{datasets} Zelig/man/eidat.Rd0000644000176200001440000000051212722061504013451 0ustar liggesusers\name{eidat} \alias{eidat} \title{Simulation Data for Ecological Inference} \description{ This dataframe contains a simulated data set to illustrate the models for ecological inference. } \usage{data(eidat)} \format{ A table containing 4 variables ("t0", "t1", "x0", "x1") and 10 observations. } \keyword{datasets} Zelig/man/Zelig-oprobit-bayes-class.Rd0000644000176200001440000000053012722061503017314 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-oprobit-bayes.R \docType{class} \name{Zelig-oprobit-bayes-class} \alias{Zelig-oprobit-bayes-class} \alias{zoprobitbayes} \title{Bayesian Ordered Probit Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-oprobitbayes.html} } Zelig/man/rocplot.Rd0000644000176200001440000000425512722061504014055 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{rocplot} \alias{rocplot} \title{Receiver Operator Characteristic Plots} \usage{ rocplot(z1, z2, cutoff = seq(from=0, to=1, length=100), lty1="solid", lty2="dashed", lwd1=par("lwd"), lwd2=par("lwd"), col1=par("col"), col2=par("col"), main="ROC Curve", xlab = "Proportion of 1's Correctly Predicted", ylab="Proportion of 0's Correctly Predicted", plot = TRUE, ... ) } \arguments{ \item{z1}{first model} \item{z2}{second model} \item{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{lty1}{the line type of the first model (defaults to 'line')} \item{lty2}{the line type of the second model (defaults to 'dashed')} \item{lwd1}{the line width of the first model (defaults to 1)} \item{lwd2}{the line width of the second model (defaults to 1)} \item{col1}{the color of the first model (defaults to 'black')} \item{col2}{the color of the second model (defaults to 'black')} \item{main}{a title for the plot (defaults to "ROC Curve")} \item{xlab}{a label for the X-axis} \item{ylab}{a lavel for the Y-axis} \item{plot}{whether to generate a plot to the selected device} \item{\dots}{additional parameters to be passed to the plot} } \value{ if plot is TRUE, rocplot simply generates a plot. Otherwise, a list with the following is produced: \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.} \item{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{area1}{the area under the first ROC curve, calculated using Reimann sums.} \item{area2}{the area under the second ROC curve, calculated using Reimann sums.} } \description{ The '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. } Zelig/man/Zelig-lognorm-class.Rd0000644000176200001440000000101612722061503016212 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-lognorm.R \docType{class} \name{Zelig-lognorm-class} \alias{Zelig-lognorm-class} \alias{zlognorm} \title{Log-Normal Regression for Duration Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-lognorm.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/Zelig-logit-bayes-class.Rd0000644000176200001440000000050512722061503016756 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-logit-bayes.R \docType{class} \name{Zelig-logit-bayes-class} \alias{Zelig-logit-bayes-class} \alias{zlogitbayes} \title{Bayesian Logit Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-logitbayes.html} } Zelig/man/coef-Zelig-method.Rd0000644000176200001440000000072612722061504015634 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-zelig.R \docType{methods} \name{coef,Zelig-method} \alias{coef,Zelig-method} \title{Method for extracting estimated coefficients from Zelig objects} \usage{ \S4method{coef}{Zelig}(object, ...) } \arguments{ \item{object}{An Object of Class Zelig} \item{...}{Additional parameters to be passed to coef} } \description{ Method for extracting estimated coefficients from Zelig objects } Zelig/man/sanction.Rd0000644000176200001440000000134012722061504014201 0ustar liggesusers\name{sanction} \alias{sanction} \title{Multilateral Economic Sanctions} \description{ Data on bilateral sanctions behavior for selected years during the general period 1939-1983. This data contains errors that have since been corrected. Please contact Lisa Martin before using this data for publication. } \usage{data(sanction)} \format{A table containing 8 variables ("mil", "coop", "target", "import", "export", "cost", "num", and "ncost") and 78 observations. For full variable description, see Martin, 1992. } \source{Martin, 1992} \references{ Martin, Lisa (1992). \emph{Coercive Cooperation: Explaining Multilateral Economic Sanctions}, Princeton: Princeton University Press. } \keyword{datasets} Zelig/man/stat.Rd0000644000176200001440000000073512722061504013345 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{stat} \alias{stat} \title{Pass Quantities of Interest to Appropriate Summary Function} \usage{ stat(qi, num) } \arguments{ \item{qi}{quantity of interest (e.g., estimated value or predicted value)} \item{num}{number of simulations} } \value{ a formatted qi } \description{ Pass Quantities of Interest to Appropriate Summary Function } \author{ Christine Choirat } \keyword{internal} Zelig/man/Zelig-ma-class.Rd0000644000176200001440000000044612722061503015140 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-ma.R \docType{class} \name{Zelig-ma-class} \alias{Zelig-ma-class} \alias{zma} \title{Time-Series Model with Moving Average} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-ma.html} } Zelig/man/Zelig-tobit-class.Rd0000644000176200001440000000100612722061504015656 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-tobit.R \docType{class} \name{Zelig-tobit-class} \alias{Zelig-tobit-class} \alias{ztobit} \title{Linear Regression for a Left-Censored Dependent Variable} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-tobit.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/fitted-Zelig-method.Rd0000644000176200001440000000074012722061504016173 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-zelig.R \docType{methods} \name{fitted,Zelig-method} \alias{fitted,Zelig-method} \title{Method for extracting estimated fitted values from Zelig objects} \usage{ \S4method{fitted}{Zelig}(object, ...) } \arguments{ \item{object}{An Object of Class Zelig} \item{...}{Additional parameters to be passed to fitted} } \description{ Method for extracting estimated fitted values from Zelig objects } Zelig/man/predict-Zelig-method.Rd0000644000176200001440000000072012722061504016344 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-zelig.R \docType{methods} \name{predict,Zelig-method} \alias{predict,Zelig-method} \title{Method for getting predicted values from Zelig objects} \usage{ \S4method{predict}{Zelig}(object, ...) } \arguments{ \item{object}{An Object of Class Zelig} \item{...}{Additional parameters to be passed to predict} } \description{ Method for getting predicted values from Zelig objects } Zelig/man/Zelig-weibull-class.Rd0000644000176200001440000000101312722061504016176 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-weibull.R \docType{class} \name{Zelig-weibull-class} \alias{Zelig-weibull-class} \alias{zweibull} \title{Weibull Regression for Duration Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-weibull.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/statmat.Rd0000644000176200001440000000051412722061504014042 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{statmat} \alias{statmat} \title{Describe Here} \usage{ statmat(qi) } \arguments{ \item{qi}{quantity of interest in the discrete case} } \value{ a formatted qi } \description{ Describe Here } \author{ Christine Choirat } \keyword{internal} Zelig/man/Zelig-gamma-survey-class.Rd0000644000176200001440000000052512722061503017156 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-gamma-survey.R \docType{class} \name{Zelig-gamma-survey-class} \alias{Zelig-gamma-survey-class} \alias{zgammasurvey} \title{Gamma Regression with Survey Weights} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-gammasurvey.html} } Zelig/man/qi.plot.Rd0000644000176200001440000000074512722061504013761 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{qi.plot} \alias{qi.plot} \title{Default Plot Design For Zelig Model QI's} \usage{ qi.plot(obj, ...) } \arguments{ \item{obj}{A reference class zelig5 object} \item{...}{Parameters to be passed to the `truehist' function which is implicitly called for numeric simulations} } \description{ Default Plot Design For Zelig Model QI's } \author{ James Honaker with panel layouts from Matt Owen } Zelig/man/relogit.Rd0000644000176200001440000000057312722061504014037 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-relogit.R \name{relogit} \alias{relogit} \title{Estimation function for rare events logit models} \usage{ relogit(formula, data = sys.parent(), tau = NULL, bias.correct = TRUE, case.control = "prior", ...) } \description{ Estimation function for rare events logit models } \keyword{internal} Zelig/man/Zelig-poisson-survey-class.Rd0000644000176200001440000000054112722061503017564 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-poisson-survey.R \docType{class} \name{Zelig-poisson-survey-class} \alias{Zelig-poisson-survey-class} \alias{zpoissonsurvey} \title{Poisson Regression with Survey Weights} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-poissonsurvey.html} } Zelig/man/zeligARMAlongrun.Rd0000644000176200001440000000056612722061504015554 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-arima.R \name{zeligARMAlongrun} \alias{zeligARMAlongrun} \title{Calculate the Long Run Exquilibrium for Fixed X} \usage{ zeligARMAlongrun(y.init = NULL, x, simparam, order, sd, tol = NULL, burnin = 20) } \description{ Calculate the Long Run Exquilibrium for Fixed X } \keyword{internal} Zelig/man/tobin.Rd0000644000176200001440000000103312722061504013475 0ustar liggesusers\name{tobin} \alias{tobin} \title{Tobin's Tobit Data} \description{ Economists fit a parametric censored data model called the `tobit'. These data are from Tobin's original paper. } \usage{data(tobin)} \format{ A data frame with 20 observations on the following 3 variables. durable: Durable goods purchase age: Age in years quant: Liquidity ratio (x 1000) } \source{ J. Tobin, Estimation of relationships for limited dependent variables, Econometrica, v26, 24-36, 1958. } \keyword{datasets} Zelig/man/zeligARMAnextstep.Rd0000644000176200001440000000061412722061504015734 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-arima.R \name{zeligARMAnextstep} \alias{zeligARMAnextstep} \title{Construct Simulated Next Step in Dynamic Series} \usage{ zeligARMAnextstep(yseries = NULL, xseries, wseries = NULL, beta, ar = NULL, i = NULL, ma = NULL, sd) } \description{ Construct Simulated Next Step in Dynamic Series } \keyword{internal} Zelig/man/free1.Rd0000644000176200001440000000776512722061504013406 0ustar liggesusers\name{free1} \alias{free1} \title{Freedom of Speech Data} \usage{data(free1)} \description{ Selection of individual-level survey data for freedom of speech. } \details{ A table with 150 observations and 12 variables. \itemize{ \item{sex}{1 for men and 0 for women} \item{age}{Age of respondent in years} \item{educ}{Levels of education, coded as a numeric variable with \itemize{ \item{1}{No formal education} \item{2}{Less than primary school education} \item{3}{Completed primary school} \item{4}{Completed secondary school} \item{5}{Completed high school} \item{6}{Completed college} \item{7}{Completed post-graduate degree} } } \item{country}{Character strings consisting of "Oceana", "Eurasia", and "Eastasia", after Orwell's \emph{1984}.} \item{y}{Self assessment (see below).} \item{v1-v6}{Response to vignettes (see below).} } Survey respondents were asked in almost the same language for a self-assessment and for an assessment of several hypothetical persons described by written vignettes. The self assessment (\code{self}, in the data set), "How free do you think [name/you] [is/are] to express [him-her/your]self without fear of government reprisal?" was first asked of the survey respondent with respect to him or herself, and then after each of vignette. The possible response categories are: \itemize{ \item{1}{Completely free} \item{2}{Very free} \item{3}{Moderately free} \item{4}{Slightly free} \item{5}{Not free at all} } The vignettes, ordered from most free to least free, are: \itemize{ \item{vign1}{[Kay] does not like many of the government's policies. She frequently publishes her opinion in newspapers, criticizing decisions by officials and calling for change. She sees little reason these actions could lead to government reprisal.} \item{vign2}{[Michael] disagrees with many of the government's policies. Though he knows criticism is frowned upon, he doesn't believe the government would punish someone for expressing critical views. He makes his opinion known on most issues without regard to who is listening.} \item{vign3}{[Bob] has political views at odds with the government. He has heard of people occasionally being arrested for speaking out against the government, and government leaders sometimes make political speeches condemning those who criticize. He sometimes writes letters to newspapers about politics, but he is careful not to use his real name.} \item{vign4}{[Connie] does not like the government's stance on many issues. She has a friend who was arrested for being too openly critical of governmental leaders, and so she avoids voicing her opinions in public places.} \item{vign5}{[Vito] disagrees with many of the government's policies, and is very careful about whom he says this to, reserving his real opinions for family and close friends only. He knows several men who have been taken away by government officials for saying negative things in public.} \item{vign6}{[Sonny] lives in fear of being harassed for his political views. Everyone he knows who has spoken out against the government has been arrested or taken away. He never says a word about anything the government does, not even when he is at home alone with his family. } } } \references{ \emph{WHO's World Health Survey} by Lydia Bendib, Somnath Chatterji, Alena Petrakova, Ritu Sadana, Joshua A. Salomon, Margie Schneider, Bedirhan Ustun, Maria Villanueva Jonathan Wand, Gary King and Olivia Lau. (2007) ``Anchors: Software for Anchoring Vignettes''. \emph{Journal of Statistical Software}. Forthcoming. copy at http://wand.stanford.edu/research/anchors-jss.pdf Gary King and Jonathan Wand. "Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes," Political Analysis, 15, 1 (Winter, 2007): Pp. 46-66, copy at http://gking.harvard.edu/files/abs/c-abs.shtml. } \keyword{datasets} Zelig/man/zeligARMAbreakforecaster.Rd0000644000176200001440000000064212722061504017225 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-arima.R \name{zeligARMAbreakforecaster} \alias{zeligARMAbreakforecaster} \title{Construct Simulated Series with Internal Discontinuity in X} \usage{ zeligARMAbreakforecaster(y.init = NULL, x, x1, simparam, order, sd, t1 = 5, t2 = 10) } \description{ Construct Simulated Series with Internal Discontinuity in X } \keyword{internal} Zelig/man/Zelig-class.Rd0000644000176200001440000000700513006170425014543 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-zelig.R \docType{class} \name{Zelig-class} \alias{Zelig-class} \alias{z} \title{Zelig reference class} \description{ Zelig website: \url{http://zeligproject.org/} } \section{Fields}{ \describe{ \item{\code{fn}}{R function to call to wrap} \item{\code{formula}}{Zelig formula} \item{\code{weights}}{forthcoming} \item{\code{name}}{name of the Zelig model} \item{\code{data}}{data frame or matrix} \item{\code{by}}{split the data by factors} \item{\code{mi}}{work with imputed dataset} \item{\code{idx}}{model index} \item{\code{zelig.call}}{Zelig function call} \item{\code{model.call}}{wrapped function call} \item{\code{zelig.out}}{estimated zelig model(s)} \item{\code{setx.out}}{set values} \item{\code{setx.labels}}{pretty-print qi} \item{\code{bsetx}}{is x set?} \item{\code{bsetx1}}{is x1 set?} \item{\code{bsetrange}}{is range set?} \item{\code{bsetrange1}}{is range1 set?} \item{\code{range}}{range} \item{\code{range1}}{range1} \item{\code{test.statistics}}{list of test statistics} \item{\code{sim.out}}{simulated qi's} \item{\code{simparam}}{simulated parameters} \item{\code{num}}{number of simulations} \item{\code{authors}}{Zelig model authors} \item{\code{zeligauthors}}{Zelig authors} \item{\code{modelauthors}}{wrapped model authors} \item{\code{packageauthors}}{wrapped package authors} \item{\code{refs}}{citation information} \item{\code{year}}{model is released} \item{\code{description}}{model description} \item{\code{url}}{model URL} \item{\code{url.docs}}{model documentation URL} \item{\code{category}}{model category} \item{\code{vignette.url}}{vignette URL} \item{\code{json}}{JSON export} \item{\code{ljson}}{JSON export} \item{\code{outcome}}{JSON export} \item{\code{wrapper}}{JSON export} \item{\code{explanatory}}{JSON export} \item{\code{mcunit.test}}{unit testing} \item{\code{with.feedback}}{Feedback} }} \section{Methods}{ \describe{ \item{\code{ATT(treatment, treated = 1, quietly = TRUE, num = NULL)}}{Generic Method for Computing Simulated (Sample) Average Treatment Effects on the Treated} \item{\code{cite()}}{Provide citation information about Zelig and Zelig model, and about wrapped package and wrapped model} \item{\code{feedback()}}{Send feedback to the Zelig team} \item{\code{getcoef()}}{Get estimated model coefficients} \item{\code{getpredict()}}{Get predicted values} \item{\code{getqi(qi = "ev", xvalue = "x", subset = NULL)}}{Get quantities of interest} \item{\code{getvcov()}}{Get estimated model variance-covariance matrix} \item{\code{graph()}}{Plot the quantities of interest} \item{\code{help()}}{Open the model vignette from http://zeligproject.org/} \item{\code{packagename()}}{Automatically retrieve wrapped package name} \item{\code{references(style = "sphinx")}}{Construct a reference list specific to a Zelig model.} \item{\code{set(..., fn = list(numeric = mean, ordered = Median, other = Mode))}}{Setting Explanatory Variable Values} \item{\code{sim(num = NULL)}}{Generic Method for Computing and Organizing Simulated Quantities of Interest} \item{\code{simATT(simparam, data, depvar, treatment, treated)}}{Simulate an Average Treatment on the Treated} \item{\code{summarise(...)}}{Display a Zelig object} \item{\code{summarize(...)}}{Display a Zelig object} \item{\code{toJSON()}}{Convert Zelig object to JSON format} \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/macro.Rd0000644000176200001440000000146512722061504013474 0ustar liggesusers\name{macro} \alias{macro} \title{Macroeconomic Data} \description{ Selected macroeconomic indicators for Austria, Belgium, Canada, Denmark, Finland, France, Italy, Japan, the Netherlands, Norway, Sweden, the United Kingdom, the United States, and West Germany for the period 1966-1990. } \usage{data(macro)} \format{ A table containing 6 variables ("country", "year", "gdp", "unem", "capmob", and "trade") and 350 observations. } \source{ICPSR} \references{ King, Gary, Michael Tomz and Jason Wittenberg. ICPSR Publication Related Archive, 1225. King, Gary, Michael Tomz and Jason Wittenberg (2000). ``Making the Most of Statistical Analyses: Improving Interpretation and Presentation,'' \emph{American Journal of Political Science}, vol. 44, pp. 341-355. } \keyword{datasets} Zelig/man/Zelig.url.Rd0000644000176200001440000000026412722061504014242 0ustar liggesusers\name{Zelig.url} \alias{Zelig.url} \title{Table of links for Zelig} \description{ Table of linds for \code{help.zelig} for the core Zelig package. } \keyword{datasets} Zelig/man/vcov-Zelig-method.Rd0000644000176200001440000000066012722061504015672 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-zelig.R \docType{methods} \name{vcov,Zelig-method} \alias{vcov,Zelig-method} \title{Variance-covariance method for Zelig objects} \usage{ \S4method{vcov}{Zelig}(object, ...) } \arguments{ \item{object}{An Object of Class Zelig} \item{...}{Additional parameters to be passed to vcov} } \description{ Variance-covariance method for Zelig objects } Zelig/man/Zelig-ls-class.Rd0000644000176200001440000000077212722061503015163 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-ls.R \docType{class} \name{Zelig-ls-class} \alias{Zelig-ls-class} \alias{zls} \title{Least Squares Regression for Continuous Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-ls.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/Zelig-poisson-bayes-class.Rd0000644000176200001440000000052112722061503017330 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-poisson-bayes.R \docType{class} \name{Zelig-poisson-bayes-class} \alias{Zelig-poisson-bayes-class} \alias{zpoissonbayes} \title{Bayesian Poisson Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-poissonbayes.html} } Zelig/man/setx.Rd0000644000176200001440000000446612722061504013362 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/wrappers.R \name{setx} \alias{setx} \title{Setting Explanatory Variable Values} \usage{ setx(obj, fn = NULL, data = NULL, cond = FALSE, ...) } \arguments{ \item{obj}{the saved output from zelig} \item{fn}{a list of functions to apply to the data frame} \item{data}{a new data frame used to set the values of explanatory variables. If data = NULL (the default), the data frame called in zelig is used} \item{cond}{a logical value indicating whether unconditional (default) or conditional (choose \code{cond = TRUE}) prediction should be performed. If you choose \code{cond = TRUE}, \code{setx} will coerce \code{fn = NULL} and ignore the additional arguments in \code{\dots}. If \code{cond = TRUE} and \code{data = NULL}, \code{setx} will prompt you for a data frame.} \item{...}{user-defined values of specific variables for overwriting the default values set by the function \code{fn}. For example, adding \code{var1 = mean(data\$var1)} or \code{x1 = 12} explicitly sets the value of \code{x1} to 12. In addition, you may specify one explanatory variable as a range of values, creating one observation for every unique value in the range of values} } \value{ For unconditional prediction, \code{x.out} is a model matrix based on the specified values for the explanatory variables. For multiple analyses (i.e., when choosing the \code{by} option in \code{\link{zelig}}, \code{setx} returns the selected values calculated over the entire data frame. If you wish to calculate values over just one subset of the data frame, the 5th subset for example, you may use: \code{x.out <- setx(z.out[[5]])} } \description{ The \code{setx} command uses the variables identified in the \code{formula} generated by \code{zelig} and sets the values of the explanatory variables to the selected values. Use \code{setx} after \code{zelig} and before \code{sim} to simulate quantities of interest. } \examples{ # Unconditional prediction: data(turnout) z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout) x.out <- setx(z.out) s.out <- sim(z.out, x = x.out) } \author{ Matt Owen \email{mowen@iq.harvard.edu}, Olivia Lau and Kosuke Imai } \seealso{ The full Zelig manual may be accessed online at \url{http://gking.harvard.edu/zelig} } \keyword{file} Zelig/man/ci.plot.Rd0000644000176200001440000000400212722061504013731 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{ci.plot} \alias{ci.plot} \title{Method for plotting qi simulations across a range within a variable, with confidence intervals} \usage{ ci.plot(obj, qi="ev", var=NULL, ..., main = NULL, sub = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, legcol="gray20", col=NULL, leg=1, legpos= NULL, ci = c(80, 95, 99.9), discont=NULL) } \arguments{ \item{obj}{A reference class zelig5 object} \item{qi}{a character-string specifying the quantity of interest to plot} \item{var}{The variable to be used on the x-axis. Default is the variable across all the chosen values with smallest nonzero variance} \item{...}{Parameters to be passed to the `truehist' function which is implicitly called for numeric simulations} \item{main}{a character-string specifying the main heading of the plot} \item{sub}{a character-string specifying the sub heading of the plot} \item{xlab}{a character-string specifying the label for the x-axis} \item{ylab}{a character-string specifying the label for the y-axis} \item{xlim}{Limits to the x-axis} \item{ylim}{Limits to the y-axis} \item{legcol}{``legend color'', an valid color used for plotting the line colors in the legend} \item{col}{a valid vector of colors of at least length 3 to use to color the confidence intervals} \item{leg}{``legend position'', an integer from 1 to 4, specifying the position of the legend. 1 to 4 correspond to ``SE'', ``SW'', ``NW'', and ``NE'' respectively} \item{legpos}{``legend type'', exact coordinates and sizes for legend. Overrides argment ``leg.type''} \item{ci}{vector of length three of confidence interval levels to draw.} \item{discont}{optional point of discontinuity along the x-axis at which to interupt the graph} } \value{ the current graphical parameters. This is subject to change in future implementations of Zelig } \description{ Method for plotting qi simulations across a range within a variable, with confidence intervals } \author{ James Honaker } Zelig/man/setfactor.Rd0000644000176200001440000000070412722061504014360 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{setfactor} \alias{setfactor} \title{Set new value of a factor variable, checking for existing levels} \usage{ setfactor(fv, v) } \arguments{ \item{fv}{factor variable} \item{v}{value} } \value{ a factor variable with a value \code{val} and the same levels } \description{ Set new value of a factor variable, checking for existing levels } \keyword{internal} Zelig/man/coalition.Rd0000644000176200001440000000212412722061504014345 0ustar liggesusers\name{coalition} \alias{coalition} \title{Coalition Dissolution in Parliamentary Democracies} \description{ This data set contains survival data on government coalitions in parliamentary democracies (Belgium, Canada, Denmark, Finland, France, Iceland, Ireland, Israel, Italy, Netherlands, Norway, Portugal, Spain, Sweden, and the United Kingdom) for the period 1945-1987. For parsimony, country indicator variables are omitted in the sample data. } \usage{data(coalition)} \format{ A table containing 7 variables ("duration", "ciep12", "invest", "fract", "polar", "numst2", "crisis") and 314 observations. For variable descriptions, please refer to King, Alt, Burns and Laver (1990). } \source{ICPSR} \references{ King, Gary, James E. Alt, Nancy Elizabeth Burns and Michael Laver (1990). ``A Unified Model of Cabinet Dissolution in Parliamentary Democracies,'' \emph{American Journal of Political Science}, vol. 34, no. 3, pp. 846-870. Gary King, James E. Alt, Nancy Burns, and Michael Laver. ICPSR Publication Related Archive, 1115. } \keyword{datasets} Zelig/man/cluster.formula.Rd0000644000176200001440000000071412722061504015514 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{cluster.formula} \alias{cluster.formula} \title{Generate Formulae that Consider Clustering} \usage{ cluster.formula(formula, cluster) } \arguments{ \item{formula}{a formula object} \item{cluster}{a vector} } \value{ a formula object describing clustering } \description{ This method is used internally by the "Zelig" Package to interpret clustering in GEE models. } Zelig/man/Zelig-probit-bayes-class.Rd0000644000176200001440000000051312722061503017136 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-probit-bayes.R \docType{class} \name{Zelig-probit-bayes-class} \alias{Zelig-probit-bayes-class} \alias{zprobitbayes} \title{Bayesian Probit Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-probitbayes.html} } Zelig/man/Zelig-binchoice-gee-class.Rd0000644000176200001440000000070212722061503017217 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-binchoice-gee.R \docType{class} \name{Zelig-binchoice-gee-class} \alias{Zelig-binchoice-gee-class} \alias{zbinchoicegee} \title{Object for Binary Choice outcomes in Generalized Estimating Equations for inheritance across models in Zelig} \description{ Object for Binary Choice outcomes in Generalized Estimating Equations for inheritance across models in Zelig } Zelig/man/Zelig-normal-survey-class.Rd0000644000176200001440000000057612722061503017372 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-normal-survey.R \docType{class} \name{Zelig-normal-survey-class} \alias{Zelig-normal-survey-class} \alias{znormalsurvey} \title{Normal Regression for Continuous Dependent Variables with Survey Weights} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-normalsurvey.html} } Zelig/man/statlevel.Rd0000644000176200001440000000057212722061504014374 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{statlevel} \alias{statlevel} \title{Describe Here} \usage{ statlevel(qi, num) } \arguments{ \item{qi}{quantity of interest in the discrete case} \item{num}{number of simulations} } \value{ a formatted qi } \description{ Describe Here } \author{ Christine Choirat } \keyword{internal} Zelig/man/Zelig-relogit-class.Rd0000644000176200001440000000103312722061504016202 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-relogit.R \docType{class} \name{Zelig-relogit-class} \alias{Zelig-relogit-class} \alias{zrelogit} \title{Rare Events Logistic Regression for Dichotomous Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-relogit.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/Zelig-poisson-gee-class.Rd0000644000176200001440000000054212722061503016770 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-poisson-gee.R \docType{class} \name{Zelig-poisson-gee-class} \alias{Zelig-poisson-gee-class} \alias{zpoissongee} \title{Generalized Estimating Equation for Poisson Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-poissongee.html} } Zelig/man/Zelig-gee-class.Rd0000644000176200001440000000104712722061503015301 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-gee.R \docType{class} \name{Zelig-gee-class} \alias{Zelig-gee-class} \alias{zgee} \title{Generalized Estimating Equations Model object for inheritance across models in Zelig} \description{ Generalized Estimating Equations Model object for inheritance across models in Zelig } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/Zelig-exp-class.Rd0000644000176200001440000000077312722061503015342 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-exp.R \docType{class} \name{Zelig-exp-class} \alias{Zelig-exp-class} \alias{zexp} \title{Exponential Regression for Duration Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-exp.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/approval.Rd0000644000176200001440000000062112722061504014210 0ustar liggesusers\name{approval} \alias{approval} \title{U.S. Presidential Approval Data} \description{ Monthy public opinion data for 2001-2006. } \usage{data(approval)} \format{ A table containing 8 variables ("month", "year", "approve", "disapprove", "unsure", "sept.oct.2001", "iraq.war", and "avg.price") and 65 observations. } \source{ICPSR} \references{ Stuff here } \keyword{datasets} Zelig/man/setval.Rd0000644000176200001440000000065312722061504013667 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{setval} \alias{setval} \title{Set new value of a variable as approrpriate to data type} \usage{ setval(val, newval) } \arguments{ \item{val}{old value} \item{newval}{new value} } \value{ a variable of the same type with a value \code{val} } \description{ Set new value of a variable as approrpriate to data type } \keyword{internal} Zelig/man/createJSON.Rd0000644000176200001440000000063212722061504014323 0ustar liggesusers\name{createJSON} \alias{createJSON} \title{Utility function for constructing JSON file that encodes the hierarchy of available statistical models in Zelig} \usage{ createJSON() } \value{ Returns TRUE on successful completion of json file } \description{ Utility function for construction a JSON file that encodes the hierarchy of available statistical models. } \author{ Christine Choirat, Vito D'Orazio } Zelig/man/simacf.Rd0000644000176200001440000000056412722061504013634 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-arima.R \name{simacf} \alias{simacf} \title{Construct Autocorrelation Function from Zelig object and simulated parameters} \usage{ simacf(coef, order, params, alpha = 0.5) } \description{ Construct Autocorrelation Function from Zelig object and simulated parameters } \keyword{internal} Zelig/man/Mode.Rd0000644000176200001440000000073313006174543013260 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{Mode} \alias{Mode} \alias{mode} \title{Compute the Statistical Mode of a Vector} \usage{ Mode(x) } \arguments{ \item{x}{a vector of numeric, factor, or ordered values} } \value{ the statistical mode of the vector. If two modes exist, one is randomly selected (by design) } \description{ Compute the Statistical Mode of a Vector } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/Weimar.Rd0000644000176200001440000000157612722061503013621 0ustar liggesusers\name{Weimar} \alias{Weimar} \title{1932 Weimar election data} \description{ This data set contains election results for 10 kreise (equivalent to precincts) from the 1932 Weimar (German) election. } \usage{data(Weimar)} \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} %\references{ %} \keyword{datasets} Zelig/man/Zelig-quantile-class.Rd0000644000176200001440000000102312722061504016356 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-quantile.R \docType{class} \name{Zelig-quantile-class} \alias{Zelig-quantile-class} \alias{zquantile} \title{Quantile Regression for Continuous Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-quantile.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/zeligArimaWrapper.Rd0000644000176200001440000000067612722061504016023 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-arima.R \name{zeligArimaWrapper} \alias{zeligArimaWrapper} \title{Estimation wrapper function for arima models, to easily fit with Zelig architecture} \usage{ zeligArimaWrapper(formula, order = c(1, 0, 0), ..., include.mean = TRUE, data) } \description{ Estimation wrapper function for arima models, to easily fit with Zelig architecture } \keyword{internal} Zelig/man/Zelig-logit-class.Rd0000644000176200001440000000050712722061503015657 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-logit.R \docType{class} \name{Zelig-logit-class} \alias{Zelig-logit-class} \alias{zlogit} \title{Logistic Regression for Dichotomous Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-logit.html} } Zelig/man/Zelig-logit-survey-class.Rd0000644000176200001440000000052512722061503017212 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-logit-survey.R \docType{class} \name{Zelig-logit-survey-class} \alias{Zelig-logit-survey-class} \alias{zlogitsurvey} \title{Logit Regression with Survey Weights} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-logitsurvey.html} } Zelig/man/mid.Rd0000644000176200001440000000156712722061504013147 0ustar liggesusers\name{mid} \alias{mid} \title{Militarized Interstate Disputes} \description{ A small sample from the militarized interstate disputes (MID) database. } \usage{data(mid)} \format{ A table containing 6 variables ("conflict", "major", "contig", "power", "maxdem", "mindem", and "years") and 3,126 observations. For full variable descriptions, please see King and Zeng, 2001. } \source{Militarized Interstate Disputes database} \references{ King, Gary, and Lanche Zeng (2001). ``Explaining Rare Events in International Relations,'' \emph{International Organization}, vol. 55, no. 3, pp. 693-715. Jones, Daniel M., Stuart A. Bremer and David Singer (1996). ``Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns,'' \emph{Conflict Management and Peace Science}, vol. 15, no. 2, pp. 163-213. } \keyword{datasets} Zelig/man/Zelig-factor-bayes-class.Rd0000644000176200001440000000100712722061503017114 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-factor-bayes.R \docType{class} \name{Zelig-factor-bayes-class} \alias{Zelig-factor-bayes-class} \alias{zfactorbayes} \title{Bayesian Factor Analysis} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-factorbayes.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/table.levels.Rd0000644000176200001440000000126312722061504014747 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{table.levels} \alias{table.levels} \title{Create a table, but ensure that the correct columns exist. In particular, this allows for entires with zero as a value, which is not the default for standard tables} \usage{ table.levels(x, levels, ...) } \arguments{ \item{x}{a vector} \item{levels}{a vector of levels} \item{...}{parameters for table} } \value{ a table } \description{ Create a table, but ensure that the correct columns exist. In particular, this allows for entires with zero as a value, which is not the default for standard tables } \author{ Matt Owen \email{mowen@iq.harvard.edu} } Zelig/man/PErisk.Rd0000644000176200001440000000555712722061503013575 0ustar liggesusers\name{PErisk} \alias{PErisk} \title{Political Economic Risk Data from 62 Countries in 1987} \description{ Political Economic Risk Data from 62 Countries in 1987. } \usage{data(PErisk)} \format{ A data frame with 62 observations on the following 6 variables. All data points are from 1987. See Quinn (2004) for more details. country: a factor with levels 'Argentina' 'Australia' 'Austria' 'Bangladesh' 'Belgium' 'Bolivia' 'Botswana' 'Brazil' 'Burma' 'Cameroon' 'Canada' 'Chile' 'Colombia' 'Congo-Kinshasa' 'Costa Rica' 'Cote d'Ivoire' 'Denmark' 'Dominican Republic' 'Ecuador' 'Finland' 'Gambia, The' 'Ghana' 'Greece' 'Hungary' 'India' 'Indonesia' 'Iran' 'Ireland' 'Israel' 'Italy' 'Japan' 'Kenya' 'Korea, South' 'Malawi' 'Malaysia' 'Mexico' 'Morocco' 'New Zealand' 'Nigeria' 'Norway' 'Papua New Guinea' 'Paraguay' 'Philippines' 'Poland' 'Portugal' 'Sierra Leone' 'Singapore' 'South Africa' 'Spain' 'Sri Lanka' 'Sweden' 'Switzerland' 'Syria' 'Thailand' 'Togo' 'Tunisia' 'Turkey' 'United Kingdom' 'Uruguay' 'Venezuela' 'Zambia' 'Zimbabwe' courts: an ordered factor with levels '0' < '1'.'courts' is an indicator of whether the country in question is judged to have an independent judiciary. From Henisz (2002). barb2: a numeric vector giving the natural log of the black market premium in each country. The black market premium is coded as the black market exchange rate (local currency per dollar) divided by the official exchange rate minus 1. From Marshall, Gurr, and Harff (2002). prsexp2: an ordered factor with levels '0' < '1' < '2' < '3' < '4' < '5', giving the lack of expropriation risk. From Marshall, Gurr, and Harff (2002). prscorr2: an ordered factor with levels '0' < '1' < '2' < '3' < '4' < '5', measuring the lack of corruption. From Marshall, Gurr, and Harff (2002). gdpw2: a numeric vector giving the natural log of real GDP per worker in 1985 international prices. From Alvarez et al. (1999). } \source{ Mike Alvarez, Jose Antonio Cheibub, Fernando Limongi, and Adam Przeworski. 1999. ``ACLP Political and Economic Database.'' . Witold J. Henisz. 2002. ``The Political Constraint Index (POLCON) Dataset.'' \ . Monty G. Marshall, Ted Robert Gurr, and Barbara Harff. 2002. ``State Failure Task Force Problem Set.'' . } \references{ Kevin M. Quinn. 2004. ``Bayesian Factor Analysis for Mixed Ordinal and Continuous Response.'' \emph{Political Analyis}. Vol. 12, pp.338--353. } \keyword{datasets} Zelig/man/Zelig-negbin-class.Rd0000644000176200001440000000102512722061503015777 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-negbinom.R \docType{class} \name{Zelig-negbin-class} \alias{Zelig-negbin-class} \alias{znegbin} \title{Negative Binomial Regression for Event Count Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-negbin.html} } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/Zelig-logit-gee-class.Rd0000644000176200001440000000052612722061503016416 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-logit-gee.R \docType{class} \name{Zelig-logit-gee-class} \alias{Zelig-logit-gee-class} \alias{zlogitgee} \title{Generalized Estimating Equation for Logit Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-logitgee.html} } Zelig/man/plot-Zelig-ANY-method.Rd0000644000176200001440000000065112722061504016320 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-zelig.R \docType{methods} \name{plot,Zelig,ANY-method} \alias{plot,Zelig,ANY-method} \title{Plot method for Zelig objects} \usage{ \S4method{plot}{Zelig,ANY}(x, y, ...) } \arguments{ \item{x}{An Object of Class Zelig} \item{y}{unused} \item{...}{Additional parameters to be passed to plot} } \description{ Plot method for Zelig objects } Zelig/man/grunfeld.Rd0000644000176200001440000000122112722061504014167 0ustar liggesusers\name{grunfeld} \alias{grunfeld} \title{Simulation Data for model Seemingly Unrelated Regression (sur) that corresponds to method SUR of systemfit} \description{ Dataframe contains 20 annual observations from 1935 to 1954 of 7 variables for two firms General Electric and Westinghouse. Columns are Year; Ige and Iw = Gross investment for GE and W,respectively; Fge and Fw=Market value of Firm as of begin of the year; Cge and Cw= Capital stock measure as of begin of the year. } \usage{data(grunfeld)} \format{ A table containing 7 variables ("Year", "Ige", "Fge", "Cge","Iw", "Fw","Cw") and 20 observations. } \keyword{datasets} Zelig/man/Zelig-normal-class.Rd0000644000176200001440000000051112722061503016024 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-normal.R \docType{class} \name{Zelig-normal-class} \alias{Zelig-normal-class} \alias{znormal} \title{Normal Regression for Continuous Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-normal.html} } Zelig/man/Zelig-survey-class.Rd0000644000176200001440000000101112722061504016066 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-survey.R \docType{class} \name{Zelig-survey-class} \alias{Zelig-survey-class} \alias{zsurvey} \title{Survey models in Zelig for weights for complex sampling designs} \description{ Survey models in Zelig for weights for complex sampling designs } \section{Methods}{ \describe{ \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/summary-Zelig-method.Rd0000644000176200001440000000064612722061504016416 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-zelig.R \docType{methods} \name{summary,Zelig-method} \alias{summary,Zelig-method} \title{Summary method for Zelig objects"} \usage{ \S4method{summary}{Zelig}(object, ...) } \arguments{ \item{object}{An Object of Class Zelig} \item{...}{Additional parameters to be passed to summary} } \description{ Summary method for Zelig objects" } Zelig/man/Zelig-ar-class.Rd0000644000176200001440000000046212722061503015143 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-ar.R \docType{class} \name{Zelig-ar-class} \alias{Zelig-ar-class} \alias{zar} \title{Time-Series Model with Autoregressive Disturbance} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-ar.html} } Zelig/man/MatchIt.url.Rd0000644000176200001440000000027712722061503014524 0ustar liggesusers\name{MatchIt.url} \alias{MatchIt.url} \title{Table of links for Zelig} \description{ Table of linds for \code{help.zelig} for the companion MatchIt package. } \keyword{datasets} Zelig/man/coalition2.Rd0000644000176200001440000000221512722061504014430 0ustar liggesusers\name{coalition2} \alias{coalition2} \docType{data} \title{Coalition Dissolution in Parliamentary Democracies, Modified Version} \description{ This data set contains survival data on government coalitions in parliamentary democracies (Belgium, Canada, Denmark, Finland, France, Iceland, Ireland, Israel, Italy, Netherlands, Norway, Portugal, Spain, Sweden, and the United Kingdom) for the period 1945-1987. Country indicator variables are included in the sample data. } \usage{data(coalition2)} \format{ A data frame containing 8 variables ("duration", "ciep12", "invest", "fract", "polar", "numst2", "crisis", "country") and 314 observations. For variable descriptions, please refer to King, Alt, Burns and Laver (1990). } \source{ICPSR} \references{ King, Gary, James E. Alt, Nancy Elizabeth Burns and Michael Laver (1990). ``A Unified Model of Cabinet Dissolution in Parliamentary Democracies,'' \emph{American Journal of Political Science}, vol. 34, no. 3, pp. 846-870. Gary King, James E. Alt, Nancy Burns, and Michael Laver. ICPSR Publication Related Archive, 1115. } \keyword{datasets}Zelig/man/Zelig-normal-gee-class.Rd0000644000176200001440000000053412722061503016567 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-normal-gee.R \docType{class} \name{Zelig-normal-gee-class} \alias{Zelig-normal-gee-class} \alias{znormalgee} \title{Generalized Estimating Equation for Normal Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-normalgee.html} } Zelig/man/reduce.Rd0000644000176200001440000000141513006170425013634 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{reduce} \alias{reduce} \title{Calculate the reduced dataset to be used in code{\link{setx}}} \usage{ reduce(dataset, s, formula, data, avg = avg) } \arguments{ \item{dataset}{Zelig object data, possibly split to deal with \code{by} argument} \item{s}{list of variables and their tentative \code{setx} values} \item{formula}{a simplified version of the Zelig object formula (typically with 1 on the lhs)} \item{data}{Zelig object data} \item{avg}{function of data transformations} } \value{ a list of all the model variables either at their central tendancy or their \code{setx} value } \description{ #' This method is used internally } \author{ Christine Choirat } \keyword{internal} Zelig/man/Zelig-timeseries-class.Rd0000644000176200001440000000121013006170425016702 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-timeseries.R \docType{class} \name{Zelig-timeseries-class} \alias{Zelig-timeseries-class} \alias{ztimeseries} \title{Time-series models in Zelig} \description{ Time-series models in Zelig } \section{Methods}{ \describe{ \item{\code{packagename()}}{Automatically retrieve wrapped package name} \item{\code{sim(num = NULL)}}{Generic Method for Computing and Organizing Simulated Quantities of Interest} \item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig command estimates a variety of statistical models} }} Zelig/man/zelig.Rd0000644000176200001440000000534712722061504013510 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/wrappers.R \name{zelig} \alias{zelig} \title{Estimating a Statistical Model} \usage{ zelig(formula, model, data, ..., by = NULL, cite = TRUE) } \arguments{ \item{formula}{a symbolic representation of the model to be estimated, in the form \code{y \~\, x1 + x2}, where \code{y} is the dependent variable and \code{x1} and \code{x2} are the explanatory variables, and \code{y}, \code{x1}, and \code{x2} are contained in the same dataset. (You may include more than two explanatory variables, of course.) The \code{+} symbol means ``inclusion'' not ``addition.'' You may also include interaction terms and main effects in the form \code{x1*x2} without computing them in prior steps; \code{I(x1*x2)} to include only the interaction term and exclude the main effects; and quadratic terms in the form \code{I(x1^2)}} \item{model}{the name of a statistical model. Type \code{help.zelig("models")} to see a list of currently supported 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})} \item{...}{additional arguments passed to \code{zelig}, depending on the model to be estimated} \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 which will allow you to save a considerable amount of effort. For example, to run the same model on all fifty states, you could type: \code{z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "state")} You may also use \code{by} to run models using MatchIt subclass} \item{cite}{If is set to "TRUE" (default), the model citation will be} } \value{ Depending on the class of model selected, \code{zelig} will return an object with elements including \code{coefficients}, \code{residuals}, and \code{formula} which may be summarized using \code{summary(z.out)} or individually extracted using, for example, \code{z.out\$coefficients}. See the specific models listed above for additional output values, or simply type \code{names(z.out)}. } \description{ The zelig command estimates a variety of statistical models. Use \code{zelig} output with \code{setx} and \code{sim} to compute quantities of interest, such as predicted probabilities, expected values, and first differences, along with the associated measures of uncertainty (standard errors and confidence intervals). } \author{ Matt Owen \email{mowen@iq.harvard.edu}, Kosuke Imai, Olivia Lau, and Gary King Maintainer: Matt Owen \email{mowen@iq.harvard.edu} } \keyword{package} Zelig/man/newpainters.Rd0000644000176200001440000000222112722061504014721 0ustar liggesusers\name{newpainters} \alias{newpainters} \title{The Discretized Painter's Data of de Piles} \description{ The original painters data contain the subjective assessment, on a 0 to 20 integer scale, of 54 classical painters. The newpainters data discretizes the subjective assessment by quartiles with thresholds 25\%, 50\%, 75\%. The painters were assessed on four characteristics: composition, drawing, colour and expression. The data is due to the Eighteenth century art critic, de Piles. } \usage{data(newpainters)} \format{A table containing 5 variables ("Composition", "Drawing", "Colour", "Expression", and "School") and 54 observations.} \source{ A. J. Weekes (1986).``A Genstat Primer''. Edward Arnold. M. Davenport and G. Studdert-Kennedy (1972). ``The statistical analysis of aesthetic judgement: an exploration.'' \emph{Applied Statistics}, vol. 21, pp. 324--333. I. T. Jolliffe (1986) ``Principal Component Analysis.'' Springer. } \references{ Venables, W. N. and Ripley, B. D. (2002) ``Modern Applied Statistics with S,'' Fourth edition. Springer. } \keyword{datasets} Zelig/man/hoff.Rd0000644000176200001440000000135712722061504013315 0ustar liggesusers\name{hoff} \alias{hoff} \title{Social Security Expenditure Data} \description{ This data set contains annual social security expenditure (as percent of budget lagged by two years), the relative frequency of mentions social justice received in the party's platform in each year, and whether the president is Republican or Democrat. } \usage{data(hoff)} \format{A table containing 5 variables ("year", "L2SocSec", "Just503D", "Just503R", "RGovDumy") and 36 observations.} \source{ICPSR (replication dataset s1109)} \references{ Gary King and Michael Laver. ``On Party Platforms, Mandates, and Government Spending,'' \emph{American Political Science Review}, Vol. 87, No. 3 (September, 1993): pp. 744-750. } \keyword{datasets} Zelig/man/friendship.Rd0000644000176200001440000000150012722061504014514 0ustar liggesusers\name{friendship} \alias{friendship} \title{Simulated Example of Schoolchildren Friendship Network} \description{ This data set contains six sociomatrices of simulated data on friendship ties among schoolchildren.} \usage{data(friendship)} \format{ Each variable in the dataset is a 15 by 15 matrix representing some form of social network tie held by the fictitious children. The matrices are labeled "friends", "advice", "prestige", "authority", "perpower" and "per". The sociomatrices were combined into the friendship dataset using the format.network.data function from the netglm package by Skyler Cranmer as shown in the example. } \source{fictitious} \examples{ \dontrun{ friendship <- format.network.data(friends, advice, prestige, authority, perpower, per) }} \keyword{datasets} Zelig/man/avg.Rd0000644000176200001440000000057512722061504013151 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{avg} \alias{avg} \title{Compute central tendancy as approrpriate to data type} \usage{ avg(val) } \arguments{ \item{val}{a vector of values} } \value{ a mean (if numeric) or a median (if ordered) or mode (otherwise) } \description{ Compute central tendancy as approrpriate to data type } Zelig/man/Zelig-gamma-class.Rd0000644000176200001440000000051512722061503015622 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-gamma.R \docType{class} \name{Zelig-gamma-class} \alias{Zelig-gamma-class} \alias{zgamma} \title{Gamma Regression for Continuous, Positive Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-gamma.html} } Zelig/man/Zelig-normal-bayes-class.Rd0000644000176200001440000000052212722061503017127 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-normal-bayes.R \docType{class} \name{Zelig-normal-bayes-class} \alias{Zelig-normal-bayes-class} \alias{znormalbayes} \title{Bayesian Normal Linear Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-normalbayes.html} } Zelig/man/swiss.Rd0000644000176200001440000000272512722061504013543 0ustar liggesusers\name{swiss} \alias{swiss} \title{Swiss Fertility and Socioeconomic Indicators (1888) Data} \description{ Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888. } \usage{data(swiss)} \format{ A data frame with 47 observations on 6 variables, each of which is in percent, i.e., in [0,100]. [,1] Fertility Ig, "common standardized fertility measure" [,2] Agriculture % of males involved in agriculture as occupation [,3] Examination % "draftees" receiving highest mark on army exami nation [,4] Education % education beyond primary school for "draftees". [,5] Catholic % catholic (as opposed to "protestant"). [,6] Infant.Mortality live births who live less than 1 year. All variables but 'Fert' give proportions of the population. } \source{ Project "16P5", pages 549-551 in Mosteller, F. and Tukey, J. W. (1977) ``Data Analysis and Regression: A Second Course in Statistics''. Addison-Wesley, Reading Mass. indicating their source as "Data used by permission of Franice van de Walle. Office of Population Research, Princeton University, 1976. Unpublished data assembled under NICHD contract number No 1-HD-O-2077." } \references{ Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) ``The New S Language''. Wadsworth & Brooks/Cole. } \keyword{datasets} Zelig/man/turnout.Rd0000644000176200001440000000145312722061504014110 0ustar liggesusers\name{turnout} \alias{turnout} \title{Turnout Data Set from the National Election Survey} \description{ This data set contains individual-level turnout data. It pools several American National Election Surveys conducted during the 1992 presidential election year. Only the first 2,000 observations (from a total of 15,837 observations) are included in the sample data. } \usage{data(turnout)} \format{A table containing 5 variables ("race", "age", "educate", "income", and "vote") and 2,000 observations.} \source{National Election Survey} \references{ King, Gary, Michael Tomz, Jason Wittenberg (2000). ``Making the Most of Statistical Analyses: Improving Interpretation and Presentation,'' \emph{American Journal of Political Science}, vol. 44, pp.341--355. } \keyword{datasets} Zelig/man/Zelig-gamma-gee-class.Rd0000644000176200001440000000052612722061503016362 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-gamma-gee.R \docType{class} \name{Zelig-gamma-gee-class} \alias{Zelig-gamma-gee-class} \alias{zgammagee} \title{Generalized Estimating Equation for Gamma Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-gammagee.html} } Zelig/man/Zelig-binchoice-survey-class.Rd0000644000176200001440000000065412722061503020022 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-binchoice-survey.R \docType{class} \name{Zelig-binchoice-survey-class} \alias{Zelig-binchoice-survey-class} \alias{zbinchoicesurvey} \title{Object for Binary Choice outcomes with Survey Weights for inheritance across models in Zelig} \description{ Object for Binary Choice outcomes with Survey Weights for inheritance across models in Zelig } Zelig/man/Zelig-probit-gee-class.Rd0000644000176200001440000000053412722061503016576 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-probit-gee.R \docType{class} \name{Zelig-probit-gee-class} \alias{Zelig-probit-gee-class} \alias{zprobitgee} \title{Generalized Estimating Equation for Probit Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-probitgee.html} } Zelig/man/summary.Arima.Rd0000644000176200001440000000062312722061504015113 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-timeseries.R \name{summary.Arima} \alias{summary.Arima} \title{Summary of an object of class Arima} \usage{ \method{summary}{Arima}(object, ...) } \arguments{ \item{object}{An object of class Arima} \item{...}{Additional parameters} } \value{ The original object } \description{ Summary of an object of class Arima } Zelig/man/Zelig-probit-class.Rd0000644000176200001440000000051212722061503016034 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-probit.R \docType{class} \name{Zelig-probit-class} \alias{Zelig-probit-class} \alias{zprobit} \title{Probit Regression for Dichotomous Dependent Variables} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-probit.html} } Zelig/man/immigration.Rd0000644000176200001440000000156212722061504014710 0ustar liggesusers\name{immigration} \alias{immigration} \alias{immi1} \alias{immi2} \alias{immi3} \alias{immi4} \alias{immi5} \title{Individual Preferences Over Immigration Policy} \description{These five datasets are part of a larger set of 10 multiply imputed data sets describing individual preferences toward immigration policy. Imputation was performed via Amelia. } \format{ Each multiply-inputed data set consists of a table with 7 variables ("ipip", "wage1992", "prtyid", "ideol", "gender") and 2,485 observations. For variable descriptions, please refer to Scheve and Slaugher, 2001. } \source{National Election Survey} \references{ Scheve, Kenneth and Matthew Slaughter (2001). ``Labor Market Competition and Individual Preferences Over Immigration Policy,'' \emph{The Review of Economics and Statistics}, vol. 83, no. 1, pp. 133-145. } \keyword{datasets} Zelig/man/Zelig-tobit-bayes-class.Rd0000644000176200001440000000050512722061504016762 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model-tobit-bayes.R \docType{class} \name{Zelig-tobit-bayes-class} \alias{Zelig-tobit-bayes-class} \alias{ztobitbayes} \title{Bayesian Tobit Regression} \description{ Vignette: \url{http://docs.zeligproject.org/en/latest/zelig-tobitbayes.html} } Zelig/man/sim.Rd0000644000176200001440000001163012722061504013156 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/wrappers.R \name{sim} \alias{sim} \title{Generic Method for Computing and Organizing Simulated Quantities of Interest} \usage{ sim(obj, x = NULL, x1 = NULL, y = NULL, num = 1000, bootstrap = F, bootfn = NULL, cond.data = NULL, ...) } \arguments{ \item{obj}{the output object from zelig} \item{x}{values of explanatory variables used for simulation, generated by setx} \item{x1}{optional values of explanatory variables (generated by a second call of setx) particular computations of quantities of interest} \item{y}{a parameter reserved for the computation of particular quantities of interest (average treatment effects). Few models currently support this parameter} \item{num}{an integer specifying the number of simulations to compute} \item{bootstrap}{currently unsupported} \item{bootfn}{currently unsupported} \item{cond.data}{currently unsupported} \item{...}{arguments reserved future versions of Zelig} } \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: \item{x}{the \code{\link{setx}} values for the explanatory variables, used to calculate the quantities of interest (expected values, predicted values, etc.). } \item{x1}{the optional \code{\link{setx}} object used to simulate first differences, and other model-specific quantities of interest, such as risk-ratios.} \item{call}{the options selected for \code{\link{sim}}, used to replicate quantities of interest. } \item{zelig.call}{the original command and options for \code{\link{zelig}}, used to replicate analyses. } \item{num}{the number of simulations requested. } \item{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{qi\$ev}{simulations of the expected values given the model and \code{x}. } \item{qi\$pr}{simulations of the predicted values given by the fitted values. } \item{qi\$fd}{simulations of the first differences (or risk difference for binary models) for the given \code{x} and \code{x1}. The difference is calculated by subtracting the expected values given \code{x} from the expected values given \code{x1}. (If do not specify \code{x1}, you will not get first differences or risk ratios.) } \item{qi\$rr}{simulations of the risk ratios for binary and multinomial models. See specific models for details.} \item{qi\$ate.ev}{simulations of the average expected treatment effect for the treatment group, using conditional prediction. Let \eqn{t_i} be a binary explanatory variable defining the treatment (\eqn{t_i=1}) and control (\eqn{t_i=0}) groups. Then the average expected treatment effect for the treatment group is \deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - E[Y_i(t_i=0)] \mid t_i=1 \,],} where \eqn{Y_i(t_i=1)} is the value of the dependent variable for observation \eqn{i} in the treatment group. Variation in the simulations are due to uncertainty in simulating \eqn{E[Y_i(t_i=0)]}, the counterfactual expected value of \eqn{Y_i} for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to \eqn{t_i=0}. } \item{qi\$ate.pr}{simulations of the average predicted treatment effect for the treatment group, using conditional prediction. Let \eqn{t_i} be a binary explanatory variable defining the treatment (\eqn{t_i=1}) and control (\eqn{t_i=0}) groups. Then the average predicted treatment effect for the treatment group is \deqn{ \frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - \widehat{Y_i(t_i=0)} \mid t_i=1 \,],} where \eqn{Y_i(t_i=1)} is the value of the dependent variable for observation \eqn{i} in the treatment group. Variation in the simulations are due to uncertainty in simulating \eqn{\widehat{Y_i(t_i=0)}}, the counterfactual predicted value of \eqn{Y_i} for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to \eqn{t_i=0}.} } \description{ Simulate quantities of interest from the estimated model output from \code{zelig()} given specified values of explanatory variables established in \code{setx()}. For classical \emph{maximum likelihood} models, \code{sim()} uses asymptotic normal approximation to the log-likelihood. For \emph{Bayesian models}, Zelig simulates quantities of interest from the posterior density, whenever possible. For \emph{robust Bayesian models}, simulations are drawn from the identified class of Bayesian posteriors. Alternatively, you may generate quantities of interest using bootstrapped parameters. } \author{ Matt Owen \email{mowen@iq.harvard.edu}, Olivia Lau and Kosuke Imai } Zelig/man/bivariate.Rd0000644000176200001440000000106312722061504014333 0ustar liggesusers\name{bivariate} \alias{bivariate} \title{Sample data for bivariate probit regression} \description{ Sample data for the bivariate probit regression. } \usage{data(bivariate)} \format{A table containing 6 variables ("y1", "y2", "x1", "x2", "x3", and "x4") and 78 observations.} \source{This is a cleaned and relabelled version of the sanction data set, available in Zelig.} \references{ Martin, Lisa (1992). \emph{Coercive Cooperation: Explaining Multilateral Economic Sanctions}, Princeton: Princeton University Press. } \keyword{datasets} Zelig/man/voteincome.Rd0000644000176200001440000000301612722061504014535 0ustar liggesusers\name{voteincome} \alias{voteincome} \docType{data} \title{Sample Turnout and Demographic Data from the 2000 Current Population Survey} \description{ This data set contains turnout and demographic data from a sample of respondents to the 2000 Current Population Survey (CPS). The states represented are South Carolina and Arkansas. The data represent only a sample and results from this example should not be used in publication. } \usage{data(voteincome)} \format{ A data frame containing 7 variables ("state", "year", "vote", "income", "education", "age", "female") and 1500 observations. \describe{ \item{\code{state}}{a factor variable with levels equal to "AR" (Arkansas) and "SC" (South Carolina)} \item{\code{year}}{an integer vector} \item{\code{vote}}{an integer vector taking on values "1" (Voted) and "0" (Did Not Vote)} \item{\code{income}}{an integer vector ranging from "4" (Less than \$5000) to "17" (Greater than \$75000) denoting family income. See the CPS codebook for more information on variable coding} \item{\code{education}}{an integer vector ranging from "1" (Less than High School Education) to "4" (More than a College Education). See the CPS codebook for more information on variable coding} \item{\code{age}}{an integer vector ranging from "18" to "85"} \item{\code{female}}{an integer vector taking on values "1" (Female) and "0" (Male)} } } \source{Census Bureau Current Population Survey} \references{\url{http://www.census.gov/cps}} \keyword{datasets}