R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for a HTML browser interface to help. Type 'q()' to quit R. > ### *
> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("pscl-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('pscl') Loading required package: MASS Loading required package: mvtnorm pscl.................................................................... R classes and methods developed in the Political Science Computational Laboratory Department of Political Science, Stanford University Simon Jackman http://pscl.stanford.edu > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "hurdle" > > ### * hurdle > > flush(stderr()); flush(stdout()) > > ### Name: hurdle > ### Title: Fit hurdle models to count data > ### Aliases: hurdle print.hurdle logLik.hurdle > ### Keywords: models regression > > ### ** Examples > > data(bioChemists) > hp <- hurdle(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + data=bioChemists,trace=TRUE) Hurdle Count Model Using logit to model zero vs non-zero Using Poisson for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done initial value 1625.139141 iter 2 value 1623.624356 Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced iter 3 value 1612.056056 Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced iter 4 value 1610.471020 iter 5 value 1609.368117 Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced iter 6 value 1609.158772 iter 7 value 1608.135177 iter 8 value 1608.065546 iter 9 value 1608.032577 iter 10 value 1607.329272 iter 11 value 1605.524929 iter 12 value 1605.478613 iter 13 value 1605.466509 iter 14 value 1605.452177 iter 15 value 1605.336005 iter 16 value 1605.321699 iter 17 value 1605.311764 iter 18 value 1605.311720 iter 18 value 1605.311720 iter 18 value 1605.311708 final value 1605.311708 converged > summary(hp) Hurdle Model Summary -------------------- Call: hurdle(count = art ~ ., x = ~fem + mar + kid5 + phd + ment, z = ~fem + mar + kid5 + phd + ment, data = bioChemists, trace = TRUE) Total Log-likelihood: -1605.31170810656 Hurdle Model (Zeros vs Other Outcomes) was fit by logit Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.52203 0.32289 -1.6168 1.059e-01 femWomen 0.25115 0.15911 1.5785 1.144e-01 marMarried -0.32624 0.18082 -1.8042 7.120e-02 kid5 0.28525 0.11113 2.5668 1.026e-02 phd -0.02221 0.07956 -0.2792 7.801e-01 ment -0.08012 0.01302 -6.1552 7.499e-10 Count Model for Non-Zero Outcomes (Poisson) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.81337 0.133570 6.0895 1.133e-09 femWomen -0.22863 0.065212 -3.5059 4.551e-04 marMarried 0.09648 0.072821 1.3249 1.852e-01 kid5 -0.14215 0.048450 -2.9340 3.347e-03 phd -0.01268 0.031302 -0.4052 6.853e-01 ment 0.01874 0.002279 8.2194 2.045e-16 > > hnb <- hurdle(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + dist="negbin", + data=bioChemists,trace=TRUE) Hurdle Count Model Using logit to model zero vs non-zero Using Negative Binomial for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done initial value 1564.927210 iter 2 value 1559.902307 iter 3 value 1556.475887 iter 4 value 1555.276172 iter 5 value 1555.214496 iter 6 value 1554.479147 iter 7 value 1554.461638 iter 8 value 1554.421256 iter 9 value 1554.292492 iter 10 value 1553.953268 iter 11 value 1552.780271 iter 12 value 1552.775277 iter 13 value 1552.611751 iter 14 value 1552.610354 iter 15 value 1552.604919 iter 16 value 1552.600392 iter 17 value 1552.598859 iter 18 value 1552.597471 iter 19 value 1552.596592 iter 19 value 1552.596592 iter 19 value 1552.596592 final value 1552.596592 converged initial value 1552.596592 iter 1 value 1552.596592 final value 1552.596592 converged > summary(hnb) Hurdle Model Summary -------------------- Call: hurdle(count = art ~ ., x = ~fem + mar + kid5 + phd + ment, z = ~fem + mar + kid5 + phd + ment, data = bioChemists, dist = "negbin", trace = TRUE) Total Log-likelihood: -1552.59659159976 Hurdle Model (Zeros vs Other Outcomes) was fit by logit Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.52198 0.32289 -1.6166 1.060e-01 femWomen 0.25115 0.15911 1.5785 1.145e-01 marMarried -0.32623 0.18082 -1.8042 7.120e-02 kid5 0.28524 0.11113 2.5667 1.027e-02 phd -0.02222 0.07956 -0.2793 7.800e-01 ment -0.08013 0.01302 -6.1553 7.495e-10 Count Model for Non-Zero Outcomes (Negative Binomial) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.508352 0.213981 2.37569 1.752e-02 femWomen -0.244673 0.097218 -2.51673 1.184e-02 marMarried 0.103406 0.109430 0.94495 3.447e-01 kid5 -0.153261 0.072229 -2.12186 3.385e-02 phd -0.002936 0.048068 -0.06108 9.513e-01 ment 0.023741 0.004287 5.53729 3.072e-08 1.828459 0.411446 4.44398 8.831e-06 > > > > cleanEx(); ..nameEx <- "odTest" > > ### * odTest > > flush(stderr()); flush(stdout()) > > ### Name: odTest > ### Title: Likelihood ratio test for over-dispersion in count data > ### Aliases: odTest > ### Keywords: regression > > ### ** Examples > > data(bioChemists) > require(MASS) [1] TRUE > modelnb <- glm.nb(art ~ ., + data=bioChemists, + trace=TRUE) Theta( 1 ) = 2.26883 , 2(Ls - Lm) = 1004.93 Theta( 2 ) = 2.26441 , 2(Ls - Lm) = 1004.28 Theta( 3 ) = 2.2644 , 2(Ls - Lm) = 1004.28 Theta( 4 ) = 2.26439 , 2(Ls - Lm) = 1004.28 Theta( 5 ) = 2.26439 , 2(Ls - Lm) = 1004.28 Theta( 6 ) = 2.26439 , 2(Ls - Lm) = 1004.28 Theta( 7 ) = 2.26439 , 2(Ls - Lm) = 1004.28 Theta( 8 ) = 2.26439 , 2(Ls - Lm) = 1004.28 > odTest(modelnb) Likelihood ratio test of HO: Poisson, as restricted NB model: Chi-Square Test Statistic = 180.196 p-value = < 2.2e-16 > > > > cleanEx(); ..nameEx <- "predict.zeroinfl" > > ### * predict.zeroinfl > > flush(stderr()); flush(stdout()) > > ### Name: predict.zeroinfl > ### Title: Generate predictions from zero-inflated regression count models > ### Aliases: predict.zeroinfl > ### Keywords: models regression > > ### ** Examples > > data(bioChemists) > zip <- zeroinfl(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + data=bioChemists,trace=TRUE) Zero-Inflated Count Model Using logit to model zero vs non-zero Using Poisson for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done MLE begins... initial value 1665.398520 iter 2 value 1659.747182 iter 3 value 1616.143856 iter 4 value 1614.089518 iter 5 value 1611.536072 iter 6 value 1610.326118 iter 7 value 1608.700803 iter 8 value 1608.547054 iter 9 value 1608.449363 iter 10 value 1607.752291 iter 11 value 1605.593420 iter 12 value 1605.454372 iter 13 value 1605.121811 iter 14 value 1604.979139 iter 15 value 1604.812503 iter 16 value 1604.780089 iter 17 value 1604.774508 iter 18 value 1604.772901 iter 19 value 1604.772871 iter 19 value 1604.772871 iter 19 value 1604.772871 final value 1604.772871 converged done > > newdata <- expand.grid(list(fem="Men", + mar="Married", + kid5=1, + phd=3.103, + ment=0:77)) > > yhat <- predict(zip,newdata=newdata, + se.fit=TRUE,MC=2500) commencing Monte Carlo simulations for predicted counts MC iterate 1 of 2500 MC iterate 2 of 2500 MC iterate 3 of 2500 MC iterate 4 of 2500 MC iterate 5 of 2500 MC iterate 6 of 2500 MC iterate 7 of 2500 MC iterate 8 of 2500 MC iterate 9 of 2500 MC iterate 10 of 2500 MC iterate 11 of 2500 MC iterate 12 of 2500 MC iterate 13 of 2500 MC iterate 14 of 2500 MC iterate 15 of 2500 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2500 MC iterate 1061 of 2500 MC iterate 1062 of 2500 MC iterate 1063 of 2500 MC iterate 1064 of 2500 MC iterate 1065 of 2500 MC iterate 1066 of 2500 MC iterate 1067 of 2500 MC iterate 1068 of 2500 MC iterate 1069 of 2500 MC iterate 1070 of 2500 MC iterate 1071 of 2500 MC iterate 1072 of 2500 MC iterate 1073 of 2500 MC iterate 1074 of 2500 MC iterate 1075 of 2500 MC iterate 1076 of 2500 MC iterate 1077 of 2500 MC iterate 1078 of 2500 MC iterate 1079 of 2500 MC iterate 1080 of 2500 MC iterate 1081 of 2500 MC iterate 1082 of 2500 MC iterate 1083 of 2500 MC iterate 1084 of 2500 MC iterate 1085 of 2500 MC iterate 1086 of 2500 MC iterate 1087 of 2500 MC iterate 1088 of 2500 MC iterate 1089 of 2500 MC iterate 1090 of 2500 MC iterate 1091 of 2500 MC iterate 1092 of 2500 MC iterate 1093 of 2500 MC iterate 1094 of 2500 MC iterate 1095 of 2500 MC iterate 1096 of 2500 MC iterate 1097 of 2500 MC iterate 1098 of 2500 MC iterate 1099 of 2500 MC iterate 1100 of 2500 MC iterate 1101 of 2500 MC iterate 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2500 MC iterate 1311 of 2500 MC iterate 1312 of 2500 MC iterate 1313 of 2500 MC iterate 1314 of 2500 MC iterate 1315 of 2500 MC iterate 1316 of 2500 MC iterate 1317 of 2500 MC iterate 1318 of 2500 MC iterate 1319 of 2500 MC iterate 1320 of 2500 MC iterate 1321 of 2500 MC iterate 1322 of 2500 MC iterate 1323 of 2500 MC iterate 1324 of 2500 MC iterate 1325 of 2500 MC iterate 1326 of 2500 MC iterate 1327 of 2500 MC iterate 1328 of 2500 MC iterate 1329 of 2500 MC iterate 1330 of 2500 MC iterate 1331 of 2500 MC iterate 1332 of 2500 MC iterate 1333 of 2500 MC iterate 1334 of 2500 MC iterate 1335 of 2500 MC iterate 1336 of 2500 MC iterate 1337 of 2500 MC iterate 1338 of 2500 MC iterate 1339 of 2500 MC iterate 1340 of 2500 MC iterate 1341 of 2500 MC iterate 1342 of 2500 MC iterate 1343 of 2500 MC iterate 1344 of 2500 MC iterate 1345 of 2500 MC iterate 1346 of 2500 MC iterate 1347 of 2500 MC iterate 1348 of 2500 MC iterate 1349 of 2500 MC iterate 1350 of 2500 MC iterate 1351 of 2500 MC iterate 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2102 of 2500 MC iterate 2103 of 2500 MC iterate 2104 of 2500 MC iterate 2105 of 2500 MC iterate 2106 of 2500 MC iterate 2107 of 2500 MC iterate 2108 of 2500 MC iterate 2109 of 2500 MC iterate 2110 of 2500 MC iterate 2111 of 2500 MC iterate 2112 of 2500 MC iterate 2113 of 2500 MC iterate 2114 of 2500 MC iterate 2115 of 2500 MC iterate 2116 of 2500 MC iterate 2117 of 2500 MC iterate 2118 of 2500 MC iterate 2119 of 2500 MC iterate 2120 of 2500 MC iterate 2121 of 2500 MC iterate 2122 of 2500 MC iterate 2123 of 2500 MC iterate 2124 of 2500 MC iterate 2125 of 2500 MC iterate 2126 of 2500 MC iterate 2127 of 2500 MC iterate 2128 of 2500 MC iterate 2129 of 2500 MC iterate 2130 of 2500 MC iterate 2131 of 2500 MC iterate 2132 of 2500 MC iterate 2133 of 2500 MC iterate 2134 of 2500 MC iterate 2135 of 2500 MC iterate 2136 of 2500 MC iterate 2137 of 2500 MC iterate 2138 of 2500 MC iterate 2139 of 2500 MC iterate 2140 of 2500 MC iterate 2141 of 2500 MC iterate 2142 of 2500 MC iterate 2143 of 2500 MC 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2477 of 2500 MC iterate 2478 of 2500 MC iterate 2479 of 2500 MC iterate 2480 of 2500 MC iterate 2481 of 2500 MC iterate 2482 of 2500 MC iterate 2483 of 2500 MC iterate 2484 of 2500 MC iterate 2485 of 2500 MC iterate 2486 of 2500 MC iterate 2487 of 2500 MC iterate 2488 of 2500 MC iterate 2489 of 2500 MC iterate 2490 of 2500 MC iterate 2491 of 2500 MC iterate 2492 of 2500 MC iterate 2493 of 2500 MC iterate 2494 of 2500 MC iterate 2495 of 2500 MC iterate 2496 of 2500 MC iterate 2497 of 2500 MC iterate 2498 of 2500 MC iterate 2499 of 2500 MC iterate 2500 of 2500 > > ## Not run: > ##D plot(x=newdata$ment, > ##D y=yhat$yhat, > ##D xlab="Mentor Articles", > ##D ylab="Predicted Counts", > ##D ylim=range(zip$y), > ##D type="n") > ##D polygon(x=c(newdata$ment,rev(newdata$ment)), > ##D y=c(yhat$lower,rev(yhat$upper)), > ##D border=FALSE, > ##D col=gray(.75)) > ##D lines(x=newdata$ment, > ##D y=yhat$yhat, > ##D lwd=2) > ##D rug(quantile(bioChemists$ment,c(.05,.50,.95))) > ##D title("Predicted Counts and 95 > ## End(Not run) > > > > cleanEx(); ..nameEx <- "predprob" > > ### * predprob > > flush(stderr()); flush(stdout()) > > ### Name: predprob > ### Title: Compute predicted probabilities from fitted models > ### Aliases: predprob > ### Keywords: regression models > > ### ** Examples > > data(bioChemists) > zip <- zeroinfl(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + dist="poisson", + data=bioChemists,trace=TRUE) ## zero-infl poisson Zero-Inflated Count Model Using logit to model zero vs non-zero Using Poisson for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done MLE begins... initial value 1665.398520 iter 2 value 1659.747182 iter 3 value 1616.143856 iter 4 value 1614.089518 iter 5 value 1611.536072 iter 6 value 1610.326118 iter 7 value 1608.700803 iter 8 value 1608.547054 iter 9 value 1608.449363 iter 10 value 1607.752291 iter 11 value 1605.593420 iter 12 value 1605.454372 iter 13 value 1605.121811 iter 14 value 1604.979139 iter 15 value 1604.812503 iter 16 value 1604.780089 iter 17 value 1604.774508 iter 18 value 1604.772901 iter 19 value 1604.772871 iter 19 value 1604.772871 iter 19 value 1604.772871 final value 1604.772871 converged done > phat <- predprob(zip) > > newdata <- expand.grid(list(fem="Men",mar="Married", + kid5=1,phd=3.103, + ment=0:77)) > phat <- predprob(zip,newdata=newdata) > > > > cleanEx(); ..nameEx <- "predprob.glm" > > ### * predprob.glm > > flush(stderr()); flush(stdout()) > > ### Name: predprob.glm > ### Title: Predicted Probabilties for GLM Fits > ### Aliases: predprob.glm > ### Keywords: models regression > > ### ** Examples > > data(bioChemists) > glm1 <- glm(art ~ ., + data=bioChemists, + family=poisson, + trace=TRUE) ## poisson GLM Deviance = 2002.609 Iterations - 1 Deviance = 1647.697 Iterations - 2 Deviance = 1634.404 Iterations - 3 Deviance = 1634.371 Iterations - 4 Deviance = 1634.371 Iterations - 5 > phat <- predprob(glm1) > apply(phat,1,sum) ## almost all 1.0 [1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [22] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [50] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [57] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [64] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [71] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [78] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [85] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [92] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [99] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [106] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [113] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [120] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [127] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [134] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [141] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [148] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [155] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [162] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [169] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [176] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [183] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [190] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [197] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [204] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [211] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [218] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [225] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [232] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [239] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [246] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [253] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [260] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [267] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [274] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [281] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [288] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [295] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [302] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [309] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [316] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [323] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9977228 1.0000000 [330] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [337] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [344] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [351] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [358] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [365] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [372] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [379] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [386] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [393] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [400] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [407] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [414] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [421] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [428] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [435] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [442] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [449] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [456] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [463] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [470] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [477] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9999999 [484] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [491] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [498] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [505] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [512] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [519] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [526] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [533] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [540] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [547] 0.9999989 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [554] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [561] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [568] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [575] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [582] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [589] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [596] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [603] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [610] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [617] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [624] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [631] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [638] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [645] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [652] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [659] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [666] 1.0000000 1.0000000 1.0000000 0.9999963 1.0000000 1.0000000 1.0000000 [673] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [680] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [687] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [694] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [701] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [708] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [715] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [722] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [729] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [736] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [743] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [750] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [757] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [764] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [771] 0.9999999 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [778] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [785] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [792] 1.0000000 1.0000000 1.0000000 0.9999999 1.0000000 1.0000000 1.0000000 [799] 0.9999963 1.0000000 1.0000000 1.0000000 0.9999905 1.0000000 1.0000000 [806] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9999999 [813] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [820] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [827] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [834] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [841] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [848] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [855] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [862] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [869] 1.0000000 1.0000000 1.0000000 0.9999989 1.0000000 1.0000000 1.0000000 [876] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [883] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [890] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [897] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [904] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9999999 1.0000000 [911] 1.0000000 1.0000000 1.0000000 1.0000000 0.9999998 > > > > cleanEx(); ..nameEx <- "predprob.zeroinfl" > > ### * predprob.zeroinfl > > flush(stderr()); flush(stdout()) > > ### Name: predprob.zeroinfl > ### Title: Predicted probabilities from zero-inflated regression models > ### Aliases: predprob.zeroinfl > ### Keywords: models regression > > ### ** Examples > > data(bioChemists) > zip <- zeroinfl(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + dist="poisson", + data=bioChemists,trace=TRUE) ## zero-infl poisson Zero-Inflated Count Model Using logit to model zero vs non-zero Using Poisson for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done MLE begins... initial value 1665.398520 iter 2 value 1659.747182 iter 3 value 1616.143856 iter 4 value 1614.089518 iter 5 value 1611.536072 iter 6 value 1610.326118 iter 7 value 1608.700803 iter 8 value 1608.547054 iter 9 value 1608.449363 iter 10 value 1607.752291 iter 11 value 1605.593420 iter 12 value 1605.454372 iter 13 value 1605.121811 iter 14 value 1604.979139 iter 15 value 1604.812503 iter 16 value 1604.780089 iter 17 value 1604.774508 iter 18 value 1604.772901 iter 19 value 1604.772871 iter 19 value 1604.772871 iter 19 value 1604.772871 final value 1604.772871 converged done > phat <- predprob(zip) > newdata <- expand.grid(list(fem="Men",mar="Married", + kid5=1,phd=3.103, + ment=0:77)) > phat <- predprob(zip,newdata=newdata) > > > > cleanEx(); ..nameEx <- "prussian" > > ### * prussian > > flush(stderr()); flush(stdout()) > > ### Name: prussian > ### Title: Prussian Army Horse Kick Data > ### Aliases: prussian > ### Keywords: datasets > > ### ** Examples > > data(prussian) > corpP <- glm(y ~ corp, family=poisson,data=prussian) > summary(corpP) Call: glm(formula = y ~ corp, family = poisson, data = prussian) Deviance Residuals: Min 1Q Median 3Q Max -1.5811 -1.0954 -0.8367 0.5438 2.0079 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.231e-01 2.500e-01 -0.893 0.3721 corpI 4.072e-09 3.535e-01 1.15e-08 1.0000 corpII -2.877e-01 3.819e-01 -0.753 0.4512 corpIII -2.877e-01 3.819e-01 -0.753 0.4512 corpIV -6.931e-01 4.330e-01 -1.601 0.1094 corpIX -2.076e-01 3.734e-01 -0.556 0.5781 corpV -3.747e-01 3.917e-01 -0.957 0.3387 corpVI 6.062e-02 3.483e-01 0.174 0.8618 corpVII -2.877e-01 3.819e-01 -0.753 0.4512 corpVIII -8.267e-01 4.532e-01 -1.824 0.0681 . corpX -6.454e-02 3.594e-01 -0.180 0.8575 corpXI 4.463e-01 3.202e-01 1.394 0.1633 corpXIV 4.055e-01 3.227e-01 1.256 0.2090 corpXV -6.931e-01 4.330e-01 -1.601 0.1094 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 323.23 on 279 degrees of freedom Residual deviance: 297.09 on 266 degrees of freedom AIC: 630.17 Number of Fisher Scoring iterations: 5 > > > > cleanEx(); ..nameEx <- "summary.hurdle" > > ### * summary.hurdle > > flush(stderr()); flush(stdout()) > > ### Name: summary.hurdle > ### Title: Summarzing Hurdle Regression Models for Count Data > ### Aliases: summary.hurdle print.summary.hurdle coef.hurdle > ### Keywords: models regression > > ### ** Examples > data(bioChemists) > hp <- hurdle(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + data=bioChemists,trace=TRUE) Hurdle Count Model Using logit to model zero vs non-zero Using Poisson for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done initial value 1625.139141 iter 2 value 1623.624356 Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced iter 3 value 1612.056056 Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced iter 4 value 1610.471020 iter 5 value 1609.368117 Warning in dpois(x, lambda, log) : NaNs produced Warning in dpois(x, lambda, log) : NaNs produced iter 6 value 1609.158772 iter 7 value 1608.135177 iter 8 value 1608.065546 iter 9 value 1608.032577 iter 10 value 1607.329272 iter 11 value 1605.524929 iter 12 value 1605.478613 iter 13 value 1605.466509 iter 14 value 1605.452177 iter 15 value 1605.336005 iter 16 value 1605.321699 iter 17 value 1605.311764 iter 18 value 1605.311720 iter 18 value 1605.311720 iter 18 value 1605.311708 final value 1605.311708 converged > summary(hp) Hurdle Model Summary -------------------- Call: hurdle(count = art ~ ., x = ~fem + mar + kid5 + phd + ment, z = ~fem + mar + kid5 + phd + ment, data = bioChemists, trace = TRUE) Total Log-likelihood: -1605.31170810656 Hurdle Model (Zeros vs Other Outcomes) was fit by logit Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.52203 0.32289 -1.6168 1.059e-01 femWomen 0.25115 0.15911 1.5785 1.144e-01 marMarried -0.32624 0.18082 -1.8042 7.120e-02 kid5 0.28525 0.11113 2.5668 1.026e-02 phd -0.02221 0.07956 -0.2792 7.801e-01 ment -0.08012 0.01302 -6.1552 7.499e-10 Count Model for Non-Zero Outcomes (Poisson) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.81337 0.133570 6.0895 1.133e-09 femWomen -0.22863 0.065212 -3.5059 4.551e-04 marMarried 0.09648 0.072821 1.3249 1.852e-01 kid5 -0.14215 0.048450 -2.9340 3.347e-03 phd -0.01268 0.031302 -0.4052 6.853e-01 ment 0.01874 0.002279 8.2194 2.045e-16 > > hnb <- hurdle(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + dist="negbin", + data=bioChemists,trace=TRUE) Hurdle Count Model Using logit to model zero vs non-zero Using Negative Binomial for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done initial value 1564.927210 iter 2 value 1559.902307 iter 3 value 1556.475887 iter 4 value 1555.276172 iter 5 value 1555.214496 iter 6 value 1554.479147 iter 7 value 1554.461638 iter 8 value 1554.421256 iter 9 value 1554.292492 iter 10 value 1553.953268 iter 11 value 1552.780271 iter 12 value 1552.775277 iter 13 value 1552.611751 iter 14 value 1552.610354 iter 15 value 1552.604919 iter 16 value 1552.600392 iter 17 value 1552.598859 iter 18 value 1552.597471 iter 19 value 1552.596592 iter 19 value 1552.596592 iter 19 value 1552.596592 final value 1552.596592 converged initial value 1552.596592 iter 1 value 1552.596592 final value 1552.596592 converged > summary(hnb) Hurdle Model Summary -------------------- Call: hurdle(count = art ~ ., x = ~fem + mar + kid5 + phd + ment, z = ~fem + mar + kid5 + phd + ment, data = bioChemists, dist = "negbin", trace = TRUE) Total Log-likelihood: -1552.59659159976 Hurdle Model (Zeros vs Other Outcomes) was fit by logit Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.52198 0.32289 -1.6166 1.060e-01 femWomen 0.25115 0.15911 1.5785 1.145e-01 marMarried -0.32623 0.18082 -1.8042 7.120e-02 kid5 0.28524 0.11113 2.5667 1.027e-02 phd -0.02222 0.07956 -0.2793 7.800e-01 ment -0.08013 0.01302 -6.1553 7.495e-10 Count Model for Non-Zero Outcomes (Negative Binomial) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.508352 0.213981 2.37569 1.752e-02 femWomen -0.244673 0.097218 -2.51673 1.184e-02 marMarried 0.103406 0.109430 0.94495 3.447e-01 kid5 -0.153261 0.072229 -2.12186 3.385e-02 phd -0.002936 0.048068 -0.06108 9.513e-01 ment 0.023741 0.004287 5.53729 3.072e-08 1.828459 0.411446 4.44398 8.831e-06 > > > > cleanEx(); ..nameEx <- "summary.zeroinfl" > > ### * summary.zeroinfl > > flush(stderr()); flush(stdout()) > > ### Name: summary.zeroinfl > ### Title: Summarizing Zero-Inflated Regression Models for Count Data > ### Aliases: summary.zeroinfl coef.zeroinfl print.summary.zeroinfl > ### Keywords: models regression > > ### ** Examples > > data(bioChemists) > zip <- zeroinfl(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + data=bioChemists,trace=TRUE) Zero-Inflated Count Model Using logit to model zero vs non-zero Using Poisson for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done MLE begins... initial value 1665.398520 iter 2 value 1659.747182 iter 3 value 1616.143856 iter 4 value 1614.089518 iter 5 value 1611.536072 iter 6 value 1610.326118 iter 7 value 1608.700803 iter 8 value 1608.547054 iter 9 value 1608.449363 iter 10 value 1607.752291 iter 11 value 1605.593420 iter 12 value 1605.454372 iter 13 value 1605.121811 iter 14 value 1604.979139 iter 15 value 1604.812503 iter 16 value 1604.780089 iter 17 value 1604.774508 iter 18 value 1604.772901 iter 19 value 1604.772871 iter 19 value 1604.772871 iter 19 value 1604.772871 final value 1604.772871 converged done > summary(zip) Zero-Inflated Count Model Summary --------------------------- Call: zeroinfl(count = art ~ ., x = ~fem + mar + kid5 + phd + ment, z = ~fem + mar + kid5 + phd + ment, data = bioChemists, trace = TRUE) Total Log-likelihood: -1604.77287068948 Zero-Inflated Model was fit with a logit link Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.794242 0.56519 -1.40526 0.159943 femWomen 0.109760 0.28003 0.39195 0.695092 marMarried -0.354161 0.31756 -1.11526 0.264739 kid5 0.217138 0.19645 1.10529 0.269035 phd 0.001479 0.14524 0.01018 0.991874 ment -0.134180 0.04523 -2.96629 0.003014 --------------------------------------------------------------------------- Count Model (Poisson) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.784132 0.132852 5.9023 3.585e-09 femWomen -0.209164 0.063402 -3.2990 9.703e-04 marMarried 0.103727 0.071108 1.4587 1.446e-01 kid5 -0.143289 0.047428 -3.0212 2.518e-03 phd -0.006112 0.031007 -0.1971 8.437e-01 ment 0.018084 0.002294 7.8830 3.196e-15 > coef(zip) (Intercept) femWomen marMarried kid5 phd ment -0.794241894 0.109759642 -0.354161125 0.217137785 0.001479174 -0.134179755 (Intercept) femWomen marMarried kid5 phd ment 0.784131679 -0.209164438 0.103727148 -0.143288564 -0.006111670 0.018084453 > logLik(zip) [1] -1604.773 > > > > cleanEx(); ..nameEx <- "vuong" > > ### * vuong > > flush(stderr()); flush(stdout()) > > ### Name: vuong > ### Title: Vuong's non-nested hypothesis test > ### Aliases: vuong > ### Keywords: models > > ### ** Examples > > data(bioChemists) > glm1 <- glm(art ~ ., + data=bioChemists, + family=poisson,trace=TRUE) ## poisson GLM Deviance = 2002.609 Iterations - 1 Deviance = 1647.697 Iterations - 2 Deviance = 1634.404 Iterations - 3 Deviance = 1634.371 Iterations - 4 Deviance = 1634.371 Iterations - 5 > > zip <- zeroinfl(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + dist="poisson", + data=bioChemists,trace=TRUE) ## zero-infl poisson Zero-Inflated Count Model Using logit to model zero vs non-zero Using Poisson for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done MLE begins... initial value 1665.398520 iter 2 value 1659.747182 iter 3 value 1616.143856 iter 4 value 1614.089518 iter 5 value 1611.536072 iter 6 value 1610.326118 iter 7 value 1608.700803 iter 8 value 1608.547054 iter 9 value 1608.449363 iter 10 value 1607.752291 iter 11 value 1605.593420 iter 12 value 1605.454372 iter 13 value 1605.121811 iter 14 value 1604.979139 iter 15 value 1604.812503 iter 16 value 1604.780089 iter 17 value 1604.774508 iter 18 value 1604.772901 iter 19 value 1604.772871 iter 19 value 1604.772871 iter 19 value 1604.772871 final value 1604.772871 converged done > > vuong(glm1,zip) Vuong Non-Nested Hypothesis Test-Statistic: -4.179006 (test-statistic is asymptotically distributed N(0,1) under the null that the models are indistinguishible) in this case: model2 > model1, with p-value 1.46393e-05 > > ## compare negbin with zero-inflated negbin > require(MASS) [1] TRUE > nb1 <- glm.nb(art ~ ., + data=bioChemists, + trace=TRUE) Theta( 1 ) = 2.26883 , 2(Ls - Lm) = 1004.93 Theta( 2 ) = 2.26441 , 2(Ls - Lm) = 1004.28 Theta( 3 ) = 2.2644 , 2(Ls - Lm) = 1004.28 Theta( 4 ) = 2.26439 , 2(Ls - Lm) = 1004.28 Theta( 5 ) = 2.26439 , 2(Ls - Lm) = 1004.28 Theta( 6 ) = 2.26439 , 2(Ls - Lm) = 1004.28 Theta( 7 ) = 2.26439 , 2(Ls - Lm) = 1004.28 Theta( 8 ) = 2.26439 , 2(Ls - Lm) = 1004.28 > zinb <- zeroinfl(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + dist="negbin", + data=bioChemists,trace=TRUE) Zero-Inflated Count Model Using logit to model zero vs non-zero Using Negative Binomial for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done MLE begins... initial value 1702.797924 iter 2 value 1689.637397 iter 3 value 1638.075496 iter 4 value 1612.254390 iter 5 value 1608.250215 iter 6 value 1603.664346 iter 7 value 1602.760824 iter 8 value 1598.613806 iter 9 value 1593.278058 iter 10 value 1579.281667 iter 11 value 1572.057123 iter 12 value 1568.009187 iter 13 value 1565.033552 iter 14 value 1561.086152 iter 15 value 1558.865116 iter 16 value 1555.990904 iter 17 value 1555.164922 iter 18 value 1553.965979 iter 19 value 1552.756866 iter 20 value 1551.406096 iter 21 value 1550.558232 iter 22 value 1550.224317 iter 23 value 1550.088402 iter 24 value 1550.027455 iter 25 value 1549.999960 iter 26 value 1549.993086 iter 27 value 1549.991543 iter 28 value 1549.991080 iter 28 value 1549.991080 final value 1549.991080 converged done > vuong(nb1,zinb) Vuong Non-Nested Hypothesis Test-Statistic: -2.239637 (test-statistic is asymptotically distributed N(0,1) under the null that the models are indistinguishible) in this case: model2 > model1, with p-value 0.01255724 > > > > cleanEx(); ..nameEx <- "zeroinfl" > > ### * zeroinfl > > flush(stderr()); flush(stdout()) > > ### Name: zeroinfl > ### Title: Zero-inflated regression models for count data > ### Aliases: zeroinfl print.zeroinfl logLik.zeroinfl > ### Keywords: models regression > > ### ** Examples > > data(bioChemists) > zip <- zeroinfl(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + data=bioChemists,trace=TRUE) Zero-Inflated Count Model Using logit to model zero vs non-zero Using Poisson for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done MLE begins... initial value 1665.398520 iter 2 value 1659.747182 iter 3 value 1616.143856 iter 4 value 1614.089518 iter 5 value 1611.536072 iter 6 value 1610.326118 iter 7 value 1608.700803 iter 8 value 1608.547054 iter 9 value 1608.449363 iter 10 value 1607.752291 iter 11 value 1605.593420 iter 12 value 1605.454372 iter 13 value 1605.121811 iter 14 value 1604.979139 iter 15 value 1604.812503 iter 16 value 1604.780089 iter 17 value 1604.774508 iter 18 value 1604.772901 iter 19 value 1604.772871 iter 19 value 1604.772871 iter 19 value 1604.772871 final value 1604.772871 converged done > summary(zip) Zero-Inflated Count Model Summary --------------------------- Call: zeroinfl(count = art ~ ., x = ~fem + mar + kid5 + phd + ment, z = ~fem + mar + kid5 + phd + ment, data = bioChemists, trace = TRUE) Total Log-likelihood: -1604.77287068948 Zero-Inflated Model was fit with a logit link Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.794242 0.56519 -1.40526 0.159943 femWomen 0.109760 0.28003 0.39195 0.695092 marMarried -0.354161 0.31756 -1.11526 0.264739 kid5 0.217138 0.19645 1.10529 0.269035 phd 0.001479 0.14524 0.01018 0.991874 ment -0.134180 0.04523 -2.96629 0.003014 --------------------------------------------------------------------------- Count Model (Poisson) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.784132 0.132852 5.9023 3.585e-09 femWomen -0.209164 0.063402 -3.2990 9.703e-04 marMarried 0.103727 0.071108 1.4587 1.446e-01 kid5 -0.143289 0.047428 -3.0212 2.518e-03 phd -0.006112 0.031007 -0.1971 8.437e-01 ment 0.018084 0.002294 7.8830 3.196e-15 > > zinb <- zeroinfl(count=art ~ ., + x = ~ fem + mar + kid5 + phd + ment, + z = ~ fem + mar + kid5 + phd + ment, + dist="negbin", + data=bioChemists,trace=TRUE) Zero-Inflated Count Model Using logit to model zero vs non-zero Using Negative Binomial for counts dependent variable y: Y 0 1 2 3 4 5 6 7 8 9 10 11 12 16 19 275 246 178 84 67 27 17 12 1 2 1 1 2 1 1 generating start values...done MLE begins... initial value 1702.797924 iter 2 value 1689.637397 iter 3 value 1638.075496 iter 4 value 1612.254390 iter 5 value 1608.250215 iter 6 value 1603.664346 iter 7 value 1602.760824 iter 8 value 1598.613806 iter 9 value 1593.278058 iter 10 value 1579.281667 iter 11 value 1572.057123 iter 12 value 1568.009187 iter 13 value 1565.033552 iter 14 value 1561.086152 iter 15 value 1558.865116 iter 16 value 1555.990904 iter 17 value 1555.164922 iter 18 value 1553.965979 iter 19 value 1552.756866 iter 20 value 1551.406096 iter 21 value 1550.558232 iter 22 value 1550.224317 iter 23 value 1550.088402 iter 24 value 1550.027455 iter 25 value 1549.999960 iter 26 value 1549.993086 iter 27 value 1549.991543 iter 28 value 1549.991080 iter 28 value 1549.991080 final value 1549.991080 converged done > summary(zinb) Zero-Inflated Count Model Summary --------------------------- Call: zeroinfl(count = art ~ ., x = ~fem + mar + kid5 + phd + ment, z = ~fem + mar + kid5 + phd + ment, data = bioChemists, dist = "negbin", trace = TRUE) Total Log-likelihood: -1549.99107953094 Zero-Inflated Model was fit with a logit link Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.83353 1.3732 -0.6070 0.543854 femWomen 0.63907 0.8471 0.7544 0.450601 marMarried -1.49411 0.9351 -1.5978 0.110084 kid5 0.62908 0.4419 1.4235 0.154597 phd -0.03370 0.3072 -0.1097 0.912647 ment -0.88478 0.3173 -2.7885 0.005295 --------------------------------------------------------------------------- Count Model (Negative Binomial) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.5685215 0.155884 3.64708 2.652e-04 femWomen -0.1954416 0.075565 -2.58641 9.698e-03 marMarried 0.0976266 0.084433 1.15626 2.476e-01 kid5 -0.1517195 0.054201 -2.79919 5.123e-03 phd -0.0007393 0.036267 -0.02038 9.837e-01 ment 0.0247863 0.003493 7.09692 1.276e-12 log(theta) 0.9764759 0.135464 7.20838 5.662e-13 Theta = 2.6551 > > > > ### *