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> ### > 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("logistf-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('logistf') LOGISTF library by Meinhard Ploner, Daniela Dunkler*, Harry Southworth, Georg Heinze*, *Medical University of Vienna Version 1.03 (Build 2005.03.30) for comments mailto:georg.heinze@meduniwien.ac.at > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "logistf" > > ### * logistf > > flush(stderr()); flush(stdout()) > > ### Name: logistf > ### Title: Bias-reduced logistic regression > ### Aliases: logistf logistpl print.logistf summary.logistf > ### Keywords: regression models > > ### ** Examples > > data(sex2) > fit<-logistf(case ~ age+oc+vic+vicl+vis+dia, data=sex2) > summary(fit) logistf(formula = case ~ age + oc + vic + vicl + vis + dia, data = sex2) Model fitted by Penalized ML Confidence intervals and p-values by Profile Likelihood coef se(coef) lower 0.95 upper 0.95 Chisq p (Intercept) 0.1202541 0.4855415 -0.8185777 1.0731445 0.06286298 8.020268e-01 age -1.1059815 0.4236601 -1.9737949 -0.3074251 7.50773092 6.143472e-03 oc -0.0688167 0.4437934 -0.9414289 0.7891995 0.02467044 8.751911e-01 vic 2.2688747 0.5484159 1.2730212 3.4354329 22.93139022 1.678877e-06 vicl -2.1114083 0.5430823 -3.2608638 -1.1177349 19.10407252 1.237805e-05 vis -0.7883170 0.4173676 -1.6080866 0.0151937 3.69740975 5.449701e-02 dia 3.0960078 1.6750220 0.7745682 8.0302936 7.89693139 4.951873e-03 Likelihood ratio test=49.09064 on 6 df, p=7.15089e-09, n=239 Wald test = 30.64062 on 6 df, p = 2.968434e-05 Covariance-Matrix: (Intercept) age oc vic vicl (Intercept) 0.23575053 -0.0274268412 -0.195208786 -0.12109972 -0.028931307 age -0.02742684 0.1794879029 0.001817975 -0.02316590 0.034595718 oc -0.19520879 0.0018179746 0.196952606 0.08788775 0.025843014 vic -0.12109972 -0.0231659050 0.087887752 0.30076002 -0.195031732 vicl -0.02893131 0.0345957183 0.025843014 -0.19503173 0.294938423 vis -0.04648040 0.0007177051 0.042053095 -0.01532463 -0.047269908 dia -0.02697946 -0.0655716489 0.025480432 0.02521151 -0.008037505 vis dia (Intercept) -0.0464803993 -0.026979464 age 0.0007177051 -0.065571649 oc 0.0420530953 0.025480432 vic -0.0153246333 0.025211511 vicl -0.0472699084 -0.008037505 vis 0.1741956783 -0.045265574 dia -0.0452655740 2.805698651 > > > > cleanEx(); ..nameEx <- "logistftest" > > ### * logistftest > > flush(stderr()); flush(stdout()) > > ### Name: logistftest > ### Title: Penalized likelihood ratio test > ### Aliases: logistftest print.logistftest > ### Keywords: regression models > > ### ** Examples > > data(sex2) > logistftest(case ~ age+oc+vic+vicl+vis+dia, sex2, + test = ~ vic + vicl - 1, values = c(2, 0)) logistftest(formula = case ~ age + oc + vic + vicl + vis + dia, data = sex2, test = ~vic + vicl - 1, values = c(2, 0)) Model fitted by Penalized ML Factors fixed as follows: (Intercept) age oc vic vicl vis NA NA NA 2 0 NA dia NA Likelihoods: Restricted model Full model difference -144.49700 -132.53938 11.95762 Likelihood ratio test=23.91523 on 2 df, p=6.410225e-06 > > > > cleanEx(); ..nameEx <- "sex2" > > ### * sex2 > > flush(stderr()); flush(stdout()) > > ### Name: sex2 > ### Title: Condom use and first time urinary tract infection > ### Aliases: sex2 > ### Keywords: datasets > > ### ** Examples > > data(sex2) > > > > ### *