## ----------------------------------------------------------------------------- library(rioplot) data(Kenworthy99) m1 <- lm(scale(dv) ~ scale(gdp) + scale(pov) + scale(tran) -1,data=Kenworthy99) decompose.model(m1,group.by = c(rep(1,5),rep(2,5),rep(3,5)),include.int = "no") ## ----------------------------------------------------------------------------- data("Hilbe") Hilbe <- data.frame(Hilbe,binAffairs=ifelse(Hilbe$naffairs>0,1,0)) m2<-glm(binAffairs ~ avgmarr + hapavg + vryhap + smerel + vryrel + yrsmarr4 + yrsmarr5 + yrsmarr6,data=Hilbe, family=binomial()) decompose.model(m2,group.by = c(rep(1,201),rep(2,200),rep(3,200)), model.type = "logit") ## ----------------------------------------------------------------------------- m3<-glm(naffairs~avgmarr + hapavg + vryhap + smerel + vryrel + yrsmarr4 + yrsmarr5 + yrsmarr6,data=Hilbe,family=poisson(link="log")) decompose.model(m3,group.by = c(rep(1,201),rep(2,200),rep(3,200)), model.type="poisson") ## ----------------------------------------------------------------------------- library(MASS) m4<-glm.nb(naffairs~avgmarr + hapavg + vryhap + smerel + vryrel + yrsmarr4 + yrsmarr5 + yrsmarr6,data=Hilbe) decompose.model(m4,group.by = c(rep(1,201),rep(2,200),rep(3,200)),model.type="nb")