##### Chapter 12 #####

table.12.1<-read.table("primeminister.txt",col.names=c("case", "occasion", "response", "count"))

temp1<-cbind(case=rep(1:794,each=2),table.12.1[rep(1:2,794),2:3])
temp2<-cbind(case=rep(795:(794+150),each=2),table.12.1[rep(3:4,150),2:3])
temp3<-cbind(case=rep(945:(944+86),each=2),table.12.1[rep(5:6,86),2:3])
temp4<-cbind(case=rep(1031:(1030+570),each=2),table.12.1[rep(7:8,570),2:3])

table.12.1a<-rbind(temp1,temp2,temp3,temp4)
table.12.1a$case<-factor(table.12.1a$case)

library(MASS)
fit.glmmPQL<-glmmPQL(response~occasion, random=~1 | case , family=binomial, data=table.12.1a)

library(repeated)
fit.glmm<-glmm(response~occasion , nest=case, family=binomial, data=table.12.1a, points=10)

library(glmmML)
fit.glmmML<-glmmML(response~occasion , cluster=table.12.1a$case, family=binomial, data=table.12.1a)