Mprobit=rep(1,n+1)
Mlogit=rep(1,n+1)
Mcll=rep(1,n+1)

Mprobit[1]=mean(Mprobitd)
Mlogit[1]=mean(Mlogitd)
Mcll[1]=mean(Mclld)

for(i in 1:n){
yy=y[-i]
NN=N[-i]

pprobit=pnorm(X[-i,] %*% Xtildeinv %*% qnorm(ptilde))
plogit=logistic(X[-i,] %*% Xtildeinv %*% logit(ptilde))
pcll=Gum(X[-i,] %*% Xtildeinv %*% cll(ptilde))

for(r in 1:iterates){
Mprobitd[r]=den(pprobit[,r],yy,NN)
Mlogitd[r]=den(plogit[,r],yy,NN)
Mclld[r]=den(pcll[,r],yy,NN)
}

Mprobit[i+1]=mean(Mprobitd)
Mlogit[i+1]=mean(Mlogitd)
Mcll[i+1]=mean(Mclld)
}

# Figure 13.13 in text
plot(seq(0,n),Mcll/Mlogit,xlab="Index",ylab="Bayes Factor",
ylim=c(1,1.5),type="l",lty=2)
lines(seq(0,n),Mprobit/Mlogit)
legend("left",c("Log-Log","Probit"),lty=c(2,1))