`table10pt2.fn` <-
function(ni=3000,nb=2000,nthin=1,nchain=3,nz=160){
    
    
# Jolly-Seber Model - Schwarz-Arnason parameterization by data augmentation.
# This R script and model produced the results in Table 10.2.
 
    
library("R2WinBUGS")

y<-dipper.data$y
y[is.na(y)]<-0
y<-rbind(y,matrix(rep(0,ncol(y)*nz),ncol=ncol(y),byrow=TRUE))
M<-nrow(y)
T<-ncol(y)
z<-y


sink("jsmodel.txt")
cat("
model {

for(j in 1:(T-1)){
phi0[j]~dunif(0,1)
lphi[j]<-log(phi0[j]/(1-phi0[j]))
}
psi~dunif(0,1)

# arbitrary constrains on p[]
ptmp[1]<- 1
ptmp[T]<- 1
for(i in 2:(T-1)){
p[i]~dunif(0,1)
ptmp[i]<-p[i]
}

for(j in 1:M){
for(i in 1:(T-1)){
logit(phi[j,i])<-lphi[i] 
}
}

# Dirichlet prior for entrance probabilities
for(i in 1:T){
beta[i]~dgamma(1,1)
gamma[i]<-beta[i]/sum(beta[1:T])
}
# convert entrance probs to conditional entrance probs
# the number of individuals recruited is a probability, expressed
# relative to individuals in the super-population of size sum(w[1:M])
# that have not yet recruited.
cprob[1]<-gamma[1]
for(j in 2:T){
 cprob[j]<-gamma[j]/(1-sum(gamma[1:(j-1)]))
}

for(i in 1:M){

w[i]~dbin(psi,1)
z[i,1]~dbin(gamma[1],1)
mu[i]<-z[i,1]*ptmp[1]*w[i]
y[i,1]~dbin(mu[i],1)
recruitable[i,1]<-1-z[i,1]

for(j in 2:T){
 part1[i,j]<- phi[i,j-1]*z[i,j-1]
 recruitable[i,j]<- recruitable[i,(j-1)]*(1-z[i,j])
 muz[i,j]<-  part1[i,j] + cprob[j]*recruitable[i,j-1] 
 z[i,j]~dbin(muz[i,j],1)
 muy[i,j]<-z[i,j]*ptmp[j]*w[i]
 y[i,j]~dbin(muy[i,j],1)
}

} 

for(i in 1:T){
N[i]<-inprod(z[1:M,i],w[1:M])
}
Nsuper<-sum(w[1:M])


}
",fill=TRUE)
sink()


data <- list ("y","M","T")
inits <- function(){
  list (psi=runif(1), w=rbinom(M,1,.5), phi0=runif(T-1,.2,.9), z=z)
}

parameters <- c("psi","p","phi0","N","Nsuper","gamma")


out <- bugs (data, inits, parameters, "jsmodel.txt", n.thin=nthin,n.chains=nchain, n.burnin=nb,n.iter=ni,debug=TRUE)

return(out)






}