model {
# prior distributions
psi~dunif(0,1)
mu~dnorm(0,0.001)
tau~dgamma(.001,.001) # zero-inflated binomial mixture model for
# the augmented data
for(i in 1: nind + nz){
z[i] ~ dbin(psi,1)
eta[i]~ dnorm(mu, tau)
logit(p[i])<- eta[i]
muy[i]<-p[i] * z[i]
y[i] ~ dbin(muy[i], J)
}
# Derived parameters
N<-sum(z[1 : nind+nz])
sigma<-sqrt(1 /tau)
}