model
{
# Prior distributions
theta~dunif(0,10)
theta2<-theta*theta
psi~dunif(0,1)
for(i in 1:(nind+nz)){
z[i]~dbern(psi) # latent indicator variables from data augmentation
x[i]~dunif(0,4) # distance is a random variable
logp[i]<- -((x[i]*x[i])/theta2)
p[i]<-exp(logp[i])
mu[i]<-z[i]*p[i]
y[i]~dbern(mu[i]) # observation model
}
N<-sum(z[1:(nind+nz)])
D<- N/48 # 48 km*km = total area of transects
}