model { for( i in 1 : N ) { r[i] ~ dbin(p[i],n[i]) b[i] ~ dnorm(0.0,tau) logit(p[i]) <- alpha0 + alpha1 * x1[i] + b[i] } alpha0 ~ dnorm(0.0,1.0E-6) alpha1 ~ dnorm(0.0,1.0E-6) # Choice of priors for random effects variance # Prior 1: uniform on SD sigma~ dunif(0,100) tau<-1/(sigma*sigma) }