model { for (i in 1 : N) { # Likelihood O[i] ~ dpois(mu[i]) log(mu[i]) <- log(E[i]) + alpha + beta * depriv[i] + b[i] + h[i] # Area-specific relative risk (for maps) RR[i] <- exp(alpha + beta * depriv[i] + b[i] + h[i]) # Exchangeable prior on unstructured random effects h[i] ~ dnorm(0, tau.h) } # CAR prior distribution for spatial random effects: b[1 : N] ~ car.normal(adj[], weights[], num[], tau.b) for(k in 1:sumNumNeigh) { weights[k] <- 1 } # Other priors: alpha ~ dflat() beta ~ dnorm(0.0, 1.0E-5) tau.b ~ dgamma(0.5, 0.0005) sigma.b <- sqrt(1 / tau.b) tau.h ~ dgamma(0.5, 0.0005) sigma.h <- sqrt(1 / tau.h) }