model {
# Likelihood
for (i in 1 : N) {
O[i] ~ dpois(mu[i])
log(mu[i]) <- log(E[i]) + alpha0 + alpha1 * X[i]/10 + b[i]
# Area-specific relative risk (for maps)
RR[i] <- exp(alpha0 + alpha1 * X[i]/10 + b[i])
}
# CAR prior distribution for random effects:
b[1:N] ~ car.normal(adj[], weights[], num[], tau)
for(k in 1:sumNumNeigh) {
weights[k] <- 1
}
# Other priors:
alpha0 ~ dflat()
alpha1 ~ dnorm(0.0, 1.0E-5)
tau ~ dgamma(0.5, 0.0005) # prior on precision
sigma <- sqrt(1 / tau) # standard deviation
b.mean <- sum(b[])
}