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)
}