model
{
for (i in 1:I) {
cases[i] ~ dpois(mu[i])
log(mu[i]) <- log(pyr[i]) + alpha[age[i]] + beta[year[i]]
}
betamean[1] <- 2 * beta[2] - beta[3]
Nneighs[1] <- 1
betamean[2] <- (2 * beta[1] + 4 * beta[3] - beta[4]) / 5
Nneighs[2] <- 5
for (k in 3 : K - 2) {
betamean[k] <- (4 * beta[k - 1] + 4 * beta[k + 1]- beta[k - 2] - beta[k + 2]) / 6
Nneighs[k] <- 6
}
betamean[K - 1] <- (2 * beta[K] + 4 * beta[K - 2] - beta[K - 3]) / 5
Nneighs[K - 1] <- 5
betamean[K] <- 2 * beta[K - 1] - beta[K - 2]
Nneighs[K] <- 1
for (k in 1 : K) {
betaprec[k] <- Nneighs[k] * tau
}
for (k in 1 : K) {
beta[k] ~ dnorm(betamean[k], betaprec[k])
logRR[k] <- beta[k] - beta[5]
tau.like[k] <- Nneighs[k] * beta[k] * (beta[k] - betamean[k])
}
alpha[1] <- 0.0
for (j in 2 : Nage) {
alpha[j] ~ dnorm(0, 1.0E-6)
}
d <- 0.0001 + sum(tau.like[]) / 2
r <- 0.0001 + K / 2
tau ~ dgamma(r, d)
sigma <- 1 / sqrt(tau)
}