model
{
	Tau.noninformative <- 1.0E-4
	for (i in 1:N) {
		Y[i] ~ dnorm(mu[i], tau)
		log(mu[i]) <- (
			beta[1]
			+ (beta[2] + r[1, Id[i]]) * Year[i]
		)
	}
	for (j in 1:N.beta) {
		beta[j] ~ dnorm(0.0, Tau.noninformative)
	}
	for (j in 1:N.r) {
		for (k in 1:N.id) {
			r[j, k] ~ dnorm(0.0, tau.r[j])
		}
		tau.r[j] <- 1 / (sd.r[j] * sd.r[j])
		sd.r[j] ~ dunif(0, 1.0E+4)
	}
	tau <- 1 / (sd * sd)
	sd ~ dunif(0, 1.0E+4)
}