model
{
	Tau.noninformative <- 1.0E-2
	P.gamma <- 1.0E-2
	for (i in 1:N) {
		Y[i] ~ dbin(q[i], 8)
		logit(q[i]) <- beta + r[i]
	}
	beta ~ dnorm(0, Tau.noninformative)
	for (i in 1:N) {
		r[i] ~ dnorm(0, tau)
	}
	tau ~ dgamma(P.gamma, P.gamma)
	sigma <- sqrt(1 / tau)
}