model
{
	# likelihood
	for (i in 1:N.plant) {
		Y[i] ~ dbin(q[i], N.ovule)
		logit(q[i]) <- beta + alpha[i]
	}
	# fixed effects
	beta ~ dnorm(0.0, Tau.noninformative)
	# random effects
	for (i in 1:N.plant) {
		alpha[i] ~ dnorm(0.0, tau)
	}
	# hyper priors
	tau ~ dgamma(Hyper.gamma, Hyper.gamma)
	sigma <- 1 / tau
}