model
{
	for (i in 1:N.sample) {
		Y[i] ~ dpois(lambda[i])
		log(lambda[i]) <- beta1 + beta2 * F[i] + r[i] + rp[Pot[i]]
	}
	beta1 ~ dnorm(0, 1.0E-4)
	beta2 ~ dnorm(0, 1.0E-4)
	for (i in 1:N.sample) {
		r[i] ~ dnorm(0, tau[1])
	}
	for (j in 1:N.pot) {
		rp[j] ~ dnorm(0, tau[2])
	}
	for (k in 1:N.tau) {
		tau[k] <- 1.0 / (s[k] * s[k])
		s[k] ~ dunif(0, 1.0E+4)
	}
}