model
{
	for (i in 1:N.sample) {
		Y[i] ~ dpois(lambda[i])
		log(lambda[i]) <- a + b * F[i] + r[i] + rp[Pot[i]]
	}
	a ~ dnorm(0, 1.0E-4)
	b ~ 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 / (sigma[k] * sigma[k])
		sigma[k] ~ dunif(0, 1.0E+4)
	}
}