model
{
	Tau.noninformative <- 1.0E-4
	P.gamma <- 1.0E-4
	Tau.err <- 25
	for (i in 1:N) {
		Y[i] ~ dnorm(y[i], Tau.err) # above ground weight
		X[i] ~ dnorm(x[i], Tau.err) # below ground weight
		y[i] <- q[i] * w[i]
		x[i] <- (1 - q[i]) * w[i]
		logit(q[i]) <- a + re[i]
		w[i] <- exp(log.w[i])
		log.w[i] ~ dnorm(0, Tau.noninformative) # total weight
	}
	a ~ dnorm(0, Tau.noninformative)
	for (i in 1:N) {
		re[i] ~ dnorm(0, tau)
	}
	tau ~ dgamma(P.gamma, P.gamma)
}