# Negative Binomial mean model

model{

	# Likelihood

	for(i in 1:n.rec){

	log(mu[i]) <- beta[1]*intercept.rec[i] + beta[2]*ski[i] +
		beta[3]*fee[i] + beta[4]*income[i]

	p[i] <- r/(r + mu[i])
	
	trips[i] ~ dnegbin(p[i], r)
	}

	# prior distributions

	for(i in 1:4){beta[i] ~ dnorm(0, 0.001)}
	
	r ~ dgamma(1, 1)
}