model
{
	Tau.noninformative <- 1.0E-2
	P.gamma <- 1.0E-2
	for (i in 1:N.sample) {
		R.strep[i] ~ dpois(lamda[i])
		lamda[i] <- exp(log.lamda[i])
		log.lamda[i] ~ dnorm(mean.log.lamda[i], tau[1])
		mean.log.lamda[i] <- (
			r[Id[i]] # individual effects
			+ beta[1] 
			+ beta[2] * Male[i]
			+ beta[3] * Season2[i] 
			+ beta[4] * Season3[i] 
			+ beta[5] * Season4[i] 
			+ beta[6] * Age2[i]
			+ beta[7] * Age3[i]
			+ beta[8] * Age4[i]
			+ beta[9] * Age5[i]
		)
	}
	for (j in 1:N.id) { # individual effects
		# j = 1, 2, 3, .., 50
		r[j] ~ dnorm(0, tau[2])
	}
	for (k in 1:N.beta) { # coefficient beta
		beta[k] ~ dnorm(0, Tau.noninformative) # non-informative
	}
	for (k in 1:N.tau) {
		tau[k] ~ dgamma(P.gamma, P.gamma) # non-informative
	}
}