model
{
	for(i in 1:N)
	{
		for(j in 1:n[i])
		{
		
		y[cumn[i]+j] ~ dnorm(mu[cumn[i]+j], prece[i])

		mu[cumn[i]+j] <- alpha + bhhpers*hhpers[cumn[i]+j] + bhhemp1*hhemp1[cumn[i]+j] +bedterhh*edterhh[cumn[i]+j] +bagehh*agehh[cumn[i]+j] +bsexhh*sexhh[cumn[i]+j] +u[i]+v[i]

		}

		prece[i] ~ dgamma (a2,b2)
		sigmae[i]<-1/prece[i]

		u[i] ~ dnorm(0, precu)
		
		mug[i]<-alpha+u[i]+v[i]+bhhpers*HHPERS[i]+bhhemp1*HHEMP1[i]+bedterhh*EDTERHH[i]+bagehh*AGEHH[i]+bsexhh*SEXHH[i]

                rankmu[i]<-rank(mug[1:N], i)
                rankmu10[i]<- (1-step(rankmu[i]-29))
                rankmu20[i]<- (1-step(rankmu[i]-58))
                rankpovline[i]<-step(mug[i]-693.695)

	}

	v[1:N] ~ car.normal(adj[], weights[], num[], precv)

	precu ~ dgamma (a0,b0)
	precv ~ dgamma (a1,b1)

	alpha ~ dflat()
	bhhpers ~ dflat()
	bhhemp1 ~ dflat()
	bedterhh ~ dflat()
	bagehh ~ dflat()
	bsexhh ~ dflat()

	sigmau<-1/precu
	sigmav<-1/precv
}