model
{

  for(i in 1:N)
  {
     O[i] ~ dpois(mu[i])
     mu[i]<-theta[i]*E[i]
     log(theta[i]) <-  alpha + beta[1]*PCTAGE65P[i] + 
	beta[2]*PCTOWNHOME[i] + beta[3]*PEXPOSURE[i] + u[i] + v[i] 

     u[i] ~ dnorm(0, precu)

     SMR[i]<-O[i]/E[i]
     prob[i]<-step(theta[i]-1)
  }

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

  alpha ~ dflat()
  for(i in 1:3) {beta[i] ~ dnorm(0,1.0E-5)}
  precu ~ dgamma(0.001, 0.001)
  precv ~ dgamma(0.1, 0.1)

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


