model {
#eps is random effect mean zero variance sigma squared

for (i in 1:N) {
	positives[i]~dbin(p[i], examined[i])
	logit(p[i])<-b[1]+b[2]*distriver[i]+b[3]*evi[i]+eps[i] }
	

for (l in 1:N) {
	eps[l]~dnorm(0,tau) }

for (i in 1:3) {
	b[i]~dnorm(0.0,0.01) 
	or[i]<-exp(b[i]) }

tau~dgamma(0.001,0.001)
sigma<-1/tau

}
