model {
#eps is random effect mean zero variance sigma squared
#w is for spatial effects
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]+w[i] }
	
#spatial effects
w[1:N]~spatial.exp(mu1[],longitude[],latitude[],tau.sp,phi,1)

for (i in 1:N) {
	mu1[i]<-0}

tau.sp~dgamma(2.01,1.01)
sigma.sp<-1/tau.sp

phi~dunif(0.2,150)
range<-3/phi

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

#Predict
for (i in 1:P) {
	logit(p.pred[i])<-b[1]+b[2]*distriver.pred[i]+b[3]*evi.pred[i]+eps.pred[i]+
		w.pred[i] }

#see GeoBugs manual
for (i in 1:P) {
	eps.pred[i]~dnorm(0,tau)
	w.pred[i]~spatial.unipred(mu.pred[i],x.pred[i],y.pred[i],w[])
	mu.pred[i]<-0  }

}
