
model {
for (i in 1:N) {
	rdt[i]~dbern(p[i])
	logit(p[i])<-alpha + beta[1]*age[i] + beta[2]*distriver[i] + beta[3]*alt[i] + beta[4]*evi[i] + eps[psu[i]] + w[psu[i]]  }

#spatial effects
w[1:M]~spatial.exp(mu1[],longitude[],latitude[],tau.sp,phi,1)
for (j in 1:M)  {
	mu1[j]<-0}
	
tau.sp~dgamma(0.001,0.001)
sigma.sp<-1/tau.sp

#phi~dgamma(0.01,0.01)
phi~dunif(0.2,150)
range<-3/phi

alpha~dnorm(0.0,0.001)

for (k in 1:4) {
	beta[k]~dnorm(0.0,0.001)
	or[k]<-exp(beta[k])}

#non-spatial effects
for (l in 1:M) {
	eps[l]~dnorm(0,tau) }

#tau is hyperparameter with gamma prior
#for variance at level 2
tau~dgamma(0.001,0.001)
sigma<-1/tau

}

