model
{
for (i in 1:Nobservations)
	{
	#Outcome model
	Y[i]~dbern(pY[i])
	logit(pY[i])<-beta[1]+beta[2]*X[i]+beta[3]*Z[i]
	
	#Exposure model
	X[i]~dnorm(meanX[i],taux)
	meanX[i]<-alpha[1]+alpha[2]*Z[i]
	
	#Replication model
	for (j in 1:Nreplications)
			{W[i,j]~dnorm(X[i],tauu)}
	}

# Define the atenuation	
	tauu<-taux/lambda
	
#Noninformative priors on the model parameters
lambda~dunif(0,0.5)
taux~dgamma(3,1)


for (i in 1:nalphas){alpha[i]~dnorm(0,1.0E-6)}
for (i in 1:nbetas){beta[i]~dnorm(0,1.0E-6)}

#Deterministic transformations
sigmax2<-1/taux
sigmax<-1/sqrt(taux)
sigmau2<-1/tauu
sigmau<-1/sqrt(tauu)
	
}