model {
for(i in 1:N){

mu_M[i] <- alpha*x[i]
M[i] ~ dnorm(mu_M[i],prec1)

logit(mu_y[i])<- c*x[i]+beta*M[i]
y[i] ~ dbern(mu_y[i])

logit(mu_y2[i])<-c*x[i]+beta*(M[i]+deltam)
logit(mu_y3[i])<-beta*(M[i]+deltam)
logit(mu_y4[i])<-c+beta*(M[i]+deltam)

ie[i]<-alpha/deltax*(mu_y2[i]-mu_y[i])/deltam
de[i]<-(mu_y4[i]-mu_y3[i])/deltax
te[i]<-ie[i]+de[i]
}

alpha ~ dnorm(0, 1.0E-2)
beta ~ dnorm(0, 1.0E-2)  
c ~ dnorm(0, 1.0E-2) 
var1 ~ dgamma(1,0.1)
prec1 <-1/var1
}