model {
for(i in 1:N){

mu_M[i] <- alpha*x[i]
M[i] ~ dnorm(mu_M[i],prec1)
mu_y[i]<- c*x[i]+beta*M[i]
y[i] ~ dnorm(mu_y[i],prec2)

mu_y3[i]<-beta*M[i] #when x=0
mu_y4[i]<-c+beta*M[i] #when x=1
de[i]<-(mu_y4[i]-mu_y3[i])/deltax

mu_y2[i]<-c*x[i]+beta*(M[i]+deltam)
ie[i]<-alpha/deltax*(mu_y2[i]-mu_y[i])/deltam #alpha is alpha(1-0)

te[i]<-ie[i]+de[i]
}

alpha ~ dnorm(0.0,0.000001)
beta ~ dnorm(0.0,0.000001)  
c ~ dnorm(0.0,0.000001) 
var1 ~ dgamma(1,0.1)
var2 ~ dgamma(1,0.1)
prec1 <-1/var1
prec2 <-1/var2
}