model {
for(i in 1:N){

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

mu_M1[i] <- alpha*(x[i]+deltax)*(x[i]+deltax)
M1[i] ~ dnorm(mu_M1[i],prec1)

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

mu_y1[i]<- c*(x[i]+deltax)+beta*M[i]
y1[i] ~ dnorm(mu_y1[i],prec2)

mu_y2[i]<- c*x[i]+beta*(M[i]+deltam)
y2[i] ~ dnorm(mu_y2[i],prec2)

ie[i]<-(mu_M1[i]-mu_M[i])/deltax*(mu_y2[i]-mu_y[i])/deltam
de[i]<-(mu_y1[i]-mu_y[i])/deltax
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
}

}