model{
    # Priors beta and sigma
    for (i in 1:K) {beta[i] ~ dnorm(0, 0.0001)}
    tau  <- 1 / (sigma * sigma)
    sigma ~ dunif(0.0001, 20)
    
    # Priors random effects and sigma_Elev
    for (i in 1:Nre) {a[i] ~ dnorm(0, tau_Elev)}
    tau_Elev <- 1 / (sigma_Elev * sigma_Elev)
    sigma_Elev ~ dunif(0.0001, 20)
    
    # Likelihood
    for (i in 1:N) {
    Y[i]    ~ dnorm(mu[i], tau)
    mu[i]  <- eta[i] + a[Elev[i]] # random intercept
    eta[i] <- inprod(beta[], X[i,])
    
    # Residuals
    Res[i]    <- Y[i] - mu[i]
    }
    }