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] } }