model { for(p in 1 : N) { Y[p] ~ dnorm(mu[p], tau[p]) mu[p] <- alpha[school[p], 1] + alpha[school[p], 2] * LRT[p] + alpha[school[p], 3] * VR[p, 1] + beta[1] * LRT2[p] + beta[2] * VR[p, 2] + beta[3] * Gender[p] + beta[4] * School.gender[p, 1] + beta[5] * School.gender[p, 2] + beta[6] * School.denom[p, 1] + beta[7] * School.denom[p, 2] + beta[8] * School.denom[p, 3] log(tau[p]) <- theta + phi * LRT[p] sigma2[p] <- 1 / tau[p] LRT2[p] <- LRT[p] * LRT[p] } min.var <- exp(-(theta + phi * (-34.6193))) # lowest LRT score = -34.6193 max.var <- exp(-(theta + phi * (37.3807))) # highest LRT score = 37.3807 # Priors for fixed effects: for (k in 1 : 8) { beta[k] ~ dnorm(0.0, 0.0001) } theta ~ dnorm(0.0, 0.0001) phi ~ dnorm(0.0, 0.0001) # Priors for random coefficients: for (j in 1 : M) { alpha[j, 1 : 3] ~ dmnorm(gamma[1:3 ], T[1:3 ,1:3 ]); alpha1[j] <- alpha[j,1] } # Hyper-priors: gamma[1 : 3] ~ dmnorm(mn[1:3 ], prec[1:3 ,1:3 ]); T[1 : 3, 1 : 3 ] ~ dwish(R[1:3 ,1:3 ], 3) }