jags_3mlqmm_n <-
function (...) {
for(q in 1:Q){
c1[q] <- (1-2*tau[q])/(tau[q]*(1-tau[q]))
c2[q] <- 2/(tau[q]*(1-tau[q]))
}
# likelihood
for (i in 1:I){
# longitudinal part
for(j in offset[i]:(offset[i+1]-1)){
# define object
W[j, 1] ~ dexp(1/sigma[1])
W[j, 2] ~ dexp(1/sigma[2])
prec1[j] <- 1/(W[j, 1]*sigma[1]*c2[1])
prec2[j] <- 1/(W[j, 2]*sigma[2]*c2[2])
# first quantile distribution
y[j, 1] ~ dnorm(mu1[j], prec1[j])
mu1[j] <- inprod(beta[1, 1:ncX], X[j, 1:ncX]) + inprod(b[i, 1:ncU], U[j, 1:ncU]) + c1[1]*W[j, 1]
# conditional distrubtion for second quantile given y[j, 1]
y[j, 2] ~ dnorm(mu2[j], prec2[j])
mu2[j] <- inprod(beta[2, 1:ncX], X[j, 1:ncX]) + inprod(b[i, (ncU+1):(2*ncU)], U[j, 1:ncU]) + c1[2]*W[j, 2]
}#end of j loop
# random effects
b[i, 1:(ncU*Q)] ~ dmnorm(mu0[], prec.Sigma2[, ])
}#end of i loop
# priors for parameters
prec.Sigma2[1:(ncU*Q), 1:(ncU*Q)] ~ dwish(priorR.Sigma2[, ], priorK.Sigma2)
covariance.b <- inverse(prec.Sigma2[, ])
for(qqq in 1:Q){
beta[qqq, 1:ncX] ~ dmnorm(priorMean.beta[qqq, ], priorTau.beta[, ])
sigma[qqq] ~ dgamma(priorA.sigma, priorB.sigma)
}
}
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