R/jags_lqmm_IG.R

In AntoineBbi/BQt: BQt: Bayesian Quantile regression for joint modeling

jags_lqmm_IG <-
  function () {
  # constants
  c1 <- (1-2*tau)/(tau*(1-tau))
  c2 <- 2/(tau*(1-tau))
  # likelihood
  for (i in 1:I){
    # longitudinal part
    for(j in offset[i]:(offset[i+1]-1)){
      y[j] ~ dnorm(mu[j], prec[j])
      va1[j] ~ dexp(1/sigma)
      prec[j] <- 1/(sigma*c2*va1[j])
      mu[j] <- inprod(beta[1:ncX], X[j, 1:ncX]) + inprod(b[i, 1:ncU], U[j, 1:ncU]) + c1*va1[j]
    }#end of j loop
    # random effects
    for(r in 1:ncU){
      b[i,r] ~ dnorm(0, prec.Sigma2[r])
    }
  }#end of i loop
  # priors for parameters
  for(rr in 1:ncU){
    prec.Sigma2[rr] ~ dgamma(priorA.Sigma2, priorB.Sigma2)
    covariance.b[rr] <- 1/prec.Sigma2[rr]
  }
  beta[1:ncX] ~ dmnorm(priorMean.beta[], priorTau.beta[, ])
  sigma ~ dgamma(priorA.sigma, priorB.sigma)
  }