R/jags_qrjm.weib.value.IG.R

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

jags_qrjm.weib.value.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])
      }
      # survival part
      etaBaseline[i] <- inprod(alpha[1: ncZ], Z[i, 1:ncZ])
      shareY[i] <- inprod(beta[1:ncX], Xtime[i, 1:ncX]) + inprod(b[i, 1:ncU], Utime[i, 1:ncU])
      log_h1[i] <- log(shape) + (shape - 1) * log(Time[i]) + etaBaseline[i] + alpha.assoc * shareY[i]
      for (k in 1:K) {
        shareY.s[i, k] <- inprod(beta[1:ncX], Xs[K * (i - 1) + k, 1:ncX]) + inprod(b[i, 1:ncU], Us[K * (i - 1) + k, 1:ncU])
        SurvLong[i, k] <- wk[k] * shape * pow(st[i, k], shape - 1) * exp(alpha.assoc * shareY.s[i, k])
      }
      log_S1[i] <- (-exp(etaBaseline[i]) * P[i] * sum(SurvLong[i, ]))
      logL[i] <- event[i]*log_h1[i] + log_S1[i]
      mlogL[i] <- -logL[i] + C
      zeros[i] ~ dpois(mlogL[i])
    }#end of i loop
    # priors for longitudinal 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)
    # priors for survival parameters
    alpha[1:ncZ] ~ dmnorm(priorMean.alpha[], priorTau.alpha[, ])
    shape ~ dgamma(priorA.shape, priorB.shape)
    alpha.assoc ~ dnorm(0, priorTau.alphaA)
  }