R/jags_3mqrjm_n.const.lin_value.R

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

jags_3mqrjm_n.const.lin_value <-
  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])
        W[j, 3] ~ dexp(1/sigma[3])
        prec1[j] <- 1/(W[j, 1]*sigma[1]*c2[1])
        prec2[j] <- 1/(W[j, 2]*sigma[2]*c2[2])
        prec3[j] <- 1/(W[j, 3]*sigma[3]*c2[3])
        # first quantile distribution
        y[j, 1] ~ dnorm(mu1[j], prec1[j])
        mu1[j] <- inprod(beta[1, 1:ncX], X[j, 1:ncX]) + inprod(b1[i, 1:ncU], U[j, 1:ncU]) + c1[1]*W[j, 1]
        # second quantile distribution
        y[j, 2] ~ dnorm(mu2[j], prec2[j])
        mu2[j] <- inprod(beta[2, 1:ncX], X[j, 1:ncX]) + inprod(b2[i, 1:ncU], U[j, 1:ncU]) + c1[2]*W[j, 2]
        # third quantile distribution
        y[j, 3] ~ dnorm(mu3[j], prec3[j])
        mu3[j] <- inprod(beta[3, 1:ncX], X[j, 1:ncX]) + inprod(b3[i, 1:ncU], U[j, 1:ncU]) + c1[3]*W[j, 3]
      }#end of j loop
      # random effects
      b1[i, 1:ncU] ~ dmnorm(mu0[], prec1.Sigma2[, ])
      b2[i, 1:ncU] ~ dmnorm(mu0[], prec2.Sigma2[, ])
      b3[i, 1:ncU] ~ dmnorm(mu0[], prec3.Sigma2[, ])
      # survival part
      etaBaseline[i] <- inprod(alpha[1: ncZ], Z[i, 1:ncZ])
      share.intercept[i] <- etaBaseline[i] + alpha.assoc[1] * (beta[2, 1] + b2[i, 1]) + alpha.assoc[2] * ( beta[3, 1] + b3[i, 1] - beta[1, 1] - b1[i, 1] )
      share.slope[i] <- alpha.assoc[1] * (beta[2, 2] + b2[i, 2]) + alpha.assoc[2] * ( beta[3, 2] + b3[i, 2] - beta[1, 2] - b1[i, 2] )
      log_haz[i] <- share.intercept[i] + share.slope[i] * Time[i]
      log_surv[i] <- - exp(share.intercept[i]) * ( exp(share.slope[i] * Time[i]) - 1 ) / share.slope[i]
      logL[i] <- event[i] * log_haz[i] + log_surv[i]
      mlogL[i] <- -logL[i] + C
      zeros[i] ~ dpois(mlogL[i])
    }#end of i loop
    # priors for parameters
    prec1.Sigma2[1:ncU, 1:ncU] ~ dwish(priorR.Sigma2[, ], priorK.Sigma2)
    covariance.b1 <- inverse(prec1.Sigma2[, ])
    prec2.Sigma2[1:ncU, 1:ncU] ~ dwish(priorR.Sigma2[, ], priorK.Sigma2)
    covariance.b2 <- inverse(prec2.Sigma2[, ])
    prec3.Sigma2[1:ncU, 1:ncU] ~ dwish(priorR.Sigma2[, ], priorK.Sigma2)
    covariance.b3 <- inverse(prec3.Sigma2[, ])
    for(qqq in 1:Q){
      beta[qqq, 1:ncX] ~ dmnorm(priorMean.beta[qqq, ], priorTau.beta[, ])
      sigma[qqq] ~ dgamma(priorA.sigma, priorB.sigma)
    }
    # priors for survival parameters
    alpha[1:ncZ] ~ dmnorm(priorMean.alpha[], priorTau.alpha[, ])
    alpha.assoc[1:2] ~ dmnorm(priorMean.alpha.assoc[], priorTau.alpha.assoc[, ])
  }