R/getLoglike.R

In JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data

getLoglike <- function(beta, tau, gamma1, gamma2, alpha1, alpha2, vee1, vee2, 
                       H01, H02, Sig, Z, X1, W, Y, X2, survtime, cmprsk, mdata, 
                       mdataS, xsmatrix, wsmatrix, method, initial.optimizer) {
  
  n <- nrow(X2)
  p1a <- ncol(Z)
  nsig <- p1a + 1
  CUH01 <- rep(0, n)
  CUH02 <- rep(0, n)
  HAZ01 <- rep(0, n)
  HAZ02 <- rep(0, n)
  
  CumuH01 <- cumsum(H01[, 3])
  CumuH02 <- cumsum(H02[, 3])
  
  getHazard(CumuH01, CumuH02, survtime, cmprsk, H01, H02, CUH01, CUH02, HAZ01, HAZ02)
  
  if (method == "standard") {
    status = getloglikeC(beta, tau, gamma1, gamma2, alpha1, alpha2, vee1, vee2,
                         Sig, Z, X1, W, Y, X2, survtime, cmprsk, 
                         mdata, mdataS, xsmatrix, wsmatrix, 
                         CUH01, CUH02, HAZ01, HAZ02)
  } else {
    posterior.mode <- matrix(0, nrow = n, ncol = nsig)
    posterior.var <- matrix(0, nrow = nsig*n, ncol = nsig)
    for (i in 1:n) {
      
      if (i != n) {
        subY <- Y[mdataS[i]:(mdataS[i+1]-1)]
        subW <- matrix(W[mdataS[i]:(mdataS[i+1]-1), ], ncol = ncol(W))
        subX1 <- matrix(X1[mdataS[i]:(mdataS[i+1]-1), ], ncol = ncol(X1))
        subZ <- matrix(Z[mdataS[i]:(mdataS[i+1]-1), ], ncol = ncol(Z))
      } else {
        subY <- Y[mdataS[i]:length(Y)]
        subW <- matrix(W[mdataS[i]:length(Y), ], ncol = ncol(W))
        subX1 <- matrix(X1[mdataS[i]:length(Y), ], ncol = ncol(X1))
        subZ <- matrix(Z[mdataS[i]:length(Y), ], ncol = ncol(Z))
      }
      CH001 <- CUH01[i]
      CH002 <- CUH02[i]
      HAZ001 <- HAZ01[i]
      HAZ002 <- HAZ02[i]
      
      data <- list(subY, subX1, subZ, subW, t(as.matrix(X2[i, ])), CH001, CH002, 
                   HAZ001, HAZ002, beta, tau, gamma1, gamma2, alpha1, alpha2, 
                   vee1, vee2, Sig, cmprsk[i])
      names(data) <- c("Y", "X", "Z", "W", "X2", "CH01", "CH02", 
                       "HAZ01", "HAZ02", "beta", "tau",
                       "gamma1", "gamma2", "alpha1", "alpha2", "nu1", "nu2", "Sig", "D")
      opt <- optim(rep(0, nsig), logLikCR.learn, data = data, method = initial.optimizer, hessian = TRUE)
      posterior.mode[i, ] <- opt$par
      posterior.var[(nsig*(i-1) + 1):(i * nsig), 1:nsig] <- solve(opt$hessian)
    }
    
    status = getloglikeCad(beta, tau, gamma1, gamma2, alpha1, alpha2, vee1, vee2,
                           Sig, Z, X1, W, Y, X2, survtime, cmprsk, 
                           mdata, mdataS, xsmatrix, wsmatrix, 
                           CUH01, CUH02, HAZ01, HAZ02, posterior.mode, posterior.var)
  }

  return(status)
  
}