R/SDGM2.R

In SurvMetrics: Predictive Evaluation Metrics in Survival Analysis

#' Survival Data Generation Method 2
#'
#' Survival data generation method. The dataset represents mild violations of the proportional hazards assumption.
#'
#' @param N  The sample size of the simulated dataset.
#' @param p  The covariate dimension of the simulated dataset.
#' @param u_max  The parameter which is used to control the censoring rate.
#'
#' @return the simulated dataset
#'
#' @author Hanpu Zhou \email{zhouhanpu@csu.edu.cn}
#' @references
#' Steingrimsson, J. A. ,  Diao, L. , &  Strawderman, R. L. . (2019). Censoring unbiased regression trees and ensembles. Journal of the American Statistical Association, 114.
#'
#' Zhu, R. , &  Kosorok, M. R. . (2012). Recursively imputed survival trees. Journal of the American Statistical Association, 107(497), 331-340.
#'
#' Ishwaran, H. ,  Kogalur, U. B. ,  Gorodeski, E. Z. ,  Minn, A. J. , &  Lauer, M. S. . (2010). High-dimensional variable selection for survival data. Journal of the American Statistical Association, 105(489), 205-217.
#'
#' @examples
#' SDGM2(N = 200, p = 15, u_max = 4)
#'
#' @importFrom stats rexp
#' @importFrom stats runif
#'
#' @export
#'
SDGM2 <- function(N = 200, p = 15, u_max = 4) {
  finalcenrate <- rep(0, 20)
  for (repnum in 1:20) {
    W <- matrix(0, N, p)
    for (i in 1:p) {
      W[, i] <- runif(N)
    }
    Ti <- rep(0, N)
    for (i in 1:N) {
      t.mu <- sin(W[i, 1] * pi) + 2 * abs(W[i, 2] - 0.5) + W[i, 3]^3
      Ti[i] <- rexp(1, 1 / t.mu)
    }

    c.time <- runif(N, 0, u_max)
    mydata.x <- as.data.frame(W)
    mydata.time <- data.frame("time" = Ti, "c.time" = c.time, "status" = 0)
    mydata.time$status <- ifelse(mydata.time$time < mydata.time$c.time, 1, 0)
    mydata.time$time <- apply(mydata.time[, 1:2], 1, min)

    mydata0 <- data.frame("time" = mydata.time$time, "status" = mydata.time$status, W)
    cen.rate <- 1 - sum(mydata0$status) / N

    finalcenrate[repnum] <- cen.rate
  }

  print(paste("censoring rate is:", round(mean(finalcenrate), 3)))
  return(mydata0)
}