R/exampleData.R

In psbcSpeedUp: Penalized Semiparametric Bayesian Cox Models

#' @title Simulated data set
#'
#' @description
#' Simulated data set for a quick test. The data set is a list with six
#' components: survival times \code{"t"}, event status \code{"di"}, covariates
#' \code{"x"}, number of genomics variables \code{"p"}, number of clinical
#' variables \code{"1"} and true effects of covariates \code{"beta_true"}.
#' The R code for generating the simulated data is given in the Examples
#' paragraph.
#'
#' @importFrom MASS mvrnorm
#'
#' @examples
#' # Load the example dataset
#' data("exampleData", package = "psbcSpeedUp")
#' str(exampleData)
#'
#' # ===============
#' # The code below is to show how to generate the dataset "exampleData.rda"
#' # ===============
#'
#' requireNamespace("MASS", quietly = TRUE)
#'
#' ########################### Predefined Functions
#'
#' Expo <- function(times, surv) {
#'   z1 <- -log(surv[1])
#'   t1 <- times[1]
#'   lambda <- z1 / (t1)
#'   list(rate = lambda)
#' }
#'
#' Weibull <- function(times, surv) {
#'   z1 <- -log(surv[1])
#'   z2 <- -log(surv[2])
#'   t1 <- times[1]
#'   t2 <- times[2]
#'   gamma <- log(z2 / z1) / log(t2 / t1)
#'   lambda <- z1 / (t1^gamma)
#'   list(scale = lambda, shape = gamma)
#' }
#'
#' ########################### Problem Dimensions
#' n <- 200
#' p <- 30
#' q <- 5
#' s <- 10
#'
#' ############################ Simulate a set of n x p covariates
#'
#' # effects
#' bg <- c(0.75, -0.75, 0.5, -0.5, 0.25, -0.25, rep(0, p - 6))
#' bc <- c(-1.0, 1.0, 0.3, 0, -0.3)
#' bX <- c(bg, bc)
#'
#' # covariates
#' # genomic
#' means <- rep(0, p)
#' Sigma <- diag(1, p)
#' Xg <- MASS::mvrnorm(n, means, Sigma)
#' # clinical
#' x1 <- rbinom(n = n, size = 1, prob = 0.7)
#' x2 <- rbinom(n = n, size = 1, prob = 0.3)
#' x3 <- rnorm(n = n, mean = 0, sd = 1)
#' x4 <- rnorm(n = n, mean = 0, sd = 1)
#' x5 <- rnorm(n = n, mean = 0, sd = 1)
#' Xc <- cbind(x1, x2, x3, x4, x5)
#' # all
#' X <- data.frame(Xg, Xc)
#' names(X) <- c(paste("G", 1:p, sep = ""), paste("C", 1:q, sep = ""))
#' X <- scale(X)
#'
#' # censoring function
#' # - follow-up time 36 to 72 months
#' # - administrative censoring: uniform data entry (cens1)
#' # - loss to follow-up: exponential, 20% loss in 72 months (cens2)
#' ACT <- 36
#' FUT <- 72
#' cens.start <- FUT
#' cens.end <- ACT + FUT
#' cens1 <- runif(n, cens.start, cens.end)
#' loss <- Expo(times = 72, surv = 0.8)
#' cens2 <- rexp(n, rate = loss$rate)
#' cens <- pmin(cens1, cens2)
#'
#' # survival distribution (Weibull, survival probs 0.5 and 0.9 at 12 and 36 months)
#' h0 <- round(log(2) / 36, 2)
#' surv <- Weibull(times = c(12, 36), surv = c(0.9, 0.5))
#'
#' dt <- (-log(runif(n)) * (1 / surv$scale) * exp(-as.matrix(X) %*% bX))^(1 / surv$shape)
#'
#' # survival object
#' status <- ifelse(dt <= cens, 1, 0)
#' os <- pmin(dt, cens)
#'
#' exampleData <- list("t" = os, "di" = status, "x" = X, "beta_true" = bX)
#'
"exampleData"