R/GendataPM.R

In MFSIS: Model-Free Sure Independent Screening Procedures

#' Generate simulation data (Discrete response data based on poisson model)
#'
#' This function helps you quickly generate simulation data based on poisson model.
#' You just need to input the sample and dimension of the data
#' you want to generate and the covariance parameter rho.
#' The simulated examples based on poisson model are significant popular
#' in the screening procedures, such as Model 1.f in Liu et al.(2020).
#'
#' @param n Number of subjects in the dataset to be simulated. It will also equal to the
#' number of rows in the dataset to be simulated, because it is assumed that each
#' row represents a different independent and identically distributed subject.
#' @param p Number of predictor variables (covariates) in the simulated dataset.
#' These covariates will be the features screened by model-free procedures.
#' @param rho The correlation between adjacent covariates in the simulated matrix X.
#' The within-subject covariance matrix of X is assumed to has the same form as an
#' AR(1) auto-regressive covariance matrix, although this is not meant to imply
#' that the X covariates for each subject are in fact a time series. Instead, it is just
#' used as an example of a parsimonious but nontrivial covariance structure. If
#' rho is left at the default of zero, the X covariates will be independent and the
#' simulation will run faster.
#' @param beta A vector with length of n, which are the coefficients that you want to generate
#' about Linear model. The default is beta=(1,1,1,1,1,0,...,0)^T;
#'
#' @return the list of your simulation data
#' @import MASS
#' @importFrom MASS mvrnorm
#' @importFrom stats rpois
#' @export
#' @author Xuewei Cheng \email{xwcheng@hunnu.edu.cn}
#' @examples
#' n <- 100
#' p <- 200
#' rho <- 0.5
#' data <- GendataPM(n, p, rho)
#'
#' @references
#'
#' Liu, W., Y. Ke, J. Liu, and R. Li (2020). Model-free feature screening and FDR control with knockoff features. Journal of the American Statistical Association, 1–16.
GendataPM <- function(n, p, rho,
                      beta = c(rep(1, 5), rep(0, p - 5))) # n sample size; p dimension size.
{
  sig <- matrix(0, p, p)
  sig <- rho^abs(row(sig) - col(sig))
  diag(sig) <- rep(1, p)
  X <- mvrnorm(n, rep(0, p), sig)
  myrates <- exp(X %*% beta)
  Y <- rpois(n, myrates)
  return(list(X = X, Y = Y))
}