R/generateXY.R

In valse: Variable Selection with Mixture of Models

#' generateXY
#'
#' Generate a sample of (X,Y) of size n
#'
#' @param n sample size
#' @param prop proportion for each cluster
#' @param meanX matrix of group means for covariates (of size p)
#' @param covX covariance for covariates (of size p*p)
#' @param beta regression matrix, of size p*m*k
#' @param covY covariance for the response vector (of size m*m)
#'
#' @return list with X (of size n*p) and Y (of size n*m)
#'
#' @export
generateXY <- function(n, prop, meanX, beta, covX, covY)
{
  p <- dim(covX)[1]
  m <- dim(covY)[1]
  k <- dim(beta)[3]

  X <- matrix(nrow = 0, ncol = p)
  Y <- matrix(nrow = 0, ncol = m)

  # random generation of the size of each population in X~Y (unordered)
  sizePop <- stats::rmultinom(1, n, prop)
  class <- c() #map i in 1:n --> index of class in 1:k

  for (i in 1:k)
  {
    class <- c(class, rep(i, sizePop[i]))
    newBlockX <- MASS::mvrnorm(sizePop[i], meanX, covX)
    X <- rbind(X, newBlockX)
    Y <- rbind(Y, t(apply(newBlockX, 1, function(row) MASS::mvrnorm(1, row %*%
      beta[, , i], covY[,]))))
  }

  shuffle <- sample(n)
  list(X = X[shuffle, ], Y = Y[shuffle, ], class = class[shuffle])
}