#' 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 and Y #' #' @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]) }