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0ba1b11c 1#' generateXY
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2#'
3#' Generate a sample of (X,Y) of size n
4#'
5#' @param n sample size
6#' @param π proportion for each cluster
7#' @param meanX matrix of group means for covariates (of size p)
8#' @param covX covariance for covariates (of size p*p)
9#' @param β regression matrix, of size p*m*k
10#' @param covY covariance for the response vector (of size m*m*K)
11#'
12#' @return list with X and Y
13#'
14#' @export
15generateXY <- function(n, π, meanX, β, covX, covY)
16{
17 p <- dim(covX)[1]
18 m <- dim(covY)[1]
19 k <- dim(covY)[3]
20
21 X <- matrix(nrow = 0, ncol = p)
22 Y <- matrix(nrow = 0, ncol = m)
23
24 # random generation of the size of each population in X~Y (unordered)
25 sizePop <- rmultinom(1, n, π)
26 class <- c() #map i in 1:n --> index of class in 1:k
27
28 for (i in 1:k)
29 {
30 class <- c(class, rep(i, sizePop[i]))
31 newBlockX <- MASS::mvrnorm(sizePop[i], meanX, covX)
32 X <- rbind(X, newBlockX)
0ba1b11c 33 Y <- rbind(Y, t(apply(newBlockX, 1, function(row) MASS::mvrnorm(1, row %*%
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34 β[, , i], covY[, , i]))))
35 }
36
37 shuffle <- sample(n)
38 list(X = X[shuffle, ], Y = Y[shuffle, ], class = class[shuffle])
39}