#' Generate a sample of (X,Y) of size n
#'
#' @param n sample size
-#' @param π proportion for each cluster
+#' @param p 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 β regression matrix, of size p*m*k
+#' @param beta regression matrix, of size p*m*k
#' @param covY covariance for the response vector (of size m*m*K)
#'
#' @return list with X and Y
#'
#' @export
-generateXY <- function(n, π, meanX, β, covX, covY)
+generateXY <- function(n, p, meanX, beta, covX, covY)
{
p <- dim(covX)[1]
m <- dim(covY)[1]
Y <- matrix(nrow = 0, ncol = m)
# random generation of the size of each population in X~Y (unordered)
- sizePop <- rmultinom(1, n, π)
+ sizePop <- stats::rmultinom(1, n, p)
class <- c() #map i in 1:n --> index of class in 1:k
for (i in 1:k)
newBlockX <- MASS::mvrnorm(sizePop[i], meanX, covX)
X <- rbind(X, newBlockX)
Y <- rbind(Y, t(apply(newBlockX, 1, function(row) MASS::mvrnorm(1, row %*%
- β[, , i], covY[, , i]))))
+ beta[, , i], covY[, , i]))))
}
shuffle <- sample(n)