essai fusion

[valse.git] / pkg / R / generateXY.R

diff --git a/pkg/R/generateXY.R b/pkg/R/generateXY.R
deleted file mode 100644 (file)
index 064b54b..0000000
--- a/pkg/R/generateXY.R
+++ /dev/null
@@ -1,39 +0,0 @@
-#' generateXY 
-#'
-#' Generate a sample of (X,Y) of size n
-#'
-#' @param n sample size
-#' @param π 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 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)
-{
-  p <- dim(covX)[1]
-  m <- dim(covY)[1]
-  k <- dim(covY)[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 <- rmultinom(1, n, π)
-  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 %*% 
-      β[, , i], covY[, , i]))))
-  }
-
-  shuffle <- sample(n)
-  list(X = X[shuffle, ], Y = Y[shuffle, ], class = class[shuffle])
-}