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 496d4e5..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, pi)
-       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, apply( newBlockX, 1, function(row)
-                       mvrnorm(1, row %*% beta[,,i], covY[,,i]) ) )
-       }
-
-       shuffle = sample(n)
-       list("X"=X[shuffle,], "Y"=Y[shuffle,], "class"=class[shuffle])
-}