X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FgenerateXY.R;h=064b54b2d083bb3bc9d9ddd26a8654962e5b93c2;hp=069c4702960603083fefea62f8431b9f72d1cb8b;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=321e13a991a5a0e6c97225fdca436870e5e805d1

diff --git a/pkg/R/generateXY.R b/pkg/R/generateXY.R
deleted file mode 100644
index 069c470..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])
-}