X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FgenerateXY.R;h=069c4702960603083fefea62f8431b9f72d1cb8b;hp=496d4e508db450e51dec1f364e5c7117eda91e0d;hb=321e13a991a5a0e6c97225fdca436870e5e805d1;hpb=2e813ad23c796bbed3d5ba685b8fa002bdc6689d

diff --git a/pkg/R/generateXY.R b/pkg/R/generateXY.R
index 496d4e5..069c470 100644
--- a/pkg/R/generateXY.R
+++ b/pkg/R/generateXY.R
@@ -17,12 +17,12 @@ 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)
+	sizePop <- rmultinom(1, n, π)
 	class <- c() #map i in 1:n --> index of class in 1:k
 
 	for (i in 1:k)
@@ -30,8 +30,8 @@ generateXY = function(n, π, meanX, β, covX, covY)
 		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]) ) )
+		Y <- rbind( Y, t(apply( newBlockX, 1, function(row)
+			MASS::mvrnorm(1, row %*% β[,,i], covY[,,i]) )) )
 	}
 
 	shuffle = sample(n)