X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FgenerateIO.R;h=5f19488bb80ef7b04d16e305ff2dcef7d4cdcdc9;hb=c3bc47052f3ccb659659c59a82e9a99ea842398d;hp=f8c8194daea0e44d9206c583ab99b7884d9a1a5f;hpb=39046da6016f15d625bd99cf0303ea8beb838c79;p=valse.git

diff --git a/R/generateIO.R b/R/generateIO.R
index f8c8194..5f19488 100644
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,23 +1,33 @@
+#' Generate a sample of (X,Y) of size n
+#' @param covX covariance for covariates (of size p*p*K)
+#' @param covY covariance for the response vector (of size m*m*K)
+#' @param pi	 proportion for each cluster
+#' @param beta regression matrix
+#' @param n		sample size
+#' 
+#' @return list with X and Y
+#' @export
+#-----------------------------------------------------------------------
 generateIO = function(covX, covY, pi, beta, n)
 {
-	size_covX = dim(covX)
-	p = size_covX[1]
-	k = size_covX[3]
+	p = dim(covX)[1]
 
-	size_covY = dim(covY)
-	m = size_covY[1]
+	m = dim(covY)[1]
+	k = dim(covY)[3]
 
 	Y = matrix(0,n,m)
-	BX = array(0, dim=c(n,m,k))
+	require(mvtnorm)
+	X = rmvnorm(n, mean = rep(0,p), sigma = covX)
 
 	require(MASS) #simulate from a multivariate normal distribution
 	for (i in 1:n)
 	{
+		
 		for (r in 1:k)
 		{
 			BXir = rep(0,m)
 			for (mm in 1:m)
-				Bxir[[mm]] = X[i,] %*% beta[,mm,r]
+				BXir[mm] = X[i,] %*% beta[,mm,r]
 			Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
 		}
 	}