X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FgenerateIO.R;h=5f19488bb80ef7b04d16e305ff2dcef7d4cdcdc9;hb=c3bc47052f3ccb659659c59a82e9a99ea842398d;hp=0e776d0502261ca26d4061036d661f78b8c39d55;hpb=f2a9120810d7e1e423c7b5c2c4320f4e27221f50;p=valse.git

diff --git a/R/generateIO.R b/R/generateIO.R
index 0e776d0..5f19488 100644
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,34 +1,36 @@
 #' 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 pi	 proportion for each cluster
 #' @param beta regression matrix
-#' @param n    sample size
+#' @param n		sample size
 #' 
 #' @return list with X and Y
 #' @export
 #-----------------------------------------------------------------------
 generateIO = function(covX, covY, pi, beta, n)
 {
-  p = dim(covX)[1]
-  
-  m = dim(covY)[1]
-  k = dim(covY)[3]
-  
-  Y = matrix(0,n,m)
-  BX = array(0, dim=c(n,m,k))
-  
-  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]
-      Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
-    }
-  }
-  
-  return (list(X=X,Y=Y))
+	p = dim(covX)[1]
+
+	m = dim(covY)[1]
+	k = dim(covY)[3]
+
+	Y = matrix(0,n,m)
+	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]
+			Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
+		}
+	}
+
+	return (list(X=X,Y=Y))
 }