X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FgenerateIO.R;h=5f19488bb80ef7b04d16e305ff2dcef7d4cdcdc9;hb=b4476024967cdd601bf82ff4108f5c2beb855e9e;hp=9e84af5a282d4f81d683c38946cd5b7a15ebb364;hpb=493a35bfea6d1210c94ced8fbfe3e572f0389ea5;p=valse.git

diff --git a/R/generateIO.R b/R/generateIO.R
index 9e84af5..5f19488 100644
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,25 +1,36 @@
-library(MASS) #simulate from a multivariate normal distribution
+#' 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)
+{
+	p = dim(covX)[1]
 
-generateIO = function(meanX, covX, covY, pi, beta, n){ #don't need meanX
-  size_covX = dim(covX)
-  p = size_covX[1]
-  k = size_covX[3]
-  
-  size_covY = dim(covY)
-  m = size_covY[1]
-  
-  Y = matrix(0,n,m)
-  BX = array(0, dim=c(n,m,k))
-  
-  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,Y))
-}
\ No newline at end of file
+	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))
+}