X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FgenerateIO.R;h=f8c8194daea0e44d9206c583ab99b7884d9a1a5f;hp=9e84af5a282d4f81d683c38946cd5b7a15ebb364;hb=39046da6016f15d625bd99cf0303ea8beb838c79;hpb=8266149c7d93aa0543cee2a1b22e1233e7b82617

diff --git a/R/generateIO.R b/R/generateIO.R
index 9e84af5..f8c8194 100644
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,25 +1,26 @@
-library(MASS) #simulate from a multivariate normal distribution
+generateIO = function(covX, covY, pi, beta, n)
+{
+	size_covX = dim(covX)
+	p = size_covX[1]
+	k = size_covX[3]
 
-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
+	size_covY = dim(covY)
+	m = size_covY[1]
+
+	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))
+}