X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FgenerateIO.R;h=83d8cc9f19bcc4f3689817547cd5e9db7b83c23b;hp=f8c8194daea0e44d9206c583ab99b7884d9a1a5f;hb=d1531659214edd6eaef0ac9ec835455614bba16c;hpb=6bd2e869a17f3980d52820643c1c1b5f3725738e

diff --git a/R/generateIO.R b/R/generateIO.R
index f8c8194..83d8cc9 100644
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,26 +1,35 @@
+#' Generate a sample of (X,Y) of size n
+#' @param covX covariance for covariates
+#' @param covY covariance for the response vector
+#' @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]
-
-	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))
+  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))
+  
+  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))
 }