X-Git-Url: https://git.auder.net/?a=blobdiff_plain;ds=sidebyside;f=R%2FgenerateIO.R;fp=R%2FgenerateIO.R;h=0000000000000000000000000000000000000000;hb=ef67d338c7f28ba041abe40ca9a8ab69f8365a90;hp=5f19488bb80ef7b04d16e305ff2dcef7d4cdcdc9;hpb=c3bc47052f3ccb659659c59a82e9a99ea842398d;p=valse.git

diff --git a/R/generateIO.R b/R/generateIO.R
deleted file mode 100644
index 5f19488..0000000
--- a/R/generateIO.R
+++ /dev/null
@@ -1,36 +0,0 @@
-#' 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]
-
-	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))
-}