X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FgenerateSampleInputs.R;h=c7aa3c6a78bd2c0b033467b3fa317d00af0dd5a4;hb=390625126a7ca58dafd2b4834f2f1d7a527d019f;hp=7ec361f0306abd620e0890b92a5033729021e660;hpb=f72201cc938d66d8a3bf71f990a34731305a0fa9;p=valse.git

diff --git a/pkg/R/generateSampleInputs.R b/pkg/R/generateSampleInputs.R
index 7ec361f..c7aa3c6 100644
--- a/pkg/R/generateSampleInputs.R
+++ b/pkg/R/generateSampleInputs.R
@@ -1,10 +1,10 @@
 #' Generate a sample of (X,Y) of size n
-#' @param meanX matrix of group means for covariates (p x K)
-#' @param covX covariance for covariates (p x p x K)
-#' @param covY covariance for the response vector (m x m x K)
-#' @param pi proportion for each cluster
+#' @param meanX matrix of group means for covariates (of size p)
+#' @param covX covariance for covariates (of size p*p)
+#' @param covY covariance for the response vector (of size m*m*K)
+#' @param pi	 proportion for each cluster
 #' @param beta regression matrix, of size p*m*k
-#' @param n sample size
+#' @param n		sample size
 #'
 #' @return list with X and Y
 #' @export
@@ -13,19 +13,19 @@ generateXY = function(meanX, covX, covY, pi, beta, n)
 	p = dim(covX)[1]
 	m = dim(covY)[1]
 	k = dim(covY)[3]
-
+	
 	X = matrix(nrow=n,ncol=p)
 	Y = matrix(nrow=n,ncol=m)
 	class = matrix(nrow = n)
-
+	
 	require(MASS) #simulate from a multivariate normal distribution
 	for (i in 1:n)
 	{
 		class[i] = sample(1:k, 1, prob=pi)
-		X[i,] = mvrnorm(1, meanX[,class[i]], covX[,,class[i]])
+		X[i,] = mvrnorm(1, meanX, covX)
 		Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
 	}
-
+	
 	return (list(X=X,Y=Y, class = class))
 }
 
@@ -38,13 +38,11 @@ generateXY = function(meanX, covX, covY, pi, beta, n)
 #' @export
 generateXYdefault = function(n, p, m, k)
 {
-	rangeX = 100
-	meanX = rangeX * matrix(1 - 2*runif(p*k), ncol=k)
-	covX = array(dim=c(p,p,k))
+	meanX = rep(0, p)
+	covX = diag(p)
 	covY = array(dim=c(m,m,k))
 	for(r in 1:k)
 	{
-		covX[,,r] = diag(p)
 		covY[,,r] = diag(m)
 	}
 	pi = rep(1./k,k)