X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=test%2Fgenerate_test_data%2Fhelper.R;fp=pkg%2FR%2FgenerateSampleInputs.R;h=49cd1b5743e66448d6b39fda03474991d3850190;hp=c7aa3c6a78bd2c0b033467b3fa317d00af0dd5a4;hb=086ca318ed5580e961ceda3f1e122a2da58e4427;hpb=4e8267487c83c27273305b1379e44bc7abebf4b5

diff --git a/pkg/R/generateSampleInputs.R b/test/generate_test_data/helper.R
similarity index 63%
rename from pkg/R/generateSampleInputs.R
rename to test/generate_test_data/helper.R
index c7aa3c6..49cd1b5 100644
--- a/pkg/R/generateSampleInputs.R
+++ b/test/generate_test_data/helper.R
@@ -1,34 +1,3 @@
-#' Generate a sample of (X,Y) of size n
-#' @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
-#'
-#' @return list with X and Y
-#' @export
-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, covX)
-		Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
-	}
-	
-	return (list(X=X,Y=Y, class = class))
-}
-
 #' Generate a sample of (X,Y) of size n with default values
 #' @param n sample size
 #' @param p number of covariates
@@ -42,9 +11,7 @@ generateXYdefault = function(n, p, m, k)
 	covX = diag(p)
 	covY = array(dim=c(m,m,k))
 	for(r in 1:k)
-	{
 		covY[,,r] = diag(m)
-	}
 	pi = rep(1./k,k)
 	#initialize beta to a random number of non-zero random value
 	beta = array(0, dim=c(p,m,k))