utilisation de k-means au lieu de hierarchique dans initSmallEM - PB de dimensions...

[valse.git] / R / generateIO.R

diff --git a/R/generateIO.R b/R/generateIO.R
index 83d8cc9..0e776d0 100644 (file)
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,20 +1,19 @@
 #' Generate a sample of (X,Y) of size n
-#' @param covX covariance for covariates
-#' @param covY covariance for the response vector
+#' @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)
 {
-  size_covX = dim(covX)
-  p = size_covX[1]
-  k = size_covX[3]
+  p = dim(covX)[1]
   
-  size_covY = dim(covY)
-  m = size_covY[1]
+  m = dim(covY)[1]
+  k = dim(covY)[3]
   
   Y = matrix(0,n,m)
   BX = array(0, dim=c(n,m,k))