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 9e84af5..0e776d0 100644 (file)
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,25 +1,34 @@
-library(MASS) #simulate from a multivariate normal distribution
-
-generateIO = function(meanX, covX, covY, pi, beta, n){ #don't need meanX
-  size_covX = dim(covX)
-  p = size_covX[1]
-  k = size_covX[3]
+#' 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]
   
-  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))
   
-  for(i in 1:n){
-    for(r in 1: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){
+      for (mm in 1:m)
         Bxir[[mm]] = X[i,] %*% beta[,mm,r]
-      }
-      Y[i,]=Y[i,] + pi[[r]] * mvrnorm(1,BXir, covY[,,r])
+      Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
     }
   }
   
-  return(list(X,Y))
-}
\ No newline at end of file
+  return (list(X=X,Y=Y))
+}