X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FgenerateIO.R;h=4527f0897705bc9065e93f203567f53549352212;hb=0b216f854a21821f9be375d07c2932b31e227e78;hp=83d8cc9f19bcc4f3689817547cd5e9db7b83c23b;hpb=d1531659214edd6eaef0ac9ec835455614bba16c;p=valse.git

diff --git a/R/generateIO.R b/R/generateIO.R
index 83d8cc9..4527f08 100644
--- a/R/generateIO.R
+++ b/R/generateIO.R
@@ -1,32 +1,33 @@
 #' 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))
+  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]
+        BXir[mm] = X[i,] %*% beta[,mm,r]
       Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
     }
   }