update EMGLLF.R and some few details

[valse.git] / R / generateSampleInputs.R

diff --git a/R/generateSampleInputs.R b/R/generateSampleInputs.R
index fb67b08..8edd031 100644 (file)
--- a/R/generateSampleInputs.R
+++ b/R/generateSampleInputs.R
@@ -1,9 +1,9 @@
 #' Generate a sample of (X,Y) of size n
-#' @param meanX matrix of group means for covariates (of size p*K)
-#' @param covX covariance for covariates (of size p*p*K)
+#' @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
+#' @param beta regression matrix, of size p*m*k
 #' @param n            sample size
 #'
 #' @return list with X and Y
@@ -12,20 +12,23 @@ generateXY = function(meanX, covX, covY, pi, beta, n)
 {
        p = dim(covX)[1]
        m = dim(covY)[1]
-       k = dim(covX)[3]
+       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 = sample(1:k, 1, prob=pi)
-               X[i,] = mvrnorm(1, meanX[,class], covX[,,class])
-               Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class], covY[,,class])
+               class[i] = sample(1:k, 1, prob=pi)
+               X[i,] = mvrnorm(1, meanX, covX)
+               print(X[i,])
+               print(beta[,,class[i]])
+               Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
        }
 
-       return (list(X=X,Y=Y))
+       return (list(X=X,Y=Y, class = class))
 }
 
 #' Generate a sample of (X,Y) of size n with default values