-library(MASS) #simulate from a multivariate normal distribution
-
-generateIO = function(meanX, covX, covY, pi, beta, n){ #don't need meanX
+#' Generate a sample of (X,Y) of size n
+#' @param covX covariance for covariates
+#' @param covY covariance for the response vector
+#' @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]
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))
+}