-library(MASS) #simulate from a multivariate normal distribution
+#' 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]
-generateIO = function(meanX, covX, covY, pi, beta, n){ #don't need meanX
- size_covX = dim(covX)
- p = size_covX[1]
- k = size_covX[3]
-
- size_covY = dim(covY)
- m = size_covY[1]
-
- Y = matrix(0,n,m)
- BX = array(0, dim=c(n,m,k))
-
- 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]
- }
- Y[i,]=Y[i,] + pi[[r]] * mvrnorm(1,BXir, covY[,,r])
- }
- }
-
- return(list(X,Y))
-}
\ No newline at end of file
+ m = dim(covY)[1]
+ k = dim(covY)[3]
+
+ Y = matrix(0,n,m)
+ 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]
+ Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
+ }
+ }
+
+ return (list(X=X,Y=Y))
+}