#' 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 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
+#' @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]
-
- size_covY = dim(covY)
- m = size_covY[1]
-
- Y = matrix(0,n,m)
- BX = array(0, dim=c(n,m,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)
- Bxir[[mm]] = X[i,] %*% beta[,mm,r]
- Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r])
- }
- }
-
- return (list(X=X,Y=Y))
+ p = dim(covX)[1]
+
+ 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))
}