#' Generate a sample of (X,Y) of size n
-#' @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 meanX matrix of group means for covariates (p x K)
+#' @param covX covariance for covariates (p x p x K)
+#' @param covY covariance for the response vector (m x m x K)
+#' @param pi proportion for each cluster
#' @param beta regression matrix, of size p*m*k
-#' @param n sample size
+#' @param n sample size
#'
#' @return list with X and Y
#' @export
for (i in 1:n)
{
class[i] = sample(1:k, 1, prob=pi)
- X[i,] = mvrnorm(1, meanX, covX)
- print(X[i,])
- print(beta[,,class[i]])
+ X[i,] = mvrnorm(1, meanX[,class[i]], covX[,,class[i]])
Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
}