#' 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
{
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