-#' 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 beta regression matrix, of size p*m*k
-#' @param n sample size
-#'
-#' @return list with X and Y
-#' @export
-generateXY = function(meanX, covX, covY, pi, beta, n)
-{
- p = dim(covX)[1]
- m = dim(covY)[1]
- 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[i] = sample(1:k, 1, prob=pi)
- X[i,] = mvrnorm(1, meanX, covX)
- Y[i,] = mvrnorm(1, X[i,] %*% beta[,,class[i]], covY[,,class[i]])
- }
-
- return (list(X=X,Y=Y, class = class))
-}
-
#' Generate a sample of (X,Y) of size n with default values
#' @param n sample size
#' @param p number of covariates
covX = diag(p)
covY = array(dim=c(m,m,k))
for(r in 1:k)
- {
covY[,,r] = diag(m)
- }
pi = rep(1./k,k)
#initialize beta to a random number of non-zero random value
beta = array(0, dim=c(p,m,k))