#' Generate a sample of (X,Y) of size n
-#' @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 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
+#' @param n sample size
#'
#' @return list with X and Y
#' @export
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[,class[i]], covX[,,class[i]])
+ 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))
}
#' @export
generateXYdefault = function(n, p, m, k)
{
- rangeX = 100
- meanX = rangeX * matrix(1 - 2*runif(p*k), ncol=k)
- covX = array(dim=c(p,p,k))
+ meanX = rep(0, p)
+ covX = diag(p)
covY = array(dim=c(m,m,k))
for(r in 1:k)
{
- covX[,,r] = diag(p)
covY[,,r] = diag(m)
}
pi = rep(1./k,k)