pkg/R/generateXY.R

   1 #' generateXY
   2 #'
   3 #' Generate a sample of (X,Y) of size n
   4 #'
   5 #' @param n sample size
   6 #' @param π proportion for each cluster
   7 #' @param meanX matrix of group means for covariates (of size p)
   8 #' @param covX covariance for covariates (of size p*p)
   9 #' @param β regression matrix, of size p*m*k
  10 #' @param covY covariance for the response vector (of size m*m*K)
  11 #'
  12 #' @return list with X and Y
  13 #'
  14 #' @export
  15 generateXY = function(n, π, meanX, β, covX, covY)
  16 {
  17     p <- dim(covX)[1]
  18     m <- dim(covY)[1]
  19     k <- dim(covY)[3]
  20
  21     X <- matrix(nrow=0, ncol=p)
  22     Y <- matrix(nrow=0, ncol=m)
  23
  24     #random generation of the size of each population in X~Y (unordered)
  25     sizePop <- rmultinom(1, n, pi)
  26     class <- c() #map i in 1:n --> index of class in 1:k
  27
  28     for (i in 1:k)
  29     {
  30         class <- c(class, rep(i, sizePop[i]))
  31         newBlockX <- MASS::mvrnorm(sizePop[i], meanX, covX)
  32         X <- rbind( X, newBlockX )
  33         Y <- rbind( Y, apply( newBlockX, 1, function(row)
  34             mvrnorm(1, row %*% beta[,,i], covY[,,i]) ) )
  35     }
  36
  37     shuffle = sample(n)
  38     list("X"=X[shuffle,], "Y"=Y[shuffle,], "class"=class[shuffle])
  39 }