R/mvrnorm.R

In flashxio/FlashR-learn: Scale machine learning for massive datasets

# file MASS/R/mvrnorm.R
# copyright (C) 1994-2015 W. N. Venables and B. D. Ripley
#
#  This program is free software; you can redistribute it and/or modify
#  it under the terms of the GNU General Public License as published by
#  the Free Software Foundation; either version 2 or 3 of the License
#  (at your option).
#
#  This program is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#  GNU General Public License for more details.
#
#  A copy of the GNU General Public License is available at
#  http://www.r-project.org/Licenses/
#
mvrnorm <-
    function(n = 1, mu, Sigma, tol=1e-6, empirical = FALSE, EISPACK = FALSE)
{
    p <- length(mu)
    if(!all(dim(Sigma) == c(p,p))) stop("incompatible arguments")
    if(EISPACK) stop("'EISPACK' is no longer supported by R", domain = NA)
    eS <- eigen(Sigma, symmetric = TRUE)
    ev <- eS$values
    if(!all(ev >= -tol*abs(ev[1L]))) stop("'Sigma' is not positive definite")
    X <- fm.rnorm.matrix(n, p)
    if(empirical) {
        X <- scale(X, TRUE, FALSE) # remove means
        X <- X %*% fm.svd(X, nu = 0)$v # rotate to PCs
        X <- scale(X, FALSE, TRUE) # rescale PCs to unit variance
    }
    X <- drop(mu) + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X)
    nm <- names(mu)
    if(is.null(nm) && !is.null(dn <- dimnames(Sigma))) nm <- dn[[1L]]
    dimnames(X) <- list(nm, NULL)
    if(n == 1) drop(X) else t(X)
}