DIRECT (version 1.1.0)

MVNorm: The Multivariate Normal Distribution

Description

Functions to compute the density of a multivariate normal distribution and to generate random realizations from such a distribution.

Usage

dMVNorm (x, mean, sigma, log = FALSE)
rMVNorm (n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)),
         method=c("eigen", "svd", "chol"))

Value

rMVNorm returns a vector of the same length as mean if n=1, or a matrix with each row being an independent realization otherwise.

Arguments

x

Vector or matrix of quantiles. If x is a matrix, each row is taken to be a quantile.

n

Number of realizations.

mean

Mean vector, default is rep(0, length = ncol(x)).

sigma

Covariance matrix, default is diag(ncol(x)).

log

Logical; if TRUE, densities are log-transformed.

method

Matrix decomposition used to determine the matrix root of sigma, possible methods are eigenvalue decomposition ("eigen", default), singular value decomposition ("svd"), and Cholesky decomposition ("chol").

Author

The code for both functions is taken from similar functions written by Friedrich Leisch and Fabian Scheipl in R package mvtnorm. Audrey Q. Fu modified dMVNorm to use a different method to compute the matrix determinants.

Examples

if (FALSE) {
x <- rMVNorm (10, mean=rep(0,3), method="svd")
dMVNorm (x, mean=rep(0,3), log=TRUE)
}