MVNorm: The Multivariate Normal Distribution

Description Usage Arguments Value Author(s) Examples

Description

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

Usage

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

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").

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.

Author(s)

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

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## Not run: 
x <- rMVNorm (10, mean=rep(0,3), method="svd")
dMVNorm (x, mean=rep(0,3), log=TRUE)

## End(Not run)

DIRECT documentation built on May 1, 2019, 8:08 p.m.