MVNorm {DIRECT} R Documentation

## 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"))


### 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

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

## End(Not run)


[Package DIRECT version 1.0.1 Index]