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Last data update: 2014.03.03

R: Multivariate Normal Density and Random Deviates

Mvnorm

R Documentation

Multivariate Normal Density and Random Deviates

Description

These functions provide the density function and a random number generator for the multivariate normal distribution with mean equal to mean and covariance matrix sigma.

Usage

dmvnorm(x, mean = rep(0, p), sigma = diag(p), log = FALSE)
rmvnorm(n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)),
        method=c("eigen", "svd", "chol"), pre0.9_9994 = FALSE)

Arguments

`x`	vector or matrix of quantiles. If `x` is a matrix, each row is taken to be a quantile.
`n`	number of observations.
`mean`	mean vector, default is `rep(0, length = ncol(x))`.
`sigma`	covariance matrix, default is `diag(ncol(x))`.
`log`	logical; if `TRUE`, densities d are given as log(d).
`method`	string specifying the 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"`). The Cholesky is typically fastest, not by much though.
`pre0.9_9994`	logical; if `FALSE`, the output produced in mvtnorm versions up to 0.9-9993 is reproduced. In 0.9-9994, the output is organized such that `rmvnorm(10,...)` has the same first ten rows as `rmvnorm(100, ...)` when called with the same seed.

Author(s)

Friedrich Leisch and Fabian Scheipl

Examples

dmvnorm(x=c(0,0))
dmvnorm(x=c(0,0), mean=c(1,1))

sigma <- matrix(c(4,2,2,3), ncol=2)
x <- rmvnorm(n=500, mean=c(1,2), sigma=sigma)
colMeans(x)
var(x)

x <- rmvnorm(n=500, mean=c(1,2), sigma=sigma, method="chol")
colMeans(x)
var(x)

plot(x)