mvrnorm: Simulate from a Multivariate Normal Distribution

In MASS: Support Functions and Datasets for Venables and Ripley's MASS

Simulate from a Multivariate Normal Distribution

Description

Produces one or more samples from the specified multivariate normal distribution.

Usage

mvrnorm(n = 1, mu, Sigma, tol = 1e-6, empirical = FALSE, EISPACK = FALSE)

Arguments

n	the number of samples required.
mu	a vector giving the means of the variables.
Sigma	a positive-definite symmetric matrix specifying the covariance matrix of the variables.
tol	tolerance (relative to largest variance) for numerical lack of positive-definiteness in Sigma.
empirical	logical. If true, mu and Sigma specify the empirical not population mean and covariance matrix.
EISPACK	logical: values other than FALSE are an error.

Details

The matrix decomposition is done via eigen; although a Choleski decomposition might be faster, the eigendecomposition is stabler.

Value

If n = 1 a vector of the same length as mu, otherwise an n by length(mu) matrix with one sample in each row.

Side Effects

Causes creation of the dataset .Random.seed if it does not already exist, otherwise its value is updated.

References

B. D. Ripley (1987) Stochastic Simulation. Wiley. Page 98.

See Also

rnorm

Examples

Sigma <- matrix(c(10,3,3,2),2,2)
Sigma
var(mvrnorm(n = 1000, rep(0, 2), Sigma))
var(mvrnorm(n = 1000, rep(0, 2), Sigma, empirical = TRUE))

MASS documentation built on June 22, 2024, 10:42 a.m.