mvrnorm

0th

Percentile

Simulate from a Multivariate Normal Distribution

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

Keywords
multivariate, 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. Set to true to reproduce results from MASS versions prior to 3.1-21.
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.

rnorm

• mvrnorm
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))
Documentation reproduced from package MASS, version 7.3-38, License: GPL-2 | GPL-3

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