# mvrnorm {MASS}

### 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.

### Values

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

### Examples

Documentation reproduced from package MASS, version 7.3-45. License: GPL-2 | GPL-3