R: Multivariate Normal (Gaussian) Distribution

rmvnorm {splus2R} | R Documentation

## Multivariate Normal (Gaussian) Distribution

### Description

Random generation for the multivariate normal (also called Gaussian) distribution.

### Usage

    rmvnorm(n, mean=rep(0,d), cov=diag(d), sd, rho, d=2)

### Arguments

`n` | sample size – number of random vectors of length d to return (as rows in a matrix).
`cov` | covariance or correlation matrix with d rows and columns.
`d` | dimension of the multivariate normal.
`mean` | vector of length d, or matrix with n rows and d columns.
`rho` | scalar, vector, or bdVector of length n, containing correlations for bivariate data. This is ignored if cov is supplied.
`sd` | vector of length d, or matrix with n rows and d columns, containing standard deviations. If supplied, the rows and columns of cov are multiplied by sd. In particular, if cov is a correlation matrix and sd is a vector of standard deviations, the result is a covariance matrix. If sd is a matrix then one row is used for each observation.

### Value

random sample ( rmvnorm) for the multivariate normal distribution.

### See Also

`rnorm`,`set.seed`.

### Examples

    ## 5 rows and 2 independent columns
    rmvnorm(5)

    ## 5 rows and 3 independent columns
    rmvnorm(5, mean=c(9,3,1))

    ## 2 columns, std. dev. 1, correlation .9
    rmvnorm(5, rho=.9)

    ## specify variable means and covariance matrix
    rmvnorm(5, mean=c(9,3), cov=matrix(c(4,1,1,2), 2))

[Package splus2R version 1.2-2 Index]