rmvnorm: Multivariate Normal (Gaussian) Distribution

In splus2R: Supplemental S-PLUS Functionality in R

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))

splus2R documentation built on May 29, 2024, 7:29 a.m.