rmvnorm: Multivariate normal and t random values simulation

In Rfast: A Collection of Efficient and Extremely Fast R Functions

Multivariate normal and t random values simulation

Description

Multivariate normal and t random values simulation.

Usage

rmvnorm(n, mu, sigma, seed = NULL)
rmvt(n, mu, sigma, v, seed = NULL)

Arguments

n	The sample size, a numerical value.
mu	The mean vector in R^d.
sigma	The covariance matrix in R^d.
v	The degrees of freedom.
seed	If you want the same to be generated again use a seed for the generator, an integer number.

Details

The algorithm uses univariate normal random values and transforms them to multivariate via a spectral decomposition. It is faster than the command "mvrnorm" available from MASS, and it allows for singular covariance matrices.

Value

A matrix with the simulated data.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[e-mail]>

References

Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.

See Also

racg, rmvlaplace, rmvt

Examples

x <- as.matrix(iris[, 1:4])
m <- colmeans(x)
s <- cov(x)
y <- rmvnorm(1000, m, s)
res<-colmeans(y)
res<-cov(y)
y <- NULL

Rfast documentation built on Nov. 9, 2023, 5:06 p.m.