mvrnormsim: Simulate from a Multivariate Normal Distribution

In gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Simulate from a Multivariate Normal Distribution

Description

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

Usage

mvrnormsim(n = 1, mu, Sigma, tol = 1e-6, empirical = 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.

Details

Original function mvrnorm developed by Venables, W. N. & Ripley. in package MASS, https://CRAN.R-project.org/package=MASS.

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.

Author(s)

Venables, W. N. & Ripley, with modifications by Felix Pretis, https://felixpretis.climateeconometrics.org/

References

Venables, W. N. & Ripley, B. D. (2019): 'MASS: Support Functions and Datasets for Venables and Ripley's MASS'. https://CRAN.R-project.org/package=MASS

Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0

See Also

outlierscaletest

Examples

Sigma <- matrix(c(3,2,1,7),2,2)
mvrnormsim(n=2, mu=c(1,2), Sigma)

gets documentation built on Sept. 11, 2024, 9:03 p.m.