SimDesign: rmgh – R documentation

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rmgh

Generate data with the multivariate g-and-h distribution

Description

Generate non-normal distributions using the multivariate g-and-h distribution. Can be used to generate several different classes of univariate and multivariate distributions.

Usage

rmgh(n, g, h, mean = rep(0, length(g)), sigma = diag(length(mean)))

Arguments

`n`	number of samples to draw
`g`	the g parameter(s) which control the skew of a distribution in terms of both direction and magnitude
`h`	the h parameter(s) which control the tail weight or elongation of a distribution and is positively related with kurtosis
`mean`	a vector of k elements for the mean of the variables
`sigma`	desired k x k covariance matrix between bivariate non-normal variables

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. doi: 10.20982/tqmp.16.4.p248

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. doi: 10.1080/10691898.2016.1246953

Examples

set.seed(1)

# univariate
norm <- rmgh(10000,1e-5,0)
hist(norm)

skew <- rmgh(10000,1/2,0)
hist(skew)

neg_skew_platykurtic <- rmgh(10000,-1,-1/2)
hist(neg_skew_platykurtic)

# multivariate
sigma <- matrix(c(2,1,1,4), 2)
mean <- c(-1, 1)
twovar <- rmgh(10000, c(-1/2, 1/2), c(0,0),
    mean=mean, sigma=sigma)
hist(twovar[,1])
hist(twovar[,2])
plot(twovar)

SimDesign

Structure for Organizing Monte Carlo Simulation Designs

v2.3

GPL (>= 2)

Authors

Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>), Matthew Sigal [ctb], Ogreden Oguzhan [ctb]

Initial release