rCGHD: Pseudo random number generation from a coalesced generalized...

In MixGHD: Model Based Clustering, Classification and Discriminant Analysis Using the Mixture of Generalized Hyperbolic Distributions

Pseudo random number generation from a coalesced generalized hyperbolic distribution (MSGHD).

Description

Generate n pseudo random numbers from a p dimensional coalesced generalized hyperbolic distribution.

Usage

rCGHD(n,p,mu=rep(0,p),alpha=rep(0,p),sigma=diag(p),omega=1,lambda=0.5
,omegav=rep(1,p),lambdav=rep(0.5,p),wg=0.5)

Arguments

n	number of observations.
p	number of variables.
mu	(optional) the p dimensional mean
alpha	(optional) the p dimensional skewness parameter alpha
sigma	(optional) the p x p dimensional scale matrix
lambda	(optional) the 1 dimensional index parameter lambda
omega	(optional) the 1 dimensional concentration parameter omega
omegav	(optional) the p dimensional concentration parameter omega
lambdav	(optional) the p dimensional index parameter lambda
wg	(optional) the weight

Details

The default values are: 0 for the mean and the skweness parameter alpha, diag(p) for sigma, 1 for omega, and 0.5 for lambda.

Value

A n times p matrix of numbers psudo randomly generated from a coalesced generilzed hyperbolic distribution

Author(s)

Cristina Tortora, Aisha ElSherbiny, Ryan P. Browne, Brian C. Franczak, and Paul D. McNicholas. Maintainer: Cristina Tortora <[e-mail]>

References

C. Tortora, B.C. Franczak, R.P. Browne, and P.D. McNicholas (2019). A Mixture of Coalesced Generalized Hyperbolic Distributions. Journal of Classification (to appear).

Examples

data=rCGHD(300,2,alpha=c(2,-2),omegav=c(2,2),omega=3)

plot(data)

MixGHD documentation built on May 11, 2022, 5:12 p.m.