ghdist: Random Sample from G-H Distribution

In groupcompare: Comparing Two Groups Using Various Descriptive Statistics

Random Sample from G-H Distribution

Description

Generates a random sample from the G-and-H (GH) distribution with specified parameters.

Usage

ghdist(n = 30, A = 0, B = 1, g = 0, h = 0)

Arguments

n	An integer specifying the sample size. The default is 30.
A	A numeric value specifying the location parameter. The default is 0.
B	A numeric value specifying the scale parameter. The default is 1. Must be positive.
g	A numeric value specifying the skewness parameter. The default is 0.
h	A numeric value specifying the kurtosis parameter. The default is 0. Must be zero or positive.

Details

The gh distribution is a flexible distribution defined by four parameters: A (location), B (scale), g (skewness), and h (kurtosis). The parameter B must be positive, and h must be zero or positive. This function generates random samples from the gh distribution using these parameters.

The GH distribution was introduced by John W. Tukey in 1977 as a way to model data with varying degrees of skewness and kurtosis. The distribution is defined by transforming standard normal random variables using the g and h parameters to control skewness and kurtosis, respectively.

Value

A numeric vector of length n containing the generated random samples.

Author(s)

Zeynel Cebeci, A. Firat Ozdemir, Engin Yildiztepe

References

Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.

See Also

rnorm

Examples

 set.seed(50) 
 A <- ghdist(100, 50, 2, g=0, h=0)
 B <- ghdist(100, 50, 3, g=0.5, h=0.5)
 ds <- data.frame(A=A, B=B)
 head(ds)
 bivarplot(ds)

groupcompare documentation built on June 26, 2025, 1:08 a.m.