Gld {Davies}R Documentation

The Generalized Lambda Distribution

Description

Density, distribution function, quantile function and random generation for the Generalized Lambda Distribution

Usage

dgld(x, params)
dgld.p(x, params)
pgld(x, params)
qgld(p, params)
rgld(n, params)

Arguments

x vector of quantiles.
p vector of probabilities.
n number of observations. If length(n)> 1, the length is taken to be the number required
params vector of parameters: params[1]==lambda1 etseq

Details

The Generalized Lambda distribution has quantile function

f(x)=lambda1 +(p^lambda3 - (1-p)^lambda_4)/lambda_2

Value

dgld gives the density, dgld.p gives the density in terms of the quantile, pgld gives the distribution function, qgld gives the quantile function, and rgld generates random deviates.

References

Wichura, M. J. (1988) Algorithm AS 241: The Percentage Points of the Normal Distribution. Applied Statistics, 37, 477–484.

A. "{O}zt"{u}rk and R. F. Dale, "Least squares estimation of the parameters of the generalized lambda distribution", Technometrics 1985, 27(1):84

See Also

Davies, expected.gld

Examples

params <- c(4.114,0.1333,0.0193,0.1588)  #taken straight from some paper

gld.rv <- rgld(100,params)

hist(gld.rv)
fit.davies.q(gld.rv)  #remember the Davies distn has 3 DF and the GLD 4...

[Package Davies version 0.1-4 Index]