GLDEX: fun.RPRS.ml.m – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

fun.RPRS.ml.m

Fit RS generalised lambda distribution to data set using maximum likelihood estimation

Description

This function fits RS generalised lambda distribution to data set using maximum likelihood estimation using faster implementation through C programming

Usage

fun.RPRS.ml.m(data, rs.init = c(-1.5, 1.5), leap = 3, FUN = "runif.sobol", 
no = 10000)

Arguments

`data`	Dataset to be fitted
`rs.init`	Initial values for RS distribution optimization, `c(-1.5,1.5)` tends to work well.
`leap`	Scrambling (0,1,2,3) for the Sobol sequence for the distribution fit. See scrambling/leap argument for `runif.sobol`, `runif.halton` or `QUnif`.
`FUN`	A character string of either `"runif.sobol"` (default), `"runif.halton"` or `"QUnif"`.
`no`	Number of initial random values to find the best initial values for optimisation.

Details

This function provides one of the definitive fit to data set using generalised lambda distributions. Note this function can fail if there are no defined percentiles from the data set or if the initial values do not lead to a valid RS generalised lambda distribution.

Value

A vector representing four parameters of the RS generalised lambda distribution.

Author(s)

Steve Su

References

Su, S. (2007). Numerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions. Computational statistics and data analysis 51(8) 3983-3998.

Su (2007). Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R. Journal of Statistical Software: *21* 9.

Examples

## Fitting the normal distribution
# fun.RPRS.ml.m(data=rnorm(1000,2,3),rs.init=c(-1.5,1.5),leap=3)

GLDEX

Fitting Single and Mixture of Generalised Lambda Distributions (RS and FMKL) using Various Methods

v2.0.0.7

GPL (>= 3)

Authors

Steve Su, with contributions from: Diethelm Wuertz, Martin Maechler and Rmetrics core team members for low discrepancy algorithm, Juha Karvanen for L moments codes, Robert King for gld C codes and starship codes, Benjamin Dean for corrections and input in ks.gof code and R core team for histsu function.

Initial release

2020-02-04