fun.RPRS.ml.m: Fit RS generalised lambda distribution to data set using...

In GLDEX: Fitting Single and Mixture of Generalised Lambda Distributions

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	See scrambling argument in fun.gen.qrn.
FUN	A character string of either "runif.sobol" (default), "runif.sobol.owen", "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.

See Also

fun.RPRS.ml

Examples

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

GLDEX documentation built on Aug. 21, 2023, 9:08 a.m.