fun.data.fit.ml {GLDEX}

Fit data using RS, FMKL maximum likelihood estimation and the FMKL starship method.

Description

This function fits generalised lambda distributions to data using RPRS, RMFMKL and starship methods.

Usage

fun.data.fit.ml(data, rs.leap = 3, fmkl.leap = 3, rs.init = c(-1.5, 1.5), 
fmkl.init = c(-0.25, 1.5),FUN="runif.sobol",no=10000)

Arguments

data

Dataset to be fitted.

rs.leap

See scrambling argument in fun.gen.qrn.

fmkl.leap

See scrambling argument in fun.gen.qrn.

rs.init

Inititial values (lambda3 and lambda4) for the RS generalised lambda distribution.

fmkl.init

Inititial values (lambda3 and lambda4) for the FMKL generalised lambda distribution.

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 consolidates fun.RPRS.ml, fun.RMFMKL.ml and starship and gives all the fits in one output.

Value

A matrix showing the parameters of generalised lambda distribution for RPRS, FMFKL and STAR methods.

Note

RPRS can sometimes fail if it is not possible to calculate the percentiles of the data set. This usually happens when the number of data point is small.

Author(s)

Steve Su

References

King, R.A.R. & MacGillivray, H. L. (1999), A starship method for fitting the generalised lambda distributions, Australian and New Zealand Journal of Statistics, 41, 353-374

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, fun.RMFMKL.ml, starship, fun.data.fit.hs, fun.data.fit.hs.nw , fun.data.fit.qs , fun.data.fit.mm , fun.data.fit.lm

Examples

# Fitting normal(3,2) distriution using the default setting
 junk<-rnorm(50,3,2)
 fun.data.fit.ml(junk)