Fit data using quantile matching estimation for RS and FMKL GLD

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Description

This function fits generalised lambda distributions to data using quantile matching method

Usage

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fun.data.fit.qs(data, rs.leap = 3, fmkl.leap = 3, rs.init = c(-1.5, 1.5), 
fmkl.init = c(-0.25, 1.5), FUN = "runif.sobol", trial.n = 100, len = 1000, 
type = 7, no = 10000)

Arguments

data

Dataset to be fitted.

rs.leap

Scrambling (0,1,2,3) for the sobol sequence for the RPRS distribution fit. See scrambling/leap argument for runif.sobol, runif.halton or QUnif.

fmkl.leap

Scrambling (0,1,2,3) for the sobol sequence for the RMFMKL distribution fit. See scrambling/leap argument for runif.sobol, runif.halton or QUnif.

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.halton" or "QUnif".

trial.n

Number of evenly spaced quantile ranging from 0 to 1 to be used in the checking phase, to find the best set of initial values for optimisation, this is intended to be lower than len to speed up the fitting algorithm. Default is 100.

len

Number of evenly spaced quantile ranging from 0 to 1 to be used, default is 1000

type

Type of quantile to be used, default is 7, see quantile

no

Number of initial random values to find the best initial values for optimisation.

Details

This function consolidates fun.RPRS.qs and fun.RMFMKL.qs and gives all the fits in one output.

Value

A matrix showing the parameters of RS and FMKL generalised lambda distributions.

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

Su (2008). Fitting GLD to data via quantile matching method. (Book chapter to appear)

See Also

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

Examples

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

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