GLDEX (version 2.0.0.9.3)

fun.data.fit.hs: Fit RS and FMKL generalised distributions to data using discretised approach with weights.

Description

This function fits RS and FMKL generalised distribution to data using discretised approach with weights. It is designed to act as a smoother device rather than a definitive fit.

Usage

fun.data.fit.hs(data, rs.default = "Y", fmkl.default = "Y", rs.leap = 3, 
fmkl.leap = 3, rs.init = c(-1.5, 1.5), fmkl.init = c(-0.25, 1.5), no.c.rs = 50, 
no.c.fmkl = 50,FUN="runif.sobol", no=10000)

Value

A matrix showing the four parameters of the RS and FMKL distribution fit.

Arguments

data

Dataset to be fitted

rs.default

If yes, this function uses the default method fun.nclass.e to calculate number of classes required for the RS distribution fits.

fmkl.default

If yes, this function uses the default method fun.nclass.e to calculate number of classes required for the FMKL distribution fits.

rs.leap

See scrambling argument in fun.gen.qrn.

fmkl.leap

See scrambling argument in fun.gen.qrn.

rs.init

Initial values for RS distribution optimization, c(-1.5,1.5) tends to work well.

fmkl.init

Initial values for FMKL distribution optimization, c(-0.25,1.5) tends to work well.

no.c.rs

Number of classes or bins of histogram to be optimized over for the RS GLD. This argument is ineffective if default="Y".

no.c.fmkl

Number of classes or bins of histogram to be optimized over for the FMKL GLD. This argument is ineffective if default="Y".

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.

Author

Steve Su

Details

This function optimises the deviations of frequency of the bins to that of the theoretical so it has the effect of "fitting clothes" onto the data set. The user can decide the frequency of the bins they want the distribution to smooth over. The resulting fit may or may not be an adequate fit from a formal statistical point of view such as satisfying the goodness of fit for example, but it can be useful to suggest the range of different distributions exhibited by the data set. The default number of classes calculates the mean and variance after categorising the data into different bins and uses the number of classes that best matches the mean and variance of the original, ungrouped data.The weighting is designed to accentuate the peak or the dense part of the distribution and suppress the tails.

References

Su, S. (2005). A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data. Journal of Modern Applied Statistical Methods (November): 408-424.

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.hs.nw, fun.RMFMKL.hs.nw, fun.RMFMKL.hs, fun.RPRS.hs, fun.data.fit.hs.nw, fun.data.fit.ml

Examples

# \donttest{
# Fitting normal(3,2) distriution using the default setting
 junk<-rnorm(1000,3,2)
 fun.data.fit.hs(junk)
# }