Fit data using moment matching estimation for RS and FMKL GLD
This function fits generalised lambda distributions to data using moment matching method
fun.data.fit.mm(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)
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 |
fmkl.leap |
Scrambling (0,1,2,3) for the sobol sequence for the RMFMKL
distribution fit. See scrambling/leap argument for |
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 |
no |
Number of initial random values to find the best initial values for optimisation. |
This function consolidates fun.RPRS.mm and
fun.RMFMKL.mm and gives all the fits in
one output.
A matrix showing the parameters of RS and FMKL generalised lambda distributions.
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.
Karian, Z. and E. Dudewicz (2000). Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalised Bootstrap Methods. New York, Chapman and Hall.
## Fitting normal(3,2) distriution using the default setting # junk<-rnorm(50,3,2) # fun.data.fit.mm(junk)
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