Fit data using quantile matching estimation for RS and FMKL GLD
This function fits generalised lambda distributions to data using quantile matching method
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)
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 |
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 |
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 |
no |
Number of initial random values to find the best initial values for optimisation. |
This function consolidates fun.RPRS.qs and
fun.RMFMKL.qs 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.
Steve Su
Su (2008). Fitting GLD to data via quantile matching method. (Book chapter to appear)
## Fitting normal(3,2) distriution using the default setting # junk<-rnorm(50,3,2) # fun.data.fit.qs(junk)
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