Fit RS generalised lambda distribution to data set using quantile matching
This function fits RS generalised lambda distribution to data set using quantile matching
fun.RPRS.qs(data, rs.init = c(-1.5, 1.5), leap = 3, FUN = "runif.sobol", trial.n = 100, len = 1000, type = 7, no = 10000)
data |
Dataset to be fitted |
rs.init |
Initial values for RS distribution optimization,
|
leap |
Scrambling (0,1,2,3) for the Sobol sequence for the distribution
fit. See scrambling/leap argument for |
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 provides quantile matching fitting scheme for RS GLD. Note this function can fail if there are no defined percentiles from the data set or if the initial values do not lead to a valid RS generalised lambda distribution.
A vector representing four parameters of the RS generalised lambda distribution.
Steve Su
Su (2008). Fitting GLD to data via quantile matching method. (Book chapter to appear)
## Fitting the normal distribution # fun.RPRS.qs(data=rnorm(1000,2,3),rs.init=c(-1.5,1.5),leap=3)
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