gld
)
is a transformation of the uniform distribution. This method finds the
parameters that transform the data closest to the uniform distribution.
This function uses a grid-based search.
starship.adaptivegrid(data, initgrid,inverse.eps = 1e-08, param="FMKL")
lcvect
, a vector of values for $lambda 3$,
ldvect
, a vector of values for $lambda 4$ and
levect
, a vector of values for $lambda 5$
(levect
is only required if param
is fm5
).
The parameter values given in initgrid
are not checked with gl.check.lambda
.
fmkl
uses Freimer, Mudholkar, Kollia and Lin (1988) (default).
rs
uses Ramberg and Schmeiser (1974)
fm5
uses the 5 parameter version of the FMKL parameterisation
(paper to appear)gld
)
is a transformation of the uniform distribution. Thus the inverse of this
transformation is the distribution function for the gld. The starship method
applies different values of the parameters of the distribution to the
distribution function, calculates the depths q corresponding to the data
and chooses the parameters that make the depths closest to a uniform
distribution.The closeness to the uniform is assessed by calculating the Anderson-Darling
goodness-of-fit test on the transformed data against the uniform, for a
sample of size length(data)
.
This function carries out a grid-based search. This was the original method
of King \& MacGillivray, 1999, but you are advised to instead use
starship
which uses a grid-based search together with an
optimisation based search.
See GeneralisedLambdaDistribution
for details on
parameterisations.
Ramberg, J. S. & Schmeiser, B. W. (1974), An approximate method for generating asymmetric random variables, Communications of the ACM 17, 78--82. King, R.A.R. & MacGillivray, H. L. (1999), A starship method for fitting the generalised $lambda$ distributions, Australian and New Zealand Journal of Statistics 41, 353--374
Owen, D. B. (1988), The starship, Communications in Statistics - Computation and Simulation 17, 315--323.
starship
,
starship.obj
data <- rgl(100,0,1,.2,.2)
starship.adaptivegrid(data,list(lcvect=(0:4)/10,ldvect=(0:4)/10))
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