swh:1:snp:d1587d616651317fdcebcbb237dce82c32266449
Tip revision: dfe6577bb164f53feaedf2da9530457197edfb1a authored by Georgi N. Boshnakov on 20 October 2022, 11:25:10 UTC
version 4021.93
version 4021.93
Tip revision: dfe6577
dist-gldFit.Rd
\name{gldFit}
\alias{gldFit}
\title{GH Distribution Fit}
\description{
Estimates the distrinbutional parameters for a
generalized lambda distribution.
}
\usage{
gldFit(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8,
method = c("mle", "mps", "gof", "hist", "rob"),
scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE,
title = NULL, description = NULL, \dots)
}
\arguments{
\item{x}{
a numeric vector.
}
\item{lambda1, lambda2, lambda3, lambda4}{
are numeric values where
\code{lambda1} is the location parameter,
\code{lambda2} is the location parameter,
\code{lambda3} is the first shape parameter, and
\code{lambda4} is the second shape parameter.
}
\item{method}{
a character string, the estimation approach to
fit the distributional parameters, see details.
}
\item{scale}{
not used.
%a logical flag, by default \code{TRUE}. Should the time series
%be scaled by its standard deviation to achieve a more stable
%optimization?
}
\item{doplot}{
a logical flag. Should a plot be displayed?
}
\item{add}{
a logical flag. Should a new fit added to an existing plot?
}
\item{span}{
x-coordinates for the plot, by default 100 values
automatically selected and ranging between the 0.001,
and 0.999 quantiles. Alternatively, you can specify
the range by an expression like \code{span=seq(min, max,
times = n)}, where, \code{min} and \code{max} are the
left and rigldt endpoints of the range, and \code{n} gives
the number of the intermediate points.
}
\item{trace}{
a logical flag. Should the parameter estimation process be
traced?
}
\item{title}{
a character string which allows for a project title.
}
\item{description}{
a character string which allows for a brief description.
}
\item{\dots}{
parameters to be parsed.
}
}
\value{
an object from class \code{"fDISTFIT"}.
Slot \code{fit} is a list with the following components:
\item{estimate}{
the point at which the maximum value of the log liklihood
function is obtained.
}
\item{minimum}{
the value of the estimated maximum, i.e. the value of the
log liklihood function.
}
\item{code}{
an integer indicating why the optimization process terminated.\cr
1: relative gradient is close to zero, current iterate is probably
solution; \cr
2: successive iterates within tolerance, current iterate is probably
solution; \cr
3: last global step failed to locate a point lower than \code{estimate}.
Either \code{estimate} is an approximate local minimum of the
function or \code{steptol} is too small; \cr
4: iteration limit exceeded; \cr
5: maximum step size \code{stepmax} exceeded five consecutive times.
Either the function is unbounded below, becomes asymptotic to a
finite value from above in some direction or \code{stepmax}
is too small.
}
\item{gradient}{
the gradient at the estimated maximum.
}
\item{steps}{
number of function calls.
}
}
\details{
The function \code{\link{nlminb}} is used to minimize the objective
function. The following approaches have been implemented:
\code{"mle"}, maximimum log likelihoo estimation.
\code{"mps"}, maximum product spacing estimation.
\code{"gof"}, goodness of fit approaches,
\code{type="ad"} Anderson-Darling,
\code{type="cvm"} Cramer-vonMise,
\code{type="ks"} Kolmogorov-Smirnov.
\code{"hist"}, histogram binning approaches,
\code{"fd"} Freedman-Diaconis binning,
\code{"scott"}, Scott histogram binning,
\code{"sturges"}, Sturges histogram binning.
\code{"rob"}, robust moment matching.
}
\examples{
## gldFit -
# Simulate Random Variates:
set.seed(1953)
s = rgld(n = 1000, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8)
## gldFit -
# Fit Parameters:
gldFit(s, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8,
doplot = TRUE, trace = TRUE)
}
\keyword{distribution}