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-sghFit.Rd
\name{sghFit}
\alias{sghFit}
\concept{Standardized generalized hyperbolic distribution}
\title{Standardized GH distribution fit}
\description{
Estimates the distributional parameters for a standardized generalized
hyperbolic distribution.
}
\usage{
sghFit(x, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE,
scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE,
title = NULL, description = NULL, \dots)
}
\arguments{
\item{x}{
a numeric vector.
}
\item{zeta, rho, lambda}{
shape parameter \code{zeta} is positive,
skewness parameter \code{rho} is in the range (-1, 1).
and index parameter \code{lambda}, by default 1.
}
\item{include.lambda}{
a logical flag, by default \code{TRUE}. Should the index
parameter \code{lambda} included in the parameter estimate?
}
\item{scale}{
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{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 right 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 likelihood 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.
}
}
\examples{
## sghFit -
# Simulate Random Variates:
set.seed(1953)
s = rsgh(n = 2000, zeta = 0.7, rho = 0.5, lambda = 0)
## sghFit -
# Fit Parameters:
sghFit(s, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE,
doplot = TRUE)
}
\keyword{distribution}