| ghtFit {fBasics} | R Documentation |
Estimates the distributional parameters for a generalized hyperbolic Student-t distribution.
ghtFit(x, beta = 0.1, delta = 1, mu = 0, nu = 10,
scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE,
title = NULL, description = NULL, ...)
x |
a numeric vector. |
beta, delta, mu |
The parameters are beta, delta, and mu:skewness parameter beta is in the range (0, alpha);
scale parameter delta must be zero or positive;
location parameter mu, by default 0; and
lambda parameter lambda, by default 1.
|
nu |
defines the number of degrees of freedom.
Note, alpha takes the limit of abs(beta),
and lambda=-nu/2.
|
scale |
a logical flag, by default TRUE. Should the time series
be scaled by its standard deviation to achieve a more stable
optimization?
|
doplot |
a logical flag. Should a plot be displayed? |
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 span=seq(min, max,
times = n), where, min and max are the
left and right endpoints of the range, and n gives
the number of the intermediate points.
|
trace |
a logical flag. Should the parameter estimation process be traced? |
title |
a character string which allows for a project title. |
description |
a character string which allows for a brief description. |
... |
parameters to be parsed. |
The function nlm is used to minimize the "negative"
maximum log-likelihood function. nlm carries out a minimization
using a Newton-type algorithm.
returns a list with the following components:
estimate |
the point at which the maximum value of the log liklihood function is obtained. |
minimum |
the value of the estimated maximum, i.e. the value of the log liklihood function. |
code |
an integer indicating why the optimization process terminated. 1: relative gradient is close to zero, current iterate is probably solution; 2: successive iterates within tolerance, current iterate is probably solution; 3: last global step failed to locate a point lower than estimate.
Either estimate is an approximate local minimum of the
function or steptol is too small; 4: iteration limit exceeded; 5: maximum step size stepmax exceeded five consecutive times.
Either the function is unbounded below, becomes asymptotic to a
finite value from above in some direction or stepmax
is too small.
|
gradient |
the gradient at the estimated maximum. |
steps |
number of function calls. |
## ghtFit - # Simulate Random Variates: set.seed(1953) ## ghtFit - # Fit Parameters: