mle.ghypuv-class {ghyp}R Documentation

Class mle.ghypuv

Description

The class “mle.ghypuv” inherits from the class “ghypuv”. In addition to the class “ghypuv” this class stores fitting information. Namely the number of iterations n.iter, the log likelihood value llh, a boolean vector stating which parameters were fitted fitted.params, the Akaike Information Criterion aic, a boolean converged whether the fitting procedure converged or not, an error.code which stores the status of a possible error, the corresponding error.message and the parameter.variance.

Objects from the Class

Objects should only be created by calls to the fitting routines like fit.ghypuv, fit.hypuv, fit.NIGuv, fit.VGuv and fit.tuv.

Slots

lambda:
Shape parameter of class "numeric".
alpha.bar:
Shape parameter of class "numeric".
chi:
Shape parameter of an alternative parametrization. Object of class "numeric".
psi:
Shape parameter of an alternative parametrization. Object of class "numeric".
mu:
Location parameter of lass "numeric".
sigma:
Dispersion parameter of class "numeric".
gamma:
Skewness parameter of class "numeric".
model:
Model, i.e., (a)symmetric generalized hyperbolic distribution or (a)symmetric special case. Object of class "character".
dimension:
Dimension of the generalized hyperbolic distribution. Object of class "numeric".
expected.value:
The expected value of a generalized hyperbolic distribution. Object of class "numeric".
variance:
The expected value of a generalized hyperbolic distribution. Object of class "numeric".
data:
Data vector of class "numeric". When an object of class ghypuv is instantiated the user can decide whether data should be stored within the object or not. This may be useful when fitting eneralized hyperbolic distributions to data and perform further analysis afterwards.
n.iter:
The number of iterations of class "numeric".
llh:
The log likelihood value of class "numeric".
converged:
A boolean whether optim converged or not. Object of class "logical".
error.code:
An error code of class "numeric".
error.message:
An error message of class "character".
parameter.variance:
The parameter variance is calculated to be the inverse of the fisher information matrix. Parameter.variance is of class "matrix".
fitted.params:
A boolean vector stating which parameters were fitted of class "logical".
aic:
The value of the Akaike Information Criterion of class "numeric".

Extends

Class "ghypuv", directly. Class "ghypbase", by class "ghypuv", distance 2.

Methods

A “hist” method (see hist).
A “mean” method (see mean).
A “vcov” method (see vcov).

Note

When showing special cases of the generalized hyperbolic distribution the corresponding fixed parameters are plotted in brackets.

Author(s)

David Lüthi

See Also

optim for an interpretation of error.code and error.message, fit.ghypuv where objects of class mle.ghypuv are created, ghypuv-class to have a look on the base class.

Examples

  data(smi.stocks)
  fit.ghypuv(data=smi.stocks[,"SMI"],opt.pars=c(alpha.bar=FALSE,lambda=FALSE),
             alpha.bar=1)

[Package ghyp version 0.9.2 Index]