ghyp (version 1.5.6)

ghyp-mle.ghyp-classes: Classes ghyp and mle.ghyp

Description

The class “ghyp” basically contains the parameters of a generalized hyperbolic distribution. The class “mle.ghyp” inherits from the class “ghyp”. The class “mle.ghyp” adds some additional slots which contain information about the fitting procedure. Namely, these are the number of iterations (n.iter), the log likelihood value (llh), the Akaike Information Criterion (aic), a boolean vector (fitted.params) stating which parameters were fitted, a boolean converged whether the fitting procedure converged or not, an error.code which stores the status of a possible error and the corresponding error.message. In the univariate case the parameter variance is also stored in parameter.variance.

Objects from the Class

Objects should only be created by calls to the constructors ghyp, hyp, NIG, VG, student.t and gauss or by calls to the fitting routines like fit.ghypuv, fit.ghypmv, fit.hypuv, fit.hypmv et cetera.

Slots

Slots of class ghyp:

call:

The function-call of class call.

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 matrix.

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 variance of a generalized hyperbolic distribution of class matrix.

data:

The data-slot is of class matrix. When an object of class ghypmv is instantiated the user can decide whether data should be stored within the object or not. This is the default and may be useful when fitting eneralized hyperbolic distributions to data and perform further analysis afterwards.

parametrization:

Parametrization of the generalized hyperbolic distribution of class character. These are currently either “chi.psi”, “alpha.bar” or “alpha.delta”.

Slots added by class mle.ghyp:

n.iter:

The number of iterations of class numeric.

llh:

The log likelihood value of class numeric.

converged:

A boolean whether converged or not. Object of class logical.

error.code:

An error code of class numeric.

error.message:

An error message of class character.

fitted.params:

A boolean vector stating which parameters were fitted of class logical.

aic:

The value of the Akaike Information Criterion of class numeric.

parameter.variance:

The parameter variance is the inverse of the fisher information matrix. This slot is filled only in the case of an univariate fit. This slot is of class matrix.

trace.pars:

Contains the parameter value evolution during the fitting procedure. trace.pars of class list.

Extends

Class “mle.ghyp” extends class "ghyp", directly.

Methods

A “pairs” method (see pairs). A “hist” method (see hist). A “plot” method (see plot). A “lines” method (see lines). A “coef” method (see coef). A “mean” method (see mean). A “vcov” method (see vcov). A “scale” method (see scale). A “transform” method (see transform). A “[.ghyp” method (see [). A “logLik” method for objects of class “mle.ghyp” (see logLik). An “AIC” method for objects of class “mle.ghyp” (see AIC). A “summary” method for objects of class “mle.ghyp” (see summary).

See Also

optim for an interpretation of error.code, error.message and parameter.variance. ghyp, hyp, NIG, VG, student.t and gauss for constructors of the class ghyp in the “alpha.bar” and “chi/psi” parametrization. xxx.ad for all the constructors in the “alpha/delta” parametrization. fit.ghypuv, fit.ghypmv et cetera for the fitting routies and constructors of the class mle.ghyp.

Examples

  data(smi.stocks)
  multivariate.fit <- fit.ghypmv(data = smi.stocks,
                                 opt.pars = c(lambda = FALSE, alpha.bar = FALSE),
                                 lambda = 2)
  summary(multivariate.fit)

  vcov(multivariate.fit)
  mean(multivariate.fit)
  logLik(multivariate.fit)
  AIC(multivariate.fit)
  coef(multivariate.fit)

  univariate.fit <- multivariate.fit[1]
  hist(univariate.fit)

  plot(univariate.fit)
  lines(multivariate.fit[2])