ghyp-mle.ghyp-classes {ghyp} | R Documentation |
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 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 of class ghyp:
call
:call
.lambda
:numeric
.alpha.bar
:numeric
.chi
:numeric
.psi
:numeric
.mu
:numeric
.sigma
:matrix
.gamma
:numeric
.model
:character
.dimension
:numeric
.expected.value
:numeric
.variance
:matrix
.data
: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
:character
.
These are currently either “chi.psi”, “alpha.bar” or “alpha.delta”.Slots added by class mle.ghyp:
n.iter
:numeric
.llh
:numeric
.converged
:logical
.error.code
:numeric
.error.message
:character
.fitted.params
:logical
.aic
:numeric
.parameter.variance
:matrix
.trace.pars
:trace.pars
of class list
.
Class “mle.ghyp” extends class "ghyp"
, directly.
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
).
When showing special cases of the generalized hyperbolic distribution the corresponding fixed parameters are not printed.
David Luethi
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
.
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])