| mle.ghypuv-class {ghyp} | R Documentation |
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 should only be created by calls to the fitting routines like fit.ghypuv,
fit.hypuv, fit.NIGuv, fit.VGuv and fit.tuv.
lambda:"numeric".alpha.bar:"numeric".chi:"numeric".psi:"numeric".mu:"numeric".sigma:"numeric".gamma:"numeric".model:"character".dimension:"numeric".expected.value:"numeric".variance:"numeric".data:"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:"numeric".llh:"numeric".converged:optim converged or not.
Object of class "logical".error.code:"numeric".error.message:"character".parameter.variance:"matrix".fitted.params:"logical".aic:"numeric".
Class "ghypuv", directly.
Class "ghypbase", by class "ghypuv", distance 2.
A “hist” method (see hist).
A “mean” method (see mean).
A “vcov” method (see vcov).
When showing special cases of the generalized hyperbolic distribution the corresponding fixed parameters are plotted in brackets.
David Lüthi
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.
data(smi.stocks)
fit.ghypuv(data=smi.stocks[,"SMI"],opt.pars=c(alpha.bar=FALSE,lambda=FALSE),
alpha.bar=1)