| mle.ghypmv-class {ghyp} | R Documentation |
The class “mle.ghypmv” inherits from the class “ghypmv”. In addition
to the class “ghypmv” this class stores fitting information. Namely the
number of iterations n.iter, the log likelihood value llh,
the Akaike Information Criterion aic, a boolean vector stating which parameters were fitted
fitted.params,
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
Objects should only be created by calls to the fitting routines like fit.ghypmv,
fit.hypmv, fit.NIGmv , fit.VGmv and fit.tmv .
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 may be useful
when fitting eneralized hyperbolic distributions to data and
perform further analysis afterwards.n.iter:"numeric".llh:"numeric".converged:"logical".error.code:"numeric".error.message:"character".fitted.params:"logical".aic:"numeric".
Class "ghypmv", directly.
Class "ghypbase", by class "ghypmv", distance 2.
A “pairs” method (see pairs).
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.ghypmv where objects of class mle.ghypuv were created,
ghypmv-class to have a look on the base class.
data(smi.stocks) fit.ghypmv(data=smi.stocks,opt.pars=c(lambda=FALSE, alpha.bar=FALSE),lambda=2)