| ghyp-data {ghyp} | R Documentation |
These functions simply return data stored within generalized hyperbolic
distribution objects, i.e. slots of the classes ghyp and mle.ghyp.
ghyp.fit.info extracts information about the fitting procedure from objects of
class mle.ghyp.
ghyp.fit.info(object) ghyp.data(object) ghyp.name(object, abbr = FALSE, skew.attr = TRUE)
object |
An object inheriting from class
ghyp. |
abbr |
If TRUE the abbreviation of the ghyp distribution will be returned. |
skew.attr |
If TRUE an attribute will be added to the name of the ghyp distribution stating whether the distribution is symmetric or not. |
ghyp.fit.info returns list with components:
logLikelihood | The maximized log-likelihood value. |
aic | The Akaike information criterion. |
fitted.params | A boolean vector stating which parameters were fitted. |
converged | A boolean whether optim converged or not. |
n.iter | The number of iterations. |
error.code | Error code from optim. |
error.message | Error message from optim. |
parameter.variance | Parameter variance (only for univariate fits). |
ghyp.data returns NULL if no data is stored within the
object, a vector if it is an univariate generalized hyperbolic distribution
and matrix if it is an multivariate generalized hyperbolic distribution.ghyp.name returns the name of the ghyp distribution which can be the name of a special case.
Depending on the arguments abbr and skew.attr one of the following is returned.
abbr == FALSE & skew.attr == TRUE | abbr == TRUE & skew.attr == TRUE |
| (A)symmetric Generalized Hyperbolic | (A)symm ghyp |
| (A)symmetric Hyperbolic | (A)symm hyp |
| (A)symmetric Normal Inverse Gaussian | (A)symm NIG |
| (A)symmetric Variance Gamma | (A)symm VG |
| (A)symmetric Student-t | (A)symm t |
| Gaussian | Gauss |
abbr == FALSE & skew.attr == FALSE | abbr == TRUE & skew.attr == FALSE |
| Generalized Hyperbolic | ghyp |
| Hyperbolic | hyp |
| Normal Inverse Gaussian | NIG |
| Variance Gamma | VG |
| Student-t | t |
| Gaussian | Gauss |
ghyp.fit.info requires an object of
class mle.ghyp. In the univariate case the parameter
variance is returned as well. The parameter variance is defined as the inverse of the
negative hesse-matrix computed by optim. Note that this makes sense only
in the case that the estimates are asymptotically normal distributed.
The class ghyp contains a data slot.
Data can be stored either when an object is initialized or via the fitting routines and
the argument save.data.
David Lüthi
coef, mean, vcov,
logLik, AIC for other accessor functions,
fit.ghypmv, fit.ghypuv, ghyp for constructor functions,
optim for possible error messages.
## multivariate generalized hyperbolic distribution
ghyp.mv <- ghyp(lambda = 1, alpha.bar = 0.1, mu = rep(0, 2), sigma = diag(rep(1, 2)),
gamma = rep(0, 2), data = matrix(rt(1000, df = 4), ncol = 2))
## Get data
ghyp.data(ghyp.mv)
## Get the name of the ghyp object
ghyp.name(ghyp(alpha.bar = 0))
ghyp.name(ghyp(alpha.bar = 0, lambda = -4), abbr = TRUE)
## 'ghyp.fit.info' does only work when the object is of class 'mle.ghyp',
## i.e. is created by 'fit.ghypuv' etc.
mv.fit <- fit.tmv(data = ghyp.data(ghyp.mv), control = list(abs.tol = 1e-3))
ghyp.fit.info(mv.fit)