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)
object |
An object inheriting from class
ghyp . |
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.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) ## '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)