Log-Likelihood and Akaike's Information Criterion {ghyp} | R Documentation |
The functions logLik
and AIC
extract the Log-Likelihood and the Akaike's Information Criterion from fitted
generalized hyperbolic distribution objects.
## S4 method for signature 'mle.ghypmv': logLik(object, ...) ## S4 method for signature 'mle.ghypuv': logLik(object, ...) ## S4 method for signature 'mle.ghypuv': AIC(object, ..., k = 2) ## S4 method for signature 'mle.ghypmv': AIC(object, ..., k = 2)
object |
Either an object of class
mle.ghypuv or
mle.ghypmv . |
k |
The “penalty” per parameter to be used; the default k = 2 is the classical AIC. |
... |
An arbitrary number of objects of classes
mle.ghypuv or
mle.ghypmv . |
Either the Log-Likelihood or the Akaike's Information Criterion.
The Log-Likelihood as well as the Akaike's Information Criterion can be obtained from
the function ghyp.fit.info
. However, the benefit of logLik
and AIC
is that these functions allow a call with an arbitrary number of objects and are better known
because they are generic.
David Lüthi
fit.ghypuv
, fit.ghypmv
,
ghyp.fit.info
, mle.ghypuv-class
, mle.ghypmv-class
data(smi.stocks) ## Multivariate fit fit.mv <- fit.hypmv(smi.stocks,nit=10) AIC(fit.mv) logLik(fit.mv) ## Univariate fit fit.uv <- fit.tuv(smi.stocks[,"CS"],control=list(maxit=10)) AIC(fit.uv) logLik(fit.uv)