ghyp (version 1.1.0)

logLik-AIC-methods: Extract Log-Likelihood and Akaike's Information Criterion

Description

The functions logLik and AIC extract the Log-Likelihood and the Akaike's Information Criterion from fitted generalized hyperbolic distribution objects. The Akaike information criterion is calculated according to the formula $-2 \mbox{log-likelihood} + k n_{par}$, where $n_{par}$ represents the number of parameters in the fitted model, and $k = 2$ for the usual AIC.

Usage

## S3 method for class 'mle.ghyp':
logLik(object, \dots)
## S3 method for class 'mle.ghyp':
AIC(object, ..., k = 2)

Arguments

object

An object of class mle.ghyp.

k

The penalty per parameter to be used; the default k = 2 is the classical AIC.

...

An arbitrary number of objects of class mle.ghyp.

Value

• Either the Log-Likelihood or the Akaike's Information Criterion.

See Also

fit.ghypuv, fit.ghypmv, lik.ratio.test, ghyp.fit.info, mle.ghyp-class

Examples

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)