ghyp (version 1.6.5)

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 \(-2 \cdot \mbox{log-likelihood} + k \cdot n_{par}\), where \(n_{par}\) represents the number of parameters in the fitted model, and \(k = 2\) for the usual AIC.

Usage

# S4 method for mle.ghyp
logLik(object, ...)
# S4 method for mle.ghyp
AIC(object, ..., k = 2)

Value

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

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.

Author

David Luethi

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)

  # Both together
  AIC(fit.uv, fit.mv)
  logLik(fit.uv, fit.mv)