ghyp: logLik-AIC-methods – R documentation

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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 * log-likelihood + k * npar, where npar represents the number of parameters in the fitted model, and k = 2 for the usual AIC.

Usage

## S4 method for signature 'mle.ghyp'
logLik(object, ...)

## S4 method for signature '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.

Note

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.

Author(s)

David Luethi

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)

ghyp

Generalized Hyperbolic Distribution and Its Special Cases

v1.6.1

GPL (>= 2)

Authors

Marc Weibel, David Luethi, Wolfgang Breymann

Initial release

2020-04-27