# logLik-AIC-methods: Extract Log-Likelihood and Akaike's Information Criterion In ghyp: A package on the generalized hyperbolic distribution and its special cases

## 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

 ```1 2 3 4 5``` ```## 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

`fit.ghypuv`, `fit.ghypmv`, `lik.ratio.test`, `ghyp.fit.info`, `mle.ghyp-class`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ``` 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) ```