ghyp (version 0.9.3)

Log-Likelihood and Akaike's Information Criterion: 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.

Usage

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

Arguments

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.

Value

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

See Also

fit.ghypuv, fit.ghypmv, ghyp.fit.info, mle.ghypuv-class, mle.ghypmv-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)