AIC.dlmodeler.fit: Log-likelihood and AIC of a model

In dlmodeler: Generalized Dynamic Linear Modeler

Description

Returns the log-likelihood or the AIC for a fitted DLM object.

Usage

## S3 method for class 'dlmodeler.filtered'
logLik(object, ...)

## S3 method for class 'dlmodeler.fit'
logLik(object, ...)

## S3 method for class 'dlmodeler.fit'
AIC(object, ..., k = 2)

Arguments

object	fitted DLM as given by a call to one of the dlmodeler.fit() functions, or filtered DLM as given by a call to dlmodeler.filter.
...	not used.
k	penalty parameter.

Details

The AIC is computed 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, or k = log(n) (n the number of observations) for the BIC or SBC (Schwarz's Bayesian criterion).

Value

Returns a numeric value with the corresponding log-likelihiid, AIC, BIC, or ..., depending on the value of k.

Author(s)

Cyrille Szymanski <[e-mail]>

References

Durbin, and Koopman, Time Series Analysis by State Space Methods, Oxford University Press (2001), page 152.

See Also

dlmodeler.fit.MLE

Examples

## Example TODO

Example output

dlmodeler documentation built on May 29, 2017, 11:33 a.m.