AIC.dlmodeler.fit:
Log-likelihood and AIC of a model
Description
Returns the log-likelihood or the AIC for a fitted DLM object.
Usage
"logLik"(object, ...)
"logLik"(object, ...)
"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.
Value
Returns a numeric value with the corresponding log-likelihiid, AIC,
BIC, or ..., depending on the value of k.
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).
References
Durbin, and Koopman, Time Series Analysis by State Space Methods, Oxford University Press (2001),
page 152.