Akaike Information Criterion
Usage
AIC(object, ...)
Arguments
object
|
a fitted model object, for which there exists a
logLik method to extract the corresponding log-likelihood, or
an object inheriting from class logLik .
|
...
|
optional fitted model objects.
|
Description
This generic function calculates the Akaike information criterion for
one or several fitted model objects for which a log-likelihood value
can be obtained, according to the formula -2*log-likelihood + 2*npar, where npar
represents the number of parameters in the fitted model. When comparing
fitted objects, the smaller the AIC, the better the fit.Value
if just one object is provided, returns a numeric value
with the corresponding AIC; if more than one object are provided,
returns a data.frame
with rows corresponding to the objects and
columns representing the number of parameters in the model
(df
) and the AIC.Author(s)
Jose Pinheiro and Douglas BatesReferences
Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986) "Akaike
Information Criterion Statistics", D. Reidel Publishing Company.See Also
logLik
, BIC
, AIC.logLik
Examples
library(lme)
data(Orthodont)
fm1 <- lm(distance ~ age, data = Orthodont) # no random effects
fm1 <- lme(distance ~ age, data = Orthodont) # random is ~age
AIC(fm1, fm2)