AIC(object, ...)
logLik
method to extract the corresponding log-likelihood, or
an object inheriting from class
logLik
.
data.frame
or
bdFrame
with rows corresponding to the objects and
columns representing the number of parameters in the model
(
df
) and the AIC.
Although the Akaike Information Criterion is a measure of goodness
of fit for a more general class of models, this function is part of
the Non-Linear Mixed Effects library in S-PLUS (nlme3) and
currently there are methods developed only for functions within
that library. To learn what functions have AIC methods in your
current implementation of S-PLUS, enter the command
methods(AIC)
at the S-PLUS prompt.
Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986) "Akaike Information Criterion Statistics", D. Reidel Publishing Company.
fm1 <- lm(distance ~ age, data = Orthodont) # no random effects fm2 <- lme(distance ~ age, data = Orthodont) # random is ~age AIC(fm1, fm2)