extractAIC {stats}  R Documentation 
Computes the (generalized) Akaike An Information Criterion for a fitted parametric model.
extractAIC(fit, scale, k = 2, ...)
fit 
fitted model, usually the result of a fitter like
lm . 
scale 
optional numeric specifying the scale parameter of the
model, see scale in step .

k 
numeric specifying the “weight” of the
equivalent degrees of freedom (=: edf )
part in the AIC formula. 
... 
further arguments (currently unused in base R). 
This is a generic function, with methods in base R for "aov"
,
"coxph"
, "glm"
, "lm"
, "negbin"
and "survreg"
classes.
The criterion used is
AIC =  2*log L + k * edf,
where L is the likelihood
and edf
the equivalent degrees of freedom (i.e., the number of
parameters for usual parametric models) of fit
.
For linear models with unknown scale (i.e., for lm
and
aov
), 2log L is computed from the
deviance and uses a different additive constant to AIC
.
k = 2
corresponds to the traditional AIC, using k =
log(n)
provides the BIC (Bayes IC) instead.
For further information, particularly about scale
, see
step
.
A numeric vector of length 2, giving
edf 
the “equivalent degrees of freedom”
of the fitted model fit . 
AIC 
the (generalized) Akaike Information Criterion for fit . 
These functions are used in add1
,
drop1
and step
and that may be their
main use.
B. D. Ripley
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer (4th ed).
example(glm) extractAIC(glm.D93)#>> 5 15.129