| 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