| AIC {stats} | R Documentation |
Generic function calculating 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 + 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 so-called BIC or SBC (Schwarz's Bayesian criterion).
AIC(object, ..., k = 2)
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. |
... |
optionally more fitted model objects. |
k |
numeric, the “penalty” per parameter to be used; the
default k = 2 is the classical AIC. |
The default method for AIC, AIC.default() entirely
relies on the existence of a logLik method
computing the log-likelihood for the given class.
When comparing fitted objects, the smaller the AIC, the better the fit.
The log-likelihood and hence the AIC is only defined up to an additive
constant. Different constants have conventionally be used for
different purposes and so extractAIC and AIC may
give different values (and do for models of class "lm": see the
help for extractAIC).
If just one object is provided, returns a numeric value
with the corresponding AIC (or BIC, or ..., depending on k);
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.
Jose Pinheiro and Douglas Bates
Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986). Akaike Information Criterion Statistics. D. Reidel Publishing Company.
lm1 <- lm(Fertility ~ . , data = swiss)
AIC(lm1)
stopifnot(all.equal(AIC(lm1),
AIC(logLik(lm1))))
## a version of BIC or Schwarz' BC :
AIC(lm1, k = log(nrow(swiss)))