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.
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)))