| AIC {nlme} | R Documentation |
This generic function calculates 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 + 2*npar, where npar represents the number of parameters in the fitted model. When comparing fitted objects, the smaller the AIC, the better the fit.
AIC(object, ...)
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. |
... |
optional fitted model objects. |
if just one object is provided, returns a numeric value
with the corresponding AIC; 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.
data(Orthodont) fm1 <- lm(distance ~ age, data = Orthodont) # no random effects AIC(fm1) fm2 <- lme(distance ~ age, data = Orthodont) # random is ~age AIC(fm1, fm2)