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, ..., k)
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. |
k |
numeric, the ``penalty'' per parameter to be used; the
default k = 2 is the classical AIC. |
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 Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu
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)