.\" -*- nroff -*- generated from .Rd format
.BG
.FN AIC
.TL
Akaike Information Criterion
.DN
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.
.CS
AIC(object, ...)
.RA
.AG 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'.
.OA
.AG ...
optional fitted model objects.
.RT
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.
.SH REFERENCES
Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986) "Akaike
Information Criterion Statistics", D. Reidel Publishing Company.
.SA
`logLik', `BIC', `AIC.logLik'
.EX
fm1 <- lm(distance ~ age, data = Orthodont) # no random effects
fm2 <- lme(distance ~ age, data = Orthodont) # random is ~age
AIC(fm1, fm2)
.KW models
.WR
