nlme (version 3.1-29)

AIC.lme: Akaike Information Criterion

Description

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 $-2 \mbox{log-likelihood} + 2 n_{par}$, where $n_{par}$ represents the number of parameters in the fitted model. When comparing fitted objects, the smaller the AIC, the better the fit.

Usage

## S3 method for class 'lme':
AIC(object, ..., k)

Value

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.

References

Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986) "Akaike Information Criterion Statistics", D. Reidel Publishing Company.

See Also

logLik, BIC, AIC.logLik

Examples

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)