AICc: Second-order Akaike Information Criterion

AICcR Documentation

Second-order Akaike Information Criterion


Calculate Second-order Akaike Information Criterion for one or several fitted model objects (AICc, AIC for small samples).


AICc(object, ..., k = 2, REML = NULL)



a fitted model object for which there exists a logLik method, or a "logLik" object.


optionally more fitted model objects.


the ‘penalty’ per parameter to be used; the default k = 2 is the classical AIC.


optional logical value, passed to the logLik method indicating whether the restricted log-likelihood or log-likelihood should be used. The default is to use the method used for model estimation.


If just one object is provided, returns a numeric value with the corresponding AICc; 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 AICc.


AICc should be used instead AIC when sample size is small in comparison to the number of estimated parameters (Burnham & Anderson 2002 recommend its use when n / K < 40).


Kamil Bartoń


Burnham, K. P. and Anderson, D. R (2002) Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York, Springer-Verlag.

Hurvich, C. M. and Tsai, C.-L. (1989) Regression and time series model selection in small samples, Biometrika 76: 297–307.

See Also

Akaike's An Information Criterion: AIC

Other implementations: AICc in package AICcmodavg, AICc in package bbmle and aicc in package glmulti


#Model-averaging mixed models

options(na.action = "")

data(Orthodont, package = "nlme")

# Fit model by REML
fm2 <- lme(distance ~ Sex*age, data = Orthodont,
    random = ~ 1|Subject / Sex, method = "REML")

# Model selection: ranking by AICc using ML
ms2 <- dredge(fm2, trace = TRUE, rank = "AICc", REML = FALSE)

(attr(ms2, ""))

# Get the models (fitted by REML, as in the global model)
fmList <- get.models(ms2, 1:4)

# Because the models originate from 'dredge(..., rank = AICc, REML = FALSE)',
# the default weights in 'model.avg' are ML based:

## Not run: 
# the same result:
model.avg(fmList, rank = "AICc", rank.args = list(REML = FALSE))

## End(Not run)

MuMIn documentation built on March 18, 2022, 5:28 p.m.