AICc  R Documentation 
Calculate Secondorder Akaike Information Criterion for one or several fitted
model objects (AIC_{c}
, AIC for small samples).
AICc(object, ..., k = 2, REML = NULL)
object 
a fitted model object for which there exists a 
... 
optionally more fitted model objects. 
k 
the ‘penalty’ per parameter to be used; the default

REML 
optional logical value, passed to the 
If just one object is provided, returns a numeric value with the
corresponding AIC_{c}
; 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 AIC_{c}
.
AIC_{c}
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 informationtheoretic approach. 2nd ed. New York, SpringerVerlag.
Hurvich, C. M. and Tsai, C.L. 1989 Regression and time series model selection in small samples, Biometrika 76, 297–307.
Akaike's An Information Criterion: AIC
Some other implementations:
AICc
in package AICcmodavg,
AICc
in package bbmle,
aicc
in package glmulti
#Modelaveraging mixed models
options(na.action = "na.fail")
data(Orthodont, package = "nlme")
# Fit model by REML
fm2 < lme(distance ~ Sex*age, data = Orthodont,
random = ~ 1Subject / Sex, method = "REML")
# Model selection: ranking by AICc using ML
ms2 < dredge(fm2, trace = TRUE, rank = "AICc", REML = FALSE)
(attr(ms2, "rank.call"))
# 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:
summary(model.avg(fmList))
## Not run:
# the same result:
model.avg(fmList, rank = "AICc", rank.args = list(REML = FALSE))
## End(Not run)
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