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
optionally more fitted model objects.
the ‘penalty’ per parameter to be used; the default
optional logical value, passed to the
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).
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.
Akaike's An Information Criterion:
AICc in package AICcmodavg,
AICc in package bbmle and
aicc in package glmulti
#Model-averaging mixed models options(na.action = "na.fail") 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, "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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