Akaike's Information Criterion with small-sample correction - AICc
a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood, number of parameters, and number of observations.
optionally more fitted model objects.
the value to use for the effective sample size; overrides the value contained in the model object(s).
AICc is Akaike's information Criterion (AIC) with a small sample correction. It is
AICc = AIC + 2K(K + 1) / (n - K - 1)
where K is the number of parameters and n is the number of observations.
This is an S3 generic, with a default method which calls
logLik, and should work with any class that has a
If just one object is provided, the corresponding AICc.
If multiple objects are provided, a data frame with rows corresponding to the objects and columns representing the number of parameters in the model (df) and the AICc.
The result will be
Inf for overparameterised models, ie. when
df >= nobs - 1.
For some data types, including occupancy data, there is debate on the appropriate effective sample size to use.
Essentially the same as
AIC in package
stats. Modified to return AICc by Mike Meredith.
Burnham, K P; D R Anderson 2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer-Verlag.
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