Akaike's Information Criterion with small-sample correction - AICc

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`object` |
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. |

`nobs` |
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 `logLik`

method.

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.

`AIC`

.

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