performance: performance_aicc – R documentation

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performance_aicc

Compute the AIC or second-order AIC

Description

Compute the AIC or the second-order Akaike's information criterion (AICc). performance_aic() is a small wrapper that returns the AIC. It is a generic function that also works for some models that don't have a AIC method (like Tweedie models). performance_aicc() returns the second-order (or "small sample") AIC that incorporates a correction for small sample sizes.

Usage

performance_aicc(x, ...)

performance_aic(x, ...)

Arguments

`x`	A model object.
`...`	Currently not used.

Value

Numeric, the AIC or AICc value.

References

Akaike, H. (1973) Information theory as an extension of the maximum likelihood principle. In: Second International Symposium on Information Theory, pp. 267–281. Petrov, B.N., Csaki, F., Eds, Akademiai Kiado, Budapest.
Hurvich, C. M., Tsai, C.-L. (1991) Bias of the corrected AIC criterion for underfitted regression and time series models. Biometrika 78, 499–509.

Examples

m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
AIC(m)
performance_aicc(m)

performance

Assessment of Regression Models Performance

v0.7.1

GPL-3

Authors

Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>), Dominique Makowski [aut, ctb] (<https://orcid.org/0000-0001-5375-9967>), Mattan S. Ben-Shachar [aut, ctb] (<https://orcid.org/0000-0002-4287-4801>), Indrajeet Patil [aut, ctb] (<https://orcid.org/0000-0003-1995-6531>), Philip Waggoner [aut, ctb] (<https://orcid.org/0000-0002-7825-7573>), Vincent Arel-Bundock [ctb] (<https://orcid.org/0000-0003-2042-7063>)

Initial release