performance (version 0.4.3)

performance_aicc: Compute AIC and second order AIC

Description

Compute the second-order Akaike's information criterion (AICc). The second-order (or small sample) is a AIC with a correction for small sample sizes. 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).

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<U+2013>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<U+2013>509.

Examples

# NOT RUN {
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
AIC(m)
performance_aicc(m)
# }