aic {ESTER} | R Documentation |
Computes the Akaike Information Criterion
Description
Computes the Akaike Information Criterion of a model. Except when the number of observations is much larger than the number of parameters (i.e., n / k > 40), we apply the second-order bias correction for small samples (AICc), as suggested by Burnham & Anderson (2002, 2004).
Usage
aic(mod)
Arguments
mod |
A fitted model of class |
Author(s)
Ladislas Nalborczyk <ladislas.nalborczyk@gmail.com>
References
Burnham, K. P., \& Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretical Approach. 2d ed. New York: Springer-Verlag.
Burnham, K. P., \& Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research, 33(2), 261-304.
See Also
Examples
data(mtcars)
mod1 <- lm(mpg ~ cyl, mtcars)
aic(mod1)
[Package ESTER version 0.2.0 Index]