aic: Computes the Akaike Information Criterion

In lnalborczyk/ESTER: Efficient Sequential Testing with Evidence Ratios

Description

Computes the Akaike Information Criterion (AIC) of a model, or the second-order bias correction for small samples (AICc), as suggested by Burnham & Anderson (2002, 2004).

Usage

aic(mod, correction = TRUE)

Arguments

mod	A fitted model of class lm or merMod.
correction	Should we apply the second-order correction (default to TRUE) ?

Author(s)

Ladislas Nalborczyk <[e-mail]>

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

bic, ictab

Examples

data(mtcars)
mod1 <- lm(mpg ~ cyl, mtcars)
aic(mod1)

lnalborczyk/ESTER documentation built on May 21, 2019, 7:36 a.m.