ESTER (version 0.2.0)

aic: 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 lm or merMod.

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

# NOT RUN {
data(mtcars)
mod1 <- lm(mpg ~ cyl, mtcars)
aic(mod1)

# }