summary.aic: Akaike Information Statistics

In aod: Analysis of Overdispersed Data

Akaike Information Statistics

Description

Computes Akaike difference and Akaike weights from an object of formal class “aic”.

Usage

  ## S4 method for signature 'aic'
summary(object, which = c("AIC", "AICc"))
  

Arguments

object	An object of formal class “aic”.
which	A character string indicating which information criterion is selected to compute Akaike difference and Akaike weights: either “AIC” or “AICc”.

Methods

summary

The models are ordered according to AIC or AICc and 3 statistics are computed:

- the Akaike difference \Delta: the change in AIC (or AICc) between successive (ordered) models,

- the Akaike weight W: when r models are compared, W = e^{-0.5 * \Delta} / \sum_r{e^{-\frac{1}{2} * \Delta}},

- the cumulative Akaike weight cum.W: the Akaike weights sum to 1 for the r models which are compared.

References

Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference: a practical information-theoretic approach. New-York, Springer-Verlag, 496 p.
Hurvich, C.M., Tsai, C.-L., 1995. Model selection for extended quasi-likelihood models in small samples. Biometrics, 51 (3): 1077-1084.

See Also

Examples in betabin and AIC in package stats.

aod documentation built on June 22, 2024, 12:21 p.m.