summary,aic-method {aod} | R Documentation |
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
: whenr
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 ther
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.