AIC: Akaike Information Criterion

In rebmix: Finite Mixture Modeling, Clustering & Classification

Akaike Information Criterion

Description

Returns the Akaike information criterion at pos.

Usage

## S4 method for signature 'REBMIX'
AIC(x = NULL, pos = 1, ...)
## S4 method for signature 'REBMIX'
AIC3(x = NULL, pos = 1, ...)
## S4 method for signature 'REBMIX'
AIC4(x = NULL, pos = 1, ...)
## S4 method for signature 'REBMIX'
AICc(x = NULL, pos = 1, ...)
## S4 method for signature 'REBMIX'
CAIC(x = NULL, pos = 1, ...)
## ... and for other signatures

Arguments

x	see Methods section below.
pos	a desired row number in x@summary for which the information criterion is calculated. The default value is 1.
...	currently not used.

Methods

signature(x = "REBMIX")

an object of class REBMIX.

signature(x = "REBMVNORM")

an object of class REBMVNORM.

Author(s)

Marko Nagode

References

H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(51):716-723, 1974.

A. F. M. Smith and D. J. Spiegelhalter. Bayes factors and choice criteria for linear models. Journal of the Royal Statistical Society. Series B, 42(2):213-220, 1980. https://www.jstor.org/stable/2984964.

H. Bozdogan. Model selection and akaike's information criterion (aic): The general theory and its analytical extensions. Psychometrika, 52(3):345-370, 1987. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02294361")}.

C. M. Hurvich and C.-L. Tsai. Regression and time series model selection in small samples. Biometrika, 76(2):297-307, 1989. https://www.jstor.org/stable/2336663.

rebmix documentation built on July 26, 2023, 5:32 p.m.