AIC-methods {rebmix} | R Documentation |
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 |
... |
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. 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.