CAIC: Consistent AIC

In psolymos/ResourceSelection: Resource Selection (Probability) Functions for Use-Availability Data

Consistent AIC

Description

Consistent AIC

Usage

CAIC(object, ..., alpha)
## Default S3 method:
CAIC(object, ..., alpha)
CAICtable(object, ..., alpha)

Arguments

object	A fitted model object.
...	More fitted model objects.
alpha	Weight factor between 0 and 1 (see Details). Default value is 0.5.

Details

CAIC = alpha * AIC + (1 - alpha) * BIC

Value

Atomic vector if only one input object provided, a data frame similar to what is returned by AIC and BIC if there are more than one input objects.

CAICtable returns a data frame with delta CAIC (dCAIC = CAIC - min(CAIC)) and CAIC weights (wCAIC = exp(-0.5 dCAIC_i) / sum(exp(-0.5 dCAIC_i))) where i = 1,...,m are candidate models.

Author(s)

Subhash Lele and Peter Solymos

References

Bozdogan, H. 1987. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52, 345-370.

Taper, M. 2004. Model identification from many candidates. In: Taper, M. and Lele, S. R. (eds), The Nature of Scientific Evidence: Statistical, Philosophical, and Empirical Considerations. The University of Chicago Press, Chicago, IL, 567 pp.

See Also

AIC, BIC

Examples

## compare some random models
y <- rnorm(10)
a <- lm(y ~ runif(10))
b <- lm(y ~ runif(10))

0.5*(AIC(a) + BIC(a))
CAIC(a)
AIC(a)
CAIC(a, alpha=1)
BIC(a)
CAIC(a, alpha=0)

CAIC(a, b)
CAIC(a, b, alpha=0.2)

CAICtable(a, b, alpha=1)

## you can use global option
## useful when inside of xv or bootstrap
## no need for extra argument
getOption("CAIC_alpha")
op <- options(CAIC_alpha = 0.2)
getOption("CAIC_alpha")
CAIC(a,b)
options(op)
getOption("CAIC_alpha")

psolymos/ResourceSelection documentation built on July 8, 2023, 7:25 p.m.