MuMIn (version 1.5.0)

Information criteria: Various information criteria

Description

Calculate Mallows' Cp and Bozdogan's ICOMP information criterion

Usage

Cp(object, dispersion = NULL)
ICOMP(object, ..., REML = NULL)

Arguments

object

a fitted model object (in case of ICOMP, a logLik method must exist for the object)

...

optionally more fitted model objects

dispersion

the dispersion parameter. If NULL, it is inferred from object.

REML

optional logical value, passed to the logLik method indicating whether the restricted log-likelihood or log-likelihood should be used. The default is to use the method used for model estimation.

Value

• If just one object is provided, the functions return a numeric value with the corresponding IC; otherwise a data.frame with rows corresponding to the objects is returned.

encoding

utf-8

Details

Mallows' Cp statistic is the residual deviance plus twice the estimate of $\sigma^{2}$ times the residual degrees of freedom. It is closely related to AIC (and a multiple of it if the dispersion is known).

ICOMP (I for informational and COMP for complexity) penalizes the covariance complexity of the model, rather than the number of parameters directly.

References

Mallows, C. L. (1973) Some Comments on CP. Technometrics 15: 661–675

Bozdogan, H. and Haughton, D.M.A. (1998) Information complexity criteria for regression models. Comp. Stat. & Data Analysis 28: 51-76

See Also

AIC, AICc and BIC