Calculate Mallows' Cp and Bozdogan's ICOMP and CAIFC information criteria.
Extract or calculate Deviance Information Criterion from MCMCglmm and
merMod object.
Cp(object, ..., dispersion = NULL)
ICOMP(object, ..., REML = NULL)
CAICF(object, ..., REML = NULL)
DIC(object, ...)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.
a fitted model object (in case of ICOMP and CAICF, logLik
and vcov methods must exist for the object). For DIC, an
object of class "MCMCglmm" or "merMod".
optionally more fitted model objects.
the dispersion parameter. If NULL, it is inferred
from object.
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.
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.
CAICF (C is for ‘consistent’ and F denotes the use of the Fisher information matrix) includes with penalty the natural logarithm of the determinant of the estimated Fisher information matrix.
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.
Anderson, D. R. and Burnham, K. P. 1999 Understanding information criteria for selection among capture-recapture or ring recovery models. Bird Study 46, 14--21.
Spiegelhalter, D. J., Best, N. G., Carlin, B. R., van der Linde, A. 2002 Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B-Statistical Methodology 64, 583--616.