Information criteria | R Documentation |
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, ...)
object |
a fitted model object (in case of ICOMP and CAICF, |
... |
optionally more fitted model objects. |
dispersion |
the dispersion parameter. If |
REML |
optional logical value, passed to the |
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.
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.
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.
AIC
and BIC
in stats, AICc
.
QIC
for GEE model selection.
extractDIC
in package arm, on which the (non-visible)
method extractDIC.merMod
used by DIC
is based.
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