Package ‘MuMIn’

22 merge.model.selection
Details
Mallows’ Cp statistic is the residual deviance plus twice the estimate of σ 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.
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.
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.
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.
See Also
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.
merge.model.selection
Combine model selection tables
Description
Combine two or more model selection tables.
Usage
## S3 method for class model.selection
merge(x, y, suffixes = c(".x", ".y"), ...)
## S3 method for class model.selection
rbind(..., deparse.level = 1, make.row.names = TRUE)