cAIC4-package {cAIC4} | R Documentation |
Conditional Akaike Information Criterion for 'lme4' and 'nlme'
Description
Provides functions for the estimation of the conditional Akaike information in generalized mixed-effect models fitted with (g)lmer() from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'. For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.
Details
The DESCRIPTION file:
Package: | cAIC4 |
Type: | Package |
Title: | Conditional Akaike Information Criterion for 'lme4' and 'nlme' |
Version: | 1.1 |
Date: | 2025-04-04 |
Authors@R: | c(person(given = "Benjamin", family = "Saefken", role = "aut"), person(given = "David", family = "Ruegamer", role = c("aut", "cre"), email = "david.ruegamer@gmail.com"), person(given = "Philipp", family = "Baumann", role = "aut"), person(given = "Rene-Marcel", family = "Kruse", role = "aut"), person(given = "Sonja", family = "Greven", role = "aut"), person(given = "Thomas", family = "Kneib", role = "aut")) |
Depends: | lme4(>= 1.1-6), methods, Matrix, stats4, nlme |
Imports: | RLRsim, mgcv, mvtnorm |
Suggests: | gamm4 |
Description: | Provides functions for the estimation of the conditional Akaike information in generalized mixed-effect models fitted with (g)lmer() from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'. For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>. |
License: | GPL (>= 2) |
Packaged: | 2021-09-22 12:34:56 UTC; david |
NeedsCompilation: | no |
Date/Publication: | 2014-08-12 11:48:10 |
RoxygenNote: | 7.3.2 |
Encoding: | UTF-8 |
Author: | Benjamin Saefken [aut], David Ruegamer [aut, cre], Philipp Baumann [aut], Rene-Marcel Kruse [aut], Sonja Greven [aut], Thomas Kneib [aut] |
Maintainer: | David Ruegamer <david.ruegamer@gmail.com> |
Index of help topics:
Zambia Subset of the Zambia data set on childhood malnutrition anocAIC Comparison of several lmer objects via cAIC cAIC Conditional Akaike Information for 'lme4' and 'lme' cAIC4-package Conditional Akaike Information Criterion for 'lme4' and 'nlme' deleteZeroComponents Delete random effect terms with zero variance family.lme family function for lme objects to have a generic function getModelComponents Generic getModelComponents method getModelComponents.lme getModelComponents for lme objects getModelComponents.merMod getModelComponents for merMods getWeights Optimize weights for model averaging. getcondLL Function to calculate the conditional log-likelihood guWahbaData Data from Gu and Wahba (1991) modelAvg Model Averaging for Linear Mixed Models predictMA Prediction of model averaged linear mixed models print.cAIC Print method for cAIC stepcAIC Function to stepwise select the (generalized) linear mixed model fitted via (g)lmer() or (generalized) additive (mixed) model fitted via gamm4() with the smallest cAIC. summaryMA Summary of model averaged linear mixed models
Author(s)
Benjamin Saefken [aut], David Ruegamer [aut, cre], Philipp Baumann [aut], Rene-Marcel Kruse [aut], Sonja Greven [aut], Thomas Kneib [aut]
Maintainer: David Ruegamer <david.ruegamer@gmail.com>
References
Saefken, B., Kneib T., van Waveren C.-S. and Greven, S. (2014) A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models. Electronic Journal Statistics Vol. 8, 201-225.
Greven, S. and Kneib T. (2010) On the behaviour of marginal and conditional AIC in linear mixed models. Biometrika 97(4), 773-789.
Efron , B. (2004) The estimation of prediction error. J. Amer. Statist. Ass. 99(467), 619-632.
See Also
Examples
b <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
cAIC(b)