cAIC4-package: Conditional Akaike Information Criterion for 'lme4' and...

In cAIC4: 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

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Author(s)

Benjamin Saefken, David Ruegamer, Philipp Baumann and Rene-Marcel Kruse, with contributions from Sonja Greven and Thomas Kneib

Maintainer: David Ruegamer <[e-mail]>

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

lme4

Examples

b <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)

cAIC(b)

Example output

Loading required package: lme4
Loading required package: Matrix
Loading required package: stats4
                                                   
               Conditional log-likelihood:  -824.51
                       Degrees of freedom:    31.30
 Conditional Akaike information criterion:  1711.62

cAIC4 documentation built on Sept. 22, 2021, 5:07 p.m.