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cAIC4 (version 1.0)

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) .

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Version

Install

install.packages('cAIC4')

Monthly Downloads

1,655

Version

1.0

License

GPL (>= 2)

Maintainer

David Ruegamer

Last Published

September 22nd, 2021

Functions in cAIC4 (1.0)

cAIC4-package

cAIC4
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.
print.cAIC

Print method for cAIC
summaryMA

Summary of model averaged linear mixed models
anocAIC

Comparison of several lmer objects via cAIC
predictMA

Prediction of model averaged linear mixed models
guWahbaData

Data from Gu and Wahba (1991)
getWeights

Optimize weights for model averaging.
getcondLL

Function to calculate the conditional log-likelihood
modelAvg

Model Averaging for Linear Mixed Models
Zambia

Subset of the Zambia data set on childhood malnutrition
cAIC

Conditional Akaike Information for 'lme4' and 'lme'
deleteZeroComponents

Delete random effect terms with zero variance