lmer.compare: Compare Multiple Mixed-Effects Models on AIC and BIC

In sfraundorf/psycholing: R Functions for Common Psycholingusitic and Cognitive Designs

Compare Multiple Mixed-Effects Models on AIC and BIC

Description

Compares a set of mixed-effects models on their AIC and BIC values, highlighting the best model according to each of these criteria.

Usage

lmer.compare(
  listofmodels,
  show.formulae = TRUE,
  summarize.aic = FALSE,
  summarize.bic = FALSE
)

Arguments

listofmodels	a list of merMod objects.
show.formulae	a logical value whether the model formula should be displayed for each model.
summarize.aic	a logical value indicating whether the model summary should be displayed for the model that is the best-fitting according to AIC.
summarize.bic	a logical value indicating whether the model summary should be displayed for the model that is the best-fitting according to BIC. Note that if summarize.aic and summarize.bic are both TRUE but the same model wins according to both criteria, only one summary will be displayed.

Details

Log likelihood and deviance are not reported because likelihood-ratio tests are only applicable for NESTED models, and the models may or may not be nested.

sfraundorf/psycholing documentation built on April 23, 2022, 2:50 a.m.