Journal of Statistical Software· 2017Q1
<b>lmerTest</b> Package: Tests in Linear Mixed Effects Models
- 23,710citations
- Q1SCImago
- 2017year
Short summary
The lmerTest R package now provides p-values for F and t tests in linear mixed effects models, addressing a common user need for the lme4 package's lmer function.
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Key points
- Provides p-values for F and t tests for linear mixed effects models fitted with lmer.
- Implements Satterthwaite's and Kenward-Roger methods for degrees of freedom approximation.
- Offers Type I-III ANOVA tables and backward elimination of nonsignificant effects.
- Includes tools for calculating population means and performing multiple comparison tests.
AI-generated from the title and abstract; the full text is not read.
Abstract
One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects - both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.
The authors' abstract, as published at the source. Journal of Statistical Software, 2017 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics