mlm_assumptions

Reports the results from testing all assumptions of a multilevel model and provides suggestions if an assumption is not passed

Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, 'mlmtools', is a suite of pre- and post-estimation tools for multilevel models in 'R'. Package implements post-estimation tools designed to work with models estimated using 'lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) <doi:10.18637/jss.v067.i01>.

Laura Jamison

mlmtools

Multi-Level Model Assessment Kit

Laura Jamison 

Jessica Mazen 

Erik Ruzek 

Gus Sjobeck 

mlm_assumptions function

<dl><dt>model</dt>
<dd>A linear mixed-effects model of class lmerMod, lmerModLmerTest, or glmerMod of type binomial.</dd></dl>

Arguments

Reports the output of testing all assumptions for a multilevel model — mlm_assumptions

<dl>

<dt>model</dt>
<dd>A linear mixed-effects model of class lmerMod, lmerModLmerTest, or glmerMod of type binomial.</dd>

</dl>

mlm_assumptions: Reports the output of testing all assumptions for a multilevel model

Description

Usage

Value

Arguments

References

Examples