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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

r2mlm: An R package calculating R-squared measures for multilevel models.

Mairead Shaw¹, Jason D Rights², Sonya S Sterba³

¹Department of Psychology, McGill University, 2001 McGill College, 7th Floor, Montreal, QC, H3A 1G1, Canada. mairead.shaw@mail.mcgill.ca.

Behavior Research Methods

|July 7, 2022

Summary

This study introduces the r2mlm R package to simplify calculating R-squared effect sizes for multilevel models. The package automates complex computations, making these important statistical measures more accessible to researchers.

Keywords:

Effect sizes Multilevel models R-squared r2mlm

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Area of Science:

Social and behavioral sciences research.
Statistical modeling and data analysis.

Background:

Multilevel models are widely used in social and behavioral sciences.
Effect sizes are crucial for interpreting results from multilevel models.
A recent framework for R-squared effect sizes in multilevel models exists but has a steep learning curve.

Purpose of the Study:

To introduce and demonstrate the new R package, r2mlm.
To automate the computation of R-squared effect sizes for multilevel models.
To provide accompanying graphics for visualizing multilevel R-squared measures.

Main Methods:

The study demonstrates the use of the r2mlm R package.
Accessible illustrations with open data and code are used.
The package automates intensive computations for implementing the R-squared framework by Rights and Sterba (2019).

Main Results:

The r2mlm package simplifies the implementation of a comprehensive framework for multilevel R-squared measures.
The package automates complex calculations, reducing the learning curve.
Accompanying graphics aid in the visualization and interpretation of various R-squared measures.

Conclusions:

The r2mlm R package makes advanced multilevel model effect size calculations more accessible.
Researchers can more easily contextualize their findings using R-squared measures.
The package facilitates the understanding and application of the Rights and Sterba (2019) framework.