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Journal of Data Science


Data-Driven Model Structure Diagrams for Hierarchical Linear Mixed Models
Pub. online: 26 March 2026      Type: Statistical Data Science      Open accessOpen Access
Received
11 August 2025
Accepted
21 February 2026
Published
26 March 2026

Abstract

Hierarchical linear mixed models are commonly used in many scientific fields. However, without a strong statistical background, it can be hard to understand the relationships between the random effect variables and the inferences that can be made when a model has nested random effects. Visualizing relationships makes it easier for the practitioner to understand what relationships the model is capable of estimating and testing. We present an R package modeldiagramR that seamlessly creates a visualization of the model based on the data and the model object created when fitting a linear mixed model using either lme4 or nlme.

Supplementary material

 Supplementary Material
The supplementary material is an R script file that contains all the code used to create the figures and model diagrams from this manuscript.

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Copyright
2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
data visualization diagnostic plots model assumption assessment model visualization multivariate high dimensional data random effects

Funding
This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under Award Number 1R01AR081489 and the National Institute of General Medical Sciences of the National Institutes of Health under Award Number P20GM103474. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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