Data-Driven Model Structure Diagrams for Hierarchical Linear Mixed Models

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.