Field Guide to the R Mixed Model Wilderness

Preface

“Path in the Wilderness” by Erich Taeubel, Jr.

Running mixed models in R is no easy task. There are dozens of packages supporting these aims, each with varying functionality, syntax, and conventions. The linear mixed model ecosystem in R consists of over 80 libraries that either construct and solve mixed model equations or helper packages the process the results from mixed model analysis. These libraries provide a patchwork of overlapping and unique functionality regarding the fundamental structure of mixed models: allowable distributions, nested and crossed random effects, heterogeneous error structures and other facets. No single library has all possible functionality enabled.

This patchwork of packages makes it very challenging for statisticians to conduct mixed model analysis and to teach others how to run mixed models in R. The purpose of this guide to to provide some recipes for handling common analytical scenario’s that require mixed models. As a field guide, it is intended to be succinct, and help researchers meet their analytic goals.

In general, the content from this website may not be copied or reproduced without attribution. However, the example code and required data sets to run the code are MIT licensed. These can be accessed on GitHub.

What This Does Not Cover

• Generalized linear models. We do address cases of unequal variance, but if another distribution and/or a link function is required for the model, that is not addressed in this guide.

• Basic principles of experimental design. We assume you know this, but if you do not, please check out the Grammar of Experimental Design.

• Instructions in using R. We assume familiarity with R. If you need help in learning R, there are numerous guides, including our introductory R course.

Notice!

This is a work-in-progress and will be updated over time.