Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners

TL;DR

This paper introduces a unified visual inference framework for diagnosing linear mixed-effects models, addressing limitations of traditional methods and simplifying the process for analysts.

Contribution

It proposes a new, generally applicable visual inference approach for diagnosing and selecting LME models, validated through multiple datasets and a large-scale study.

Findings

Effective visual diagnostics for LME models

Improved model selection procedures

Validated methods on diverse datasets

Abstract

The complexity of linear mixed-effects (LME) models means that traditional diagnostics are rendered less effective. This is due to a breakdown of asymptotic results, boundary issues, and visible patterns in residual plots that are introduced by the model fitting process. Some of these issues are well known and adjustments have been proposed. Working with LME models typically requires that the analyst keeps track of all the special circumstances that may arise. In this paper we illustrate a simpler but generally applicable approach to diagnosing LME models. We explain how to use new visual inference methods for these purposes. The approach provides a unified framework for diagnosing LME fits and for model selection. We illustrate the use of this approach on several commonly available data sets. A large-scale Amazon Turk study was used to validate the methods. R code is provided for the analyses.