Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web

arXiv:2606.24236 · stat.ML, cs.CV, cs.LG · Submitted 2026-06-23 · Read on arXiv

Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, Klaus Ackermann

stat.ML, cs.CV, cs.LG

Submitted: 2026-06-23

Comments: Published in Australian & New Zealand Journal of Statistics

Journal ref: Australian & New Zealand Journal of Statistics, 68(1), e70027 (2026)

DOI: 10.1111/anzs.70027

Code: https://github.com/TengMCing/autovi

Project page: https://nowosad.github.io/rcartocolor

License: http://creativecommons.org/licenses/by/4.0/

The gist: Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across

Terminology

Abstract

Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across analysts. The lineup protocol, which embeds the observed plot among null plots, can reduce subjectivity but requires even more human effort. In today's data-driven world, such tasks are well suited for automation. We present a new R package that uses a computer vision model to automate the evaluation of residual plots. An accompanying Shiny application is provided for ease of use. Given a sample of residuals, the model predicts a visual signal strength (VSS) and offers supporting information to help analysts assess model fit.

Sources

Related papers