ArXiv

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

2026-06-23 · 07:24 UTC ·Weihao Li, Dianne Cook, Emi Tanaka... ·1 min read
Authors
Weihao Li, Dianne Cook, Emi Tanaka...
Categories
stat.ML, cs.CV, cs.LG
arXiv
https://arxiv.org/abs/2606.24236v1
PDF
https://arxiv.org/pdf/2606.24236v1

Brief

The paper introduces autovi (R) and autovi.web (Shiny), which apply a computer-vision model to automate visual assessment of residual plots by producing a visual signal strength (VSS) metric and auxiliary diagnostics. The approach targets scalability and consistency compared with manual inspection and lineup tests, offering a deployable tool for model-diagnostic workflows (arXiv 2606.24236v1; ANZJS 2026).

Why it matters

The autovi R package and autovi.web Shiny app (Li, Cook, Tanaka, VanderPlas, Ackermann) use a computer-vision model to automate residual-plot assessment by predicting a visual signal strength (VSS) score; preprint on arXiv 2606.24236v1 and published in Australian & New Zealand Journal of Statistics 68(1), e70027 (2026).

Key details

  • The tool aims to replace manual inspection and the lineup protocol (which embeds the observed plot among null plots) to scale diagnostic workflows and reduce subjectivity, returning VSS plus supporting information to help analysts judge linear-model fit.
Source evidence

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.

Comment: Published in Australian & New Zealand Journal of Statistics
Journal: Australian & New Zealand Journal of Statistics, 68(1), e70027 (2026)