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

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.

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Publication Details

Journal
Australian & New Zealand Journal of Statistics
Published
2025-10-09
DOI
https://doi.org/10.1111/anzs.70027
Citations
1
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
1.24

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article

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

Weihao Li, Emi Tanaka, Dianne Cook, Klaus Ackermann et al.
1 citations
Australian & New Zealand Journal of Statistics
Industrial Vision Systems and Defect Detection
1.24
article

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

Weihao Li, Emi Tanaka, Dianne Cook, Klaus Ackermann, Susan VanderPlas
article en
1 citations

Abstract

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.

Australian & New Zealand Journal of StatisticsVol. 68(1)
University of Nebraska–Lincoln (US), Australian National University (AU), Monash University (AU)
Monash University
Openalex Percentile: Top 14%
Industrial Vision Systems and Defect Detection
1.24
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Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web — Weihao Li, Emi Tanaka, et al. · Australian & New Zealand Journal of Statistics (2025) | TGRS Research Map | TGRS