Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web
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Statistics > Machine Learning
Title: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.
| Comments: | Published in Australian & New Zealand Journal of Statistics |
| Subjects: | Machine Learning (stat.ML); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.24236 [stat.ML] |
| (or arXiv:2606.24236v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24236
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Australian & New Zealand Journal of Statistics, 68(1), e70027 (2026) |
| Related DOI: | https://doi.org/10.1111/anzs.70027
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