Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web
| dc.contributor.author | Li, Weihao | en |
| dc.contributor.author | Cook, Dianne | en |
| dc.contributor.author | Tanaka, Emi | en |
| dc.contributor.author | VanderPlas, Susan | en |
| dc.contributor.author | Ackermann, Klaus | en |
| dc.date.accessioned | 2025-12-17T20:41:23Z | |
| dc.date.available | 2025-12-17T20:41:23Z | |
| dc.date.issued | 2025 | en |
| dc.description.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. | en |
| dc.description.status | Peer-reviewed | en |
| dc.format.extent | 14 | en |
| dc.identifier.issn | 1369-1473 | en |
| dc.identifier.other | ORCID:/0000-0003-4959-106X/work/195523504 | en |
| dc.identifier.other | ORCID:/0000-0002-1455-259X/work/195539995 | en |
| dc.identifier.scopus | 105018495936 | en |
| dc.identifier.uri | https://hdl.handle.net/1885/733796411 | |
| dc.language.iso | en | en |
| dc.provenance | This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use anddistribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. | en |
| dc.rights | © 2025 The Author(s). | en |
| dc.source | Australian and New Zealand Journal of Statistics | en |
| dc.subject | computer vision | en |
| dc.subject | data visualisation | en |
| dc.subject | hypothesis testing | en |
| dc.subject | initial data analysis | en |
| dc.subject | machine learning | en |
| dc.subject | model diagnostics | en |
| dc.subject | regression analysis | en |
| dc.subject | statistical graphics | en |
| dc.subject | visual inference | en |
| dc.title | Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web | en |
| dc.type | Journal article | en |
| dspace.entity.type | Publication | en |
| local.contributor.affiliation | Li, Weihao; Research School of Finance, Actuarial Studies and Statistics, Research School of Finance, Actuarial Studies & Statistics, ANU College of Business & Economics, The Australian National University | en |
| local.contributor.affiliation | Cook, Dianne; Monash University | en |
| local.contributor.affiliation | Tanaka, Emi; Research School of Finance, Actuarial Studies and Statistics, Research School of Finance, Actuarial Studies & Statistics, ANU College of Business & Economics, The Australian National University | en |
| local.contributor.affiliation | VanderPlas, Susan; University of Nebraska-Lincoln | en |
| local.contributor.affiliation | Ackermann, Klaus; Monash University | en |
| local.identifier.citationvolume | 68 | en |
| local.identifier.doi | 10.1111/anzs.70027 | en |
| local.identifier.pure | c3234081-f076-4060-aaab-c34074bc9183 | en |
| local.identifier.url | https://www.scopus.com/pages/publications/105018495936 | en |
| local.type.status | E-pub ahead of print | en |
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