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

dc.contributor.authorLi, Weihaoen
dc.contributor.authorCook, Dianneen
dc.contributor.authorTanaka, Emien
dc.contributor.authorVanderPlas, Susanen
dc.contributor.authorAckermann, Klausen
dc.date.accessioned2025-12-17T20:41:23Z
dc.date.available2025-12-17T20:41:23Z
dc.date.issued2025en
dc.description.abstractVisual 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.statusPeer-revieweden
dc.format.extent14en
dc.identifier.issn1369-1473en
dc.identifier.otherORCID:/0000-0003-4959-106X/work/195523504en
dc.identifier.otherORCID:/0000-0002-1455-259X/work/195539995en
dc.identifier.scopus105018495936en
dc.identifier.urihttps://hdl.handle.net/1885/733796411
dc.language.isoenen
dc.provenanceThis 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.sourceAustralian and New Zealand Journal of Statisticsen
dc.subjectcomputer visionen
dc.subjectdata visualisationen
dc.subjecthypothesis testingen
dc.subjectinitial data analysisen
dc.subjectmachine learningen
dc.subjectmodel diagnosticsen
dc.subjectregression analysisen
dc.subjectstatistical graphicsen
dc.subjectvisual inferenceen
dc.titleAutomated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.weben
dc.typeJournal articleen
dspace.entity.typePublicationen
local.contributor.affiliationLi, Weihao; Research School of Finance, Actuarial Studies and Statistics, Research School of Finance, Actuarial Studies & Statistics, ANU College of Business & Economics, The Australian National Universityen
local.contributor.affiliationCook, Dianne; Monash Universityen
local.contributor.affiliationTanaka, Emi; Research School of Finance, Actuarial Studies and Statistics, Research School of Finance, Actuarial Studies & Statistics, ANU College of Business & Economics, The Australian National Universityen
local.contributor.affiliationVanderPlas, Susan; University of Nebraska-Lincolnen
local.contributor.affiliationAckermann, Klaus; Monash Universityen
local.identifier.citationvolume68en
local.identifier.doi10.1111/anzs.70027en
local.identifier.purec3234081-f076-4060-aaab-c34074bc9183en
local.identifier.urlhttps://www.scopus.com/pages/publications/105018495936en
local.type.statusE-pub ahead of printen

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