Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web
arXiv:2606.24236v1 Announce Type: cross 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
The increasing availability of computer vision models and the demand for scalable, automated data analysis drive the development of tools like autovi.
Automating statistical diagnostics reduces human effort, improves consistency, and accelerates research workflows, especially in fields reliant on linear models.
Manual, subjective assessments of residual plots can now be augmented or replaced by objective, scalable computer vision methods.
- · Statisticians
- · Data Scientists
- · Academia
- · R package developers
- · Manual data diagnosticians (specific roles)
- · Inefficient statistical processes
Increased efficiency and consistency in statistical model diagnostics for researchers and practitioners.
Broader adoption of automated validation techniques across various scientific and industrial applications.
Potential for new standards in statistical reporting that incorporate automated diagnostic results.
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Read at arXiv cs.LG