用计算机视觉自动评估残差图，提升模型诊断效率与一致性。
Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web
- 引入计算机视觉模型自动分析残差图的视觉信号
- 输出可视信号强度（VSS）量化模型拟合质量
- 配套Shiny应用降低使用门槛，适合数据分析师
残差图的视觉评估是诊断线性模型的常用方法，但依赖人工判断，难以扩展且易导致分析者间结果不一致。行列法（lineup protocol）虽可降低主观性，却需更多人力投入。在数据驱动时代，此类任务适合自动化。本文提出一个新R包autovi，利用计算机视觉模型自动评估残差图。配合Shiny应用autovi.web，输入残差样本后，模型预测视觉信号强度（VSS），并提供辅助信息帮助评估模型拟合效果。
原文摘要 · Abstract (English)
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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