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

2026-06-24 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, extended

Summary

The R package autovi and its web interface autovi.web automate the visual assessment of residual plots for diagnosing linear models. This addresses the limitations of manual evaluation, which is subjective, inconsistent, and does not scale. autovi employs a computer vision model to predict a Visual Signal Strength (VSS) from residual plots, providing a p-value by comparing it against null and bootstrapped plots. The accompanying autovi.web Shiny application offers a user-friendly, cross-platform interface, eliminating installation complexities. The package features a modular design, allowing extension to other model classes like generalized linear models, and integrates Python libraries such as TensorFlow for model inference.

Key takeaway

For data scientists and analysts performing regression analysis, autovi and autovi.web offer a robust solution to automate residual plot interpretation. You can obtain objective Visual Signal Strength (VSS) and p-values, significantly reducing the subjectivity and time associated with manual diagnostics. Consider using the web application for quick assessments or extending the R package's modular infrastructure for custom model classes, ensuring more consistent and scalable model evaluation.

Key insights

Automating subjective visual statistical diagnostics with computer vision improves consistency and scalability.

Principles

• Visual inference reduces subjectivity.
• Computer vision automates visual assessments.
• Modular design extends model diagnostics.

Method

The autovi package uses a computer vision model to predict Visual Signal Strength (VSS) from residual plots, comparing it against null and bootstrapped plots to generate a p-value for model fit assessment.

In practice

• Use autovi for automated residual plot diagnostics.
• Extend autovi for generalized linear models.
• Access autovi.web for browser-based analysis.

Topics

• Regression Diagnostics
• Computer Vision
• R Package
• Shiny Application
• Visual Signal Strength
• Statistical Graphics
• Model Diagnostics

Code references

• TengMCing/autovi
• TengMCing/autovi_web
• TengMCing/autovi_paper
• quarto-dev/quarto-cli
• jgm/pandoc

Best for: Research Scientist, Data Scientist, AI Scientist, Software Engineer

Related on AIssential

• Demystifying Residual Analysis: A Beginner’s Guide to What Your Model Isn’t Telling You — Residual analysis diagnoses model failures by examining prediction errors for hidden patterns and assumption violations.
• 5 Useful Python Scripts to Automate Exploratory Data Analysis — Automated Python scripts streamline exploratory data analysis, addressing common pain points in data profiling, visualization, and anomaly detection.
• Synthetic Designed Experiments for Diagnosing Vision Model Failure — SDRS uses Design of Experiments and ANOVA to diagnose vision model failures and prescribe targeted synthetic data.
• Andrej Karpathy Dropped a 200-Line GPT; I Used the Same Math To Turn Datasets Into Searchable… — Automating statistical pipelines can eliminate manual data entry and streamline research execution.
• A Visual Explanation of Linear Regression — Visual, practical, and reproducible methods are key to understanding and applying linear regression effectively.
• TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment — TEVI refines image embeddings using captions to improve vision-language alignment by addressing information imbalance.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.