arXiv subjects

Susan VanderPlas

Publications and source records attributed to Susan VanderPlas.

6 recordsLinked to original sources

arXiv2026

A Mathematical Framework and Software Implementation for Uncertainty Visualisation

Random variables are the bread and butter of statistics, and visualisations are one of the most versatile tools in the field, so it is a wonder why we do not have a methodology for visualising random variables. This gap is particularly evident for exploratory data analysis (EDA). We address this gap by designing a mathematical framework for visualisation, which argues that we should consider visualisations to be continuous functions. In the case of random variable inputs, this means the visualisations should obey the continuous mapping theorem (CMT). By breaking the visual function down into its components, we are able to identify which parts of the mapping are ill-defined for random variable inputs and redefine them in a way that guarantees both the flexibility required for EDA and the statistical sensibility of CMT. This formalisation represents a complete integration of uncertainty into the grammar of graphics, which we show by implementing the theory in the R package, "ggdibbler". The ggdibbler software is a "ggplot2" extension that allows users to replace the data of any plotting function with a random variable, with the guarantee that the visualisation will have the same convergence properties as its underlying data.

stat.AP↗

arXiv2026

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

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.

stat.ML↗

arXiv2024

The Noisy Work of Uncertainty Visualisation Research: A Review

Better representation of the uncertainty in a data visualisation is a focus of recent research activity. A problem with the current literature is that there is a lack of clarity about the definition of uncertainty and what it means to represent it in a plot. This confusion results in a significant amount of conflicting results in the literature, especially in experiments that assess the effectiveness of different uncertainty representations. In this review, we summarise the current literature, provide workable definitions, and illustrate these definitions with examples. In doing so, we ask what it really takes to achieve transparency in statistical graphics. It is hoped that it will be useful for guiding new graphics methodology and experimental research.

cs.HC↗

arXiv2024

Automated Assessment of Residual Plots with Computer Vision Models

Plotting the residuals is a recommended procedure to diagnose deviations from linear model assumptions, such as non-linearity, heteroscedasticity, and non-normality. The presence of structure in residual plots can be tested using the lineup protocol to do visual inference. There are a variety of conventional residual tests, but the lineup protocol, used as a statistical test, performs better for diagnostic purposes because it is less sensitive and applies more broadly to different types of departures. However, the lineup protocol relies on human judgment which limits its scalability. This work presents a solution by providing a computer vision model to automate the assessment of residual plots. It is trained to predict a distance measure that quantifies the disparity between the residual distribution of a fitted classical normal linear regression model and the reference distribution, based on Kullback-Leibler divergence. From extensive simulation studies, the computer vision model exhibits lower sensitivity than conventional tests but higher sensitivity than human visual tests. It is slightly less effective on non-linearity patterns. Several examples from classical papers and contemporary data illustrate the new procedures, highlighting its usefulness in automating the diagnostic process and supplementing existing methods.

stat.ML↗

arXiv2023

Demonstrative Evidence and the Use of Algorithms in Jury Trials

We investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore what our study says about how this type of forensic evidence is perceived in the courtroom where individuals unfamiliar with advanced statistical methods are asked to evaluate results in order to assess guilt. In the course of our initial study, we found that individuals overwhelmingly provided high Likert scale ratings in reliability, credibility, and scientificity regardless of experimental condition. This discovery of scale compression - where responses are limited to a few values on a larger scale, despite experimental manipulations - limits statistical modeling but provides opportunities for new experimental manipulations which may improve future studies in this area.

cs.CY↗

arXiv2023

A Plot is Worth a Thousand Tests: Assessing Residual Diagnostics with the Lineup Protocol

Regression experts consistently recommend plotting residuals for model diagnosis, despite the availability of many numerical hypothesis test procedures designed to use residuals to assess problems with a model fit. Here we provide evidence for why this is good advice using data from a visual inference experiment. We show how conventional tests are too sensitive, which means that too often the conclusion would be that the model fit is inadequate. The experiment uses the lineup protocol which puts a residual plot in the context of null plots. This helps generate reliable and consistent reading of residual plots for better model diagnosis. It can also help in an obverse situation where a conventional test would fail to detect a problem with a model due to contaminated data. The lineup protocol also detects a range of departures from good residuals simultaneously. Supplemental materials for the article are available online.

stat.AP↗