Susan VanderPlas

A 'ggplot2' extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas 'ggdibbler' differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace one of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart.

In this paper, we discuss considerations and methods for experimentally testing visualizations. We discuss levels of user engagement with graphics, common issues when developing a sampling or data generation model, the importance of pilot testing, and data analysis methods. Along the way, we also provide recommendations of how to avoid some of the unique pitfalls of human testing in statistical and visualization research.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms

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.

When wires are cut, the tool produces striations on the cut surface; as in other forms of forensic analysis, these striation marks are used to connect the evidence to the source that created them. Here, we argue that the practice of comparing two wire cut surfaces introduces complexities not present in better-investigated forensic examination of toolmarks such as those observed on bullets, as wire comparisons inherently require multiple distinct comparisons, increasing the expected false discovery rate. We call attention to the multiple comparison problem in wire examination and relate it to other situations in forensics that involve multiple comparisons, such as database searches.