Scagnostics, scatterplot diagnostics, was discovered by John and Paul Tukey and later popularized by Leland Wilkinson in Graph-Theoretic Scagnostics (2005). These analyses were redefined in High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions (2006).
The beauty of scagnostics is the ability to visually explore a dataset. JMP has the inherent feature called Scatterplot Matrix (SPLOM), which allows the user to simultaneously compare the relationship between many pairs of variables.
However, SPLOMs lose their effectiveness when the number of variables get too large.
That’s where scagnostics comes in! Scagnostics assesses five aspects of scatterplots: outliers, shape, trend, density, and coherence.
This summer, I had the privilege of writing this JMP add-in that allows the user to interactively explore data using nine graph-theoretic measures. The add-in combines three current features of JMP: Distribution, Scatterplot Matrix, and Graph Builder. Each point in the scatterplot represents a 2D scatterplot. When the user selects a point in the scatterplot matrix in the bottom left, Graph Builder shows the respective scatterplot for the two variable in the bottom right.
With scagnostics, we are able to uncover much more informative and enlightening analyses when doing exploratory visual data analysis.
Update: Version 2.1 of the Scagnostics Add-In has been uploaded, which updates the launcher to optionally take a pre-computed table as an argument, and also adds a red-triangle option to turn on the box-plots for the distributions.
