ballmapper: Applying Topological Data Analysis Ball Mapper in Stata
Simon Rudkin, Wanling Rudkin

TL;DR
This paper introduces the Ball Mapper Topological Data Analysis tool for Stata, enabling model-free visualization of complex multivariate data without dimensionality reduction, preserving data structure and allowing variable coloring.
Contribution
It provides an introduction to TDABM and implements the Ball Mapper algorithm in a Stata package, expanding its accessibility for data analysis.
Findings
Enables visualization of high-dimensional data without information loss
Supports coloring by additional variables or residuals
Applicable across diverse fields like finance and medicine
Abstract
Topological Data Analysis Ball Mapper (TDABM) offers a model-free visualization of multivariate data which does not necessitate the information loss associated with dimensionality reduction. TDABM Dlotko (2019) produces a cover of a multidimensional point cloud using equal size balls, the radius of the ball is the only parameter. A TDABM visualization retains the full structure of the data. The graphs produced by TDABM can convey coloration according to further variables, model residuals, or variables within the multivariate data. An expanding literature makes use of the power of TDABM across Finance, Economics, Geography, Medicine and Chemistry amongst others. We provide an introduction to TDABM and the \texttt{ballmapper} package for Stata.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopological and Geometric Data Analysis · Morphological variations and asymmetry · Data Visualization and Analytics
