mcvis: a new framework for collinearity discovery, diagnostic, and visualization

Chen Lin, Kevin Wang, Samuel Muller

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Collinearity discovery through diagnostic tools is an important analysis step when performing linear regression. Despite their wide-spread use, collinearity indices such as the variance inflation factor and the condition number have limitations and may not be effective in some applications. In this article, we will contribute to the study of conventional collinearity indices through theoretical and empirical work. We will present mcvis, a new framework that uses resampling techniques to repeatedly learn from these conventional collinearity indices to better understand the causes of collinearity. Our framework is made available in R through the mcvis package which includes new collinearity measures and visualizations, in particular a bipartite plot that informs on the degree and structure of collinearity. Supplementary materials for this article are available online.
Original languageEnglish
Pages (from-to)125-132
Number of pages8
JournalJournal of Computational and Graphical Statistics
Volume30
Issue number1
Early online date30 Jul 2020
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • High correlations
  • Multicollinearity
  • Resampling
  • Variance inflation factor

Fingerprint

Dive into the research topics of 'mcvis: a new framework for collinearity discovery, diagnostic, and visualization'. Together they form a unique fingerprint.

Cite this