MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Lin, Chen; Wang, Kevin; Mueller, Samuel
• Is Part Of: Journal of computational and graphical statistics. Volume 30:Issue 1(2021)
• Page Start: 125
• Page End: 132
• Publication Date: 2021-01-02
• Subjects: High correlations -- Multicollinearity -- Resampling -- Variance inflation factor; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2020.1779729
• Languages: English
• ISSNs: 1061-8600

Abstract

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.