MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization

Lin, C; Wang, K; Mueller, S

MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization

Lin, C Wang, K Mueller, S

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Publisher:: Informa UK Limited
Publication Type:: Journal Article
Citation:: Journal of Computational and Graphical Statistics, 2021, 30, (1), pp. 125-132
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Lin, C
dc.contributor.author	Wang, K
dc.contributor.author	Mueller, S
dc.date.accessioned	2022-05-26T05:39:00Z
dc.date.available	2022-05-26T05:39:00Z
dc.date.issued	2021-01-01
dc.identifier.citation	Journal of Computational and Graphical Statistics, 2021, 30, (1), pp. 125-132
dc.identifier.issn	1061-8600
dc.identifier.issn	1537-2715
dc.identifier.uri	http://hdl.handle.net/10453/157720
dc.description.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.
dc.language	en
dc.publisher	Informa UK Limited
dc.relation.ispartof	Journal of Computational and Graphical Statistics
dc.relation.isbasedon	10.1080/10618600.2020.1779729
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 1403 Econometrics
dc.subject.classification	Statistics & Probability
dc.title	MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-26T05:38:53Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	1

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157720