Collinearity diagnostics are widely used, but the typical tabular output used in almost all software makes it hard to tell what to look for and how to understand the results. We describe a simple improvement to the standard tabular display, a graphic rendition of the salient information as a "tableplot," and graphic displays designed to make the information in these diagnostic methods more readily understandable. In addition, we propose a visualization of the contributions of the predictors to collinearity through a "collinearity biplot," which is simultaneously a biplot of the smallest dimensions of the correlation matrix of the predictors, R XX, and the largest dimensions of R -1 xx, on which the standard collinearity diagnostics are based.

Additional Metadata
Keywords Collinearity biplot, Condition indices, Diagnostic plots, Effect ordering, Multiple regression, Tableplot, Variance inflation
Persistent URL dx.doi.org/10.1198/tast.2009.0012
Journal American Statistician
Citation
Friendly, M. (Michael), & Kwan, E. (2009). Statistical computing and graphics where's waldo? visualizing Collinearity diagnostics. American Statistician, 63(1), 56–65. doi:10.1198/tast.2009.0012