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VisCollin  

Visualizing Collinearity Diagnostics
View on CRAN: Click here


Download and install VisCollin package within the R console
Install from CRAN:
install.packages("VisCollin")

Install from Github:
library("remotes")
install_github("cran/VisCollin")

Install by package version:
library("remotes")
install_version("VisCollin", "0.1.2")



Attach the package and use:
library("VisCollin")
Maintained by
Michael Friendly
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-08-17
Latest Update: 2023-09-05
Description:
Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Where’s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>. These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.
How to cite:
Michael Friendly (2023). VisCollin: Visualizing Collinearity Diagnostics. R package version 0.1.2, https://cran.r-project.org/web/packages/VisCollin
Previous versions and publish date:
0.1.1 (2023-08-17 09:52)
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Functions, R codes and Examples using the VisCollin R package
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