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rvif (version 3.0)

Collinearity Detection using Redefined Variance Inflation Factor and Graphical Methods

Description

The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. The objective of this package is to detect it using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., García C.B. and García J. (2018) , Salmerón, R., Rodríguez, A. and García C. (2020) , Salmerón, R., García, C.B, Rodríguez, A. and García, C. (2022) , Salmerón, R., García, C.B. and García, J. (2025) and Salmerón, R., García, C.B, García J. (2023, working paper) . You can also view the package vignette using 'browseVignettes("rvif")', the package website using 'browseURL(system.file("docs/index.html", package = "rvif"))' or version control on GitHub ().

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Install

install.packages('rvif')

Monthly Downloads

75

Version

3.0

License

GPL (>= 2)

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Maintainer

R. Salmeron

Last Published

July 29th, 2025

Functions in rvif (3.0)

SLM2

Second simulated data for the simple linear regression model
euribor

Euribor data
Wissel

Wissel data
CDpf

Cobb-Douglas data
SLM1

First simulated data for the simple linear regression model
cv_vif

VIF and CV calculation
employees

Spanish company employee data
soil

Soil characteristics data
cv_vif_plot

Scatterplot of CV vs VIF
multicollinearity

Decision Rule to Detect Troubling Multicollinearity
rvif-package

Detecting multicollinearity using RVIF and graphical methods.
rvifs

RVIF calculation