The R Journal: article published in 2016, volume 8:2

mctest: An R Package for Detection of Collinearity among Regressors
Muhammad Imdadullah, Muhammad Aslam and Saima Altaf , The R Journal (2016) 8:2, pages 495-505.

Abstract It is common for linear regression models to be plagued with the problem of multicollinearity when two or more regressors are highly correlated. This problem results in unstable estimates of regression coefficients and causes some serious problems in validation and interpretation of the model. Different diagnostic measures are used to detect multicollinearity among regressors. Many statistical software and R packages provide few diagnostic measures for the judgment of multicollinearity. Most widely used diagnostic measures in these software are: coefficient of determination (R2 ), variance inflation factor/tolerance limit (VIF/TOL), eigenvalues, condition number (CN) and condition index (CI) etc. In this manuscript, we present an R package, mctest, that computes popular and widely used multicollinearity diagnostic measures. The package also indicates which regressors may be the reason of collinearity among regressors.

Received: 2016-07-12; online 2016-12-12
CRAN packages: mctest, perturb, HH, car, fmsb, rms, faraway, usdm, VIF, leaps, bestglm, glmulti, meifly
CRAN Task Views implied by cited CRAN packages: SocialSciences, Econometrics, ChemPhys, ClinicalTrials, Finance, Multivariate, ReproducibleResearch, Survival

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-062,
  author = {Muhammad Imdadullah and Muhammad Aslam and Saima Altaf},
  title = {{mctest: An R Package for Detection of Collinearity among
          Regressors}},
  year = {2016},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2016-062},
  url = {https://doi.org/10.32614/RJ-2016-062},
  pages = {495--505},
  volume = {8},
  number = {2}
}