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.
mctest, perturb, HH, car, fmsb, rms, faraway, usdm, VIF, leaps, bestglm, glmulti, meifly
SocialSciences, Econometrics, ChemPhys, ClinicalTrials, Finance, Multivariate, ReproducibleResearch, Survival
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For attribution, please cite this work as
Imdadullah, et al., "The R Journal: mctest: An R Package for Detection of Collinearity among Regressors", The R Journal, 2016
BibTeX citation
@article{RJ-2016-062,
author = {Imdadullah, Muhammad and Aslam, Muhammad and Altaf, Saima},
title = {The R Journal: mctest: An R Package for Detection of Collinearity among Regressors},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-062},
doi = {10.32614/RJ-2016-062},
volume = {8},
issue = {2},
issn = {2073-4859},
pages = {495-505}
}