Abstract
The main goal of this paper is to show the advantages of the multiColl package in R, comparing its results with other existing packages in R for the treatment of multicollinearity.
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References
Belsley, D. (1991). Conditioning diagnostics: Collinearity and weak data in regression. Wiley.
Fox, J., Weisberg, S., & Price, B. (2018) car: Companion to Applied Regression. https://CRAN.R-project.org/package=car. R package version 3.0-2
García, C., García, J., López, M., & Salmerón, R. (2018a). Collinearity: Revisiting the variance inflation factor in ridge regression. Journal of Applied Statistics, 42(3), 648–661. https://doi.org/10.1080/02664763.2014.980789.
García, C., Salmerón, R., & García, C. (2018b). A choice of the ridge factor from the correlation matrix determinant. Journal of Statistical Computation and Simulation, 2(89), 211–231. https://doi.org/10.1080/00949655.2018.1543423?journalCode=gscs20.
Harrell Jr, F. E. (2020). rms: Regression Modeling Strategies. https://CRAN.R-project.org/package=rms. R package version 6.0-1.
Hendrickx, J. (2012) perturb: Tools for evaluating collinearity. https://CRAN.R-project.org/package=perturb. R package version 2.05.
Klein, L., & Goldberger, A. (1964). An economic model of the United States, 1929–1952. North Holland Publishing Company.
Lin, C., Wang, K., & Mueller, S. (2020). mcvis: A new framework for collinearity discovery, diagnostic and visualization. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2020.1779729.
Marquardt, D., & Snee, R. (1975). Ridge regression in practice. The American Statistician, 1(29), 3–20. https://doi.org/10.1080/00031305.1975.10479105.
Salmerón, R., García, C., García, C. (2019a) multicoll: An r package to detect multicollinearity. https://arxiv.org/abs/1910.14590
Salmerón, R., García, C., & García, C. (2020a). Detection of near-multicollinearity through centered and noncentered regression. Mathematics, 6(8), 931. https://doi.org/10.3390/math8060931.
Salmerón, R., García, C., & García, C. (2020b). A guide to using the r package “multicoll’’ for detecting multicollinearity. Computational Economics. https://doi.org/10.1007/s10614-019-09967-y.
Salmerón, R., García, C., & García, C. (2021) multicoll package and other packages to detect multicollinearity in r. arXiv. https://arxiv.org/abs/2107.03077
Salmeron, R., Garcia, C., & Garcia, J. (2019b) multiColl: Collinearity Detection in a Multiple Linear Regression Model. https://CRAN.R-project.org/package=multiColl. R package version 1.0.
Salmerón, R., García, C., & García, J. (2019c). Comment on “a note on collinearity diagnostics and centering’’ by velilla (2018). The American Statistician. https://doi.org/10.1080/00031305.2019.1635527.
Salmerón, R., Rodríguez, A., & García, C. (2019d). Diagnosis and quantification of the non-essential collinearity. Computational Statistics. https://doi.org/10.1007/s00180-019-00922-x.
Stewart, G. (1987). Collinearity and least squares regression. Statistical Science, 2(1), 68–100.
Theil, H. (1971). Principles of Econometrics. Wiley.
Ullah, D. M. I., & Aslam, D. M. (2018). mctest: Multicollinearity Diagnostic Measures. https://CRAN.R-project.org/package=mctest
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This work has been supported by Project PP2019-EI-02 of the University of Granada, Spain.
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Salmerón Gómez, R., García, C.B.G. & Pérez, J.G. The multiColl Package Versus Other Existing Packages in R to Detect Multicollinearity. Comput Econ 60, 439–450 (2022). https://doi.org/10.1007/s10614-021-10154-1
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DOI: https://doi.org/10.1007/s10614-021-10154-1