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A Guide to Using the R Package “multiColl” for Detecting Multicollinearity

Author

Listed:
  • Román Salmerón-Gómez

    (University of Granada)

  • Catalina García-García

    (University of Granada)

  • José García-Pérez

    (University of Almería)

Abstract

The detection of problematic collinearity in a linear regression model is treated in all the existing statistical software packages. However, such detection is not always done adequately. The main shortcomings relate to treatment of independent qualitative variables and completely ignoring the role of the intercept in the model (consequently, ignoring the nonessential collinearity). This paper presents the R package multiColl, which implements the usually applied measures for detecting near collinearity while overcoming the weaknesses observed in other existing packages.

Suggested Citation

  • Román Salmerón-Gómez & Catalina García-García & José García-Pérez, 2021. "A Guide to Using the R Package “multiColl” for Detecting Multicollinearity," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 529-536, February.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:2:d:10.1007_s10614-019-09967-y
    DOI: 10.1007/s10614-019-09967-y
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    References listed on IDEAS

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    1. George E. Halkos & Kyriaki D. Tsilika, 2018. "Programming Correlation Criteria with free CAS Software," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 299-311, June.
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