Limitations in detecting multicollinearity due to scaling issues in the “mcvis” package
Metadatos
Afficher la notice complèteAuteur
Salmerón Gómez, Román; García García, Catalina; Rodríguez Sánchez, Ainara; García García, ClaudiaEditorial
The R Foundation
Materia
Multicolinealidad R package detección
Date
2022-12Referencia bibliográfica
Salmerón, R., García, C.B., Rodríguez, A. and García, C. (2022). Limitations in Detecting Multicollinearity due to Scaling Issues in the mcvis Package. The R Journal, 14 (4), 264-279.
Patrocinador
This work has been supported by project PP2019-EI-02 of the University of Granada (Spain), by project A-SEJ-496-UGR20 of the Andalusian Government’s Counseling of Economic Transformation, Industry, Knowledge and Universities (Spain) and by project I+D+i PID2019-107767GA-I0 financed by MCIN/AEI/10.13039/501100011033.Résumé
Transformation of the observed data is a very common practice when a troubling degree of near multicollinearity is detected in a linear regression model. However, it is important to take into account that these transformations may affect the detection of this problem, so they should not be performed systematically. In this paper we analyze the transformation of the data when applying the R package mcvis, showing that it only detects essential near multicollinearity when the studentise transformation is performed.