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Research Article · Open access · Published 28 December 2023

Comparison of Collinearity Indices for Linear Models in Agricultural Trials

Danny Villegas Rivas1, José M. Palacios Sánchez2, Cristina A. Alzamora Rivero2, Carlos M. Franco Del Carpio2, César Osorio Carrera3, Martin Grados Vasquez1, Luis Ramírez Calderón1, Karin Ponce Rojas1, José Jorge Rodríguez Figueroa1, Felicia L. Cáceres Narrea2, Delia A. Saravia Pachas2, Arrieta Benoutt Felipe2, Arturo N. Neyra Flores2, Pedro E. Zata Pupuche2, Carlos Fabián Falcón1, Yolanda Maribel Mercedes Chipana Fernández1, Víctor Hugo Fernández Rosas1, Francisco Alejandro Espinoza Polo1, Gaby Esther Chunga Pingo1, Mercy Carolina Merejildo Vera4, Carlos Alfredo Cerna Muñoz5, Luis Orlando Miranda Diaz5, Miguel Ángel Hernández López6, Martín Desiderio Vejarano Campos7, Erick Delgado Bazán8, Zadith Garrido Campaña8, José Paredes Carranza9, Leyli J. Aguilar Ventura10, Graciela M. Monroy Correa2, Ruth A. Chicana Becerra11, Jhonny Richard Rodriguez Barboza12, Mariella M. Quipas Bellizza13, Fernando Emilio Escudero Vilchez14 & Silvia Liliana Salazar Llerena14

OnLine Journal of Biological Sciences, Volume 24, Issue 2, pp. 195–207 · doi.org/10.3844/ojbsci.2024.195.207

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Abstract

The deleterious consequences of collinearity in linear regression on the precision of estimators of regression coefficients and the interpretability of the fitted model are widely recognized. In this study, we compare several methodologies for assessing collinearity in linear models and explore the effect of outliers on collinearity. The robustness of collinearity measures (individual and overall) is validated through two detailed Monte Carlo simulation study which also considers the effect of outliers on collinearity indices. The methods are illustrated with two real-world agricultural and fish morphology l data sets to show potential applications. The results do not provide any evidence for an effect from outliers on collinearity identification using the collinearity indices (individual and overall). The FG and Fj collinearity indices more robust as both sample size and collinearity degree increase. The VIF (individual measure) had a better performance on the fitted model with a greater number of parameters.

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Rivas, D. V., Sánchez, J. M. P., Rivero, C. A. A., Del Carpio, C. M. F., Carrera, C. O., Vasquez, M. G., Calderón, L. R., Rojas, K. P., Figueroa, J. J. R., Narrea, F. L. C., Pachas, D. A. S., Felipe, A. B., Neyra Flores, A. N., Zata Pupuche, P. E., Falcón, C. F., Chipana Fernández, Y. M. M., Rosas, V. H. F., Polo, F. A. E., Chunga Pingo, G. E., Merejildo Vera, M. C., Cerna Muñoz, C. A., Miranda Diaz, L. O., Hernández López, M. &., Vejarano Campos, M. D., Bazán, E. D., Campaña, Z. G., Carranza, J. P., Ventura, L. J. A., Monroy Correa, G. M., Chicana Becerra, R. A., Barboza, J. R. R., Quipas Bellizza, M. M., Escudero Vilchez, F. E. & Salazar Llerena, S. L. (2024). Comparison of Collinearity Indices for Linear Models in Agricultural Trials. OnLine Journal of Biological Sciences, 24(2), 195–207. https://doi.org/10.3844/ojbsci.2024.195.207

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Comparison of Collinearity Indices for Linear Models in Agricultural TrialsOnLine Journal of Biological Sciences · Volume 24, Issue 2 · pp. 195–207 · CC BY 4.0Download