Comparison of Collinearity Indices for Linear Models in Agricultural Trials
- 1 Graduate School, Universidad César Vallejo, Peru
- 2 Faculty of Education, Universidad Nacional Federico Villarreal, Peru
- 3 Graduate School, Universidad Continental, Peru
- 4 Graduate School, Universidad Privada Norbert Wiener, Lima, Peru
- 5 Graduate School, Universidad Católica de Trujillo Benedicto XVI, Trujillo, Peru
- 6 Graduate School, Universidad Nacional San Luis Gonzaga, Ica, Peru
- 7 Graduate School, Universidad Señor de Sipán, Chiclayo, Peru
- 8 Faculty of Engineering. Universidad Nacional de Jaén, Cajamarca, Peru
- 9 School of Medical Technology, Universidad Nacional de Jaén, Cajamarca, Peru
- 10 Graduate School, Universidad Nacional de Trujillo, Peru
- 11 Graduate School, Universidad del Pacífico, Peru
- 12 Center for Language Studies, Universidad San Ignacio de Loyola, Peru
- 13 Graduate School, Universidad Femenina del Sagrado Corazón, Peru
- 14 Faculty of Engineering, Sciences and Administration, Universidad Autónoma de Ica, Peru
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.
DOI: https://doi.org/10.3844/ojbsci.2024.195.207
Copyright: © 2024 Danny Villegas Rivas, José M. Palacios Sánchez, Cristina A. Alzamora Rivero, Carlos M. Franco Del Carpio, César Osorio Carrera, Martin Grados Vasquez, Luis Ramírez Calderón, Karin Ponce Rojas, José Jorge Rodríguez Figueroa, Felicia L. Cáceres Narrea, Delia A. Saravia Pachas, Arrieta Benoutt Felipe, Arturo N. Neyra Flores, Pedro E. Zata Pupuche, Carlos Fabián Falcón, Yolanda Maribel Mercedes Chipana Fernández, Víctor Hugo Fernández Rosas, Francisco Alejandro Espinoza Polo, Gaby Esther Chunga Pingo, Mercy Carolina Merejildo Vera, Carlos Alfredo Cerna Muñoz, Luis Orlando Miranda Diaz, Miguel Ángel Hernández López, Martín Desiderio Vejarano Campos, Erick Delgado Bazán, Zadith Garrido Campaña, José Paredes Carranza, Leyli J. Aguilar Ventura, Graciela M. Monroy Correa, Ruth A. Chicana Becerra, Jhonny Richard Rodriguez Barboza, Mariella M. Quipas Bellizza, Fernando Emilio Escudero Vilchez and Silvia Liliana Salazar Llerena. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Multicollinearity
- Overall Some Individual Indices
- Monte Carlo Simulation
- Mctest Package