TY - JOUR
T1 - Comparison of Collinearity Indices for Linear Models in Agricultural Trials
AU - Rivas, Danny Villegas
AU - Sánchez, José M.Palacios
AU - Rivero, Cristina A.Alzamora
AU - Del Carpio, Carlos M.Franco
AU - Carrera, César Osorio
AU - Vasquez, Martin Grados
AU - Calderón, Luis Ramírez
AU - Salinas, Luis E.Cruz
AU - Rojas, Karin Ponce
AU - Rojas, Liliana Correa
AU - Figueroa, José Jorge Rodríguez
AU - Narrea, Cáceres
AU - Felicia, L.
AU - Pachas, Saravia
AU - Delia, A.
AU - Benoutt, Arrieta
AU - Felipe,
AU - Flores, Arturo N.Neyra
AU - Pupuche, Pedro E.Zata
AU - Falcón, Carlos Fabián
AU - Fernández, Yolanda Maribel Mercedes Chipana
AU - Lezama, Marilú T.Flores
AU - Tello, Asunción R.Lezcano
AU - Chávez, Pablo V.Aguilar
AU - Rosas, Víctor Hugo Fernández
AU - Polo, Francisco Alejandro Espinoza
AU - Pingo, Gaby Esther Chunga
AU - Vera, Mercy Carolina Merejildo
AU - Muñoz, Carlos Alfredo Cerna
AU - Diaz, Luis Orlando Miranda
AU - López, Miguel Ángel Hernández
AU - Campos, Martín Desiderio Vejarano
AU - Bazán, Erick Delgado
AU - Campaña, Zadith Garrido
AU - Carranza, José Paredes
AU - Ventura, Leyli J.Aguilar
AU - Correa, Graciela M.Monroy
AU - Becerra, Ruth A.Chicana
AU - Barboza, Jhonny Richard Rodriguez
AU - Prieto, Rafael Damián Villón
AU - Prieto, Claudia Rosalía Villón
AU - Bellizza, Mariella M.Quipas
AU - Vilchez, Fernando Emilio Escudero
AU - Llerena, Silvia Liliana Salazar
N1 - Publisher Copyright:
© 2024, Science Publications. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Mctest Package
KW - Monte Carlo Simulation
KW - Multicollinearity
KW - Overall Some Individual Indices
UR - http://www.scopus.com/inward/record.url?scp=85182479071&partnerID=8YFLogxK
U2 - 10.3844/ojbsci.2024.195.207
DO - 10.3844/ojbsci.2024.195.207
M3 - Article
AN - SCOPUS:85182479071
SN - 1608-4217
VL - 24
SP - 195
EP - 207
JO - OnLine Journal of Biological Sciences
JF - OnLine Journal of Biological Sciences
IS - 2
ER -