On a logistic regression model with random intercept: diagnostic analytics, simulation and biological application
Autor
Tapia, Alejandra
Leiva, Víctor
Galea, Manuel
Werneck, Rachel
Fecha
2020Resumen
This article proposes a methodology for diagnostics in a logistic regression with random intercept motivated by a biological study. The methodology includes local and global influence techniques allowing us to contrast the results of both types of influence. The proposed methodology is applied to a case study with real data to show its potential. This study corresponds to the reproduction of arachnids reporting how the local and global influence of atypical observations can modify the significance of parameters, and then the biological conclusions. The model fitting is evaluated through predictive indicators. The methodology is summarized in an algorithm and a demo example is implemented in R code to facilitate its application. To evaluate the performance of the methodology, Monte Carlo simulations are conducted.
Fuente
Journal of Statistical Computation and Simulation, 90(13), 2354-2383Link de Acceso
Click aquí para ver el documentoIdentificador DOI
doi.org/10.1080/00949655.2020.1777293Colecciones
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