On a logistic regression model with random intercept: diagnostic analytics, simulation and biological application

Abstract

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.

Keywords:

• Correlated binary data
• Metropolis–Hasting algorithm
• Monte Carlo integration and simulation
• R software
• reproductive biology

Acknowledgments

The authors thank the Editors and reviewers for their constructive comments on an earlier version of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research was partially supported by the project grants “FONDECYT 1200525” from the National Commission for Scientific and Technological Research of the Chilean government (V. Leiva) and “Puente 001/2019” from the Research Directorate of the Vice President for Research of the Pontificia Universidad Católica de Chile, Chile (M. Galea).