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