Before a logistic regression model is used to describe the relationship between a binary response variable and predictors, the fit of the model should be assessed. The nature of any model deficiency may indicate that some aspect of the model should be reformulated or that poorly fitting observations need to be considered separately. We propose graphical methodology based on a Bayesian framework to address issues such as this. Publicly available software allows diagnostic plots to be constructed quickly and easily for any model of interest. These plots are more intuitive and meaningful than traditional graphical diagnostics such as residual plots.
- Bayesian methodology
- Diagnostic plot
- Marginal model plot
- Model criticism
- Posterior predictive distribution