Calculate Leave-One-Out Prediction for GLM

In the model development, the “leave-one-out” prediction is a way of cross-validation, calculated as below:

1. First of all, after a model is developed, each observation used in the model development is removed in turn and then the model is refitted with the remaining observations

2. The out-of-sample prediction for the refitted model is calculated with the removed observation one by one to assemble the LOO, e.g. leave-one-out predicted values for the whole model development sample.

The loo_predict() function below is a general routine to calculate the LOO prediction for any GLM object, which can be further employed to investigate the model stability and predictability.