Generalized Linear Model (GLM) Unique Features - Cross-Validation and Prediction Samples
A very important step when fitting models to be used for prediction of future observation is to cross-validate the results, i.e., to apply the current results to a new set of observations that was not used to compute those results (estimate the parameters). GLM offers very flexible methods for computing detailed predicted value and residual statistics for observations (1) that were not used in the computations for fitting the current model and have observed values for the dependent variables (the cross-validation sample), and (2) that were not used in the computations for fitting the current model, and have missing data for the dependent variables (prediction sample; see the Residuals tab on the Results dialog). These indispensable facilities for evaluating the prospective validity of the model are often not included in less complete implementations of GLM.
Some Unique Features and Facilities of GLM