Model Summary (generalized linear mixed models)

Figure 1. Model Summary
Model Summary

By default, the procedure creates a model object in the output Viewer. Alternatively, you can generate pivot tables and charts by selecting Pivot tables and charts in the Output Display group on the Output tab of the Options dialog (Edit > Options).

This example uses the model object. Activate the object by double-clicking on it.

The first visible model view is a high-level summary of the model and its fit. Confirm basic model selections like the choices of target, probability distribution, and link function. Three fit values are listed:

  • Akaike Corrected. The Akaike information criterion, corrected (AICC) is a measure for selecting and comparing models based on the -2 log likelihood. Smaller values indicate better models. The AICC "corrects" the Akaike information criterion (AIC) for small sample sizes. As the sample size increases, the AICC converges to the AIC. The AIC "penalizes" overparametrized models.
  • Bayesian. The Bayesian information criterion (BIC) is a measure for selecting and comparing models based on the -2 log likelihood. Smaller values indicate better models. The BIC also penalizes overparametrized models, but more strictly than the AIC because the BIC accounts for the size of the dataset as well as the size of the model.
  • -2 log likelihood. Listed in a footnote under the table, the -2 log likelihood is the basis for the AIC and BIC.

You can use the information criteria to compare this model with other models for Post-test built on this dataset.

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