Model Summary (generalized linear mixed models)
This view is a snapshot, at-a-glance summary of the model and its fit.
Table. The table identifies the target, probability distribution, and link function specified on the Target settings. If the target is defined by events and trials, the cell is split to show the events field and the trials field or fixed number of trials. Additionally the finite sample corrected Akaike information criterion (AICC) and Bayesian information criterion (BIC) are displayed.
- Akaike Corrected. A measure for selecting and comparing mixed models based on the -2 (Restricted) log likelihood. Smaller values indicate better models. The AICC "corrects" the AIC for small sample sizes. As the sample size increases, the AICC converges to the AIC.
- Bayesian. A measure for selecting and comparing models based on the -2 log likelihood. Smaller values indicate better models. The BIC also "penalizes" overparameterized models (complex models with a large number of inputs, for example), but more strictly than the AIC.
Chart. If the target is categorical, a chart displays the accuracy of the final model, which is the percentage of correct classifications.