(Available only if the model consists of a single continuous effect.) Shows or hides the Logistic Plot report. See The Logistic Fit Report.
Shows or hides an ROC curve for the model. Receiver Operating Characteristic (ROC) curves measure the sorting efficiency of the model’s fitted probabilities to sort the response levels. ROC curves can also aid in setting criterion points in diagnostic tests. The higher the curve from the diagonal, the better the fit. An introduction to ROC curves is found in ROC Curves in the Basic Analysis book.
If the logistic fit has more than two response levels, it produces a generalized ROC curve (identical to the one in the Partition platform). In such a plot, there is a curve for each response level, which is the ROC curve of that level versus all other levels. See ROC Curve in the Predictive and Specialized Modeling book.
Shows or hides a lift curve for the model. A lift curve shows the same information as an ROC curve, but in a way to dramatize the richness of the ordering at the beginning. The Y-axis shows the ratio of how rich that portion of the population is in the chosen response level compared to the rate of that response level as a whole. If you specified a validation column, a lift curve is shown for each of the Training, Validation, and Test sets. See Lift Curve in the Predictive and Specialized Modeling book for more information about lift curves.
Shows or hides the prediction profiler, showing the fitted values for a specified response probability as the values of the factors in the model are changed. This feature is available for both nominal and ordinal responses. For detailed information about profiling features, refer to Profiler in the Profilers book.
Shows or hides the Effect Summary report, which enables you to interactively update the effects in the model. See The Logistic Fit Report.
See Local Data Filter, Redo Menus, and Save Script Menusin the Using JMP book for more information about the following options: