This example shows how to develop a prediction model for the binomial response, Severity, in the Liver Cancer.jmp sample data table.
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Select Analyze > Fit Model.
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All terms up to degree 2 (the default in the Degree box) are added to the model.
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From the Personality list, select Generalized Regression.
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Click Run.
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Select Elastic Net as the Estimation Method.
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Click Go.
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An Adaptive Elastic Net with AICc Validation report appears. The Solution Path is shown in Figure 6.3.
Figure 6.3 Solution Path Plot
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Click the red triangle next to Adaptive Elastic Net with AICc Validation and select the Select Zeroed Terms option.
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The Effect Tests report also shows that there are no significant terms at the 0.05 level. However, the Time*Markers interaction has a small p-value of 0.0626 and the Time effect has a small p-value of 0.1459.
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Click the red triangle next to Adaptive Elastic Net with AICc Validation and select Profilers > Profiler.
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Note: The predictor Hepatitis is not shown in the profiler because it does not appear in any active (nonzero) terms. Because Markers and Jaundice appear in active interaction terms, they appear in the profiler even though, as main effects, they are not active.
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Move the red dashed line for Time from left to right to see its interaction with Markers (Figure 6.4 and Figure 6.5). For patients who enter the study with small values of Time since diagnosis, Markers have little impact on Severity. But for patients who enter the study having been diagnosed for a longer time, Markers are important. For those patients, normal markers suggest a lower probability of high Severity.
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