The data in the Diabetes.jmp sample data table consist of measurements on 442 diabetics. The response of interest is Y, disease progression measured one year after a baseline measure was taken. Ten variables thought to be related to disease progression are also measured at baseline. This example shows how to develop a predictive model using generalized regression techniques.
1.

Open the Diabetes.jmp sample data table.

2.

Select Analyze > Fit Model.

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This adds all terms up to degree 2 (the default in the Degree box) to the model.
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From the Personality list, select Generalized Regression.

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Click Run.

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Click Go.

The Solution Path report (Solution Path Plot) shows a plot of the parameter estimates. The shrinkage increases as the Magnitude of Scaled Parameter Estimates decreases. The estimates at the far right of the plot are the maximum likelihood estimates. A vertical red line indicates those parameter values selected by the validation criterion, in this case, the holdback sample defined by the column Validation.
10.

Select the option Select Nonzero Terms from the Adaptive Lasso with Validation Column Validation report’s red triangle menu.

This option highlights the nonzero terms in the Parameter Estimates for Centered and Scaled Data report (Portion of Parameter Estimates for Centered and Scaled Predictors Report) and their paths in the Solution Path plot. Note that only 11 of the 55 parameter estimates are nonzero. Also note that the scale parameter for the normal distribution (sigma) is estimated and shown in the last line of the Parameter Estimates for Centered and Scaled Data report.
To save the prediction formula, select Save Columns > Save Prediction Formula from the red triangle menu for the Adaptive Lasso with Validation Column Validation report.