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.
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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.

An Adaptive Lasso with Validation Column report appears. The Solution Path report (Solution Path Plot) shows plots of the parameter estimates and scaled negative loglikelihood. 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.
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Select the option Select Nonzero Terms from the Adaptive Lasso with Validation Column report’s red triangle menu.

This option highlights the nonzero terms in the Parameter Estimates for Original Predictors report (Portion of Parameter Estimates for Original Predictors Report) and their paths in the Solution Path Plot. The corresponding columns in the data table are also selected. Note that only 6 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 Original 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 report.