Please note.......taken directly from
Help==>Books==>Fitting Linear Models
in the section entitled
Generalized Regression Models
an example that details each item in the input dialog box
Example of Generalized Regression
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. Select Help > Sample Data Library and open Diabetes.jmp.
2. Select Analyze > Fit Model.
3. Select Y from the Select Columns list and click Y.
4. Select Age through Glucose and click Macros > Factorial to degree.
This adds all terms up to degree 2 (the default in the Degree box) to the model.
5. Select Validation from the Select Columns list and click Validation.
6. From the Personality list, select Generalized Regression.
7. Click Run.
The Generalized Regression report that appears contains a Model Launch control panel
and a Standard Least Squares with Validation Column report.
In the Model Launch control panel, note the following:
– The default estimation method is the adaptive Lasso.
– The Validation Method is set to Validation Column because you specified a validation
column in the Fit Model window.
8. Click Go.
An Adaptive Lasso with Validation Column report appears. The Solution Path report
(Figure 6.2) shows plots of the parameter estimates and scaled negative log-likelihood.
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.
I believe that item 5 is what you were looking for
Jim