Modeling and Multivariate Methods - SAS

508 Fitting Partial Least Squares Models Chapter 21
Example of Partial Least Squares
Number of Folds under Validation Method and then click Go. This is equivalent to leave-one-out cross
validation, which is the default method in JMP.
1. Open the Baltic.jmp sample data table.
2. Select Analyze > Multivariate Methods > Partial Least Squares.
3. Assign ls, ha, and dt to the Y, Response role.
4. Assign Intensities, which contains the 27 intensity variables v1 through v27, to the X, Factor role.
5. Click OK.
The Partial Least Squares Model Launch control panel appears.
6. Click Go.
A portion of the report appears in Figure 21.2.
Figure 21.2 Partial Least Squares Report
JMP uses leave-one-out cross validation in assessing the number of factors to extract. The Cross
Validation report shows that the optimum number of factors to extract, based on Root Mean PRESS, is
seven. A report entitled NIPALS Fit with 7 Factors is produced. A portion of that report is shown in
Figure 21.3.
The van der Voet T 2 statistic tests to determine whether a model with a different number of factors
differs significantly from the model with the minimum PRESS value. Some authors recommend
extracting the smallest number of factors for which the van der Voet significance level exceeds 0.10.
Were you to apply this thinking in this example, you would fit a new model by entering 6 as the
Number of Factors in the Model Launch window.