Parameters | Predictive Modeling | PROC PLS Statement Options

PROC PLS Statement Options
Use this field to specify advanced PROC PLS options in the PROC PLS statement.
You can specify PROC PLS options using the following syntax:
Option is the PROC PLS option
Note: Do not specify the NOCENTER and NOSCALE options, since they are always set. Rather, if you want to standardize predictors, use the Standardization Method for Predictor Continuous Variables parameter on the Predictor Reduction tab.
Some PROC PLS options are described in the following table:
Specifies how observations with missing values are to be handled in computing the fit.
The method MISSING=AVG specifies that the fit be computed by filling in missing values with the average of the nonmissing values for the corresponding variable.
The default is MISSING=NONE, for which observations with any missing variables (dependent or independent) are excluded from the analysis.
If you specify MISSING=EM, then the procedure first computes the model with MISSING=AVG and then fills in missing values by their predicted values based on that model and computes the model again.
The default is MISSING=NONE, for which observations with any missing variables (dependent or independent) are excluded from the analysis.
Specifies the cross validation method to be used. By default, no cross validation is performed.
The method CV=ONE requests one-at-a-time cross validation.
The method CV=RANDOM requests that observations be excluded at random.
NTEST=n specifies the number of observations in each random subset chosen for exclusion. The default value is one-tenth of the total number of observations.
NITER=n specifies the number of random subsets to exclude. The default value is 10.
Seed=n specifies an integer used to start the pseudo-random number generator for selecting the random test set. If you do not specify a seed, or specify a value less than or equal to zero, the seed is by default generated from reading the time of day from the computer’s clock.
Specifies that van der Voet’s (1994)1 randomization-based model comparison test be performed to test models with different numbers of extracted factors against the model that minimizes the predicted residual sum of squares.
Specifies the general factor extraction method to be used.
Note: The default is METHOD=PLS. The value PLS requests partial least squares.

van der Voet, H. (1994) Comparing the Predictive Accuracy of Models Using a Simple Randomization Test. Chemometrics and Intelligent Laboratory Systems 25: 313–323.

To Specify One or More PROC PLS Options:
For example, to use the average value of nonmissing observations for missing data, type MISSING=AVG in the text box.
Refer to the SAS PROC PLS documentation for more information.