Parameters | Expression Parameters | PROC PLS Options

PROC PLS 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
where:
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 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 c 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 factor against the model that minimizes the predicted residual sum of squares.
Note : The default is METHOD=PLS. The value PLS requests partial least squares

1
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:
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Refer to the SAS PROC PLS documentation for more information.