PROC PLS Statement Options

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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

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 for Predictor Continuous Variables parameter on the Predictor Reduction tab.

Some PROC PLS options are described in the following table:

Option

Definition

MISSING=AVG

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.

MISSING=EM

Specifies how observations with missing values are to be handled in computing the fit.

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.

CV=ONE

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.

CV=RANDOM(NTEST=5 NITER=10 SEED=3243807)

Specifies the cross validation method to be used. By default, no cross validation is performed.

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.

CVTEST

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.

METHOD=PCR

Specifies the general factor extraction method to be used.

PCR requests principal components regression.

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:

For example, to use the average value of nonmissing observations for missing data, type MISSING=AVG in the text box.

Be sure to separate individual options with a space .

Refer to the SAS PROC PLS documentation for more information.

Option	Definition
MISSING=AVG	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.
MISSING=EM	Specifies how observations with missing values are to be handled in computing the fit. 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.
CV=ONE	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.
CV=RANDOM(NTEST=5 NITER=10 SEED=3243807)	Specifies the cross validation method to be used. By default, no cross validation is performed. 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.
CVTEST	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.
METHOD=PCR	Specifies the general factor extraction method to be used. PCR requests principal components regression. Note : The default is METHOD=PLS. The value PLS requests partial least squares .