PROC PLS Statement

The PLS Procedure

PROC PLS Statement

PROC PLS < options > ;

You use the PROC PLS statement to invoke the PLS procedure and, optionally, to indicate the analysis data and method. The following options are available.

DATA=SAS-data-set

names the SAS data set to be used by PROC PLS. The default is the most recently created data set.

METHOD=PLS < ( PLS-options ) >

METHOD=SIMPLS

METHOD=PCR

METHOD=RRR

specifies the general factor extraction method to be used. The value PLS requests partial least squares, SIMPLS requests the SIMPLS method of de Jong (1993), PCR requests principal components regression, and RRR requests reduced rank regression. The default is METHOD=PLS. You can also specify the following optional PLS-options in parentheses after METHOD=PLS:

ALGORITHM=NIPALS | SVD | EIG | RLGW: names the specific algorithm used to compute extracted PLS factors. NIPALS requests the usual iterative NIPALS algorithm, SVD bases the extraction on the singular value decomposition of X'Y, EIG bases the extraction on the eigenvalue decomposition of Y'XX'Y, and RLGW is an iterative approach that is efficient when there are many predictors (R $\ddot{a}$ nner et al. 1994). ALGORITHM=SVD is the most accurate but least efficient approach; the default is ALGORITHM=NIPALS.
MAXITER=n: specifies the maximum number of iterations for the NIPALS and RLGW algorithms. The default value is 200.
EPSILON=n: specifies the convergence criterion for the NIPALS and RLGW algorithms. The default value is 10^-12.

CV=ONE

CV=SPLIT < (n) >

CV=BLOCK < (n) >

CV=RANDOM < (cv-random-opts) >

CV=TESTSET(SAS-data-set)

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=SPLIT requests that every nth observation be excluded, CV=BLOCK requests that blocks of n observations be excluded, CV=RANDOM requests that observations be excluded at random, and CV=TESTSET( SAS-data-set) specifies a test set of observations to be used for validation (formally, this is called "test set validation" rather than "cross validation"). You can, optionally, specify n for CV=SPLIT and CV=BLOCK; the default is n=7. You can also specify the following optional cv-random-options in parentheses after the CV=RANDOM option:

NITER=n: specifies the number of random subsets to exclude. The default value is 10.
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.
SEED=n: specifies the seed value for random number generation (the clock time is used by default).

CVTEST < (cvtest-options) >

specifies that van der Voet's (1994) 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; see the "Cross Validation" section for more information. You can also specify the following cv-test-options in parentheses after the CVTEST option:

PVAL=n: specifies the cut-off probability for declaring an insignificant difference. The default value is 0.10.
STAT=test-statistic: specifies the test statistic for the model comparison. You can specify either T2, for Hotelling's T² statistic, or PRESS, for the predicted residual sum of squares. The default value is T2.
NSAMP=n: specifies the number of randomizations to perform. The default value is 1000.
SEED=n: specifies the seed value for randomization generation (the clock time is used by default).

NFAC=n

specifies the number of factors to extract. The default is min{15,p,N}, where p is the number of predictors (the number of dependent variables for METHOD=RRR) and N is the number of runs. This is probably more than you need for most applications. Extracting too many factors can lead to an over-fit model, one that matches the training data too well, sacrificing predictive ability. Thus, if you use the default NFAC= specification, you should also either use the CV= option to select the appropriate number of factors for the final model or consider the analysis to be preliminary and examine the results to determine the appropriate number of factors for a subsequent analysis.

NOPRINT

suppresses the normal display of results. This is useful when you want only the output statistics saved in a data set. Note that this option temporarily disables the Output Delivery System (ODS); see Chapter 15, "Using the Output Delivery System," for more information.

NOSCALE

suppresses scaling of the responses and predictors before fitting. This is useful if the analysis variables are already centered and scaled. See the "Centering and Scaling" section for more information.

NOCENTER

suppresses centering of the responses and predictors before fitting. This is useful if the analysis variables are already centered and scaled. See the "Centering and Scaling" section for more information.

NOCVSTDIZE

suppresses re-centering and re-scaling of the responses and predictors before each model is fit in the cross validation. See the "Centering and Scaling" section for more information.

CENSCALE

lists the centering and scaling information for each response and predictor.

VARSCALE

specifies that continuous model variables should be centered and scaled prior to centering and scaling the model effects in which they are involved. The rescaling specified by the VARSCALE option may be more appropriate if the model involves cross products between model variables; however, the VARSCALE option still may not produce the model you expect. See the "Centering and Scaling" section for more information.

VARSS

lists, in addition to the average response and predictor sum of squares accounted for by each successive factor, the amount of variation accounted for in each response and predictor.

DETAILS

lists the details of the fitted model for each successive factor. The details listed are different for different extraction methods: see the "Displayed Output" section for more information.

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