Capabilities

The PLS procedure

principal components regression, which extracts factors to explain as much predictor sample variation as possible
reduced rank regression, which extracts factors to explain as much response variation as possible. This technique, also known as (maximum) redundancy analysis, differs from multivariate linear regression only when there are multiple responses.
partial least squares regression, which balances the two objectives of explaining response variation and explaining predictor variation. Two different formulations for partial least squares are available: the original predictive method of Wold (1966) and the SIMPLS method of de Jong (1993).
choose the number of extracted factors by cross validation
use the general linear modeling approach of the GLM procedure to specify a model for your design, allowing for general polynomial effects as well as classification or ANOVA effects
save the model fit by the PLS procedure in a data set and apply it to new data by using the SCORE procedure
supports ODS Graphics
obtain separate analyses on observations in groups
creates an output data set to receive quantities that can be computed for every input observation, such as extracted factors and predicted values

For further details see the SAS/STAT User's Guide: The PLS Procedure
( PDF | HTML )

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