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The PLS Procedure |
By default, the predictors and the responses are centered and scaled to have mean 0 and standard deviation 1. Centering the predictors and the responses ensures that the criterion for choosing successive factors is based on how much variation they explain, in either the predictors or the responses or both. (See the "Regression Methods" section for more details on how different methods explain variation.) Without centering, both the mean variable value and the variation around that mean are involved in selecting factors. Scaling serves to place all predictors and responses on an equal footing relative to their variation in the data. For example, if Time and Temp are two of the predictors, then scaling says that a change of std( Time) in Time is roughly equivalent to a change of std( Temp) in Temp.
Usually, both the predictors and responses should be centered and scaled. However, if their values already represent variation around a nominal or target value, then you can use the NOCENTER option in the PROC PLS statement to suppress centering. Likewise, if the predictors or responses are already all on comparable scales, then you can use the NOSCALE option to suppress scaling.
Note that, if the predictors involve crossproduct terms, then, by default, the variables are not standardized before standardizing the cross product. That is, if the ith values of two predictors are denoted x^{1}_{i} and x^{2}_{i}, then the default standardized ith value of the cross product is
When cross validation is performed for the number of effects, there is some disagreement among practitioners as to whether each cross validation training set should be retransformed. By default, PROC PLS does so, but you can suppress this behavior by specifying the NOCVSTDIZE option in the PROC PLS statement.
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