Modeling and Multivariate Methods - SAS

Chapter 21 Fitting Partial Least Squares Models 511
Example of Partial Least Squares
Scaling Divides each column by its standard deviation. For more information, see “Centering and
Scaling” on page 511.
Impute Missing Data Imputes values for observations with missing X or Y values. For more information,
see “Impute Missing Data” on page 511.
After completing the launch window and clicking OK, the Model Launch control panel appears. See “Model
Launch Control Panel” on page 512.
Launching through Fit Model
In JMP Pro, you can launch Partial Least Squares through Fit Model. Select Analyze > Fit Model and select
Partial Least Squares from the Personality menu. The options for Centering, Scaling, and Impute Missing
Data appear in the Fit Model window, once the personality has been set to Partial Least Squares.
After completing the Fit Model window and clicking Run, the Model Launch control panel appears. See
“Model Launch Control Panel” on page 512.
Note the following about using the Partial Least Squares personality of Fit Model:
• If you access Partial Least Squares through Fit Model, you can include categorical, polynomial, and
interaction effects in the model.
• In JMP 10, the following Fit Model features are not available for the Partial Least Squares personality:
Weight, Freq, Nest, Attributes, Transform, and No Intercept. The following Macros are not available:
Mixture Response Surface, Scheffé Cubic, and Radial.
Centering and Scaling
By default, the predictors and 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. Without centering, both the variable’s mean and its variation around
that mean are involved in selecting factors.
Scaling places all predictors and responses on an equal footing relative to their variation. Suppose that Time
and Temp are two of the predictors. Scaling them indicates that a change of one standard deviation in Time
is approximately equivalent to a change of one standard deviation in Temp.
Impute Missing Data
Ordinarily, rows that are missing observations on any X variable are excluded from the analysis and no
predictions are computed for these rows. Rows with no missing observations on X variables but with
missing observations on Y variables are also excluded from the analysis, but predictions are computed.
However, JMP Pro can compensate for missing observations on any X or Y variable. Select Impute Missing
Data on the PLS launch or in the Fit Model window. Missing data for each variable is imputed using the
average of the nonmissing values for that variable.