The PLSMOD Procedure

To isolate a few underlying spectral factors that provide a good predictive model, you can fit a PLS model to the 16 samples by using the following SAS statements:

proc plsmod data=mycas.sample;
   model ls ha dt = v1-v27;
run;

By default, the PLSMOD procedure extracts at most 15 factors. The default output from this analysis is presented in Figure 1 and Figure 2.

Figure 1 displays the "Model Information," "Dimensions," and "Number of Observations" tables.

The "Model Information" table identifies the data source and shows that the factor extraction method is partial least squares regression (which is the default) and that the nonlinear iterative partial least squares (NIPALS) algorithm (which is also the default) is used to compute extracted PLS factors.

The "Dimensions" table shows the number of response variables, the number of effects, the number of predictor parameters, and the number of factors to extract.

The "Number of Observations" table shows that all 16 of the sample observations in the input data are used in the analysis; all the samples are used because they all contain complete data.

Figure 2 lists the amount of variation, both individual and cumulative, that is accounted for by each of the 15 factors. All the variation in both the predictors and the responses is accounted for by only 15 factors because there are only 16 sample observations. More important, almost all the variation is accounted for by even fewer factors—one or two for the predictors and three to eight for the responses.