Modeling and Multivariate Methods - SAS

Chapter 11 Modeling Relationships With Gaussian Processes 297
The Gaussian Process Report
Figure 11.3 Gaussian Process Default Report
Actual by Predicted Plot
The Actual by Predicted plot shows the actual Y values on the y-axis and the jackknife predicted values on
the x-axis. One measure of goodness-of-fit is how well the points lie along the 45 degree diagonal line.
The jackknife values are really pseudo-jackknife values because they are not refit unless the row is excluded.
Therefore, the correlation parameters still have the contribution of that row in them, but the prediction
formula does not. If the row is excluded, neither the correlation parameters nor the prediction formula have
the contribution.
Model Report
The Model Report shows a functional ANOVA table for the model parameters that the platform estimates.
Specifically, it is an analysis of variance table, but the variation is computed using a function-driven method.
The Total Variation is the integrated variability over the entire experimental space.
For each covariate, we can create a marginal prediction formula by averaging the overall prediction formula
over the values of all the other factors. The functional main effect of X1 is the integrated total variation due
to X1 alone. In this case, we see that 37.6% of the variation in Y is due to X1.
The ratio of (Functional X1 effect)/(Total Variation) is the value listed as the Main Effect in the Model
report. A similar ratio exists for each factor in the model.