Data Smoothing: Thin-Plate Spline |
The Plots Tab
You can use the Plots tab (Figure 19.3) to create plots that graphically display results of the Thin-Plate Spline analysis. The raw residuals are computed as , where indicates the variable containing the predicted values of the response.
Creating a plot often adds one or more variables to the data table. The following plots are available:
- Observed vs. Explanatory with smoother
- creates a scatter plot of the X and Y variables, overlaid with a smoother.
- Confidence limits for means
- specifies whether to add 95% upper and lower confidence curves to the Observed vs. Explanatory plot. The meaning of the curves is described in the section "Computational Formulas" in the TPSPLINE documentation.
- Observed vs. Predicted
- creates a scatter plot of the Y variable versus the predicted values.
- Raw residuals vs. Predicted
- creates a scatter plot of the residuals versus the predicted values.
- Raw residuals vs. Explanatory
- creates a scatter plot of the residuals versus the X variable.
- Residual normal QQ
- creates a normal Q-Q plot of the residuals.
- GCV vs. log(n*lambda)
- creates a scatter plot of the GCV criterion versus the smoothing parameter value for a range of smoothing parameter values.
- Minimum log(n*lambda)
- specifies the minimum value of the smoothing parameter to consider.
- Maximum log(n*lambda)
- specifies the maximum value of the smoothing parameter to consider.
- Number of subdivisions
- specifies the number of smoothing parameters to consider. The value in this field is combined with the values in the previous two fields to form a list of values for the LOGNLAMBDA= option.
Note: SAS/IML Studio adds a smoother to an existing scatter plot when both of the following conditions are satisfied:
- The scatter plot is the active window when you select the analysis.
- The scatter plot variables match the analysis variables.
Chapter 18, "Data Smoothing: Loess," discusses how to display multiple smoothers in a single scatter plot, and how to remove smoothers from a scatter plot.
Copyright © 2009 by SAS Institute Inc., Cary, NC, USA. All rights reserved.