Basic Analysis and Graphing - SAS

108 Performing Bivariate Analysis Chapter 4
Kernel Smoother
• If you want to use a lambda value that is not listed on the menu, select Fit Spline > Other. If the scaling
of the X variable changes, the fitted model also changes. To prevent this from happening, select the
Standardize X option. This option guarantees that the fitted model remains the same for either the
original x variable or the scaled X variable.
• You might find it helpful to try several λ values. You can use the Lambda slider beneath the Smoothing
Spline report to experiment with different λ values. However, λ is not invariant to the scaling of the
data. For example, the λ value for an X measured in inches, is not the same as the λ value for an X
measured in centimeters.
Smoothing Spline Fit Report
The Smoothing Spline Fit report contains the R-Square for the spline fit and the Sum of Squares Error.
You can use these values to compare the spline fit to other fits, or to compare different spline fits to each
other.
Table 4.9 Description of the Smoothing Spline Fit Report
R-Square
Sum of Squares Error
Change Lambda
Measures the proportion of variation accounted for by the smoothing spline
model. For more information, see “Statistical Details for the Smoothing Fit
Reports” on page 126.
Sum of squared distances from each point to the fitted spline. It is the
unexplained error (residual) after fitting the spline model.
Enables you to change the λ value, either by entering a number, or by moving
the slider.
Related Information
• “Fitting Menus” on page 115
• “Statistical Details for Fit Spline” on page 124
Kernel Smoother
The Kernel Smoother command produces a curve formed by repeatedly finding a locally weighted fit of a
simple curve (a line or a quadratic) at sampled points in the domain. The many local fits (128 in total) are
combined to produce the smooth curve over the entire domain. This method is also called Loess or Lowess,
which was originally an acronym for Locally Weighted Scatterplot Smoother. See Cleveland (1979).
Use this method to quickly see the relationship between variables and to help you determine the type of
analysis or fit to perform.