The SMOOTH or smoothing spline transformation is the same as it has always been. However, how the results of the transformation are processed in PROC TRANSREG has changed with this release. In particular, some aspects of the syntax along the coefficients and predicted values have changed. The new behavior was required to make the smoothing splines work properly with ODS Graphics and to make SMOOTH work consistently with the new PBSPLINE (penalized B-spline; see the section Penalized B-Splines) capabilities. However, you can use the new NSR a-option, if you want the old functionality. Here are two typical uses of the SMOOTH transformation:

proc transreg;
   model identity(y) = smooth(x / sm=50);
   output p;
run;

proc transreg;
   model identity(y) = class(group / zero=none) * smooth(x / sm=50);
   output p;
run;

For the first model, the variable x is smoothly transformed by using a smoothing parameter of SM=50, and the results are stored in the transformed variable Tx. The second model has two groups of observations corresponding to group=1 and Group=2. Separate curves are fit through each group. The results for the first group are stored in the transformed variable TGroup1x, and the results for the second group are stored in the transformed variable TGroup2x. The predicted values are stored in Py. In the first case, Py = Tx, and in the second case, Py = TGroup1x + TGroup2x. These represent the two standard usages of the SMOOTH transformation, and you can use ODS Graphics to display fit plots with a single or multiple smooth functions. For the first model, which is the most typical usage, the syntax has not changed, nor has the transformed variable. For the second model, the syntax has slightly changed, but the transformed variables have not. The details of the syntax changes are discussed later in this section. The primary change involves what happens after the SMOOTH transformation is found. Now, by default, ordinary least squares (OLS) is no longer used to find the coefficients when there are smooth transformations, and in the iteration history table the OLS R square is no longer produced.

Here is some background for the change. The first three of the four models shown next have much in common:

   model identity(y) = smooth(x / sm=50);
   model identity(y) = rank(x);
   model identity(y) = log(x);
   model identity(y) = spline(x);

Previously, the SMOOTH, RANK, and LOG transformations all requested that PROC TRANSREG preprocess the data, nonlinearly transforming x before using OLS to fit a model to the preprocessed results. All of these first three transformations of x are nonoptimal in the sense that none of them is based in any way on the OLS regression model that follows the preprocessing of the data. In contrast, the fourth model requests a spline transformation. In this model, both the nonlinear transformation and the final regression model seek to minimize the same OLS criterion. Some PROC TRANSREG transformations, such as SPLINE, MSPLINE, OPSCORE, MONOTONE, and so on, seek to minimize squared error, whereas others, such as SMOOTH, LOG, EXP, and RANK, do not. For the latter, the data are simply preprocessed before analysis. There is a philosophical difference, however, between SMOOTH and the nonoptimal transformations. The SMOOTH and PBSPLINE transformations use the dependent variable and a model (but not OLS) to compute the transformation, whereas LOG, EXP, RANK, and the other nonoptimal transformations do not. A log transformation, for example, would be the same, regardless of context, whereas the SMOOTH and PBSPLINE transformations depend on the model.

The principal change to SMOOTH, with this release of PROC TRANSREG, involves making PROC TRANSREG aware of the underlying smoothing spline model. This makes SMOOTH and PBSPLINE perform similarly, and less like LOG, EXP, RANK, and the other nonoptimal transformations. Previously, if you specified SMOOTH and then examined the regression coefficients, you would probably get an intercept very close to but not exactly 0, and the remaining coefficients would be very close to but not exactly 1. This is because PROC TRANSREG was using OLS to find the coefficients. This has changed. Now, PROC TRANSREG recognizes that the SMOOTH transformation has an implicit intercept (see the section Implicit and Explicit Intercepts); hence there is no separate intercept. Furthermore, now the other parameters are exactly 1, which are the correct parameters for the non-OLS smoothing spline model. Hence, the predicted values are now the sum of the transformed variables. When there is no CLASS variable, the predicted values exactly match the transformed variable. The SMOOTH transformation is no longer a form of preprocessing; it now changes the nature of the model from OLS to a true smoothing-spline model. If you still want the old behavior, preprocessing and then OLS, you can get the old default functionality by specifying the NSR a-option.

The new, default functionality assumes that you either want to fit a smooth function through the data or fit separate functions, one for each level of a CLASS variable. It also recognizes the smoothing-spline model as a model with an implicit intercept. For these reasons, the syntax for models with a CLASS variable has slightly changed, as is shown next:

proc transreg nsr; /* old */
   model identity(y) = class(group / zero=none) |
                       smooth(x / after sm=50);
   output p;
run;

proc transreg; /* new */
   model identity(y) = class(group / zero=none) *
                       smooth(x / sm=50);
   output p;
run;

Previously, the AFTER t-option was required when you wanted to fit separate and independent functions within each group. This t-option specifies that PROC TRANSREG should find the smoothing spline transformations after it crosses the independent variable with the CLASS variable. Previously, by default, PROC TRANSREG found an overall smooth transformation and then crossed it with the CLASS variable, which is probably not what you want. You can still specify the AFTER t-option, but now it is assumed with CLASS * SMOOTH. If you specify AFTER without the NSR a-option, PROC TRANSREG suppresses the note that AFTER is assumed. It does not affect the model. If you do not want AFTER to be in effect by default, you must specify the NSR a-option. Also previously, you typically needed to specify the vertical bar instead of the asterisk to cross the CLASS and SMOOTH variables. The difference is that the bar adds both crossed variables and separate group intercepts to the model, whereas the asterisk adds only the crossed variables to the model. Since the SMOOTH transformation is now recognized as providing an implicit intercept, you should use the asterisk and not the vertical bar.

The default behavior of the SMOOTH transformation needed to change for several reasons. SMOOTH was originally provided as nothing more than a way to get PROC GPLOT’s smoothing splines into an output data set in the transformed variables. However, with new enhancements to PROC TRANSREG such as ODS Graphics and PBSPLINE, the old method for SMOOTH did not fit well. The old method produced predicted values that were not the correct values to plot in order to show the smoothing spline fit. Now, with this change, ODS Graphics can always plot the predicted values. Also, PBSPLINE is new with this release; it and SMOOTH are similar in spirit, and for both, OLS results are not truly appropriate. Previously, PROC TRANSREG fit linear models, linear models with nonlinearly preprocessed variables, and linear models with optimal nonlinear transformations that minimized squared error. Now it also has the ability to fit non-OLS models for scatter plot smoothing.

One aspect of the SMOOTH transformation has unconditionally changed with this release. Previously, PROC TRANSREG did not evaluate the effective degrees of freedom by examining the trace of the transformation hat matrix. It simply used the number of categories in the df calculations, which for continuous variables is the number of observations. This made it impossible to get a sensible ANOVA test for the overall fit. With this release, the degrees of freedom are always based on the trace. This df change also affects the SSPLINE transformation, which finds a smooth transformation by using the same algorithm as SMOOTH. The difference is that the SMOOTH transformation occurs once, as an analysis preprocessing step, whereas SSPLINE transformations occur iteratively and in the body of the alternating least-squares algorithm.