I think the easiest approach is to do the spline fitting by using PROC GLMSELECT instead of TRANSREG. The GLMSELECT procedure supports the STORE statement, which stores the model in an item store. You can use the PLM procedure to score additional data (and graph the results), as discussed in the article "Techniques for scoring a regression model in SAS."
If you want to stick with PROC TRANSREG, you can use the "missing value trick" to score the model on a new set of data.
If you use PROC GLMSELECT, the code will look similar to the following:
/* Generate training and scoring data by randomly splitting the Sashelp.Cars data into two parts */
data Cars1 Cars2;
set sashelp.cars(where=(weight<6000));
if rand("Bernoulli", 0.6) then output Cars1;
else output Cars2;
run;
/* Create and store model with PROC GLMSELECT. Score with PROC PLM */
/* fit model on Cars1 data */
proc glmselect data=Cars1;
effect spl = spline(weight/knotmethod=equal(9));
model mpg_city = spl;
store out=SplineModel;
run;
/* score model on Cars2 */
proc plm restore=SplineModel;
score data=Cars2 out=Pred;
effectplot;
run;
... View more
