The SCORE statement calculates predicted statistics for a new data set. If you have multiple data sets to predict, you can
specify multiple SCORE statements. You must use a SCORE statement for each data set.
You can request diagnostic measures that are calculated for each observation in the SCORE data set. The new data set contains
all the variables in the SCORE data set in addition to the requested variables. If no keyword is present, the data set contains only the predicted values.
The following keywords must be specified in the SCORE statement:

DATA=SASdataset

specifies the input SAS data set that contains the smoothing variables and regression variables . The predicted response () value is computed for each pair. The data set must include all independent variables specified in the MODEL statement.

OUT=SASdataset

specifies the name of the SAS data set to contain the predictions.

keyword

specifies the statistics to include in the output data set for the current SCORE statement. The names of the new variables
that contain the statistics are formed by using a prefix of one or more characters to identify the statistic, followed by
an underscore (_), followed by the dependent variable name. The keywords and the statistics they represent are as follows:
 PRED

predicted values
 STD

standard error of the mean predicted value
 UCLM

upper limit of the Bayesian confidence interval for the expected value of the dependent variables. By default, PROC TPSPLINE
computes 95% confidence limits.
 LCLM

lower limit of the Bayesian confidence interval for the expected value of the dependent variables. By default, PROC TPSPLINE
computes 95% confidence limits.