The REG Procedure

The OUTEST= specification produces a TYPE=EST output SAS data set containing estimates and optional statistics from the regression models. For each BY group on each dependent variable occurring in each MODEL statement, PROC REG outputs an observation to the OUTEST= data set. The variables output to the data set are as follows:

If you specify the COVOUT option, the covariance matrix of the estimates is output after the estimates; the _TYPE_ variable is set to the value ’COV’ and the names of the rows are identified by the character variable, _NAME_.

If you specify the TABLEOUT option, the following statistics listed by _TYPE_ are added after the estimates:

Specifying the option ADJRSQ, AIC, BIC, CP, EDF, GMSEP, JP, MSE, PC, RSQUARE, SBC, SP, or SSE in the PROC REG or MODEL statement automatically outputs these statistics and the model for each model selected, regardless of the model selection method. Additional variables, in order of occurrence, are as follows:

The following statements produce and display the OUTEST= data set. This example uses the population data given in the section Polynomial Regression. Figure 79.18 through Figure 79.20 show the regression equations and the resulting OUTEST= data set.

proc reg data=USPopulation outest=est;
   m1: model Population=Year;
   m2: model Population=Year YearSq;
run;
proc print data=est;
run;

The following modification of the previous example uses the TABLEOUT and ALPHA= options to obtain additional information in the OUTEST= data set:

proc reg data=USPopulation outest=est tableout alpha=0.1;
   m1: model Population=Year/noprint;
   m2: model Population=Year YearSq/noprint;
run;
proc print data=est;
run;

Notice that the TABLEOUT option causes standard errors, t statistics, p-values, and confidence limits for the estimates to be added to the OUTEST= data set. Also note that the ALPHA= option is used to set the confidence level at 90%. The OUTEST= data set is shown in Figure 79.21.

A slightly different OUTEST= data set is created when you use the RSQUARE selection method. The following statements request only the “best” model for each subset size but ask for a variety of model selection statistics, as well as the estimated regression coefficients. An OUTEST= data set is created and displayed. See Figure 79.22 and Figure 79.23 for the results.

proc reg data=fitness outest=est;
   model Oxygen=Age Weight RunTime RunPulse RestPulse MaxPulse
         / selection=rsquare mse jp gmsep cp aic bic sbc b best=1;
run;
proc print data=est;
run;

The OUTSSCP= option produces a TYPE=SSCP output SAS data set containing sums of squares and crossproducts. A special row (observation) and column (variable) of the matrix called Intercept contain the number of observations and sums. Observations are identified by the character variable _NAME_. The data set contains all variables used in MODEL statements. You can specify additional variables that you want included in the crossproducts matrix with a VAR statement.

The SSCP data set is used when a large number of observations are explored in many different runs. The SSCP data set can be saved and used for subsequent runs, which are much less expensive since PROC REG never reads the original data again. If you run PROC REG once to create only a SSCP data set, you should list all the variables that you might need in a VAR statement or include all the variables that you might need in a MODEL statement.

The following statements use the fitness data from Example 79.2 to produce an output data set with the OUTSSCP= option. The resulting output is shown in Figure 79.24.

proc reg data=fitness outsscp=sscp;
   var Oxygen RunTime Age Weight RestPulse RunPulse MaxPulse;
run;
proc print data=sscp;
run;

Since a model is not fit to the data and since the only request is to create the SSCP data set, a MODEL statement is not required in this example. However, since the MODEL statement is not used, the VAR statement is required.