The REG Procedure

PROC REG does not compute new regressors. For example, if you want a quadratic term in your model, you should create a new variable when you prepare the input data. For example, the statement

model y=x1 x1*x1;

is not valid. Note that this MODEL statement is valid in the GLM procedure.

The input data set for most applications of PROC REG contains standard rectangular data, but special TYPE=CORR, TYPE=COV, and TYPE=SSCP data sets can also be used. TYPE=CORR and TYPE=COV data sets created by the CORR procedure contain means and standard deviations. In addition, TYPE=CORR data sets contain correlations and TYPE=COV data sets contain covariances. TYPE=SSCP data sets created in previous runs of PROC REG that used the OUTSSCP= option contain the sums of squares and crossproducts of the variables.

See Appendix A: Special SAS Data Sets, and the “SAS Files” section in SAS Language Reference: Concepts for more information about special SAS data sets.

These summary files save CPU time. It takes operations (where n = number of observations and k = number of variables) to calculate crossproducts; the regressions are of the order . When n is in the thousands and k is less than 10, you can save 99% of the CPU time by reusing the SSCP matrix rather than recomputing it.

When you want to use a special SAS data set as input, PROC REG must determine the TYPE for the data set. PROC CORR and PROC REG automatically set the type for their output data sets. However, if you create the data set by some other means (such as a DATA step), you must specify its type with the TYPE= data set option. If the TYPE for the data set is not specified when the data set is created, you can specify TYPE= as a data set option in the DATA= option in the PROC REG statement. For example:

proc reg data=a(type=corr);

When a TYPE=CORR, TYPE=COV, or TYPE=SSCP data set is used with PROC REG, statements and options that require the original data values have no effect. The OUTPUT, PAINT, PLOT, and REWEIGHT statements and the MODEL and PRINT statement options P, R, CLM, CLI, DW, INFLUENCE, and PARTIAL are disabled since the original observations needed to calculate predicted and residual values are not present.

proc corr data=fitness outp=r noprint;
   var Oxygen RunTime Age Weight RunPulse MaxPulse RestPulse;
run;
proc print data=r;
run;
proc reg data=r;
   model Oxygen=RunTime Age Weight;
run;

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