Many SAS procedures add the variables from the input data set when an observationwise output data set is created. The assumption of high-performance analytics procedures is that the input data sets can be large and can contain many variables. For performance reasons, the OUT= data set contains the following:
variables that are explicitly created by the statement
variables that are listed in the ID statement
distribution keys or hash keys that are transferred from the input data set
Having these variables and keys in the OUT= data set enables you to add output data set information that is necessary for subsequent SQL joins without copying the entire input data set to the output data set. For more information about output data sets that are produced when PROC HPPRINCOMP is run in distributed mode, see the section Output Data Sets in Chapter 3: Shared Concepts and Topics.
The new variables that are created for the OUT= data set contain the principal component scores. The N= option determines the number of new variables. The names of the new variables are formed by concatenating the value given
by the PREFIX= option (or Prin
if PREFIX= is omitted) to the numbers 1, 2, 3, and so on. The new variables have mean 0 and a variance equal to the corresponding
eigenvalue, unless you specify the STANDARD option to standardize the scores to unit variance. Also, if you specify the COV option, PROC HPPRINCOMP computes the principal component scores from the corrected or uncorrected (if the NOINT option is specified) variables rather than from the standardized variables.
If you use a PARTIAL statement, the OUT= data set also contains the residuals from predicting the VAR variables from the PARTIAL variables.