The HPSUMMARY Procedure

CLASS Statement

Subsections:

  • CLASS variables </ options>;

The CLASS statement names the classification variables to be used as explanatory variables in the analysis. These variables enter the analysis not through their values, but through levels to which the unique values are mapped. For more information, see the section Levelization of Classification Variables in SAS/STAT 14.1 User's Guide: High-Performance Procedures.

Levels of classification variables are ordered by their external formatted values, except for numeric variables with no explicit format, which are ordered by their unformatted (internal) values.

Required Argument

variables

specifies one or more variables that the procedure uses to group the data. Variables in a CLASS statement are referred to as classification variables. Classification variables are numeric or character. Classification variables can have continuous values, but they typically have a few discrete values that define levels of the variable. You do not have to sort the data by classification variables.

Use the TYPES statement or the WAYS statement to control which classification variables PROC HPSUMMARY uses to group the data. See the section How PROC HPSUMMARY Groups Data.

To reduce the number of classification variable levels, use a FORMAT statement to combine variable values. When a format combines several internal values into one formatted value, PROC HPSUMMARY outputs the lowest internal value.

Optional Arguments

GROUPINTERNAL

specifies that formats are not to be applied to the classification variables when PROC HPSUMMARY groups the values to create combinations of classification variables. This option saves computer resources when the numeric classification variables contain discrete values. See the section Computational Resources.

MISSING

considers missing values as valid values for the classification variable levels. Special missing values that represent numeric values—the letters A through Z and the underscore (_) character—are each considered as a separate value. If you omit the MISSING option, then PROC HPSUMMARY excludes the observations with a missing classification variable value from the analysis.

By default, if an observation contains a missing value for any classification variable, then PROC HPSUMMARY excludes that observation from the analysis. If you specify the MISSING option in the PROC HPSUMMARY statement, then the procedure considers missing values as valid levels for the combination of classification variables.

Specifying the MISSING option in the CLASS statement enables you to control the acceptance of missing values for individual classification variables.

See SAS Language Reference: Concepts for a discussion of missing values that have special meaning.