Shared Statistical Concepts

reverses the sort order of the classification variable. If both the DESCENDING and ORDER= options are specified, high-performance statistical procedures order the categories according to the ORDER= option and then reverse that order.

specifies the sort order for the levels of classification variables. This ordering determines which parameters in the model correspond to each level in the data. By default, ORDER=FORMATTED. For ORDER=FORMATTED and ORDER=INTERNAL, the sort order is machine-dependent. When ORDER=FORMATTED is in effect for numeric variables for which you have supplied no explicit format, the levels are ordered by their internal values.

The following table shows how high-performance statistical procedures interpret values of the ORDER= option.

For more information about sort order, see the chapter about the SORT procedure in Base SAS Procedures Guide and the discussion of BY-group processing in SAS Language Reference: Concepts.

specifies the reference level that is used when you specify PARAM= REFERENCE. For an individual (but not a global) variable REF= option, you can specify the level of the variable to use as the reference level. Specify the formatted value of the variable if a format is assigned. For a REF= option or global-option, you can use one of the following keywords. The default is REF=LAST.

If you choose a reference level for any CLASS variable, all variables are parameterized in the reference parameterization for computational efficiency. In other words, high-performance statistical procedures apply a single parameterization method to all classification variables.

Suppose that the variable temp has three levels (hot, warm, and cold) and that the variable gender has two levels (M and F). The following statements fit a logistic regression model:

proc hplogistic;
   class gender(ref='F') temp;
   model y = gender gender*temp;
run;

Both CLASS variables are in reference parameterization in this model. The reference levels are F for the variable gender and warm for the variable temp, because the statements are equivalent to the following statements:

proc hplogistic;
   class gender(ref='F') temp(ref=last);
   model y = gender gender*temp;
run;

requests that the columns of the design matrix that correspond to any effect that contains a split classification variable can be selected to enter or leave a model independently of the other design columns of that effect. This option is specific to the HPREG procedure.

Suppose that the variable temp has three levels (hot, warm, and cold), that the variable gender has two levels (M and F), and that the variables are used in a PROC HPREG run as follows:

proc hpreg;
   class temp gender / split;
   model y = gender gender*temp;
run;

The two effects in the MODEL statement are split into eight independent effects. The effect "gender" is split into two effects that are labeled "gender_M" and "gender_F". The effect "gender*temp" is split into six effects that are labeled "gender_M*temp_hot", "gender_F*temp_hot", "gender_M*temp_warm", "gender_F*temp_warm", "gender_M*temp_cold", and "gender_F*temp_cold". The previous PROC HPREG step is equivalent to the following:

proc hpreg;
   model y = gender_M gender_F
             gender_M*temp_hot  gender_F*temp_hot
             gender_M*temp_warm gender_F*temp_warm
             gender_M*temp_cold gender_F*temp_cold;
run;

The SPLIT option can be used on individual classification variables. For example, consider the following PROC HPREG step:

proc hpreg;
   class temp(split) gender;
   model y = gender gender*temp;
run;

In this case, the effect "gender" is not split and the effect "gender*temp" is split into three effects, which are labeled "gender*temp_hot", "gender*temp_warm", and "gender*temp_cold". Furthermore, each of these three split effects now has two parameters that correspond to the two levels of "gender." The PROC HPREG step is equivalent to the following:

proc hpreg;
   class gender;
   model y = gender gender*temp_hot gender*temp_warm gender*temp_cold;
run;

You can specify the following global-options:

treats missing values (".", ".A", …, ".Z" for numeric variables and blanks for character variables) as valid values for the CLASS variable.

If you do not specify the MISSING option, observations that have missing values for CLASS variables are removed from the analysis, even if the CLASS variables are not used in the model formulation.

specifies the parameterization method for the classification variable or variables. You can specify the following keywords:

The GLM parameterization is the default. For more information about how parameterization of classification variables affects the construction and interpretation of model effects, see the section Specification and Parameterization of Model Effects.

specifies the truncation width of formatted values of CLASS variables when the optional n is specified.

If n is not specified, the TRUNCATE option requests that classification levels be determined by using no more than the first 16 characters of the formatted values of CLASS variables.