Shared Concepts

reverses the sort order of the classification variable. If both the DESCENDING and ORDER= options are specified, the procedure orders the categories according to the ORDER= option and then reverse that order.

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.

specifies the sort order for the levels of classification variables. This order determines which parameters in the model correspond to each level in the data. The following table shows how to interpret values of the ORDER= keywords:

PROC LOGSELECT also enables you to specify a particular order for variables by listing the levels in quotes in the desired order. For example, in the following statement, the global specification (after the slash) specifies that the levels of all variables except the pet variable are to be sorted by their formatted value, and the individual specification of the food variable overrides the FORMATTED order:

class pet food(order='wet' 'dry') / order=formatted(pet='dog' 'cat');

Any level of the pet and food variables that you do not specify is added to the end of the variable list in formatted order.

The ORDER= option does not apply to missing values, which are always ordered first.

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 the "Grouping Data" section of SAS Programmers Guide: Essentials. By default, ORDER=FORMATTED.

specifies the parameterization method for the classification variable or variables. You can specify any of the keywords shown in the following table; design matrix columns are created from CLASS variables according to the corresponding coding schemes.

All parameterizations are full rank, except for the GLM parameterization. If you specify a full rank parameterization for any CLASS variable, then every CLASS variable without a specified coding is given the EFFECT coding.

By default, PARAM=GLM. 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 reference level that is used when you specify a nonsingular parameterization. You can specify the following values:

By default, REF=LAST.

specifies that design matrix columns 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 applies to procedures that perform model selection.

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 REGSELECT run as follows:

proc regselect data=mylib.data;
   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 REGSELECT step is equivalent to the following:

proc regselect data=mylib.data;
   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 REGSELECT step:

proc regselect data=mylib.data;
   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 REGSELECT step is equivalent to the following:

proc regselect data=mylib.data;
   class gender;
   model y = gender gender*temp_hot gender*temp_warm gender*temp_cold;
run;