The following statements are available in PROC CATMOD:
You can use all of the statements in PROC CATMOD interactively. The first RUN statement executes all of the previous statements. Any subsequent RUN statement executes only those statements that appear between the previous RUN statement and the current one. However, if you specify a BY statement, interactive processing is disabled. That is, all statements through the following RUN statement are processed for each BY group in the data set, but no additional statements are accepted by the procedure.
If more than one CONTRAST statement appears between two RUN statements, all the CONTRAST statements are processed. If more than one RESPONSE statement appears between two RUN statements, then analyses associated with each RESPONSE statement are produced. For all other statements, there can be only one occurrence of the statement between any two RUN statements. For example, if there are two LOGLIN statements between two RUN statements, the first LOGLIN statement is ignored.
The PROC CATMOD and MODEL statements are required. If specified, the DIRECT statement must precede the MODEL statement. As a result, if you use the DIRECT statement interactively, you need to specify a MODEL statement in the same RUN group. See the section DIRECT Statement for an example.
The CONTRAST statements, if any, must follow the MODEL statement.
You can specify only one of the LOGLIN, REPEATED, and FACTORS statements between any two RUN statements, because they all specify the same information: how to partition the variation among the response functions within a population.
A QUIT statement executes any statements that have not been processed and then ends the CATMOD procedure run.
The purpose of each statement, other than the PROC CATMOD statement, is summarized in the following list:
determines groups in which data are to be processed separately.
specifies a hypothesis to test.
specifies independent variables that are to be treated quantitatively (like continuous variables) rather than qualitatively (like classification or discrete variables). These variables also help to determine the rows of the contingency table and distinguish response functions in one population from those in other populations.
specifies (1) the factors that distinguish response functions from others in the same population and (2) model effects, based on these factors, which help to determine the design matrix.
specifies log-linear model effects.
specifies (1) dependent variables, which determine the columns of the contingency table, (2) independent variables, which distinguish response functions in one population from those in other populations, and (3) model effects, which determine the design matrix and the way in which total variation among the response functions is partitioned.
specifies variables that determine the rows of the contingency table and distinguish response functions in one population from those in other populations.
specifies (1) the repeated measurement factors that distinguish response functions from others in the same population and (2) model effects, based on these factors, which help to determine the design matrix.
determines the response functions that are to be modeled.
restricts values of parameters to the values you specify.
specifies a variable containing frequency counts.