where a factor-description is defined as follows:
and factor-descriptions are separated from each other by a comma. The $ is required for character-valued factors. The value of levels provides the number of levels of the factor identified by a given factor-name. For only one factor, levels is optional; for two or more factors, it is required.
The FACTORS statement identifies factors that distinguish response functions from others in the same population. It also specifies how those factors are incorporated into the model. You can use the FACTORS statement whenever there is more than one response function per population and the keyword _RESPONSE_ is specified in the MODEL statement. You can specify the name, type, and number of levels of each factor and the identification of each level.
The FACTORS statement is most useful when the response functions and their covariance matrix are read directly from the input data set. In this case, PROC CATMOD reads the response functions as though they are from one population (this poses no problem in the multiple-population case because the appropriately constructed covariance matrix is also read directly). Thus, you can use the FACTORS statement to partition the variation among the response functions into appropriate sources, even when the functions actually represent separate populations.
The format of the FACTORS statement is identical to that of the REPEATED statement. In fact, repeated measurement factors are simply special cases of factors in which some of the response functions correspond to multiple dependent variables that are measurements on the same experimental (or sampling) units.
You cannot specify the FACTORS statement for an analysis that also contains the REPEATED or LOGLIN statement since all of them specify the same information: how to partition the variation among the response functions within a population.
You can specify the following terms in the FACTORS statement:
names a factor that corresponds to two or more response functions. This name must be a valid SAS variable name, and it should not be the same as the name of a variable that already exists in the data set being analyzed.
indicates that the factor is character-valued. If the $ is omitted, then the CATMOD procedure assumes that the factor is numeric. The type of the factor is relevant only when you use the PROFILE= option or when the _RESPONSE_= option (described later in this section) specifies nested-by-value effects.
specifies the number of levels of the corresponding factor. If there is only one such factor, and the number is omitted, then PROC CATMOD assumes that the number of levels is equal to the number of response functions per population (q). Unless you specify the PROFILE= option, the number q must either be equal to or be a multiple of the product of the number of levels of all the factors.
You can specify the following options in the FACTORS statement after a slash.