The CATMOD Procedure
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Overview
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Getting Started
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Syntax
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Details
Missing Values Input Data Sets Ordering of Populations and Responses Specification of Effects Output Data Sets Logistic Analysis Log-Linear Model Analysis Repeated Measures Analysis Generation of the Design Matrix Cautions Computational Method Computational Formulas Memory and Time Requirements Displayed Output ODS Table Names
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Examples
Linear Response Function, r=2 Responses Mean Score Response Function, r=3 Responses Logistic Regression, Standard Response Function Log-Linear Model, Three Dependent Variables Log-Linear Model, Structural and Sampling Zeros Repeated Measures, 2 Response Levels, 3 Populations Repeated Measures, 4 Response Levels, 1 Population Repeated Measures, Logistic Analysis of Growth Curve Repeated Measures, Two Repeated Measurement Factors Direct Input of Response Functions and Covariance Matrix Predicted Probabilities
- References
| FACTORS Statement |
- FACTORS factor-description <,
, factor-description></ options> ;
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:
- factor-name
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.
- levels
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 (
). Unless you specify the PROFILE= option, the number 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.
- PROFILE=(matrix)
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specifies the values assumed by the factors for each response function. There should be one column for each factor, and the values in a given column (character or numeric) should match the type of the corresponding factor. Character values are restricted to 16 characters or less. If there are
response functions per population, then the matrix must have 
rows, where must either be equal to or be a multiple of . Adjacent rows of the matrix should be separated by a comma. The values in the PROFILE matrix are useful for specifying models in those situations where the study design is not a full factorial with respect to the factors. They can also be used to specify nested-by-value effects in the _RESPONSE_= option. If you specify character values in both places (the PROFILE= option and the _RESPONSE_= option), then the values must match with respect to whether or not they are enclosed in quotes (that is, enclosed in quotes in both places or in neither place).
For an example of using the PROFILE= option, see Example 29.10.
- _RESPONSE_=effects
specifies design effects. The variables named in the effects must be factor-names that appear in the FACTORS statement. If the _RESPONSE_= option is omitted, then PROC CATMOD builds a full factorial _RESPONSE_ effect with respect to the factors.
- TITLE=’title’
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displays the title at the top of certain pages of output that correspond to the current FACTORS statement.
For an example of how the FACTORS statement is useful, consider the case where the response functions and their covariance matrix are read directly from the input data set. The TYPE=EST data set might be created in the following manner:
data direct(type=est); input b1-b4 _type_ $ _name_ $8.; datalines; 0.590463 0.384720 0.273269 0.136458 parms . 0.001690 0.000911 0.000474 0.000432 cov b1 0.000911 0.001823 0.000031 0.000102 cov b2 0.000474 0.000031 0.001056 0.000477 cov b3 0.000432 0.000102 0.000477 0.000396 cov b4 ;
Suppose the response functions correspond to four populations that represent the cross-classification of age (two groups) by sex. You can use the FACTORS statement to identify these two factors and to name the effects in the model. The statements required to fit a main-effects model to these data are as follows:
proc catmod data=direct; response read b1-b4; model _f_=_response_; factors age 2, sex 2 / _response_=age sex; run;
If you want to specify some nested-by-value effects, you can change the FACTORS statement to the following:
factors age $ 2, sex $ 2 / _response_=age sex(age='under 30') sex(age='30 & over') profile=('under 30' male, 'under 30' female, '30 & over' male, '30 & over' female);
If, by design or by chance, the study contains no male subjects under 30 years of age, then there are only three response functions, and you can specify a main-effects model as follows:proc catmod data=direct; response read b2-b4; model _f_=_response_; factors age $ 2, sex $ 2 / _response_=age sex profile=('under 30' female, '30 & over' male, '30 & over' female); run;When you specify two or more factors and omit the PROFILE= option, PROC CATMOD presumes that the response functions are ordered so that the levels of the rightmost factor change most rapidly. For the preceding example, the order implied by the FACTORS statement is as follows:
Response
Dependent
Function
Variable
Age
Sex
1
b1
1
1
2
b2
1
2
3
b3
2
1
4
b4
2
2
For additional examples of how to use the FACTORS statement, see the section Repeated Measures Analysis. All of the examples in that section are applicable, with the REPEATED statement replaced by the FACTORS statement.