The CATMOD procedure performs categorical data modeling of data that can be represented by a contingency table. PROC CATMOD fits linear models to functions of response frequencies, and it can be used for linear modeling, log-linear modeling, logistic regression, and repeated measurement analysis. PROC CATMOD uses the following estimation methods:
weighted least squares (WLS) estimation of parameters for a wide range of general linear models
maximum likelihood (ML) estimation of parameters for log-linear models and the analysis of generalized logits
The CATMOD procedure provides a wide variety of categorical data analyses, many of which are generalizations of continuous data analysis methods. For example, analysis of variance, in the traditional sense, refers to the analysis of means and the partitioning of variation among the means into various sources. Here, the term analysis of variance is used in a generalized sense to denote the analysis of response functions and the partitioning of variation among those functions into various sources. The response functions might be mean scores if the dependent variables are ordinally scaled. But they can also be marginal probabilities, cumulative logits, or other functions that incorporate the essential information from the dependent variables.
Note: PROC CATMOD specializes in WLS modeling and analysis of a wide range of models on contingency tables. For ML modeling on standard models, especially with continuous predictors, it might be more appropriate to use a procedure such as PROC GENMOD or PROC LOGISTIC; see Chapter 42: The GENMOD Procedure, and Chapter 58: The LOGISTIC Procedure, for more information.