CATMOD Procedure
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,
loglinear modeling, logistic regression, and repeated measurement analysis.
The procedure enables you to do the following:
 estimate model parameters by using weighted least squares (WLS) for a wide range of general linear
models or maximum likelihood (ML) for loglinear models and the analysis of generalized logits
 supply raw data, where each observation is a subject, supply cell count data,
where each observation is a cell in a contingency table, or directly input a covariance matrix
 construct linear functions of the model parameters or loglinear effects and test the hypothesis that the linear combination equals zero
 perform constrained estimation
 perform BY group precessing, which enables you to obtain separate analyses on grouped observations

 create a data set that contains the observed and predicted values of the response
functions, their standard errors, the residuals, and variables that describe the population and response
profiles. In addition, if you use the standard response functions, the data set includes observed
and predicted values for the cell frequencies or the cell probabilities, together with their standard errors and residuals.
 create a data set that contains the estimated parameter vector and its estimated covariance matrix
 create a data set that corresponds to any output table

For further details see the SAS/STAT User's Guide:
The CATMOD Procedure
( PDF  HTML )
Examples
 Example 32.1: Linear Response Function, r=2 Responses
 Example 32.2: Mean Score Response Function, r=3 Responses
 Example 32.3: Logistic Regression, Standard Response Function
 Example 32.4: LogLinear Model, Three Dependent Variables
 Example 32.5: LogLinear Model, Structural and Sampling Zeros
 Example 32.6: Repeated Measures, 2 Response Levels, 3 Populations
 Example 32.7: Repeated Measures, 4 Response Levels, 1 Population
 Example 32.8: Repeated Measures, Logistic Analysis of Growth Curve
 Example 32.9: Repeated Measures, Two Repeated Measurement Factors
 Example 32.10: Direct Input of Response Functions and Covariance Matrix
 Example 32.11: Predicted Probabilities