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• SAS/STAT Procedures A-Z

The CATMOD Procedure

• performs categorical data modeling of data that can be represented by a contingency table. The types of models and analyses include the following:
  • linear model
  • log-linear model
  • logistic regression
  • repeated measures
  • analysis of variance
  • linear regression
  • logistic analysis of ordinal data
  • sample survey 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
• you can supply raw data, where each observation is a subject, you can supply cell count data, where each observation is a cell in a contingency table, or you can directly input a covariance matrix
• obtain separate analyses on observations in groups
• construct linear functions of the model parameters or log-linear effects and test the hypothesis that the linear combination equals zero
• performs constrained estimation (i.e., restrict the values of certain model parameters)
• creates an output data set containing 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.
• creates an output data set containing the estimated parameter vector and its estimated covariance matrix
• create output data set for every table using ODS

For further details see the SAS/STAT User's Guide: The CATMOD Procedure
( PDF | HTML )

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

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