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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, log-linear 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 log-linear 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 log-linear 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