Introduction to Categorical Data Analysis Procedures

Comparison of CATMOD, GENMOD, LOGISTIC, and PROBIT Procedures

The LOGISTIC, GENMOD, PROBIT, and CATMOD procedures can all be used for statistical modeling of categorical data. The CATMOD procedure provides maximum likelihood estimation for logistic regression, including the analysis of logits for dichotomous outcomes and the analysis of generalized logits for polychotomous outcomes. It provides weighted least squares estimation of many other response functions, such as means, cumulative logits, and proportions, and you can also compute and analyze other response functions that can be formed from the proportions corresponding to the rows of a contingency table. In addition, a user can input and analyze a set of response functions and user-supplied covariance matrix with weighted least squares. With the CATMOD procedure, by default, all explanatory (independent) variables are treated as classification variables.

The GENMOD procedure is also a general statistical modeling tool which fits generalized linear models to data: it fits several useful models to categorical data including logistic regression, the proportional odds model, and Poisson regression. The GENMOD procedures also provides a facility for fitting generalized estimating equations to correlated response data that are categorical, such as repeated dichotomous outcomes. The GENMOD procedure fits models using maximum likelihood estimation, and you include classification variables in your models with a CLASS statement. PROC GENMOD can perform type I and type III tests, and it provides predicted values and residuals.

The LOGISTIC procedure is specifically designed for logistic regression. For dichotomous outcomes, it performs the usual logistic regression and for ordinal outcomes, it fits the proportional odds model. Note that any polychotomous response variable will be treated as an ordinal outcome by PROC LOGISTIC. This procedure has capabilities for a variety of model-building techniques, including stepwise, forward, and backwards selection. It produces predicted values and can create output data sets containing these values and other statistics including ROC, and it produces a number of regression diagnostics. The current version does not contain a CLASS statement, so that you have to code classification effects using indicator variables.

The PROBIT procedure is designed for quantal assay or other discrete event data. It performs logistic regression. This procedure includes a CLASS statement.

Stokes, Davis, and Koch (1995) provide substantial discussion of these procedures, particularly the use of the LOGISTIC and CATMOD procedures for statistical modeling.

Logistic Regression

Parameterization

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