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Introduction to Categorical Data Analysis Procedures

Logistic Regression

Dichotomous Response

You have many options for performing logistic regression in the SAS System. For the dichotomous outcome, most of the time you would use the LOGISTIC procedure or the GENMOD procedure; you will need to code indicator variables for classification effects in PROC LOGISTIC but can use the CLASS statement in PROC GENMOD. The LOGISTIC procedure provides model-building, so you may choose to use it for that reason. (Note that a future release of PROC LOGISTIC will include a CLASS statement).

You may want to consider the CATMOD procedure for logistic regression since it handles classification variables; however it isn't efficient for this purpose when you have continuous variables with a large number of different values. For a continuous variable with a very limited number of values, PROC CATMOD may be useful. You list the continuous variables in the DIRECT statement.

The PROBIT procedure also performs logistic regression, and the LOGISTIC, GENMOD, and PROBIT procedures allow you to use events/trials input for the responses; the ratio of events to trials must be between 0 and 1.

Ordinal Response

The LOGISTIC and PROBIT procedures treat all response variables with more than two levels as ordinal responses and fit the proportional odds model. The GENMOD procedure fits this model with a link function of CLOGIT and the specification of the multinomial distribution.

Nominal Response

When the response variable is nominal, that is, there is no concept of ordering of the values, you can fit a logistic model to response functions called generalized logits. Only the CATMOD procedure presently performs a generalized logits analysis.

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