LOGISTIC Procedure
The LOGISTIC procedure fits linear logistic regression models for discrete response data by the method of maximum likelihood.
It can also perform conditional logistic regression for binary response data and exact logistic regression for binary and nominal
response data. The maximum likelihood estimation is carried out with either the Fisher scoring algorithm or the NewtonRaphson
algorithm, and you can perform the biasreducing penalized likelihood optimization as discussed by Firth (1993) and Heinze and
Schemper (2002). You can specify starting values for the parameter estimates. The logit link function in the logistic regression
models can be replaced by the probit function, the complementary loglog function, or the generalized logit function.
The LOGISTIC procedure also enables you to do the following:
 fit stratified conditional logistic regression of binary response data
 fit partial proportional odds logistic regression models
 fit adjacentcategory logit models to ordinal response data
 add or relax constraints on parameters in nominal response models and partial proportional odds models
 compute the partial correlation statistic for each model parameter (excluding the intercept)
 control the ordering of the response categories
 compute a generalized R^{2} measure for the fitted model
 reclassify binary response observations according to their predicted response probabilities
 test linear hypotheses about the regression parameters
 perform exact tests of the parameters for the specified effects and optionally estimates
the parameters and exact conditional distributions
 specify contrasts to compare several receiver operating characteristic curves

 score a data set by using a previously fitted model
 specify units of change for continuous explanatory variables so that customized odds ratios can be estimated
 perform BY group processing, which enables you to obtain separate analyses on grouped observations
 perform weighted estimation
 create a data set for producing a receiver operating characteristic curve for each fitted model
 create a data set that contains the estimated response probabilities, residuals, and influence diagnostics
 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
 automatically create graphs by using ODS Graphics

For further details see the LOGISTIC Procedure
Examples