SAS/STAT Software

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 Newton-Raphson algorithm, and you can perform the bias-reducing 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 log-log 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 adjacent-category 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 R2 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