• support.sas.com

• Knowledge Base

• Support

• Training & Bookstore

• Community

Capabilities

• Analysis of Variance
• Bayesian Analysis
• Categorical Data Analysis
• Cluster Analysis
• Descriptive Statistics
• Distribution Analysis
• Discriminant Analysis
• Exact Methods
• Group Sequential Design and Analysis
• Market Research
• Missing Value Imputation
• Mixed Models
• Multivariate Analysis
• Nonparametric Statistics
• Power and Sample Size
• Psychometric Analysis
• Regression
• Spatial Analysis
• Structural Equations
• Survey Sampling and Analysis
• Survival Analysis
• Transformations
• SAS/STAT Procedures A-Z

The LOGISTIC Procedure

• fits models with binary, ordinal, or nominal dependent variables with the following link functions:
  • logit
  • probit
  • complementary log-log
  • generalized logit
• fits stratified conditional logistic regression of binary response data
• estimation methods include the following:
  • maximum likelihood via the Fisher scoring or Newton-Raphson algorithms
  • Firth's bias-reducing penalized likelihood optimization
• 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
• create a data set for producing a receiver operating characteristic curve for each fitted model
• 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
• obtain separate analyses on observations in groups
• perform weighted estimation
• create a data set containing the estimated response probabilities, residuals, and influence diagnostics
• creates an output data set containing the estimated parameter vector and its estimated covariance matrix
• uses ODS to create a SAS data set corresponding to any table
• supports ODS Graphics

For further details see the SAS/STAT User's Guide: The LOGISTIC Procedure
( PDF | HTML )

Examples

• Stepwise Logistic Regression and Predicted Values
• Logistic Modeling with Categorical Predictors
• Ordinal Logistic Regression
• Nominal Response Data: Generalized Logits Model
• Stratified Sampling
• Logistic Regression Diagnostics
• ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
• Comparing Receiver Operating Characteristic Curves
• Goodness-of-Fit Tests and Subpopulations
• Overdispersion
• Conditional Logistic Regression for Matched Pairs Data
• Firth’s Penalized Likelihood Compared with Other Approaches
• Complementary Log-Log Model for Infection Rates
• Complementary Log-Log Model for Interval-Censored Survival Times
• Scoring Data Sets
• Using the LSMEANS Statement

Statistics and Operations Research Home Page | SAS/STAT Software