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The LOGISTIC Procedure

Overview
Getting Started
Syntax
PROC LOGISTIC Statement BY Statement CLASS Statement CODE Statement CONTRAST Statement EFFECT Statement EFFECTPLOT Statement ESTIMATE Statement EXACT Statement EXACTOPTIONS Statement FREQ Statement ID Statement LSMEANS Statement LSMESTIMATE Statement MODEL Statement NLOPTIONS Statement ODDSRATIO Statement OUTPUT Statement ROC Statement ROCCONTRAST Statement SCORE Statement SLICE Statement STORE Statement STRATA Statement TEST Statement UNITS Statement WEIGHT Statement
Details
Missing Values Response Level Ordering Link Functions and the Corresponding Distributions Determining Observations for Likelihood Contributions Iterative Algorithms for Model Fitting Convergence Criteria Existence of Maximum Likelihood Estimates Effect-Selection Methods Model Fitting Information Generalized Coefficient of Determination Score Statistics and Tests Confidence Intervals for Parameters Odds Ratio Estimation Rank Correlation of Observed Responses and Predicted Probabilities Linear Predictor, Predicted Probability, and Confidence Limits Classification Table Overdispersion The Hosmer-Lemeshow Goodness-of-Fit Test Receiver Operating Characteristic Curves Testing Linear Hypotheses about the Regression Coefficients Joint Tests and Type 3 Tests Regression Diagnostics Scoring Data Sets Conditional Logistic Regression Exact Conditional Logistic Regression Input and Output Data Sets Computational Resources Displayed Output ODS Table Names ODS Graphics
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 Exact Conditional Logistic Regression 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 Partial Proportional Odds Model
References

Examples: LOGISTIC Procedure

Subsections:

60.1 Stepwise Logistic Regression and Predicted Values
60.2 Logistic Modeling with Categorical Predictors
60.3 Ordinal Logistic Regression
60.4 Nominal Response Data: Generalized Logits Model
60.5 Stratified Sampling
60.6 Logistic Regression Diagnostics
60.7 ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
60.8 Comparing Receiver Operating Characteristic Curves
60.9 Goodness-of-Fit Tests and Subpopulations
60.10 Overdispersion
60.11 Conditional Logistic Regression for Matched Pairs Data
60.12 Exact Conditional Logistic Regression
60.13 Firth’s Penalized Likelihood Compared with Other Approaches
60.14 Complementary Log-Log Model for Infection Rates
60.15 Complementary Log-Log Model for Interval-Censored Survival Times
60.16 Scoring Data Sets
60.17 Using the LSMEANS Statement
60.18 Partial Proportional Odds Model

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