The LOGISTIC Procedure
- Overview
- Getting Started
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Syntax
PROC LOGISTIC Statement BY Statement CLASS Statement CONTRAST Statement EFFECT Statement EFFECTPLOT Statement ESTIMATE Statement EXACT Statement EXACTOPTIONS Statement FREQ Statement LSMEANS Statement LSMESTIMATE Statement MODEL 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 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
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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
- References
| Generalized Coefficient of Determination |
Cox and Snell (1989, pp. 208–209) propose the following generalization of the coefficient of determination to a more general linear model:
 |
where 
is the likelihood of the intercept-only model, 
is the likelihood of the specified model, 
is the sample size, 
is the frequency of the 
th observation, and 
is the number of trials when events/trials syntax is specified or 
with single-trial syntax.
The quantity 
achieves a maximum of less than one for discrete models, where the maximum is given by
 |
Nagelkerke (1991) proposes the following adjusted coefficient, which can achieve a maximum value of one:
 |
Specifying the NORMALIZE option in the WEIGHT statement makes these coefficients invariant to the scale of the weights.
Like the AIC and SC statistics described in the section Model Fitting Information, and 
are most useful for comparing competing models that are not necessarily nested—larger values indicate better models. More properties and interpretation of and are provided in Nagelkerke (1991). In the "Testing Global Null Hypothesis: BETA=0" table, is labeled as "RSquare" and is labeled as "Max-rescaled RSquare." Use the RSQUARE option to request and .