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
| LSMEANS Statement |
- LSMEANS <model-effects> </ options> ;
The LSMEANS statement computes and compares least squares means (LS-means) of fixed effects. LS-means are predicted population margins—that is, they estimate the marginal means over a balanced population. In a sense, LS-means are to unbalanced designs as class and subclass arithmetic means are to balanced designs.
Table 53.5 summarizes important options in the LSMEANS statement.
Option |
Description |
|---|---|
Construction and Computation of LS-Means |
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Modifies the covariate value in computing LS-means |
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Computes separate margins |
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Requests differences of LS-means |
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Specifies the weighting scheme for LS-means computation as determined by the input data set |
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Tunes estimability checking |
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Degrees of Freedom and p-values |
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Determines the method for multiple comparison adjustment of LS-means differences |
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Determines the confidence level ( |
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Adjusts multiple comparison p-values further in a step-down fashion |
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Statistical Output |
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Constructs confidence limits for means and mean differences |
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Displays the correlation matrix of LS-means |
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Displays the covariance matrix of LS-means |
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Prints the |
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Produces a "Lines" display for pairwise LS-means differences |
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Prints the LS-means |
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Requests ODS statistical graphics of means and mean comparisons |
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Specifies the seed for computations that depend on random numbers |
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Generalized Linear Modeling |
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Exponentiates and displays estimates of LS-means or LS-means differences |
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Computes and displays estimates and standard errors of LS-means (but not differences) on the inverse linked scale |
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Reports (simple) differences of least squares means in terms of odds ratios if permitted by the link function |
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) 
matrix For details about the syntax of the LSMEANS statement, see the section LSMEANS Statement of Chapter 19, Shared Concepts and Topics.
Note: If you have classification variables in your model, then the LSMEANS statement is allowed only if you also specify the PARAM=GLM option.