The LOGISTIC Procedure
- Overview
- Getting Started
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Syntax
PROC LOGISTIC StatementBY StatementCLASS StatementCODE StatementCONTRAST StatementEFFECT StatementEFFECTPLOT StatementESTIMATE StatementEXACT StatementEXACTOPTIONS StatementFREQ StatementID StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementNLOPTIONS StatementODDSRATIO StatementOUTPUT StatementROC StatementROCCONTRAST StatementSCORE StatementSLICE StatementSTORE StatementSTRATA StatementTEST StatementUNITS StatementWEIGHT Statement -
DetailsMissing ValuesResponse Level OrderingLink Functions and the Corresponding DistributionsDetermining Observations for Likelihood ContributionsIterative Algorithms for Model FittingConvergence CriteriaExistence of Maximum Likelihood EstimatesEffect-Selection MethodsModel Fitting InformationGeneralized Coefficient of DeterminationScore Statistics and TestsConfidence Intervals for ParametersOdds Ratio EstimationRank Correlation of Observed Responses and Predicted ProbabilitiesLinear Predictor, Predicted Probability, and Confidence LimitsClassification TableOverdispersionThe Hosmer-Lemeshow Goodness-of-Fit TestReceiver Operating Characteristic CurvesTesting Linear Hypotheses about the Regression CoefficientsJoint Tests and Type 3 TestsRegression DiagnosticsScoring Data SetsConditional Logistic RegressionExact Conditional Logistic RegressionInput and Output Data SetsComputational ResourcesDisplayed OutputODS Table NamesODS Graphics
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ExamplesStepwise Logistic Regression and Predicted ValuesLogistic Modeling with Categorical PredictorsOrdinal Logistic RegressionNominal Response Data: Generalized Logits ModelStratified SamplingLogistic Regression DiagnosticsROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence LimitsComparing Receiver Operating Characteristic CurvesGoodness-of-Fit Tests and SubpopulationsOverdispersionConditional Logistic Regression for Matched Pairs DataExact Conditional Logistic RegressionFirth’s Penalized Likelihood Compared with Other ApproachesComplementary Log-Log Model for Infection RatesComplementary Log-Log Model for Interval-Censored Survival TimesScoring Data SetsUsing the LSMEANS StatementPartial Proportional Odds Model
- References
LSMEANS Statement
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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 72.6 summarizes the options available in the LSMEANS statement.
Table 72.6: LSMEANS Statement Options
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Option |
Description |
|---|---|
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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 graphs 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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For details about the syntax of the LSMEANS statement, see the section LSMEANS Statement in 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.