The GENMOD Procedure
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Overview
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Getting Started
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SyntaxPROC GENMOD StatementASSESS StatementBAYES StatementBY StatementCLASS StatementCODE StatementCONTRAST StatementDEVIANCE StatementEFFECTPLOT StatementESTIMATE StatementEXACT StatementEXACTOPTIONS StatementFREQ StatementFWDLINK StatementINVLINK StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementOUTPUT StatementProgramming StatementsREPEATED StatementSLICE StatementSTORE StatementSTRATA StatementVARIANCE StatementWEIGHT StatementZEROMODEL Statement
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DetailsGeneralized Linear Models TheorySpecification of EffectsParameterization Used in PROC GENMODType 1 AnalysisType 3 AnalysisConfidence Intervals for ParametersF StatisticsLagrange Multiplier StatisticsPredicted Values of the MeanResidualsMultinomial ModelsZero-Inflated ModelsTweedie Distribution For Generalized Linear ModelsGeneralized Estimating EquationsAssessment of Models Based on Aggregates of ResidualsCase Deletion Diagnostic StatisticsBayesian AnalysisExact Logistic and Exact Poisson RegressionMissing ValuesDisplayed Output for Classical AnalysisDisplayed Output for Bayesian AnalysisDisplayed Output for Exact AnalysisODS Table NamesODS Graphics
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ExamplesLogistic RegressionNormal Regression, Log Link Gamma Distribution Applied to Life DataOrdinal Model for Multinomial DataGEE for Binary Data with Logit Link FunctionLog Odds Ratios and the ALR AlgorithmLog-Linear Model for Count DataModel Assessment of Multiple Regression Using Aggregates of ResidualsAssessment of a Marginal Model for Dependent DataBayesian Analysis of a Poisson Regression ModelExact Poisson RegressionTweedie Regression
- References
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 43.5 summarizes the options available in the LSMEANS statement. If you specify the BAYES statement, the ADJUST=, STEPDOWN, and LINES options are ignored. The PLOTS= option is not available for a maximum likelihood analysis; it is available only for a Bayesian analysis.
If you specify a zero-inflated model (that is, a model for either the zero-inflated Poisson or the zero-inflated negative binomial distribution), then the least squares means are computed only for effects in the model for the distribution mean, and not for effects in the zero-inflation probability part of the model.
Table 43.5: 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.