The GLIMMIX Procedure
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Overview
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Getting Started
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SyntaxPROC GLIMMIX StatementBY StatementCLASS StatementCODE StatementCONTRAST StatementCOVTEST StatementEFFECT StatementESTIMATE StatementFREQ StatementID StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementNLOPTIONS StatementOUTPUT StatementPARMS StatementRANDOM StatementSLICE StatementSTORE StatementWEIGHT StatementProgramming StatementsUser-Defined Link or Variance Function
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DetailsGeneralized Linear Models TheoryGeneralized Linear Mixed Models TheoryGLM Mode or GLMM ModeStatistical Inference for Covariance ParametersDegrees of Freedom MethodsEmpirical Covariance ("Sandwich") EstimatorsExploring and Comparing Covariance MatricesProcessing by SubjectsRadial Smoothing Based on Mixed ModelsOdds and Odds Ratio EstimationParameterization of Generalized Linear Mixed ModelsResponse-Level Ordering and ReferencingComparing the GLIMMIX and MIXED ProceduresSingly or Doubly Iterative FittingDefault Estimation TechniquesDefault OutputNotes on Output StatisticsODS Table NamesODS Graphics
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ExamplesBinomial Counts in Randomized BlocksMating Experiment with Crossed Random EffectsSmoothing Disease Rates; Standardized Mortality RatiosQuasi-likelihood Estimation for Proportions with Unknown DistributionJoint Modeling of Binary and Count DataRadial Smoothing of Repeated Measures DataIsotonic Contrasts for Ordered AlternativesAdjusted Covariance Matrices of Fixed EffectsTesting Equality of Covariance and Correlation MatricesMultiple Trends Correspond to Multiple Extrema in Profile LikelihoodsMaximum Likelihood in Proportional Odds Model with Random EffectsFitting a Marginal (GEE-Type) ModelResponse Surface Comparisons with Multiplicity AdjustmentsGeneralized Poisson Mixed Model for Overdispersed Count DataComparing Multiple B-SplinesDiallel Experiment with Multimember Random EffectsLinear Inference Based on Summary DataWeighted Multilevel Model for Survey DataQuadrature Method for Multilevel Model
- References
ODS Graph Names
The GLIMMIX procedure does not produce graphs by default. You can reference every graph produced through ODS Graphics with a name. The names of the graphs that PROC GLIMMIX generates are listed in Table 45.26, along with the required statements and options.
Table 45.26: Graphs Produced by PROC GLIMMIX
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ODS Graph Name |
Plot Description |
Option |
|---|---|---|
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Plot of LS-mean differences against the average LS-mean |
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Box plots of residuals and/or observed values for model effects |
PLOTS=BOXPLOT |
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Plot of LS-mean differences against a control level |
PLOTS=CONTROLPLOT
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Plot of LS-mean pairwise differences |
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Plot of least squares means |
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ORPlot |
Plot of odds ratios |
PLOTS=ODDSRATIO |
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PearsonBoxplot |
Box plot of Pearson residuals |
PLOTS=PEARSONPANEL (UNPACK) |
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PearsonByPredicted |
Pearson residuals vs. mean |
PLOTS=PEARSONPANEL (UNPACK) |
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PearsonHistogram |
Histogram of Pearson residuals |
PLOTS=PEARSONPANEL (UNPACK) |
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Panel of Pearson residuals |
PLOTS=PEARSONPANEL |
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PearsonQQplot |
Q-Q plot of Pearson residuals |
PLOTS=PEARSONPANEL (UNPACK) |
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ResidualBoxplot |
Box plot of (raw) residuals |
PLOTS=RESIDUALPANEL (UNPACK) |
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ResidualByPredicted |
Residuals vs. mean or linear predictor |
PLOTS=RESIDUALPANEL (UNPACK) |
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ResidualHistogram |
Histogram of (raw) residuals |
PLOTS=RESIDUALPANEL (UNPACK) |
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Panel of (raw) residuals |
PLOTS=RESIDUALPANEL |
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ResidualQQplot |
Q-Q plot of (raw) residuals |
PLOTS=RESIDUALPANEL (UNPACK) |
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StudentBoxplot |
Box plot of studentized residuals |
PLOTS=STUDENTPANEL (UNPACK) |
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StudentByPredicted |
Studentized residuals vs. mean or linear predictor |
PLOTS=STUDENTPANEL (UNPACK) |
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StudentHistogram |
Histogram of studentized residuals |
PLOTS=STUDENTPANEL (UNPACK) |
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Panel of studentized residuals |
PLOTS=STUDENTPANEL |
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StudentQQplot |
Q-Q plot of studentized residuals |
PLOTS=STUDENTPANEL (UNPACK) |
When ODS Graphics is enabled, the SLICE statement can produce plots that are associated with its analysis. For information about these plots, see the section SLICE Statement in Chapter 19: Shared Concepts and Topics.