The GLIMMIX Procedure
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Overview
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Getting Started
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Syntax
PROC GLIMMIX Statement BY Statement CLASS Statement CONTRAST Statement COVTEST Statement EFFECT Statement ESTIMATE Statement FREQ Statement ID Statement LSMEANS Statement LSMESTIMATE Statement MODEL Statement NLOPTIONS Statement OUTPUT Statement PARMS Statement RANDOM Statement SLICE Statement STORE Statement WEIGHT Statement Programming Statements User-Defined Link or Variance Function
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Details
Generalized Linear Models Theory Generalized Linear Mixed Models Theory GLM Mode or GLMM Mode Statistical Inference for Covariance Parameters Satterthwaite Degrees of Freedom Approximation Empirical Covariance (Sandwich) Estimators Exploring and Comparing Covariance Matrices Processing by Subjects Radial Smoothing Based on Mixed Models Odds and Odds Ratio Estimation Parameterization of Generalized Linear Mixed Models Response-Level Ordering and Referencing Comparing the GLIMMIX and MIXED Procedures Singly or Doubly Iterative Fitting Default Estimation Techniques Default Output Notes on Output Statistics ODS Table Names ODS Graphics
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Examples
Binomial Counts in Randomized Blocks Mating Experiment with Crossed Random Effects Smoothing Disease Rates; Standardized Mortality Ratios Quasi-likelihood Estimation for Proportions with Unknown Distribution Joint Modeling of Binary and Count Data Radial Smoothing of Repeated Measures Data Isotonic Contrasts for Ordered Alternatives Adjusted Covariance Matrices of Fixed Effects Testing Equality of Covariance and Correlation Matrices Multiple Trends Correspond to Multiple Extrema in Profile Likelihoods Maximum Likelihood in Proportional Odds Model with Random Effects Fitting a Marginal (GEE-Type) Model Response Surface Comparisons with Multiplicity Adjustments Generalized Poisson Mixed Model for Overdispersed Count Data Comparing Multiple B-Splines Diallel Experiment with Multimember Random Effects Linear Inference Based on Summary Data
- 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 40.21, along with the required statements and options.
ODS Graph Name |
Plot Description |
Option |
|---|---|---|
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 |
PearsonBoxplot |
Box plot of Pearson residuals |
PLOTS=PEARSONPANEL(UNPACK) |
PearsonByPredicted |
Pearson residuals vs. mean |
PLOTS=PEARSONPANEL(UNPACK) |
PearsonHistogram |
Histogram of Pearson residuals |
PLOTS=PEARSONPANEL(UNPACK) |
Panel of Pearson residuals |
PLOTS=PEARSONPANEL |
|
PearsonQQplot |
Q-Q plot of Pearson residuals |
PLOTS=PEARSONPANEL(UNPACK) |
ResidualBoxplot |
Box plot of (raw) residuals |
PLOTS=RESIDUALPANEL(UNPACK) |
ResidualByPredicted |
Residuals vs. mean or linear predictor |
PLOTS=RESIDUALPANEL(UNPACK) |
ResidualHistogram |
Histogram of (raw) residuals |
PLOTS=RESIDUALPANEL(UNPACK) |
Panel of (raw) residuals |
PLOTS=RESIDUALPANEL |
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ResidualQQplot |
Q-Q plot of (raw) residuals |
PLOTS=RESIDUALPANEL(UNPACK) |
StudentBoxplot |
Box plot of studentized residuals |
PLOTS=STUDENTPANEL(UNPACK) |
StudentByPredicted |
Studentized residuals vs. mean or linear predictor |
PLOTS=STUDENTPANEL(UNPACK) |
StudentHistogram |
Histogram of studentized residuals |
PLOTS=STUDENTPANEL(UNPACK) |
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 of Chapter 19, Shared Concepts and Topics.