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The GLIMMIX Procedure

Overview
Basic Features Assumptions Notation for the Generalized Linear Mixed Model PROC GLIMMIX Contrasted with Other SAS Procedures
Getting Started
Logistic Regressions with Random Intercepts
Syntax
PROC GLIMMIX Statement BY Statement CLASS Statement CODE 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
Details
Generalized Linear Models Theory Generalized Linear Mixed Models Theory GLM Mode or GLMM Mode Statistical Inference for Covariance Parameters Degrees of Freedom Methods 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
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 Weighted Multilevel Model for Survey Data
References

Generalized Linear Mixed Models Theory

Subsections:

Model or Integral Approximation
Pseudo-likelihood Estimation Based on Linearization
Maximum Likelihood Estimation Based on Laplace Approximation
Maximum Likelihood Estimation Based on Adaptive Quadrature
Aspects Common to Adaptive Quadrature and Laplace Approximation
Notes on Bias of Estimators
Pseudo-likelihood Estimation for Weighted Multilevel Models

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