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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
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
UserDefined Link or Variance Function
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
ResponseLevel 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
Quasilikelihood 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 (GEEType) Model
Response Surface Comparisons with Multiplicity Adjustments
Generalized Poisson Mixed Model for Overdispersed Count Data
Comparing Multiple BSplines
Diallel Experiment with Multimember Random Effects
Linear Inference Based on Summary Data
References
Examples: GLIMMIX Procedure
Binomial Counts in Randomized Blocks
Mating Experiment with Crossed Random Effects
Smoothing Disease Rates; Standardized Mortality Ratios
Quasilikelihood 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 (GEEType) Model
Response Surface Comparisons with Multiplicity Adjustments
Generalized Poisson Mixed Model for Overdispersed Count Data
Comparing Multiple BSplines
Diallel Experiment with Multimember Random Effects
Linear Inference Based on Summary Data
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