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
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Iteration History
The "Iteration History" table describes the progress of the estimation process. In singly iterative methods, the table displays the following:
the iteration count, Iteration
the number of restarts, Restarts
the number of function evaluations, Evaluations
the objective function, Objective
the change in the objective function, Change
the absolute value of the largest (projected) gradient, MaxGradient
Note that the change in the objective function is not the convergence criterion monitored by the GLIMMIX procedure. PROC GLIMMIX tracks several convergence criteria simultaneously; see the ABSCONV=, ABSFCONV=, ABSGCONV=, ABSXCONV=, FCONV=, or GCONV= option in the NLOPTIONS statement.
For doubly iterative estimation methods, the "Iteration History" table does not display the progress of the individual optimizations; instead, it reports on the progress of the outer iterations. Every row of the table then corresponds to an update of the linearization, the computation of a new set of pseudo-data, and a new optimization. In the listing, PROC GLIMMIX displays the following:
the optimization count, Iteration
the number of restarts, Restarts
the number of iterations per optimization, Subiterations
the change in the parameter estimates, Change
the absolute value of the largest (projected) gradient at the end of the optimization, MaxGradient
By default, the change in the parameter estimates is expressed in terms of the relative PCONV criterion. If you request an absolute criterion with the ABSPCONV option of the PROC GLIMMIX statement, the change reflects the largest absolute difference since the last optimization.
If you specify the ITDETAILS option in the PROC GLIMMIX statement, parameter estimates and their gradients are added to the "Iteration History" table. For ODS purposes, the name of the "Iteration History" table is "IterHistory."