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
| NLOPTIONS Statement |
Most models fit with the GLIMMIX procedure typically have one or more nonlinear parameters. Estimation requires nonlinear optimization methods. You can control the optimization through options in the NLOPTIONS statement.
Several estimation methods of the GLIMMIX procedure (METHOD=RSPL, MSPL, RMPL, MMPL) are doubly iterative in the following sense. The generalized linear mixed model is approximated by a linear mixed model based on current values of the covariance parameter estimates. The resulting linear mixed model is then fit, which is itself an iterative process (with some exceptions). On convergence, new covariance parameters and fixed-effects estimates are obtained and the approximated linear mixed model is updated. Its parameters are again estimated iteratively. It is thus reasonable to refer to outer and inner iterations. The outer iterations involve the repeated updates of the linear mixed models, and the inner iterations are the iterative steps that lead to parameter estimates in any given linear mixed model. The NLOPTIONS statement controls the inner iterations. The outer iteration behavior can be controlled with options in the PROC GLIMMIX statement, such as the MAXLMMUPDATE=, PCONV=, and ABSPCONV= options. If the estimation method involves a singly iterative approach, then there is no need for the outer cycling and the model is fit in a single optimization controlled by the NLOPTIONS statement (see the section Singly or Doubly Iterative Fitting).
The syntax and options of the NLOPTIONS statement are described in the section NLOPTIONS Statement of Chapter 19, Shared Concepts and Topics.
Note that in a GLMM with pseudo-likelihood estimation, specifying TECHNIQUE=NONE has the same effect as specifying the NOITER option in the PARMS statement. If you estimate the parameters by METHOD=LAPLACE or METHOD=QUAD, TECHNIQUE=NONE applies to the optimization after starting values have been determined.
The GLIMMIX procedure applies the default optimization technique shown in Table 40.10, depending on your model.
Model Family |
Setting |
TECHNIQUE= |
|---|---|---|
GLM |
DIST=NORMAL |
NONE |
LINK=IDENTITY |
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GLM |
otherwise |
NEWRAP |
GLMM |
PARMS NOITER, PL |
NONE |
GLMM |
binary data, PL |
NRRIDG |
GLMM |
otherwise |
QUANEW |