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GLIMMIX Procedure


The GLIMMIX procedure fits statistical models to data with correlations or nonconstant variability and where the response is not necessarily normally distributed. These models are known as generalized linear mixed models (GLMM). GLMMs, like linear mixed models, assume normal (Gaussian) random effects. Conditional on these random effects, data can have any distribution in the exponential family. The following are highlights of the GLIMMIX procedure's features:

  • provides the following built-in link functions:
    • cumulative complementary log-log
    • cumulative logit
    • cumulative log-log
    • cumulative probit
    • complementary log-log
    • generalized logit
    • identity
    • log
    • logit
    • log-log
    • probit
    • power with exponent λ = number
    • power with exponent -2
    • reciprocal
  • provides the following built-in distributions and associated variance functions:
    • beta
    • binary
    • binomial
    • exponential
    • gamma
    • normal
    • geometric
    • inverse gaussian
    • lognormal
    • negative binomial
    • Poisson
    • t
  • use SAS programming statements within the procedure to compute model effects, weights, frequency, subject, group, and other variables, and to define mean and variance functions
  • fits covariance structures including:
    • ANTE(1)
    • AR(1)
    • ARH(1)
    • ARMA(1,1)
    • Cholesky
    • compound symmetry
    • heterogeneous compound symmetry
    • factor analytic
    • Huynh-Feldt
    • general linear
    • P-spline
    • radial smoother
    • simple
    • exponential spatial
    • gaussian
    • Matern
    • power
    • anisitropic power
    • spherical
    • Toeplitz
    • unstructured
  • permits subject and group effects that enable blocking and heterogeneity, respectively
  • permits weighted multilevel models for analyzing survey data that arise from multistage sampling
  • choice of linearization approach or integral approximation by quadrature or Laplace method for mixed models with nonlinear random effects or nonnormal distribution
  • choice of linearization about expected values or expansion about current solutions of best linear unbiased predictors (BLUP)
  • flexible covariance structures for random and residual random effects, including variance components, unstructured, autoregressive, and spatial structures
  • produce hypothesis tests and estimable linear combinations of effects
  • provides a mechanism to obtain inferences for the covariance parameters. Significance tests are based on the ratio of (residual) likelihoods or pseudo-likelihoods. Confidence limits and bounds are computed as Wald or likelihood ratio limits.
  • construct special collections of columns for the design matrices in your model. These special collections, which are referred to as constructed effects can include the following:
    • COLLECTION is a collection effect defining one or more variables as a single effect with multiple degrees of freedom. The variables in a collection are considered as a unit for estimation and inference.
    • MULTIMEMBER | MM is a multimember classification effect whose levels are determined by one or more variables that appear in a CLASS statement.
    • POLYNOMIAL | POLY is a multivariate polynomial effect in the specified numeric variables.
    • SPLINE is a regression spline effect whose columns are univariate spline expansions of one or more variables. A spline expansion replaces the original variable with an expanded or larger set of new variables.
  • provides the following estimation methods:
    • RSPL
    • MSPL
    • RMPL
    • MMPL
    • Laplace
    • adaptive quadrature
  • enables you to exercise control over the numerical optimization. You can choose techniques, update methods, line search algorithms, convergence criteria, and more. Or, you can choose the default optimization strategies selected for the particular class of model you are fitting.
  • enables you to generate variables with SAS programming statements inside of PROC GLIMMIX (except for variables listed in the CLASS statement).
  • performs grouped data analysis
  • supports BY group processing, which enebales you to obtain separate analyses on grouped observations
  • use ODS to create a SAS data set corresponding to any table
  • automaticlly generates graphs by using ODS Graphics

For further details see the SAS/STAT User's Guide: The GLIMMIX Procedure
( PDF | HTML )

Examples