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
Response Variable Options
Response variable options determine how the GLIMMIX procedure models probabilities for binary and multinomial data.
You can specify the following options by enclosing them in parentheses after the response variable. See the section Response-Level Ordering and Referencing for more detail and examples.
- DESCENDING
- DESC
reverses the order of the response categories. If both the DESCENDING and ORDER= options are specified, PROC GLIMMIX orders the response categories according to the ORDER= option and then reverses that order.
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EVENT=’category’
|keyword
- specifies the event category for the binary response model. PROC GLIMMIX models the probability of the event category. The EVENT= option has no effect when there are more than two response categories. You can specify the value (formatted, if a format is applied) of the event category in quotes, or you can specify one of the following keywords:
- FIRST
designates the first ordered category as the event. This is the default.
- LAST
designates the last ordered category as the event.
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ORDER=DATA|FORMATTED|FREQ|INTERNAL
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specifies the sort order for the levels of the response variable. When ORDER=FORMATTED (the default) for numeric variables for which you have supplied no explicit format (that is, for which there is no corresponding FORMAT statement in the current PROC GLIMMIX run or in the DATA step that created the data set), the levels are ordered by their internal (numeric) value. If you specify the ORDER= option in the MODEL statement and the ORDER= option in the PROC GLIMMIX statement, the former takes precedence. The following table shows the interpretation of the ORDER= values:
Table 40.7 continued Value of ORDER=
Levels Sorted By
DATA
order of appearance in the input data set
FORMATTED
external formatted value, except for numeric variables with no explicit format, which are sorted by their unformatted (internal) value
FREQ
descending frequency count; levels with the most observations come first in the order
INTERNAL
unformatted value
By default, ORDER=FORMATTED. For the FORMATTED and INTERNAL values, the sort order is machine dependent.
For more information about sort order, see the chapter on the SORT procedure in the Base SAS Procedures Guide and the discussion of BY-group processing in SAS Language Reference: Concepts.
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REFERENCE=’category’|keyword
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REF=’category’|keyword
- specifies the reference category for the generalized logit model and the binary response model. For the generalized logit model, each nonreference category is contrasted with the reference category. For the binary response model, specifying one response category as the reference is the same as specifying the other response category as the event category. You can specify the value (formatted if a format is applied) of the reference category in quotes, or you can specify one of the following keywords:
- FIRST
designates the first ordered category as the reference category.
- LAST
designates the last ordered category as the reference category. This is the default.