The GLIMMIX Procedure 
MODEL Statement 
The MODEL statement is required and names the dependent variable and the fixed effects. The fixedeffects determine the matrix of the model (see the section Notation for the Generalized Linear Mixed Model for details). The specification of effects is the same as in the GLM or MIXED procedure. In contrast to PROC GLM, you do not specify random effects in the MODEL statement. However, in contrast to PROC GLM and PROC MIXED, continuous variables on the left and right side of the MODEL statement can be computed through PROC GLIMMIX programming statements.
An intercept is included in the fixedeffects model by default. It can be removed with the NOINT option.
The dependent variable can be specified by using either the response syntax or the events/trials syntax. The events/trials syntax is specific to models for binomial data. A binomial(,) variable is the sum of independent Bernoulli trials with event probability . Each Bernoulli trial results in either an event or a nonevent (with probability ). You use the events/trials syntax to indicate to the GLIMMIX procedure that the Bernoulli outcomes are grouped. The value of the second variable, trials, gives the number of Bernoulli trials. The value of the first variable, events, is the number of events out of . The values of both events and (trialsevents) must be nonnegative and the value of trials must be positive. Observations for which these conditions are not met are excluded from the analysis. If the events/trials syntax is used, the GLIMMIX procedure defaults to the binomial distribution. The response is then the events variable. The trials variable is accounted in model fitting as an additional weight. If you use the response syntax, the procedure defaults to the normal distribution.
There are two sets of options in the MODEL statement. The responseoptions determine how the GLIMMIX procedure models probabilities for binary and multinomial data. The modeloptions control other aspects of model formation and inference. Table 38.6 summarizes important responseoptions and modeloptions. These are subsequently discussed in detail in alphabetical order by option category.
Option 
Description 

Response Variable Options 

reverses the order of response categories 

specifies the event category in binary models 

specifies the sort order for the response variable 

specifies the reference category in generalized logit models 

Model Building 

specifies the response distribution 

specifies the link function 

excludes fixedeffect intercept from model 

specifies the offset variable for linear predictor 

Statistical Computations 

determines the confidence level () 

requests chisquare tests 

specifies the denominator degrees of freedom (list) 

specifies the method for computing denominator degrees of freedom 

selects the type of hypothesis test 

suppresses centering and scaling of columns during the estimation phase 

tunes sensitivity in computing Type III functions 

Statistical Output 

displays confidence limits for fixedeffects parameter estimates 

displays the correlation matrix of fixedeffects parameter estimates 

displays the covariance matrix of fixedeffects parameter estimates 

displays the inverse covariance matrix of fixedeffects parameter estimates 

displays the matrix coefficients 

adds a row for the intercept to test tables 

displays odds ratios and confidence limits 

displays fixedeffects parameter estimates (and scale parameter in GLM models) 

displays standardized coefficients 
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