MODEL events/trials = <effects </ options>>;
MODEL variable <(voptions)> = <effects> </ options>;
The MODEL statement names the response variable and the explanatory effects, including covariates, main effects, interactions, and nested effects; see the section Specification of Effects in Chapter 46: The GLM Procedure, for more information. If you omit the explanatory variables, the procedure fits an interceptonly model. Model options can be specified after a slash (/).
Two forms of the MODEL statement can be specified. The first form, referred to as singletrial syntax, is applicable to binary, ordinal, and nominal response data. The second form, referred to as events/trials syntax, is restricted to the case of binary response data. The singletrial syntax is used when each observation in the DATA= data set contains information about only a single trial, such as a single subject in an experiment. When each observation contains information about multiple binaryresponse trials, such as the counts of the number of subjects observed and the number responding, then events/trials syntax can be used.
In the events/trials syntax, you specify two variables that contain count data for a binomial experiment. These two variables are separated by a slash. The value of the first variable, events, is the number of positive responses (or events), and it must be nonnegative. The value of the second variable, trials, is the number of trials, and it must not be less than the value of events.
In the singletrial syntax, you specify one variable (on the left side of the equal sign) as the response variable. This variable can be character or numeric. Options specific to the response variable can be specified immediately after the response variable with parentheses around them.
For both forms of the MODEL statement, explanatory effects follow the equal sign. Variables can be either continuous or classification variables. Classification variables can be character or numeric, and they must be declared in the CLASS statement. When an effect is a classification variable, the procedure enters a set of coded columns into the design matrix instead of directly entering a single column containing the values of the variable.
You specify the following options by enclosing them in parentheses after the response variable:
Model options can be specified after a slash (/). Table 111.7 summarizes the options available in the MODEL statement.
Table 111.7: MODEL Statement Options
Option 
Description 

Model Specification Options 

Specifies link function 

Suppresses intercept(s) 

Specifies offset variable 

Convergence Criterion Options 

Specifies absolute function convergence criterion 

Specifies relative function convergence criterion 

Specifies relative gradient convergence criterion 

Specifies relative parameter convergence criterion 

Specifies maximum number of iterations 

Suppresses checking for infinite parameters 

Specifies technique used to improve the loglikelihood function when its value is worse than that of the previous step 

Specifies tolerance for testing singularity 

Specifies iterative algorithm for maximization 

Options for Adjustment to Variance Estimation 

Chooses variance estimation adjustment method 

Options for Confidence Intervals 

Specifies the degrees of freedom 

Specifies for the confidence intervals 

Specifies the type of likelihood ratio chisquare test 

Computes confidence intervals for parameters 

Computes confidence intervals for odds ratios 

Options for Display of Details 

Displays correlation matrix 

Displays covariance matrix 

Displays exponentiated values of estimates 

Displays gradients evaluated at null hypothesis 

Displays iteration history 

Suppresses "Class Level Information" table 

Displays parameter labels 

Displays generalized 

Displays standardized estimates 
The following list describes these options: