MODEL response <(variableoptions)> = <effects> </ options>;
MODEL events/trials = <effects> </ options>;
The MODEL statement specifies the response, or dependent variable, and the effects, or explanatory variables. If you omit the explanatory variables, the procedure fits an interceptonly model. An intercept term is included in the model by default. The intercept can be removed with the NOINT option.
You can specify the response in the form of a single variable or in the form of a ratio of two variables denoted events/trials. The first form is applicable to all responses. The second form is applicable only to summarized binomial response data. When each observation in the input data set contains the number of events (for example, successes) and the number of trials from a set of binomial trials, use the events/trials syntax.
In the events/trials model syntax, you specify two variables that contain the event and trial counts. These two variables are separated by a slash (/). The values of both events and (trials–events) must be nonnegative, and the value of the trials variable must be greater than 0 for an observation to be valid. The variable events or trials can take noninteger values.
When each observation in the input data set contains a single trial from a binomial or multinomial experiment, use the first form of the preceding MODEL statements. The response variable can be numeric or character. Variable options specific to the response variable can be specified in parentheses immediately after the response variable. Identifying the event level for binomial responses and ordering of response levels for multinomial responses is critical in these models. You can use the response variable options to do this.
Responses for the Poisson distribution must be all nonnegative, but they can be noninteger values.
The effects in the MODEL statement consist of an explanatory variable or combination of variables. Explanatory variables can be continuous or classification variables. Classification variables can be character or numeric. Explanatory variables representing nominal, or classification, data must be declared in a CLASS statement. Interactions between variables can also be included as effects. Columns of the design matrix are automatically generated for classification variables and interactions. The syntax for specification of effects is the same as for the GLM procedure. See the section Specification of Effects for more information. Also see Chapter 46: The GLM Procedure.
Table 44.7 summarizes the options available in the MODEL statement.
Table 44.7: MODEL Statement Options
Option 
Description 

Specifies the subpopulations 

Sets the confidence coefficient 

Sets the convergence criterion for profile likelihood confidence intervals 

Displays confidence limits for predicted values 

Uses effect coding for all classification variables 

Sets the convergence criterion 

Sets the relative Hessian convergence criterion 

Displays the parameter estimate correlation matrix 

Displays the parameter estimate covariance matrix 

Reverses the order of the response categories 

Displays case deletion diagnostic statistics 

Specifies the builtin probability distribution 

Specifies the event category for the binary response model 

Names a variable used for performing an exact Poisson regression 

Computes covariances and associated statistics by using the expected Fisher information matrix 

Displays the values of variable in the input data set in the OBSTATS table 

Sets initial values for parameter estimates 

Initializes the intercept term 

Displays the iteration history for all iterative processes 

Specifies the link function 

Computes the maximum likelihood estimate and confidence limits of kbased 

Computes twosided confidence intervals for the partially likelihood function 

Sets the maximum allowable number of iterations for all iterative computation processes 

Requests that no intercept term 

Computes the maximum likelihood estimate and confidence limits of k based on k 

Holds the scale parameter fixed 

Displays an additional table of statistics 

Specifies a variable in the input data set to be used as an offset 

Specifies the sort order for the levels of the response variable 

Displays predicted values and associated statistics 

Specifies the reference category for the binary response model 

Displays residuals and standardized residuals 

Sets the value used for the scale 

Computes the Hessian matrix using the Fisher scoring method 

Sets the tolerance for testing singularity 

Performs a Type 1 analysis 

Computes statistics for Type 3 contrasts 

Requests Wald statistics for Type 3 contrasts 

Computes twosided Wald confidence intervals 

Includes the regression variables in the OBSTATS table 
You can specify the following options in the MODEL statement after a slash (/).