<label:> MODEL
response<(response_options)> = effects </ options> ;
<label:> MODEL
events/trials = effects </ options> ;
The MODEL statement names the variables used as the response and the independent variables. Additionally, you can specify the distribution used to model the response, as well as other options. Only a single MODEL statement can be used with one invocation of the PROBIT procedure. If multiple MODEL statements are present, only the last is used. Main effects and interaction terms can be specified in the MODEL statement, as in the GLM procedure.
The optional label, which must be a valid SAS name, is used to label output from the matching MODEL statement.
The response can be a single variable with a value that is used to indicate the level of the observed response. For example, the response
might be a variable called Symptoms
that takes on the values ‘None,’ ‘Mild,’ or ‘Severe.’ Note that, for dichotomous response variables, the probability of the
lower sorted value is modeled by default (see the section Details: PROBIT Procedure). Because the model fit by the PROBIT procedure requires ordered response levels, you might need to use either the ORDER=DATA
option in the PROC PROBIT statement or a numeric coding of the response to get the desired ordering of levels.
Alternatively, the response can be specified as a pair of variable names separated by a slash (/). The value of the first
variable, events, is the number of positive responses (or events). The value of the second variable, trials, is the number of trials. Both variables must be numeric and nonnegative, and the ratio of the first variable value to the
second variable value must be between 0 and 1, inclusive. For example, the variables might be hits
, a variable containing the number of hits for a baseball player, and AtBats
, a variable containing the number of times at bat. A model for hitting proportion (batting average) as a function of age
could be specified as
model hits/AtBats=age;
The effects following the equal sign are the covariates in the model. Higherorder effects, such as interactions and nested terms, are allowed in the list, as in the GLM procedure. Variable names and combinations of variable names representing higherorder terms are allowed to appear in this list. Classification variables can be used as effects, and indicator variables are generated for the class levels. If you do not specify any covariates following the equal sign, an interceptonly model is fit.
Table 79.31 summarizes the options available in the MODEL statement.
Table 79.31: MODEL Statement Options
Option 
Description 

Specifies the subpopulations 

Sets the significance level 

Specifies the convergence criterion 

Displays the estimated correlation matrix 

Displays the estimated covariance matrix 

Reverses the order of the response categories 

Specifies the cumulative distribution function 

Specifies the event category for the binary response model 

Specifies a minimum probability level 

Sets initial values for the parameters 

Initializes the intercept parameter 

Computes confidence limits 

Displays the iteration history, the final evaluation of the gradient, and the second derivative matrix 

Performs two goodnessoffit tests 

Specifies the maximum number of iterations 

Fits a model with no intercept parameter 

Specifies the sort order for the levels of the response variable 

Specifies the reference category for the binary response model 

Specifies the method for estimating the dispersion parameter 

Specifies the singularity criterion 