MODEL processname = <trendname, …> </modeloptions>;
The MODEL statement enables you to fit an inhomogeneous Poisson process model. You must specify a processname as the dependent variable. In addition, the MODEL statement enables you to specify multiple trends as covariates. If you do not specify any trends as covariates in the MODEL statement, PROC SPP fits a seconddegree polynomial. The processname must be defined in a preceding PROCESS statement, and each trendname must be defined in a preceding TREND statement. Table 105.2 summarizes the modeloptions that you can specify.
Table 105.2: MODEL Statement Options
Option 
Description 

Displays optimization centering and scaling information 

Requests the approximate correlation matrix 

Requests the approximate covariance matrix 

Constructs a ttype confidence interval 

Performs a chisquarebased goodnessoffit test 

Specifies the intensity response GRID size 

Requests the optimization iteration history 

Requests a specific type of model to be fit 

Specifies an output data set to store the intensity estimates 

Specifies an output data set to store the simulations from an intensity model 

Requests an additional polynomial component to be included in the model fitting process 

Requests residual computations and specifies the bandwidth for smoothed residuals 

Requests display of raw results 
You can specify the following modeloptions:
You can specify additional options that are related to the nonlinear optimization aspects of the MODEL fitting process via the NLOPTIONS statement.