PROC GAMPL <options>;
The PROC GAMPL statement invokes the procedure. Table 42.1 summarizes the available options in the PROC GAMPL statement by function. The options are then described fully in alphabetical order.
Table 42.1: PROC GAMPL Statement Options
Option 
Description 

Basic Options 

Specifies a global significance level 

Specifies the input data set 

Limits the length of effect names 

Sets the seed for pseudorandom number generation 

Display Options 

Displays the "Iteration History" table 

Limits or suppresses the display of classification variable levels 

Suppresses ODS output 

Controls plots that are produced through ODS Graphics 

Optimization Subject Options 

Sets optimization parameters for likelihood estimation 

Sets optimization parameters for smoothing parameter estimation 

Tolerance Options 

Tunes the singularity criterion for Cholesky decompositions 

Tunes the singularity criterion for the sweep operator 

Tunes the general singularity criterion 

UserDefined Format Options 

Specifies the file reference for a format stream 
You can specify the following options in the PROC GAMPL statement.
You can specify optimizationparameters for both the PLIKEOPTIONS and SMOOTHOPTIONS options. Depending on the modeling context, some optimization parameters might have no effect. For parametric generalized linear models or generalized additive models that have fixed smoothing parameters, any optimization parameters that you specify in the SMOOTHOPTIONS option are ignored. For the performance iteration method , only the ABSFCONV= , FCONV= , and MAXITER= options are effective for PLIKEOPTIONS . The optimization algorithm is considered to have converged when any one of the convergence criteria that are specified in optimizationparameters is satisfied. Table 42.2 lists the available optimization parameters for both the PLIKEOPTIONS and SMOOTHOPTIONS options.
Table 42.2: Optimization Parameters
Option 
Description 

Tunes the absolute function convergence criterion 

Tunes the absolute function difference convergence criterion 

Tunes the absolute gradient convergence criterion 

Tunes the relative function difference convergence criterion 

Tunes the relative gradient convergence criterion 

Specifies the maximum number of function evaluations in any optimization 

Chooses the maximum number of iterations in any optimization 

Specifies the upper limit of CPU time (in seconds) for any optimization 

Specifies the minimum number of iterations in any optimization 

Selects the optimization technique 
You can specify the following optimizationparameters: