Generalized Linear Models Iterations

You can set the convergence parameters for estimating the generalized linear model.

Iterations. The following options are available:

• Maximum iterations. The maximum number of iterations the algorithm will execute. Specify a non-negative integer.
• Maximum step-halving. At each iteration, the step size is reduced by a factor of 0.5 until the log-likelihood increases or maximum step-halving is reached. Specify a positive integer.
• Check for separation of data points. When selected, the algorithm performs tests to ensure that the parameter estimates have unique values. Separation occurs when the procedure can produce a model that correctly classifies every case. This option is available for binomial responses with binary format .

Convergence Criteria. The following options are available

• Parameter convergence. When selected, the algorithm stops after an iteration in which the absolute or relative change in the parameter estimates is less than the value specified, which must be positive.
• Log-likelihood convergence. When selected, the algorithm stops after an iteration in which the absolute or relative change in the log-likelihood function is less than the value specified, which must be positive.
• Hessian convergence. For the Absolute specification, convergence is assumed if a statistic based on the Hessian convergence is less than the positive value specified. For the Relative specification, convergence is assumed if the statistic is less than the product of the positive value specified and the absolute value of the log-likelihood.