Multinomial Logistic Regression Criteria
You can specify the following criteria for your Multinomial Logistic Regression:
Iterations. Allows you to specify the maximum number of times you want to cycle through the algorithm, the maximum number of steps in the step-halving, the convergence tolerances for changes in the log-likelihood and parameters, how often the progress of the iterative algorithm is printed, and at what iteration the procedure should begin checking for complete or quasi-complete separation of the data.
- Log-likelihood convergence. Convergence is assumed if the absolute change in the log-likelihood function is less than the specified value. The criterion is not used if the value is 0. Specify a non-negative value.
- Parameter convergence. Convergence is assumed if the absolute change in the parameter estimates is less than this value. The criterion is not used if the value is 0.
Delta. Allows you to specify a non-negative value less than 1. This value is added to each empty cell of the crosstabulation of response category by covariate pattern. This helps to stabilize the algorithm and prevent bias in the estimates.
Singularity tolerance. Allows you to specify the tolerance used in checking for singularities.
From the menus choose:
This feature requires Custom Tables and Advanced Statistics.
- From the menus choose:
- In the Multinomial Logistic Regression dialog box, click Criteria.
- Define the criteria.