# Ordinal Regression Options

The Options dialog box allows you to adjust parameters used in the iterative estimation algorithm, choose a level of confidence for your parameter estimates, and select a link function.

Iterations. You can customize the iterative algorithm.

• Maximum iterations. Specify a non-negative integer. If 0 is specified, the procedure returns the initial estimates.
• Maximum step-halving. Specify a positive integer.
• Log-likelihood convergence. The algorithm stops if the absolute or relative change in the log-likelihood is less than this value. The criterion is not used if 0 is specified.
• Parameter convergence. The algorithm stops if the absolute or relative change in each of the parameter estimates is less than this value. The criterion is not used if 0 is specified.

Confidence interval. Specify a value greater than or equal to 0 and less than 100.

Delta. The value added to zero cell frequencies. Specify a non-negative value less than 1.

Singularity tolerance. Used for checking for highly dependent predictors. Select a value from the list of options.

Link function. The link function is a transformation of the cumulative probabilities that allows estimation of the model. The following five link functions are available.

• Logit. f(x)=log(x/(1−x) ). Typically used for evenly distributed categories.
• Complementary log-log. f(x)=log(−log(1−x)). Typically used when higher categories are more probable.
• Negative log-log. f(x)=−log(−log(x)). Typically used when lower categories are more probable.
• Probit. f(x)=Φ−1(x). Typically used when the latent variable is normally distributed.
• Cauchit (inverse Cauchy). f(x)=tan(π(x−0.5)). Typically used when the latent variable has many extreme values.

To Specify Ordinal Regression Options

This feature requires the Statistics Base option.