# Nonlinear Canonical Correlation Analysis Options

The Options dialog box allows you to select optional statistics and plots, save object scores as new variables in the active dataset, specify iteration and convergence criteria, and specify an initial configuration for the analysis.

Display. Available statistics include marginal frequencies (counts), centroids, iteration history, weights and component loadings, category quantifications, object scores, and single and multiple fit statistics.

• Centroids. Category quantifications, and the projected and the actual averages of the object scores for the objects (cases) included in each set for those cases belonging to the same category of the variable.
• Weights and component loadings. The regression coefficients in each dimension for every quantified variable in a set, where the object scores are regressed on the quantified variables, and the projection of the quantified variable in the object space. Provides an indication of the contribution each variable makes to the dimension within each set.
• Single and multiple fit. Measures of goodness of fit of the single- and multiple-category coordinates/category quantifications with respect to the objects.
• Category quantifications. Optimal scale values assigned to the categories of a variable.
• Object scores. Optimal score assigned to an object (case) in a particular dimension.

Plot. You can produce plots of category coordinates, object scores, component loadings, category centroids, and transformations.

Save object scores. You can save the object scores as new variables in the active dataset. Object scores are saved for the number of dimensions that are specified in the main dialog box.

Use random initial configuration. A random initial configuration should be used if some or all of the variables are single nominal. If this option is not selected, a nested initial configuration is used.

Criteria. You can specify the maximum number of iterations that the nonlinear canonical correlation analysis can go through in its computations. You can also select a convergence criterion value. The analysis stops iterating if the difference in total fit between the last two iterations is less than the convergence value or if the maximum number of iterations is reached.

To Specify OVERALS Options

This feature requires the Categories option.