Categorical Principal Components Analysis Options
The Options dialog box provides controls to select the initial configuration, specify iteration and convergence criteria, select a normalization method, choose the method for labeling plots, and specify supplementary objects.
Supplementary Objects. Specify the case number of the object, or the first and last case numbers of a range of objects, that you want to make supplementary and then click Add. If an object is specified as supplementary, then case weights are ignored for that object.
Normalization Method. You can specify one of five options for normalizing the object scores and the variables. Only one normalization method can be used in each analysis.
- Variable Principal. This option optimizes the association between variables. The coordinates of the variables in the object space are the component loadings (correlations with principal components, such as dimensions and object scores). This method is useful when you are primarily interested in the correlation between the variables.
- Object Principal. This option optimizes distances between objects. This method is useful when you are primarily interested in differences or similarities between the objects.
- Symmetrical. Use this normalization option if you are primarily interested in the relation between objects and variables.
- Independent. Use this normalization option if you want to examine distances between objects and correlations between variables separately.
- Custom. You can specify any real value in the closed interval [–1, 1]. A value of 1 is equal to the Object Principal method. A value of 0 is equal to the Symmetrical method. A value of –1 is equal to the Variable Principal method. By specifying a value greater than –1 and less than 1, you can spread the eigenvalue over both objects and variables. This method is useful for making a tailor-made biplot or triplot.
Criteria. You can specify the maximum number of iterations the procedure can go through in its computations. You can also select a convergence criterion value. The algorithm 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.
Label Plots By. You can specify whether variables and value labels or variable names and values are used in the plots. You can also specify a maximum length for labels.
Plot Dimensions. You can control the dimensions that are displayed in the output.
- Display all dimensions in the solution. All dimensions in the solution are displayed in a scatterplot matrix.
- Restrict the number of dimensions. The displayed dimensions are restricted to plotted pairs. If you restrict the dimensions, you must select the lowest and highest dimensions to be plotted. The lowest dimension can range from 1 to the number of dimensions in the solution minus 1 and is plotted against higher dimensions. The highest dimension value can range from 2 to the number of dimensions in the solution and indicates the highest dimension to be used in plotting the dimension pairs. This specification applies to all requested multidimensional plots.
Rotation. You can select a rotation method to obtain rotated results.
Note: These rotation methods are not available if you select Perform bootstrapping in the Bootstrap dialog.
- Varimax. An orthogonal rotation method that minimizes the number of variables that have high loadings on each component. It simplifies the interpretation of the components.
- Quartimax. A rotation method that minimizes the number of components that are needed to explain each variable. It simplifies the interpretation of the observed variables.
- Equamax. A rotation method that is a combination of the Varimax method, which simplifies the components, and the Quartimax method, which simplifies the variables. The number of variables that load highly on a component and the number of components that are needed to explain a variable are minimized.
- Oblimin. A method for oblique (non-orthogonal) rotation. When delta equals 0, components are most oblique. As delta becomes more negative, the components become less oblique. Positive values permit additional component correlation. The value of Delta must be less than or equal to 0.8.
- Promax. An oblique (non-orthogonal) rotation, which allows components to be correlated. It can be calculated more quickly than a direct Oblimin rotation, so it is useful for large datasets. The amount of correlation (obliqueness) that is allowed is controlled by the kappa parameter. The value of Kappa must be greater than or equal to 1 and less 10,000.
Configuration. You can read data from a file that contains the coordinates of a configuration. The first variable in the file contains the coordinates for the first dimension. The second variable contains the coordinates for the second dimension, and so on.
- Initial. The configuration in the file that is specified is used as the starting point of the analysis.
- Fixed. The configuration in the file that is specified is used to fit in the variables. The variables that are fitted in must be selected as analysis variables, but because the configuration is fixed, they are treated as supplementary variables (so they do not need to be selected as supplementary variables).
To Specify CATPCA Options
This feature requires the Categories option.
- From the menus, choose:
- In the Categorical Principal Components dialog box, click Options.