General Discriminant Analysis (GDA) Models Startup Panel and Quick Tab
Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Mult/Exploratory and from the menu, select General Discriminant to display the General Discriminant Analysis (GDA) Models Startup Panel.
Classic menus. On the Statistics - Multivariate Exploratory Techniques submenu, select General Discriminant Analysis Models to display the General Discriminant Analysis (GDA) Models Startup Panel.
The Startup Panel contains one tab: Quick. Use the options on this tab to select the Type of analysis and Specification method.
See also the General Discriminant Analysis (GDA) Models Index, Overviews, and Examples.
As described in the Introductory Overview, the General Discriminant Analysis (GDA) Models module of Statistica provides an extension to the traditional approach to discriminant function analysis (e.g., as described in the context of the Discriminant Analysis module) by (dummy-)coding the categorical dependent variable values (i.e., class or group memberships) into multiple dependent variables. Statistica then analyzes the data as a multivariate regression problem, as described in the Introductory Overview of the General Regression Models (GRM) module. While traditionally discriminant function analysis is typically applied to data files with single degree of freedom continuous predictor variables, with the GDA module, you can specify complex models involving continuous and categorical predictor variables and effects, and perform stepwise and best-subset selection of those predictors. However, refer to the Note of caution for models with categorical predictors, and other advanced techniques, to learn about the possible limitations of this approach.
Most, if not all, textbook treatments of discriminant function analysis are limited to simple and stepwise analyses with single degree of freedom continuous predictors. No "experience" (in the literature) exists regarding issues of robustness and effectiveness of these techniques, when they are generalized in the manner provided in this very powerful module. Refer to the Note of caution for models with categorical predictors, and other advanced techniques to learn about the possible limitations of this approach.
Note. Refer to the Introductory Overview for details concerning analyses in GDA that involve categorical and/or continuous predictor effects, in an ANOVA-like design.
OK. Click the OK button to display the appropriate analysis specification dialog box, depending on the Specification method selected on the Quick tab.
Cancel. Click the Cancel button to close the Startup Panel without performing an analysis.
Options. See Options Menu for descriptions of the commands on this menu.
Open Data. Click the Open Data button to display the Select Data Source dialog box, which contains options to choose the spreadsheet on which to perform the analysis. The Select Data Source dialog box contains a list of the spreadsheets that are currently active.
Select Cases. Click the Select Cases button to display the Analysis/Graph Case Selection Conditions dialog box, which contains options to create conditions for which cases will be included (or excluded) in the current analysis. More information is available in the case selection conditions overview, syntax summary, and dialog description.
W. Click the W (Weight) button to display the Analysis/Graph Case Weights dialog box, which contains options to adjust the contribution of individual cases to the outcome of the current analysis by "weighting" those cases in proportion to the values of a selected variable.
Weighted moments. Select the Weighted moments check box to specify that each observation contributes the weighting variable's value for that observation. The weight values need not be integers. This module can use fractional case weights in most computations. Some other modules use case weights as integer case multipliers or frequency values. This option will only be available after you have defined a weight variable via the W option (see above).
See also the General Discriminant Analysis (GDA) Models Index, Overviews, and Examples.