General Discriminant Analysis Models

  • Best-Subset and Stepwise GDA ANCOVA
    Best-subset and stepwise discriminant function analysis with categorical factor effects; builds a linear discriminant function model for continuous and categorical predictor variables, using ANCOVA-like designs. By default, only main effects will be evaluated for categorical predictors; you can also construct factorial designs up to a certain degree (e.g., to degree 3, to include all 2-way and 3-way interactions of categorical predictors). Note that the algorithm for stepwise and best subset selection of categorical factor effects ensures that complete (possibly multiple-degrees-of-freedom) effects are moved into and out of the model. The General Discriminant Analysis module provides functionality that makes this technique a general tool for classification and data mining. However, 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. The use of best-subset methods, in particular when used in conjunction with categorical predictors, should be considered a heuristic search method, rather than a statistical analysis technique.
  • General Best-Subset and Stepwise Discriminant Analysis
    General best-subset and stepwise discriminant analysis; builds a linear discriminant function model for continuous and categorical predictor variables, using ANCOVA-like designs. The parameters in Statistica allow full access to the GDA syntax for specifying ANCOVA-like models, and for controlling the parameters for stepwise and best-subset selection of predictor effects (for categorical and continuous predictor variables). Note that the algorithm for stepwise and best subset selection of categorical factor effects ensures that complete (possibly multiple-degrees-of-freedom) effects are moved into and out of the model. The General Discriminant Analysis module provides functionality that makes this technique a general tool for classification and data mining. However, 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. The use of best-subset methods, in particular when used in conjunction with categorical predictors, should be considered a heuristic search method, rather than a statistical analysis technique.