Transformation and Mixture Discriminant Analysis

Volstorff A

The Discriminant Analysis, which includes Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Mixture Discriminant Analysis(MDA), is recognized for its versatile and reliable approach to classification tasks, and it has countless applications across various fields of study. However, its effectiveness is often constrained by the assumption that each group or subgroup follows a Gaussian distribution, which may not hold for real-world data. Our R package transDA addresses this limitation by integrating transformation into discriminant analysis, allowing for skewness within data groups or subgroups. transDA can handle both non-transformation methods such as LDA, QDA, and MDA and transformation methods such as Transformation Linear Discriminant Analysis (TLDA), Transformation Quadratic Discriminant Analysis(TQDA), and Transformation Mixture Discriminant Analysis (TMDA). This paper provides a solid theoretical foundation about TMDA and detailed descriptions of the functions of transDA, along with illustrative examples. Real-life datasets are used to demonstrate the flexibility and usefulness of the package.