Year: 2018
Type: preprint
Abstract: In multiple correspondence analysis, both individuals (observations) and categories can be represented in a biplot that jointly depicts the relationships across categories and individuals, as well as the associations between them. Additional information about the individuals can enhance interpretation capacities, such as by including class information for which the interdependencies are not of immediate concern, but that facilitate the interpretation of the plot with respect to relationships between individuals and categories. This article proposes a new method which we call multiple-class cluster correspondence analysis that identifies clusters specific to classes. The proposed method can construct a biplot that depicts heterogeneous tendencies of individual members, as well as their relationships with the original categorical variables. A simulation study to investigate the performance of the proposed method and an application to data regarding road accidents in the United Kingdom confirms the viability of this approach. Supplementary materials for this article are available online.
Source: arXiv (Cornell University)
Authors: Takagishi, Mariko, van de Velden, Michel
Institutions: Doshisha University, Erasmus University Rotterdam
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2898835182