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PreprintCC BY-NC-ND 4.0arXiv

Visualizing Class Specific Heterogeneous Tendencies using R

  • Mariko Takagishi
  • Michel van de Velden
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Abstract

In this paper we introduce the R package mccca, which implements multiple-class cluster correspondence analysis (MCCCA) proposed in M.Takagishi et al., (2022). MCCCA is a statistical method that identifies and visualizes heterogeneous tendencies specific to ``classes'' (e.g., gender and nationality) in a low dimensional space. In MCCCA, two kinds of variables, external and active variables, are distinguished. External variables directly define classes whereas the active variables are used to derive class-specific clusters of observations (individuals). In this paper, we show how to apply MCCCA and how to visualize its results using mccca. We illustrate the procedure for performing MCCCA by applying it to two data sets.

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Cite

BibTeXRIS
BibTeX
@misc{takagishi2026visualizing,
  title = {{Visualizing Class Specific Heterogeneous Tendencies using R}},
  author = {Mariko Takagishi and Michel van de Velden},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.04608},
  eprint = {2609.04608},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.04608},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - UNPB
TI  - Visualizing Class Specific Heterogeneous Tendencies using R
AU  - Mariko Takagishi
AU  - Michel van de Velden
PY  - 2026
DA  - 2026/09/04
PB  - arXiv
DO  - 10.48550/arxiv.2609.04608
UR  - https://arxiv.org/abs/2609.04608
AB  - In this paper we introduce the R package mccca, which implements multiple-class cluster correspondence analysis (MCCCA) proposed in M.Takagishi et al., (2022). MCCCA is a statistical method that identifies and visualizes heterogeneous tendencies specific to ``classes'' (e.g., gender and nationality) in a low dimensional space. In MCCCA, two kinds of variables, external and active variables, are distinguished. External variables directly define classes whereas the active variables are used to derive class-specific clusters of observations (individuals). In this paper, we show how to apply MCCCA and how to visualize its results using mccca. We illustrate the procedure for performing MCCCA by applying it to two data sets.
ER  -