Visualizing Class Specific Heterogeneous Tendencies in Categorical Data

References

• Böckenholt, U., and Böckenholt, I. (1990), “Canonical Analysis of Contingency Tables with Linear Constraints,” Psychometrika, 55, 633–639. DOI: 10.1007/BF02294612.
   Web of Science ®Google Scholar
• Böckenholt, U., and Takane, Y. (1994), “Linear Constraints in Correspondence Analysis,” in Correspondence Analysis in Social Sciences, eds. M. J. Greenacre and J. Blasius, pp. 112–127, London: Academic Press.
   Google Scholar
• Gabriel, K. R. (2002), “Goodness of Fit of Biplots and Correspondence Analysis,” Biometrika, 89, 423–436. DOI: 10.1093/biomet/89.2.423.
   Web of Science ®Google Scholar
• Gower, J. C., Groenen, P., and van de Velden, M. (2010), “Area Biplots,” Journal of Computational and Graphical Statistics, 19, 46–61. DOI: 10.1198/jcgs.2010.07134.
   Web of Science ®Google Scholar
• Gower, J. C., and Hand, D. J. (1996), Biplots, London: Chapman & Hall.
   Google Scholar
• Greenacre, M. J. (1984), Theory and Applications of Correspondence Analysis, London: Academic Press.
   Google Scholar
• Greenacre, M. J. (1993), “Biplots in Correspondence Analysis,” Journal of Applied Statistics, 20, 251–269.
   Google Scholar
• Greenacre, M. J. (2017), Correspondence Analysis in Practice, Boca Raton, FL: CRC press.
   Google Scholar
• Hubert, L., and Arabie, P. (1985), “Comparing Partitions,” Journal of Classification, 2, 193–218. DOI: 10.1007/BF01908075.
   Web of Science ®Google Scholar
• Hwang, H., and Takane, Y. (2002), “Generalized Constrained Multiple Correspondence Analysis,” Psychometrika, 67, 211–224. DOI: 10.1007/BF02294843.
   Web of Science ®Google Scholar
• Hwang, H., Yang, B., and Takane, Y. (2005), “A Simultaneous Approach to Constrained Multiple Correspondence Analysis and Cluster Analysis for Market Segmentation,” Asia Pacific Advances in Consumer Research, 6, 197–199.
   Google Scholar
• Krzanowski, W. J., and Lai, Y. (1988), “A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clusteringm,” Biometrics, 44, 23–34. DOI: 10.2307/2531893.
   Web of Science ®Google Scholar
• Lorenzo-Seva, U., and Ten Berge, J. M. (2006), “Tucker’s Congruence Coefficient as a Meaningful Index of Factor Similarity,” Methodology, 2, 57–64. DOI: 10.1027/1614-2241.2.2.57.
   Google Scholar
• R Core Team (2021). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/
   Google Scholar
• Takagishi, M. (2022). “mccca: Visualizing Class Specific Heterogeneous Tendencies in Categorical Data.” Available at https://CRAN.R-project.org/package=mccca (R package version 0.1.0)
   Google Scholar
• Takane, Y., and Hwang, H. (2002), “Generalized Constrained Canonical Correlation Analysis,” Multivariate Behavioral Research, 37, 163–195. DOI: 10.1207/S15327906MBR3702_01.
   Web of Science ®Google Scholar
• Takane, Y., and Shibayama, T. (1991), “Principal Component Analysis with External Information on Both Subjects and Variables,” Psychometrika, 56, 97–120. DOI: 10.1007/BF02294589.
   Web of Science ®Google Scholar
• Takane, Y., Yanai, H., and Mayekawa, S. (1991), “Relationships Among Several Methods of Linearly Constrained Correspondence Analysis,” Psychometrika, 56, 667–684. DOI: 10.1007/BF02294498.
   Web of Science ®Google Scholar
• Ter Braak, C. J. (1986), “Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis,” Ecology, 67, 1167–1179. DOI: 10.2307/1938672.
   Web of Science ®Google Scholar
• Van Buuren, S., and Heiser, W. J. (1989), “Clustering n Objects into k Groups Under Optimal Scaling of Variables,” Psychometrika, 54, 699–706. DOI: 10.1007/BF02296404.
   Web of Science ®Google Scholar
• Van Buuren, S., and de Leeuw, J. (1992), “Equality Constraints in Multiple Correspondence Analysis,” Multivariate Behavioral Research, 27, 567–583. DOI: 10.1207/s15327906mbr2704_4.
   PubMed Web of Science ®Google Scholar
• van de Velden, M., D’Enza, A. I., and Palumbo, F. (2017), “Cluster Correspondence Analysis,” Psychometrika, 82, 158–185. DOI: 10.1007/s11336-016-9514-0.
   PubMed Web of Science ®Google Scholar
• Yanai, H. (1986), “Some Generalizations of Correspondence Analysis in Terms of Projectors,” in Data Analysis and Informatics IV, eds. E. Diday, Y. Escoufier, L. Lebaxt, J. E. Pages, Y. Schektman, and R. Thomassone, pp. 193–207, Amsterdam: North Holland.
   Google Scholar
• Yanai, H. (1988), “Partial Correspondence Analysis and Its Properties,” in Recent Developments in Clustering and Data Analysis, eds. C. Hayashi, M. Jambu, E. Diday, and N. Ohsumi, pp. 259–266, Boston: Academic Press.
   Google Scholar
• Yanai, H., and Maeda, T. (2002), “Partial Multiple Correspondence Analysis,” in Measurement and Multivariate Analysis, eds. S. Nishisato, Y. Baba, H. Bozdogan, and K. Kanefuji, pp. 57–68, Tokyo: Springer.
   Google Scholar