Correspondence Analysis in R, with Two- and Three-dimensional Graphics: The ca Package. We describe an implementation of simple, multiple and joint correspondence analysis in R. The resulting package comprises two parts, one for simple correspondence analysis and one for multiple and joint correspondence analysis. Within each part, functions for computation, summaries and visualization in two and three dimensions are provided, including options to display supplementary points and perform subset analyses. Special emphasis has been put on the visualization functions that offer features such as different scaling options for biplots and three-dimensional maps using the rgl package. Graphical options include shading and sizing plot symbols for the points according to their contributions to the map and masses respectively.

References in zbMATH (referenced in 22 articles , 1 standard article )

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  1. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2021)
  2. Márcio A. Diniz, Gillian Gresham, Sungjim Kim, Michael Luu, N. Lynn Henry, Mourad Tighiouart, Greg Yothers, Patricia Ganz, André Rogatko: Visualizing adverse events using correspondence analysis (2021) arXiv
  3. Derek Beaton: Generalized eigen, singular value, and partial least squares decompositions: The GSVD package (2020) arXiv
  4. Alfonso Iodice D’Enza, Angelos Markos, Michel van de Velden: Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R (2019) not zbMATH
  5. Fotuhi, Hatef; Amiri, Amirhossein; Taheriyoun, Ali Reza: A novel approach based on multiple correspondence analysis for monitoring social networks with categorical attributed data (2019)
  6. Urbano Lorenzo-Seva; Michel van de Velden: MultipleCar: A Graphical User Interface MATLAB Toolbox to Compute Multiple Correspondence Analysis (2019) not zbMATH
  7. Mair, Patrick: Modern psychometrics with R (2018)
  8. Greenacre, Michael: Correspondence analysis in practice (2017)
  9. Ranalli, Monia; Rocci, Roberto: A model-based approach to simultaneous clustering and dimensional reduction of ordinal data (2017)
  10. D’Enza, Alfonso Iodice; Markos, Angelos: Low-dimensional tracking of association structures in categorical data (2015)
  11. Gianmarco Alberti: CAinterprTools: An R package to help interpreting Correspondence Analysis’ results (2015) not zbMATH
  12. Beaton, Derek; Chin Fatt, Cherise R.; Abdi, Hervé: An exposition of multivariate analysis with the singular value decomposition in R (2014)
  13. D’enza, Alfonso Iodice; Greenacre, Michael: Multiple correspondence analysis for the quantification and visualization of large categorical data sets (2012) ioport
  14. Aşan, Zerrin; Greenacre, Michael: Biplots of fuzzy coded data (2011) ioport
  15. Greenacre, Michael: Power transformations in correspondence analysis (2009)
  16. Greenacre, Michael; Lewi, Paul: Distributional equivalence and subcompositional coherence in the analysis of compositional data, contingency tables and ratio-scale measurements (2009)
  17. Jan de Leeuw; Patrick Mair: Simple and Canonical Correspondence Analysis Using the R Package anacor (2009) not zbMATH
  18. Jan de Leeuw; Patrick Mair: Gifi Methods for Optimal Scaling in R: The Package homals (2009) not zbMATH
  19. Urbano Lorenzo-Seva; Michel van de Velden; Henk Kiers: CAR: A MATLAB Package to Compute Correspondence Analysis with Rotations (2009) not zbMATH
  20. Greenacre, Michael: Correspondence analysis in practice. (2007)

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