AFMULT

Multiple factor analysis (AFMULT package). Multiple Factor Analysis (MFA) studies several groups of variables (numerical and/or categorical) defined on the same set of individuals. MFA approaches this kind of data according to many points of view already used in others methods as: factor analysis in which groups of variables are weighted, canonical analysis, Procrustes analysis, STATIS, INDSCAL. In MFA, these points of view are considered in a unique framework. This paper presents the different outputs provided by MFA and an example about sensory analysis of wines.


References in zbMATH (referenced in 15 articles )

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  1. Marie Chavent, Vanessa Kuentz-Simonet, Amaury Labenne, J. Saracco: Multivariate analysis of mixed data: The PCAmixdata R package (2014) arXiv
  2. Abdi, Hervé; Williams, Lynne J.; Connolly, Andrew C.; Gobbini, M.Ida; Dunlop, Joseph P.; Haxby, James V.: Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): how to assign scans to categories without using spatial normalization (2012)
  3. Bénasséni, Jacques; Dosse, Mohammed Bennani: Analyzing multiset data by the power STATIS-ACT method (2012)
  4. Liberati, Caterina; Mariani, Paolo: Banking customer satisfaction evaluation: a three-way factor perspective (2012)
  5. Thioulouse, Jean: Simultaneous analysis of a sequence of paired ecological tables: A comparison of several methods (2011)
  6. Abascal, Elena; Lautre, Ignacio García; Mallor, Fermín: Tracking customer portfolio composition: a factor analysis approach (2010)
  7. Henseler, Jörg: On the convergence of the partial least squares path modeling algorithm (2010)
  8. Zárraga, A.; Goitisolo, B.: Simultaneous analysis and multiple factor analysis for contingency tables: two methods for the joint study of contingency tables (2009)
  9. Bécue-Bertaut, Mónica; Pagès, Jér^ome: Multiple factor analysis and clustering of a mixture of quantitative, categorical and frequency data (2008)
  10. Tenenhaus, Michel; Vinzi, Vincenzo Esposito; Chatelin, Yves-Marie; Lauro, Carlo: PLS path modeling (2005)
  11. Bécue-Bertaut, Mónica; Pagès, Jér^ome: A principal axes method for comparing contingency tables: MFACT (2004)
  12. García Lautre, Ignacio; Abascal Fernández, Elena: A methodology for measuring latent variables based on multiple factor analysis (2004)
  13. Vivien, Myrtille; Sabatier, Robert: A generalization of STATIS-ACT strategy: DO-ACT for two multiblocks tables (2004)
  14. Casin, Ph.: A generalization of principal component analysis to $K$ sets of variables. (2001)
  15. Escofier, B.; Pages, J.: Multiple factor analysis (AFMULT package) (1994)