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Biplots of compositional data


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The singular value decomposition and its interpretation as a linear biplot has proved to be a powerful tool for analysing many forms of multivariate data. Here we adapt biplot methodology to the speciffic case of compositional data consisting of positive vectors each of which is constrained to have unit sum. These relative variation biplots have properties relating to special features of compositional data: the study of ratios, subcompositions and models of compositional relationships. The methodology is demonstrated on a data set consisting of six-part colour compositions in 22 abstract paintings, showing how the singular value decomposition can achieve an accurate biplot of the colour ratios and how possible models interrelating the colours can be diagnosed.

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Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 557.

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Date of creation: Jun 2001
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Handle: RePEc:upf:upfgen:557

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Keywords: Logratio transformation; principal component analysis; relative variation biplot; singular value decomposition; subcomposition;

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Cited by:
  1. Michael Greenacre & Paul Lewi, 2005. "Distributional equivalence and subcompositional coherence in the analysis of contingency tables, ratio-scale measurements and compositional data," Economics Working Papers 908, Department of Economics and Business, Universitat Pompeu Fabra, revised Aug 2007.
  2. Michael Greenacre, 2003. "Singular value decomposition of matched matrices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1101-1113.
  3. Michael Greenacre & Rafael Pardo, 2004. "Subset correspondence analysis: Visualizing relationships among a selected set of response categories from a questionnaire survey," Economics Working Papers 791, Department of Economics and Business, Universitat Pompeu Fabra.
  4. Aerni, Philipp, 2009. "What is sustainable agriculture? Empirical evidence of diverging views in Switzerland and New Zealand," Ecological Economics, Elsevier, vol. 68(6), pages 1872-1882, April.
  5. Michael Greenacre & Anna Torres, 2002. "Measuring asymmetries in brand associations using correspondence analysis," Economics Working Papers 630, Department of Economics and Business, Universitat Pompeu Fabra.
  6. Michael Greenacre, 2002. "Ratio maps and correspondence analysis," Economics Working Papers 598, Department of Economics and Business, Universitat Pompeu Fabra.
  7. Peter Filzmoser & Karel Hron & Matthias Templ, 2012. "Discriminant analysis for compositional data and robust parameter estimation," Computational Statistics, Springer, vol. 27(4), pages 585-604, December.
  8. Michael Greenacre, 2006. "Tying up the loose ends in simple correspondence analysis," Economics Working Papers 940, Department of Economics and Business, Universitat Pompeu Fabra.
  9. Michael Greenacre, 2009. "Contribution biplots," Economics Working Papers 1162, Department of Economics and Business, Universitat Pompeu Fabra, revised Jan 2011.
  10. Frederic Udina, . "Interactive Biplot Construction," Journal of Statistical Software, American Statistical Association, vol. 13(i05).
  11. Michael Greenacre, 2007. "Power transformations in correspondence analysis," Economics Working Papers 1044, Department of Economics and Business, Universitat Pompeu Fabra, revised Mar 2008.
  12. Juan Manuel Larrosa, 2003. "A Compositional Statistical Analysis of Capital per Worker," Macroeconomics 0301006, EconWPA.
  13. Michael Greenacre & Paul Lewi, 2009. "Distributional Equivalence and Subcompositional Coherence in the Analysis of Compositional Data, Contingency Tables and Ratio-Scale Measurements," Journal of Classification, Springer, vol. 26(1), pages 29-54, April.


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