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The Contributions of Rare Objects in Correspondence Analysis


  • Michael Greenacre


Correspondence analysis, when used to visualize relationships in a table of counts (for example, abundance data in ecology), has been frequently criticized as being too sensitive to objects (for example, species) that occur with very low frequency or in very few samples. In this statistical report we show that this criticism is generally unfounded. We demonstrate this in several data sets by calculating the actual contributions of rare objects to the results of correspondence analysis and canonical correspondence analysis, both to the determination of the principal axes and to the chi-square distance. It is a fact that rare objects are often positioned as outliers in correspondence analysis maps, which gives the impression that they are highly influential, but their low weight offsets their distant positions and reduces their effect on the results. An alternative scaling of the correspondence analysis solution, the contribution biplot, is proposed as a way of mapping the results in order to avoid the problem of outlying and low contributing rare objects.

Suggested Citation

  • Michael Greenacre, 2011. "The Contributions of Rare Objects in Correspondence Analysis," Working Papers 571, Barcelona Graduate School of Economics.
  • Handle: RePEc:bge:wpaper:571

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    References listed on IDEAS

    1. Eve Chiapello & A. Hurand, 2011. "Contribution," Post-Print hal-00681170, HAL.
    2. Nenadic, Oleg & Greenacre, Michael, 2007. "Correspondence Analysis in R, with Two- and Three-dimensional Graphics: The ca Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i03).
    3. Michael Greenacre, 2008. "Correspondence analysis of raw data," Economics Working Papers 1112, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2009.
    4. Michael Greenacre, 2009. "Contribution biplots," Economics Working Papers 1162, Department of Economics and Business, Universitat Pompeu Fabra, revised Jan 2011.
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    More about this item


    Biplot; canonical correspondence analysis; contribution; correspondence analysis; influence; outlier; scaling;

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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