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Discarding Variables in a Principal Component Analysis. Ii: Real Data

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  • I. T. Jolliffe

Abstract

In this paper it is shown for four sets of real data, all published examples of principal component analysis, that the number of variables used can be greatly reduced with little effect on the results obtained. Five methods for discarding variables, which have previously been successfully tested on artificial data (Jolliffe, 1972), are used. The methods are compared and all are shown to be satisfactory for real, as well as artificial, data, although none is shown to be overwhelmingly superior to the others.

Suggested Citation

  • I. T. Jolliffe, 1973. "Discarding Variables in a Principal Component Analysis. Ii: Real Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 21-31, March.
    • Handle: RePEc:bla:jorssc:v:22:y:1973:i:1:p:21-31
    DOI: 10.2307/2346300
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    Cited by:

    1. Michael Brusco & Renu Singh & Douglas Steinley, 2009. "Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 705-726, December.
    2. Cadima, Jorge & Cerdeira, J. Orestes & Minhoto, Manuel, 2004. "Computational aspects of algorithms for variable selection in the context of principal components," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 225-236, September.
    3. Tomson Ogwang & Abdella Abdou, 2003. "The Choice of Principal Variables for Computing some Measures of Human Well-being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 64(1), pages 139-152, October.
    4. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    5. Diego Bernardo Avanzini, 2009. "Designing Composite Entrepreneurship Indicators: An Application Using Consensus PCA," WIDER Working Paper Series RP2009-41, World Institute for Development Economic Research (UNU-WIDER).
    6. Daymara Rodríguez-Alfonso & Miriam Isidrón-Pérez & Odalys Barrios & Zoila Fundora & José Ignacio Hormaza & María José Grajal-Martín & Lisset Herrera-Isidrón, 2020. "Minimal morphoagronomic descriptors for Cuban pineapple germplasm characterisation," Horticultural Science, Czech Academy of Agricultural Sciences, vol. 47(1), pages 28-35.
    7. Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.
    8. Jolliffe, Ian, 2022. "A 50-year personal journey through time with principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    9. Gianluca Gucciardi, 2022. "Measuring the relative development and integration of EU countries’ capital markets using composite indicators and cluster analysis," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(4), pages 1043-1083, November.
    10. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
    11. Bauer, Jan O. & Drabant, Bernhard, 2021. "Principal loading analysis," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    12. António Pedro Duarte Silva, 2002. "Discarding Variables in a Principal Component Analysis: Algorithms for All-Subsets Comparisons," Computational Statistics, Springer, vol. 17(2), pages 251-271, July.
    13. Montanari, Angela & Lizzani, Laura, 2001. "A projection pursuit approach to variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 35(4), pages 463-473, February.
    14. Luca Scrucca, 2006. "Subset selection in dimension reduction methods," Quaderni del Dipartimento di Economia, Finanza e Statistica 23/2006, Università di Perugia, Dipartimento Economia.
    15. Brint, Andrew & Genovese, Andrea & Piccolo, Carmela & Taboada-Perez, Gerardo J., 2021. "Reducing data requirements when selecting key performance indicators for supply chain management: The case of a multinational automotive component manufacturer," International Journal of Production Economics, Elsevier, vol. 233(C).

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