Chapter 14. Principal Component Analysis (PCA) and Factor Analysis (FA)

  1. Clemens Reimann M.Sc. in Mineralogy and Petrology, Ph.D. in Geosciences, D.Sc. in Applied Geochemistry lecturer senior geochemist director professor chairman acting vice president associate editor1,
  2. Peter Filzmoser Applied Mathematics visiting professor2,
  3. Robert G. Garrett Mining Geology and Applied Geochemistry Emeritus Scientist3,
  4. Rudolf Dutter M.Sc., Ph.D. senior statistician full professor post-doctoral fellow2

Published Online: 18 APR 2008

DOI: 10.1002/9780470987605.ch14

Book Title

Statistical Data Analysis Explained: Applied Environmental Statistics with R

Statistical Data Analysis Explained: Applied Environmental Statistics with R

Additional Information

How to Cite

Reimann, C., Filzmoser, P., Garrett, R. G. and Dutter, R. (2008) Principal Component Analysis (PCA) and Factor Analysis (FA), in Statistical Data Analysis Explained: Applied Environmental Statistics with R, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470987605.ch14

Author Information

  1. 1

    Geological Survey of Norway, Norway

  2. 2

    Vienna University of Technology, Austria

  3. 3

    Geological Survey of Canada, Canada

Publication History

  1. Published Online: 18 APR 2008
  2. Published Print: 4 APR 2008

ISBN Information

Print ISBN: 9780470985816

Online ISBN: 9780470987605

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CHAPTER TOOLS

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Keywords:

  • principal component analysis (PCA);
  • maximum likelihood (ML) method and principal factor analysis (PFA);
  • data point majority fit;
  • inhomogeneous data sets;
  • robust versus classical PCA;
  • Factor analysis (FA), PCA and dimensionality reduction;
  • dimension reduction PCA and FA techniques;
  • varimax rotation and log-transformed Kola Project moss data

Summary

This chapter contains sections titled:

  • Conditioning the data for PCA and FA

  • Principal component analysis (PCA)

  • Factor analysis

  • Summary

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