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2. Principal component analysis (PCA)
2.1 Objectives
The purpose of principal component analysis is to study data resulting from the observation of p quantitative variables on n individuals, arranged in a matrix X (n x p). The objectives are :
the "optimal" graphical representation of the individuals (lines), minimizing the distortions of the point cloud, in a subspace E q of dimension q (q < p) of the vector space
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Principal component analysis (PCA)
Other factorial methods
Bibliography
Websites
Other resources (handouts, practical exercises, functions written in R) are available on the website :
https://www.math.univ-toulouse.fr/
R Development Core TeamR: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing
Find out more
The most useful general and introductory references for this theme are: Bouroche & Saporta (1980), Jobson (1992), Lebart, Morineau & Piron (2006), Mardia, Kent & Bibby (1979), Saporta (2006). More recent additions and developments can be found in: Droesbeke, Fichet & Tassi (1992), Govaert (2003).
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