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Statistics - Computational Statistics | Principal Component and Correspondence Analyses Using R

Principal Component and Correspondence Analyses Using R

Abdi, Hervé, Beaton, Derek

2015, X, 110 p. 40 illus., 10 illus. in color.

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  • Fills a void in the literature for how to do PCA and CA with R, a wildly popular and open source software
  • All analyses use R packages, including a package created for this book, and examples provide all data and code
  • The "how to" approach used here can be used in courses and self-learning
With the right R packages, R is uniquely suited to perform Principal Component Analysis (PCA), Correspondence Analysis (CA),  Multiple Correspondence Analysis (MCA), and metric multidimensional scaling (MMDS). The analyses depicted in this book use several packages specially developed for theses analyses and include (among others): the ExPosition suite, FactoMiner , ade4, and ca. The authors present each technique with one or several small  examples that demonstrate how to enter the data, perform the standard analyses, and obtain professional quality graphics. Through explanations of the major options for how to carry out each method, readers can tailor the content of this book to their particular goals. Explanations include the effects of using particular packages. ExPosition is a great choice for the methods as it was written specifically for this book. However, options abound and are illustrated within unique scenarios. The first chapter includes installation of the packages. At the end of the book, a short appendix presents critical mathematical material for readers who want to go deeper into the theory.

Content Level » Research

Keywords » Correspondence Analysis with R - Data Mining with R - Multiple Correspondence Analysis - Multivariate Analysis - Principal Component Analysis with R - R Software

Related subjects » Computational Statistics - Life Sciences, Medicine & Health - Statistical Theory and Methods

Table of contents 

​Minimum of R.- Notations.- Principal Component Analysis.- Correspondence Analysis.- Multiple Correspondence Analysis & Alternative.- Appendix: The Singular Value Decomposition (SVD).- References.- Index.

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