Logo - springer
Slogan - springer

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.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 

ISBN 978-3-319-09256-0

digitally watermarked, no DRM

The eBook version of this title will be available soon


learn more about Springer eBooks

add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$54.99

(net) price for USA

ISBN 978-3-319-09255-3

free shipping for individuals worldwide

Due: June 4, 2015


add to marked items

  • 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.

Popular Content within this publication 

 

Articles

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Statistics and Computing / Statistics Programs.