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Exploratory Multivariate Analysis by Example Using R. 2nd ed. Rを用いた実例による探索的多変量解析 第2版
・ISBN 978-1-138-19634-6 2017 hard GB£ 124.99
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・ISBN 978-0-367-65802-1 2020 paper GB£ 55.99
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電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-0-429-22543-7
| 著者・編者 | Husson, Francois / Le, S. / Pagès, J., |
|---|---|
| シリーズ | Chapman & Hall/CRC Computer Science and Data Analysis |
| 出版社 | (Chapman & Hall / CRC, US) |
| ページ数 | 262 pp. |
| 言語 | ENG |
| ニュース番号 | ＜638-351 638-P717＞ |
解説
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.
The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.