Exploratory multivariate analysis by example using R
Author(s)
Bibliographic Information
Exploratory multivariate analysis by example using R
（Series in computer science and data analysis）（A Chapman & Hall book）
CRC Press, c2017
2nd ed.
- : hbk
Available at 6 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
Preface
Principal Component Analysis (PCA)
Correspondence Analysis (CA)
Multiple Correspondence Analysis (MCA)
Clustering
Visualisation
Appendix
by "Nielsen BookData"