TY  - BOOK
AU  - Husson,Francois
AU  - Le,Sebastien
AU  - Pages,Jerome
TI  - Exploratory multivariate analysis by example using R
T2  - Computer science and Data analysis Series
SN  - 9781439835807
U1  - SA.07 23rd. 
PY  - 2010///
CY  - Boca Raton
PB  - CRC Press
KW  - Mathematics
KW  - Computer Science
KW  - Data Analysis
N1  - Includes index and bibliography; Principal Component Analysis PCA -- Correspondence Analysis CA -- Multiple Correspondence Analysis MCA -- Clustering
N2  - Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R 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 visualizing 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 and the ways they can be exploited using examples from various fields.

Throughout the text, each result correlates with an R command accessible in the FactoMineR package developed by the authors. All of the data sets and code are available at http://factominer.free.fr/book

By using the theory, examples, and software presented in this book, readers will be fully equipped to tackle real-life multivariate data
ER  -