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）
CRC Press, c2011
- : hardback
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Note
"A Chapman & Hall book"
"Bibliography of software packages": p. 221-222
Includes bibliographical references (p. 223-224) and index
Description and Table of Contents
Description
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.
Table of Contents
Principal Component Analysis (PCA)
Data - Notation - Examples
Objectives
Studying Individuals
Studying Variables
Relationships between the Two Representations NI and NK
Interpreting the Data
Implementation with FactoMineR
Additional Results
Example: The Decathlon Dataset
Example: The Temperature Dataset
Example of Genomic Data: The Chicken Dataset
Correspondence Analysis (CA)
Data - Notation - Examples
Objectives and the Independence Model
Fitting the Clouds
Interpreting the Data
Supplementary Elements (= Illustrative)
Implementation with FactoMineR
CA and Textual Data Processing
Example: The Olympic Games Dataset
Example: The White Wines Dataset
Example: The Causes of Mortality Dataset
Multiple Correspondence Analysis (MCA)
Data - Notation - Examples
Objectives
Defining Distances between Individuals and Distances between Categories
CA on the Indicator Matrix
Interpreting the Data
Implementation with FactoMineR
Addendum
Example: The Survey on the Perception of Genetically Modified Organisms
Example: The Sorting Task Dataset
Clustering
Data - Issues
Formalising the Notion of Similarity
Constructing an Indexed Hierarchy
Ward's Method
Direct Search for Partitions: K-means Algorithm
Partitioning and Hierarchical Clustering
Clustering and Principal Component Methods
Example: The Temperature Dataset
Example: The Tea Dataset
Dividing Quantitative Variables into Classes
Appendix
Percentage of Inertia Explained by the First Component or by the First Plane
R Software
Bibliography of Software Packages
Bibliography
Index
by "Nielsen BookData"
