Exploratory Multivariate Analysis by Example Using R

Francois Husson

ISBN 10: 036765802X / ISBN 13: 9780367658021

Published by Chapman And Hall/CRC Sep 2020, 2020

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 264 pp. Englisch.

Seller Inventory # 9780367658021

Bibliographic Details

• Title: Exploratory Multivariate Analysis by Example Using R
• Publisher: Chapman And Hall/CRC Sep 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 036765802X
• ISBN 13: 9780367658021
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 234W x 156H x 14D millimeters 234 millimeters width by 156 millimeters height by 14 millimeters depth
• Weight: 406 grams

About this title

Synopsis

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.

About the Author

Francois Husson, Sebastien Le, Jérôme Pagès