Description
Exploratory Multivariate Analysis by Example Using R (2nd Ed.)
Chapman & Hall/CRC Computer Science & Data Analysis Series
Authors: Husson Francois, Le Sebastien, Pagès Jérôme
Language: EnglishSubjects for Exploratory Multivariate Analysis by Example Using R:
Keywords
Total Inertia; Standardise PCA; multiple; Tea Data; correspondence; Theoretical Sample Size; total; Row Profile; inertia; Lolita Lempicka; quantitative; Principal Component Method; variables; FactoMineR Package; principal; Aromatics Elixir; component; PCA Function; tea; MCA; data; DNA Chip; Confidence Ellipses; Categorical Variable; Supplementary Variables; MCA Analysis; Column Profiles; Average Profile; Usual Euclidean Distance; Blind Wine Tasting; Eau De; Supplementary Rows; Independence Model; Coco Mademoiselle; Malignant Tumour
60.02 €
In Print (Delivery period: 14 days).
Add to cart the book of Husson Francois, Le Sebastien, Pagès JérômePublication date: 09-2020
· 15.6x23.4 cm · Paperback
162.53 €
In Print (Delivery period: 14 days).
Add to cart the book of Husson Francois, Le Sebastien, Pagès JérômePublication date: 04-2017
· 15.6x23.4 cm · Hardback
Description
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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.