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.
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EAN: 9780367658021
UPC: 9780367658021
ISBN: 9780367658021
MPN: N/A
Item Length: 23.4 cm
Number of Pages: 248 Pages
Publication Name: Exploratory Multivariate Analysis by Example Using R
Language: English
Publisher: Taylor & Francis LTD
Item Height: 234 mm
Subject: Mathematics
Publication Year: 2020
Type: Textbook
Item Weight: 485 g
Subject Area: Experimental Psychology
Author: Sebastien Le, Jerome Pages, Francois Husson
Item Width: 156 mm
Format: Paperback
