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May 3, 2017 · 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 ...
- Francois Husson, Sebastien Le, Jérôme Pagès
- 2010
techniques of multivariate analysis (MVA): principal component analysis (PCA), correspon-dence analysis (CA), multiple correspondence analysis (MCA), and clustering. The approach is to provide just enough mathematics to remove some of the black-box feeling of the meth-ods.
Exploratory Multivariate Analysis by Example Using R provides a very good overview of the application of three multivariate analysis techniques: principal components analysis, correspon-dence analysis and hierarchical cluster analysis.
Feb 14, 2012 · Exploratory Multivariate Analysis by Example Using R provides a very good overview of the application of three multivariate analysis techniques: principal components analysis, correspondence analysis and hierarchical cluster analysis.
- Eric J. Beh
- 2012
Sep 30, 2020 · 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.
- Francois Husson
- September 30, 2020
Nov 21, 2011 · Exploratory Multivariate Analysis by Example Using R by François Husson, Sébastien Lê, Jérôme Pagès. John H. Maindonald. First published: 21 November 2011. https://doi.org/10.1111/j.1751-5823.2011.00159_19.x. Citations: 2. Read the full text.
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This book explores four essential and basic methods for multivariate exploratory data analysis: principal component analysis, correspondence analysis, multiple correspondence analysis, and hierarchical ascendant classification.
