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Original Essays | September 15, 2014

Lois Leveen: IMG Forsooth Me Not: Shakespeare, Juliet, Her Nurse, and a Novel



There's this writer, William Shakespeare. Perhaps you've heard of him. He wrote this play, Romeo and Juliet. Maybe you've heard of it as well. It's... Continue »
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    Lois Leveen 9781476757445

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Chapman & Hall/CRC Computer Science & Data Analysis #15: Exploratory Multivariate Analysis by Example Using R

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Chapman & Hall/CRC Computer Science & Data Analysis #15: Exploratory Multivariate Analysis by Example Using R Cover

 

Synopses & Reviews

Publisher Comments:

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.

Book News Annotation:

Writing for scientists who have no desire to become statisticians, but want to analyze their own data, Husson, Sébastien Lê, and Jérôme Pagés describe four basic methods of multivariate exploratory data analysis that have the largest potential for applications. They are principal component analysis when variables are quantitative, correspondence analysis and multiple correspondence analysis when variables are categorical, and hierarchical cluster analysis. Their treatment is practical rather than theoretical, substituting examples and intuition for formalism and mathematics when possible. An undergraduate understanding of mathematics should suffice, along with at least an introduction to the R programming language. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

"An introduction to exploratory techniques for multivariate data analysis, this book covers the key methodology, including principal components analysis, correspondence analysis, mixed models and multiple factor analysis. The authors take a practical approach, with examples leading the discussion of the methods and lots of graphics to emphasize visualization. They present the concepts in the most intuitive way possible, keeping mathematical content to a minimum or relegating it to the appendices. The book includes examples that use real data from a range of scientific disciplines and implemented using an R package developed by the authors"--

Product Details

ISBN:
9781439835807
Author:
Husson, Francois
Publisher:
CRC Press
Author:
Pages, Jerome
Author:
Le, Sebastien
Subject:
Probability & Statistics - General
Subject:
Mathematics - General
Series:
Chapman & Hall/CRC Computer Science & Data Analysis
Series Volume:
15
Publication Date:
20101131
Binding:
Hardcover
Language:
English
Pages:
240

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Chapman & Hall/CRC Computer Science & Data Analysis #15: Exploratory Multivariate Analysis by Example Using R New Hardcover
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Product details 240 pages CRC Press - English 9781439835807 Reviews:
"Synopsis" by , "An introduction to exploratory techniques for multivariate data analysis, this book covers the key methodology, including principal components analysis, correspondence analysis, mixed models and multiple factor analysis. The authors take a practical approach, with examples leading the discussion of the methods and lots of graphics to emphasize visualization. They present the concepts in the most intuitive way possible, keeping mathematical content to a minimum or relegating it to the appendices. The book includes examples that use real data from a range of scientific disciplines and implemented using an R package developed by the authors"--
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