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Exploratory Multivariate Analysis by Example Using R

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Release : 2010-11-15
Genre : Mathematics
Kind : eBook
Book Rating : 810/5 ( reviews)

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Book Synopsis Exploratory Multivariate Analysis by Example Using R by : Francois Husson

Download or read book Exploratory Multivariate Analysis by Example Using R written by Francois Husson. This book was released on 2010-11-15. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Exploratory Multivariate Analysis by Example Using R

Download Exploratory Multivariate Analysis by Example Using R PDF Online Free

Author :
Release : 2017-04-25
Genre : Mathematics
Kind : eBook
Book Rating : 865/5 ( reviews)

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Book Synopsis Exploratory Multivariate Analysis by Example Using R by : Francois Husson

Download or read book Exploratory Multivariate Analysis by Example Using R written by Francois Husson. This book was released on 2017-04-25. Available in PDF, EPUB and Kindle. Book excerpt: 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) a

Exploratory Multivariate Analysis by Example Using R

Download Exploratory Multivariate Analysis by Example Using R PDF Online Free

Author :
Release : 2020-09-30
Genre :
Kind : eBook
Book Rating : 021/5 ( reviews)

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Book Synopsis Exploratory Multivariate Analysis by Example Using R by : Francois Husson

Download or read book Exploratory Multivariate Analysis by Example Using R written by Francois Husson. This book was released on 2020-09-30. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Exploratory Multivariate Analysis by Example Using R

Download Exploratory Multivariate Analysis by Example Using R PDF Online Free

Author :
Release : 2017
Genre : MATHEMATICS
Kind : eBook
Book Rating : 668/5 ( reviews)

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Book Synopsis Exploratory Multivariate Analysis by Example Using R by : François Husson

Download or read book Exploratory Multivariate Analysis by Example Using R written by François Husson. This book was released on 2017. Available in PDF, EPUB and Kindle. Book excerpt:

An Introduction to Applied Multivariate Analysis with R

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Author :
Release : 2011-04-23
Genre : Mathematics
Kind : eBook
Book Rating : 508/5 ( reviews)

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Book Synopsis An Introduction to Applied Multivariate Analysis with R by : Brian Everitt

Download or read book An Introduction to Applied Multivariate Analysis with R written by Brian Everitt. This book was released on 2011-04-23. Available in PDF, EPUB and Kindle. Book excerpt: The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

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