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Exploratory multivariate analysis by example using R

£69.99

Product description

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 https://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.

Item details

Author(s):
Francois Husson, Sebastien Le and Jerome Pages
Condition:
Used: very good
Edition:
2011
Format:
Hardback
ISBN-10:
1439835802
ISBN-13:
9781439835807
Number of pages:
228
Publisher:
CRC Press
Title:
Exploratory multivariate analysis by example using R

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About this item

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 https://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.

Author(s):
Francois Husson, Sebastien Le and Jerome Pages
Condition:
Used: very good
Edition:
2011
Format:
Hardback
ISBN-10:
1439835802
ISBN-13:
9781439835807
Number of pages:
228
Publisher:
CRC Press
Title:
Exploratory multivariate analysis by example using R

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