Exploratory Multivariate Analysis by Example Using R,2nd Edition

Exploratory Multivariate Analysis by Example Using R,Second Edition (Chapman & Hall/CRC Computer Science & Data Analysis)
by Francois Husson,Sebastien Le,Jérôme Pagès
B071HLBP4P
Print Length 页数: 262 pages
Publisher finelybook 出版社: Chapman and Hall/CRC; 2 edition (24 May 2017)
Language 语言: English
ISBN-10: 1138196347
ISBN-13: 9781138196346
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.
The book has been written using minimal mathematics so as to appeal to applied statisticians,as well as researchers from various disciplines,including medical research and the social sciences. Readers can use the theory,examples,and software presented in this book in order to be fully equipped to tackle real-life multivariate data.
Contents
Chapter 1: Principal Component Analysis (Pca)
Chapter 2: Correspondence Analysis (Ca)
Chapter 3: Multiple Correspondence Analysis (Mca)
Chapter 4: Clustering
Chapter 5: Visualisation
充分的现实案例研究和实践建议，使用R的示例探索性多变量分析，第二版重点介绍最适合应用的多变量探索性数据分析的四个基本方法。当变量分类时，它包括变量定量，对应分析（CA）和多重对应分析（MCA）的主成分分析（PCA）和层次聚类分析。
作者以几何观点为探索多变量数据表提供了统一的观点。在这个框架内，他们介绍了代表和可视化探索方法共同的对象的原则，指标和方法。作者展示了如何在PCA上下文中使用分类变量，其中变量是量化的，如何处理原始两个变量的CA上下文中的两个以上的分类变量，以及如何在MCA上下文中添加定量变量变量是分类的。他们还说明了使用各种领域的示例的方法，相关的R代码可以在作者开发的FactoMineR包中访问。
这本书是使用最小数学写的，以吸引应用统计学家，以及来自各个学科的研究人员，包括医学研究和社会科学。读者可以使用本书中介绍的理论，实例和软件，以便能够全面地处理现实生活中的多变量数据。
目录
第1章: 主成分分析（Pca）
第二章: 信函分析（Ca）
第3章: 多重对应分析（Mca）
第4章: 聚类
第5章: 可视化

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