Full of real-world case studies and pratical advice, Exploratoy Multivariate Analysis by Exemple 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 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 and ways these 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.
Features
- Illustrates each statistical method with several real-world examples
- Contains datasets from different areas of application, including genomics, marketing, and sensory analysis
- Uses clustering techniques in a principal components framework
- Provides datasets and code on the book’s Web site
By using the theory, examples, and software presented in this book, readers will be fully equipped to tackle real-life multivariate data.
