zbMATH — the first resource for mathematics

Principal component and correspondence analyses using R (to appear). (English) Zbl 06403069
SpringerBriefs in Statistics. Cham: Springer (ISBN 978-3-319-09255-3/pbk; 978-3-319-09256-0/ebook). x, 110 p. (2021).
Publisher’s description: With the right R packages, R is uniquely suited to perform Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), and metric multidimensional scaling (MMDS). The analyses depicted in this book use several packages specially developed for theses analyses and include (among others): the ExPosition suite, FactoMiner , ade4, and ca. The authors present each technique with one or several small examples that demonstrate how to enter the data, perform the standard analyses, and obtain professional quality graphics. Through explanations of the major options for how to carry out each method, readers can tailor the content of this book to their particular goals. Explanations include the effects of using particular packages. ExPosition is a great choice for the methods as it was written specifically for this book. However, options abound and are illustrated within unique scenarios. The first chapter includes installation of the packages. At the end of the book, a short appendix presents critical mathematical material for readers who want to go deeper into the theory.
62-02 Research exposition (monographs, survey articles) pertaining to statistics
62-04 Software, source code, etc. for problems pertaining to statistics
62H25 Factor analysis and principal components; correspondence analysis
R; ade4; ExPosition; FactoMineR; ca