(Ebook) Practical Guide to Principal Component Methods in R by Alboukadel Kassambara ISBN 9781138196346, 9780387954424, 0387954422, 1138196347

(Ebook) Practical Guide to Principal Component Methods in R by Alboukadel Kassambara ISBN 9781138196346, 9780387954424, 0387954422, 1138196347

This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods (PCMs) in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra.Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include:■  Principal Component Analysis (PCA, for continuous variables),■  Simple correspondence analysis (CA, for large contingency tables formed by two categorical variables)■  Multiple correspondence analysis (MCA, for a data set with more than 2 categorical variables).In Part III, you’ll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups:■  Factor Analysis of Mixed Data (FAMD) and,■  Multiple Factor Analysis (MFA).Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variablesKey features of this book:This book presents the basic principles of the different methods and provide many examples in R. This book offers solid guidance in data mining for students and researchers.■  Covers principal component methods and implementation in R■  Highlights the most important information in your data set using ggplot2-based elegant visualization■  Short, self-contained chapters with tested examples that allow for flexibility in designing a course and for easy referenceAt the end of each chapter, we present R lab sections in which we systematically work through applications of the various methods discussed in that chapter. Additionally, we provide links to other resources and to our hand-curated list of videos on principal component methods for further learning.