Practical Guide To Principal Component Methods in R: Volume 2 (Multivariate Analysis) - Image 1

£47.99

CREATESPACE Practical Guide To Principal Component Methods in R: Volume 2 (Multivariate Analysis)

Name: Practical Guide To Principal Component Methods in R: Volume 2 (Multivariate Analysis)
Brand: CREATESPACE
Price: 47.99 GBP
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43241-46311

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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 analysis 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) and Multiple CA (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 variables.

Product Specifications

Brand: CREATESPACE
Model: 43241-46311
Format: paperback
ASIN: 1975721136
Category: Books > Subjects
Domain: Amazon UK
Release Date: 22 August 2017
Listed Since: 23 August 2017

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CREATESPACE Practical Guide To Principal Component Methods in R: Volume 2 (Multivariate Analysis)

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