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Practical Guide To Principal Component Methods In R Multivariate Analysis Book 2 Alboukadel Kassambara

  • SKU: BELL-6856044
Practical Guide To Principal Component Methods In R Multivariate Analysis Book 2 Alboukadel Kassambara
$ 31.00 $ 45.00 (-31%)

4.0

26 reviews

Practical Guide To Principal Component Methods In R Multivariate Analysis Book 2 Alboukadel Kassambara instant download after payment.

Publisher: STHDA
File Extension: PDF
File size: 8.16 MB
Pages: 205
Author: Alboukadel Kassambara
ISBN: 9781975721138, 1975721136
Language: English
Year: 2017

Product desciption

Practical Guide To Principal Component Methods In R Multivariate Analysis Book 2 Alboukadel Kassambara by Alboukadel Kassambara 9781975721138, 1975721136 instant download after payment.

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.

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