Multiple Factor Analysis by Example Using R  book cover
1st Edition

Multiple Factor Analysis by Example Using R

ISBN 9781482205473
Published November 20, 2014 by Chapman and Hall/CRC
272 Pages 94 B/W Illustrations

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Book Description

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR).

The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.

Table of Contents

Principal Component Analysis

Multiple Correspondence Analysis

Factor Analysis for Mixed Data

Weighting Groups of Variables

Comparing Clouds of Partial Individuals

Factors Common to Different Groups of Variables

Comparing Groups of Variables and Indscal Model

Qualitative and Mixed Data

Multiple Factor Analysis and Procrustes Analysis

Hierarchical Multiple Factor Analysis

Matrix Calculus and Euclidean Vector Space


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Jérôme Pagès is a professor of statistics at Agrocampus (Rennes, France), where he heads the Laboratory of Applied Mathematics (LMA²).