Multiple Factor Analysis by Example Using R (Hardcover)

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.

多重因素分析（MFA）使使用者能夠分析包含定量、定性或混合群組結構的個體和變數表。《Multiple Factor Analysis by Example Using R》是這種方法的共同開發者所撰寫，將MFA的理論和方法論結合在一起。它還包括應用示例和使用R套件（FactoMineR）實施MFA的詳細資訊。

前兩章介紹了基本的因子分析方法，包括主成分分析（PCA）和多重對應分析（MCA）。接下來的一章討論了混合數據的因子分析（FAMD），這是一種不區分群組同時分析定量和定性變數的鮮為人知的方法。接下來的章節專注於MFA，探討了在定量變數以及定性和混合數據背景下的MFA的關鍵點。作者還比較了MFA和Procrustes分析，並介紹了MFA的自然擴展：階層MFA（HMFA）。最後一章探討了本書中使用的矩陣計算和度量空間的幾個要素。