Principal Component and Correspondence Analyses Using R

With the right R packages, R is uniquely suited to perform Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), and metric multidimensional scaling (MMDS). The analyses depicted in this book use several packages specially developed for theses analyses and include (among others): the ExPosition suite, FactoMiner, ade4, and ca. The authors present each technique with one or several small examples that demonstrate how to enter the data, perform the standard analyses, and obtain professional quality graphics. Through explanations of the major options for how to carry out each method, readers can tailor the content of this book to their particular goals. Explanations include the effects of using particular packages. ExPosition is a great choice for the methods as it was written specifically for this book. However, options abound and are illustrated within unique scenarios. The first chapter includes installation of the packages. At the end of the book, a short appendix presents critical mathematical material for readers who want to go deeper into the theory.

使用合適的 R 套件，R 語言非常適合進行主成分分析 (PCA)、對應分析 (CA)、多重對應分析 (MCA) 和度量多維尺度分析 (MMDS)。本書中所呈現的分析使用了幾個專為這些分析而開發的套件，包括 ExPosition 套件、FactoMiner、ade4 和 ca 等。作者通過一個或多個小例子來介紹每種技術，演示如何輸入數據、進行標準分析並獲得專業質量的圖形。通過解釋每種方法的主要選項，讀者可以根據自己的目標來定制本書的內容。解釋還包括使用特定套件的效果。ExPosition 是這些方法的一個很好的選擇，因為它是專為本書而寫的。然而，還有其他選項，並且在獨特的場景中進行了演示。第一章介紹了套件的安裝。在書的末尾，附錄提供了一些重要的數學材料，供希望深入研究理論的讀者參考。

Hervé Abdi is currently a full professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas and is the author or co-author of more than 250 publications (including 12 books). His recent work is concerned with face and person perception, odor perception, and with computational modeling of these processes. He is also developing statistical techniques to analyze the structure of large data sets as found, for example, in Genomics, brain imaging, and sensory evaluation (e.g., principal component analysis, correspondence analysis, Partial Least Square methods, STATIS, DISTATIS, discriminant correspondence analysis, multiple factor analysis, multi-table analysis, and additive tree representations). He is co-author (with Derek Beaton) of several R packages implementing these techniques. He teaches or has taught classes in cognition, computational modeling, experimental design, multivariate statistics, and the analysis of brain imaging data.

Derek Beaton has a background in computer science and is currently working towards his PhD in Cognition and Neuroscience under his advisor, Dr. Hervé Abdi. Derek's interests are in developing new statistical approaches to better understand the contributions of genetics to brain and behavior. Recently, Derek was awarded a National Institutes of Health Ruth Kirschstein F31 fellowship via National Institute of Drug Abuse. His fellowship (co-sponsored by Drs. Hervé Abdi and Francesca Filbey) aims to reveal the genetic contributions to substance abuse and related traits. He is the main author of several R packages implementing the techniques described in this book.

Hervé Abdi目前是德克薩斯大學達拉斯分校行為和腦科學學院的正教授，並且是超過250篇出版物（包括12本書）的作者或合著者。他最近的工作涉及面孔和人物感知、氣味感知以及這些過程的計算建模。他還正在開發統計技術，以分析大數據集的結構，例如基因組學、腦成像和感官評估中的數據（例如主成分分析、對應分析、偏最小二乘法、STATIS、DISTATIS、判別對應分析、多因素分析、多表分析和加法樹表示）。他與Derek Beaton合著了幾個實現這些技術的R軟件包。他教授或曾教授認知、計算建模、實驗設計、多變量統計和腦成像數據分析等課程。

Derek Beaton具有計算機科學背景，目前在他的指導教授Hervé Abdi的指導下攻讀認知和神經科學的博士學位。Derek的興趣在於開發新的統計方法，以更好地理解基因對大腦和行為的貢獻。最近，Derek通過國家藥物濫用研究所獲得了國家衛生研究院Ruth Kirschstein F31獎學金。他的獎學金（由Hervé Abdi博士和Francesca Filbey博士共同贊助）旨在揭示基因對物質濫用和相關特徵的貢獻。他是幾個實現本書所描述技術的R軟件包的主要作者。