Many introductory books about principal component (PC) analysis using R have been published. These books describe how to use the princomp() function of R. The unique feature of this book is that it explains step-by-step the background theory of the princomp() function and helps readers identify PCs using examples ranging from simple to complex.
The prerequisites that readers of this book should be familiar with are as follows: the basics of differentiation; the basics of vectors; including the inner product of vectors; and the basics of matrix analysis, including the product, transpose, inverse, and eigenvalues of matrices.
The scripts used in this book can be downloaded from the site:
http://www.mybook-pub-site.sakura.ne.jp/multi_variate_analysis/PCA/index.html
This book uses R 3.0.2 for Windows. The software can be downloaded from http://cran.r-project.org/ for free. This book uses the Windows versions of scripts. Mac users only have to change the file path to load data files.
1. Introduction
2. Principal Component Analysis
2.1 Basic Theory
2.2 Calculation Using R
2.3 Data Centering and Normalization
2.4 Calculation using R – data centering and normalization
2.5 Contribution Ratio, PC Scores, and PC Loadings
2.6 Multi-Dimensional Data
2.7 Calculation using R (4 dimensional data)
2.8 Calculation using princomp() Function
3. Concluding Remarks
About the Author
Professor, Dept. of Computational Science and Eng., Nagoya University
The prerequisites that readers of this book should be familiar with are as follows: the basics of differentiation; the basics of vectors; including the inner product of vectors; and the basics of matrix analysis, including the product, transpose, inverse, and eigenvalues of matrices.
The scripts used in this book can be downloaded from the site:
http://www.mybook-pub-site.sakura.ne.jp/multi_variate_analysis/PCA/index.html
This book uses R 3.0.2 for Windows. The software can be downloaded from http://cran.r-project.org/ for free. This book uses the Windows versions of scripts. Mac users only have to change the file path to load data files.
1. Introduction
2. Principal Component Analysis
2.1 Basic Theory
2.2 Calculation Using R
2.3 Data Centering and Normalization
2.4 Calculation using R – data centering and normalization
2.5 Contribution Ratio, PC Scores, and PC Loadings
2.6 Multi-Dimensional Data
2.7 Calculation using R (4 dimensional data)
2.8 Calculation using princomp() Function
3. Concluding Remarks
About the Author
Professor, Dept. of Computational Science and Eng., Nagoya University
