Chemometrics with R

This book offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a comprehensive description of the general data analysis paradigm, from exploratory analysis (principal component analysis, self-organizing maps and clustering) to modeling (classification, regression) and validation (including variable selection). It also includes a special section discussing several more specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all the examples in the book; and scripts, functions and data are available in a separate R package. This second revised edition features not only updates on many of the topics covered, but also several sections of new material (e. g. , on handling missing values in PCA, multivariate process monitoring and batch correction).

Introduction. - Data. - Preprocessing. - Principal Component Analysis. - Self-Organizing Maps. - Clustering. - Classification. - Multivariate Regression. - Validation. - Variable Selection. - Chemometric Applications.