Chemometrics with R: Multivariate Data Analysis in the Natural and Life Sciences

Preface to the Second Edition
Preface to the First Edition
Contents
1 Introduction
Part I Preliminaries
2 Data
3 Preprocessing
	3.1 Dealing with Noise
	3.2 Baseline Removal
	3.3 Aligning Peaks—Warping
		3.3.1 Parametric Time Warping
		3.3.2 Dynamic Time Warping
		3.3.3 Practicalities
	3.4 Peak Picking
	3.5 Scaling
	3.6 Missing Data
	3.7 Conclusion
Part II Exploratory Analysis
4 Principal Component Analysis
	4.1 The Machinery
	4.2 Doing It Yourself
	4.3 Choosing the Number of PCs
		4.3.1 Scree Plots
		4.3.2 Statistical Tests
	4.4 Projections
	4.5 R Functions for PCA
	4.6 Related Methods
		4.6.1 Multidimensional Scaling
		4.6.2 Independent Component Analysis and Projection Pursuit
		4.6.3 Factor Analysis
		4.6.4 Discussion
5 Self-Organizing Maps
	5.1 Training SOMs
	5.2 Visualization
	5.3 Application
	5.4 R Packages for SOMs
	5.5 Discussion
6 Clustering
	6.1 Hierarchical Clustering
	6.2 Partitional Clustering
		6.2.1 K-Means
		6.2.2 K-Medoids
	6.3 Probabilistic Clustering
	6.4 Comparing Clusterings
	6.5 Discussion
Part III Modelling
7 Classification
	7.1 Discriminant Analysis
		7.1.1 Linear Discriminant Analysis
		7.1.2 Crossvalidation
		7.1.3 Fisher LDA
		7.1.4 Quadratic Discriminant Analysis
		7.1.5 Model-Based Discriminant Analysis
		7.1.6 Regularized Forms of Discriminant Analysis
	7.2 Nearest-Neighbor Approaches
	7.3 Tree-Based Approaches
		7.3.1 Recursive Partitioning and Regression Trees
		7.3.2 Discussion
	7.4 More Complicated Techniques
		7.4.1 Support Vector Machines
		7.4.2 Artificial Neural Networks
8 Multivariate Regression
	8.1 Multiple Regression
		8.1.1 Limits of Multiple Regression
	8.2 PCR
		8.2.1 The Algorithm
		8.2.2 Selecting the Optimal Number of Components
	8.3 Partial Least Squares (PLS) Regression
		8.3.1 The Algorithm(s)
		8.3.2 Interpretation
	8.4 Ridge Regression
	8.5 Continuum Methods
	8.6 Some Non-linear Regression Techniques
		8.6.1 SVMs for Regression
		8.6.2 ANNs for Regression
	8.7 Classification as a Regression Problem
		8.7.1 Regression for LDA
		8.7.2 PCDA
		8.7.3 PLSDA
		8.7.4 Discussion
Part IV Model Inspection
9 Validation
	9.1 Representativity and Independence
	9.2 Error Measures
	9.3 Model Selection
		9.3.1 Permutation Approaches
		9.3.2 Model Selection Indices
		9.3.3 Including Model Selection in the Validation
	9.4 Crossvalidation Revisited
		9.4.1 LOO Crossvalidation
		9.4.2 Leave-Multiple-Out Crossvalidation
		9.4.3 Double Crossvalidation
	9.5 The Jackknife
	9.6 The Bootstrap
		9.6.1 Error Estimation with the Bootstrap
		9.6.2 Confidence Intervals for Regression Coefficients
		9.6.3 Other R Packages for Bootstrapping
	9.7 Integrated Modelling and Validation
		9.7.1 Bagging
		9.7.2 Random Forests
		9.7.3 Boosting
10 Variable Selection
	10.1 Coefficient Significance
		10.1.1 Confidence Intervals for Individual Coefficients
		10.1.2 Tests Based on Overall Error Contributions
	10.2 Explicit Coefficient Penalization
	10.3 Global Optimization Methods
		10.3.1 Simulated Annealing
		10.3.2 Genetic Algorithms
		10.3.3 Discussion
Part V Applications
11 Chemometric Applications
	11.1 PCA in the Presence of Missing Values
		11.1.1 Ignoring the Missing Values
		11.1.2 Single Imputation
		11.1.3 Multiple Imputation
	11.2 Outlier Detection with Robust PCA
		11.2.1 Robust PCA
		11.2.2 Discussion
	11.3 Multivariate Process Monitoring
	11.4 Orthogonal Signal Correction and OPLS
	11.5 Biomarker Identification
	11.6 Calibration Transfer
	11.7 Batch Correction
	11.8 Multivariate Curve Resolution
		11.8.1 Theory
		11.8.2 Finding Suitable Initial Estimates
		11.8.3 Applying MCR
		11.8.4 Constraints
		11.8.5 Combining Data Sets
Appendix  References
Index