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

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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