Centre documentaire

Numerical ecology with R

DOC1 Numerical ecology
DOC2 Numerical ecology with R

DOC1 Numerical ecology
Preface
1. Complex ecological data sets
1.0. Numerical analysis of ecological data
1.1. Autocorrelation and spatial structure
1.2. Statistical testing by permutation
1.3. Computers
1.4. Ecological descriptors
1.5. Coding
1.6. Missing data
2. Matrix algebra: a summary
2.0. Matrix algebra
2.1. The ecological data matrix
2.2. Association matrices
2.3. Special matrices
2.4. Vectors and scaling
2.5. Matrix addition and multiplication
2.6. Determinant
2.7. The rank of a matrix
2.8. Matrix inversion
2.9. Eigenvalues and eigenvectors
2.10. Some properties of eigenvalues and eigenvectors
2.11. Singular value decomposition
3. Dimensional analysis in ecology
3.0. Dimensional analysis
3.1. Dimensions
3.2. Fundamental principles and the Pi theorem
3.3. The complete set of dimensionless products
3.4. Scale factors and models
4. Multidimensional quantitative data
4.0. Multidimensional statistics
4.1. Multidimensional variables and dispersion matrix
4.2. Correlation matrix
4.3. Multinormal distribution
4.4. Principal axes
4.5. Multiple and partial correlations
4.6. Multinormal conditional distribution
4.7. Tests of normality and multinormality
5. Multidimensional semiquantitative data
5.0. Nonparametric statistics
5.1. Quantitative, semiquantitative, and qualitative multivariates
5.2. One-dimensional nonparametric statistics
5.3. Multidimensional ranking tests
6. Multidimensional qualitative data
6.0. General principles
6.1. Information and entropy
6.2. Two-way contingency tables
6.3. Multiway contingency tables
6.4. Contingency tables: correspondence
6.5. Species diversity
7. Ecological resemblance
7.0. The basis for clustering and ordination
7.1. Q and R analyses
7.2. Association coefficients
7.3. Q mode: similarity coefficients
7.4. Q mode: distance coefficients
7.5. R mode: coefficients of dependence
7.6. Choice of a coefficient
7.7. Computer programs and packages
8. Cluster analysis
8.0. A search for discontinuities
8.1. Definitions
8.2. The basic model: single linkage clustering
8.3. Cophenetic matrix and ultrametric property
8.4. The panoply of methods
8.5. Hierarchical agglomerative clustering
8.6. Reversals
8.7. Hierarchical divisive clustering
8.8. Partitioning by K-means
8.9. Species clustering: biological associations
8.10. Seriation
8.11. Clustering statistics
8.12. Cluster validation
8.13. Cluster representation and choice of a method
9. Ordination in reduced space
9.0. Projecting data sets in a few dimensions
9.1. Principal component analysis (PCA)
9.2. Principal coordinate analysis (PCoA)
9.3. Nonmetric multidimensional scaling (MDS)
9.4. Correspondence analysis (CA)
9.5. Factor analysis
10. Interpretation of ecological structures
10.0. Ecological structures
10.1. Clustering and ordination
10.2. The mathematics of ecological interpretation
10.3. Regression
10.4. Path analysis
10.5. Matrix comparisons
10.6. The 4th-corner problem
11. Canonical analysis
11.0. Principles of canonical analysis
11.1. Redundancy analysis (RDA)
11.2. Canonical correspondence analysis (CCA)
11.3. Partial RDA and CCA
11.4. Canonical correlation analysis (CCorA)
11.5. Discriminant analysis
11.6. Canonical analysis of species data
12. Ecological data series
12.0. Ecological series
12.1. Characteristics of data series and research objectives
12.2. Trend extraction and numerical filters
12.3. Periodic variability: correlogram
12.4. Periodic variability: periodogram
12.5. Periodic variability: spectral analysis
12.6. Detection of discontinuities in multivariate series
12.7. Box-Jenkins models
12.8. Computer programs
13. Spatial analysis
13.0. Spatial patterns
13.1. Structure functions
13.2. Maps
13.3. Patches and boundaries
13.4. Unconstrained and constrained ordination maps
13.5. Causal modelling: partial canonical analysis
13.6. Causal modelling: partial Mantel analysis
13.7. Computer programs
Bibliography
Tables
Subject index

DOC2 Numerical ecology with R
1. Introduction
1.1. Why Numerical Ecology?
1.2. Why R?
1.3. Readership and Structure of the Book
1.4. How to Use This Book
1.5. The Data Sets
1.6. A Quick Reminder about Help Sources
1.7. Now It Is Time
2. Exploratory Data Analysis
2.1. Objectives
2.2. Data Exploration
2.3. Conclusion
3. Association Measures and Matrices
3.1. Objectives
3.2. The Main Categories of Association Measures (Short Overview)
3.3. Q Mode: Computing Distance Matrices Among Objects
3.4. R Mode: Computing Dependence Matrices Among Variables
3.5. Pre-transformations for Species Data
3.6. Conclusion
4. Cluster Analysis
4.1. Objectives
4.2. Clustering Overview
4.3. Hierarchical Clustering Based on Links
4.4. Average Agglomerative Clustering
4.5. Ward’s Minimum Variance Clustering
4.6. Flexible Clustering
4.7. Interpreting and Comparing Hierarchical Clustering Results
4.8. Non-hierarchical Clustering
4.9. Comparison with Environmental Data
4.10. Species Assemblages
4.11. Multivariate Regression Trees: Constrained Clustering
4.12. A Very Different Approach: Fuzzy Clustering
4.13. Conclusion
5. Unconstrained Ordination
5.1. Objectives
5.2. Ordination Overview
5.3. Principal Component Analysis
5.4. Correspondence Analysis
5.5. Principal Coordinate Analysis
5.6. Nonmetric Multidimensional Scaling
5.7. Handwritten Ordination Function
6. Canonical Ordination
6.1. Objectives
6.2. Canonical Ordination Overview
6.3. Redundancy Analysis
6.4. Canonical Correspondence Analysis
6.5. Linear Discriminant Analysis
6.6. Other Asymmetrical Analyses
6.7. Symmetrical Analysis of Two (or More) Data Sets
6.8. Canonical Correlation Analysis
6.9. Co-inertia Analysis
6.10. Multiple Factor Analysis
6.11. Conclusion
7. Spatial Analysis of Ecological Data
7.1. Objectives
7.2. Spatial Structures and Spatial Analysis: A Short Overview
7.3. Multivariate Trend-Surface Analysis
7.4. Eigenvector-Based Spatial Variables and Spatial Modelling
7.5. Another Way to Look at Spatial Structures: Multiscale Ordination
7.6. Conclusion
Bibliographical References
Index