Numerical Ecology with R

Name: Numerical Ecology with R
Availability: OutOfStock
ISBN: 9781441979759

ISBN-10: 1441979751

ISBN-13: 9781441979759

Edition: 2011

Authors: Daniel Borcard, Francois Gillet, Pierre Legendre

List price: $49.99

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Book details

List price: $49.99
Copyright year: 2011
Publisher: Springer London, Limited
Publication date: 1/14/2011
Binding: Paperback
Pages: 306
Size: 6.25" wide x 9.25" long x 0.75" tall
Weight: 1.012
Language: English




Introduction



Why Numerical Ecology?



Why R?



Readership and Structure of the Book



How to Use This Book



The Data Sets



The Doubs Fish Data



The Oribatid Mite Data



A Quick Reminder about Help Sources



Now It Is Time



Exploratory Data Analysis



Objectives



Data Exploration



Data Extraction



Species Data: First Contact



Species Data: A Closer Look



Species Data Transformation



Environmental Data



Conclusion



Association Measures and Matrices



Objectives



The Main Categories of Association Measures (Short Overview)



Q Mode and R Mode



Symmetrical or Asymmetrical Coefficients in Q Mode: The Double-Zero Problem



Association Measures for Qualitative or Quantitative Data



To Summarize



Q Mode: Computing Distance Matrices Among Objects



Q Mode: Quantitative Species Data



Q Mode: Binary (Presence-Absence) Species Data



Q Mode: Quantitative Data (Excluding Species Abundances)



Q Mode: Binary Data (Excluding Species Presence-Absence Data)



Q Mode: Mixed Types, Including Categorical (Qualitative Multiclass) Variables



R Mode: Computing Dependence Matrices Among Variables



R Mode: Species Abundance Data



R Mode: Species Presence-Absence Data



R Mode: Quantitative and Ordinal Data (Other than Species Abundances)



R Mode: Binary Data (Other than Species Abundance Data)



Pre-transformations for Species Data



Conclusion



Cluster Analysis



Objectives



Clustering Overview



Hierarchical Clustering Based on Links



Single Linkage Agglomerative Clustering



Complete Linkage Agglomerative Clustering



Average Agglomerative Clustering



Ward's Minimum Variance Clustering



Flexible Clustering



Interpreting and Comparing Hierarchical Clustering Results



Introduction



Cophenetic Correlation



Looking for Interpretable Clusters



Non-hierarchical Clustering



K-Means Partitioning



Partitioning Around Medoids



Comparison with Environmental Data



Comparing a Typology with External Data (ANOVA Approach)



Comparing Two Typologies (Contingency Table Approach)



Species Assemblages



Simple Statistics on Group Contents



Kendall's W Coefficient of Concordance



Species Assemblages in Presence-Absence Data



IndVal: Species Indicator Values



Multivariate Regression Trees: Constrained Clustering



Introduction



Computation (Principle)



Application Using Packages mvpart and MVPARTwrap



Combining MRT and IndVal



MRT as a ï¿½Chronologicalï¿½ Clustering Method



A Very Different Approach: Fuzzy Clustering



Fuzzy c-Means Clustering Using cluster's Function fanny()



Conclusion



Unconstrained Ordination



Objectives



Ordination Overview



Multidimensional Space



Ordination in Reduced Space



Principal Component Analysis



Overview



PCA on the Environmental Variables of the Doubs Data Set Using rda()



PCA on Transformed Species Data



Domain of Application of PCA



PCA Using Function PCA()



Correspondence Analysis



Introduction



CA Using Function cca() of Package vegan



CA Using Function CA()



Arch Effect and Detrended Correspondence Analysis



Multiple Correspondence Analysis



Principal Coordinate Analysis



Introduction



Application to the Doubs Data Set Using cmdscale and vegan



Application to the Doubs Data Set Using pcoa()



Nontnetric Multidimensional Scaling



Introduction



Application to the Fish Data



Handwritten Ordination Function



Canonical Ordination



Objectives



Canonical Ordination Overview



Redundancy Analysis



Introduction



RDA of the Doubs River Data



A Hand-Written RDA Function



Canonical Correspondence Analysis



Introduction



CCA of the Doubs Data



Linear Discriminant Analysis



Introduction



Discriminant Analysis Using lda()



Other Asymmetrical Analyses



Symmetrical Analysis of Two (or More) Data Sets



Canonical Correlation Analysis



Introduction



Canonical Correlation Analysis using CCorA



Co-inertia Analysis



Introduction



Co-inertia Analysis Using ade4



Multiple Factor Analysis



Introduction



Multiple Factor Analysis Using FactoMineR



Conclusion



Spatial Analysis of Ecological Data



Objectives



Spatial Structures and Spatial Analysis: A Short Overview



Introduction



Induced Spatial Dependence and Spatial Autocorrelation



Spatial Scale



Spatial Heterogeneity



Spatial Correlation or Autocorrelation Functions and Spatial Correlograms



Testing for the Presence of Spatial Correlation: Conditions



Modelling Spatial Structures



Multivariate Trend-Surface Analysis



Introduction



Trend-Surface Analysis in Practice



Eigenvector-Based Spatial Variables and Spatial Modelling



Introduction



Classical Distance-Based MEM, Formerly Called Principal Coordinates of Neighbour Matrices



MEM in a Wider Context: Weights Other than Geographic Distances



MEM with Positive or Negative Spatial Correlation: Which Ones Should Be Used?



Asymmetric Eigenvector Maps: When Directionality Matters



Another Way to Look at Spatial Structures: Multiscale Ordination



Principle



Application to the Mite Data: Exploratory Approach



Application to the Detrended Mite and Environmental Data



Conclusion


Bibliographical References


Index