Numerical Ecology with R

ISBN-10: 1441979751
ISBN-13: 9781441979759
Edition: 2011
Buy it from $63.16
This item qualifies for FREE shipping

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

30 day, 100% satisfaction guarantee

If an item you ordered from TextbookRush does not meet your expectations due to an error on our part, simply fill out a return request and then return it by mail within 30 days of ordering it for a full refund of item cost.

Learn more about our returns policy

New Starting from $82.84
what's this?
Rush Rewards U
Members Receive:
coins
coins
You have reached 400 XP and carrot coins. That is the daily max!
You could win $10,000

Get an entry for every item you buy, rent, or sell.

Study Briefs

Limited time offer: Get the first one free! (?)

All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.

Add to cart
Study Briefs
History of Western Art Online content $4.95 $1.99
Add to cart
Study Briefs
History of World Philosophies Online content $4.95 $1.99
Add to cart
Study Briefs
American History Volume 1 Online content $4.95 $1.99
Add to cart
Study Briefs
History of Western Music Online content $4.95 $1.99

Customers also bought

Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading

Book details

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 �Chronological� 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

×
Free shipping on orders over $35*

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

Learn more about the TextbookRush Marketplace.

×