Multivariate Statistics for Wildlife and Ecology Research

Multivariate Statistics for Wildlife and Ecology Research

Detalles Bibliográficos
Autores principales: McGarigal, Kevin. (Autor), Cushman, Samuel A. (Autor), Stafford, Susan. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: eBook
Lenguaje:English
Publicado: New York, NY : Springer New York : Imprint: Springer, 2000.
Edición:1st ed. 2000.
Materias:
Ecology .
Biomathematics.
Environmental sciences.
Wildlife.
Fish.
Statistics .
Theoretical Ecology/Statistics.
Ecology.
Mathematical and Computational Biology.
Math. Appl. in Environmental Science.
Fish & Wildlife Biology & Management.
Statistics for Life Sciences, Medicine, Health Sciences.
Acceso en línea:https://doi.org/10.1007/978-1-4612-1288-1
Tabla de Contenidos:
  • 1 Introduction and Overview
  • 1.1 Objectives
  • 1.2 Multivariate Statistics: An Ecological Perspective
  • 1.3 Multivariate Description and Inference
  • 1.4 Multivariate Confusion!
  • 1.5 Types of Multivariate Techniques
  • 2 Ordination: Principal Components Analysis
  • 2.1 Objectives
  • 2.2 Conceptual Overview
  • 2.3 Geometric Overview
  • 2.4 The Data Set
  • 2.5 Assumptions
  • 2.6 Sample Size Requirements
  • 2.7 Deriving the Principal Components
  • 2.8 Assessing the Importance of the Principal Components
  • 2.9 Interpreting the Principal Components
  • 2.10 Rotating the Principal Components
  • 2.11 Limitations of Principal Components Analysis
  • 2.12 R-Factor Versus Q-Factor Ordination
  • 2.13 Other Ordination Techniques
  • Appendix 2.1
  • 3 Cluster Analysis
  • 3.1 Objectives
  • 3.2 Conceptual Overview
  • 3.3 The Definition of Cluster
  • 3.4 The Data Set
  • 3.5 Clustering Techniques
  • 3.6 Nonhierarchical Clustering
  • 3.7 Hierarchical Clustering
  • 3.8 Evaluating the Stability of the Cluster Solution
  • 3.9 Complementary Use of Ordination and Cluster Analysis
  • 3.10 Limitations of Cluster Analysis
  • Appendix 3.1
  • 4 Discriminant Analysis
  • 4.1 Objectives
  • 4.2 Conceptual Overview
  • 4.3 Geometric Overview
  • 4.4 The Data Set
  • 4.5 Assumptions
  • 4.6 Sample Size Requirements
  • 4.7 Deriving the Canonical Functions
  • 4.8 Assessing the Importance of the Canonical Functions
  • 4.9 Interpreting the Canonical Functions
  • 4.10 Validating the Canonical Functions
  • 4.11 Limitations of Discriminant Analysis
  • Appendix 4.1
  • 5 Canonical Correlation Analysis
  • 5.1 Objectives
  • 5.2 Conceptual Overview
  • 5.3 Geometric Overview
  • 5.4 The Data Set
  • 5.5 Assumptions
  • 5.6 Sample Size Requirements
  • 5.7 Deriving the Canonical Variates
  • 5.8 Assessing the Importance of the Canonical Variates
  • 5.9 Interpreting the Canonical Variates
  • 5.10 Validating the Canonical Variates
  • 5.11 Limitations of Canonical Correlation Analysis
  • Appendix 5.1
  • 6 Summary and Comparison
  • 6.1 Objectives
  • 6.2 Relationship Among Techniques
  • 6.3 Complementary Use of Techniques
  • Appendix: Acronyms Used in This Book. .

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