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Ecological Models and Data in R

Author Benjamin M. Bolker
Release Date: 2008/07/01
ISBN: 9781400840908
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Book Description

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics.


  • Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R

  • Step-by-step instructions for fitting models to messy, real-world data

  • Balanced view of different statistical approaches

  • Wide coverage of techniques--from simple (distribution fitting) to complex (state-space modeling)

  • Techniques for data manipulation and graphical display

  • Companion Web site with data and R code for all examples

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Contents
  5. Acknowledgments
  6. 1 - Introduction and Background
    1. 1.1 Introduction
    2. 1.2 What This Book is Not About
    3. 1.3 Frameworks for Modeling
    4. 1.4 Frameworks for Statistical Inference
    5. 1.5 Frameworks for Computing
    6. 1.6 Outline of the Modeling Process
    7. 1.7 R Supplement
  7. 2 - Exploratory Data Analysis and Graphics
    1. 2.1 Introduction
    2. 2.2 Getting Data into R
    3. 2.3 Data Types
    4. 2.4 Exploratory Data Analysis and Graphics
    5. 2.5 Conclusion
    6. 2.6 R Supplement
  8. 3 - Deterministic Functions for Ecological Modeling
    1. 3.1 Introduction
    2. 3.2 Finding Out About Functions Numerically
    3. 3.3 Finding Out About Functions Analytically
    4. 3.4 Bestiary of Functions
    5. 3.5 Conclusion
    6. 3.6 R Supplement
  9. 4 - Probability and Stochastic Distributions for Ecological Modeling
    1. 4.1 Introduction: Why does Variability Matter?
    2. 4.2 Basic Probability Theory
    3. 4.3 Bayes' Rule
    4. 4.4 Analyzing Probability Distributions
    5. 4.5 Bestiary of Distributions
    6. 4.6 Extending Simple Distributions: Compounding and Generalizing
    7. 4.7 R Supplement
  10. 5 - Stochastic Simulation and Power Analysis
    1. 5.1 Introduction
    2. 5.2 Stochastic Simulation
    3. 5.3 Power Analysis
  11. 6 - Likelihood and All That
    1. 6.1 Introduction
    2. 6.2 Parameter Estimation: Single Distributions
    3. 6.3 Estimation for More Complex Functions
    4. 6.4 Likelihood Surfaces, Profiles, and Confidence Intervals
    5. 6.5 Confidence Intervals for Complex Models: Quadratic Approximation
    6. 6.6 Comparing Models
    7. 6.7 Conclusion
  12. 7 - Optimization and All That
    1. 7.1 Introduction
    2. 7.2 Fitting Methods
    3. 7.3 Markov Chain Monte Carlo
    4. 7.4 Fitting Challenges
    5. 7.5 Estimating Confidence Limits of Functions of Parameters
    6. 7.6 R Supplement
  13. 8 - Likelihood Examples
    1. 8.1 Tadpole Predation
    2. 8.2 Goby Survival
    3. 8.3 Seed Removal
  14. 9 - Standard Statistics Revisited
    1. 9.1 Introduction
    2. 9.2 General Linear Models
    3. 9.3 Nonlinearity: Nonlinear Least Squares
    4. 9.4 Nonnormal Errors: Generalized Linear Models
    5. 9.5 R Supplement
  15. 10 - Modeling Variance
    1. 10.1 Introduction
    2. 10.2 Changing Variance within Blocks
    3. 10.3 Correlations: Time-Series and Spatial Data
    4. 10.4 Multilevel Models: Special Cases
    5. 10.5 General Multilevel Models
    6. 10.6 Challenges
    7. 10.7 Conclusion
    8. 10.8 R Supplement
  16. 11 - Dynamic Models
    1. 11.1 Introduction
    2. 11.2 Simulating Dynamic Models
    3. 11.3 Observation and Process Error
    4. 11.4 Process and Observation Error
    5. 11.5 SIMEX
    6. 11.6 State-Space Models
    7. 11.7 Conclusions
    8. 11.8 R Supplement
  17. 12 - Afterword
  18. Appendix Algebra and Calculus Basics
    1. A.1 Exponentials and Logarithms
    2. A.2 Differential Calculus
    3. A.3 Partial Differentiation
    4. A.4 Integral Calculus
    5. A.5 Factorials and the Gamma Function
    6. A.6 Probability
    7. A.7 The Delta Method
    8. A.8 Linear Algebra Basics
  19. Bibliography
  20. Index of R Arguments, Functions, and Packages
  21. General Index
18.118.144.12