Ecological Models and Data In R (ebook)

Argumento de Ecological Models and Data In R (ebook)

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

"[T]his book succeeds both in explaining how to analyze many types of ecological data, and in clearly describing the theoretical background behind some common analyses and approaches. I expect to refer to it often."--Lynda D. Prior, Austral Ecology

"[A] must for natural scientists and for statisticians who are interested in ecological applications. . . . Numerous enlightening footnotes, meaningful graphics and direct speech are evidence of substantial classroom experience of the author. . . . The book addresses students and researchers who have or have had some basic knowledge in ecology, mathematics and statistics. Delivering many examples and profound details on numerical aspects of maximum likelihood estimation, the book may also give a red line for a course in computational statistics."--Martin Schlather, Biometrical Journal

"This user-friendly introduction to likelihood and Bayesian statistical methods for ecology students is set apart by its emphasis on implementation in R. This alone will make it more useful than previous books. In contrast to other texts, Bolker's book explains how to fit models to data in enough detail that even students with little programming experience will be able to follow along. I expect this to become an exceedingly popular textbook."--Stephan B. Munch, Stony Brook University

"Benjamin Bolker is a pioneer in helping ecology students make the leap from a casual understanding of modern statistical methods to a hands-on application of these tools to their own precious data sets. This book shows the lessons learned from teaching this material to several cohorts of graduate students. No other book I've read gives such a good feel for the compromises scientists have to make in searching for good statistical models."--Brian Inouye, Florida State University

"I have no doubt that this book will become a fixture on many ecologists' bookshelves (it certainly will be on mine). With a presentation that is gentle and encouraging rather than jargon-filled and intimidating, it empowers ecologists to develop their own statistical procedures. I strongly recommend it."--Timothy Essington, University of Washington

"Bolker's book is a must-buy for anyone wanting to fit data to models and go beyond hypothesis testing, but it is certainly not an 'introductory' text in the sense of 'simple'. This book is a tour de force for anyone who studied ecology for his or her interest of nature's working. But it is the one single book that can propel the statistical novice to the cutting edge of statistical ecology--albeit with blood, sweat and tears."--Carsten F. Dormann, Basic and Applied Ecology0Acknowledgments ix

Chapter 1: Introduction and Background 1
1.1 Introduction 1
1.2 What This Book Is Not About 3
1.3 Frameworks for Modeling 5
1.4 Frameworks for Statistical Inference 10
1.5 Frameworks for Computing 17
1.6 Outline of the Modeling Process 20
1.7 R Supplement 22

Chapter 2: Exploratory Data Analysis and Graphics 29
2.1 Introduction 29
2.2 Getting Data into R 30
2.3 Data Types 34
2.4 Exploratory Data Analysis and Graphics 40
2.5 Conclusion 59
2.6 R Supplement 59

Chapter 3: Deterministic Functions for Ecological Modeling 72
3.1 Introduction 72
3.2 Finding Out about Functions Numerically 73
3.3 Finding Out about Functions Analytically 76
3.4 Bestiary of Functions 87
3.5 Conclusion 100
3.6 R Supplement 100

Chapter 4: Probability and Stochastic Distributions for Ecological Modeling 103
4.1 Introduction: Why Does Variability Matter? 103
4.2 Basic Probability Theory 104
4.3 Bayes? Rule 107
4.4 Analyzing Probability Distributions 115
4.5 Bestiary of Distributions 120
4.6 Extending Simple Distributions: Compounding and Generalizing 137
4.7 R Supplement 141

Chapter 5: Stochastic Simulation and Power Analysis 147
5.1 Introduction 147
5.2 Stochastic Simulation 148
5.3 Power Analysis 156

Chapter 6: Likelihood and All That 169
6.1 Introduction 169
6.2 Parameter Estimation: Single Distributions 169
6.3 Estimation for More Complex Functions 182
6.4 Likelihood Surfaces, Profiles, and Confidence Intervals 187
6.5 Confidence Intervals for Complex Models: Quadratic Approximation 196
6.6 Comparing Models 201
6.7 Conclusion 220

Chapter 7: Optimization and All That 222
7.1 Introduction 222
7.2 Fitting Methods 223
7.3 Markov Chain Monte Carlo 233
7.4 Fitting Challenges 241
7.5 Estimating Confidence Limits of Functions of Parameters 250
7.6 R Supplement 258

Chapter 8: Likelihood Examples 263
8.1 Tadpole Predation 263
8.2 Goby Survival 276
8.3 Seed Removal 283

Chapter 9: Standard Statistics Revisited 298
9.1 Introduction 298
9.2 General Linear Models 300
9.3 Nonlinearity: Nonlinear Least Squares 306
9.4 Nonnormal Errors: Generalized Linear Models 308
9.5 R Supplement 312

Chapter 10: Modeling Variance 316
10.1 Introduction 316
10.2 Changing Variance within Blocks 318
10.3 Correlations: Time-Series and Spatial Data 320
10.4 Multilevel Models: Special Cases 324
10.5 General Multilevel Models 327
10.6 Challenges 333
10.7 Conclusion 334
10.8 R Supplement 335

Chapter 11: Dynamic Models 337
11.1 Introduction 337
11.2 Simulating Dynamic Models 338
11.3 Observation and Process Error 342
11.4 Process and Observation Error 344
11.5 SIMEX 346
11.6 State-Space Models 348
11.7 Conclusions 357
11.8 R Supplement 360

Chapter 12: Afterword 362
Appendix Algebra and Calculus Basics 363
A.1 Exponentials and Logarithms 363
A.2 Differential Calculus 364
A.3 Partial Differentiation 364
A.4 Integral Calculus 365
A.5 Factorials and the Gamma Function 365
A.6 Probability 365
A.7 The Delta Method 366
A.8 Linear Algebra Basics 366

Bibliography 369
Index of R Arguments, Functions, and Packages 383
General Index 389