Ecological models and data in R

Title:

Personal Author:

Bolker, Benjamin M., 1967-

Publication Information:

Princeton, N.J. : Princeton University Press, c2008

Physical Description:

vii, 396 p. : ill. ; 26 cm.

ISBN:

9780691125220

Subject Term:

Ecology -- Statistical methods

Ecology -- Mathematical models

Ecology -- Simulation methods

R (Computer program language)

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010278095	QH541.15.S72 B65 2008	Open Access Book	Book	Searching... Unknown

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

Author Notes

Benjamin M. Bolker is a theoretical ecologist in the departments of Mathematics & Statistics and Biology at McMaster University.

Acknowledgments	p. ix
1 Introduction and Background	p. 1
1.1 Introduction	p. 1
1.2 What This Book Is Not About	p. 3
1.3 Frameworks for Modeling	p. 5
1.4 Frameworks for Statistical Inference	p. 10
1.5 Frameworks for Computing	p. 17
1.6 Outline of the Modeling Process	p. 20
1.7 R Supplement	p. 22
2 Exploratory Data Analysis and Graphics	p. 29
2.1 Introduction	p. 29
2.2 Getting Data into R	p. 30
2.3 Data Types	p. 34
2.4 Exploratory Data Analysis and Graphics	p. 40
2.5 Conclusion	p. 59
2.6 R Supplement	p. 59
3 Deterministic Functions for Ecological Modeling	p. 72
3.1 Introduction	p. 72
3.2 Finding Out about Functions Numerically	p. 73
3.3 Finding Out about Functions Analytically	p. 76
3.4 Bestiary of Functions	p. 87
3.5 Conclusion	p. 100
3.6 R Supplement	p. 100
4 Probability and Stochastic Distributions for Ecological Modeling	p. 103
4.1 Introduction: Why Does Variability Matter?	p. 103
4.2 Basic Probability Theory	p. 104
4.3 Bayes' Rule	p. 107
4.4 Analyzing Probability Distributions	p. 115
4.5 Bestiary of Distributions	p. 120
4.6 Extending Simple Distributions: Compounding and Generalizing	p. 137
4.7 R Supplement	p. 141
5 Stochastic Simulation and Power Analysis	p. 147
5.1 Introduction	p. 147
5.2 Stochastic Simulation	p. 148
5.3 Power Analysis	p. 156
6 Likelihood and All That	p. 169
6.1 Introduction	p. 169
6.2 Parameter Estimation: Single Distributions	p. 169
6.3 Estimation for More Complex Functions	p. 182
6.4 Likelihood Surfaces, Profiles, and Confidence Intervals	p. 187
6.5 Confidence Intervals for Complex Models: Quadratic Approximation	p. 196
6.6 Comparing Models	p. 201
6.7 Conclusion	p. 220
7 Optimization and All That	p. 222
7.1 Introduction	p. 222
7.2 Fitting Methods	p. 223
7.3 Markov Chain Monte Carlo	p. 233
7.4 Fitting Challenges	p. 241
7.5 Estimating Confidence Limits of Functions of Parameters	p. 250
7.6 R Supplement	p. 258
8 Likelihood Examples	p. 263
8.1 Tadpole Predation	p. 263
8.2 Goby Survival	p. 276
8.3 Seed Removal	p. 283
9 Standard Statistics Revisited	p. 298
9.1 Introduction	p. 298
9.2 General Linear Models	p. 300
9.3 Nonlinearity: Nonlinear Least Squares	p. 306
9.4 Nonnormal Errors: Generalized Linear Models	p. 308
9.5 R Supplement	p. 312
10 Modeling Variance	p. 316
10.1 Introduction	p. 316
10.2 Changing Variance within Blocks	p. 318
10.3 Correlations: Time-Series and Spatial Data	p. 320
10.4 Multilevel Models: Special Cases	p. 324
10.5 General Multilevel Models	p. 327
10.6 Challenges	p. 333
10.7 Conclusion	p. 334
10.8 R Supplement	p. 335
11 Dynamic Models	p. 337
11.1 Introduction	p. 337
11.2 Simulating Dynamic Models	p. 338
11.3 Observation and Process Error	p. 342
11.4 Process and Observation Error	p. 344
11.5 SIMEX	p. 346
11.6 State-Space Models	p. 348
11.7 Conclusions	p. 357
11.8 R Supplement	p. 360
12 Afterword	p. 362
Appendix Algebra and Calculus Basics	p. 363
A.1 Exponentials and Logarithms	p. 363
A.2 Differential Calculus	p. 364
A.3 Partial Differentiation	p. 364
A.4 Integral Calculus	p. 365
A.5 Factorials and the Gamma Function	p. 365
A.6 Probability	p. 365
A.7 The Delta Method	p. 366
A.8 Linear Algebra Basics	p. 366
Bibliography	p. 369
Index of R Arguments, Functions, and Packages	p. 383
General Index	p. 389

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Summary

Summary

Author Notes

Table of Contents