Ecological Models and Data in R

ISBN-10: 0691125228
ISBN-13: 9780691125220
Edition: 2008
List price: $65.00 Buy it from $53.49
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Description: Ecological Models and Data in Ris 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  More...

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Book details

List price: $65.00
Copyright year: 2008
Publisher: Princeton University Press
Publication date: 7/21/2008
Binding: Hardcover
Pages: 408
Size: 7.25" wide x 10.00" long x 1.00" tall
Weight: 2.112

Ecological Models and Data in Ris 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

Acknowledgments
Introduction and Background
Introduction
What This Book Is Not About
Frameworks for Modeling
Frameworks for Statistical Inference
Frameworks for Computing
Outline of the Modeling Process
R Supplement
Exploratory Data Analysis and Graphics
Introduction
Getting Data into R
Data Types
Exploratory Data Analysis and Graphics
Conclusion
R Supplement
Deterministic Functions for Ecological Modeling
Introduction
Finding Out about Functions Numerically
Finding Out about Functions Analytically
Bestiary of Functions
Conclusion
R Supplement
Probability and Stochastic Distributions for Ecological Modeling
Introduction: Why Does Variability Matter?
Basic Probability Theory
Bayes' Rule
Analyzing Probability Distributions
Bestiary of Distributions
Extending Simple Distributions: Compounding and Generalizing
R Supplement
Stochastic Simulation and Power Analysis
Introduction
Stochastic Simulation
Power Analysis
Likelihood and All That
Introduction
Parameter Estimation: Single Distributions
Estimation for More Complex Functions
Likelihood Surfaces, Profiles, and Confidence Intervals
Confidence Intervals for Complex Models: Quadratic Approximation
Comparing Models
Conclusion
Optimization and All That
Introduction
Fitting Methods
Markov Chain Monte Carlo
Fitting Challenges
Estimating Confidence Limits of Functions of Parameters
R Supplement
Likelihood Examples
Tadpole Predation
Goby Survival
Seed Removal
Standard Statistics Revisited
Introduction
General Linear Models
Nonlinearity: Nonlinear Least Squares
Nonnormal Errors: Generalized Linear Models
R Supplement
Modeling Variance
Introduction
Changing Variance within Blocks
Correlations: Time-Series and Spatial Data
Multilevel Models: Special Cases
General Multilevel Models
Challenges
Conclusion
R Supplement
Dynamic Models
Introduction
Simulating Dynamic Models
Observation and Process Error
Process and Observation Error
SIMEX
State-Space Models
Conclusions
R Supplement
Afterword
Algebra and Calculus Basics
Exponentials and Logarithms
Differential Calculus
Partial Differentiation
Integral Calculus
Factorials and the Gamma Function
Probability
The Delta Method
Linear Algebra Basics
Bibliography
Index of R Arguments, Functions, and Packages
General Index

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