Ecological Models and Data in R / / Benjamin M. Bolker.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Bolker, Benjamin M.,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2008]; ©2008
• Year of Publication: 2008
• Language: English
• Physical Description: 1 online resource (408 p.)

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

Frontmatter -- Contents -- Acknowledgments -- 1. Introduction and Background -- 2. Exploratory Data Analysis and Graphics -- 3. Deterministic Functions for Ecological Modeling -- 4. Probability and Stochastic Distributions for Ecological Modeling -- 5. Stochastic Simulation and Power Analysis -- 6. Likelihood and All That -- 7. Optimization and All That -- 8. Likelihood Examples -- 9. Standard Statistics Revisited -- 10. Modeling Variance -- 11. Dynamic Models -- 12. Afterword -- Appendix. Algebra and Calculus Basics -- Bibliography -- Index of R Arguments, Functions, and Packages -- General Index