Ecological Models and Data in R

Princeton University Press
2
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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

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About the author

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

Publisher
Princeton University Press
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Published on
Jul 1, 2008
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Pages
408
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ISBN
9781400840908
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Language
English
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Genres
Computers / Mathematical & Statistical Software
Mathematics / Probability & Statistics / General
Nature / Ecology
Reference / Research
Science / Research & Methodology
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Content Protection
This content is DRM protected.
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Read Aloud
Available on Android devices
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Eligible for Family Library

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Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance.Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGSProvides every detail of R and WinBUGS code required to conduct all analysesCompanion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)
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