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Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses Paperback – July 1, 2010

ISBN-13: 978-0123786050 ISBN-10: 0123786053 Edition: 1st

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Editorial Reviews

Review

"I don't believe this book was written with the goal of being treated as the primary text of an intro Bayesian statistics course. That said, it could prove to be a useful supplemental text for an introductory Bayesian course or even a linear models course. Although the book was geared towards ecologists, I believe it would be an excellent library addition for any applied modeler interested in applying Bayesian methodologies in their work."--The American Statistician

From the Back Cover

Introduction to WINbugs for Ecologists is an introduction to Bayesian statistical modeling, written for ecologists by an ecologist, using the widely available WinBUGS package. Examples are placed within a comprehensive and largely non-mathematical overview of linear, generalized linear (GLM), mixed and generalized linear mixed models (GLMM). This book will be of interest to any quantitative scientist who uses regression-type models, especially ecologists, agronomists, geologists, epidemiologists, sociologists, and psychologists. This book:

  • Teaches by example rather than by equations.
  • Contains examples based on simulated data along with fully commented R code for the generation of these data sets.
  • Is full integrated with program R-all analyses are conducted by calling program WinBUGS from within program R.

Dr. Kéry is a population ecologist with the Swiss Ornithological Institute. He is the author of over 40 peer-reviewed journal articles on a wide range of topics, including the analysis of large-scale monitoring programs, demographic population analyses, experimental design for animal and plant surveys, and the population ecology of rare species.

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Product Details

  • Paperback: 320 pages
  • Publisher: Academic Press; 1 edition (July 1, 2010)
  • Language: English
  • ISBN-10: 0123786053
  • ISBN-13: 978-0123786050
  • Product Dimensions: 6 x 0.8 x 9 inches
  • Shipping Weight: 1.3 pounds (View shipping rates and policies)
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (12 customer reviews)
  • Amazon Best Sellers Rank: #127,684 in Books (See Top 100 in Books)

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Customer Reviews

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If you've felt the same and been stymied, and you do classical stats in R, Kery has the perfect book to learn Bayesian approaches.
Sitting in Seattle
It is actually also a pretty good book for performing classical linear modeling in R. All the analyses are performed in both R and WinBUGS.
Jared Becksfort
Regardless of what beginner book you go with, running Winbugs or Openbugs in R requires at least an intermediate understanding of R.
D. Collingridge

Most Helpful Customer Reviews

6 of 6 people found the following review helpful By Jared Becksfort on December 16, 2010
Format: Paperback Verified Purchase
This is a great book for using WinBUGS through R with the R library R2WinBUGS. It is actually also a pretty good book for performing classical linear modeling in R. All the analyses are performed in both R and WinBUGS. Much of the data is simulated but realistic, and the author shows you how he generated the data, which is also useful. Solutions to the exercises are available at the book's web site.

There is no reason to be an ecologist to use this book. The examples translate very well to other fields.

Despite this book being very useful to me, I gave it 4 instead of 5 stars for a few reasons. Very little attempt is made to explain the theory (to be fair, he says this at the outset, and it is a book about WinBUGS, not Bayesian statistics). The expected understanding of statistics and R is somewhat uneven throughout. For example, the author in one chapter shows you how to load libraries in R and other basic housekeeping tasks, but a few chapters later he shows more advanced model specification code in R's lm function without explaining it. Expect to spend some time in the R manual if you want to understand it all. Similarly, he repeatedly says that much of the statistics behind the code is too advanced for most ecologists, which might annoy me if I were an ecologist, but then he tends to assume a lot of the theoretical statistics is already well understood by the reader.

There is a quick introduction to Generalized Linear Models which I found helpful. Basically, this is a great practical book but you will need to look elsewhere for mathematical understanding. I like Peter Hoff's "A First Course in Bayesian Statistical Methods."
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Format: Paperback Verified Purchase
If you are an R user and a non-statistician (i.e., a professional researcher but without rigorous Math/Stats background) and want to learn Bayesian methods: get this book! It is one of the single best statistics books I've ever read for applied researchers.

Most Bayesian method books start off fine with Bayes' Rule, followed by reasonable coverage of the binomial distribution, and then plunge off the deep end with formulas for likelihood and details on how to write MCMC samplers. When I'm in a bad mood, I suspect those books reflect disdain for non-statisticians and a desire to keep Bayesian methods a secret fraternity. If you've felt the same and been stymied, and you do classical stats in R, Kery has the perfect book to learn Bayesian approaches.

Other reviewers have commented on how approachable the book is (true), how you need to know R first because it is too much heavy lifting to learn R and Bayes and WinBUGS all at the same time (I agree), and that it is light on Bayes theory and math (a very positive thing, in my opinion).

What you might also wonder is: (a) can you use it with a non-Windows computer? and (b) does it only work with WinBUGS, or could one use openbugs or JAGS instead? Answers: yes and yes. I've been working through it with JAGS running on a Mac and using the "rjags" package. The primary changes that are required involve the fact that commands to call JAGS from R (in the rjags package) are different from those to call WinBUGS.

I don't want to get too technical in a review, but the rjags approach is simple, works on Macs and Linux as well as Windows, and is not a big stretch from the book. For reference, here is rjags syntax for the first live example ("y1000" model in chapter 5.
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This book is very well written and easy to read. Throughout the book there are comparisons of frequentist and Bayesian approaches to statistical modelling (simple general linear models, generalized linear models, linear and generalized linear mixed-effects models), with side-by-side examples of R and WinBUGS code. This side-by-side presentation makes the statistical concepts and the code syntax crystal clear. A brilliant short course on practical statistical modelling methods for scientists who are comfortable with basic statistical concepts and who have introductory R skills. For those coming from other GUI-based statistical software packages, consider using the R-Commander implementation of R, as there is a scripting window where code examples from this book can be implemented.
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By Bryant C. Dossman on February 26, 2014
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I am an ecologists and am beginning to get started with bayesian data analysis. This books helps introduce bayesian statistics but is primarily a "how to" book for the software.
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By B. Johnson on April 22, 2013
Format: Paperback
This book is a perfect introduction for the WinBUGS beginner--and a good reference for the intermediate to advanced user. For the beginner, what sets this book apart is the author's use of simulated data--along with the R code so that the user can replicate the work. By generating simulated data--and then recovering the same parameter estimates using WinBUGS--the user gains a very clear understanding of random effects models, how they differ from fixed effects models, and what WinBUGS is doing. The examples start small and simple and then move to moderately complex, never missing a step. For the intermediate user, there are very intuitive explanations of such concepts as exchangeability, overdispersion zero-inflated and mixture models. I highly recommend this one.
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