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Ecological Models and Data in R [Hardcover]

Benjamin M. Bolker (Author)
4.0 out of 5 stars  See all reviews (8 customer reviews)

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Book Description

July 1, 2008 0691125228 978-0691125220 508

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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Editorial Reviews

Review

Bolker's book is a must-buy for anyone wanting to fit data to models and go beyond hypothesis testing, but it is certainly not an 'introductory' text in the sense of 'simple'. This book is a tour de force for anyone who studied ecology for his or her interest of nature's working. But it is the one single book that can propel the statistical novice to the cutting edge of statistical ecology--albeit with blood, sweat and tears. -- Carsten F. Dormann, Basic and Applied Ecology

[A] must for natural scientists and for statisticians who are interested in ecological applications. . . . Numerous enlightening footnotes, meaningful graphics and direct speech are evidence of substantial classroom experience of the author. . . . The book addresses students and researchers who have or have had some basic knowledge in ecology, mathematics and statistics. Delivering many examples and profound details on numerical aspects of maximum likelihood estimation, the book may also give a red line for a course in computational statistics. -- Martin Schlather, Biometrical Journal

[T]his book succeeds both in explaining how to analyze many types of ecological data, and in clearly describing the theoretical background behind some common analyses and approaches. I expect to refer to it often. -- Lynda D. Prior, Austral Ecology

From the Inside Flap

"This user-friendly introduction to likelihood and Bayesian statistical methods for ecology students is set apart by its emphasis on implementation in R. This alone will make it more useful than previous books. In contrast to other texts, Bolker's book explains how to fit models to data in enough detail that even students with little programming experience will be able to follow along. I expect this to become an exceedingly popular textbook."--Stephan B. Munch, Stony Brook University

"Benjamin Bolker is a pioneer in helping ecology students make the leap from a casual understanding of modern statistical methods to a hands-on application of these tools to their own precious data sets. This book shows the lessons learned from teaching this material to several cohorts of graduate students. No other book I've read gives such a good feel for the compromises scientists have to make in searching for good statistical models."--Brian Inouye, Florida State University

"I have no doubt that this book will become a fixture on many ecologists' bookshelves (it certainly will be on mine). With a presentation that is gentle and encouraging rather than jargon-filled and intimidating, it empowers ecologists to develop their own statistical procedures. I strongly recommend it."--Timothy Essington, University of Washington


Product Details

  • Hardcover: 408 pages
  • Publisher: Princeton University Press; 508 edition (July 1, 2008)
  • Language: English
  • ISBN-10: 0691125228
  • ISBN-13: 978-0691125220
  • Product Dimensions: 10.1 x 7.1 x 1.1 inches
  • Shipping Weight: 2.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #107,968 in Books (See Top 100 in Books)

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Customer Reviews

8 Reviews
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Average Customer Review
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Most Helpful Customer Reviews

10 of 10 people found the following review helpful:
5.0 out of 5 stars An excellent resource for ecologists, March 10, 2009
By 
James R. Vonesh (Richmond, VA, USA) - See all my reviews
(REAL NAME)   
This review is from: Ecological Models and Data in R (Hardcover)
This book, in part, was developed from Dr. Bolker's graduate course in Ecological Models and Data at the University of Florida. This was the best course I took as a graduate student, it transformed the set of quantitative tools I was able to bring to bear on ecological questions. There was so much worthwhile material covered in this class that I took it twice (UF only counted the first time:). Since graduate school I still frequently refer to my notes from the class. With the publication of "Ecological Models and Data in R" even those who didn't have the good fortune of being in Bolker's class can learn approaches for integrating ecological theory and data. Bolker's book covers much of the material from his course and thus is an excellent resource for graduate students and faculty alike.
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5 of 5 people found the following review helpful:
5.0 out of 5 stars Great hands-on textbook, October 28, 2009
This review is from: Ecological Models and Data in R (Hardcover)
I'm doing infectious disease modeling for a living, and I got a lot out of this book. I was not too familiar with R and with stochastic models. Reading and working through this book taught me a lot. The book is really meant to be worked through carefully. Ben drops nuggets of wisdom everywhere - but you need to read carefully to catch them. It's not the ideal book if you need a quick reference on how to do "X". But as a textbook and to really learn things, it is great. That said, I would hesitate to use it for a real beginner's class. Some background with statistical concepts and a solid math foundation are necessary. And some programming experience, with either R or another language, helps a lot. If students are too weak in any of these areas, it would be hard to teach the material in a single semester course. But the great thing about this book is that anyone motivated to learn the subject matter can "simply" sit down and work through it on their own and at their own pace. It will take time, but it's totally worth it.
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2 of 2 people found the following review helpful:
4.0 out of 5 stars Nice book, September 5, 2008
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This review is from: Ecological Models and Data in R (Hardcover)
I am a molecular biologist, trying to work my way through some ecological modeling. I found this book quite useful, since it has lot of examples and details. There is an online supplement for this book, where you can get all the scripts and pdf versions of the chapters, if you want. the R supplements, and the scripts give you a hands-on experience in handling the data in R. Tests like maximum likelihood, monte carlo are explained very well, and the R scripts help in understanding the nitty-gritties of programming. All in all, a good book.
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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
seed removal data, myxomatosis data, profile confidence limits, population prediction intervals, liana class, likelihood slice, functional response experiment, predation data, tadpole predation, predation probability, tadpole size, formula interface, lattice package, lattice graphics, likelihood profile, proportion eaten, predation probabilities, predator visits, overdispersion parameter, modified logistic, true population size, likelihood surface, trajectory matching, focal parameter, fir data
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Likelihood Ratio, Value Figure, Central Limit Theorem, Estimate Std, Model Builder
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