Mixed Effects Models and Extensions in Ecology with R (Statistics for Biology and Health) |  | Authors: Alain F. Zuur, Elena N. Ieno, Neil Walker, Anatoly A. Saveliev, Graham M. Smith
Publisher: Springer
List Price: $84.95
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Rating:  4 reviews
Sales Rank: 112823
Media: Hardcover
Edition: 1
Pages: 574
Number Of Items: 1
Shipping Weight (lbs): 2.2
Dimensions (in): 9.1 x 6.4 x 1.6
ISBN: 0387874577
Dewey Decimal Number: 519
EAN: 9780387874579
ASIN: 0387874577
Publication Date: March 12, 2009
Availability: Usually ships in 1-2 business days
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Product Description
Building on the successful Analysing Ecological Data (2007) by Zuur, Ieno and Smith, the authors now provide an expanded introduction to using regression and its extensions in analysing ecological data. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout. The first part of the book is a largely non-mathematical introduction to linear mixed effects modelling, GLM and GAM, zero inflated models, GEE, GLMM and GAMM. The second part provides ten case studies that range from koalas to deep sea research. These chapters provide an invaluable insight into analysing complex ecological datasets, including comparisons of different approaches to the same problem. By matching ecological questions and data structure to a case study, these chapters provide an excellent starting point to analysing your own data. Data and R code from all chapters are available from www.highstat.com.
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| Customer Reviews:
 Excellent Book, too many typos December 15, 2009
Ubuntu Diego (Ithaca, NY United States)
1 out of 1 found this review helpful
This book is very good in both introducing statistical concepts and describing the R commands to implement those concepts. It is required, however, a relatively deep understanding of Linear Regression. I read this book from A to Z, however, each chapter is as independent as possible, and therefore it is possible to read the individual chapters. I did not try the code on the web page of the book yet, but I did type some of the examples and the code from the book works OK. In addition in the web site there is a set of instructions to install a package with all the code from the examples and updates on the R libraries and packages explained in the book.
Each methodology explained in the book covers step by step both the statistical (and mathematical) details as well as the construction of the R code (including importing the dataset and formating of columns for later analysis).
One of the most important "extra points" in this book is the use of a consistent methodology to approach the problem of modeling ecological data from a statistical point of view.
My only complain is that there are lots (LOTS) of typos, nothing too serious (since I was able to catch them) but still, I'm a little disappointed, because a good reviewer should got those.
Another Great Book by Zuur and Company November 2, 2009
Martin L. Jones
I really enjoyed this group's first book, Analysing Ecological Data, but this book is even better. The second book follows the style and format of the first book in that the authors explain the concepts in non-technical terms, but don't gloss over the important ideas. Moreover, they use real data sets that are quite messy and they show how these data sets can be analyzed through the numerous case studies in the text. All of the case studies are from published ecological papers or PhD theses. What makes this book even better than their first is that R code is included in the text and they carefully show how R can be used to help with the analysis and to construct the elaborate and beautiful graphics displayed in the text. If you're looking to analyze your own ecological data, you must have a copy of this book. It is an invaluable resource both for statistical methodology and for understanding how to use R with statistical models. These guys have done a spectacular job with this book and I look forward to future work from them.
Very nice applied text July 12, 2009
Philip Turk (Morgantown, WV)
3 out of 3 found this review helpful
Many applications in ecology clearly are not amenable to use of the general linear model due to violations of its assumptions. In fact, in most projects I work on, things like correlation among the errors, nonconstant error variance, etc., are the rule, rather than the exception. If you are looking for an applied text dealing with these types of situations with lots of examples, and demonstrations on analysis in R, then you should get this book. It does not delve into theory; there are plenty of other textbooks where you can fill in those details if you are interested. Rather, this book would be ideally suited for quantitative ecologists, biometricians, and statistical consultants who work in life sciences. Another nice thing is that the book does not assume you are an "R expert". Well done.
An excellent guide April 4, 2009
BlueDaisy (Northern US)
4 out of 4 found this review helpful
Mixed effects models and extensions in ecology with R (Statistics for Biology and Health)
The authors extend the expertise and practicality of Analysing Ecological Data (2007) to more types of data that are encountered in the world of living things. Many "real world" data are characterized by problems that traditional methods cannot cope with very well: nested data, heterogeneity of variances, spatial and temporal correlations, and more. These authors discuss these issues using ecological problems, but their approaches can be easily translated into other areas, such as human behavior and health (my area).
In a highly readable style, they begin with clear explanations of the special problems of messy and complex data, and why they require special handling. They use a gentle mathematical and theoretical touch when conceptualizing problems, so the analyst understands why the authors suggest handling data in the way they do. Then they guide the analyst through the process of statistical decision making through a step by step process, explaining options at various points. Finally, they end with suggestions on methods for communicating the results to other scientists. At the end of the analysis, the reader understands the reasoning underlying the statistical methods and decisions made along the way.
The R code for analyzing data sets is clearly presented, so the reader who attempts the examples learns how to apply this powerful statistical language as well.
This is a book that I expect to use again and again. Highly recommended.
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