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(12 reviews)
Author: Alain Zuur Elena N. Ieno Neil Walker Anatoly A. Saveliev Graham M. Smith
ISBN : 0387874577
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Download file now Free Mixed Effects Models and Extensions in Ecology with R (Statistics for Biology and Health) [Hardcover]
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This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.
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- Series: Statistics for Biology and Health
- Hardcover: 574 pages
- Publisher: Springer; 2009 edition (March 12, 2009)
- Language: English
- ISBN-10: 0387874577
- ISBN-13: 978-0387874579
- Product Dimensions: 1.3 x 6.1 x 9.1 inches
- Shipping Weight: 2.2 pounds (View shipping rates and policies)
Free Mixed Effects Models and Extensions in Ecology with R
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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.
By Philip Turk
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.
By Diego RM
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