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Mixed Effects Models and Extensions in Ecology With R (Anglais) Relié – 1 mars 2009

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Présentation de l'éditeur

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. --Ce texte fait référence à l'édition Broché .

Détails sur le produit

  • Relié: 574 pages
  • Editeur : Springer-Verlag New York Inc. (1 mars 2009)
  • Collection : Statistics for Biology and Health
  • Langue : Anglais
  • ISBN-10: 0387874577
  • ISBN-13: 978-0387874579
  • Dimensions du produit: 15,6 x 3,3 x 23,4 cm
  • Moyenne des commentaires client : 5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)
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2 internautes sur 2 ont trouvé ce commentaire utile  Par Villers le 18 juin 2009
Format: Relié
Ce livre très complet aborde à la fois les aspects fondamentaux des analyses statistiques en écologie, avec une petite dose de maths pour comprendre ce qu'il y a derrière (pour ceux que ça intéresse), mais aussi les aspects plus techniques (code sous R, sélection de modèles, etc.)
Indispensable pour les étudiants et chercheurs qui veulent faire des analyses "justes" en se dépatouillant des corrélations spatiales ou temporelles.
You must have it !!!
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Amazon.com: 17 commentaires
14 internautes sur 14 ont trouvé ce commentaire utile 
Very nice applied text 12 juillet 2009
Par Philip Turk - Publié sur Amazon.com
Format: Relié Achat vérifié
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.
6 internautes sur 6 ont trouvé ce commentaire utile 
Excellent Book, too many typos 15 décembre 2009
Par Diego RM - Publié sur Amazon.com
Format: Relié Achat vérifié
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.
6 internautes sur 7 ont trouvé ce commentaire utile 
An excellent guide 4 avril 2009
Par BlueDaisy - Publié sur Amazon.com
Format: Relié
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.
3 internautes sur 3 ont trouvé ce commentaire utile 
A pleasure to read 26 juin 2012
Par C. Andersen - Publié sur Amazon.com
Format: Relié Achat vérifié
I've read through the first 6 chapters during the past few days, and have quite enjoyed it -- it reads smoothly, almost like a novel; quite unexpected for a text written by multiple authors. There's even a bit of humor. I've worked a bit with mixed models in the SAS world, but needed to learn how to deal with them in R, and this book has turned-out to be rather better than expected in this regard (I'm really liking how mixed models are done in R as opposed to SAS). At first I thought the blend of topics covered a bit odd, wondering what the heck a "Generalized Additive Model" was and what it was doing in a book on mixed models, but it turns out that GAMs are really nifty and not too difficult to grasp and in fact appear relevant to problems I'm currently working on. The authors have a preference for working with the distribution of the data as given rather than attempting to transform it to an approximation of normality, and I'm coming to appreciate this as well. For an applied text, it has unexpected depths. Great book.
3 internautes sur 3 ont trouvé ce commentaire utile 
One of the best guides to mixed models out there 27 avril 2011
Par GG - Publié sur Amazon.com
Format: Relié Achat vérifié
I am a plant ecologist. Even when I try to design simple experiments, it seems everything has autocorrelation (how did they do ecology in the past!?). So, I'm always using mixed models.

This book is great on two fronts. First, it is an excellent "how to" guide for using mixed models in R. It gives you examples, output, and a roadmap to the code you need to write to do the analysis. Second, it explains the theory behind mixed models in a way that is easy to understand for a non-statistician. It walks you through what output means and the theory behind what R is doing, and the limitations of what R won't do.

Every ecologist should buy this book.
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