Mixed Effects Models and Extensions in Ecology with R

by Alain F. Zuur, Elena N. Ieno, Neil Walker

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.

Genres: Science, Nonfiction, Textbooks, Biology

596 pages, Hardcover

First published March 12, 2009

Reviews

[Tomek · Rating 3 out of 5]

Although I have yet to find such a comprehensive overview of modeling ecological data in R (which is precisely what I needed in my research), I found the presentation wanting. While the authors sometimes clearly explain their thought processes and logic behind making certain decisions, others are left to the reader to divine or are explained later, which can cause a good deal of frustration up to that point. Also, while it is advertised as "non-mathematical" it certainly assumes a good deal of familiarity with statistical notation, which I feel many applied ecologists do not. While, I do think this is a useful resource for ecologists looking to do higher level statistical analysis, but I feel it would be best paired with a course to clear up any questions and uncertainties.

[Karina · Rating 5 out of 5]

This is definitely not a book for beginners, but is an extremely valuable resource for postgraduates or research scientists attempting to analyse biological data that do not conform to the basic assumptions of the simple statistical analyses that are usually taught during undergraduate degrees. Most real biological datasets do not meet these assumptions and researchers are left floundering for solutions. This book covers many of the typical issues and provides clear r code and explanations to attempt their application to real data. It is a dense read and reliance on the index to skip to relevant sections is required, but all of the information is there or suggestions for further reading provided. An incredibly valuable piece of work!

[Jenna Black · Rating 5 out of 5]

i'm pretty sure this book saved my honours project... bless ya

[Sophie · Rating 5 out of 5]

they don't tell you that when you go into environmental science you are actually about to be balls deep in statistics and code. so like fyi.

[Barbara · Rating 3 out of 5]

This is a pretty good text if you are already familiar and comfortable with regression modelling, including GLM and GAM, probability and error distributions and heteroscedastic data. I first read this book when some of these things were new to me, and found it very confusing. Revisiting it now, it seems much clearer and has a lot of useful content, but I would not recommend it for beginners.

[Sofia · Rating 4 out of 5]

This book really gives a great insight for working with mixed effects models in R! Although, I would like to see more insight for using the lme4 package over the nlme package.