Mixed Effects Models and Extensions in Ecology with R

"For many people dealing with statistics is like jumping into ice-cold water. This metaphor is depicted by the cover of this book [...] . full of excellent example code and for most graphs and analyses the code is printed and explained in detail. [...] Each example finishes with [...] valuable information for a person new to a technique. In summary, I highly recommend the book to anyone who is familiar with basic statistics [...] who wants to expand his/her statistical knowledge to analyse ecological data."
– Bernd Gruber, Basic and Applied Ecology, Vol. 10, 2009

"This book is written in a very approachable conversational style. The additional focus on the heuristics of the process rather than just a rote recital of theory and equations is commendable. This type of approach helps the reader get behind the 'why' of what's being done rather than blindly follow a simple list of rules. [...] In short, this text is good for researchers with at least a little familiarity with the basic concepts of modeling and who want some solid stop-by-stop guidance with examples on how common ecological modeling tasks are accomplished using R."
– Aaron Christ, Journal of Statistical Software, November 2009, Vol. 32

"The authors succeed in explaining complex extensions of regression in largely nonmathematical terms and clearly present appropriate R code for each analysis. A major strength of the text is that instead of relying on idealized datasets [...] the authors use data from consulting projects or dissertation research to expose issues associated with 'real' data. [...] The book is well written and accessible [...] . the volume should be a useful reference for advanced graduate students, postdoctoral researchers, and experienced professionals working in the biological sciences."
– Paul E. Bourdeau, The Quarterly Review of Biology, Vol. 84, December, 2009

"This is a companion volume to Analyzing Ecology Data by the same authors. [...] It extends the previous work by looking at more complex general and generalized linear models involving mixed effects or heterogeneity in variances. It is aimed at statistically sophisticated readers who have a good understanding of multiple regression models [...] .The pedagogical style is informal [...] . The authors are pragmatists – they use combinations of informal graphical approaches, formal hypothesis testing, and information-theoretical model selection methods when analyzing data. [...] Advanced graduate students in ecology or ecologists with several years of experience with 'messy' data would find this book useful. [...] Statisticians would find this book interesting for the nice explorations of many of the issues with messy data. This book would be (very) suitable for a graduate course on statistical consulting – indeed, students would learn a great deal about the use of sophisticated statistical models in ecology! [...] I very much liked this book (and also the previous volume). I enjoyed the nontechnical presentations of the complex ideas and their emphasis that a good analysis uses 'simple statistical methods wherever possible, but doesn't use them simplistically.'"
– Biometrics, Summer 2009, 65, 992–993

"This book is a great introduction to a wide variety of regression models. [...] This text examines how to fit many alternative models using the statistical package R. [...] The text is a valuable reference [...] . A large number of real datasets are used as examples. Discussion on which model to use and the large number of recent references make the book useful for self study [...] ."
– David J. Olive, Technometrics, Vol. 52 (4), November, 2010