Synopses & Reviews
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.
Review
From the reviews: "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) "... Das vorgestellte anwendungsorientierte Buch kann als eine Erweiterung des 2007 erschienenen Buches 'Analysing Ecological Data' ... angesehen werden. Die gute ... Einführung im ersten Buchteil erleichtert den Einstieg in die verschiedenen Auswertungskonzepte für alle ... Zur mathematischen Vertiefüng der Methoden und anderer Aspekte statistischer Analysen sind die vielen weiterführenden Literaturhinweise sehr hilfreich. Der zweite Teil des Buches mit zehn eingehend erläuterten Beispielen gibt einen nützlichen Einblick in die verschiedenen Analysemöglichkeiten komplexer ökologischer Daten, wodurch die Untersuchung des eigenen Datensatzes erleichtert werden kann ..." (Rene Wördehoff, in: Forstarchiv, 2009, Vol. 80, Issue 4, S. 134) "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
Review
From the reviews:
"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
Synopsis
Building on their previous book on the subject, the authors provide an expanded introduction to using Regression to analyze ecological data. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout.
Synopsis
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.
Table of Contents
Limitations of linear regression applied on ecological data. - Things are not always linear; additive modelling. - Dealing with hetergeneity. - Mixed modelling for nested data. - Violation of independence - temporal data. - Violation of independence; spatial data. - Generalised linear modelling and generalised additive modelling. - Generalised estimation equations. - GLMM and GAMM. - Estimating trends for Antarctic birds in relation to climate change. - Large-scale impacts of land-use change in a Scottish farming catchment. - Negative binomial GAM and GAMM to analyse amphibian road killings. - Additive mixed modelling applied on deep-sea plagic bioluminescent organisms. - Additive mixed modelling applied on phyoplankton time series data. - Mixed modelling applied on American Fouldbrood affecting honey bees larvae. - Three-way nested data for age determination techniques applied to small cetaceans. - GLMM applied on the spatial distribution of koalas in a fragmented landscape. - GEE and GLMM applied on binomial Badger activity data.