Mixed Effects Models and Extensions in Ecology with R

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Springer Science & Business Media, Mar 5, 2009 - Science - 574 pages

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.

 

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I highly recommend this book. It manages to make the most complex statistics fathomable to the non-mathematical, which let’s face it, most of us are. For years I’ve struggled with the fact that most ecological data cannot be analysed correctly using simple statistical tests. Here, in one easy-to-read book, is the answer to most of the statistical problems I’ve struggled with for the last 15 years of my academic career. I now make it essential reading for members of my research group. 

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Mixed effect models has become familiar even in non statisticians.modelling trend shows that data usually come from such a nature that neither all variables are fixed nor random. That is why mixed moddeling is used. R package is well known software for handelling such type of problems has

Contents

2 Limitations of Linear Regression Applied on Ecological Data
11
3 Things Are Not Always Linear Additive Modelling
34
4 Dealing with Heterogeneity
71
5 Mixed Effects Modelling for Nested Data
101
6 Violation of Independence Part I
143
7 Violation of Independence Part II
161
8 Meet the Exponential Family
192
9 GLM and GAM for Count Data
209
15 LargeScale Impacts of LandUse Change in a Scottish Farming Catchment
362
16 Negative Binomial GAM and GAMM to Analyse Amphibian Roadkills
383
17 Additive Mixed Modelling Applied on DeepSea Pelagic Bioluminescent Organisms
398
18 Additive Mixed Modelling Applied on Phytoplankton Time Series Data
423
19 Mixed Effects Modelling Applied on American Foulbrood Affecting Honey Bees Larvae
447
20 ThreeWay Nested Data for Age Determination Techniques Applied to Cetaceans
459
21 GLMM Applied on the Spatial Distribution of Koalas in a Fragmented Landscape
469
22 A Comparison of GLM GEE and GLMM Applied to Badger Activity Data
493

10 GLM and GAM for AbsencePresence and Proportional Data
245
11 ZeroTruncated and ZeroInflated Models for Count Data
260
12 Generalised Estimation Equations
295
13 GLMM and GAMM
322
14 Estimating Trends for Antarctic Birds in Relation to Climate Change
343
23 Incorporating Temporal Correlation in Seal Abundance Data with MCMC
503
A Linear Regression and Additive Modelling Example
531
References
553
Index
563
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