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.

 

Contents

Introduction
1
Limitations of Linear Regression Applied on Ecological Data
11
Things Are Not Always Linear Additive Modelling
34
5
59
6
66
1
101
Violation of Independence Part I
143
Violation of Independence Part II
161
Estimating Trends for Antarctic Birds in Relation
343
LargeScale Impacts of LandUse Change in a Scottish
362
101
376
8
381
Negative Binomial GAM and GAMM to Analyse Amphibian
383
Additive Mixed Modelling Applied on DeepSea Pelagic
398
Additive Mixed Modelling Applied on Phytoplankton Time Series
423
Mixed Effects Modelling Applied on American Foulbrood Affecting
447

Meet the Exponential Family 193
192
GLM and GAM for Count Data
209
GLM and GAM for AbsencePresence and Proportional Data
245
ZeroTruncated and ZeroInflated Models for Count Data 261
260
Generalised Estimation Equations
295
90
317
GLMM and GAMM
322
92
335
ThreeWay Nested Data for Age Determination Techniques Applied
459
GLMM Applied on the Spatial Distribution of Koalas in
469
A Comparison of GLM GEE and GLMM Applied to Badger
493
Incorporating Temporal Correlation in Seal Abundance Data with
503
A Linear Regression and Additive
531
References
553
Index
563
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