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Zuur A.F. et al. Mixed Effects Models and Extensions in Ecology with R

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Zuur A.F. et al. Mixed Effects Models and Extensions in Ecology with R
Springer, 2009. — 549 p. — ISBN 0387874577.
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 Текст ссылки.
Limitations of Linear Regression Applied on Ecological Data
Things Are Not Always Linear; Additive Modelling
Dealing with Heterogeneity
Mixed Effects Modelling for Nested Data
Violation of Independence – Part I
Violation of Independence – Part II
Meet the Exponential Family
GLM and GAM for Count Data
GLM and GAM for Absence–Presence and Proportional Data
Zero-Truncated and Zero-Inflated Models for Count Data
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 Roadkills
Additive Mixed Modelling Applied on Deep-Sea Pelagic Bioluminescent Organisms
Additive Mixed Modelling Applied on Phytoplankton Time Series Data
Mixed Effects Modelling Applied on American Foulbrood Affecting Honey Bees Larvae
Three-Way Nested Data for Age Determination Techniques Applied to Cetaceans
GLMM Applied on the Spatial Distribution of Koalas in a Fragmented Landscape
A Comparison of GLM, GEE, and GLMM Applied to Badger Activity Data
Incorporating Temporal Correlation in Seal Abundance Data with MCMC
Required Pre-knowledge: A Linear Regression and Additive Modelling Example
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