Mixed effects models and extensions in ecology with R

Building on the successful Analysing Ecological Data (2007) by Zuur, Ieno andSmith, the authors now provide an expanded introduction to using regression and its extensions in analysing ecological data. As with the earlier book, realdata 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. Explains essential statistical tools for the ecologist Includes detailed case studies describing how to choose the most appropriate analysis Uses the R statistical program throughout INDICE: Revision of regression and additive models.- Regression and additive models with different variances.- Regression and additive models for time series or spatial data.- Mixed effects models.- Revision of GLM and GAM.- GLMM and GAMM for time series or spatial data.- Case study chapter showing regression or additive model.- Case study chapter showing the use of regression or additive models for time series data.- Case study chapter showing the use of regression or additive model for spatial data.- Case study chapter showing the useof a mixed effect model.- Case study chapter showing the use of GLMM and GAMMfor time series.- Case study chapter showing the use of GLM or GAMM for spatial data.

• ISBN: 978-0-387-87457-9
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 530
• Fecha Publicación: 01/04/2009
• Nº Volúmenes: 1
• Idioma: Inglés