[R] New book: Beginner's Guide to GLM and GLMM with R

[Highland Statistics Ltd]

Members of this mailing list may be interested in the following new book:


Beginner's Guide to GLM and GLMM with R.
- A frequentist and Bayesian perspective for ecologists -

Zuur AF, Hilbe JM and Ieno EN


This book is only available from:
http://www.highstat.com/BGGLM.htm



This book presents Generalized Linear Models (GLM) and Generalized 
Linear Mixed Models (GLMM) based on both frequency-based and Bayesian 
concepts. Using ecological data from real-world studies, the text 
introduces the reader to the basics of GLM and mixed effects models, 
with demonstrations of binomial, gamma, Poisson, negative binomial 
regression, and beta and beta-binomial GLMs and GLMMs. The book uses the 
functions glm, lmer, glmer, glmmADMB, and also JAGS from within R. JAGS 
results are compared with frequentist results.

R code to construct, fit, interpret, and comparatively evaluate models 
is provided at every stage. Otherwise challenging procedures are 
presented in a clear and comprehensible manner with each step of the 
modelling process explained in detail, and all code is provided so that 
it can be reproduced by the reader.

Readers of this book have free access to:

Chapter 1 of Zero Inflated Models and Generalized Linear Mixed Models 
with R. (2012a) Zuur, Saveliev, Ieno.
Chapter 1 of Beginner's Guide to Generalized Additive Models with R. 
(2012b) Zuur, AF.


Keywords
Introduction to GLM
Poisson GLM and Negative binomial GLM for count data
Binomial GLM for binary data
Binomial GLM for proportional data
Other distributions
GLM applied to red squirrel data
Bayesian approach – running the Poisson GLM
Running JAGS via R
Applying a negative binomial GLM in JAGS
GLM applied to presence-absence Polychaeta data
Model selection using AIC, DIC and BIC in jags
Introduction to mixed effects models
GLMM applied on honeybee pollination data
Poisson GLMM using glmer and JAGS
Negative binomial GLMM using glmmADMD and JAGS
GLMM with auto-regressive correlation
GLMM for strictly positive data: biomass of rainforest trees
gamma GLM using a frequentist approach
Fitting a gamma GLM using JAGS
Truncated Gaussian linear regression
Tobit model in JAGS
Tobit model with random effects in JAGS
Binomial, beta-binomial, and beta GLMM applied to cheetah data

Kind regards,

Alain Zuur




-- 

Dr. Alain F. Zuur
First author of:

1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7


2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.
http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9


3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer
http://www.springer.com/statistics/computational/book/978-0-387-93836-3


4. Zero Inflated Models and Generalized Linear Mixed Models with R. (2012) Zuur, Saveliev, Ieno.
http://www.highstat.com/book4.htm

Other books: http://www.highstat.com/books.htm


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