We are pleased to announce the following book:
Title: Beginner's Guide to Zero-Inflated Models with R
Authors: Zuur, Ieno
Book website: http://www.highstat.com/BGZIM.htm
Paperback or EBook can be order (exclusively) from:
Keywords: 430 pages. Zero inflated count data. Zero inflated continuous
data. Zero inflated proportional data. Frequentist and Bayesian
approaches. Random effects. Introduction to Bayesian statistics and
MCMC. JAGS. Bayesian model selection. Multivariate GLMM.
R code and data sets available.
The minimum prerequisite for Beginner's Guide to Zero-Inflated Models
with R is knowledge of multiple linear regression, and in Chapter 2 we
start with brief explanations of the Poisson, negative binomial,
Bernoulli, binomial and gamma distributions. The motivation for doing
this is that zero-inflated models consist of two distributions ‘glued’
together, one of which is the Bernoulli distribution. We begin Chapter 3
with a brief revision of the Poisson generalised linear model (GLM) and
the Bernoulli GLM, followed by a gentle introduction to zero-inflated
Poisson (ZIP) models. Chapters 4 and 5 contain detailed case studies
using count data of orange-crowned warblers and sharks. Just like all
other chapters, these case studies are based on real datasets used in
In Chapter 6 we use zero-altered Poisson (ZAP) models to deal with the
excessive number of zeros in count data. In Chapter 7 we analyse
continuous data with a large number of zeros. Biomass of Chinese tallow
trees is analysed with zero-altered gamma (ZAG) models.
In Chapter 8, which begins the second part of the book, we explain how
to deal with dependency. Mixed models are introduced, using beaver and
monkey datasets. In Chapter 9 we encounter a rather complicated dataset
in terms of dependency. Reproductive indices are sampled from plants.
But the seeds come from the same source and are planted in the same bed
in the same garden. We apply models that take care of the excessive
number of zeros in count data, crossed random effects and nested random
Up to this point we have done everything in a frequentist setting, but
at this stage of the book you will see that we are reaching the limit of
what we can achieve with the frequentist software. For this reason we
switch to Bayesian techniques in the third part of the book. Chapter 10
contains an excellent beginner’s guide to Bayesian statistics and Markov
Chain Monte Carlo (MCMC) techniques. The chapter also contains exercises
and a video solution file for one of the exercises. This means that you
can see our screen and listen to our voices (just in case you have
trouble falling asleep at night). A large number of students reviewed
this chapter and we incorporated their suggestions for improvement, so
Chapter 10 should be very easy to understand.
In Chapter 11 we show how to implement the Poisson, negative binomial
and ZIP models in MCMC. We do the same for mixed models in Chapter 12.
In Chapter 13 we discuss a method, called the ‘zero trick’, that allows
you to fit nearly every distribution in JAGS.
A major stumbling block in Bayesian analysis is model selection. Chapter
14 provides an easy-to-understand overview of various Bayesian model
selection tools. Chapter 15 contains an example of Bayesian model
selection using butterfly data.
In Chapter 16 we discuss methods for the analysis of proportional data
(seagrass coverage time series) with a large number of zeros. We use a
zero-altered beta model with nested random effects. Finally, in Chapters
17 and 18 we discuss various topics, including multivariate GLMMs and
generalised Poisson models (these can be used for underdispersion). We
also discuss zero-inflated binomial models.
Dr. Alain F. Zuur
First author of:
1. Beginner's Guide to GAMM with R (2014).
2. Beginner's Guide to GLM and GLMM with R (2013).
3. Beginner's Guide to GAM with R (2012).
4. Zero Inflated Models and GLMM with R (2012).
5. A Beginner's Guide to R (2009).
6. Mixed effects models and extensions in ecology with R (2009).
7. Analysing Ecological Data (2007).
Highland Statistics Ltd.
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