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BENTHOS  April 2016

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Subject:

New book: Beginner's Guide to Zero-Inflated Models with R

From:

Highland Statistics Ltd <[log in to unmask]>

Reply-To:

Highland Statistics Ltd <[log in to unmask]>

Date:

Thu, 28 Apr 2016 19:09:30 +0100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (97 lines)

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:
http://www.highstat.com/bookorder.htm
TOC: http://www.highstat.com/BGS/ZIM/pdfs/TOCOnly.pdf

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.


-----------------------------------------------------
Outline
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 
scientific papers.

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 
effects.

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.
9 St Clair Wynd
UK - AB41 6DZ Newburgh
Tel:   0044 1358 788177
Email: [log in to unmask]
URL:   www.highstat.com

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