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Based on the 2012 book Zero Inflated Models and Generalized Linear Mixed Models with R, this book is intended for the beginner.
The minimum prerequisite for Beginner's Guide to Zero-Inflated Models with R is knowledge of multiple linear regression. Chapter 2 starts with brief explanations of the Poisson, negative binomial, Bernoulli, binomial and gamma distributions. Chapters 3-5 presents a brief revision of the Poisson generalised linear model (GLM) and the Bernoulli GLM, followed by an introduction to zero-inflated Poisson (ZIP) models, followed by detailed case studies. Chapter 6 and 7 introduces zero-altered Poisson (ZAP) models to deal with the excessive number of zeros in count data, followed by a case study. The second part of the book explains how to deal with dependency, introducing mixed models that take care of excessive numbers of zeros in count data, crossed random effects and nested random effects.
To achieve more complex analyses requires Bayesian techniques, which are the subject of the third part of book. Chapter 10 contains a beginner’s guide to Bayesian statistics and Markov Chain Monte Carlo (MCMC) techniques. Chapters 11 and 12 shows how to implement the Poisson, negative binomial and ZIP models, and mixed models in MCMC. A major stumbling block in Bayesian analysis is model selection, the subject of chapter 14-16 provides an easy-to-understand overview of various Bayesian model selection tools, followed by case studies. Finally, chapters 17 and 18 discuss various topics, including multivariate GLMMs and generalised Poisson models (useful for underdispersion), and zero-inflated binomial models.