9780367760670-0367760673-Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Chapman & Hall/CRC The R Series)

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Chapman & Hall/CRC The R Series)

ISBN-13: 9780367760670
ISBN-10: 0367760673
Edition: 1
Author: Andrew B. Lawson
Publication date: 2023
Publisher: Chapman and Hall/CRC
Format: Paperback 284 pages
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ISBN-13: 9780367760670
ISBN-10: 0367760673
Edition: 1
Author: Andrew B. Lawson
Publication date: 2023
Publisher: Chapman and Hall/CRC
Format: Paperback 284 pages

Summary

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Chapman & Hall/CRC The R Series) (ISBN-13: 9780367760670 and ISBN-10: 0367760673), written by authors Andrew B. Lawson, was published by Chapman and Hall/CRC in 2023. With an overall rating of 3.5 stars, it's a notable title among other books. You can easily purchase or rent Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Chapman & Hall/CRC The R Series) (Paperback, New) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $1.01.

Description

Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.

Features:

  • Review of R graphics relevant to spatial health data
  • Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data
  • Bayesian Computation and goodness-of-fit
  • Review of basic Bayesian disease mapping models
  • Spatio-temporal modeling with MCMC and INLA
  • Special topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling
  • Software for fitting models based on BRugs, Nimble, CARBayes and INLA
  • Provides code relevant to fitting all examples throughout the book at a supplementary website

The book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatio-temporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of geo-referenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science.

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