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Using R for Bayesian spatial and spatio-temporal health modeling / Andrew B. Lawson.

By: Lawson, Andrew B. (Andrew B.) [author.].
Publisher: U.S : CRC , 2021Edition: First edition.Description: 284p.ISBN: 9780367490126.Subject(s): Medical statistics -- Data processing | Medical mapping -- Data processing | Medical statistics -- Computer programs | Geospatial data -- Computer processing | Geographic information systems | Information modeling -- Simulation methods | R (Computer program language) | Bayesian statistical decision theoryDDC classification: 610.21
Contents:
Introduction and data sets -- R graphics and spatial health data -- Bayesian hierarchical models -- Computation -- Bayesian model goodness of fit criteria -- Bayesian disease mapping models -- BRugs/OpenBUGS -- Nimble -- CARBayes -- INLA and R-INLA -- Clustering, latent variable and mixture modeling -- Spatio-temporal modeling with MCMC -- Spatio-temporal modeling with INLA -- Multivariate models -- Survival modeling -- Missingness, measurement error and variable selection -- Individual event modeling -- Infectious disease modeling.
Summary: "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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Includes bibliographical references and index.

Introduction and data sets -- R graphics and spatial health data -- Bayesian hierarchical models -- Computation -- Bayesian model goodness of fit criteria -- Bayesian disease mapping models -- BRugs/OpenBUGS -- Nimble -- CARBayes -- INLA and R-INLA -- Clustering, latent variable and mixture modeling -- Spatio-temporal modeling with MCMC -- Spatio-temporal modeling with INLA -- Multivariate models -- Survival modeling -- Missingness, measurement error and variable selection -- Individual event modeling -- Infectious disease modeling.

"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"--

English.

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