Using R For Bayesian Spatial And Spatio Temporal Health Modeling
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Using R For Bayesian Spatial And Spatio Temporal Health Modeling
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Author : Andrew B. Lawson
language : en
Publisher: CRC Press
Release Date : 2021-04-28
Using R For Bayesian Spatial And Spatio Temporal Health Modeling written by Andrew B. Lawson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Mathematics categories.
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.
Spatial And Spatio Temporal Bayesian Models With R Inla
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Author : Marta Blangiardo
language : en
Publisher: John Wiley & Sons
Release Date : 2015-06-02
Spatial And Spatio Temporal Bayesian Models With R Inla written by Marta Blangiardo and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-02 with Mathematics categories.
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations
Spatio Temporal Methods In Environmental Epidemiology
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Author : Gavin Shaddick
language : en
Publisher: CRC Press
Release Date : 2015-06-17
Spatio Temporal Methods In Environmental Epidemiology written by Gavin Shaddick and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-17 with Mathematics categories.
Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological StudiesSpatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and
Bayesian Modeling Of Spatio Temporal Data With R
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Author : Sujit Sahu
language : en
Publisher: CRC Press
Release Date : 2022-02-23
Bayesian Modeling Of Spatio Temporal Data With R written by Sujit Sahu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-23 with Mathematics categories.
Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modelling, which aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modelling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems. Key features of the book: • Accessible detailed discussion of a majority of all aspects of Bayesian methods and computations with worked examples, numerical illustrations and exercises • A spatial statistics jargon buster chapter that enables the reader to build up a vocabulary without getting clouded in modeling and technicalities • Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr, allowing the reader to use well-known packages and platforms, such as rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, CARBayesST, etc • Included are R code notes detailing the algorithms used to produce all the tables and figures, with data and code available via an online supplement • Two dedicated chapters discuss practical examples of spatio-temporal modeling of point referenced and areal unit data • Throughout, the emphasis has been on validating models by splitting data into test and training sets following on the philosophy of machine learning and data science This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience of students and researchers, from mathematicians and statisticians to practitioners in the applied sciences. It presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. It does not compromise on rigour, as it presents the underlying theories of Bayesian inference and computation in standalone chapters, which would be appeal those interested in the theoretical details. By avoiding hard core mathematics and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.
Spatiotemporal Analysis Of Air Pollution And Its Application In Public Health
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Author : Lixin Li
language : en
Publisher: Elsevier
Release Date : 2019-11-13
Spatiotemporal Analysis Of Air Pollution And Its Application In Public Health written by Lixin Li and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-13 with Science categories.
Spatiotemporal Analysis of Air Pollution and Its Application in Public Health reviews, in detail, the tools needed to understand the spatial temporal distribution and trends of air pollution in the atmosphere, including how this information can be tied into the diverse amount of public health data available using accurate GIS techniques. By utilizing GIS to monitor, analyze and visualize air pollution problems, it has proven to not only be the most powerful, accurate and flexible way to understand the atmosphere, but also a great way to understand the impact air pollution has in diverse populations. This book is essential reading for novices and experts in atmospheric science, geography and any allied fields investigating air pollution. - Introduces readers to the benefits and uses of geo-spatiotemporal analyses of big data to reveal new and greater understanding of the intersection of air pollution and health - Ties in machine learning to improve speed and efficacy of data models - Includes developing visualizations, historical data, and real-time air pollution in large geographic areas
Spatio Temporal Statistics With R
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Author : Christopher K. Wikle
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2019
Spatio Temporal Statistics With R written by Christopher K. Wikle and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computers categories.
The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.
The Science Of Health Disparities Research
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Author : Irene Dankwa-Mullan
language : en
Publisher: John Wiley & Sons
Release Date : 2021-03-16
The Science Of Health Disparities Research written by Irene Dankwa-Mullan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-16 with Science categories.
Integrates the various disciplines of the science of health disparities in one comprehensive volume The Science of Health Disparities Research is an indispensable source of up-to-date information on clinical and translational health disparities science. Building upon the advances in health disparities research over the past decade, this authoritative volume informs policies and practices addressing the diseases, disorders, and gaps in health outcomes that are more prevalent in minority populations and socially disadvantaged communities. Contributions by recognized scholars and leaders in the field—featuring contemporary research, conceptual models, and a broad range of scientific perspectives—provide an interdisciplinary approach to reducing inequalities in population health, encouraging community engagement in the research process, and promoting social justice. In-depth chapters help readers better understand the specifics of minority health and health disparities while demonstrating the importance of advancing theory, refining measurement, improving investigative methods, and diversifying scientific research. In 26 chapters, the book examines topics including the etiology of health disparities research, the determinants of population health, research ethics, and research in African American, Asians, Latino, American Indian, and other vulnerable populations. Providing a unified framework on the principles and applications of the science of health disparities research, this important volume: Defines the field of health disparities science and suggests new directions in scholarship and research Explains basic definitions, principles, and concepts for identifying, understanding and addressing health disparities Provides guidance on both conducting health disparities research and translating the results Examines how social, historical and contemporary injustices may influence the health of racial and ethnic minorities Illustrates the increasing national and global importance of addressing health disparities Discusses population health training, capacity-building, and the transdisciplinary tools needed to advance health equity A significant contribution to the field, The Science of Health Disparities Research is an essential resource for students and basic and clinical researchers in genetics, population genetics, and public health, health care policymakers, and epidemiologists, medical students, and clinicians, particularly those working with minority, vulnerable, or underserved populations.
R For Health Data Science
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Author : Ewen Harrison
language : en
Publisher: CRC Press
Release Date : 2020-12-31
R For Health Data Science written by Ewen Harrison and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-31 with Medical categories.
In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms.
Spatial Cluster Modelling
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Author : Andrew B. Lawson
language : en
Publisher: CRC Press
Release Date : 2002-05-16
Spatial Cluster Modelling written by Andrew B. Lawson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-05-16 with Mathematics categories.
Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this b
Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan
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Author : Franzi Korner-Nievergelt
language : en
Publisher: Academic Press
Release Date : 2015-04-04
Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan written by Franzi Korner-Nievergelt and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-04 with Science categories.
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. - Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest - Written in a step-by-step approach that allows for eased understanding by non-statisticians - Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data - All example data as well as additional functions are provided in the R-package blmeco