Title
Using R for Bayesian Spatial and SpatioTemporal Health Modeling (Chapman & Hall/CRC The R Series),Used
Sold by Ergodebooks, an authorized reseller.
Returns accepted within 30 days | support@ergodebooks.com
Shipping Information
- Free Standard Shipping — United States only
- Processing Time: 1–3 business days
- Estimated Delivery: 3–5 business days after dispatch
- Double-boxed, fully insured & discreetly packaged
- Tracking number sent via email once dispatched
- Orders over $250 require signature upon delivery. Taxes calculated at checkout.
Returns & Refund
Returns accepted within 30 days of delivery.
Damaged or Defective Item
Free return shipping + replacement or full refund
Wrong Item Received
Free return shipping + replacement or full refund
Change of Mind
Return shipping at customer's expense · 25% restocking fee applies
Payment Option
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 SpatioTemporal 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 dataOverview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial dataBayesian Computation and goodnessoffitReview of basic Bayesian disease mapping modelsSpatiotemporal modeling with MCMC and INLASpecial topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modelingSoftware for fitting models based on BRugs, Nimble, CARBayes and INLAProvides code relevant to fitting all examples throughout the book at a supplementary websiteThe 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 spatiotemporal 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 georeferenced 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.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.