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http://hdl.handle.net/1942/21025
Title: | Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping. | Authors: | Carroll, R. LAWSON, Andrew FAES, Christel Kirby, R.S. AREGAY, Mehreteab WATJOU, Kevin |
Issue Date: | 2015 | Source: | Spatial and spatio-temporal epidemiology (Print), 14-15, p. 45-54 | Abstract: | The recently developed R package INLA (Integrated Nested Laplace Approximation) is becoming a more widely used package for Bayesian inference. The INLA software has been promoted as a fast alternative to MCMC for disease mapping applications. Here, we compare the INLA package to the MCMC approach by way of the BRugs package in R, which calls OpenBUGS. We focus on the Poisson data model commonly used for disease mapping. Ultimately, INLA is a computationally efficient way of implementing Bayesian methods and returns nearly identical estimates for fixed parameters in comparison to OpenBUGS, but falls short in recovering the true estimates for the random effects, their precisions, and model goodness of fit measures under the default settings. We assumed default settings for ground truth parameters, and through altering these default settings in our simulation study, we were able to recover estimates comparable to those produced in OpenBUGS under the same assumptions. | Keywords: | Bayesian; spatial; Integrated Nested Laplace Approximation; BRugs; Poisson; MCMC | Document URI: | http://hdl.handle.net/1942/21025 | ISSN: | 1877-5845 | e-ISSN: | 1877-5853 | DOI: | 10.1016/j.sste.2015.08.001 | Rights: | © 2015 Elsevier Ltd. All rights reserved. | Category: | A1 | Type: | Journal Contribution | Validations: | vabb 2017 |
Appears in Collections: | Research publications |
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Carrol et al 2015 SSTE.pdf Restricted Access | Published version | 1.08 MB | Adobe PDF | View/Open Request a copy |
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