Abstract
In this paper we introduce a new R package, PrevMap, for the analysis of spatially referenced prevalence data, including both classical maximum likelihood and Bayesian approaches to parameter estimation and plug-in or Bayesian prediction. More specifically, the new package implements fitting of geostatistical models for binomial data, based on two distinct approaches. The first approach uses a generalized linear mixed model with logistic link function, binomial error distribution and a Gaussian spatial process as a stochastic component in the linear predictor. A simpler, but approximate, alternative approach consists of fitting a linear Gaussian model to empirical-logit-transformed data. The package also includes implementations of convolution-based low-rank approximations to the Gaussian spatial process to enable computationally efficient analysis of large spatial datasets. We illustrate the use of the package through the analysis of Loa loa prevalence data from Cameroon and Nigeria. We illustrate the use of the low rank approximation using a simulated geostatistical dataset.
Highlights
This article introduces PrevMap, an R package for classical and Bayesian inference on spatially referenced prevalence data
PrevMap provides the following features: implementation of a convolution-based low-rank approximation that can be used to reduce the computational burden when analysing large spatial data-sets; accurate numerical computation of Monte Carlo maximum likelihood (MCML) standard errors for both regression and covariance parameters estimates; inclusion of both individual-level and location-level explanatory variables with random effects defined at location-level when repeated observations are made at the same location; more flexible prior specifications for the covariance parameters; implementation of an efficient Hamiltonian Markov chain Monte Carlo algorithm for Bayesian parameter estimation
The resulting parameter estimates can be substantially biased in the case of binomial observations with small denominators (Joe 2008), whereas the MCML method delivers asymptotically unbiased estimates
Summary
This article introduces PrevMap, an R package for classical and Bayesian inference on spatially referenced prevalence data. The package implements fitting and spatial prediction for the standard geostatistical model used in the context of prevalence mapping (Diggle, Tawn, and Moyeed 1998). PrevMap provides the following features: implementation of a convolution-based low-rank approximation that can be used to reduce the computational burden when analysing large spatial data-sets; accurate numerical computation of MCML standard errors for both regression and covariance parameters estimates; inclusion of both individual-level and location-level explanatory variables with random effects defined at location-level when repeated observations are made at the same location; more flexible prior specifications for the covariance parameters; implementation of an efficient Hamiltonian Markov chain Monte Carlo algorithm for Bayesian parameter estimation.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
