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PrevMap: An R Package for Prevalence Mapping

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  • Giorgi, Emanuele
  • Diggle, Peter J.

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.

Suggested Citation

  • Giorgi, Emanuele & Diggle, Peter J., 2017. "PrevMap: An R Package for Prevalence Mapping," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i08).
    • Handle: RePEc:jss:jstsof:v:078:i08
    DOI: http://hdl.handle.net/10.18637/jss.v078.i08
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    References listed on IDEAS

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    1. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Joe, Harry, 2008. "Accuracy of Laplace approximation for discrete response mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5066-5074, August.
    4. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
    5. Emanuele Giorgi & Sanie S. S. Sesay & Dianne J. Terlouw & Peter J. Diggle, 2015. "Combining data from multiple spatially referenced prevalence surveys using generalized linear geostatistical models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(2), pages 445-464, February.
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    Cited by:

    1. Ujjal Kumar Mukherjee & Benjamin E. Bagozzi & Snigdhansu Chatterjee, 2023. "A Bayesian framework for studying climate anomalies and social conflicts," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    2. Reihaneh Entezari & Patrick E. Brown & Jeffrey S. Rosenthal, 2020. "Bayesian spatial analysis of hardwood tree counts in forests via MCMC," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.

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