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geoCount: An R Package for the Analysis of Geostatistical Count Data

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  • Jing, Liang
  • De Oliveira, Victor

Abstract

We describe the R package geoCount for the analysis of geostatistical count data. The package performs Bayesian analysis for the Poisson-lognormal and binomial-logitnormal spatial models, which are subclasses of the class of generalized linear spatial models proposed by Diggle, Tawn, and Moyeed (1998). The package implements the computational intensive tasks in C++ using an R/C++ interface, and has parallel computation capabilities to speed up the computations. geoCount also implements group updating, Langevin- Hastings algorithms and a data-based parameterization, algorithmic approaches proposed by Christensen, Roberts, and Sköld (2006) to improve the efficiency of the Markov chain Monte Carlo algorithms. In addition, the package includes functions for simulation and visualization, as well as three geostatistical count datasets taken from the literature. One of those is used to illustrate the package capabilities. Finally, we provide a side-by-side comparison between geoCount and the R packages geoRglm and INLA.

Suggested Citation

  • Jing, Liang & De Oliveira, Victor, 2015. "geoCount: An R Package for the Analysis of Geostatistical Count Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i11).
    • Handle: RePEc:jss:jstsof:v:063:i11
    DOI: http://hdl.handle.net/10.18637/jss.v063.i11
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    References listed on IDEAS

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    1. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
    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. Hao Zhang, 2002. "On Estimation and Prediction for Spatial Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 58(1), pages 129-136, March.
    4. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    5. Ole F. Christensen & Rasmus Waagepetersen, 2002. "Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 58(2), pages 280-286, June.
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    Cited by:

    1. Farman Ali & Zulfiqar Ali & Bing-Zhao Li & Sadia Qamar & Amna Nazeer & Saba Riaz & Muhammad Asif Khan & Rabia Fayyaz & Javeria Nawaz Abbasi, 2022. "Exploring Regional Profile of Drought History- a New Procedure to Characterize and Evaluate Multi-Scaler Drought Indices Under Spatial Poisson Log-Normal Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 2989-3005, July.
    2. Victor De Oliveira, 2017. "Geostatistical Binary Data: Models, Properties And Connections," Working Papers 0151mss, College of Business, University of Texas at San Antonio.
    3. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).

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