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sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R

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  • Davies, Tilman M.
  • Hazelton, Martin L.
  • Marshall, Jonathan. C

Abstract

The estimation of kernel-smoothed relative risk functions is a useful approach to examining the spatial variation of disease risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have been very few contributions to date that have focused on estimation of a relative risk function per se. Use of a variable or adaptive smoothing parameter for estimation of the individual densities has been shown to provide additional benefits in estimating relative risk and specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to an estimated risk function. To facilitate analyses in the field, the R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and risk functions, identify statistically significant fluctuations in an estimated risk function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways.

Suggested Citation

  • Davies, Tilman M. & Hazelton, Martin L. & Marshall, Jonathan. C, 2011. "sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i01).
  • Handle: RePEc:jss:jstsof:v:039:i01
    DOI: http://hdl.handle.net/10.18637/jss.v039.i01
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    Citations

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    Cited by:

    1. Davies, Tilman M. & Jones, Khair & Hazelton, Martin L., 2016. "Symmetric adaptive smoothing regimens for estimation of the spatial relative risk function," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 12-28.
    2. Akimitsu Inoue, 2016. "Density estimation based on pointwise mutual information," Economics Bulletin, AccessEcon, vol. 36(2), pages 1138-1148.
    3. Emily Walker & Melen Leclerc & Jean‐François Rey & Rémy Beaudouin & Samuel Soubeyrand & Antoine Messéan, 2019. "A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 54-70, January.
    4. Christoph Lambio & Tillman Schmitz & Richard Elson & Jeffrey Butler & Alexandra Roth & Silke Feller & Nicolai Savaskan & Tobia Lakes, 2023. "Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln," IJERPH, MDPI, vol. 20(10), pages 1-22, May.
    5. Fernando A. Campos & Linda M. Fedigan, 2014. "Spatial ecology of perceived predation risk and vigilance behavior in white-faced capuchins," Behavioral Ecology, International Society for Behavioral Ecology, vol. 25(3), pages 477-486.
    6. Marcus Groß & Ulrich Rendtel & Timo Schmid & Sebastian Schmon & Nikos Tzavidis, 2017. "Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected via measurement error," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 161-183, January.
    7. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).
    8. Peter Howe & Hilary Boudet & Anthony Leiserowitz & Edward Maibach, 2014. "Mapping the shadow of experience of extreme weather events," Climatic Change, Springer, vol. 127(2), pages 381-389, November.
    9. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2013. "lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i04).

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