The R Journal: accepted article

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Mapping Smoothed Spatial Effect Estimates from Individual-Level Data: MapGAM PDF download
Lu Bai, Daniel L. Gillen, Scott M. Bartell and Verónica M. Vieira

Abstract We introduce and illustrate the utility of MapGAM, a user-friendly R package that provides a unified framework for estimating, predicting and drawing inference on covariate-adjusted spatial effects using individual-level data. The package also facilitates visualization of spatial effects via automated mapping procedures. MapGAM estimates covariate-adjusted spatial associations with a univariate or survival outcome using generalized additive models that include a non-parametric bivariate smooth term of geolocation parameters. Estimation and mapping methods are implemented for continuous, discrete, and right-censored survival data. In the current manuscript, we summarize the methodology implemented in MapGAM and illustrate the package using two example simulated datasets: the first considering a case-control study design from the state of Massachusetts and the second considering right-censored survival data from California.

Received: 2018-04-04; online 2020-03-31, supplementary material, (1.7 Kb)
CRAN packages: MapGAM


CC BY 4.0
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@article{RJ-2020-001,
  author = {Lu Bai and Daniel L. Gillen and Scott M. Bartell and
          Verónica M. Vieira},
  title = {{Mapping Smoothed Spatial Effect Estimates from Individual-
          Level Data: MapGAM}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-001},
  url = {https://journal.r-project.org/archive/2020/RJ-2020-001/index.html}
}