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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2020-001.zip
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For attribution, please cite this work as
Bai, et al., "The R Journal: Mapping Smoothed Spatial Effect Estimates from Individual-Level Data: MapGAM ", The R Journal, 2020
BibTeX citation
@article{RJ-2020-001,
author = {Bai, Lu and Gillen, Daniel L. and Bartell, Scott M. and Vieira, Verónica M.},
title = {The R Journal: Mapping Smoothed Spatial Effect Estimates from Individual-Level Data: MapGAM },
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2020-001},
doi = {10.32614/RJ-2020-001},
volume = {12},
issue = {1},
issn = {2073-4859},
pages = {32-48}
}