bspme

Go to the package website: [link]

See a vignette with NO2 exposure and simulated health data: [link]

See bspme_1.0.1.pdf for the pdf file of the package manual.

bspme is an R package that provides fast, scalable inference tools for Bayesian spatial exposure measurement error models, namely, the Bayesian linear and generalized linear models with the presence of spatial exposure measurement error of covariate(s). These models typically arise from a two-stage Bayesian analysis of environmental exposures and health outcomes. From a first-stage model, predictions of the covariate of interest (“exposure”) and their uncertainty information (typically contained in MCMC samples) are obtained and used to form a multivariate normal prior distribution $X\sim N(\mu, \Sigma)$ for exposure in a second-stage regression model. Naive, non-sparse choices of the precision matrix $Q = \Sigma^{-1}$ of the multivariate normal (such as a sample precision matrix) lead to the MCMC posterior inference algorithm being infeasible to run for a large number of subjects $n$ because of the cubic computational cost associated with the $n$-dimensional MVN prior. With a sparse precision matrix $Q$ obtained from the Vecchia approximation, the bspme package offers fast, scalable algorithms to conduct posterior inference for large health datasets, with the number of subjects $n$ possibly reaching tens of thousands. For more details, please see the following paper:

Lee, C. J., Symanski, E., Rammah, A., Kang, D. H., Hopke, P. K., & Park, E. S. (2024). A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology. arXiv preprint arXiv:2401.00634. https://arxiv.org/abs/2401.00634

Installation

You can install the R package bspme with the following code:

# install.packages("devtools")
devtools::install_github("changwoo-lee/bspme", build_vignettes = T)

To browse and see vignettes, run

browseVignettes("bspme")

Functionality

Function	Description
blm_me()	Bayesian linear regression models with spatial exposure measurement error.
bglm_me()	Bayesian generalized linear models with spatial exposure measurement error.
vecchia_cov()	Run Vecchia approximation given a covariance matrix.

To see function description in R environment, run the following lines:

?blm_me
?bglm_me
?vecchia_cov

datasets

Dataset call	Description
data("NO2_Jan2012")	Daily average NO2 concentrations in and around Harris County, Texas, in Jan 2012.
data("health_sim")	Simulated health data associated with ln(NO2) concentration on Jan 10, 2012.

Examples

Please see the vignette “NO2-exposure-and-health-data-analysis”.

Acknowldegements

This work was supported by the National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health (NIH) under R01ES031990.