In rudeboybert/SpatialEpi: Methods and Data for Spatial Epidemiology

knitr::opts_chunk$set(
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SpatialEpi

Package of data and methods for spatial epidemiology.

Installation

Get the released version from CRAN:

install.packages("SpatialEpi")

Or the development version from GitHub:

# If you haven't installed devtools yet, do so:
# install.packages("devtools")
devtools::install_github("rudeboybert/SpatialEpi")

Note: In order for all C++ code to compile correctly you may need to

1. Install the cpp11 package
2. Install an older version of RcppArmadillo by running
   r packageurl <- "https://cran.r-project.org/src/contrib/Archive/RcppArmadillo/RcppArmadillo_0.9.900.3.0.tar.gz" install.packages(packageurl, repos=NULL, type="source")

Example

We load the data and convert the coordinate system from latitude/longitude to a grid-based system.

library(SpatialEpi)

data(NYleukemia)
sp.obj <- NYleukemia$spatial.polygon
centroids <- latlong2grid(NYleukemia$geo[, 2:3])
population <- NYleukemia$data$population
cases <- NYleukemia$data$cases

We plot the incidence of leukemia for each census tract.

plotmap(cases/population, sp.obj, log=TRUE, nclr=5)
points(grid2latlong(centroids), pch=4)

We run the Bayesian Cluster Detection method from Wakefield and Kim (2013):

y <- cases
E <- expected(population, cases, 1)
max.prop <- 0.15
shape <- c(2976.3, 2.31)
rate <- c(2977.3, 1.31)
J <- 7
pi0 <- 0.95
n.sim.lambda <- 10^4
n.sim.prior <- 10^5
n.sim.post <- 10^5

# Compute output
output <- bayes_cluster(y, E, population, sp.obj, centroids, max.prop,
                        shape, rate, J, pi0, n.sim.lambda, n.sim.prior,
                        n.sim.post)

plotmap(output$post.map$high.area, sp.obj)

rudeboybert/SpatialEpi documentation built on Feb. 27, 2023, 5:09 a.m.