https://github.com/rudeboybert/spatialepi

R Package of methods and data for spatial epidemiology
https://github.com/rudeboybert/spatialepi

Last synced: 17 days ago
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R Package of methods and data for spatial epidemiology

• Host: GitHub
• URL: https://github.com/rudeboybert/spatialepi
• Owner: rudeboybert
• Created: 2014-05-28T19:10:05.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2023-02-22T21:35:25.000Z (over 1 year ago)
• Last Synced: 2024-10-09T22:06:14.161Z (about 1 month ago)
• Language: R
• Homepage:
• Size: 30.9 MB
• Stars: 30
• Watchers: 1
• Forks: 8
• Open Issues: 1
• Metadata Files:
  • Readme: README.Rmd

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README

        
---

output: github_document

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README_figure/README-"

)

```

# SpatialEpi

[![R-CMD-check](https://github.com/rudeboybert/SpatialEpi/workflows/R-CMD-check/badge.svg)](https://github.com/rudeboybert/SpatialEpi/actions)

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/SpatialEpi)](https://cran.r-project.org/package=SpatialEpi) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/SpatialEpi)](https://www.r-pkg.org/pkg/SpatialEpi)

Package of data and methods for spatial epidemiology. 

## Installation

Get the released version from CRAN:

```{r, eval=FALSE}

install.packages("SpatialEpi")

```

Or the development version from GitHub:

```{r, eval=FALSE}

# 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

1. Install an older version of `RcppArmadillo` by running  

    ```{r, eval=FALSE}

    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.

```{r, message=FALSE, warning=FALSE}

library(SpatialEpi)

```

```{r, message=FALSE, warning=FALSE}

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.

```{r, message=FALSE, warning=FALSE, fig.height=6.5}

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)](https://www.researchgate.net/publication/235896508_A_Bayesian_model_for_cluster_detection):

```{r bayes_cluster, message=FALSE, warning=FALSE, cache=TRUE}

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)

```

```{r, fig.height=6.5}

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

```