README.Rmd

---
output: github_document
---

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```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README_figure/README-"
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```

# 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)
```