README.md
In jpfrench81/neastbenchmark: Benchmark Data for Disease Clusters
neastbenchmark
An R package containing benchmark data for disease clusters.
The package can be installed in R via the command
devtools::install_github("jfrench/neastbenchmark")
assuming the devtools package is installed.
This package contains benchmark data for evaluating the power of disease clustering methods. The data are based on the female at-risk population in 245 counties (and equivalents) based on the 1990 United States census. The data consist of regions from the Northeastern United States, including regions in Connecticut, Delaware, the District of Columbia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont.
The observational data were originally used to identify clusters of breast cancer mortality in Kulldorff et al. (1997). The original circular cluster benchmark data (rural*, mixed*, and urban* data sets) were produced by Song and Kulldorff (2003). The irregularly-shaped cluster benchmark data (a-k) were produced by Duczmal et al. (2006). The remaining irregularly clustered benchmark data (irural*, imixed*, iurban*) were produced by French et al. (2022).
Martin Kulldorff, Eric J. Feuer, Barry A. Miller, Laurence S. Freedman; Breast Cancer Clusters in the Northeast United States: A Geographic Analysis, American Journal of Epidemiology, Volume 146, Issue 2, 15 July 1997, Pages 161–170, https://doi.org/10.1093/oxfordjournals.aje.a009247.
Song, C., & Kulldorff, M. (2003). Power evaluation of disease clustering tests. International Journal of Health Geographics, 2, 9. https://doi.org/10.1186/1476-072X-2-9.
Duczmal, L., Kulldorff, M., & Huang, L. (2006). Evaluation of Spatial Scan Statistics for Irregularly Shaped Clusters. Journal of Computational and Graphical Statistics, 15(2), 428-442. Retrieved from http://www.jstor.org/stable/27594187.
Joshua P. French, Mohammad Meysami, Lauren M.
Hall, Nicholas E. Weaver, Minh C. Nguyen & Lee Panter
(2022) A comparison of spatial scan methods for cluster
detection, Journal of Statistical Computation and
Simulation, 92:16, 3343-3372. https://doi.org/10.1080/00949655.2022.2065676.
jpfrench81/neastbenchmark documentation built on July 26, 2023, 3:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
neastbenchmark
An R package containing benchmark data for disease clusters.
The package can be installed in R via the command
devtools::install_github("jfrench/neastbenchmark")
assuming the devtools package is installed.
This package contains benchmark data for evaluating the power of disease clustering methods. The data are based on the female at-risk population in 245 counties (and equivalents) based on the 1990 United States census. The data consist of regions from the Northeastern United States, including regions in Connecticut, Delaware, the District of Columbia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont.
The observational data were originally used to identify clusters of breast cancer mortality in Kulldorff et al. (1997). The original circular cluster benchmark data (rural*, mixed*, and urban* data sets) were produced by Song and Kulldorff (2003). The irregularly-shaped cluster benchmark data (a-k) were produced by Duczmal et al. (2006). The remaining irregularly clustered benchmark data (irural*, imixed*, iurban*) were produced by French et al. (2022).
Martin Kulldorff, Eric J. Feuer, Barry A. Miller, Laurence S. Freedman; Breast Cancer Clusters in the Northeast United States: A Geographic Analysis, American Journal of Epidemiology, Volume 146, Issue 2, 15 July 1997, Pages 161–170, https://doi.org/10.1093/oxfordjournals.aje.a009247.
Song, C., & Kulldorff, M. (2003). Power evaluation of disease clustering tests. International Journal of Health Geographics, 2, 9. https://doi.org/10.1186/1476-072X-2-9.
Duczmal, L., Kulldorff, M., & Huang, L. (2006). Evaluation of Spatial Scan Statistics for Irregularly Shaped Clusters. Journal of Computational and Graphical Statistics, 15(2), 428-442. Retrieved from http://www.jstor.org/stable/27594187.
Joshua P. French, Mohammad Meysami, Lauren M. Hall, Nicholas E. Weaver, Minh C. Nguyen & Lee Panter (2022) A comparison of spatial scan methods for cluster detection, Journal of Statistical Computation and Simulation, 92:16, 3343-3372. https://doi.org/10.1080/00949655.2022.2065676.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.