SpatialVS
Spatial Variable Selection
Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, doi:10.48550/arXiv.1809.06418). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.
- Version1.1
- R versionunknown
- LicenseGPL-2
- Needs compilation?No
- Last release11/10/2018
Team
Yili Hong
Yimeng Xie
Show author detailsRolesAuthorZhongnan Jin
Show author detailsRolesAuthorLi Xu
Show author detailsRolesAuthor
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Last 30 days
This package has been downloaded 112 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1 times.
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Last 365 days
This package has been downloaded 1,594 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 26 downloads.
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Dependencies
- Imports3 packages