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, <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.
