CRAN/E | SpatialVS

SpatialVS

Spatial Variable Selection

Installation

About

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, ). 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.

Key Metrics

Version 1.1
R ≥ 3.3.0
Published 2018-11-10 1844 days ago
Needs compilation? no
License GPL-2
CRAN checks SpatialVS results

Downloads

Last 24 hours 0
Last 7 days 13 -75%
Last 30 days 134 +28%
Last 90 days 349 -2%
Last 365 days 1.580 -38%

Maintainer

Maintainer

Yili Hong

yilihong@vt.edu

Authors

Yili Hong
Li Xu
Yimeng Xie
Zhongnan Jin

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

SpatialVS archive

Depends

R ≥ 3.3.0

Imports

MASS
nlme
fields