Other packages > Find by keyword >

SpatialVS  

Spatial Variable Selection
View on CRAN: Click here


Download and install SpatialVS package within the R console
Install from CRAN:
install.packages("SpatialVS")

Install from Github:
library("remotes")
install_github("cran/SpatialVS")

Install by package version:
library("remotes")
install_version("SpatialVS", "1.1")



Attach the package and use:
library("SpatialVS")
Maintained by
Yili Hong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-09-30
Latest Update: 2018-11-10
Description:
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.
How to cite:
Yili Hong (2018). SpatialVS: Spatial Variable Selection. R package version 1.1, https://cran.r-project.org/web/packages/SpatialVS. Accessed 05 Mar. 2026.
Previous versions and publish date:
1.0 (2018-09-30 17:30)
Other packages that cited SpatialVS R package
View SpatialVS citation profile
Other R packages that SpatialVS depends, imports, suggests or enhances
Complete documentation for SpatialVS
Functions, R codes and Examples using the SpatialVS R package
Some associated functions: SpatialVS . SpatialVS.summary . control.default . lyme.svs.eco0.dat . lyme.svs.eco1.dat . small.test.dat . 
Some associated R codes: globV.R . lyme.svs.eco0.dat.R . lyme.svs.eco1.dat.R . small.test.R . spatialVS.R . summary.R .  Full SpatialVS package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

pinp  
'pinp' is not 'PNAS'
A 'PNAS'-alike style for 'rmarkdown', derived from the 'Proceedings of the National Academy of Scie ...
Download / Learn more Package Citations See dependency  
imagefx  
Extract Features from Images
Synthesize images into characteristic features for time-series analysis or machine learning applicat ...
Download / Learn more Package Citations See dependency  
roccv  
ROC for Cross Validation Results
Cross validate large genetic data while specifying clinical variables that should always be in the m ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
neat  
Efficient Network Enrichment Analysis Test
Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in ...
Download / Learn more Package Citations See dependency  
ClimClass  
Climate Classification According to Several Indices
Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indi ...
Download / Learn more Package Citations See dependency  

26,264

R Packages

223,360

Dependencies

70,244

Author Associations

26,265

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA