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spfilter

Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models

https://github.com/sjuhl/spfilter

Science Score: 23.0%

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    Low similarity (11.9%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models

Basic Info
Statistics
  • Stars: 7
  • Watchers: 1
  • Forks: 2
  • Open Issues: 0
  • Releases: 6
Created almost 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog License

README.md

spfilteR

codecov Travis build status license CRAN/METACRAN <!-- badges: end -->

This package provides a number of useful functions that facilitate the analysis of spatially autocorrelated data based on the eigenfunction decomposition of an exogenously given connectivity matrix W. The main function getEVs() specifies a projection matrix M and symmetrizes the connectivity matrix by V=1/2(_W+W_') before decomposing the transformed matrix _MVM_. If covariates are supplied, this function constructs the projection matrix by: _M=I-X(X'X_)-1 _X_'. Eigenvectors obtained from this specification are not only mutually uncorrelated but also orthogonal to the covariates in _X_. In contrast, if no covariates are supplied, the projection matrix simplifies to _M_= _I-11'/n_, where _1*_ is a vector of ones and n is the number of units. Besides the eigenvectors and eigenvalues, getEVs() also provides the Moran coefficient associated with each eigenvector.

Subsequently, these eigenvectors can be used to perform semiparametric spatial filtering in a regression framework. The functions lmFilter() and glmFilter() in this package implement unsupervised eigenvector selection based on a stepwise regression procedure and different objective functions, including i) the maximization of model fit, ii) minimization of residual autocorrelation, iii) the statistical significance of residual autocorrelation, and iv) the statistical significance of candidate eigenvectors. If other selection criteria are required or a model needs to be fitted that is currently not supported by these two functions, supervised eigenvector selection can be performed as well using the basic stats::lm() and stats::glm() commands in conjunction with the output generated by getEVs(). This option provides additional flexibility.

Owner

  • Name: Sebastian Juhl
  • Login: sjuhl
  • Kind: user
  • Location: Hamburg, Germany
  • Company: SAP SE

Data Scientist

GitHub Events

Total
  • Release event: 1
  • Push event: 15
  • Create event: 1
Last Year
  • Release event: 1
  • Push event: 15
  • Create event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 264
  • Total Committers: 2
  • Avg Commits per committer: 132.0
  • Development Distribution Score (DDS): 0.379
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
sjuhl s****l@g****e 164
sjuhl 3****l 100
Committer Domains (Top 20 + Academic)
gess.uni-mannheim.de: 1

Issues and Pull Requests

Last synced: almost 2 years ago

All Time
  • Total issues: 0
  • Total pull requests: 4
  • Average time to close issues: N/A
  • Average time to close pull requests: 36 minutes
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.25
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
Pull Request Authors
  • sjuhl (4)
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 301 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 8
  • Total maintainers: 1
cran.r-project.org: spfilteR

Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models

  • Versions: 8
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 301 Last month
Rankings
Stargazers count: 21.1%
Forks count: 21.9%
Dependent packages count: 29.8%
Average: 32.7%
Dependent repos count: 35.5%
Downloads: 55.3%
Maintainers (1)
sebastian.juhl@t-online.de
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • stats * imports
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests
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