bsbt

Bayesian Spatial Bradley--Terry

https://github.com/rowlandseymour/bsbt

Science Score: 33.0%

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    Links to: arxiv.org
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Keywords

bayesian-inference bradley-terry comparative-judgement preference-learning
Last synced: 6 months ago · JSON representation

Repository

Bayesian Spatial Bradley--Terry

Basic Info
  • Host: GitHub
  • Owner: rowlandseymour
  • License: lgpl-3.0
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 44.3 MB
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Topics
bayesian-inference bradley-terry comparative-judgement preference-learning
Created over 5 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing License

README.md

BSBT


Bayesian Spatial Bradley--Terry

CRAN status R build status

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📦 The BSBT R package allows you to fit the Bayesian Spatial Bradley--Terry model to comparative judgement data sets. The package estimates the quality of each object in the data set based on the observed comparisons. The package includes functions to construct the prior distribution covariance matrix from a network or set of coordinates. The package also contains functions to run a number of different MCMC algorithms to estimate the level of deprivation in each aobject in the data set. Also included is a comparative judgement data set on deprivation in Dar es Salaam, Tanzania.

Installation

You can install BSBT by calling the following commands: ```{r} install.packages("BSBT")

devtools::install_github("rowlandseymour/BSBT", dependencies = TRUE) #for development version

```

Creating a Network from a Set of Areas

The first step is to create a network from the set of areas. Here's an example of a network made from Local Authority Areas in the England: England Map and Network (BSBT) There are two ways to do this in BSBT. The first is to construct an adjacency matrix, which describes which areas are neighbours. This can then be fed into constrained_adjacent_covariance_function. The second way is to use coordinates which can be used withconstrained_covariance_matrix. This uses the Euclidean distance metric.

Fitting the Model

The BSBT package uses MCMC the estimate the model parameters. The MCMC can be run by calling the run_mcmc function. This make take some time, up to a few hours, depending on how many areas there are in the model. Here are the results of the method applied to a comparative judgement data set in Tanzania:

Deprivation in Dar es Salaam, Tanzania (BSBT)

Data

In the package, there is a comparative judgement data set collected in Dar es Salaam, Tanzania. It includes over 75,000 comparisons, where citizens where are to compare subwards in the city based on deprivation. Also included are shapefiles for the 452 subwards. These can be accessed by calling data(dar.comparisons, package = "BSBT") and data(dar.shapefiles, package = "BSBT").

There is also code for simulating comparative judgement data given the underlying levels of deprivation. More information can be found by calling ?BSBT::simulate_contests

The Package in Action

R. G. Seymour, D. Sirl, S. Preston, I. L. Dryden, M. J. A. Ellis, B. Perrat, & J. Goulding. (2020). The Bayesian Spatial Bradley–Terry Model: Urban Deprivation Modeling in Tanzania. arXiv:2010.14128.

Seymour, R. G., Sirl, D, Preston, S & Goulding, J 2023, Multi-Level Spatial Comparative Judgement Models To Map Deprivation. in Proceedings of the Joint Statistical Meetings 2023. Zenodo, Joint Statistical Meetings 2023. https://doi.org/10.5281/zenodo.8314257

R. G. Seymour, A. Nyarko-Agyei, H. R. McCabe, K. Severn, T. Kypraios, D. Sirl, A. Taylor. (2023). Comparative Judgement Modeling to Map Forced Marriage at Local Levels. arXiv:2212.01202

Using citizen knowledge to model urban deprivation @ Universitas21 Early Career Researcher Workshop 2020 - Modern Slavery, Forced Labour and Human Trafficking: Research Roadmaps to 2030. Deceber 2020.

Acknowledgements

This work is supported by the Engineering and Physical Sciences Research Council [grant numbers EP/T003928/1, EP/R513283/1], the Economic and Social Sciences Research Council [ES/V015370/1], the Research England Policy Support Fund and the Big East African Data Science research group at the University of Nottingham.

The comparative judgement dataset was collected by Madeleine Ellis, James Goulding, Bertrand Perrat, Gavin Smith and Gregor Engelmann. We gratefully acknowledge the Rights Lab at the University of Nottingham for supporting funding for the comprehensive ground truth survey. We also acknowledge Humanitarian Street Mapping Team (HOT) for providing a team of experts in data collection to facilitate the surveys. This fieldwork was also supported by the EPSRC Horizon Centre for Doctoral Training - My Life in Data (EP/L015463/1) and by EPSRC grant Neodemographics (EP/L021080/1).

Owner

  • Name: Rowland Seymour
  • Login: rowlandseymour
  • Kind: user
  • Location: Nottingham
  • Company: University of Nottingham

GitHub Events

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  • Total Commits: 207
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  • Avg Commits per committer: 23.0
  • Development Distribution Score (DDS): 0.396
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Top Committers
Name Email Commits
Rowland Seymour r****r@n****k 125
Rowland Seymour R****r@n****k 24
Rowland Seymour r****d@R****n 18
rowlandseymour r****r@g****m 17
Rowland Guy Seymour p****3@i****n 15
diggerb j****s@b****k 3
Rowland G Seymour p****2@i****e 2
Rowland Seymour r****r@m****e 2
Yichi Zhang z****1@o****m 1

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  • Average comments per issue: 0
  • Average comments per pull request: 0.33
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 272 last-month
  • Total docker downloads: 41,971
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 4
  • Total maintainers: 1
cran.r-project.org: BSBT

The Bayesian Spatial Bradley–Terry Model

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 272 Last month
  • Docker Downloads: 41,971
Rankings
Forks count: 21.9%
Dependent packages count: 29.8%
Average: 34.2%
Stargazers count: 35.2%
Dependent repos count: 35.5%
Downloads: 48.5%
Last synced: about 2 years ago

Dependencies

DESCRIPTION cran
  • R >= 2.10 depends
  • MASS * imports
  • expm * imports
  • igraph * imports
  • stats * imports
  • utils * imports
  • RColorBrewer * suggests
  • RSQLite * suggests
  • deldir * suggests
  • knitr * suggests
  • rmarkdown * suggests
  • sf * suggests
  • spdep * suggests
  • surveillance * suggests
  • testthat * suggests
.github/workflows/check-standard.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite