https://github.com/vasishth/uncertainty
https://github.com/vasishth/uncertainty
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/vasishth/uncertainty
- Owner: vasishth
- Created: 2019-11-13T12:40:04.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-03-19T21:13:19.000Z (about 4 years ago)
- Last Synced: 2025-03-15T18:42:09.672Z (over 1 year ago)
- Language: R
- Homepage:
- Size: 10.9 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# uncertainty
Code and data to accompany the paper:
Shravan Vasishth and Andrew Gelman. How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis. Linguistics, 59:1311--1342, 2021
doi: https://doi.org/10.1515/ling-2019-0051
Please contact Shravan Vasishth if there are any problems with this repository.
The files:
- The directory R has some functions that I used in this work.
- code contains reproducible code and a compiled pdf file that shows the results of the code.
- model has some Stan modeling results that were precomputed (see code).
- Paper contains the original paper. Note that it may not compile on your particular machine.
To extract R code from the Rmd file (in the code directory), use purl() in the knitr library.
.
├── R
│ ├── createStanDat.R
│ ├── createStanDatAcc.R
│ ├── gen_fake_lnorm.R
│ ├── gen_fake_lnorm2x2.R
│ ├── gen_fake_norm.R
│ ├── magnifytext.R
│ ├── multiplot.R
│ ├── plotpredictions.R
│ ├── plotresults.R
│ └── stan_results.R
├── README.md
├── code
│ ├── Uncertainty.Rmd
│ ├── Uncertainty.pdf
│ data
│ ├── DillonE1.txt
│ ├── JMVV2019replication.Rda
│ ├── Lago.csv
│ ├── Tucker.RData
│ ├── Wagers.Rdata
│ ├── agrmt_mismatch.Rda
│ ├── bayesfactors.txt
│ ├── data_model.Rda
│ ├── data_model_dillonrep.Rda
│ ├── gibsonwu2012data.txt
│ ├── lmer_estimates2.Rda
│ ├── lmer_estimates3.Rda
│ ├── posteriorsTargetMismatch.Rda
│ ├── public_article_data.txt
│ ├── remafit.Rda
│ ├── smallsamplesestimates.Rda
│ └── tvals.Rda
├── model
│ └── au_predicted_meansD13rep.Rda
└── paper
├── UncertaintyVasishthGelman2021Preprint.Rnw
├── bibio.bib
├── dgruyter.sty
You can extract the R code from the Rnw file by typing
knitr::purl("")