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("")