This work is licensed under a Creative Commons Attribution 4.0 International License
http://creativecommons.org/licenses/by/4.0/
The repo is structured as:
.
├── dataprep
│ ├── graphs
│ └── outdata
├── inference
│ ├── data
│ ├── graphs
│ └── outdata
└── model
├── data
├── graphs
├── indata
└── outdata
Analyses should be run in this order:
- dataprep : preparation of pre/post data for inference. Also contains some utility functions.
- inference : statistical analyses of pre/post data - can all be run and post-processed using the bash script.
- model : the main modelling analysis
- modeldata.R : prepares effect estimates from the inference analysis and makes parts of Table 1
- modeloutcomes.R : main results for each sensitivity scenario
- runmodel.sh : bash script to run all modelling analyses at once (on multicore machine with plenty memory)
resultsupload.R in the top level parses and uploads results to googlesheets for inclusion in the article. The uploads require authetication (authors only).
The following R packages are required:
- inference: rstanarm
- plotting: ggplot2, scales, ggpubr, ggthemes
- modelling: HEdtree, discly (install using
devtools::install_github('petedodd/packagename')) - data manipulation: data.table
- utilities: glue, here,
- data I/O: googlesheets4, readxl