In bramzandbelt/irmass: Independent Race Model Analysis of Selective Stopping
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irmass - Research compendium for the report on independent race model analysis of selective stopping by Zandbelt & Van den Bosch
Compendium DOI
The files at the URL above will generate the results as found in the preprint. The files hosted at https://github.com/bramzandbelt/irmass are the development versions and may have changed since the preprint was published.
Author of this repository
Bram Zandbelt (bramzandbelt@gmail.com)
Published in:
TBA
Overview of contents
The packagae irmass is one of two research compendia of the research project Cognitive and Neurobiological Mechanisms of Selective Stopping by Bram Zandbelt and Ruben van den Bosch (the other research compendium, cnmss, can be found here). This project was conducted at the Donders Institute, Radboud University, Nijmegen, the Netherlands, and registered at the Donders Centre for Cognitive Neuroimaging under project number 3017031.05 (DCCN PI: Roshan Cools).
This research compendium contains all data, code, and text associated with the above-mentioned publication and is organized as follows (showing directories in a tree-like format with a maximum depth of two levels):
.
├── R
├── analysis
│ ├── bash
│ └── notebooks_and_scripts
├── data
│ └── derivatives
├── documents
│ ├── content
│ └── context
├── figures
│ ├── 01_preprocess_log_files
│ ├── 02_assess_task_performance_criteria
│ ├── 03_individual_analysis_effect_ssd_on_prob_responding_given_stopsignal
│ ├── 04_individual_analysis_rt_difference_nosignal_stoprespond
│ ├── 05_individual_analysis_effect_ssd_on_stoprespond_rt
│ ├── 07_group_analysis_rt_difference_nosignal_stoprespond
│ ├── 08_group_analysis_effect_ssd_on_stoprespond_rt
│ └── 09_exploration_support_for_hypotheses_under_different_bayes_factor_criteria
├── man
├── packrat
│ ├── lib
│ ├── lib-R
│ ├── lib-ext
│ └── src
└── reports
The R/ directory contains:
- R code specific to the present project; functions are organized into files (e.g. functions for plotting are in
plot_functions.R).
The analysis/ directory contains:
- R Markdown notebooks implementing the analyses (
notebooks_and_scripts/ directory), numbered in the order in which they should be run;
- shell scripts running the R Markdown notebooks with appropriate parameters, if any (
bash/ directory).
The data/ directory contains:
- the data derived from the raw data (
derivatives/ directory), organized by notebook name.
- for meaning of output variables, see the codebooks of the notebook
01_preprocess_log_files (documents/content/01_preprocess_log_files/), the notebooks (analysis/*.Rmd), and static reports (reports/)
- the simulated performance data (
simulations/ directory)
N.B. The raw data is not shared, because it contains information about date and time on which the session took place, potentially allowing for identification of participants (e.g. by participants themselves). However, the raw data are archived at the Donders Center for Cognitive Neuroimaging under project number 3017031.05
The documents/ directory contains:
- documents describing the content of the experimental data (
content/ directory), such as codebooks;
- documents describing the context of the data (
context/ directory), such as ethics documents, preregistration, and task instructions;
- documents related to the report of this research project (
manuscript/ directory).
The figures/ directory contains:
- visualizations of descriptive and inferential statistics, organized by notebook name.
The man/ directory contains:
- documentation of objects inside the package, generated by
roxygen2.
The packrat/ directory contains:
- R packages the research compendium depends on; for more info see https://rstudio.github.io/packrat/.
The reports/ directory contains:
- static HTML versions of the knitted R Markdown notebooks, organized by notebook name.
Finally, this research compendium is associated with a number of online objects, including:
| object | archived version | development version |
| ------ | -------------------------------- | -------------------------------- |
| preregistration | https://osf.io/mq64z/ | NA |
| stimulus presentation code | https://doi.org/10.5281/zenodo.3243799 | github.com/bramzandbelt/StPy |
In this experiment, we used the following StPy stimulus presentation configuration files (under config/ in StPy):
expt_3017031-05-Expt02-A.yamlexpt_3017031-05-Expt02-B.yaml
How to use
This repository is organized as an R package. The R package structure was used to help manage dependencies, to take advantage of continuous integration for automated code testing and documentation, and to be able to follow a standard format for file organization. The package irmass depends on other R packages and non-R programs, which are listed below under Dependencies.
To download the package source as you see it on GitHub, for offline browsing, use this line at the shell prompt (assuming you have Git installed on your computer):
Install irmass package from Github:
- From R:
devtools::install_github("bramzandbelt/irmass")
- From the command line:
git clone https://github.com/bramzandbelt/irmass.git
Once the download is complete, open the file irmass.Rproj in RStudio to begin working with the package and compendium files. To reproduce all analyses, run the shell script analysis/bash/run_all_analyses.sh. This will run all RMarkdown notebooks in correct order. It may take several hours to complete.
Licenses
Manuscript: CC-BY-4.0 http://creativecommons.org/licenses/by/4.0/
Code: MIT http://opensource.org/licenses/MIT, year: 2019, copyright holder: Bram B. Zandbelt
Dependencies
Below is the output of sessionInfo(), showing version information about R, the OS, and attached or loaded packages:
devtools::session_info()
Packrat takes care of dependencies in R. In addition, Stan (we used v.2.18.1) is needed.
Acknowledgment
This research project was funded through a James McDonnell Scholar Award (grant number 220020328) to Roshan Cools. We thank Roshan Cools (RC) for financial support and constructive feedback and Alexandra Sebastian (AS) for providing statistical parametric maps for the purpose of sample size estimation. We thank Ben Marwick for inspiration on how to create, organize, and describe research compendia.
Contributor roles
We specify the contribution of all people involved in the research (contributing non-authors included), according to the Contributor Role Taxonomy.
| | BBZ | RvdB | RC |
| ---------------------------- | --- | ---- | --- |
| Conceptualization | X | - | - |
| Methodology | X | - | - |
| Software | X | - | - |
| Validation | X | - | - |
| Formal analysis | X | - | - |
| Investigation | - | X | - |
| Resources | X | - | - |
| Data curation | X | - | - |
| Writing - original draft | X | - | - |
| Writing - review and editing | X | X | - |
| Visualization | X | - | - |
| Supervision | X | - | - |
| Project administration | - | X | - |
| Funding acquisition | - | - | X |
Contact
bramzandbelt/irmass documentation built on June 28, 2019, 9:07 p.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
irmass - Research compendium for the report on independent race model analysis of selective stopping by Zandbelt & Van den Bosch
Compendium DOI
The files at the URL above will generate the results as found in the preprint. The files hosted at https://github.com/bramzandbelt/irmass are the development versions and may have changed since the preprint was published.
Author of this repository
Bram Zandbelt (bramzandbelt@gmail.com)
Published in:
TBA
Overview of contents
The packagae irmass is one of two research compendia of the research project Cognitive and Neurobiological Mechanisms of Selective Stopping by Bram Zandbelt and Ruben van den Bosch (the other research compendium, cnmss, can be found here). This project was conducted at the Donders Institute, Radboud University, Nijmegen, the Netherlands, and registered at the Donders Centre for Cognitive Neuroimaging under project number 3017031.05 (DCCN PI: Roshan Cools).
This research compendium contains all data, code, and text associated with the above-mentioned publication and is organized as follows (showing directories in a tree-like format with a maximum depth of two levels):
. ├── R ├── analysis │ ├── bash │ └── notebooks_and_scripts ├── data │ └── derivatives ├── documents │ ├── content │ └── context ├── figures │ ├── 01_preprocess_log_files │ ├── 02_assess_task_performance_criteria │ ├── 03_individual_analysis_effect_ssd_on_prob_responding_given_stopsignal │ ├── 04_individual_analysis_rt_difference_nosignal_stoprespond │ ├── 05_individual_analysis_effect_ssd_on_stoprespond_rt │ ├── 07_group_analysis_rt_difference_nosignal_stoprespond │ ├── 08_group_analysis_effect_ssd_on_stoprespond_rt │ └── 09_exploration_support_for_hypotheses_under_different_bayes_factor_criteria ├── man ├── packrat │ ├── lib │ ├── lib-R │ ├── lib-ext │ └── src └── reports
The R/ directory contains:
- R code specific to the present project; functions are organized into files (e.g. functions for plotting are in
plot_functions.R).
The analysis/ directory contains:
- R Markdown notebooks implementing the analyses (
notebooks_and_scripts/directory), numbered in the order in which they should be run; - shell scripts running the R Markdown notebooks with appropriate parameters, if any (
bash/directory).
The data/ directory contains:
- the data derived from the raw data (
derivatives/directory), organized by notebook name.- for meaning of output variables, see the codebooks of the notebook
01_preprocess_log_files(documents/content/01_preprocess_log_files/), the notebooks (analysis/*.Rmd), and static reports (reports/)
- for meaning of output variables, see the codebooks of the notebook
- the simulated performance data (
simulations/directory)
N.B. The raw data is not shared, because it contains information about date and time on which the session took place, potentially allowing for identification of participants (e.g. by participants themselves). However, the raw data are archived at the Donders Center for Cognitive Neuroimaging under project number 3017031.05
The documents/ directory contains:
- documents describing the content of the experimental data (
content/directory), such as codebooks; - documents describing the context of the data (
context/directory), such as ethics documents, preregistration, and task instructions; - documents related to the report of this research project (
manuscript/directory).
The figures/ directory contains:
- visualizations of descriptive and inferential statistics, organized by notebook name.
The man/ directory contains:
- documentation of objects inside the package, generated by
roxygen2.
The packrat/ directory contains:
- R packages the research compendium depends on; for more info see https://rstudio.github.io/packrat/.
The reports/ directory contains:
- static HTML versions of the knitted R Markdown notebooks, organized by notebook name.
Finally, this research compendium is associated with a number of online objects, including:
| object | archived version | development version | | ------ | -------------------------------- | -------------------------------- | | preregistration | https://osf.io/mq64z/ | NA | | stimulus presentation code | https://doi.org/10.5281/zenodo.3243799 | github.com/bramzandbelt/StPy |
In this experiment, we used the following StPy stimulus presentation configuration files (under config/ in StPy):
expt_3017031-05-Expt02-A.yamlexpt_3017031-05-Expt02-B.yaml
How to use
This repository is organized as an R package. The R package structure was used to help manage dependencies, to take advantage of continuous integration for automated code testing and documentation, and to be able to follow a standard format for file organization. The package irmass depends on other R packages and non-R programs, which are listed below under Dependencies.
To download the package source as you see it on GitHub, for offline browsing, use this line at the shell prompt (assuming you have Git installed on your computer):
Install irmass package from Github:
- From R:
devtools::install_github("bramzandbelt/irmass")
- From the command line:
git clone https://github.com/bramzandbelt/irmass.git
Once the download is complete, open the file irmass.Rproj in RStudio to begin working with the package and compendium files. To reproduce all analyses, run the shell script analysis/bash/run_all_analyses.sh. This will run all RMarkdown notebooks in correct order. It may take several hours to complete.
Licenses
Manuscript: CC-BY-4.0 http://creativecommons.org/licenses/by/4.0/
Code: MIT http://opensource.org/licenses/MIT, year: 2019, copyright holder: Bram B. Zandbelt
Dependencies
Below is the output of sessionInfo(), showing version information about R, the OS, and attached or loaded packages:
devtools::session_info()
Packrat takes care of dependencies in R. In addition, Stan (we used v.2.18.1) is needed.
Acknowledgment
This research project was funded through a James McDonnell Scholar Award (grant number 220020328) to Roshan Cools. We thank Roshan Cools (RC) for financial support and constructive feedback and Alexandra Sebastian (AS) for providing statistical parametric maps for the purpose of sample size estimation. We thank Ben Marwick for inspiration on how to create, organize, and describe research compendia.
Contributor roles
We specify the contribution of all people involved in the research (contributing non-authors included), according to the Contributor Role Taxonomy.
| | BBZ | RvdB | RC | | ---------------------------- | --- | ---- | --- | | Conceptualization | X | - | - | | Methodology | X | - | - | | Software | X | - | - | | Validation | X | - | - | | Formal analysis | X | - | - | | Investigation | - | X | - | | Resources | X | - | - | | Data curation | X | - | - | | Writing - original draft | X | - | - | | Writing - review and editing | X | X | - | | Visualization | X | - | - | | Supervision | X | - | - | | Project administration | - | X | - | | Funding acquisition | - | - | X |
Contact
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We want your feedback!
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