The model fitting code is based on the code shared by Andreas Hula (https://github.com/AndreasHula).
All analyses can be reproduced using the accompanying docker container mpcoll2/trustgame:latest See docker.io on how to install docker on your computer.
After downloading this repository, use your path to this repository to replace $CODEPATH in all commands below.
The import_prepare_data.py script loads the raw data, creates long and wide versions of the data frame and saves the data as a binary file for the model fitting. All outputs are in the data folder.
TODO : Double check data manipulation in script
docker run -it -v $CODEPATH:/code mpcoll2/trustgame:latest python ./code/import_prepare_data.pyThe C++ files in the model_fit directory are already compiled to run in the docker container. If you are not using the container you might need to modify the scripts and compile them again for your systen. Fitting the model takes 1-2 hours/participant. However, multiple participants can be run in parallel if you have access to more than one cpu thread. RAM usage is about 1GB per participant. To fit the model to one participant or multiple participants, modify the bash script model_fit_parallel.sh according to the instructions in the script and run it using the command below.
TODO : Double check script is working correctly
One output binary file per pair is saved in the model_fit/outputs folder. The model was already fitted to this data and the outputs are already in this folder.
docker run -it -v $CODEPATH:/code mpcoll2/trustgame:latest ./code/model_fit_parallel.shThe read_model_fit.py script reads the model fitting outputs, and adds the parameters of the best fitting model for each pair int the raw and wide data files.
docker run -it -v $CODEPATH:/code mpcoll2/trustgame:latest python ./code/read_model_fit.py**To do **