We present here how to run the code to enable the stratification of Vineland scores from the NDAR database.

Python >3.6

Install libraries with:

pip install -r requirements.txt

Create an utils file including variables as reported in example_utils.py

1. Once the utils.py file is ready, run dataset method from create_dataset to obtain the long-format Vineland dataset and the dictionary of demographic information for each selected subject saved as namedtuple.

   Preprocessing steps include:

   Phenotype selection (import_data:_select_phenotype);

   Read tables from multiple instrument datasets (import_data:ReadData:data_wrangling);

   Merging of different instrument versions (e.g., renaming feature names) with create_levels:vineland_levels;

2. Run the create_dataset:prepare_imputation function that drops subjects that have a percentage of missing information greater than the desired threshold.

3. Grid search the best parameters for the Relative Clustering Validation. Considered different missing percentages thresholds and varying number of neighbors.