netneurotools.stats.get_dominance_stats

netneurotools.stats.get_dominance_stats(X, y, use_adjusted_r_sq=True, verbose=False, n_jobs=1)[source]

Return the dominance analysis statistics for multilinear regression.

This is a rewritten & simplified version of [DA1]. It is briefly tested against the original package, but still in early stages. Please feel free to report any bugs.

Warning: Still work-in-progress. Parameters might change!

Parameters:

  • X ((N, M) array_like) – Input data

  • y ((N,) array_like) – Target values

  • use_adjusted_r_sq (bool, optional) – Whether to use adjusted r squares. Default: True

  • verbose (bool, optional) – Whether to print debug messages. Default: False

  • n_jobs (int, optional) – The number of jobs to run in parallel. Default: 1

Returns:

  • model_metrics (dict) – The dominance metrics, currently containing individual_dominance, partial_dominance, total_dominance, and full_r_sq.

  • model_r_sq (dict) – Contains all model r squares

Notes

Example usage

from netneurotools.stats import get_dominance_stats
from sklearn.datasets import load_boston
X, y = load_boston(return_X_y=True)
model_metrics, model_r_sq = get_dominance_stats(X, y)

To compare with [DA1], use use_adjusted_r_sq=False

from dominance_analysis import Dominance_Datasets
from dominance_analysis import Dominance
boston_dataset=Dominance_Datasets.get_boston()
dominance_regression=Dominance(data=boston_dataset,
                               target='House_Price',objective=1)
incr_variable_rsquare=dominance_regression.incremental_rsquare()
dominance_regression.dominance_stats()

References