tedana.selection.tedpca.kundu_tedpca

kundu_tedpca(component_table, n_echos, n_independent_echos=None, kdaw=10.0, rdaw=1.0, stabilize=False)[source]

Select PCA components using Kundu’s decision tree approach.

Parameters:

  • component_table (pandas.DataFrame) – Component table with relevant metrics: kappa, rho, and normalized variance explained. Component number should be the index.

  • n_echos (int) – Number of echoes in dataset.

  • n_independent_echos (int) – Number of independent echoes to use in goodness of fit metrics (fstat). Primarily used for EPTI acquisitions. If None, number of echoes will be used. Default is None.

  • kdaw (float, optional) – Kappa dimensionality augmentation weight. Must be a non-negative float, or -1 (a special value). Default is 10.

  • rdaw (float, optional) – Rho dimensionality augmentation weight. Must be a non-negative float, or -1 (a special value). Default is 1.

  • stabilize (bool, optional) – Whether to stabilize convergence by reducing dimensionality, for low quality data. Default is False.

Returns:

  • component_table (pandas.DataFrame) – Component table with components classified as ‘accepted’, ‘rejected’, or ‘ignored’.

  • metric_metadata (dict) – Dictionary with metadata about calculated metrics. Each entry corresponds to a column in component_table.