tedana.selection
.kundu_tedpca
- kundu_tedpca(comptable, n_echos, kdaw=10.0, rdaw=1.0, stabilize=False)[source]
Select PCA components using Kundu’s decision tree approach.
- Parameters
comptable (
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.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
comptable (
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 incomptable
.