tedana.metrics.dependence

Metrics evaluating component TE-dependence or -independence.

Functions

calculate_betas(*, data, mixing)	Calculate unstandardized parameter estimates between data and mixing matrix.
calculate_dependence_metrics(*, f_t2_maps, ...)	Calculate Kappa and Rho metrics from F-statistic maps.
calculate_f_maps(*, data_cat, z_maps, ...[, ...])	Calculate pseudo-F-statistic maps for TE-dependence and -independence models.
calculate_psc(*, data_optcom, optcom_betas)	Calculate percent signal change maps for components against optimally-combined data.
calculate_varex(*, optcom_betas)	Calculate unnormalized(?) variance explained from unstandardized parameter estimate maps.
calculate_varex_norm(*, weights)	Calculate normalized variance explained from standardized parameter estimate maps.
calculate_weights(*, data_optcom, mixing)	Calculate standardized parameter estimates between data and mixing matrix.
calculate_z_maps(*, weights[, z_max])	Calculate component-wise z-statistic maps.
compute_countnoise(*, stat_maps, stat_cl_maps)	Count the number of significant voxels from non-significant clusters.
compute_countsignal(*, stat_cl_maps)	Count the number of significant voxels in a set of cluster-extent thresholded maps.
compute_dice(*, clmaps1, clmaps2[, axis])	Compute the Dice similarity index between two thresholded and binarized maps.
compute_signal_minus_noise_t(*, z_maps, ...)	Compare signal and noise t-statistic distributions with a two-sample t-test.
compute_signal_minus_noise_z(*, z_maps, ...)	Compare signal and noise z-statistic distributions with a two-sample t-test.
generate_decision_table_score(*, kappa, ...)	Generate a five-metric decision table.
threshold_map(*, maps, mask, ref_img, threshold)	Perform cluster-extent thresholding.
threshold_to_match(*, maps, n_sig_voxels, ...)	Cluster-extent threshold a map to target number of significant voxels.