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, mixing, ...[, ...])	Calculate pseudo-F-statistic maps for TE-dependence and -independence models.
calculate_marginal_r2(*, data_optcom, mixing)	Calculate mean voxel-wise marginal R-squared for each component against the data.
calculate_partial_r2(*, semipartial_r2, total_r2)	Calculate mean voxelwise partial R-squared for each regressor.
calculate_psc(*, data_optcom, optcom_betas)	Calculate percent signal change maps for components against optimally-combined data.
calculate_semipartial_r2(*, data_optcom, mixing)	Calculate mean voxelwise semi-partial R-squared for each regressor.
calculate_total_r2(*, data_optcom, mixing)	Calculate mean voxel-wise variance explained by all components against the data.
calculate_varex(*, component_maps)	Calculate relative coefficient energy from 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_kappa_rho_difference(*, kappa, rho)	Compute the proportion of pseudo-F-statistics that is dominated by either kappa or rho.
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(*[, ...])	Generate a decision table score from an arbitrary set of metrics.
threshold_map(*, maps, mask_img[, ...])	Perform cluster-extent thresholding.
threshold_to_match(*, maps, n_sig_voxels, ...)	Cluster-extent threshold a map to target number of significant voxels.