eelbrain.testnd.Vector

class eelbrain.testnd.Vector(y, match=None, sub=None, data=None, samples=10000, tmin=None, tfce=False, tstart=None, tstop=None, parc=None, force_permutation=False, norm=False, **criteria)[source]

Test a vector field for vectors with non-random direction

Parameters:

  • y (NDVar | str) – Dependent variable (needs to include one vector dimension).

  • match (Factor | Interaction | NestedEffect | str) – Combine data for these categories before testing.

  • sub (Var | ndarray | str) – Perform test with a subset of the data.

  • data (Dataset) – If a Dataset is specified, all data-objects can be specified as names of Dataset variables

  • samples (int) – Number of samples for permutation test (default 10000).

  • tmin (float) – Threshold value for forming clusters.

  • tfce (float | bool) – Use threshold-free cluster enhancement. Use a scalar to specify the step of TFCE levels (for tfce is True, 0.1 is used).

  • tstart (float) – Start of the time window for the permutation test (default is the beginning of y).

  • tstop (float) – Stop of the time window for the permutation test (default is the end of y).

  • parc (str) – Collect permutation statistics for all regions of the parcellation of this dimension. For threshold-based test, the regions are disconnected.

  • force_permutation (bool) – Conduct permutations regardless of whether there are any clusters.

  • norm (bool) – Use the vector norm as univariate test statistic (instead of Hotelling’s T-Square statistic).

  • mintime (scalar) – Minimum duration for clusters (in seconds).

  • minsource (int) – Minimum number of sources per cluster.

n

Number of cases.

Type:

int

difference

The vector field averaged across cases.

Type:

eelbrain._data_obj.NDVar

t2

Hotelling T-Square map; None if the test used norm=True.

Type:

eelbrain._data_obj.NDVar

p

Map of p-values corrected for multiple comparison (or None if no correction was performed).

Type:

eelbrain._data_obj.NDVar

tfce_map

Map of the test statistic processed with the threshold-free cluster enhancement algorithm (or None if no TFCE was performed).

Type:

eelbrain._data_obj.NDVar

clusters

For cluster-based tests, a table of all clusters. Otherwise a table of all significant regions (or None if permutations were omitted). See also the find_clusters() method.

Type:

eelbrain._data_obj.Dataset

See also

testnd

Information on the different permutation methods

Notes

The permutation test for vector data is described in [1]. Computation of the T-Square statistic relies on [2].

References

Methods

cluster(cluster_id)

Retrieve a specific cluster as NDVar

compute_probability_map(**sub)

Compute a probability map

find_clusters([pmin, maps])

Find significant regions or clusters

find_peaks()

Find peaks in a threshold-free cluster distribution

info_list([computation])

List with information about the test

masked_difference([p, name])

Difference map masked by significance

masked_parameter_map([pmin])

Statistical parameter map masked by significance