class eelbrain.testnd.VectorDifferenceRelated(y, x, c1=None, c0=None, match=None, sub=None, data=None, samples=10000, tmin=None, tfce=False, tstart=None, tstop=None, parc=None, force_permutation=False, norm=False, **criteria)

Test difference between two vector fields for non-random direction

  • y (NDVar) – Dependent variable.

  • x (categorial | NDVar) – Model containing the cells which should be compared, or NDVar to which y should be compared. In the latter case, the next three parameters are ignored.

  • c1 (str | tuple | None) – Test condition (cell of x). c1 and c0 can be omitted if x only contains two cells, in which case cells will be used in alphabetical order.

  • c0 (str | tuple | None) – Control condition (cell of x).

  • match (categorial) – Units within which measurements are related (e.g. ‘subject’ in a within-subject comparison).

  • sub (index) – Perform the 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 (scalar) – Threshold value for forming clusters.

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

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

  • tstop (scalar) – 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 (int) – Number of cases.

  • c1_mean (NDVar) – Mean in the c1 condition.

  • c0_mean (NDVar) – Mean in the c0 condition.

  • difference (NDVar) – Difference between the mean in condition c1 and condition c0.

  • t2 (NDVar | None) – Hotelling T-Square map; None if the test used norm=True.

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

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

  • clusters (None | Dataset) – 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.

See also


One-sample vector test, notes on vector test implementation


Information on the different permutation methods



Retrieve a specific cluster as NDVar


Compute a probability map

find_clusters([pmin, maps])

Find significant regions or clusters


Find peaks in a threshold-free cluster distribution


List with information about the test


c1 map masked by significance of the c1-c0 difference

masked_difference([p, name])

Difference map masked by significance


Statistical parameter map masked by significance