class eelbrain.testnd.Vector(y: Union[eelbrain._data_obj.NDVar, str], match: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str] = None, sub: Union[eelbrain._data_obj.Var, numpy.ndarray, str] = None, ds: eelbrain._data_obj.Dataset = None, samples: int = 10000, tmin: float = None, tfce: Union[float, bool] = False, tstart: float = None, tstop: float = None, parc: str = None, force_permutation: bool = False, norm: bool = False, **criteria)

Test a vector field for vectors with non-random direction

y : NDVar

Dependent variable (needs to include one vector dimension).

match : None | categorial

Combine data for these categories before testing.

sub : index

Perform test with a subset of the data.

ds : None | 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.


Vector tests are based on the Hotelling T-Square statistic. Computation of the T-Square statistic relies on [1].


[1]Kopp, J. (2008). Efficient numerical diagonalization of hermitian 3 x 3 matrices. International Journal of Modern Physics C, 19(3), 523-548. 10.1142/S0129183108012303
n : int

Number of cases.

difference : NDVar

The vector field averaged across cases.

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.


cluster(self, cluster_id) Retrieve a specific cluster as NDVar
compute_probability_map(self, **sub) Compute a probability map
find_clusters(self[, pmin, maps]) Find significant regions or clusters
find_peaks(self) Find peaks in a threshold-free cluster distribution
info_list(self[, computation]) List with information about the test
masked_difference(self[, p, name]) Difference map masked by significance
masked_parameter_map(self[, pmin]) Create a copy of the parameter map masked by significance