class eelbrain.testnd.VectorDifferenceRelated(y: Union[eelbrain._data_obj.NDVar, str], x: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str, eelbrain._data_obj.NDVar], c1: str = None, c0: str = None, 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: bool = False, tstart: float = None, tstop: float = None, parc: str = None, force_permutation: bool = False, norm: bool = 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.

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.

See also

One-sample vector test, notes on vector test implementation
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.


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_c1(self[, p]) c1 map masked by significance of the c1-c0 difference
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