eelbrain.testnd.Vector¶
-
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
Parameters: - 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.
Notes
Vector tests are based on the Hotelling T-Square statistic. Computation of the T-Square statistic relies on [1].
References
[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 Attributes: - n : int
Number of cases.
- difference : NDVar
The vector field averaged across cases.
- t2 : NDVar | None
Hotelling T-Square map;
None
if the test usednorm=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 thefind_clusters()
method.
Methods¶
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 |