- 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)
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.
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
tstop (scalar) – Stop of the time window for the permutation test (default is the end of
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.
difference (NDVar) – The vector field averaged across cases.
t2 (NDVar | None) – Hotelling T-Square map;
Noneif the test used
p (NDVar | None) – Map of p-values corrected for multiple comparison (or
Noneif 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
Noneif permutations were omitted). See also the
Information on the different permutation methods
Vector tests are based on the Hotelling T-Square statistic. Computation of the T-Square statistic relies on 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
Retrieve a specific cluster as NDVar
Compute a probability map
Find significant regions or clusters
Find peaks in a threshold-free cluster distribution
List with information about the test
Difference map masked by significance
Statistical parameter map masked by significance