eelbrain.testnd.TTestIndependent

class eelbrain.testnd.TTestIndependent(y, x, c1=None, c0=None, match=None, sub=None, ds=None, tail=0, samples=10000, pmin=None, tmin=None, tfce=False, tstart=None, tstop=None, parc=None, force_permutation=False, **criteria)

Mass-univariate independent samples t-test

The test data can be specified in two forms:

  • In long form, with y supplying the data, x specifying condition for each case.

  • With y and x supplying data for the two conditions.

Parameters
  • 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) – Combine cases with the same cell on x % match.

  • sub (index) – Perform the test with a subset of the data.

  • ds (Dataset) – If a Dataset is specified, all data-objects can be specified as names of Dataset variables.

  • tail (0 | 1 | -1) – Which tail of the t-distribution to consider: 0: both (two-tailed); 1: upper tail (one-tailed); -1: lower tail (one-tailed).

  • samples (int) – Number of samples for permutation test (default 10,000).

  • pmin (None | scalar (0 < pmin < 1)) – Threshold p value for forming clusters. None for threshold-free cluster enhancement.

  • tmin (scalar) – Threshold for forming clusters as t-value.

  • 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.

  • mintime (scalar) – Minimum duration for clusters (in seconds).

  • minsource (int) – Minimum number of sources per cluster.

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

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

  • 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.

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

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

  • p_uncorrected (NDVar) – Map of p-values uncorrected for multiple comparison.

  • t (NDVar) – Map of t-values.

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

See also

testnd

Information on the different permutation methods

Notes

Cases with zero variance are set to t=0.

Methods

cluster(cluster_id)

Retrieve a specific cluster as NDVar

compute_probability_map(**sub)

Compute a probability map

find_clusters([pmin, maps])

Find significant regions or clusters

find_peaks()

Find peaks in a threshold-free cluster distribution

info_list([computation])

List with information about the test

masked_difference([p, name])

Difference map masked by significance

masked_parameter_map([pmin])

Create a copy of the parameter map masked by significance