eelbrain.testnd.ANOVA

class eelbrain.testnd.ANOVA(y, x, sub=None, ds=None, samples=10000, pmin=None, fmin=None, tfce=False, tstart=None, tstop=None, match=None, parc=None, force_permutation=False, **criteria)

Mass-univariate ANOVA

Parameters

  • y (NDVar) – Dependent variable.

  • x (Model) – Independent variables.

  • 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.

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

  • pmin (None | scalar (0 < pmin < 1)) – Threshold for forming clusters: use an f-value equivalent to an uncorrected p-value.

  • fmin (scalar) – Threshold for forming clusters as f-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).

  • match (categorial | False) – When permuting data, only shuffle the cases within the categories of match. By default, match is determined automatically based on the random efects structure of x.

  • 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

  • effects (tuple of str) – Names of the tested effects, in the same order as in other attributes.

  • 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.

  • f (list of NDVar) – Maps of F values.

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

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

  • tfce_maps (list of NDVar | None) – Maps 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

Examples

For information on model specification see the univariate ANOVA examples.

Methods

cluster(cluster_id[, effect])

Retrieve a specific cluster as NDVar

clusters

compute_probability_map([effect])

Compute a probability map

find_clusters([pmin, maps, effect])

Find significant regions or clusters

find_peaks()

Find peaks in a TFCE distribution

info_list([computation])

List with information about the test

masked_parameter_map([effect, pmin])

Create a copy of the parameter map masked by significance

table([title, caption, clusters])

Table listing all effects and corresponding smallest p-values