eelbrain.testnd.ANOVA

class eelbrain.testnd.ANOVA(y, x, sub=None, data=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.

  • 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 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

Mass-univariate ANOVA example. For information on model specification see the univariate ANOVA examples.

Methods

cluster(cluster_id[, effect])

Retrieve a specific cluster as NDVar

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])

Statistical parameter map masked by significance

table([title, caption, clusters])

Table listing all effects and corresponding smallest p-values