class eelbrain.testnd.anova(y: Union[eelbrain._data_obj.NDVar, str], x: Union[eelbrain._data_obj.Model, eelbrain._data_obj.Interaction, eelbrain._data_obj.Categorial, eelbrain._data_obj.Var, str], sub: Union[eelbrain._data_obj.Var, numpy.ndarray, str] = None, ds: eelbrain._data_obj.Dataset = None, samples: int = 10000, pmin: float = None, fmin: float = None, tfce: Union[float, bool] = False, tstart: float = None, tstop: float = None, match: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str, bool] = None, parc: str = None, force_permutation: bool = False, **criteria)

Mass-univariate ANOVA

y : NDVar

Dependent variable.

x : Model

Independent variables.

sub : index

Perform the 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 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).

replacement : bool

whether random samples should be drawn with replacement or without

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.


For information on model specification see the univariate anova() examples.

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).


cluster(self, cluster_id[, effect]) Retrieve a specific cluster as NDVar
compute_probability_map(self[, effect]) Compute a probability map
find_clusters(self[, pmin, maps, effect]) Find significant regions or clusters
find_peaks(self) Find peaks in a TFCE distribution
info_list(self[, computation]) List with information about the test
masked_parameter_map(self[, effect, pmin]) Create a copy of the parameter map masked by significance
table(self[, title, caption]) Table listing all effects and corresponding smallest p-values