eelbrain.testnd.anova¶
-
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
Parameters: - 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 ofx
.- 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.
Examples
For information on model specification see the univariate
anova()
examples.Attributes: - 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 thefind_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).
Methods¶
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 |