eelbrain.testnd.corr¶
-
class
eelbrain.testnd.
corr
(y: Union[eelbrain._data_obj.NDVar, str], x: Union[eelbrain._data_obj.Var, str], norm: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str] = None, sub: Union[eelbrain._data_obj.Var, numpy.ndarray, str] = None, ds: eelbrain._data_obj.Dataset = None, samples: int = 10000, pmin: float = None, rmin: 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] = None, parc: str = None, **criteria)¶ Mass-univariate correlation
Parameters: - y : NDVar
Dependent variable.
- x : continuous
The continuous predictor variable.
- norm : None | categorial
Categories in which to normalize (z-score) x.
- 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 r-value equivalent to an uncorrected p-value.
- rmin : None | scalar
Threshold for forming clusters.
- 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 : None | categorial
When permuting data, only shuffle the cases within the categories of match.
- parc : str
Collect permutation statistics for all regions of the parcellation of this dimension. For threshold-based test, the regions are disconnected.
- mintime : scalar
Minimum duration for clusters (in seconds).
- minsource : int
Minimum number of sources per cluster.
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.- 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.
- r : NDVar
Map of correlation values (with threshold contours).
- tfce_map : NDVar | None
Map of the test statistic processed with the threshold-free cluster enhancement algorithm (or None if no TFCE was performed).
Methods¶
cluster (self, cluster_id) |
Retrieve a specific cluster as NDVar |
compute_probability_map (self, **sub) |
Compute a probability map |
find_clusters (self[, pmin, maps]) |
Find significant regions or clusters |
find_peaks (self) |
Find peaks in a threshold-free cluster distribution |
info_list (self[, computation]) |
List with information about the test |
masked_parameter_map (self[, pmin]) |
Create a copy of the parameter map masked by significance |