eelbrain.testnd.ttest_ind¶
-
class
eelbrain.testnd.
ttest_ind
(y: Union[eelbrain._data_obj.NDVar, str], x: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str, eelbrain._data_obj.NDVar], c1: Union[str, Tuple[str, ...]] = None, c0: Union[str, Tuple[str, ...]] = None, match: 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, tail: int = 0, samples: int = 10000, pmin: float = None, tmin: float = None, tfce: Union[float, bool] = False, tstart: float = None, tstop: float = None, parc: str = None, force_permutation: bool = False, **criteria)¶ Mass-univariate independent samples t-test
The test data can be specified in two forms:
- In long form, with
y
supplying the data,x
specifying condition for each case. - With
y
andx
supplying data for the two conditions.
Parameters: - y : NDVar
Dependent variable.
- x : categorial | NDVar
Model containing the cells which should be compared, or NDVar to which
y
should be compared. In the latter case, the next three parameters are ignored.- c1 : str | tuple | None
Test condition (cell of
x
).c1
andc0
can be omitted ifx
only contains two cells, in which case cells will be used in alphabetical order.- c0 : str | tuple | None
Control condition (cell of
x
).- match : categorial
Combine cases with the same cell on
x % match
.- 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.
- tail : 0 | 1 | -1
Which tail of the t-distribution to consider: 0: both (two-tailed); 1: upper tail (one-tailed); -1: lower tail (one-tailed).
- samples : int
Number of samples for permutation test (default 10,000).
- pmin : None | scalar (0 < pmin < 1)
Threshold p value for forming clusters. None for threshold-free cluster enhancement.
- tmin : scalar
Threshold for forming clusters as t-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
).- 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.
Notes
Cases with zero variance are set to t=0.
Attributes: - c1_mean : NDVar
Mean in the c1 condition.
- c0_mean : NDVar
Mean in the c0 condition.
- 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.- difference : NDVar
Difference between the mean in condition c1 and condition c0.
- 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.
- t : NDVar
Map of t-values.
- tfce_map : NDVar | None
Map of the test statistic processed with the threshold-free cluster enhancement algorithm (or None if no TFCE was performed).
- In long form, with
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_difference (self[, p, name]) |
Difference map masked by significance |
masked_parameter_map (self[, pmin]) |
Create a copy of the parameter map masked by significance |