- X : array, shape (n_observations, n_times, n_vertices)
Array data of the difference between two conditions.
- threshold : float | dict | None
If threshold is None, it will choose a t-threshold equivalent to
p < 0.05 for the given number of observations (only valid when
using an t-statistic). If a dict is used, then threshold-free
cluster enhancement (TFCE) will be used, and it must have keys
'start'
and 'step'
to specify the integration parameters,
see the TFCE example.
- n_permutations : int | ‘all’
The number of permutations to compute. Can be “all” to perform
an exact test.
- tail : -1 or 0 or 1 (default = 0)
If tail is 1, the statistic is thresholded above threshold.
If tail is -1, the statistic is thresholded below threshold.
If tail is 0, the statistic is thresholded on both sides of
the distribution.
- stat_fun : callable | None
Function used to compute the statistical map (default None will use
mne.stats.ttest_1samp_no_p()
).
- connectivity : scipy.sparse.spmatrix or None
Defines connectivity between features. The matrix is assumed to
be symmetric and only the upper triangular half is used.
This matrix must be square with dimension (n_vertices * n_times) or
(n_vertices). Default is None, i.e, a regular lattice connectivity.
Use square n_vertices matrix for datasets with a large temporal
extent to save on memory and computation time.
- n_jobs : int
Number of permutations to run in parallel (requires joblib package).
- seed : int | instance of RandomState | None
Seed the random number generator for results reproducibility.
- max_step : int
When connectivity is a n_vertices x n_vertices matrix, specify the
maximum number of steps between vertices along the second dimension
(typically time) to be considered connected. This is not used for full
or None connectivity matrices.
- spatial_exclude : list of int or None
List of spatial indices to exclude from clustering.
- step_down_p : float
To perform a step-down-in-jumps test, pass a p-value for clusters to
exclude from each successive iteration. Default is zero, perform no
step-down test (since no clusters will be smaller than this value).
Setting this to a reasonable value, e.g. 0.05, can increase sensitivity
but costs computation time.
- t_power : float
Power to raise the statistical values (usually t-values) by before
summing (sign will be retained). Note that t_power == 0 will give a
count of nodes in each cluster, t_power == 1 will weight each node by
its statistical score.
- out_type : str
For arrays with connectivity, this sets the output format for clusters.
If ‘mask’, it will pass back a list of boolean mask arrays.
If ‘indices’, it will pass back a list of lists, where each list is the
set of vertices in a given cluster. Note that the latter may use far
less memory for large datasets.
- check_disjoint : bool
If True, the connectivity matrix (or list) will be examined to
determine of it can be separated into disjoint sets. In some cases
(usually with connectivity as a list and many “time” points), this
can lead to faster clustering, but results should be identical.
- buffer_size: int or None
The statistics will be computed for blocks of variables of size
“buffer_size” at a time. This is option significantly reduces the
memory requirements when n_jobs > 1 and memory sharing between
processes is enabled (see set_cache_dir()), as X will be shared
between processes and each process only needs to allocate space
for a small block of variables.
- verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).