mne.stats.spatio_temporal_cluster_1samp_test¶
-
mne.stats.
spatio_temporal_cluster_1samp_test
(X, threshold=None, n_permutations=1024, tail=0, stat_fun=None, connectivity=None, n_jobs=1, seed=None, max_step=1, spatial_exclude=None, step_down_p=0, t_power=1, out_type='indices', check_disjoint=False, buffer_size=1000, verbose=None)[source]¶ Non-parametric cluster-level paired t-test for spatio-temporal data.
This function provides a convenient wrapper for data organized in the form (observations x time x space) to use
mne.stats.permutation_cluster_1samp_test()
, which contains more complete documentation.- Parameters
- 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
orNone
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
The number of jobs to run in parallel (default 1). Requires the joblib package.
- seed
None
|int
| instance ofRandomState
If
seed
is anint
, it will be used as a seed forRandomState
. IfNone
, the seed will be obtained from the operating system (seeRandomState
for details). Default isNone
.- 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
ofint
orNone
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_disjointbool
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.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
- X
- Returns
References
- 1
Maris/Oostenveld, “Nonparametric statistical testing of EEG- and MEG-data” Journal of Neuroscience Methods, Vol. 164, No. 1., pp. 177-190. doi:10.1016/j.jneumeth.2007.03.024
- 2
Smith/Nichols (2009), “Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence, and localisation in cluster inference”, NeuroImage 44 (2009) 83-98.