# mne.stats.permutation_cluster_test¶

mne.stats.permutation_cluster_test(X, threshold=None, n_permutations=1024, tail=0, stat_fun=None, connectivity=None, n_jobs=1, seed=None, max_step=1, exclude=None, step_down_p=0, t_power=1, out_type='mask', check_disjoint=False, buffer_size=1000, verbose=None)[source]

Cluster-level statistical permutation test.

For a list of nd-arrays of data, e.g. 2d for time series or 3d for time-frequency power values, calculate some statistics corrected for multiple comparisons using permutations and cluster level correction. Each element of the list X contains the data for one group of observations. Randomized data are generated with random partitions of the data.

Parameters
Xlist

List of nd-arrays containing the data. Each element of X contains the samples for one group. First dimension of each element is the number of samples/observations in this group. The other dimensions are for the size of the observations. For example if X = [X1, X2] with X1.shape = (20, 50, 4) and X2.shape = (17, 50, 4) one has 2 groups with respectively 20 and 17 observations in each. Each data point is of shape (50, 4).

threshold

If threshold is None, it will choose an F-threshold equivalent to p < 0.05 for the given number of observations (only valid when using an F-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_permutationsint

The number of permutations to compute.

tail-1 or 0 or 1 (default = 0)

If tail is 0, the statistic is thresholded on both sides of the distribution. If tail is 1, the statistic is thresholded above threshold. If tail is -1, the statistic is thresholded below threshold, and the values in threshold must correspondingly be negative.

stat_fun

Function called to calculate statistics, must accept 1d-arrays as arguments (default None uses mne.stats.f_oneway()).

connectivityscipy.sparse.spmatrix

Defines connectivity between features. The matrix is assumed to be symmetric and only the upper triangular half is used. Default is None, i.e, a regular lattice connectivity. Can also be False to assume no connectivity.

n_jobsint

Number of permutations to run in parallel (requires joblib package).

seedint | instance of RandomState | None

Seed the random number generator for results reproducibility.

max_stepint

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.

exclude

Mask to apply to the data to exclude certain points from clustering (e.g., medial wall vertices). Should be the same shape as X. If None, no points are excluded.

step_down_pfloat

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_powerfloat

Power to raise the statistical values (usually F-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_typestr

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.

verbose

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns
F_obsarray, shape (n_tests,)

Statistic (F by default) observed for all variables.

clusterslist

List type defined by out_type above.

cluster_pvarray

P-value for each cluster

H0array, shape (n_permutations,)

Max cluster level stats observed under permutation.

References

1

Maris/Oostenveld (2007), “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.