pyriemann.stats.PermutationDistance

class pyriemann.stats.PermutationDistance(n_perms=100, metric='riemann', mode='pairwise', n_jobs=1, random_state=42, estimator=None)

Permutation test based on distance.

Perform a permutation test based on distance. You have the choice of 3 different statistic:

  • ‘pairwise’ :

    the statistic is based on paiwire distance as descibed in [1]. This is the fastest option for low sample size since the pairwise distance matrix does not need to be estimated for each permutation.

  • ‘ttest’ :

    t-test based statistic obtained by the ration of the distance between each riemannian centroid and the group dispersion. The means have to be estimated for each permutation, leading to a slower procedure. However, this can be used for high sample size.

  • ‘ftest’:

    f-test based statistic estimated using the between and within group variability. As for the ‘ttest’ stats, group centroid are estimated for each permutation.

Parameters:
n_permsint, default=100

The number of permutation. The minimum should be 20 for a resolution of 0.05 p-value.

metricstring | dict, default=”riemann”

Metric used for mean estimation (for the list of supported metrics, see pyriemann.utils.mean.mean_covariance()) and for distance estimation (see pyriemann.utils.distance.distance()). The metric can be a dict with two keys, “mean” and “distance” in order to pass different metrics.

modestring, default=’pairwise’

Type of statistic to use. could be ‘pairwise’, ‘ttest’ of ‘ftest’

n_jobsinteger, default=1

The number of CPUs to use to do the computation. -1 means ‘all CPUs’.

random_stateint, default=42

random state for the permutation test.

estimatorNone or sklearn compatible estimator, default=None

If provided, data are transformed before every permutation. should not be used unless a supervised opperation must be applied on the data. This would be the case for ERP covariance.

See also

PermutationModel

References

[1]

A new method for non-parametric multivariate analysis of variance M. Anderson. Austral ecology, Volume 26, Issue 1, February 2001.

Attributes:
p_value_float

the p-value of the test

scores_list

contain all scores for all permutations. The fist element is the non-permuted score.

__init__(n_perms=100, metric='riemann', mode='pairwise', n_jobs=1, random_state=42, estimator=None)

Init.

plot(nbins=10, range=None, axes=None)

Plot results of the permutation test.

Parameters:
nbinsinteger or array_like or ‘auto’, default=10

If an integer is given, bins + 1 bin edges are returned, consistently with np.histogram() for numpy version >= 1.3. Unequally spaced bins are supported if bins is a sequence.

rangetuple or None, default=None

The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, range is (x.min(), x.max()). Range has no effect if bins is a sequence. If bins is a sequence or range is specified, autoscaling is based on the specified bin range instead of the range of x.

axesaxes handle, default=None

Axes handle for matplotlib. if None a new figure will be created.

score(X, y, groups=None)

Score of a permutation.

Parameters:
Xarray-like

The data to fit. Can be, for example a list, or an array at least 2d.

yarray-like

The target variable to try to predict in the case of supervised learning.

test(X, y, groups=None, verbose=True)

Performs the permutation test

Parameters:
Xarray-like

The data to fit. Can be, for example a list, or an array at least 2d.

yarray-like

The target variable to try to predict in the case of supervised learning.

verbosebool, default=True

If true, print progress.