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 (seepyriemann.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.
- 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.
See also
References
[1]A new method for non-parametric multivariate analysis of variance M. Anderson. Austral ecology, Volume 26, Issue 1, February 2001.
- __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.
