mne.stats.permutation_t_test

mne.stats.permutation_t_test(X, n_permutations=10000, tail=0, n_jobs=1, seed=None, verbose=None)[source]

One sample/paired sample permutation test based on a t-statistic.

This function can perform the test on one variable or simultaneously on multiple variables. When applying the test to multiple variables, the “tmax” method is used for adjusting the p-values of each variable for multiple comparisons. Like Bonferroni correction, this method adjusts p-values in a way that controls the family-wise error rate. However, the permutation method will be more powerful than Bonferroni correction when different variables in the test are correlated (see [1]).

Parameters:
X : array, shape (n_samples, n_tests)

Samples (observations) by number of tests (variables).

n_permutations : int | ‘all’

Number of permutations. If n_permutations is ‘all’ all possible permutations are tested. It’s the exact test, that can be untractable when the number of samples is big (e.g. > 20). If n_permutations >= 2**n_samples then the exact test is performed.

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

If tail is 1, the alternative hypothesis is that the mean of the data is greater than 0 (upper tailed test). If tail is 0, the alternative hypothesis is that the mean of the data is different than 0 (two tailed test). If tail is -1, the alternative hypothesis is that the mean of the data is less than 0 (lower tailed test).

n_jobs : int

Number of CPUs to use for computation.

seed : int | instance of RandomState | None

Seed the random number generator for results reproducibility.

verbose : bool, str, int, or None

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

Returns:
T_obs : array of shape [n_tests]

T-statistic observed for all variables

p_values : array of shape [n_tests]

P-values for all the tests (aka variables)

H0 : array of shape [n_permutations]

T-statistic obtained by permutations and t-max trick for multiple comparison.

Notes

If n_permutations >= 2 ** (n_samples - (tail == 0)), n_permutations and seed will be ignored since an exact test (full permutation test) will be performed.

References

[1](1, 2) Nichols, T. E. & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15, 1-25.

Examples using mne.stats.permutation_t_test