# 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
Xarray, shape (n_samples, n_tests)

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

n_permutationsint | ‘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_jobsint

The number of jobs to run in parallel (default 1). Requires the joblib package.

seedNone | int | instance of RandomState

If seed is an int, it will be used as a seed for RandomState. If None, the seed will be obtained from the operating system (see RandomState for details). Default is None.

verbose

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

Returns
T_obsarray of shape [n_tests]

T-statistic observed for all variables

p_valuesarray of shape [n_tests]

P-values for all the tests (aka variables)

H0array 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

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