mne.stats.permutation_t_test¶

mne.stats.permutation_t_test(X, n_permutations=10000, tail=0, n_jobs=1, 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.

Parameters:

Parameters:	X : array of shape [n_samples x n_tests] Data of size number of samples (aka number of observations) times number of tests (aka number of variables). n_permutations : int or ‘all’ Number of permutations. If n_permutations is ‘all’ all possible permutations are tested (2n_samples). It’s the exact test, that can be untractable when the number of samples is big (e.g. > 20). If n_permutations >= 2n_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. 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.

X : array of shape [n_samples x n_tests]

Data of size number of samples (aka number of observations) times number of tests (aka number of variables).

n_permutations : int or ‘all’

Number of permutations. If n_permutations is ‘all’ all possible permutations are tested (2**n_samples). 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.

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

A reference (among many) in field of neuroimaging: Nichols, T. E. & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15, 1-25. Overview of standard nonparametric randomization and permutation testing applied to neuroimaging data (e.g. fMRI) DOI: https://doi.org/10.1002/hbm.1058

Examples using `mne.stats.permutation_t_test`¶

Permutation T-test on sensor data

mne.stats.permutation_t_test¶

Examples using mne.stats.permutation_t_test¶

Examples using `mne.stats.permutation_t_test`¶