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 tstatistic.
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 pvalues of each variable for multiple comparisons. Like Bonferroni correction, this method adjusts pvalues in a way that controls the familywise 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.
 tail1 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
The number of jobs to run in parallel (default 1). Requires the joblib package.
 seed
None
int
 instance ofRandomState
If
seed
is anint
, it will be used as a seed forRandomState
. IfNone
, the seed will be obtained from the operating system (seeRandomState
for details). Default isNone
. verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 X
 Returns
Notes
If
n_permutations >= 2 ** (n_samples  (tail == 0))
,n_permutations
andseed
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, 125.