- mne.stats.permutation_t_test(X, n_permutations=10000, tail=0, n_jobs=1, seed=None, verbose=None)¶
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).
array, shape (n_samples, n_tests)
Samples (observations) by number of tests (variables).
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).
The number of jobs to run in parallel (default 1). Requires the joblib package.
int| instance of
n_permutations >= 2 ** (n_samples - (tail == 0)),
seedwill be ignored since an exact test (full permutation test) will be performed.