frites.stats.rfx_ttest#
- frites.stats.rfx_ttest(mi, mi_p, center=False, sigma=0.001, ttested=False)[source]#
Perform the t-test across subjects.
- Parameters
- minumpy:array_like
A list of length n_roi of array of mutual information of shape (n_suj, n_times). If ttested is True, n_suj shoud be 1.
- mi_pnumpy:array_like
A list of array of permuted mutual information of shape (n_perm, n_suj, n_times). If ttested is True, n_suj shoud be 1.
- sigma
python:float| 0.001 Hat adjustment method, a value of 1e-3 may be a reasonable choice
- center{
python:False, ‘mean’, ‘median’, ‘trimmed’, ‘zscore’} Re-center the time-series of effect arround 0 before computing the t-test. This parameters can be useful in case of a different number of data per brain region.
- ttestedbool |
python:False Specify if the inputs have already been t-tested
- Returns
- t_obsnumpy:array_like
Array of true t-values of shape (n_suj, n_times)
- tobs_surrnumpy:array_like
Array of permuted t-values of shape (n_perm, n_suj, n_times)
- pop_mean
python:float The value that have been used to compute the one-sample t-test. If the data have already been t-tested, this parameter is set to NaN
References
Giordano et al., 2017 [11]