mne.stats.
ttest_1samp_no_p
(X, sigma=0, method='relative')[source]¶Perform one-sample t-test.
This is a modified version of scipy.stats.ttest_1samp()
that avoids
a (relatively) time-consuming p-value calculation, and can adjust
for implausibly small variance values [1].
Parameters: |
|
---|---|
Returns: |
|
Notes
To use the “hat” adjustment method [1], a value of sigma=1e-3
may be a
reasonable choice.
You can use the conversion from scipy.stats.distributions.t.ppf
:
thresh = -scipy.stats.distributions.t.ppf(p_thresh, n_samples - 1) / 2.
to convert a desired p-value threshold to 2-tailed t-value threshold.
For one-tailed tests, thresh
in the above should be multiplied by 2
(and for tail=-1
, multiplied by -1
).
References
[1] | (1, 2, 3) Ridgway et al. 2012 “The problem of low variance voxels in statistical parametric mapping; a new hat avoids a ‘haircut’”, NeuroImage. 2012 Feb 1;59(3):2131-41. |
mne.stats.ttest_1samp_no_p
¶