mne.stats.ttest_1samp_no_p#

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
Xarray

Array to return t-values for.

sigmafloat

The variance estimate will be given by var + sigma * max(var) or var + sigma, depending on “method”. By default this is 0 (no adjustment). See Notes for details.

methodstr

If ‘relative’, the minimum variance estimate will be sigma * max(var), if ‘absolute’ the minimum variance estimate will be sigma.

Returns
tarray

T-values, potentially adjusted using the hat method.

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)

Gerard R. Ridgway, Vladimir Litvak, Guillaume Flandin, Karl J. Friston, and Will D. Penny. The problem of low variance voxels in statistical parametric mapping; a new hat avoids a ‘haircut’. NeuroImage, 59(3):2131–2141, 2012. doi:10.1016/j.neuroimage.2011.10.027.

Examples using mne.stats.ttest_1samp_no_p#

Statistical inference

Statistical inference

Statistical inference