statistics

pycorr.statistics.isc_corrmat_within_diff(indxA, indxB, C)

Faster within-group subject-total correlation contrast using correlation matrix

Parameters:

• indxA (list) – list of indices corresponding to group A members.
• indxB (list) – likewise for group B (should be no overlap)

Returns:

ndarray with isc for A minus isc for B.

pycorr.statistics.isc_within_diff(A, B, standardized=False)

Contrast within-group subject-total correlation for A and B.

This function operates on the timecourse data, so is slower than isc_corrmat_within_diff. Inputs may be multi-dimensional. The last dimension is used for correlations (e.g. time should be last).

Parameters:

• A (list) – List of timecourse data for each member of group A.
• B (list) – Timecourses of same length as A.

Returns:

ndarray with isc for A minus isc for B.

pycorr.statistics.perm(A, B, fun, nreps=1, out=None, **kwargs)

Permutation test. Randomly shuffles group labels, then runs fun. Group sizes are preserved.

Parameters:

• A – lists with elements (or indices) to permute across groups
• B – similar to A, but other group
• fun – function of form fun(new_A, new_B, [opt1, ... ,])
• nreps – number of repetitions
• out – optional container for results (e.g. numpy array with dtype)
• kwargs – optional parameters passed to fun