Hi all, I am trying to do a group-level permutation test in pyMVPA. I have obtained individual p-values with permutation test and I think I should average the permuted distributions across subjects to get the p-value and mean accuracy. I am not sure what I should do next and here are the current lines I have: sub=[2003,2016,2077,2098,2989,1989......] for subject in sub: ............ #basic parameters clf = LinearCSVMC() permutator = AttributePermutator('targets', count=1000) distr_est = MCNullDist(permutator, tail='left', enable_ca=['dist_samples']) cvte = CrossValidation(clf,splitter,errorfx=mean_mismatch_error, postproc=mean_sample(), null_dist=distr_est,enable_ca=['stats']) err=cvte(dataset) cvte.null_dist.append(cvte.null_dist) p = cvte.ca.null_prob assert(p.shape == (1,1)) print 'Corresponding p-value:', np.asscalar(p)
Thanks for your help!
_______________________________________________ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@alioth-lists.debian.net https://alioth-lists.debian.net/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa