Hi all, I'm rather new to pyMVPA and I would love to get your help and feedback. I'm trying do understand the different procedures of statistical inference, I can achieve for whole brain searchlight analysis, using pyMVPA.
I started by implementing the inference at the subject level (attaching the code). Is this how I'm supposed to evaluate the p values of the classifications for a single subject? What is the differences between adding the null_dist to the sl level and the cross validation level? My code: clf = LinearCSVMC() splt = NFoldPartitioner(attr='chunks') repeater = Repeater(count=100) permutator = AttributePermutator('targets', limit={'partitions': 1}, count=1) null_cv = CrossValidation(clf, ChainNode([splt, permutator],space=splt.get_space()), postproc=mean_sample()) null_sl = sphere_searchlight(null_cv, radius=3, space='voxel_indices', enable_ca=['roi_sizes']) distr_est = MCNullDist(repeater,tail='left', measure=null_sl, enable_ca=['dist_samples']) cv = CrossValidation(clf,splt, enable_ca=['stats'], postproc=mean_sample() ) sl = sphere_searchlight(cv, radius=3, space='voxel_indices', null_dist=distr_est, enable_ca=['roi_sizes']) ds = glm_dataset.copy(deep=False, sa=['targets','chunks'], fa=['voxel_indices'], a=['mapper']) sl_map = sl(ds) p_values = distr_est.cdf(sl_map.samples) # IS THIS THE RIGHT WAY?? Is there a way to make sure the permutations are exhaustive? In order to make an inference on the group level I understand I can use GroupClusterThreshold. Does anyone have a code sample for that? Do I use the MCNullDist's created at the subject level? Thanks, Roni.
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