Hi, Yaroslav and Richard, thank you so much for the quick and very helpful reply!
Though I only received it through the daily summary, so I am sure this is the wrong way to reply. Yaroslav, regarding the permutator "dance", is it necessary in cases where I have several betas in each run? Thanks again for all the help. On Tue, Aug 11, 2015 at 8:18 PM, Roni Maimon <ronimai...@gmail.com> wrote: > 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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