Yaroslav, Thank you very much for the input. Richard, in the code you referred to it is stated: "The values mapped onto each voxel represent the mean accuracy across all classification (spheres)
a voxel was included in." How is this achieved? I scanned the code and nothing popped out but I must be missing something. Thanks! On Wed, Aug 12, 2015 at 3:05 AM, Roni Maimon <ronimai...@gmail.com> wrote: > So the full design is I have 4 conditions in 8 runs. 5 blocks of each > condition in each run. > All runs have all the conditions but I'm interested only in two > classifications and the differences between these classifications. > The order of trials is different across runs. > Some recommend I only permute the labels within runs, is this what you're > referring to? Is there a quick way to do that in pyMVPA? > > On Wed, Aug 12, 2015 at 2:14 AM, Roni Maimon <ronimai...@gmail.com> wrote: > >> 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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