Thank you so much Richard! This was super helpful! One last question, do you know if the averaging can be done using the command line without sparse ROI's? Maybe by using --scatter-rois 0? or is it the default regardless to the input of scatter-rois?
And just to make sure I understand the scatter option: by using the same value here and in the neighborhood size the value of a centroid in the original map is simply the accuracy of it's neighborhood since a centroid of a calculated neighborhood can never(?) be a part of a different neighborhood? On Wed, Aug 12, 2015 at 5:36 PM, Roni Maimon <ronimai...@gmail.com> wrote: > > 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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