[pymvpa] Searchlight statistical inference

[Roni Maimon]

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 <ronimaimon at 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.
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/attachments/20150811/c7621890/attachment.html>