[pymvpa] GroupClusterThreshold data / attributes
Ryan Stolier
ryan.m.stolier at gmail.com
Tue Feb 28 02:10:43 UTC 2017
Hi Richard,
Thank you so much for the help, I am surprised you have this down to memory
—as is, your example runs perfectly with the trained 'thr' object as all
that is needed.
One last question for you, or anyone who may know - is there a way to use
the AttributePermutator in a way that maintains the same label shuffling
across subjects? Even with cross-validation? I have read several sources
saying this should be done for several reasons, eg, discussion here:
http://mvpa.blogspot.com/2012/10/permuting-searchlight-maps-stelzer.html
Thank you again for any help, this has been immense.
Best,
Ryan
On Sun, Feb 26, 2017 at 7:02 AM <
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> 1. Re: GroupClusterThreshold data / attributes (Richard Dinga)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Sat, 25 Feb 2017 19:52:11 +0100
> From: Richard Dinga <dinga92 at gmail.com>
> To: Development and support of PyMVPA
> <pkg-exppsy-pymvpa at lists.alioth.debian.org>
> Subject: Re: [pymvpa] GroupClusterThreshold data / attributes
> Message-ID:
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> sxDhGoNy1xR-chAZTOSQ at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hi,
> I am sorry, my python is bit rusty by now, and I don't have a data set
> ready to give you an reproducible example. However I think that once you
> train your GroupClusterThreshold object, then the all cluster sizes from
> your bootstraps should be in _null_cluster_sizes attribute. Then your
> p-value for your arbitrary cluster is just a it's percentile in that array.
>
> You can also use methods get_cluster_pvals or _transform_to_pvals from the
> GroupClusterThreshold class to do it for you. Somehow :)
> Actual code that computes the p-values for clusters is:
> cluster_probs_raw = _transform_to_pvals( area, self
> ._null_cluster_sizes.astype('float'))
> so MAYBE this will work for you
> cluster_probs_raw = GroupClusterThreshold._transform_to_pvals(
> np.array([40. 60, 80]), thr._null_cluster_sizes.astype('float'))
>
> On Thu, Feb 9, 2017 at 10:40 PM, Ryan Stolier <ryan.m.stolier at gmail.com>
> wrote:
>
> > Hi all,
> >
> > I recently implemented GroupClusterThreshold() based on help I found in
> > this thread: https://lists.alioth.debian.org/pipermail/pkg-
> > exppsy-pymvpa/2015q3/003202.html
> >
> > I was able to execute everything properly and get back results. However,
> I
> > am curious if there is a way to access data used along the way in the
> > algorithm and figure out corrected significance levels of *any* size
> > cluster, rather than only the clusters in my real (non-permuted) data.
> Ie,
> > can I access the table of cluster sizes from the 100000 bootstraps of the
> > permutation maps used to determine the significance levels of clusters in
> > my real data? Say I wanted to find out the hypothetical significance
> level
> > of clusters sized: 40, 60, 80 given everything besides the 'mean_map'
> input
> > in the email exchange in the listserv above. Does that make sense? Also,
> I
> > suppose the data used at steps 6/7 here: http://mvpa.blogspot.
> > com/2012/10/permuting-searchlight-maps-stelzer.html
> >
> > Thank you so much for any assistance!
> > Ryan
> >
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