[pymvpa] GroupClusterThreshold data / attributes

[Richard Dinga]

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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