[Mne_analysis] [Mne analysis] two sample t-test with spatio_temporal_cluster_test

Tue Jun 13 04:06:23 EDT 2017

One of the problems could be that scipy.stats.ttest_ind returns both t and
p values - maybe this is causing your problems? You could try using:

def ttest_no_p(*args):
    tvals, _ = ttest_ind(*args)
    return tvals

and pass ttest_no_p as the stat_fun.

13.06.2017 01:25 “Talitha Ford” <tcford at swin.edu.au> napisał(a):

Hi Dennis,
>
> I get the same error for the spatio_temporal_cluster_test:
> ValueError: could not broadcast input array from shape (1000) into shape
> (614520)
>
>
>
>
> On 13 Jun 2017, at 07:45, Denis-Alexander Engemann <
> denis.engemann at gmail.com> wrote:
>
> That does not look obviously wrong. Does it work?
> On Tue, 13 Jun 2017 at 00:43, Talitha Ford <tcford at swin.edu.au> wrote:
>
>> Thanks Dennis, I had included a ttest as the stat_fun in the previous
>> command, though.
>>
>> From what I understand, for a ttest, my code should look more like this:
>>
>> #~ X1= np.abs(X1)
>> #~ X2 np.abs(X2)
>>
>> print('Computing connectivity.')
>> connectivity = spatial_tris_connectivity(grade_to_tris(5))
>>
>> #    Note that X needs to be a list of multi-dimensional array of shape
>> #    samples (subjects_k) x time x space, so we permute dimensions
>> X1 = np.transpose(X1, [2,1,0])
>> X2 = np.transpose(X2, [2,1,0])
>> all_data = [X2, X1]
>>
>> p_threshold = 0.0001
>>
>> #~ f_threshold = stats.distributions.f.ppf(1. - p_threshold / 2.,
>>     #~ len(X2) - 1, len(X1) - 1)
>>
>> t_threshold = stats.distributions.t.ppf(p_threshold / 2.,
>>     len(X2) - 1, len(X1) - 1)
>>
>> print('Clustering.')
>> T_obs, clusters, cluster_p_values, H0 = clu =\
>> spatio_temporal_cluster_test(all_data, connectivity=connectivity,
>> n_jobs=2, threshod= t_threshold, stat_fun= scipy.stats.ttest_ind)
>>
>> Commenting out the conversation of the data to absolute values,
>> calculating a t_threshold, and including ttest_ind as the stat_fun? Sorry
>> if I have misunderstood something.
>>
>> Thanks,
>> Talitha
>>
>>
>>
>> On 12 Jun 2017, at 17:27, Denis-Alexander Engemann <
>> denis.engemann at gmail.com> wrote:
>>
>> Ahh. For two conditions F should be the abs(T**2). You can just use a
>> t-test for indpendent samples here instead as statfun.
>> On Mon, 12 Jun 2017 at 10:13, Talitha Ford <tcford at swin.edu.au> wrote:
>>
>>> Hi Dennis,
>>> Thank you, this is the script I’ve been working from. The problem I am
>>> having though, is that as f-stats are >0, they do not indicate which group
>>> is larger than the other, which is what I would like to know. I have tried
>>> to use scipy.stats.ttest_ind but I get this error:
>>> ValueError: could not broadcast input array from shape (1000) into shape
>>> (614520)
>>>
>>> The command is:
>>>  T_obs, clusters, cluster_p_values, H0 = clu =\
>>> spatio_temporal_cluster_test(all_data, connectivity=connectivity,
>>> n_jobs=2, stat_fun= scipy.stats.ttest_ind)
>>>
>>> all_data is 2 lists (2 groups) of 16 and 19 participants, with 30 time
>>> points of 20484 vertices for each participant.
>>>
>>> I hope that makes sense (and there is a possible work around!). Thanks
>>> again for you help,
>>>
>>> Talitha
>>>
>>>
>>> On 12 Jun 2017, at 05:33, Denis-Alexander Engemann <
>>> denis.engemann at gmail.com> wrote:
>>>
>>> Hi Talitha,
>>>
>>> you can run the permutation clustering with a wide array of contrasts.
>>> This might be what you are looking for:
>>>
>>> http://martinos.org/mne/dev/auto_tutorials/plot_stats_cluste
>>> r_spatio_temporal_2samp.html
>>>
>>> I hope this helps,
>>> Denis
>>>
>>> On Sun, Jun 11, 2017 at 12:00 PM Talitha Ford <tcford at swin.edu.au>
>>> wrote:
>>>
>>>> Dear all,
>>>>
>>>> Similar to conducing a pair samples t-test on source data using
>>>> spatio-temporal clustering, allowing the visualisation of clusters where
>>>> condition A > condition B and vice versa, is it possible to run an
>>>> independent samples t-test to visualise differences between two groups? The
>>>> 2 samples permutation tests currently available are limited in that they
>>>> plot F statistics that don’t give a direction of the difference between the
>>>> groups. Basically, is it possible to identify clusters that differ
>>>> significantly between the groups, as well as identify/visualise the
>>>> direction in which they differ?
>>>>
>>>> I am currently attempting to extract the cluster values for each
>>>> participant to get an overall mean for each vertex within the cluster to
>>>> compare between groups, but this seems very inefficient.
>>>>
>>>> Cheers,
>>>> Talitha
>>>>
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