>> Thanks Latitia,
>>
>>
>> >
>> > Hi Denis,
>> >
>> > I tried the spatio-temporal clustering with TFCE
>> (spatio_temporal_cluster_1samp_test) on alpha power data (size of epoch
:
>> 2s; decimation = 4 (so 501 time points)) with n_jobs = 6 and the default
>> TFCE parameter (dict(start = 0, step = 0.2)).
>>
>>
>> I think we need to improve the documentation on TFCE a bit. A good
default
>> range is probably
>>
>> dict(start=2, step=0.2)
>>
>> >
>> > According to script output, 48 thresholds were used from 0 to 9.4.
>> > After 5h (!!), 10262484 clusters were found and finally after some
>> others
>> hours the script crashed before the end ("cannot allocate memory")...
>> >
>>
>> For TFCE N clusters equals N features. Howver if you do not scan the
>> enitre
>> range of the test statistic you wont have to wait thast long.
>>
>>
> I tried dict(start=2, step=0.2) and dict(start=2, step=0.5), but I still
> had 5 120 000 clusters (15 threshold for step =0.5). How can I not scan
the
> entire range of the test, as you suggested ? I didn't understand what you
> mean by "features"...
>
>samples : subjects or trials or observations
feaures : measured value at time t, location l, condition c, frequency f,
etc.dict(start=2, step=0.2) would not scan the entire range since you start at
a value of 2. It depends on your effect size and your test statistic. with
an f-test you might want to start at 4 (roughly the point where values form
an f-dist are considered significant). Also if the maximum of your primary
test statistic is rather high, e.g. 80, you might want to jump in steps of
.05 or even 1.I'm currently using it like that (dict(start=4, step=0.5) in sensor space
analysis and with 17640 clusters, 7 jobs and I'm waiting about 6-7 minutes
for a result using a repated measures anova as stat function (slower than
t-test) .
Ok thanks, it's clearer! So, I tried dict(start=4, step=0.5), I got 5 120
000 clusters, 14 thresholds and I got no significant clusters. The
computation lasted 84min, so that remains quite long. And I didn't find the
cluster I found with p = 0.01 in classical analysis...
>
>> > I tried on shorter data (1s so 250 time points) but it was also too
long
>> and too memory-demanding. Then I tried to change the step parameter to
>> decrease the number of thresholds to test. I took step =0.5. 17
thresholds
>> were used from 0 to 8. 5121000 clusters were found and my script also
>> ended
>> up crashing. So... what do you suggest to get acceptable computation
time?
>> >
>>
>> See above. Btw for roughtly 10.000 clusters with 15-20 thresholds I'm
>> waiting for roughly 15-20 minutes per iteration (multiple iterations
with
>> step_down=0.05).
>>
>> >
>> > By the way, with p_threshold =0.001, I got no cluster; with
p_threshold
>> =0.01, I got one occipito-parietal cluster (pvalue = 0.026) lasting
around
>> 500ms, after something about 10min of computation.
>> >
>>
>> I'm not sure what you refer to by `p_threshold`. Either you pass a dict
or
>> a float. The latter will be a classical cluster permuation analysis, the
>> former TFCE.
>> You can howver use the p_threhold as start value for TFCE.
>>
>>
> Yes I was talking about classical permutation cluster analysis.
>
>Ah ok, got it now.
> >
>> > Hoping that it is useful for you,
>> > Best,
>> > Laetitia G.
>>
>>
>> Yes, thanks!
>> Best,
>> Denis
>>
>>
> Thanks a lot for your advice!
> Laetitia
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20140804/095696d5/attachment.html