olga at graphicmind.info
Thu Jan 27 04:16:51 CET 2011
I guess if you do cluster analysis, which is based on permutation
tests you do not need any correction like.
Cluster-based statistics just deal with multiple comparison problem
differently (Monte-Carlo randomization, permutation tests and examine
the probability of your cluster among the random ones). Clusters may
be formed based on time, space/frequency adjacency.
On 27.01.2011, at 2:26, Tom Campbell wrote:
> Dear Eric Maris, Robert Oostenveld and colleagues,
> I write with some queries with reference to your previous
> correspondence on the Fieldtrip listserv and would very much
> appreciate if you could please answer them.
> I am trying to use Fieldtrip to analyse timelocked ERP data from
> what is a 16(participant: [1:16]) X2(Background: congruent,
> incongruent)x2(Stimulus: Animal, Vehicle) design. The code seems to
> runing well treating this as a 16(participant: [1:16]) X4(Visual
> stimulus: animal-congruent background, animal-incongruent
> background, vehicle-congruent, vehicle-incongruent )design with 4
> conditions, though I haven't looked at the results of the tests yet.
> If I then run cluster analyses of differences of theoretical
> interest via depsamplest, please how would I bonferroni correct? I
> am interested in what clusters exist in the background and stimulus
> main effects and their background X stimulus interaction. Please is
> this possible in fieldtrip to use depsamplesF to work with a
> Participant X "2-way" design?
> Best regards,
> Tom Campbell.
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
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