cluster statistic on one sample
j.schoffelen at PSY.GLA.AC.UK
Mon Oct 20 14:13:11 CEST 2008
Dear Floris, Eric, Vladimir and all other people reading this message,
What about performing a nonparametric test, based on the bootstrap
distribution of the beta weights under the null-hypothesis?
This problem sounds similar to one I came across recently (and which
I still have to write something about on fieldtrip's wiki-page (sorry
Eric)), which has to do with the testing of the significance of the F-
value for interaction in a 2x2 repeated measure anova. Also in this
case, one also wants to test a parametric null-hypothesis, as Eric
phrased it in his last e-mail. One way to test this (I don't have the
reference at hand), is to test the observed F-statistic against a
null-distribution, obtained from bootstrapping your data, which you
preconditioned as to impose the null-hypothesis (in the case of an
anova it would be to remove from each of the observations the mean of
the cell to which the observation belongs). I don't know yet how to
impose the null-hypothesis in the regression case, but would this
line of thought be a possibility?
As to a potential implementation: Robert and I are pretty close to
have the bootstrapping implemented.
On Oct 20, 2008, at 11:05 AM, Vladimir Litvak wrote:
> Dear Floris and Eric,
> Parametric tests at scalp level taking into account spatial
> relationship between sensors can be done in SPM (with RFT correction).
> That'll require using some low-level functions to convert
> coefficients to images but in principle shouldn't be that difficult.
>> On Mon, Oct 20, 2008 at 10:17 AM, Eric Maris
>> <e.maris at donders.ru.nl> wrote:
>>> Dear Floris,
>>>> I have a question about statistical analysis on the sensor level.
>>>> I would like to make use of the cluster size thresholding of the
>>>> clusterrand routine in Fieldtrip. Unfortunately, in the current
>>>> wrapper, it seems there is no option for a one-sample T-test?
>>>> There is
>>>> an activation-baseline test, and a (in)dependent samples test
>>>> two conditions, but what I want to do is simply test whether a 14
>>>> (subjects) x 275 (channels) matrix is different from zero,
>>>> taking into
>>>> account the spatial relations between adjacent sensors. (The data
>>>> points are regression weights from a multiple-regression
>>>> analysis, so
>>>> there's no easy way to split it into two parts.)
>>>> I assume this should be easy to tweak, but I couldn't come up
>>>> with any
>>>> smart ideas how to do it.
>>>> Anyone any ideas?
>>> I'm afraid that I have to disappoint you, Floris. Your null
>>> hypothesis is a
>>> typical parametric null hypothesis; the expected value of some
>>> (matrix-valued) variable being equal to zero. The null hypothesis
>>> that is
>>> tested by a nonparametric permutation test is equality across
>>> conditions of the probability distribution from which the
>>> (condition-specific) data are drawn. Since you have single
>>> condition only, I
>>> see no way of applying the theory behind nonparametric
>>> permutation testing
>>> (of the type described by Maris & Oostenveld, 2007) to your data.
>>> To solve your problem we need a brilliant theoretical insight.
>>>> Thanks in advance!
>>>> The aim of this list is to facilitate the discussion between
>>>> users of the
>>>> toolbox, to share experiences and to discuss new ideas for MEG
>>>> and EEG
>>>> See also http://listserv.surfnet.nl/archives/fieldtrip.html and
>>> The aim of this list is to facilitate the discussion between
>>> users of the FieldTrip toolbox, to share experiences and to
>>> discuss new ideas for MEG and EEG analysis. See also http://
>>> listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/
> The aim of this list is to facilitate the discussion between users
> of the FieldTrip toolbox, to share experiences and to discuss new
> ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/
> archives/fieldtrip.html and http://www.ru.nl/fcdonders/fieldtrip.
The aim of this list is to facilitate the discussion between users of the FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/fcdonders/fieldtrip.
More information about the fieldtrip