[FieldTrip] depsamplesF

Tue Feb 8 04:26:26 CET 2011

Dear Eric,
    Thank you very much for these helpful suggestions. Plotting the suggested difference waves was rather enlightening.
Best regards,
Tom.

From: e.maris at donders.ru.nl
To: tom_campbell75 at hotmail.com; fieldtrip at donders.ru.nl
Subject: RE: [FieldTrip] depsamplesF
Date: Fri, 28 Jan 2011 10:43:42 +0100

Hi Tom,

To test main and interaction effects in your 2x2 within subjects design, you have to perform 3 tests, each using the statfun desamplesT. Say you have the output of ft_timelockanalysis for all four conditions: tlout_Ia, tlout_Ib, tlout_IIa, tlout_IIb. Your then proceed as follows:

1.   Main effect of I versus II: calculate the mean of tlout_Ia.avg and tlout_Ib.avg and put this is a a new struct variable tlout_I, which has the same fields as tlout_Ia and tlout_Ib. Do the same with tlout_IIa.avg and tlout_IIb.avg and make a new struct variable tlout_II. Then run ft_timelockstatistics with input arguments tlout_I and tlout_II. With this analysis you will test the main effect of I-versus-II.
2.   In the same way, you now test the main effect of a versus b. In your calculations, the roles of (I,II) and (a,b) are now reversed.
3.   Interaction of I-vs-II and a-vs-b. Calculate the differences (tlout_Ia.avg-tlout_Ib.avg) and (tlout_IIa.avg-tlout_IIb.avg), put them in output structures and statistically compare them using ft_timelockstatistics. With this analysis, you test the interaction of I-vs-II and a-vs-b.

There is no need for Bonferroni correction or an adjustment of cfg.clusteralpha (which does not affect the false alarm rate anyhow) and cfg.alpha.

Best,

Eric Mari

Thank you very much for this helpful advice Olga. That would have also been my impression until I found this discussion between Maya Zuckerman and Eric Maris:

http://mailman.science.ru.nl/pipermail/fieldtrip/2010-December/003335.html
http://mailman.science.ru.nl/pipermail/fieldtrip/2010-December/003338.html

, which I may have misunderstood. I can see from the list that people are interested in using depsampleF to investigate interactions between two independent variables (e.g., background: I,II x stimulus: a,b) in experiments with multiple participants. I can also see a several people have had problems with this.

Given for datasets I_a, I_b, II_a, II_b I'd like to do something like:
cfg.design =????;
...
cfg.statistic ='depsamplesF';
...
 [stat] = ft_timelockstatistics(cfg, I_a, I_b, II_a, II_b);

to find the tests of of main effect clusters for each factor and interaction clusters. How to specify up cfg.design could be one of the problems, as could be what other cfg parameters need to be specified. I may be barking up completely the wrong tree.

Assuming the interaction is significant, there are 4 differences that would be of theoretical interest: I_a vs I_b, II_a vs II_b,I_a vs II_a,I_b vs II_b). For instance, a "classical" interaction might exhibit the abolition of significance of an effect ab seen at I at level II of that factor. 

So, if I understand the Zuckerman-Maris dialogue, if I used the permutation test to test such I_a vd II_b differences as in the tutorial:

 cfg.method = 'montecarlo';
cfg.statistic = 'depsamplesT';
cfg.correctm = 'cluster';
cfg.clusteralpha = 0.05;
cfg.clusterstatistic = 'maxsum';
cfg.minnbchan = 2;
cfg.tail = 0;
cfg.clustertail = 0;
cfg.alpha = 0.025;
cfg.numrandomization = 500;
...
[I_avsb_stat] = ft_timelockstatistics(cfg, I_a, I_b);
[II_avsb_stat] = ft_timelockstatistics(cfg, II_a, II_b);

[IvsII_a_stat] = ft_timelockstatistics(cfg, I_a, II_a);
[IvsII_b_stat] = ft_timelockstatistics(cfg, I_a, II_b);

,I may have to either do some bonferonni correction:
-at the cluster level (cluster.alpha) 
-or at the level at which each t-test can be included in a cluster (cfg.alpha). 
Please would you verify which, if at all?

If I am testing 4 such differences with 4 permutation tests then if the bonferroni correction is to be at the cluster level, please should the correction depend upon the number of clusters as well as the number of such differences tested? 

Please, I look forward to any clarifications.

With best regards,

Tom Campbell.

From: olga at graphicmind.info
To: tom_campbell75 at hotmail.com; fieldtrip at donders.ru.nl
Subject: Re: [FieldTrip] depsamplesF
Date: Thu, 27 Jan 2011 06:16:51 +0300

Hi, Tom, 

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.

Best Regards,

Olga.

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.

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