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<p class="MsoNormal">Hi Jan-Mathijs,<o:p></o:p></p>
<p class="MsoNormal">Thank you so much for your response! I have some follow-up questions:<o:p></o:p></p>
<p class="MsoListParagraphCxSpFirst" style="text-indent:-18.0pt;mso-list:l0 level1 lfo1">
<![if !supportLists]><span style="mso-list:Ignore">1)<span style="font:7.0pt "Times New Roman"">
</span></span><![endif]>True, using F-statistics makes more sense for my design. But to clarify, you are recommending using F-statistics (ft_statfun_depsamplesFunivariate) and then doing pairwise tests for the main effects to get around the fact that the depsamplesF
functions only take 1xN factorial design? Or do you mean I should continue using T-statistics, and do the pairwise tests?
<o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle"><o:p> </o:p></p>
<p class="MsoListParagraphCxSpMiddle" style="text-indent:-18.0pt;mso-list:l0 level1 lfo1">
<![if !supportLists]><span style="mso-list:Ignore">2)<span style="font:7.0pt "Times New Roman"">
</span></span><![endif]>Thank you for pointing out that bit of documentation about testing interactions effects, I totally missed that before. All things considered now I am leaning towards not getting into interaction effects, since it doesn’t seem to be recommended
for my design and especially if I end up using F-statistics, these will not inform me about the pattern in the data responsible for the interaction effect. But I am still a bit uncertain as to what I should do for the main effects - should I just compare both
main effects, of cycling (averaging across vis and invis, and comparing R, L, H) and of perception (averaging across R/L/H and comparing vis versus invis), just like in a two-way RM-ANOVA and leave it at that? What would you recommend?<o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle"><o:p> </o:p></p>
<p class="MsoListParagraphCxSpMiddle" style="text-indent:-18.0pt;mso-list:l0 level1 lfo1">
<![if !supportLists]><span style="mso-list:Ignore">3)<span style="font:7.0pt "Times New Roman"">
</span></span><![endif]>I was clearly confusing cluster based and TFCE approaches. Also, I apologise for my poor choice of wording – I have read Benedikt Ehinger’s post of TFCE as well as the FieldTrip page on how not to interpret results from cluster-based
permutation tests, so I do understand it doesn’t make sense to talk about significant clusters. But one thing I am uncertain about – will I be able to plot an effect topographically using ft_clusterplot when I run an analysis with TFCE? Or is that only possible
for other methods of correcting for multiple comparisons in cluster based permutation analyses? I am asking this because the TFCE example on the FieldTrip website is only for on one channel (https://www.fieldtriptoolbox.org/example/threshold_free_cluster_enhancement/)
and I was wondering what it looks like scaled up for multiple channels. I actually tried running the TFCE analysis for all channels on the dataset used in the example but I couldn’t plot the effect using ft_clusterplot. But maybe, this is something that doesn’t
even make sense to attempt? <o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle"><o:p> </o:p></p>
<p class="MsoListParagraphCxSpMiddle" style="text-indent:-18.0pt;mso-list:l0 level1 lfo1">
<![if !supportLists]><span style="mso-list:Ignore">4)<span style="font:7.0pt "Times New Roman"">
</span></span><![endif]>The reason I was asking about this analysis without a prior constraints was also to further my understanding about these analyses. I am puzzled by the fact the same test in not significant on data obtained for very similar paradigms
with a very similar number of subjects. So in essence, if in the previous experiment, the cluster-based tests were not significant and we didn’t have a prior constraints to limit our analysis then, we would have likely missed the effect in the time window
and channels that we are now using as a prior constraints in the current experiment.
<o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle"><o:p> </o:p></p>
<p class="MsoListParagraphCxSpLast">This is also relevant to me because I will also be performing a similar cluster based analysis for other parameters (e.g. heart-evoked potential amplitudes) on the same dataset, where I don’t have any a priori topographical
constraints, and I am wondering if there are additional tests I should perform for fear of missing out on some effect that is not evident in cluster based tests?
<o:p></o:p></p>
<p class="MsoNormal">Best,<o:p></o:p></p>
<p class="MsoNormal">Ash<o:p></o:p></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Arial",sans-serif"><o:p> </o:p></span></p>
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