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</o:shapelayout></xml><![endif]--></head><body lang=NL link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Hi Steve,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Have a look here: <a href="http://fieldtrip.fcdonders.nl/faq/how_can_i_test_an_interaction_effect_using_cluster-based_permutation_tests">http://fieldtrip.fcdonders.nl/faq/how_can_i_test_an_interaction_effect_using_cluster-based_permutation_tests</a><o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Best,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Eric Maris<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><div style='border:none;border-left:solid blue 1.5pt;padding:0cm 0cm 0cm 4.0pt'><div><div style='border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0cm 0cm 0cm'><p class=MsoNormal><b><span lang=EN-US style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>From:</span></b><span lang=EN-US style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> Stephen Politzer-Ahles [mailto:spa268@nyu.edu] <br><b>Sent:</b> woensdag 24 september 2014 11:18<br><b>To:</b> fieldtrip@science.ru.nl<br><b>Subject:</b> Re: [FieldTrip] Cluster-based permutation tests for between-subject design<o:p></o:p></span></p></div></div><p class=MsoNormal><o:p> </o:p></p><div><div><p class=MsoNormal>Hi Eelke,<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Thanks for this information. I just wanted to jump in and ask: what about for interactions in a mixed 2x2 design? For example, say I expect a difference between conditions A and B for group 1, but not group 2. Would the correct way to do this be to<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>1) make difference waves (A-B) for each participant, then<o:p></o:p></p></div><div><p class=MsoNormal>2) do a between-UO test on the difference waves using indepsamplesT?<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>In the past I have always tested within-UO interactions using basically this method (based on <a href="http://mailman.science.ru.nl/pipermail/fieldtrip/2011-January/003447.html">http://mailman.science.ru.nl/pipermail/fieldtrip/2011-January/003447.html</a>), but I was under the impression that this is not OK for mixed designs (from this post: <a href="http://mailman.science.ru.nl/pipermail/fieldtrip/2011-September/004244.html">http://mailman.science.ru.nl/pipermail/fieldtrip/2011-September/004244.html</a>)<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Thanks,<o:p></o:p></p></div><div><p class=MsoNormal>Steve<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><p class=MsoNormal>><br>> ------------------------------<br>><br>> Message: 12<br>> Date: Wed, 24 Sep 2014 07:59:31 +0200<br>> From: Eelke Spaak <<a href="mailto:eelke.spaak@donders.ru.nl">eelke.spaak@donders.ru.nl</a>><br>> To: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl">fieldtrip@science.ru.nl</a>><br>> Subject: Re: [FieldTrip] Cluster-based permutation tests for<br>> between-subject design<br>> Message-ID:<br>> <<a href="mailto:CABPNLUomYPTw6m__%2BWx8jRJc8HvyU-Pqc4Q7%2BG7czOjD502dfA@mail.gmail.com">CABPNLUomYPTw6m__+Wx8jRJc8HvyU-Pqc4Q7+G7czOjD502dfA@mail.gmail.com</a>><br>> Content-Type: text/plain; charset=UTF-8<br>><br>> Hello Dylan,<br>><br>> You can analyse a between-subjects design exactly as you would a<br>> between-trials design (at least as far as the statistics step is<br>> concerned), in both cases the two conditions correspond to two groups<br>> of observations, and not to the same group of observations measured in<br>> two separate conditions (which would be a within-UO design). In<br>> FieldTrip, you would typically compute averages per subject, then use<br>> an "indepsamplesT" (or indepsamplesF with >2 conditions) statistic<br>> (not depsamples). indepsamplesT only requires one row in the design<br>> matrix, indicating the condition.<br>><br>> Note that if you have e.g. timelock structures in two (or more) cell<br>> arrays, corresponding to the conditions, you can input them into the<br>> statistics function as follows:<br>><br>> stat = ft_timelockstatistics(cfg, tlCondA{:}, tlCondB{:});<br>><br>> without having to call ft_timelockgrandaverage. In fact, the above is<br>> the preferred way to do statistics now. (The same holds for<br>> ft_freqstatistics.)<br>><br>> Hope that helps,<br>> Best,<br>> Eelke<br>><br>> On 24 September 2014 02:32, Dylan DeLosAngeles<br>> <<a href="mailto:dylan.delosangeles@gmail.com">dylan.delosangeles@gmail.com</a>> wrote:<br>> > Hello,<br>> ><br>> > So far, the tutorial on "Cluster-based permutation tests on time-frequency<br>> > data" has been very helpful.<br>> ><br>> > Out of the four combinations from the two UO-types (subjects and trials) and<br>> > the two experimental designs (between- and within-UO), the tutorial covers<br>> > statistics on data in two conditions in a between-trials, in a within-trials<br>> > and in a within-subjects design. However, I am wondering if there is any<br>> > information about the fourth type of experiment design: between-subjects.<br>> ><br>> > I have data for 2 groups with 12 subjects in each group. Both groups are<br>> > measured during 11 conditions.<br>> > Can I approach this in a similar fashion to within-subjects design (multiple<br>> > subjects in multiple experimental conditions), such that my design is<br>> > multiple groups in multiple experimental conditions. Is it a case of first<br>> > averaging over all trials belonging to each of the experimental conditions<br>> > for each subject (as instructed in tutorial), and then averaging over all<br>> > subjects in each group?<br>> ><br>> > Configuration code for setting up the design currently looks like this;<br>> > grp = 2;<br>> > subj = 11;<br>> > design = zeros(2, subj*grp);<br>> ><br>> > for i = 1:grp<br>> > design(1,i:2:end) = i;<br>> > end<br>> ><br>> > idx = 1;<br>> > for i = 1:subj<br>> > design(2,idx:idx+1) = i;<br>> > idx = idx+2;<br>> > end<br>> ><br>> > Is there anything else I need to take into consideration when doing these<br>> > statistics?<br>> ><br>> > Thank you,<br>> > Dr Dylan DeLosAngeles<br>> > Research Fellow<br>> > Brain Signal Laboratory<br>> > Flinders University<br>> ><br>> > _______________________________________________<br>> > fieldtrip mailing list<br>> > <a href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a><br>> > <a href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><br>><br> <br>><br>><br>> All the forward modelling in FieldTrip is based on a user-specified<br>> MRI (preferably an individual one, but can be a template brain if an<br>> individual MRI is not available). You probably will want to have a<br>> look at this tutorial:<br>> <a href="http://fieldtrip.fcdonders.nl/tutorial/headmodel_eeg">http://fieldtrip.fcdonders.nl/tutorial/headmodel_eeg</a><br>><o:p></o:p></p></div></div></div></body></html>