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<div class="moz-cite-prefix">Hi Stefon,<br>
<br>
Arno forgot that the LIMO_EEG toolbox (<a
class="moz-txt-link-freetext"
href="https://gforge.dcn.ed.ac.uk/gf/project/limo_eeg/">https://gforge.dcn.ed.ac.uk/gf/project/limo_eeg/</a>)
includes such design. <br>
Although the toolbox is designed for hierarchical linear
modelling, nothing prevents you to input your inter-trial
coherence matrix into the rep ANOVA function.<br>
<br>
Make a matrix Y [electrode, time frames, subjects, conditions] and
a vector gp (e.g. 1 1 1 1 1 1 2 2 2 2 2 2)<br>
Then simply call limo_random_robust(6,Y,gp,[2],0) the [2]
indicates you have 1 factor with 2 conditions and 0 indicates you
don't want to bootstrap this. This will compute the Hotelling test
for each electrodes and time frames. If you want to bootstrap
change e.g. to 1000 and then you can use LIMO EEG to look at the
results using clustering (ie accounting for multiple testing). In
this case it will require to specify the electrode configuration
etc .. <br>
<br>
Best,<br>
Cyril<br>
<br>
</div>
<blockquote
cite="mid:mailman.255.1340302805.3616.eeglablist@sccn.ucsd.edu"
type="cite">
<fieldset class="mimeAttachmentHeader"><legend
class="mimeAttachmentHeaderName">[Eeglablist] Statistics in
EEGlab.eml</legend></fieldset>
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<div class="headerdisplayname" style="display:inline;">Subject:
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[Eeglablist] Statistics in EEGlab</td>
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<div class="headerdisplayname" style="display:inline;">From:
</div>
Stefon van Noordt <a class="moz-txt-link-rfc2396E"
href="mailto:sv05lz@brocku.ca"><sv05lz@brocku.ca></a></td>
</tr>
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<td>
<div class="headerdisplayname" style="display:inline;">Date:
</div>
21/06/2012 03:29</td>
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cellspacing="0" width="100%">
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<div class="headerdisplayname" style="display:inline;">To:
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<a class="moz-txt-link-abbreviated"
href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a></td>
</tr>
</tbody>
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<br>
<div class="moz-text-flowed" style="font-family: -moz-fixed;
font-size: 13px;" lang="x-western">Dear colleagues, <br>
<br>
If the ‘parametric’ option is selected, does the study module
treat a 2 (paired) x 2 (unpaired) design as a mixed-ANOVA with
repeated measures? <br>
<br>
Comparing differences in inter-trial coherence with a 2
(feedback type – paired <br>
statistics) x 2 (gender – unpaired statistics) study design
seems to produce the same results as when both variables are set
to be paired statistics (i.e., for the purpose of testing the
outcome, setting gender as paired instead of unpaired). <br>
<br>
Thanks for your time. <br>
<br>
Best regards, <br>
Stefon van Noordt <br>
<br>
<br>
<br>
<fieldset class="mimeAttachmentHeader"><legend
class="mimeAttachmentHeaderName">Re: [Eeglablist] Statistics
in EEGlab.eml</legend></fieldset>
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<div class="headerdisplayname" style="display:inline;">Subject:
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Re: [Eeglablist] Statistics in EEGlab</td>
</tr>
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<td>
<div class="headerdisplayname" style="display:inline;">From:
</div>
Arnaud Delorme <a class="moz-txt-link-rfc2396E"
href="mailto:arno@ucsd.edu"><arno@ucsd.edu></a></td>
</tr>
<tr>
<td>
<div class="headerdisplayname" style="display:inline;">Date:
</div>
23/06/2012 17:11</td>
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cellspacing="0" width="100%">
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<td>
<div class="headerdisplayname" style="display:inline;">To:
</div>
Stefon van Noordt <a class="moz-txt-link-rfc2396E"
href="mailto:sv05lz@brocku.ca"><sv05lz@brocku.ca></a></td>
</tr>
<tr>
<td>
<div class="headerdisplayname" style="display:inline;">CC:
</div>
eeglablist <a class="moz-txt-link-rfc2396E"
href="mailto:eeglablist@sccn.ucsd.edu"><eeglablist@sccn.ucsd.edu></a>,
David Groppe <a class="moz-txt-link-rfc2396E"
href="mailto:david.m.groppe@gmail.com"><david.m.groppe@gmail.com></a></td>
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<br>
<pre wrap="">Dear Stefon,
yes you are correct. When you have a mixed design (paired) x (unpaired), EEGLAB will use (unpaired) x (unpaired) so it is better to use surrogate statistical approaches. There is a warning on the command line that indicates that EEGLAB does so if I remember well. When using surrogate methods, EEGLAB will use a balanced ANOVA approach (unpaired x unpaired) but data shuffling will be computed according to the pairing selection. In the surrogate case, ANOVA is simply use as a metric of distance for your design, so it is not (as) critical to use the correct ANOVA method.
Ideally, we would use the correct repeated ANOVA function for mixed design (paired x unpaired) but there is no such function in Matlab to our knowledge. The only function available is in R. David Groppe (copied to this message) would know more about this. We are planning in the long run to automatically bridge some statistics from Matlab to R. If anybody wants to help, let us know.
Best,
Arno</pre>
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