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<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font color="#1f497d" size="2" face="Calibri,sans-serif"><span style="FONT-SIZE: 11pt">Hi Chris,</span></font></span></font></div>
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<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font face="Calibri,sans-serif"><span lang="en-US">Just to clarify my last email: I'm referring to correlation between either single trial data within subject,
or averaged ERPs across subjects, with a behavioral variable. </span></font><font face="Calibri,sans-serif">Not linear regression across conditions as is already implemented in the ft_statfun_depsamplesregrT function.</font></span></font></div>
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<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font color="#1f497d" face="Calibri,sans-serif"></font></span></font> </div>
<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font color="#1f497d" size="2" face="Calibri,sans-serif"><span lang="en-US" style="FONT-SIZE: 11pt">For correlating/regressing single trial data of a single subject,
you use the ft_statfun_indepsamplesregrT (with the units-of-observations being the trials, which each correspond to one column of your design matrix).
</span></font></span></font></div>
<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font color="#1f497d" size="2" face="Calibri,sans-serif"><span lang="en-US" style="FONT-SIZE: 11pt"></span></font></span></font> </div>
<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font color="#1f497d" size="2" face="Calibri,sans-serif"><span lang="en-US" style="FONT-SIZE: 11pt">For correlating/regressing subject-averaged data of a group
of subjects, you also use the ft_statfun_indepsamplesregrT (with the units-of-observations being the subjects, which each correspond to one column of your design matrix). Note, in this case your behavioral data (accuracy or RT) or also subject averages.</span></font></span></font></div>
<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font color="#1f497d" size="2" face="Calibri,sans-serif"><span lang="en-US" style="FONT-SIZE: 11pt"></span></font></span></font> </div>
<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font color="#1f497d" size="2" face="Calibri,sans-serif"><span lang="en-US" style="FONT-SIZE: 11pt">Best,</span></font></span></font></div>
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<div style="MARGIN: 0px"><font size="3" face="Times New Roman,serif"><span style="FONT-SIZE: 12pt"><font color="#1f497d" size="2" face="Calibri,sans-serif"><span lang="en-US" style="FONT-SIZE: 11pt">Eric Maris</span></font></span></font></div>
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<div id="divRpF665121" style="DIRECTION: ltr"><font color="#000000" size="2" face="Tahoma"><b>Van:</b> Dr C A Brown [cb802@cam.ac.uk]<br>
<b>Verzonden:</b> zaterdag 6 december 2014 12:33<br>
<b>Aan:</b> fieldtrip@science.ru.nl<br>
<b>Onderwerp:</b> Re: [FieldTrip] Cluster analysis on correlation statistics<br>
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<div>Hello again,</div>
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<div>Just to clarify my last email: I'm referring to correlation between either single trial data within subject, or averaged ERPs across subjects, with a behavioral variable. Not linear regression across conditions as is already implemented in the ft_statfun_depsamplesregrT
function.</div>
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<div>Many thanks</div>
<div>Chris </div>
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<div>Dr Christopher Brown</div>
<div>Research Associate</div>
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<div>Sent from a mobile device, please excuse my brevity.</div>
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<div id="htc_header">----- Reply message -----<br>
From: "Christopher Brown" <cb802@cam.ac.uk><br>
To: <fieldtrip@science.ru.nl><br>
Subject: Cluster analysis on correlation statistics<br>
Date: Sat, Dec 6, 2014 07:49</div>
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<p>Dear all</p>
<p>I would like to use the cluster analysis functions in Fieldtrip on correlation statistics. I note there has been some discussion about this on this list in the past but I haven't come across the necessary ft_statfun_corr function to allow this analysis.
It would be great if somebody has this code and could share it with me; otherwise, I will embark on writing it myself and sharing my results with everyone for feedback. Being new to Fieldtrip I would greatly appreciate any collaborative help with this. May
I check my understanding on something before getting started: To allow for multiple comparisons correction using monte carlo for example, as part of a cluster analysis, I presume the output of the function must be t values rather than correlation coefficients?
Any other hints as to how to write this are most welcome.</p>
<p>Kind regards</p>
<p>Chris</p>
<div>-- <br>
<pre><strong>Dr Christopher Brown</strong>
<em>Research Associate </em><em>| CamPain Group </em><em>| Division of Anaesthesia | School of Clinical Medicine | University of Cambridge</em> </pre>
<pre><br>Box 93 <br>Addenbrooke's Hospital <br>Hills Road <br>Cambridge CB2 0QQ <br><br>01223 256 995</pre>
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