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    Hm... it seems that I may have reinvented the wheel (or perhaps
    invented a cruder wheel before Eric and co provided this more
    streamlined one).<br>
    <br>
    Eric, can indepsamplesregrT also compute nonparametric correlation
    coefficients (eg Spearman)?<br>
    <br>
    Best,<br>
    Tom<br>
    <br>
    <div class="moz-cite-prefix">On 12/8/2014 3:56 PM, Maris, E.G.G.
      (Eric) wrote:<br>
    </div>
    <blockquote
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            size="3"><span style="FONT-SIZE: 12pt"><font color="#1f497d"
                face="Calibri,sans-serif" size="2"><span
                  style="FONT-SIZE: 11pt">Hi Chris,</span></font></span></font></div>
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            size="3"><span style="FONT-SIZE: 12pt"><font color="#1f497d"
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              <div style="MARGIN: 0px"><font face="Times New
                  Roman,serif" size="3"><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 face="Times New
                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"></span></font> </div>
              <div style="MARGIN: 0px"><font face="Times New
                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"><font
                      color="#1f497d" face="Calibri,sans-serif" size="2"><span
                        style="FONT-SIZE: 11pt" lang="en-US">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 face="Times New
                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"><font
                      color="#1f497d" face="Calibri,sans-serif" size="2"><span
                        style="FONT-SIZE: 11pt" lang="en-US"></span></font></span></font> </div>
              <div style="MARGIN: 0px"><font face="Times New
                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"><font
                      color="#1f497d" face="Calibri,sans-serif" size="2"><span
                        style="FONT-SIZE: 11pt" lang="en-US">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 face="Times New
                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"><font
                      color="#1f497d" face="Calibri,sans-serif" size="2"><span
                        style="FONT-SIZE: 11pt" lang="en-US"></span></font></span></font> </div>
              <div style="MARGIN: 0px"><font face="Times New
                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"><font
                      color="#1f497d" face="Calibri,sans-serif" size="2"><span
                        style="FONT-SIZE: 11pt" lang="en-US">Best,</span></font></span></font></div>
              <div style="MARGIN: 0px"><font face="Times New
                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"><font
                      color="#1f497d" face="Calibri,sans-serif" size="2"><span
                        style="FONT-SIZE: 11pt" lang="en-US"></span></font></span></font> </div>
              <div style="MARGIN: 0px"><font face="Times New
                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"><font
                      color="#1f497d" face="Calibri,sans-serif" size="2"><span
                        style="FONT-SIZE: 11pt" lang="en-US">Eric Maris</span></font></span></font></div>
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                  Roman,serif" size="3"><span style="FONT-SIZE: 12pt"><font
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          <div id="divRpF665121" style="DIRECTION: ltr"><font
              color="#000000" face="Tahoma" size="2"><b>Van:</b> Dr C A
              Brown [<a class="moz-txt-link-abbreviated" href="mailto:cb802@cam.ac.uk">cb802@cam.ac.uk</a>]<br>
              <b>Verzonden:</b> zaterdag 6 december 2014 12:33<br>
              <b>Aan:</b> <a class="moz-txt-link-abbreviated" href="mailto:fieldtrip@science.ru.nl">fieldtrip@science.ru.nl</a><br>
              <b>Onderwerp:</b> Re: [FieldTrip] Cluster analysis on
              correlation statistics<br>
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            <div style="FONT-SIZE: 12pt; FONT-FAMILY:
              Calibri,sans-serif">
              <div>Hello again,</div>
              <div><br>
              </div>
              <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>
              <div><br>
              </div>
              <div>Many thanks</div>
              <div>Chris </div>
              <div><br>
              </div>
              <div>Dr Christopher Brown</div>
              <div>Research Associate</div>
              <div><br>
              </div>
              <div>Sent from a mobile device, please excuse my brevity.</div>
              <br>
              <div id="htc_header">----- Reply message -----<br>
                From: "Christopher Brown" <a class="moz-txt-link-rfc2396E" href="mailto:cb802@cam.ac.uk"><cb802@cam.ac.uk></a><br>
                To: <a class="moz-txt-link-rfc2396E" href="mailto:fieldtrip@science.ru.nl"><fieldtrip@science.ru.nl></a><br>
                Subject: Cluster analysis on correlation statistics<br>
                Date: Sat, Dec 6, 2014 07:49</div>
            </div>
            <br>
            <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>
Box 93 
Addenbrooke's Hospital 
Hills Road 
Cambridge CB2 0QQ 

01223 256 995</pre>
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