<div dir="ltr">Ok, many thanks for your help!<div><br></div><div>-Maria</div></div><div class="gmail_extra"><br><div class="gmail_quote">2014-12-21 18:30 GMT+02:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><br><div class="gmail_extra"><br><div class="gmail_quote"><span class="">2014-12-21 17:26 GMT+01:00 Maria Hakonen <span dir="ltr"><<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@gmail.com</a>></span>:<br></span><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div>Hi Denis,</div><div><br></div><span class=""><div>I have used Newman-Keuls test in Statistica, but maybe paired t-test would work as well if I correct the alpha. </div><div>Did you mean that I could simply use stats.ttest_rel and then correct the values with, for example, fdr_correction?</div><span><font color="#888888"><div><br></div></font></span></span></div></blockquote><div><br></div><div>Yes, or just use our 1samp ttest after computing a paired contrast</div><div><div class="h5"><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><span><font color="#888888"><div></div><div>-Maria</div></font></span></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2014-12-21 17:49 GMT+02:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Maria,<br><div class="gmail_extra"><br><div class="gmail_quote"><span>2014-12-21 16:35 GMT+01:00 Maria Hakonen <span dir="ltr"><<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@gmail.com</a>></span>:<br></span><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr">Hi Denis,<div><div><div><br></div><span><div>I tried that and got the following p values: 0.<a href="tel:02993846" value="+492993846" target="_blank">02993846</a>, <a href="tel:0.47414992" value="+4947414992" target="_blank">0.47414992</a></div></span></div><span><div>The corresponding Greenhouse-Geisser corrected values computed with Statistica were: 0.026775, <a href="tel:0.462423" value="+49462423" target="_blank">0.462423</a>. </div><div>Huynh-Feldt correction gives 0.023894, <a href="tel:0.473539" value="+49473539" target="_blank">0.473539</a> and lower-bound correction 0.036458, <a href="tel:0.427778" value="+49427778" target="_blank">0.427778</a></div><div><br></div><div>The uncorrected values calculated with Statistica and Python are equal but also the corrected values are almost equal. So, maybe <span style="font-size:13px">f_twoway_rm works correctly now? </span></div><div><br></div></span></div></div></blockquote><div><br></div><div>There might be some minor implementation-related difference, would be good to investigate.</div><div>Generally the ANOVA function is tested against R and SPSS, event for the correction:</div><div><br></div><div><a href="https://github.com/mne-tools/mne-python/blob/master/mne/stats/tests/test_parametric.py#L10" target="_blank">https://github.com/mne-tools/mne-python/blob/master/mne/stats/tests/test_parametric.py#L10</a><br></div><div><br></div><div>I would therefore trust the results.</div><span><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div><div></div><div>It would also be interesting to do post-hoc comparisons between conditions but I think that Python doesn't have function for post-hoc test, or at least I didn't found it? </div></div><span><font color="#888888"><div><br></div></font></span></div></blockquote><div><br></div></span><div>What do you mean? You simply compute paired t-tests and correct the alpha.</div><span><font color="#888888"><div><br></div><div>-Denis</div></font></span><div><div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><span><font color="#888888"><div></div><div>-Maria</div></font></span></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2014-12-21 16:49 GMT+02:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr">Should be Greenhouse-Geisser, <br><div>also see source code:</div><div><br></div><div><a href="https://github.com/mne-tools/mne-python/blob/master/mne/stats/parametric.py#L249" target="_blank">https://github.com/mne-tools/mne-python/blob/master/mne/stats/parametric.py#L249</a><br></div></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2014-12-21 15:47 GMT+01:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div style="font-size:12.8000001907349px">[f,p]=f_twoway_rm(data,factor_levels=[2,3], correction=True)</div><div><br></div></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2014-12-21 15:45 GMT+01:00 Maria Hakonen <span dir="ltr"><<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div>Hi Denis,</div><div><br></div><div>Are you sure that the sphericity correction is implemented by f_twoway_rm?</div><div>I tested it with data where the assumption of sphericity is violated. I calculated p values with Statistica and corrected them using the lower-bound estimate, Greenhouse-Geisser correction and the Huynh-Feldt correction. However, the p-values given by f_twoway_rm were equal with the uncorrected values I calculated with Statistica. I used f_twoway_rm as:</div><div><br></div><div>[f,p]=f_twoway_rm(data,factor_levels=[2,3])</div><div><br></div><div>where data is hemisphere*sesion ANOVA table.</div><span><font color="#888888"><div><br></div><div>-Maria</div></font></span></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2014-12-21 15:08 GMT+02:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><br><div class="gmail_extra"><br><div class="gmail_quote"><span>2014-12-21 14:00 GMT+01:00 Maria Hakonen <span dir="ltr"><<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div>Hi,</div><div><br></div><div>Is it that when I have hemisphere*session ANOVAs, I can use f_twoway_rm, and this gives the corrected values? </div></div></blockquote><div><br></div></span><div>with correction=True</div><span><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div>Should one-way ANOVA be used in the cases where I am not interested in hemisperic asymmetry? Is there any function for one way ANOVA that gives sphericity corrected values?</div><span><font color="#888888"><div><br></div></font></span></div></blockquote><div><br></div></span><div>A one-way ANOVA is currently not implemented. But you can just look at the relevant main effect. Should be equivalent.</div><div><div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><span><font color="#888888"><div></div><div>-Maria</div></font></span></div><div><div><div class="gmail_extra"><br><div class="gmail_quote">2014-12-21 14:21 GMT+02:00 Denis A. Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="auto"><div>Hi Maria,</div><span><div><br>On 21 Dec 2014, at 12:51, Maria Hakonen <<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@gmail.com</a>> wrote:<br><br></div><blockquote type="cite"><div><div dir="ltr"><div>Hi Denis,</div><div><br></div><div>Should I use two-way ANOVA if I also want to compare hemispheres, i.e. have a 2*3 (hemisphere*session) ANOVA table?</div></div></div></blockquote><div><br></div></span><div>Sounds like the repeated measures anova is what you want to do here indeed. I just realized the sphericity correction is actually implemented by our code. So that should fly. Checkout the correction parameter.</div><div><div><div><br></div><br><blockquote type="cite"><div><div dir="ltr"><div>I would need to do group level analysis.</div><div><br></div><div>-Maria</div></div></div></blockquote><blockquote type="cite"><div><div class="gmail_extra"><br><div class="gmail_quote">2014-12-21 12:38 GMT+02:00 Denis-Alexander Engemann <span dir="ltr"><<a href="mailto:denis.engemann@gmail.com" target="_blank">denis.engemann@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr">Hi Maria,<br><div class="gmail_extra"><br><div class="gmail_quote"><span>2014-12-21 10:18 GMT+01:00 Maria Hakonen <span dir="ltr"><<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div>Hi all,</div><div><br></div><div>I would need to find the time intervals where there are statistically significant differences between MEG responses measured under three different conditions. I study the responses by comparing the mean values in steps of 200 ms between the range of 400-2400 ms. I would like to do this for responses from several brain areas. What could be the best way to compare the mean values of the tree responses? </div><div><br></div></div></blockquote><div><br></div></span><div>Single subject or group level?</div><div>For single subject a standard F-test would be appropriate. For groups a oneway repeated measures ANOVA, if I understand your design correctly (simply 3 different conditions).</div><span><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div></div><div>I have planned to try f_twoway_rm in a loop. However, when rmANOVA is used, sphericity test is needed, and if the assumption of sphericity is violated, p values need to be corrected. Is there any function for this in Python (e.g. Mauchly's sphericity test and Greenhouse-Geisser test)? </div><div><br></div></div></blockquote><div><br></div></span><div>Note that this only needed if you have more than 2 factors with more than two levels. Currently this is not implemented in our stats code. The other questions is why you need a 2-way test for 3 conditions. I probably did not fully understand your design.</div><div><br></div><div>Denis</div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><span><div dir="ltr"><div></div><div>Many thanks already in advance!</div><div><br></div><div>Regards,</div><div>Maria </div></div>
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