<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><br></div><div>Hello FTs,</div><div>here comes a question concerning the wonderful stats function</div><div><div><br></div><div><b><span class="Apple-style-span" style="font-weight: normal;">depsampregrT</span></b></div><div><br></div><div><b><span class="Apple-style-span" style="font-weight: normal;"></span><span class="Apple-style-span" style="font-weight: normal; ">I am using it mainly for running time–frequency–electrode cluster tests. I usually use contrasts like cfg.contrastcoefs=[-3 -1 1 3] and it has proven pretty powerful.</span></b></div><div><br></div><div>With a new data set, I sadly have only three levels of data, which is of course always a bit of a pain for linear contrasts.</div><div>What puzzles me is that</div><div><br></div><div>[-2 -1 3] (i.e., linear increase; trying to keep it zero-weighted)</div><div><br></div><div>and </div><div><br></div><div>[-1 2 -1] (i.e. trying to crudely model an inverse u-shape relationship)</div><div><br></div><div>give the very same result (not exactly numerically, but very much in terms of clusters, down to very similar p-values, etc).</div><div><br></div><div>Q: Any idea why this might be the case?</div><div>Of course, linear and cubic trends can be sometimes both significant, but I was slightly worried seeing this strong concordance in this case.</div><div><br></div><div>(Also, does any of you have a better/best/gold standard way to model 3-level linear contrasts? mathematically, in [-1 0 1], the middle condition is basically switched off, isn’t it.)</div><div><br></div><div>Anyway, thanks very much for a quick reply!</div><div>Best wishes, Jonas</div></div><div><br></div><div><br></div><br><div>
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