<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Hi,<div><br></div><div>I've poked around a little more, and I'm wondering if L2 normalization would ameliorate my second issue regarding lesion size being a confounding factor in my classification? <a href="http://www.pymvpa.org/generated/mvpa.misc.transformers.l2_normed.html">http://www.pymvpa.org/generated/mvpa.misc.transformers.l2_normed.html</a></div><div><br></div><div>At the end of the day, all I mainly want to ensure that (a) 0/1 scaling is correct and (b) determine how much of the CV is due to lesion size alone. If it were a simpler univariate regression, I could include lesion size as a covariate, but I'm not sure how to do something analogous in PyMVPA.</div><div><br></div><div>Thanks!</div><div>David</div><div><br>
<br><div><div>On Sep 6, 2011, at 12:52 AM, David V. Smith wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div><br>Hello,<br><br>I have lesion data, and I am trying to test whether particular patterns of lesions distinguish two classes of patients. I have two questions:<br><br>1) What is the best way to scale the lesion data? Traditionally, these data are represented with 1s (lesion) and 0s (no lesion). I've played around with different scalings, and I've gotten different (but replicable) results using the SMLR classifier in PyMVPA 0.4. See below: first column is the leave-one-out CV; second column the value for the spared voxels; third column is the value for the damaged voxels.<br>CV<span class="Apple-tab-span" style="white-space:pre"> </span>NoLesion<span class="Apple-tab-span" style="white-space:pre"> </span>Lesion<br>83.571<span class="Apple-tab-span" style="white-space:pre"> </span>000<span class="Apple-tab-span" style="white-space:pre"> </span>001<br>75.000<span class="Apple-tab-span" style="white-space:pre"> </span>001<span class="Apple-tab-span" style="white-space:pre"> </span>002<br>77.143<span class="Apple-tab-span" style="white-space:pre"> </span>002<span class="Apple-tab-span" style="white-space:pre"> </span>004<br>81.429<span class="Apple-tab-span" style="white-space:pre"> </span>100<span class="Apple-tab-span" style="white-space:pre"> </span>200<br>81.429<span class="Apple-tab-span" style="white-space:pre"> </span>200<span class="Apple-tab-span" style="white-space:pre"> </span>400<br><br>2.) What is the best way to control for a nuisance factor? I know there is an additional variable (i.e., lesion volume) that can distinguish between my two patient groups, so I would like the resulting CV and heavily weighted voxels to be uncontaminated by this nuisance factor. Ideally, I would like to know how much additional predictive power is gained over and above this nuisance factor. <br><br>Thanks,<br>David<br><br><br>_______________________________________________<br>Pkg-ExpPsy-PyMVPA mailing list<br><a href="mailto:Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org">Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org</a><br>http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa<br></div></blockquote></div><br></div></body></html>