<div dir="ltr"><div>Hello Roma,<br></div><div><br></div><div>Use the groups from find.clusters.<br></div><div><br>It's a common misconception, but DAPC is not a method to define groups. It is a tool that allows you to create a model of your data based on your groups so that you can assess how well you can differentiate samples into individual groups (similar to AMOVA) and give you a method to predict what groups your samples belong in based on that model.<br><br></div><div>find.clusters() and snapclust() are the only functions in adegenet that can determine groups de novo from your data.<br><br></div><div>Hope that helps,</div><div>Zhian<br></div><div><br><br><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Oct 24, 2019 at 11:00 AM <<a href="mailto:adegenet-forum-request@lists.r-forge.r-project.org">adegenet-forum-request@lists.r-forge.r-project.org</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Send adegenet-forum mailing list submissions to<br>
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1. DAPC-Find optimum number of groups (Das, Roma (ICRISAT-IN))<br>
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Date: Thu, 24 Oct 2019 07:59:10 +0000<br>
From: "Das, Roma (ICRISAT-IN)" <<a href="mailto:r.das@cgiar.org" target="_blank">r.das@cgiar.org</a>><br>
To: "<a href="mailto:adegenet-forum@lists.r-forge.r-project.org" target="_blank">adegenet-forum@lists.r-forge.r-project.org</a>"<br>
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Subject: [adegenet-forum] DAPC-Find optimum number of groups<br>
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Hello everyone,<br>
<br>
* Based on DAPC analysis, I am not sure whether I should treat the final group for individuals line as 1) prior group from find.clusters() or<br>
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2) group with maximum posterior probability after xval.DAPC()<br>
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As in scatterplot from DAPC analysis individuals are plotted based on prior group. Please advise if there a way to choose optimum number of discriminating functions to be used.<br>
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<br>
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Regards,<br>
<br>
Roma<br>
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