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<p class="MsoNormal"><a name="_MailEndCompose"><span style="color:black">Hello everyone,<o:p></o:p></span></a></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l1 level1 lfo3"><![if !supportLists]><span style="font-family:Symbol;color:black"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">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 2) group with maximum posterior probability after xval.DAPC().
As in scatterplot what we get after analysis, individuals are plotted based on prior group. Please advise<o:p></o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l1 level1 lfo3"><![if !supportLists]><span style="font-family:Symbol;color:black"><span style="mso-list:Ignore">·<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">Is there a way to choose optimum number of discriminating functions to be used<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="color:black">Regards,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black">Roma</span><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="color:#1F497D"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span lang="EN-US" style="mso-fareast-language:EN-IN">From:</span></b><span lang="EN-US" style="mso-fareast-language:EN-IN"> Das, Roma (ICRISAT-IN)
<br>
<b>Sent:</b> 28 August 2019 04:21<br>
<b>To:</b> 'adegenet-forum@lists.r-forge.r-project.org' <adegenet-forum@lists.r-forge.r-project.org><br>
<b>Subject:</b> Request of DAPC analysis<o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span style="color:black">Hello Everyone,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black">I am using DAPC using adegenet package for cluster analysis. However I am not sure if I am following the correct way to select n.pca and n.clust based on cross-validation.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="color:black">I am following below steps<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l0 level1 lfo2"><![if !supportLists]><span style="color:black"><span style="mso-list:Ignore">1.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">I am using a genind object<o:p></o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l0 level1 lfo2"><![if !supportLists]><span style="color:black"><span style="mso-list:Ignore">2.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">Used find.clusters() </span>
<span style="font-size:10.0pt;font-family:"Courier New";color:black;background:yellow;mso-highlight:yellow">grp <- find.clusters()</span><span style="color:black"> and interactively chose n.pca and n.clust. Based on plot, I selected
<span style="background:aqua;mso-highlight:aqua">n.pca=200 and n.clust=21</span><o:p></o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l0 level1 lfo2"><![if !supportLists]><span style="color:black"><span style="mso-list:Ignore">3.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">Next used xvalDapc() to get some idea about number of PCs<o:p></o:p></span></p>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:10.0pt;font-family:"Courier New";color:black;background:yellow;mso-highlight:yellow">xval <- xvalDapc(tab(fdat, NA.method = "mean"), grp$grp, n.pca.max = 300, n.rep = 30)</span><span style="font-size:10.0pt;font-family:"Courier New";color:black"><o:p></o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l0 level1 lfo2"><![if !supportLists]><span style="color:black"><span style="mso-list:Ignore">4.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">Based on number of PCs achieving highest mean success and lowest MSE, I selected
<span style="background:aqua;mso-highlight:aqua">n.pca=50</span><o:p></o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l0 level1 lfo2"><![if !supportLists]><span style="color:black"><span style="mso-list:Ignore">5.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">Further, I tried to narrowed the search of PC’s with n.pca = 30:60<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-left:36.0pt"><span style="font-size:10.0pt;font-family:"Courier New";color:black;background:yellow;mso-highlight:yellow">xval_optimum <- xvalDapc(tab(fdat, NA.method = "mean"), grp$grp, n.pca = 30:60, n.rep = 100,parallel
= "multicore", ncpus = 6L )</span><span style="font-size:10.0pt;font-family:"Courier New";color:black"><o:p></o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l0 level1 lfo2"><![if !supportLists]><span style="color:black"><span style="mso-list:Ignore">6.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">Finally I selected <span style="background:aqua;mso-highlight:aqua">
n.pca=30</span> based on number of PCs achieving highest mean success and lowest MSE from xval_optimum<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="color:black">My questions are:<o:p></o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l0 level1 lfo2"><![if !supportLists]><span style="color:black"><span style="mso-list:Ignore">7.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">From cross-validation, it seems the optimum number of PCs is 30. Should I re-run find.clusters() with n.pca=30 and select n.clust interactively from plot<o:p></o:p></span></p>
<p class="MsoListParagraph" style="text-indent:-18.0pt;mso-list:l0 level1 lfo2"><![if !supportLists]><span style="color:black"><span style="mso-list:Ignore">8.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="color:black">And then re-run dapc() with n.pca=30 and output of n.clust from step 6. Please advise<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="color:black">Thanks,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:black">Roma<o:p></o:p></span></p>
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