<html>
<head>
<style><!--
.hmmessage P
{
margin:0px;
padding:0px
}
body.hmmessage
{
font-size: 12pt;
font-family:Calibri
}
--></style></head>
<body class='hmmessage'><div dir='ltr'>Hello again,<div></div><div><br></div><div>I have been running xvalDapc and have been getting variable results and am not sure how to interpret this. </div><div><br></div><div>I have a dataset of combined microsatellite (19 loci) and SNP (39 loci) data for 560 individuals. From initially running find.clusters I have 6 groups/clusters (which makes sense with my data) that I am testing with xval to eventually run a DAPC.</div><div><span style="font-size: 12pt;"><br></span></div><div><span style="font-size: 12pt;">For xvalDapc I have been using the following settings:</span></div><div><i>n.pca.max=100, n.da=NULL, training.set=0.9, n.pca=NULL </i></div><div><br></div><div>First off, if I try anything over 4 replicates I often get the following message:</div><div><div><br></div><div><i>Warning message:</i></div><div><i>In xvalDapc.matrix(objNoNa@tab, grp$grp, n.pca.max = 100, n.da = NULL, :</i></div><div><i> At least one group was absent from the training / validating sets.</i></div><div><i>Try using smaller training sets.</i></div></div><div><br></div><div>So, I have run the command many many times with both 3 and 4 reps (occasionally, but not as often, getting the above warning message) and keep getting very variable results. For instance if I run xval 6 times with 4 reps no one run gives me the same "best" number of PCAs. Some times I get 20 PCAs as best, others I get 80. Overall,<span style="font-size: 12pt;"> I never get the same thing twice, but all classifications are greater than 0.80, and most over 0.90, success.</span><span style="font-size: 12pt;"> </span><span style="font-size: 12pt;">I feel based on the xval results there is no way to unambiguously pick a best number of PCAs to use to run a subsequent DAPC. </span></div><div><br></div><div>My first thought with this inconsistency would be to run more reps, but then I get the warning message very often, and when the runs with the higher reps do proceed, I get many groups that aren't assigned to a training set. So if I am stuck with using fewer reps, and am stuck with the inconsistent results, can that be interpreted as my dataset not being very informative...and/or, I hate to say it, but that I need more loci to increase assignment consistency with DAPC?</div><div><br></div><div>Thanks for any help you can offer, it is much appreciated!</div><div><br></div><div>Nikki</div> </div></body>
</html>