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<body class='hmmessage'><div dir='ltr'>Really group size? Here are mine: 95, 43, 61, 72, 164, 125. Is 43 really that small?<br><br><br><br><div></div><br><br><div>> From: t.jombart@imperial.ac.uk<br>> To: nlv209@hotmail.com; adegenet-forum@lists.r-forge.r-project.org<br>> Subject: RE: [adegenet-forum] more xval confusion: getting variable results<br>> Date: Wed, 26 Feb 2014 11:52:00 +0000<br>> <br>> Hello, <br>> <br>> the results come from the fact that some groups probably have very small sample sizes in your data. Therefore, the re-sampling used for the cross validation may have i) no individuals to train the method on, and/or ii) no individuals to cross-validate with.<br>> <br>> Caitlin Collins has modified the cross-validation procedure for this kind of situation, but it is still in (one of ) the devel version of adegenet. You can either contact her directly, or just discard the smallest groups from your analysis.<br>> <br>> Cheers<br>> Thibaut<br>> <br>> <br>> <br>> <br>> ________________________________________<br>> From: adegenet-forum-bounces@lists.r-forge.r-project.org [adegenet-forum-bounces@lists.r-forge.r-project.org] on behalf of Nikki Vollmer [nlv209@hotmail.com]<br>> Sent: 25 February 2014 19:25<br>> To: adegenet-forum@lists.r-forge.r-project.org<br>> Subject: [adegenet-forum] more xval confusion: getting variable results<br>> <br>> Hello again,<br>> <br>> I have been running xvalDapc and have been getting variable results and am not sure how to interpret this.<br>> <br>> 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.<br>> <br>> For xvalDapc I have been using the following settings:<br>> n.pca.max=100, n.da=NULL, training.set=0.9, n.pca=NULL<br>> <br>> First off, if I try anything over 4 replicates I often get the following message:<br>> <br>> Warning message:<br>> In xvalDapc.matrix(objNoNa@tab, grp$grp, n.pca.max = 100, n.da = NULL, :<br>> At least one group was absent from the training / validating sets.<br>> Try using smaller training sets.<br>> <br>> 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, I never get the same thing twice, but all classifications are greater than 0.80, and most over 0.90, success. 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.<br>> <br>> 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?<br>> <br>> Thanks for any help you can offer, it is much appreciated!<br>> <br>> Nikki<br></div> </div></body>
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