[adegenet-forum] more xval confusion: getting variable results

[Nikki Vollmer]

Hello again,
I have been running xvalDapc and have been getting variable results and am not sure how to interpret this.  
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.
For xvalDapc I have been using the following settings:n.pca.max=100, n.da=NULL, training.set=0.9, n.pca=NULL 
First off, if I try anything over 4 replicates I often get the following message:
Warning message:In xvalDapc.matrix(objNoNa at tab, grp$grp, n.pca.max = 100, n.da = NULL,  :  At least one group was absent from the training / validating sets.Try using smaller training sets.
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. 
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?
Thanks for any help you can offer, it is much appreciated!
Nikki 		 	   		  
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