bsp22e at bangor.ac.uk
Mon Jan 17 18:36:08 CET 2011
My find.clusters analysis for species delimitation resulted in 4
clusters and I retained 10PCs (representing about 75% of cumulative
variance) and 3 eigenvalues. I used this optim.a.score command but not
sure it is right...
optim.a.score(dapc1, n.pca=1:ncol(dapc1$tab), smart=TRUE, n=10,
plot=TRUE, n.sim=10, n.da=3)
If this is right, I get the optimal number of PCs to be 3 which
represents only 45% of the variance.
Then I redid the DAPC using 3 PCs (which only represents 45% of
variance) and it gave me the following a.scores
1 2 4 3
0.2227273 0.9666667 0.9571429 0.6333333
If I used all 10 PCs, I get a.scores
1 2 4 3
0.2818182 0.7500000 0.6571429 0.5333333
I'm not sure whether to use 10 or 3 PCs as it seems to be trade-off
between total variance and a.scores. But more confusingly, each time I
redo the DAPC with a certain number of PCs (say 3) it gives me different
Could you kindly clarify this.
Thanks for your help,
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