[adegenet-forum] are xval results only relevant for the dapc part of dapc analysis and not find.cluster

[Ella Bowles]

Hello,

When trying to find the number of clusters, as is known, I get different
results when I retain different numbers of PCs.

As background, I have samples from 180 individuals over 11 different sites,
and am trying to find the best structure.

In the tutorial, it says that when you run find.clusters there is no reason
for keeping small numbers of principle components here. When I run with
n.pca.max = 60 (so, n/3), using xval I get pretty consistently that the
good number of PCs to retain is 50.



When I run find.cluster using 50 PCs I get anywhere between 7 and 9
clusters, mostly telling the same story for the data. However, when I run
find.cluster with over 100 PCs I consistently get k = 4 or 5, and the plot
is much cleaner. In addition, however, when I look at my variance explained
plots, they don’t really asymptote, either for find.cluster or for dapc.

Both of the variance explained plots look like


Using the scaled dataset

mat <- scaleGen(Stickle8c10NoOdds, NA.method="mean")

I use 120 PCs, and get


If I run with 90 PCs the number of clusters bumps up to 5,



However, if I run find.cluster and choose 50 PCs, I get


> head(NumClust$Kstat, 11)

     K=1      K=2      K=3      K=4      K=5      K=6      K=7      K=8
K=9     K=10     K=11

1492.620 1472.790 1455.980 1448.216 1443.735 1442.909 1440.166 1440.344
1440.867 1441.979 1443.101



Are the xval procedure results (i.e., 50 PCs in my case) meant to be used
only at the dapc1 <- dapc(mat, NumClust$grp) stage? And, do my variance
explained plots concern you at all given that they don’t asymptote?


I've attached a word document with all the same text and plots as are in
this message in case they don't show up on your screen.

Thank you for your time,

Ella
-- 
Ella Bowles, PhD
Postdoctoral Researcher
Department of Biology
Concordia University

Website: https://ellabowlesphd.wordpress.com/
Email: bowlese at gmail.com
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