[adegenet-forum] DAPC and components retaining

[Vladimir Mikryukov]

Dear Dr. Jombart,
Thank you for the link for the DAPC-paper.
I found it quite interesting.

But the question about the formal criterion to determine a number of PC axes
that should be retained is still opened.
Maybe it has sense to use the Horn’s Parallel Analysis (see Glorfeld)?
It’s already implemented in R package paran by Alexis Dinno, and according
to Peres-Neto it performed quite well in comparative tests with other
criteria.

But the big disadvantage of PA is its speed - even with 500 iterations it's
slow.
And maybe it's too conservative.
Taking example from your paper: for the island model (a) PA retained only 25
principal components (around 77,5% of variance). And with it, observed
proportions of overall correct assignment and correct assignment per group
are higher then in the example (with 100 PCs retained).

 library(adegenet)
 library(paran)
 data(dapcIllus)
 attach(dapcIllus)

 dim <- paran(a at tab, iterations=500, centile=95, graph=TRUE)
 clust.a_PA <- find.clusters(a, n.pca=dim$Retained, n.clust=6)
 dapc.a_PA <- dapc.genind(x = a, pop = clust.a_PA$grp, n.pca=dim$Retained,
n.da = 5)

 summary(dapc.a_PA)
 scatter.dapc(dapc.a_PA)


Well, I almost sure that you already thought about the dimension-reduction
problem.
Anyway criterion choice is up to researcher.

Best regards,
Vladimir


References:

Glorfeld L.W. An Improvement on Horn's Parallel Analysis Methodology for
Selecting the Correct Number of Factors to Retain // Educ Psychol Meas.
1995. V. 55. № 3. P. 377-393.

Peres-Neto P.R., Jackson D.A., Somers K.M. How many principal components?
stopping rules for determining the number of non-trivial axes revisited //
Comput Stat Data An. 2005. V. 49. № 4. P. 974-997.


--
Vladimir Mikryukov
PhD student
 Institute of Plant & Animal Ecology UD RAS,
Lab. of Population and Community Ecotoxicology
[8 Marta 202, 620144, Ekaterinburg, Russia]
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