[adegenet-forum] optimum number of PCs for optim.a.score()

[Jombart, Thibaut]

Hi Robert,

this has been discussed before on the forum. I would recommend using xvalDapc over optim.a.score. The procedure is documented in the vignette, and explained in more details in the last instance of the DAPC practical I use for workshop (adegenet website > documents, bottom of the page):

http://adegenet.r-forge.r-project.org/files/Barcelona2015/practical-MVAgroups.1.0.pdf

Cheers
Thibaut


==============================
Dr Thibaut Jombart
MRC Centre for Outbreak Analysis and Modelling
Department of Infectious Disease Epidemiology
Imperial College - School of Public Health
Norfolk Place, London W2 1PG, UK
Tel. : 0044 (0)20 7594 3658
http://sites.google.com/site/thibautjombart/
http://sites.google.com/site/therepiproject/
http://adegenet.r-forge.r-project.org/
Twitter: @thibautjombart


________________________________
From: adegenet-forum-bounces at lists.r-forge.r-project.org [adegenet-forum-bounces at lists.r-forge.r-project.org] on behalf of Robert Fairweather [osue37 at bangor.ac.uk]
Sent: 13 November 2015 17:53
To: adegenet-forum at lists.r-forge.r-project.org
Subject: [adegenet-forum] optimum number of PCs for optim.a.score()

Hello,
I am using the optim.a.score() function to investigate the optimum number of PCs to retain in my analysis, a function that operates on a DAPC object. To produce a DAPC object I first have to carry out a DAPC procedure on my data, which means defining the number of PCs to retain in advance. I have noticed that the number of PCs retained in the DAPC object changes the outcome of the optim.a.score function, suggesting that some optimum should be known before using the function.
For example, with the microbov data, optim.a.score(dapc(microbov, n.da=100, n.pca=50)) , as suggested in the vignette, returns an optimum number of ~22 PCs, however, increasing this to 100, 200 and 300 PCs, all explaining <100% cumulative variance, return values of 15, 27 and 59 respectively. 300 returns a series of instability warnings.
With this in mind, what is the best way to choose the number of PCs in the dapc object used in optim.a.score()? The number used in the microbov example in the vignette (50) is that explaining ~90% cumulative variance, and thus far I have been using that threshold as a guide with my own data.
Am I perhaps misunderstanding the use of the function?
Many thanks,
Robert

Robert Fairweather
Research Student
Molecular Ecology and Fisheries Genetics Laboratory
Environment Centre Wales
Bangor Unversity
LL57 2UW

Email: osue37 at bangor.ac.uk<mailto:osue37 at bangor.ac.uk>
Skype: fairweathr
Twitter: @FairweathR




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