[adegenet-forum] adegenet-forum Digest, Vol 131, Issue 2

[Das, Roma (ICRISAT-IN)]

Thanks Zhian for your reply. Really helpful. Please help me to further understand this

1. xvalDAPC() require one of the input as group membership (grp) of individuals and then it gives number of optimum PC as one of the output. However the grp what we used is from find.clusters(). Do I need to use optimum PC from xvalDAPC and re-run find.clusters()?

2. How can I use posterior probabilities from DAPC analysis? The prior and posterior group membership changing for few individuals

3. How can I select optimum number of DA functions? I used default (n.da=grp-1) in DAPC analysis. How to decide how many DA functions is required? Is there any statistic to take account of


Thanks and regards,
Roma

From: adegenet-forum [mailto:adegenet-forum-bounces at lists.r-forge.r-project.org] On Behalf Of Zhian Kamvar
Sent: 24 October 2019 10:15
To: adegenet-forum at lists.r-forge.r-project.org
Subject: Re: [adegenet-forum] adegenet-forum Digest, Vol 131, Issue 2

Hello Roma,

Use the groups from find.clusters.

It's a common misconception, but DAPC is not a method to define groups. It is a tool that allows you to create a model of your data based on your groups so that you can assess how well you can differentiate samples into individual groups (similar to AMOVA) and give you a method to predict what groups your samples belong in based on that model.
find.clusters() and snapclust() are the only functions in adegenet that can determine groups de novo from your data.
Hope that helps,
Zhian



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Today's Topics:

   1. DAPC-Find optimum number of groups (Das, Roma (ICRISAT-IN))


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Date: Thu, 24 Oct 2019 07:59:10 +0000
From: "Das, Roma (ICRISAT-IN)" <r.das at cgiar.org<mailto:r.das at cgiar.org>>
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Subject: [adegenet-forum] DAPC-Find optimum number of groups
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Hello everyone,

*         Based on DAPC analysis, I am not sure whether I should treat the final group for individuals line as 1) prior group from find.clusters() or

       2) group with maximum posterior probability after xval.DAPC()


As in scatterplot from DAPC analysis  individuals are plotted based on prior group. Please advise if there a way to choose optimum number of discriminating functions to be used.




Regards,

Roma

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