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

[Zhian Kamvar]

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




On Thu, Oct 24, 2019 at 11:00 AM <
adegenet-forum-request at lists.r-forge.r-project.org> wrote:

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>    1. DAPC-Find optimum number of groups (Das, Roma (ICRISAT-IN))
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> Message: 1
> Date: Thu, 24 Oct 2019 07:59:10 +0000
> From: "Das, Roma (ICRISAT-IN)" <r.das at cgiar.org>
> To: "adegenet-forum at lists.r-forge.r-project.org"
>         <adegenet-forum at lists.r-forge.r-project.org>
> Subject: [adegenet-forum] DAPC-Find optimum number of groups
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> DB6P195MB04211B823F362C731D17B659FA6A0 at DB6P195MB0421.EURP195.PROD.OUTLOOK.COM
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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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