[adegenet-forum] Doubt with DAPC without prior K (find.clusters)

[Thibaut Jombart]

Hi there,

yep. To add to this: you can increase the number of random starting points
e.g. n.start = 50 to smooth the results.

Best
Thibaut

--
Dr Thibaut Jombart
Associate Professor in Outbreak Analytics, London School of Hygiene and
Tropical Medicine
Senior Lecturer in Genetic Analysis, Imperial College London
President of RECON: repidemicsconsortium.org
https://thibautjombart.netlify.com
Twitter: @TeebzR


On Thu, 24 Jan 2019 at 12:44, Cathy Bouffartigue <cathy.bouffartigue at inra.fr>
wrote:

> Hi Helena,
>
>
>
> This is due to the k-means algorithm used in the find.cluster function
> which use a different seed for its initialization each time you’re running
> your code. You have to set the seed before running your find.cluster
> function to ensure the reproducibility of your results.
>
>
>
> set.seeds(24012019)
>
> *perca_grp6 <-find.clusters(perca, max.n.clust = 10)*
>
>
>
>
>
> *Good luck with your analyses!*
>
>
>
> *Cathy*
>
>
>
> [image: Description :
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>
>
> *_____________________________ **Cathy Bouffartigue*
> Doctorante/ PhD student
>
> cathy.bouffartigue at inra.fr
>
>
>
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>
>
> *De :* adegenet-forum <adegenet-forum-bounces at lists.r-forge.r-project.org>
> *De la part de* HELENA IBANEZ MARTINEZ
> *Envoyé :* jeudi 24 janvier 2019 12:58
> *À :* adegenet-forum at lists.r-forge.r-project.org
> *Objet :* Re: [adegenet-forum] Doubt with DAPC without prior K
> (find.clusters)
>
>
>
> HELENA IBANEZ MARTINEZ <him21024 at um.es> escribió:
>
> HELENA IBANEZ MARTINEZ <him21024 at um.es> escribió:
>
> *Hello, my name is Helena,*
>
>
>
>
>
>
>
> * I have a question about my DAPC analayses. I'm following the tutorial
> step by step and I get the DAPC graph of my data very well, but my problem
> is when I repeat the process my graphic results come out completely
> different every time... My question is, why do such different results come
> out if I'm repeating exactly the same (same data and same parameters)? It
> happens to me every time I do it. What can I be doing wrong? Here I leave
> the example of my code: 135 individuals with 8 loci sampled in 6 different
> locations*
>
> *perca <-read.structure("PercaPopName.str",*
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *                                      onerowperind=TRUE,
>                                      n.ind=135,
>          n.loc=8,                                      col.lab=1,
>                                      col.pop=2,
>                                      row.marknames=1,
>                                      ask=FALSE) perca_grp6
> <-find.clusters(perca, max.n.clust = 10) 200 6 dapc6 <-dapc(perca,
> perca_grp6$grp) 200 5 scatter(dapc6)*
>
> *Thank you.*
>
>
>
>
>
> Hi, my name is Helena,
>
> I have a question about my DAPC analayses.
>
> I'm following the tutorial step by step and well, I get the DAPC graph of
> my data, but the problem is that when I repeat this my graphic results come
> out completely different, therefore they are not reliable...
> My question is, why do such different results come out if I'm repeating
> exactly the same? It happens to me every time I do it. What can I be doing
> wrong?
>
> Here my code:
>
> perca <-read.structure("PercaPopName.str",
>                                       onerowperind=TRUE,
>                                       n.ind=135,
>                                       n.loc=8,
>                                       col.lab=1,
>                                       col.pop=2,
>                                       row.marknames=1,
>                                       ask=FALSE)
>
> dapc1 <-dapc(perca, perca_grp$grp)
>
> 200
> 6
>
> scatter(dapc1)
>
>
> *Hello again, I`m Helena, sorry but I introduced my code wrong yesterday:*
>
> * This is my code: *
>
> *perca <-read.structure("PercaPopName.str",*
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *                                      onerowperind=TRUE,
>                                      n.ind=135,
>          n.loc=8,                                      col.lab=1,
>                                      col.pop=2,
>                                      row.marknames=1,
>                                      ask=FALSE) perca_grp6
> <-find.clusters(perca, max.n.clust = 10) 200 6 dapc6 <-dapc(perca,
> perca_grp6$grp) 200 5 scatter(dapc6) Thank you *
>
>
>
>
>
>
>
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