Hello, thank You very much for Your reply. I'm still little confused. Dne Pá 7. června 2013 16:03:13 jste napsal(a): > Hello there, > > you could have done the PCoA on Chord's distance using adegenet, it would > have probably been simpler (see the vignette adegenet-basics, section 6 > "Multivariate Analysis“).
Function dist.genpop() contains Nei's distance, but man pages says it is only for genpop objects (I wish to analyse individuals, not populations). I imported the data using read.loci() and converted it to genind using loci2genind(). I found also function dist.genet() from ade4 package, but it requires genet object. So far I didn't find straightforward way to convert my data to it. And I haven't find any function providing the Goldstein's distance. > 'dist' is the canonical Euclidean distance, but dudi.pco will accept any > Euclidean distance. You can use "cailliez" in ade4 to make your distance > Euclidean before the PCoA. I'm not mathematician, so I don't understand one point. Let's say I have non- Euclidean distance matrix and some individuals (e.g. from same population) have zero distance (they are identical). When I use cailliez(), I have two possibilities (parameter cor.zero): 1) to add the constant also to zero-length distances, so that the shift is everywhere same. But as the result, the distance between originally identical objects is positive, so they will be treated as different in ongoing analysis, right?; or 2) keep zero-length distances, so that those objects stay identical, but their distances from another objects change, so the further analysis are biased again, right? Is there any solution? Or am I wrong? > Cheers > Thibaut Sincerely, Vojtěch > -- > ###################################### > Dr Thibaut JOMBART > MRC Centre for Outbreak Analysis and Modelling > Department of Infectious Disease Epidemiology > Imperial College - School of Public Health > St Mary’s Campus > Norfolk Place > London W2 1PG > United Kingdom > Tel. : 0044 (0)20 7594 3658 > t.jomb...@imperial.ac.uk > http://sites.google.com/site/thibautjombart/ > http://adegenet.r-forge.r-project.org/ > ________________________________________ > From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] > on behalf of Vojtěch Zeisek [vojtech.zei...@opensuse.org] > Sent: 07 June > 2013 15:46 > To: mailinglist R > Subject: [R-sig-phylo] PCoA with custom distance matrix > > Hello, > I have microsatellite data and I would like to analyze them using PCoA in R. > I would like to use following genetic distance: Goldstein's (1995) (dμ)2 > and Nei's chord distance (1983). I calculated those distances in MSA > (Dieringer and Schlötterer 2003), because I didn't find any possibility to > calculate them in R. I imported the distances like that: > dist.dms <- read.csv("dms_ind.txt", header=TRUE, sep="\t", dec=".", > row.names=1) > dioszegia.dist.dms <- as.dist(dioszegia.dist.dms) > class(dioszegia.dist.dms) > [1] "dist“ > dms_ind.txt is ordinary square matrix with diagonal. > But using of dudi.pco() from ade4 package fails: > pcoa <- dudi.pco(dist.dms, scannf=FALSE, nf=3) > Warning message: > In dudi.pco(dioszegia.dist.dms, scannf = FALSE, nf = 3) : > Non euclidean distance > When using Euclidean distance (function dist()), it works fine. It produces > some results despite the above error, but the results are very far from > those I get using dist() function. And the results don't look realistic. > What do I do wrong? :-) > Have a nice day! > Vojtěch -- Vojtěch Zeisek Komunita openSUSE GNU/Linuxu Community of the openSUSE GNU/Linux http://www.opensuse.org/ http://trapa.cz/
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