Is it alleged or a legitimate BA sample. The Ev13 Visigoth from Spain also plots with Serbs on K15. His father was probably a Thracian since the Goths settled in Moesia after fleeing the Huns when they crossed the Danube.
How to explain Slavic drift among some of La Tene & Mezocsat samples, such as these?:
Distance to: HUN_IA_La_Tene_o3:I25524
0.02948879 Lithuanian_SZ
0.03104802 Lithuanian_VA
0.03181494 Russian_Pskov
0.03249216 Lithuanian_RA
Distance to: HUN_IA_La_Tene_o3:I25509
0.02897196 Sorb_Niederlausitz
0.02966193 Slovakian
0.03318174 Polish
0.03507321 Ukrainian_Rivne
Distance to: HUN_IA_La_Tene_o:I18226
0.02066294 Russian_Pskov
0.02355747 Russian_Tver
0.02398007 Russian_Kursk
0.02439635 Russian_Kaluga
Distance to: HUN_IA_La_Tene_o:I18183
0.02571778 Polish
0.02786730 Sorb_Niederlausitz
0.02871717 Ukrainian_Rivne
0.02984511 Ukrainian_Zhytomyr
Distance to: HUN_IA_La_Tene_o:I18182
0.03551951 Slovakian
0.04015479 Hungarian
0.04079776 Ukrainian_Zakarpattia
0.04162469 Croatian
Distance to: HUN_EIA_Prescythian_Mezocsat_o1:I18241
0.07797610 Lithuanian_VA
0.07938375 Sorb_Niederlausitz
0.08015265 Russian_Pskov
0.08042445 Ukrainian_Rivne
Distance to: HUN_EIA_o3:I25525
0.03325160 Polish
0.03468322 Czech
0.03473361 German_East
0.03889267 Sorb_Niederlausitz
I use R, you must already have it installed to do averages. Download this file folder (https://we.tl/t-RWNUWnPAEs), open R, change directories and select the folder you downloaded. Now put in these commands and you're done:
In the "Samples" .txt file always remember to leave a blank line at the end and make sure that all the coordinates you want to mix are preceded by the same name before the colon. Let me know if you've succeeded.Code:source('nMonte3.R')
averages <- aggr_pops('Samples.txt')
write.csv(averages,"Samples_average.txt")
Download R from here: https://cran.r-project.org. Then open the R console application and run this:
Or to combine samples from different populations into a single average, you can also run the code below here: https://rdrr.io/snippets/.Code:t=read.csv("https://drive.google.com/uc?export=download&id=1VFry7ZfzTnLJ3-G-PJGL6jlWU4TIppjB",header=F,row.names=1)
a=data.frame(aggregate(t,list(sub(":.*","",rownames(t))),mean),row.names=1)
write.table(round(a,6),"averages.csv",quote=F,col.names=F,sep=",")
(Or install R through your package manager and run `R` to start the R REPL.)Code:t=read.csv(header=F,row.names=1,text="RUS_Veretye_Meso:PES001,0.124067,0.04773,0.145191,0.204783,0.008617,0.054384,-0.022796,-0.017307,-0.005727,-0.071254,0.014777,-0.011839,0.038652,-0.048168,0.02348,0.025325,-0.01004,0.003547,-0.00264,0.022011,-0.002371,0.01694,0.005053,-0.033017,-0.008263
RUS_Vologda_Veretye_Meso:KAR001,0.121791,0.033512,0.115022,0.167638,-0.000615,0.06024,-0.021151,-0.010384,-0.024747,-0.07581,0.018512,-0.019033,0.02438,-0.048443,0.015744,0.011668,-0.001565,0.00114,-0.023757,0.023511,0.002496,0.01793,0.01861,-0.021087,-0.009101")
paste(round(colMeans(t),6),collapse=",")
The first of those samples he listed even has Slavic Y-DNA:
https://www.yfull.com/tree/R-PF6155/
Target: Creoda_scaledCode:Distance to: Creoda_scaled
0.01968710 England_MIA_LIA:I20623
0.02018121 VK2020_NOR_Mid_MA:VK117
0.02031459 England_IA:I0160
0.02046513 Scotland_LBA:I2860
0.02185548 VK2020_ISL_Hofstadir_VA:VK123
0.02214888 England_EastYorkshire_LIA:I5502
0.02219334 VK2020_GreenlandE_VA:VK183
0.02239764 VK2020_Scotland_Orkney_VA:VK203
0.02256107 VK2020_Faroes_EM:VK234
0.02273809 England_EIA:I11149
0.02329942 VK2020_NOR_North_VA:VK528
0.02330274 England_LBA:I13713
0.02350372 England_MIA:I19874
0.02358320 England_MIA:I19654
0.02359881 Scotland_LIA:I27385
0.02360302 HUN_MA_Szolad:SZ4
0.02401730 VK2020_England_Dorset_VA:VK257
0.02439687 England_EastYorkshire_MIA_LIA:I14104
0.02459870 England_MIA_LIA:I11992
0.02476256 England_MIA_LIA:I11993
Distance: 0.7963% / 0.00796324 | R2P
64.4 England_MIA
35.6 VK2020_SWE_Gotland_VA
Target: Creoda_scaled
Distance: 0.6504% / 0.00650417 | R3P
36.0 England_MIA
32.6 England_LBA
31.4 SWE_Viking_Age_Sigtuna
Target: Creoda_scaled
Distance: 0.5405% / 0.00540526 | R4P
36.0 England_MIA
27.0 England_LBA
20.4 VK2020_SWE_Skara_VA
16.6 SWE_Viking_Age_Sigtuna
:candycane1
https://www.youtube.com/watch?v=GYtgCbQekRc
This one's better
https://youtu.be/Z-VQHChwabA