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If you are going to make a calculator with modern populations than they should be populations that more or less exist today and not based off of mixed populations that dont and share to much overlap with other modern populations. You can create sub-regions but based off of modern regions that actually exist otherwise it becomes messy and you get funny and strange admixture results. K36 seems to work very well for people who are not mixed. For those who are its even more vague than if you used less components.
“Cool Story bro”63.1% Belorussian + 36.9% French @ 3.85





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| Received: 21,537/13 Given: 27,015/0 |
It seems to me that K36 works well for you when the source is tuned to your needs.
Target: Karol_Klacansky(Slk+US)
Distance: 977.5935% / 9.77593468
79.0 Czech
21.0 English_Southwest
Target: Karol_Klacansky(Slk+US)
Distance: 915.8221% / 9.15822077
68.2 Slovak
16.4 English_East
15.4 English_Southwest
Code:Cornish,0.13,0,0,2.25,0,6.83,0,0,1.59,0,5.49,0,4.63,7.82,7.42,14.7,0,8.68,0,0,0.01,20.39,0.94,17.40,0,0.01,0,0,0,0.04,0.21,0,0.34,0,0.16,0.88 English_East,0.07,0,0.16,2.33,0.02,7.14,0,0,2.33,0,5.81,0.14,4.39,9.32,6.83,13.77,0,8.62,0,0.03,0,14.70,1.53,20.26,0,0.00,0.01,0.01,0,0.20,0.17,0,0.24,0,0.50,1.30 English_East-Midlands,0.01,0,0.05,2.25,0,7.80,0,0,2.51,0,5.79,0.03,4.11,8.88,6.38,13.22,0,9.62,0,0.00,0.01,15.89,1.31,20.17,0,0.03,0,0.01,0,0,0.25,0,0.52,0,0.13,0.91 English_Lancashire,0.09,0,0.02,2.52,0,7.89,0,0,1.72,0,6.03,0,3.63,9.83,7.37,14.07,0,7.99,0,0,0.00,16.52,1.08,19.89,0,0.00,0.00,0.02,0,0.03,0.20,0,0.23,0.00,0.03,0.70 English_Northeast,0.07,0.00,0.16,1.85,0,7.29,0,0,2.01,0,6.52,0,4.21,8.67,7.95,13.23,0,8.31,0,0,0,18.23,1.09,18.73,0,0.01,0,0,0,0.03,0.28,0,0.68,0,0.09,0.52 English_Southeast,0.05,0.00,0.17,2.19,0.00,6.57,0,0,1.96,0,6.14,0.07,3.61,9.84,7.27,13.63,0,8.99,0,0.01,0.02,15.97,1.65,19.92,0,0.04,0.01,0.00,0,0.03,0.39,0,0.31,0,0.23,0.76 English_Southwest,0.07,0.00,0.09,2.40,0,6.38,0,0,2.17,0,5.22,0,3.64,8.93,6.76,15.81,0.00,7.72,0,0,0.02,17.64,1.24,19.50,0,0.03,0,0,0,0.15,0.48,0,0.54,0,0.11,0.98 English_Yorkshire,0.08,0,0.05,2.64,0,6.96,0,0,1.46,0,6.20,0.02,3.81,10.15,6.87,12.91,0,8.22,0,0,0.03,17.29,1.11,20.69,0,0.02,0,0.01,0,0.02,0.39,0,0.24,0,0.26,0.42 English_West-Midlands,0.02,0,0.15,2.63,0.00,7.00,0.00,0,2.05,0,5.50,0.00,4.41,9.26,7.80,13.28,0,8.84,0,0,0.05,16.10,1.42,19.88,0,0.02,0.00,0.00,0,0.00,0.23,0,0.41,0,0.25,0.60 Czech,0.05,0.02,0.87,2.06,0,9.06,0,0,6.25,0,16.81,0.40,10.49,10.76,5.12,6.75,0.00,6.11,0.00,0.59,0.14,8.20,1.97,11.48,0.01,0.02,0.03,0,0.00,0.11,0.19,0,0.76,0,0.43,1.19 Czech_Bohemia,0,0.13,1.91,1.61,0,9.64,0,0,5.04,0,18.48,0.13,10.27,10.61,4.28,6.17,0,7.76,0,0,0,7.73,2.28,10.54,0,0,0,0,0,0,0.52,0,1.28,0,0.51,1.11 Czech_Moravia,0.00,0.04,0.72,1.49,0.00,8.88,0.00,0.00,5.55,0.00,17.28,0.31,11.49,9.20,4.54,8.15,0.01,8.61,0.02,0.26,0.03,8.41,1.61,10.47,0.00,0.01,0.00,0.00,0.01,0.01,0.14,0.00,0.77,0.00,0.59,1.42 Slovak-2,0.02,0.05,0.86,0.68,0.02,8.54,0.00,0.00,6.86,0.00,18.42,0.74,12.38,9.70,4.03,6.78,0.00,7.45,0.00,0.44,0.02,9.03,1.48,8.98,0.00,0.01,0.00,0.00,0.00,0.04,0.46,0.00,1.29,0.01,0.35,1.36 Slovak,0.00,0.00,0.41,0.86,0.00,8.66,0,0,6.32,0,18.63,1.88,13.72,10.57,4.05,5.80,0,6.40,0,0.41,0.00,8.92,1.16,9.40,0,0.00,0,0,0,0.00,0.14,0,1.24,0.00,0.08,1.22





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yes but this is a good example of what Im saying. If I could change the source of any calculator to just czech/slovak and the British isles I will get more accurate results. K36 works well for people who are unmixed and therefore very similar to the source populations. With 36 components the model is very sensitive so once someone is a bit mixed then u see larger distances and kind of funny results. K36 isnt bad but its borderline overfitting. I actually think a smaller amount of components already acounts for the important amount of variation between populations, and is more flexible.
So sorry for going on but in conclusion more flexible models are needed for mixed people. Un-mixed people who are very similar to source populations benefit a lot from the detail.
“Cool Story bro”63.1% Belorussian + 36.9% French @ 3.85


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| Received: 21,537/13 Given: 27,015/0 |
Ah okay, I can understand what you are saying, seems a valid point.
Interestingly, I believe it may be the fine-grained nature of this calc that allows me to get the (accurate to my paper trail) result below. I can't get a 3-way split (Hungarian/German/English) on K13/K15. But I can get it on both K36 and G25, and I had assumed the greater # of components was what allowed for the flexibility of modeling in my case. However, there are other far ranging variables (the limitations of working with averages, quality of the samples, the algorithm of the Vahaduo calc itself, etc), so it's possible I'm misinterpreting the reason why it seems to work better for me. I am just one case, after all.
I look forward to seeing how this calc performs for others as the population averages improve. Also, it may ultimately be very important to have a larger region spreadsheet and a smaller region spreadsheet, as has been done for K13.
K36
Target: Mr.G_Merged
Distance: 7.3495% / 7.34951461
53.1 Hungarian
36.3 Germany_West
10.6 English_Southwest
Target: Mr.G_Merged
Distance: 6.9713% / 6.97130624
51.4 Hungarian
41.3 German_South
7.3 English_Southwest





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| Received: 17,037/194 Given: 8,029/117 |
Good quality (20-25 kits) Scandinavian averages made by Aren added, to replace the smaller samples I had for Danes and Norwegians.
Also Moldovan averages from Ion Basecul (banned) added.Code:Danish,0,0.00,0,1.43,0,8.52,0,0,1.57,0,8.30,0,4.62,15.00,7.12,8.85,0,6.18,0,0,0,15.46,0.50,21.52,0,0.00,0,0,0,0,0.47,0,0.09,0,0.16,0.11 Swedish_2,0.02,0,0,1.49,0,8.53,0,0,1.64,0,7.36,0,5.03,17.88,6.70,8.43,0,4.47,0,0,0.00,16.34,0.76,20.82,0,0.01,0,0.02,0,0.01,0,0,0.37,0.01,0.03,0 Norwegian,0,0,0,1.27,0,7.40,0,0,2.22,0,7.66,0,5.29,17.52,6.26,8.22,0,4.92,0,0,0,15.16,0.85,22.17,0,0,0,0,0,0,0.04,0,0.42,0,0.63,0.22 Distance to: Danish_new 3.58866270 Danish 3.85167496 Icelandic 4.11324689 Norwegian 4.11324689 Norwegian-2 4.27141663 German_Schleswig-Holstein 4.30547326 German_Lower_Saxony_North 5.11185876 German_Westphalia 5.34577403 Dutch_North 5.51659315 Dutch 6.18962034 Dutch_Central 7.20698273 German_Lower_Saxony_South 7.47891035 German_Frisian 7.60013816 English_Yorkshire 7.80237784 Swedish 8.20040243 Dutch_South 8.34509437 English_Lancashire 8.50145282 English_Southeast 8.53515085 German_Mecklenburg_Center/East 8.73813481 English_West-Midlands 8.77572789 English_East 8.79562960 German_Mecklenburg_West 9.00607573 English_East-Midlands 9.27859364 English_Northeast 9.43727185 Scottish 9.63703274 German_Hither_Pomerania Distance to: Swedish_2 2.77928048 Norwegian 2.77928048 Norwegian-2 4.22177688 Icelandic 5.50527928 Swedish 5.50533378 Danish 6.05424644 German_Lower_Saxony_North 6.67985030 German_Schleswig-Holstein 7.04216586 Dutch_North 7.81959718 German_Westphalia 8.17596477 Dutch 8.23027339 Swedish_North 8.67957948 German_Frisian 8.74297432 German_Mecklenburg_Center/East 8.95822527 Dutch_Central 10.10032178 English_Yorkshire 10.34219996 German_Mecklenburg_West 10.44874155 German_Lower_Saxony_South 10.74925114 Dutch_South 10.78018553 English_Lancashire 10.94397551 German_Hither_Pomerania 11.08715924 English_Southeast 11.29830961 English_West-Midlands 11.42772068 English_East 11.45231418 Scottish 11.54634574 German_Brandenburg_Northwest Distance to: Norwegian_new 2.19045657 Norwegian 2.19045657 Norwegian-2 4.89167660 Icelandic 5.35171001 Swedish 5.39505329 Danish 5.48903452 German_Schleswig-Holstein 5.64967256 German_Lower_Saxony_North 7.06743235 Dutch_North 7.19716611 German_Westphalia 8.31900835 Dutch 8.34671792 German_Mecklenburg_Center/East 8.54517993 Swedish_North 8.57722566 German_Frisian 9.06336030 Dutch_Central 9.88922646 German_Mecklenburg_West 9.94731622 German_Lower_Saxony_South 10.08379889 English_Yorkshire 10.40377816 German_Brandenburg_Northwest 10.88651000 Dutch_South 10.91986722 English_Lancashire 10.93413920 English_Southeast 11.00845584 German_Hither_Pomerania 11.09682387 English_East 11.21833767 English_West-Midlands 11.54890038 English_East-Midlands
Spoiler!


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Code:Distance to: Token 8.20829459 German_Mecklenburg_West 8.48517531 Danish 8.68404859 German_Westphalia 8.72013761 German_Lower_Saxony_South 9.34842233 German_Schleswig-Holstein 9.54715664 English_East 9.65669198 German_Brandenburg_Northwest 9.88516565 Norwegian 10.08441372 Dutch 10.09262602 German_Lower_Saxony_North 10.23945311 German_Hither_Pomerania 10.29560586 English_Yorkshire 10.29571756 Dutch_Central 10.37240088 English_Southeast 10.42685955 English_East-Midlands 10.47304158 English_Lancashire 10.54029411 Icelandic 10.56840575 German_Saxony 10.58358162 English_West-Midlands 10.76864894 German_Mecklenburg_Center/East 10.79570285 Dutch_South 10.87699867 German_Frisian 11.03160007 Swedish_2 11.10784408 German_West_Bohemia 11.51722623 German_East_Prussia_Central Target: Token Distance: 589.0294% / 5.89029423 49.4 German_Mecklenburg_West 30.2 German_North_Rhine 20.4 German_Frisian


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I decided to try K36 only with Poland, Sweden, Norway, Denmark and Lower Saxony North and South.
Target: mariusz
Distance: 740.3023% / 7.40302319
91.4 Polish
8.6 Swedish_2
Target: mariusz
Distance: 743.3332% / 7.43333153
91.8 Polish
8.2 Norwegian
Target: mariusz
Distance: 747.8833% / 7.47883321
92.2 Polish
7.8 Danish
Target: mariusz
Distance: 749.8594% / 7.49859445
92.6 Polish
4.0 German_Lower_Saxony_South
3.4 German_Lower_Saxony_North
I'm unmixed, I have family only from eastern Poland and Hannover. So K36 is perfect.

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Hi everyone and especially Creoda!
I have been playing around with the K13 vs K36 results of a number of British Isles people.
Some of them have results that are quite similar across all calculators, however some individuals are significantly Southern shifted in the one calculator but not the other.
What could be the reason for this?
Is it possible that some people just get better results with one calculator?
If you were to rate K13 , K36 and Dodecad K12b out of 10 what would you give them?
Thanks!
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