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Thread: Automated Global25 nMonte entirely from the web

  1. #501
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    This was kind of interesting. Although, I’m not sure it’s proper procedure. I ran some models using the two Roman Gladiators I’m supposed to be the closest to and then added the two I1a samples (my haplogroup) found among the Longobards.


    England_Anglo-Saxon +England_Roman--3DT16 +England_Roman--6DT18 +Hungary_Medieval_Szolad--SZ45 +Italy_Medieval_Collegno--CL63 +Sweden_Viking_Age
    Custom:AGUser_JMcB = 1.4524 — 19.17, 30.83, 12.5, 15, 10.83, 11.67


    England_Anglo-Saxon +England_Roman--3DT16 +England_Roman--6DT18 +Germany_Roman +Hungary_Medieval_Szolad--SZ45 +Italy_Medieval_Collegno--CL63 +Sweden_Viking_Age
    Custom:AGUser_JMcB = 1.3184 — 20, 25, 15, 12.5, 5.83, 7.5, 14.7

    And a more normal run:

    England_Anglo-Saxon +England_Roman +Hungary_Medieval_Szolad +Italy_Medieval_Collegno +Sweden_Viking_Age
    Custom:AGUser_JMcB = 1.2026 — 4.17, 29.17, 25, 33.33, 8.33
    Last edited by JMcB; 09-14-2018 at 06:19 PM.
    Known Paper Trail: 45.3% English, 29.7% Scottish, 12.5% Irish, 6.25% German & 6.25% Italian. Or: 87.5% British Isles, 6.25% German & 6.25% Italian.
    LivingDNA: 88.1% British Isles (59.7% English, 27% Scottish & 1.3% Irish), 5.9% Europe South (Aegian 3.4%, Tuscany 1.3%, Sardinia 1.1%), 4.4% Europe NW (Scandinavia) & 1.6% Europe East, (Mordovia).
    FT Big Y: I1-Z140 branch I-F2642 >Y1966 >Y3649 >A13241 >Y3647 >A13248 (circa 930 AD) >A13242/YSEQ (circa 1075 AD) >A13243/YSEQ (circa 1660 AD).

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  3. #502
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    Quote Originally Posted by JMcB View Post
    This was kind of interesting. Although, I’m not sure it’s proper procedure. I ran some models using the two Roman Gladiators I’m supposed to be closest to and then added the two I1a samples (my haplogroup) found among the Longobards.


    England_Anglo-Saxon +England_Roman--3DT16 +England_Roman--6DT18 +Hungary_Medieval_Szolad--SZ45 +Italy_Medieval_Collegno--CL63 +Sweden_Viking_Age
    Custom:AGUser_JMcB = 1.4524 — 19.17, 30.83, 12.5, 15, 10.83, 11.67


    England_Anglo-Saxon +England_Roman--3DT16 +England_Roman--6DT18 +Germany_Roman +Hungary_Medieval_Szolad--SZ45 +Italy_Medieval_Collegno--CL63 +Sweden_Viking_Age
    Custom:AGUser_JMcB = 1.3184 — 20, 25, 15, 12.5, 5.83, 7.5, 14.7

    And a more normal run:

    England_Anglo-Saxon +England_Roman +Hungary_Medieval_Szolad +Italy_Medieval_Collegno +Sweden_Viking_Age
    Custom:AGUser_JMcB = 1.2026 — 4.17, 29.17, 25, 33.33, 8.33
    1 England_Anglo-Saxon +England_Roman--3DT16 +England_Roman--6DT18 +Hungary_Medieval_Szolad--SZ45 +Italy_Medieval_Collegno--CL63 +Sweden_Viking_Age Custom:AGUser_firemonkey 1.663 60.83 0 14.17 0 17.5 7.5
    2 England_Anglo-Saxon +England_Roman--3DT16 +England_Roman--6DT18 +Hungary_Medieval_Szolad--SZ45 +Italy_Medieval_Collegno--CL63 +Sweden_Viking_Age Custom:AGUser_firemonkey_father 2.2071 48.33 0 5.83 0 36.67 9.17


    England_Anglo-Saxon +England_Roman--3DT16 +England_Roman--6DT18 +Germany_Roman +Hungary_Medieval_Szolad--SZ45 +Italy_Medieval_Collegno--CL63 +Sweden_Viking_Age Custom:AGUser_firemonkey 1.6653 60.83 0 14.17 0 0 18.33 6.67
    2 England_Anglo-Saxon +England_Roman--3DT16 +England_Roman--6DT18 +Germany_Roman +Hungary_Medieval_Szolad--SZ45 +Italy_Medieval_Collegno--CL63 +Sweden_Viking_Age Custom:AGUser_firemonkey_father 2.2032 49.17 0 4.17 0 0 36.67 10

    1 England_Anglo-Saxon +England_Roman +Hungary_Medieval_Szolad +Italy_Medieval_Collegno +Sweden_Viking_Age Custom:AGUser_firemonkey 1.4332 20 14.17 25.83 35.83 4.17
    2 England_Anglo-Saxon +England_Roman +Hungary_Medieval_Szolad +Italy_Medieval_Collegno +Sweden_Viking_Age Custom:AGUser_firemonkey_father 1.4606 0 20.83 4.17 72.5 2.5
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  5. #503
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    I learn by doing and making mistakes (like a bull in a china shop). I read through the long G25 thread and found the explanation of why having the individuals in the data file rather than the averages allows nMonte3 to work its magic as this is built in to the design. So my use of averages was causing the Nordic_IA values to increase in people's results and I misunderstood what overfitting meant - I realise now that if you separate individuals from their parent group (eg. England_IA to England_IA_West, England_IA_East, etc.) this turns them into separate populations, rather than leaving them in their parent group and letting nMonte do the work.

    I re-ran nMonte with the correct approach (my result is the unlabelled top bar).

    Attachment 25913

    Then the Anthrogenica members:

    Attachment 25912

    Whew.
    Last edited by Capitalis; 09-14-2018 at 05:42 PM. Reason: Clarification

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  7. #504
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    Quote Originally Posted by Capitalis View Post
    I learn by doing and making mistakes (like a bull in a china shop). I read through the long G25 thread and found the explanation of why having the individuals in the data file rather than the averages allows nMonte3 to work its magic as this is built in to the design. So my use of averages was causing the Nordic_IA values to increase in people's results and I misunderstood what overfitting meant - I realise now that if you separate individuals from their parent group (eg. England_IA to England_IA_West, England_IA_East, etc.) this turns them into separate populations, rather than leaving them in their parent group and letting nMonte do the work.

    I re-ran nMonte with the correct approach (my result is the unlabelled top bar).

    Attachment 25913

    Then the Anthrogenica members:

    Attachment 25912

    Whew.
    nMonte3 is awesome in actually utilizing individuals and the reporting is at the group level anyway. That is the primary reason why the tool did not even bother with calculating averages during the Step1-Reference Groups. Feed in all individuals and let nmonte3 sort it out. It might also be interesting to check against the average, which you can do in the Test step2 though.

    Btw, your charts make it seem all academic
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  9. #505
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    Quote Originally Posted by poi View Post
    nMonte3 is awesome in actually utilizing individuals and the reporting is at the group level anyway. That is the primary reason why the tool did not even bother with calculating averages during the Step1-Reference Groups. Feed in all individuals and let nmonte3 sort it out. It might also be interesting to check against the average, which you can do in the Test step2 though.

    Btw, your charts make it seem all academic
    Will nMonte3 pick an individual outlier from a population - eg. a Nordic_IA shifted English-Roman (from an English-Roman population that is mostly England_IA shifted) and cause a Nordic_IA shifted test subject to be assigned a close match to English-Roman, even though they don't match the English-Roman group as a whole? This intrigues me.

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  11. #506
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    Quote Originally Posted by poi View Post
    Btw, your charts make it seem all academic
    Indeed! Love the charts!
    Known Paper Trail: 45.3% English, 29.7% Scottish, 12.5% Irish, 6.25% German & 6.25% Italian. Or: 87.5% British Isles, 6.25% German & 6.25% Italian.
    LivingDNA: 88.1% British Isles (59.7% English, 27% Scottish & 1.3% Irish), 5.9% Europe South (Aegian 3.4%, Tuscany 1.3%, Sardinia 1.1%), 4.4% Europe NW (Scandinavia) & 1.6% Europe East, (Mordovia).
    FT Big Y: I1-Z140 branch I-F2642 >Y1966 >Y3649 >A13241 >Y3647 >A13248 (circa 930 AD) >A13242/YSEQ (circa 1075 AD) >A13243/YSEQ (circa 1660 AD).

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  13. #507
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    Quote Originally Posted by Capitalis View Post
    Will nMonte3 pick an individual outlier from a population - eg. a Nordic_IA shifted English-Roman (from an English-Roman population that is mostly England_IA shifted) and cause a Nordic_IA shifted test subject to be assigned a close match to English-Roman, even though they don't match the English-Roman group as a whole? This intrigues me.
    Yes. If a group has individual very different the the rest and the test pop likes just that outlying individual refrerence, nmonte3 will certainly inflate the group's final score. That was also why I added the "filter" in step1 with a list of thresholds.

    There are individuals within certain groups that exceed the group's average fit distance by over 400%.
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  15. #508
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    Quote Originally Posted by poi View Post
    Yes. If a group has individual very different the the rest and the test pop likes just that outlying individual refrerence, nmonte3 will certainly inflate the group's final score. That was also why I added the "filter" in step1 with a list of thresholds.

    There are individuals within certain groups that exceed the group's average fit distance by over 400%.
    Thank you, that explains certain results very well. And thank you for taking the time to answer my queries.

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  17. #509
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    I will have a preliminary grouping by epoch, region, and country done hopefully tonight. The tool's selections could be more intuitive and I will open up a spreadsheet so that we can crowdsource data to catalog all G25 individuals.
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  19. #510
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    Created the preliminary groupings... I started with the data I had from Narasimhan 2018 suppl sheet. I will now work on cataloging other individuals... just the Epoch and the Country for now.
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