1. Very cool thanks

2. May I suggest that perhaps dchicn(named as dcicn on the plot) and Alfred 2/Alfred are completely switched around based on every other plot/tool as well as my direct knowledge of my ancestry; that is, I usually fall near Belgian/FrenchEast/French. I never imagined being near Czech/Slovak/Austrian. Besides I'd hate to have to re-do all my flags!

3. Ph2ter, My mother is in 2 different places on plot . One in north Italy and then again off north Italy?

4. After an exciting time trying to find myself. ;-) I think I tracked myself down just outside of England in West Germany. At least, I think I’m in West Germany. Be that as it may, what’s the difference between the two different approaches?

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JonikW (06-25-2019)

6. Originally Posted by ph2ter
Isn't this exciting? Trying to find yourself.
So how many admixture plots and calculators will ph2ter create?

ph2ter: Yes.

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digital_noise (06-25-2019),  ph2ter (06-25-2019)

8. Originally Posted by ph2ter
Probably not very good for mixed persons. You are actually inside West German cluster. Saami ancestry pools you and your brother through the Czechs towards Saami place which is beyond Russians.
Why is your father put inside Serbian cluster, what is his ancestry? Probably geometric center of his components.
I agree. Did you have both my kits on this PCA-map?

Father is 75% North Italian 25% Saami. And he ended up in Balkan.

9. Originally Posted by Nino90
I agree. Did you have both my kits on this PCA-map?

Father is 75% North Italian 25% Saami. And he ended up in Balkan.
Only V3 kit result.
Your father is exactly where he should be (between North Italy and Saami and his distance to Saami is 75% of total distance)

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Nino90 (06-25-2019)

11. Originally Posted by JMcB
After an exciting time trying to find myself. ;-) I think I tracked myself down just outside of England in West Germany. At least, I think I’m in West Germany. Be that as it may, what’s the difference between the two different approaches?
I found an interesting answer somewhere on the Internet:
The fundamental formal difference is that PCA decomposes relations between columns only (e.g. by decomposing their covariance matrix), treating rows as "cases"; while CA decomposes columns and rows simultaneously, treating them symmetrically, as cross-tabulation "categories". ...

PCA works on the values where as CA works on the relative values. Both are fine for relative abundance data of the sort you mention (with one major caveat, see later). With % data you already have a relative measure, but there will still be differences. Ask yourself
• do you want to emphasise the pattern in the abundant species/taxa (i.e. the ones with large %cover), or
• do you want to focus on the patterns of relative composition?

If the former, use PCA. If the latter use CA. What I mean by the two questions is would you want
A ={50,20,10}
B
={5,2,1}
to be considered different or the the same? A and B are two samples and the values are the % cover of three taxa shown. (This example turned out poorly, assume there is bare ground! ;-) PCA would consider these very different because of the Euclidean distance used, but CA would consider these two samples as being very similar because the have the same relative profile.

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JMcB (06-25-2019)

13. It would be really interesting seeing that on G25, but when I try that on PAST the thing just keeps processing and processing

I have no idea if this is in fact better than a PCA plot or not
Correspondence analysis (CA) or reciprocal averaging is a multivariate statistical technique proposed[1] by Herman Otto Hartley (Hirschfeld)[2] and later developed by Jean-Paul Benzécri.[3] It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data. In a similar manner to principal component analysis, it provides a means of displaying or summarising a set of data in two-dimensional graphical form.

All data should be on the same scale for CA to be applicable, keeping in mind that the method treats rows and columns equivalently. It is traditionally applied to contingency tables — CA decomposes the chi-squared statistic associated with this table into orthogonal factors. Because CA is a descriptive technique, it can be applied to tables whether or not the χ 2 {\displaystyle \chi ^{2}} \chi ^{2} statistic is appropriate.[4][5]

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JMcB (06-25-2019),  ph2ter (06-25-2019)

15. Originally Posted by Sizzles
Ph2ter, My mother is in 2 different places on plot . One in north Italy and then again off north Italy?
You supplied me with two kits:
Code:
Lightgray	Dot	sizzles_mum_new	 	36.34	13.72	16.21	9.2	16.42	4.3	2.38	0	0	0	0	0	1.44
Lightgray	Dot	SizzlesMom	 	31.33	11.71	14.85	6.89	18.63	3.96	4.69	0.83	0.91	1.89	0.22	0	4.15

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