I love this your analysis of population genetics. Even evolution supports the fact that we all share a common descent. The concept of race is too artificial to be used for division. It is all in our minds. At molecular level, we are all virtually the same except for few environmentally modified genes.
I love reading your work @ertwro. I like the way you do your analysis. Unfortunately, I do not know much about PCA, but I am going to make a point of duty to read up.
Thanks for this lovely post
Depending on your background I can help you gain the intuition on that particular form of dimensional reduction, if you have a good background in linear algebra it should be easier, if not I can also help you with that. Let me know what's your current knowledge and I'll point you to a resource that will take no more than 5 minutes.
Thanks a lot for the help offer. I have basic knowledge of regression and correlation analysis and a fuzzy knowledge of PCA. I think the application is what I cant grab yet
One must remember is not properly a statistical analysis tool but a data visualization tool. Is biased. (although you can do further PC regression and even partial least squares regressions, but that's just for completition) In this case, since the information I wanted to represent and the point I try to make, a cline and how is a linear continuum of change across geography. PCA's represent linearly changes in a continuum of a set, it sounds like dimensional reduction is a match made in heaven for us in this case.
If you want to represent a lot of information from several variables, with probable correlations analysis, in a single graph while conserving most of the variance of the data. The first advantage is you can represent it in two dimensions (draw it, I mean try to draw 7 variables and their axis), the second is that the clustering or redundancy hints at the information that was lost in the flattening or reduction of the dimensions.
Video on the intuition of PCA 1, 2
The two more common options are either PCA or Heatmaps with hierarchical clustering.
Article comparing their differences
Those clusters are methods to group the shared redundancies of the data of those variables. It saves a lot of computational and graphical costs instead of doing this:
ps: Sorry if you get lots of notifications on the edits. I'm doing this from a phone and I have OCD.
This is lovely. A lot of information at once. I will take my time to digest them in bits. thanks!