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Assessing confidence in introgression among four populations
I have a question associated with the usage of this tool, can this tool be used tool to calculate F4-statistics on my data that consists of unlinked SNPs from SNP array? Will the simulation by fastsimcoal2 (as run by F4.py) affected by it?
Hi! I'm running F4 with my data and I'm getting the error below. It already happened twice, and after a similar (although different) number of burn-in steps (this one was at step 28556 and the first time it happened at 28674). The input I'm using was successfully tested on Treemix, and I know my copy of F4.py works because I could run it with your "example.txt" without problems. My dataset has just a little more SNPs than the example (797), but maybe the issue has something to do with estimation of effective population sizes, because the burn-in never seems to end and that variable that caused the overflow becomes ridiculously big. I'm attaching my input file, so the problem can be reproduced.
Running simulations (burnin 28556)...Traceback (most recent call last):
File "/home/ericolegal/bin/f4.py", line 881, in <module>
mean_effective_population_size = int(mean_effective_population_size*mean_effective_population_size_scaler)
OverflowError: cannot convert float infinity to integer
Cheers,
Érico.
First of all thank you very much for this tool which is easy to use and also provide direct interpretation of the results.
However, I have a question concerning the effect of including the outgroup in F4-statistics on the simulation parameters implemented in F4 tool. For instance, if instead of "D", i.e. sister clade of the population "C", I use "O"- a divergent group. How the simulation process will work? Is it correct to use this topology as well in F4 tool? Does, the demographic model implemented in F4, inherently assume that "C" and "D" should be sister clades?
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