Comments (2)
I suspect that the problem is that the first population (out
) consists of only a single pseudohaploidized sample. This makes it impossible to get unbiased estimates of f3, which is why you get NAs.
f4(out,pop1;out,pop2)
and f3(out;pop1,pop2)
will be identical in the limit as the number of samples in each population increases. With small sample counts, unbiased estimates of f3 depend on a bias correction term for the first population. That term is proportional to sample size, and it can't be computed when one of the populations has only a single pseudohaploidized sample.
In addition to that, the default option for f3 when it is computed from genotype files, is that the base estimate is normalized by the heterozygosity of the first population. Just like the bias correction term, the heterozygosity can only be estimated if the first population has at least two haplotypes (one diploid sample or two pseudohaploid samples).
There are options in the f3
function to get around that limitation. The resulting f3 estimates may be biased, but that bias may be small, and sometimes the bias doesn't matter even if it is large. An example of a case where a large bias doesn't matter is qpGraph, where multiple f3-statistics with the same out
population are compared to each other, and it's only the relative difference between f3-statistics that matters.
The heterozygosity normalization can be turned off with outgroupmode = TRUE
.
The bias correction term can effectively be set to 0 with apply_corr = FALSE
. The bias correction term is positive and is normally subtracted from the uncorrected f3-estimate, so setting this to FALSE
will result in an upward biased f3-estimate.
When both options are passed to the f3 function, it should give the same result as the f4 function.
So f3('prefix', 'out', 'pop1', 'pop2', outgroupmode = TRUE, apply_corr = FALSE)
should give the same result as f4('prefix', 'out', 'pop1', 'out', 'pop2')
.
from admixtools.
Thank you very much for the explanation. Knowing that f3() expects a diploid outgroup or more than one sample, we can plan our dataset accordingly. I save the commands, however, in case that's not feasible in the future.
Best,
Pedro.
from admixtools.
Related Issues (20)
- Do qpWave, qpAdm, and f5 have requirements on the minimum number of individuals for each population? HOT 1
- est_to_boo does not preserve the SNP block names (number of SNPs), but est_to_loo() does. Is that intended? HOT 2
- large difference of f3 result between admixtools and admixtools2 HOT 1
- Zero drift edges HOT 2
- Behavior affected by other R packages? HOT 2
- Error in if (gimp > 0 && gimp%%plusminus_generations == 0) { : missing value where TRUE/FALSE needed HOT 2
- please add tags/releases HOT 2
- Inconsistent bootstrap significance testing HOT 5
- keyword argument typo lack of error HOT 3
- Comparing graphs with compare_fits HOT 2
- auto_only: change default value to FALSE HOT 1
- qpWave returning one fewer ranks than expected HOT 1
- qpAdm computation stucks when the num. of letf pops below 3
- Issue with f3 and f4 HOT 2
- running many replicates of find_graphs? HOT 1
- Issue with extract_f2 HOT 2
- Problem making f2 file from plink .bed .fam and .bim files HOT 6
- Suggestion: Change to p_emp calculation in compare_fits() HOT 1
- possible to get worst residuals from `find_graphs` models? HOT 1
- Installation / Update error HOT 4
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