weighted.jackknife-methods | R Documentation |
Weighted Jackknife calculations.
## S4 method for signature 'GENOME'
weighted.jackknife(object,
do.D=TRUE,
do.df=TRUE,
per.region=FALSE,
block.size=1)
object |
An object of class |
do.D |
Pattersons D and Martin's f statistic |
do.df |
d-fraction (distance fraction) |
per.region |
jacknife within regions |
block.size |
the block size for jackknife (SNPs) |
To perform the D and f statistic one needs to define 3 populations via the function set.populations
,
where the third population represent the archaic population. In addition, an outgroup is required and have to be
set via the function set.outgroup
. Here, only SNPs where the outgroup is monomorphic are considered.
f is the fraction of the genome that is admixed [2].
Returned value is a modified object of class "GENOME"
———————————————————
The following slots will be modified in the "GENOME"
object
———————————————————
Slot | Reference | Description | |
1. | df.z | [3] | z values (jackknife) |
2. | df.SE | [3] | standard error (jackknife) |
3. | df.pval | [3] | P values (jackknife) |
4. | D.z | [x] | z values (jackknife) |
5. | D.SE | [x] | standard error (jackknife) |
6. | D.pval | [x] | P values (jackknife) |
[1] Durand, E. Y., Patterson, N. J., Reich, D., & Slatkin, M. (2011). Testing for ancient admixture between closely related populations.
Molecular Biology and Evolution, 28(8), 2239–2252. doi:10.1093/molbev/msr048
[2] Simon H Martin, Kanchon K Dasmahapatra, Nicola J Nadeau, et al. (2013). Genome-wide evidence for speciation with gene flow in Heliconius butterflies.
Genome Res. doi:10.1101/gr.159426.113
[3] Bastian Pfeifer and Durrell D. Kapan (2019). Estimates of introgression as a function of pairwise distances.
BMC Bioinformatics. https://doi.org/10.1186/s12859-019-2747-z
# GENOME.class <- readData("\home\Alignments")
# GENOME.class <- set.populations(GENOME.class,list(1:3,4:8,9:12))
# GENOME.class <- set.outgroup(GENOME.class,13)
# GENOME.class <- introgression.stats(GENOME.class, do.D=TRUE)
# show the result:
# GENOME.class@D
# GENOME.class@f
# GENOME.class <- introgression.stats(GENOME.class, do.df=TRUE)
# show the result:
# GENOME.class@df
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