Fit qpAdm models based on the rotation strategy described in Harney et al. 2020 (bioRxiv)
Fit qpAdm models based on the rotation strategy described in Harney et al. 2020 (bioRxiv)
qpAdm_rotation( data, target, candidates, minimize = TRUE, nsources = 2, ncores = 1, fulloutput = FALSE )
data |
EIGENSTRAT dataset |
target |
Target population that is modeled as admixed |
candidates |
Potential candidates for sources and outgroups |
minimize |
Test also all possible subsets of outgroups? (default TRUE) |
nsources |
Number of sources to pull from the candidates |
ncores |
Number of CPU cores to utilize for model fitting |
fulloutput |
Report also 'ranks' and 'subsets' analysis from qpAdm in addition to the admixture proportions results? (default FALSE) |
qpAdm list with proportions, ranks and subsets elements (as with a traditional qpAdm run) or just the proportions (determined by the value of the 'fulloutput' argument)
## Not run: # download an example genomic data set and prepare it for analysis
snps <- eigenstrat(download_data(dirname = tempdir()))
# find the set of most likely two-source qpAdm models of
# a French individual - produce only the 'proportions'
# qpAdm summary
models <- qpAdm_rotation(
data = snps,
target = "French",
candidates = c("Dinka", "Mbuti", "Yoruba", "Vindija",
"Altai", "Denisova", "Chimp"),
minimize = TRUE,
nsources = 2,
ncores = 2,
fulloutput = FALSE
)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.