RDM | R Documentation |
RDM()
estimates a tree-topology from allele frequencies.
RDM( mat_allele_freq, outgroup, use = c("complete.obs", "pairwise.complete.obs", "everything", "all.obs", "na.or.complete") )
mat_allele_freq |
A (P+1) \times L matrix containing the allele frequencies, where there are P taxa, plus one outgroup, and L loci. |
outgroup |
A variable that can be either the population name or a numerical row number of the outgroup data. |
use |
Specify which part of data is used to compute the covariance matrix. The options are " |
The input matrix is the observed values of the frequencies at tips 1, 2, ..., P, P+1. A logit transformation is performed on the allele frequency data, so that the observed values are approximately normal. (The logit transformation of r refers to \log\frac{r}{1-r}.) The transformed matrix is converted into a data frame for further analyses.
An estimated tree-topology in Newick format.
Peng J, Rajeevan H, Kubatko L, and RoyChoudhury A (2021) A fast likelihood approach for estimation of large phylogenies from continuous trait data. Molecular Phylogenetics and Evolution 161 107142.
# A dataset "Human_Allele_Frequencies" is loaded with the package; # it has allele frequencies in 31,000 sites for # 4 human populations and one outgroup human population. # check data dimension dim(Human_Allele_Frequencies) # run RDM function rd_tre <- RDM(Human_Allele_Frequencies, outgroup = "San", use = "pairwise.complete.obs") # result visualization plot(rd_tre, use.edge.length = FALSE, cex = 0.5)
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