ddBD estimates parameters for birth-death model (i.e., birth rate, death rate, and sampling fraction) for dating analysis.
ddBD(tr, outgroup, root.time = 1, measure = c("SSE", "KL"))
tr is an object of class "phylo" specifying the relative times from RelTime.
outgroup is a vector of character specifying all outgroup tips
root.time is the age of root in the current time unit. The default value is 1.
measure is the method for selecting the initial values in grid search. The best initial values can be selected by minimzing the sum of squared errors (SSE) or Kullback-Leibler divergence (KL). The default is method is SSE.
Users need to provide a relative timetree inferred by RelTime without any calibrations in order to run ddBD. To get the relative timetree, one can use MEGA X (https://www.megasoftware.net).
This program will produce the parameters for birth-death model (i.e., birth rate, death rate, and sampling fraction). All three parameters are automatically estimated simultaneously. The program that only estimates birth rate and death rate with a user-specified sampling fraction rate will come soon. Currently, the estimated parameters can only be directly used in MCMCTree for dating analysis.
Note that the program currently works well with R version 3.6.x. R version 4.0.x gives an error. I will fix it soon.
"code" directory contains ddBD R function.
"simulation" directory contains all simulated data, estimated birth-death parameter values and MCMCTree results obtained using the true (BD_10_5_0.99), default flat (BD_2_2_0.1), and inferred (BD_inf) tree prior.
"empirical-data" directory contains the empirical data, estimated birth-death parameter values and MCMCTree results.
If you have more questions, please email qiqing.tao@temple.edu.
If you use ddBD, please cite: Tao Q, Barba-Montoya J, and Kumar S. 2021. Data-driven speciation tree prior for better species divergence times in calibration-poor molecular phylogenies. bioRxiv. doi:10.1101/2021.03.27.437326. (In press by Bioinformatics)
If you use RelTime from MEGA X, please also cite: Kumar S, Stecher G, Li M, Knyaz C, and Tamura K. 2018. MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol. Biol. Evol. 35:1547-1549.