View source: R/coexpansion_CEA_TH_NCT.R
sim.coexp | R Documentation |
Simulation of codemographic models
sim.coexp(
nsims,
var.zeta,
coexp.prior,
th,
Ne.prior,
NeA.prior,
time.prior,
gene.prior,
alpha = F,
append.sims = F,
path = getwd()
)
nsims |
Total number of simulations |
var.zeta |
Variation on zeta parameter. Can be "FREE" to vary or be set to a specific value (between 0-1). |
coexp.prior |
Uniform prior for the coespansion time. Vector of two numbers with the lower and upper boudary of the prior. |
th |
Threshold. Minimum time difference between Ts, time of simultaneous change and population specific times. |
Ne.prior |
Data frame with the prior values for the Ne of each population. |
NeA.prior |
Data frame with the prior values for the ancestral Ne of each population. |
time.prior |
Data frame with parameter values for the priors of the time of demographic change of each population. |
gene.prior |
Data frame with parameter values for the priors of the mutation rate of each species. |
alpha |
logical. If TRUE all demographic chages are exponential. If FALSE sudden changes. Defaut is FALSE. |
append.sims |
logical. If TRUE simulations are appended to the simulations file. Defaut is FALSE. |
path |
Path to the directiry to write the simulations. Defaut is the working directory. |
To simulate the model of Chan et al. (2014) the th parameter should be set to zero and the time.prior should have the same value of the coexp.prior.
To simulate the Threshold model the th argument need to be higher than zero. To simulate the Narrow Coexpansion Time model the th argument need to be higher than zero and the boundaries of coexp.time shoud be narrower than the time.prior values. See references for more details. Use the sim.coexp2 function to simulate the partitioned time model.
Gehara M., Garda A.A., Werneck F.P. et al. (2017) Estimating synchronous demographic changes across populations using hABC and its application for a herpetological community from northeastern Brazil. Molecular Ecology, 26, 4756–4771.
Chan Y.L., Schanzenbach D., & Hickerson M.J. (2014) Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian computation. Molecular Biology and Evolution, 31, 2501–2515.
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