sim_t_tworegime: Recursive simulation (root-to-tip) of two-regime models

In RPANDA: Phylogenetic ANalyses of DiversificAtion

Recursive simulation (root-to-tip) of two-regime models

Description

Simulates datasets for a given phylogeny under two-regime matching competition (MC), diversity dependent linear (DDlin), diversity dependent exponential (DDexp), or early burst (EB) models of trait evolution. Simulations are carried out from the root to the tip of the tree.

Usage

sim_t_tworegime(regime.map, pars, root.value, Nsegments=2500, 
                model=c("MC","DDexp","DDlin","EB"),
	            	verbose=TRUE, rnd=6)

Arguments

regime.map	a stochastic map of the two regimes stored as a simmap object output from make.simmap
pars	a vector containing the three parameters for the chosen model; all models require sig2, and additionally, the MC model requires S1 and S2, specifying the level of competition in regime 1 and 2, respectively (larger negative values correspond to higher levels of competition), the DDlin model requires b1 and b2, the DDexp model requires r1, the slope parameters (negative in cases of decline in evolutionary rates with increasing diversity). sig2 must be listed first.
root.value	a number specifying the trait value for the ancestor
Nsegments	a value specifying the total number of time segments to simulate across for the phylogeny (see Details)
model	model chosen to fit trait data, "MC" is the matching competition model, "DDlin" is the diversity-dependent linear model, "DDexp" is the diversity-dependent exponential model, and "EB" is the early burst model.
verbose	if TRUE, prints the identity of regimes corresponding to each parameter value
rnd	number of digits to round timings to (see round (see Details)

Details

Adjusting Nsegments will impact the length of time the simulations take. The length of each segment (max(nodeHeights(phylo))/Nsegments) should be much smaller than the smallest branch (min(phylo$edge.length)).

Adjusting rnd may help if function crashes.

Value

a named vector with simulated trait values for n species in the phylogeny

Author(s)

J Drury [e-mail]

References

Drury, J., Clavel, J., Manceau, M., and Morlon, H. 2016. Estimating the effect of competition on trait evolution using maximum likelihood inference. Systematic Biology doi 10.1093/sysbio/syw020

Nuismer, S. & Harmon, L. 2015. Predicting rates of interspecific interaction from phylogenetic trees. Ecology Letters 18:17-27.

Weir, J. & Mursleen, S. 2012. Diversity-dependent cladogenesis and trait evolution in the adaptive radiation of the auks (Aves: Alcidae). Evolution 67:403-416.

See Also

fit_t_comp

Examples

data(Cetacea_clades)



# Simulate data under the matching competition model
MC_tworegime.data<-sim_t_tworegime(Cetacea_clades,pars=c(sig2=0.01,S1=-0.1,S2=-0.01),
	root.value=0,Nsegments=1000,model="MC")

# Simulate data under the diversity dependent linear model
DDlin_tworegime.data<-sim_t_tworegime(Cetacea_clades,pars=c(sig2=0.01,b1=-0.0001,b2=-0.000001),
	root.value=0,Nsegments=1000,model="DDlin")

# Simulate data under the diversity dependent linear model
DDexp_tworegime.data<-sim_t_tworegime(Cetacea_clades,pars=c(sig2=0.01,r1=-0.01,r2=-0.02),
	root.value=0,Nsegments=1000,model="DDexp")

# Simulate data under the diversity dependent linear model
EB.data_tworegime<-sim_t_tworegime(Cetacea_clades,pars=c(sig2=0.01,r1=-0.01,r2=-0.02),
	root.value=0,Nsegments=1000,model="EB")

RPANDA documentation built on Oct. 24, 2022, 5:06 p.m.