paleoTS (version 0.3-1)

sim.GRW: Simulate evolutionary time-series

Description

Simulate the evolution of a trait according to general random walk or stasis models.

Usage

sim.GRW(ns = 20, ms = 0, vs = 0.1, vp = 1, nn = rep(20, ns), tt = 1:ns)
sim.Stasis(ns = 20, theta = 0, omega = 0, vp = 1, nn = rep(20,ns), tt = 1:ns)

Arguments

ns

number of samples in time-series

ms

mean of the step distribution, random walk model

vs

variance of the step distribution, random walk model

vp

within-population trait variance

nn

vector of the number of individuals in each sample

tt

vector of sample ages, increases from oldest to youngest

theta

evolutionary optimum, stasis model

omega

evolutionary variance, stasis model

Value

• A paleoTS object.

Details

See reference below for details on parameterization of the models. Briefly, the general random walk model considers time in discrete steps. The duration of steps does not matter as long as many steps occur between sampled populations. At each time step, an evolutionary change is drawn at random from a distribution of possible evolutionary "steps." It turns out that the long-term dynamics of an evolving lineage depend only on the mean and variance of this step distribution. The former, mstep, determined the directionality in a sequence and the latter, vstep, determines its volatility. The stasis model is based on the parameterization of Sheets and Mitchell (2001). Under this model, there is an evolutionary optimum, theta, with some amount of true variance, omega, around this optimum.

References

Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology 32:578--601. Sheets, H. D., and C. E. Mitchell. 2001. Why the null matters: statistical tests, random walks and evolution. Genetica 112-113:105-125.

See Also

opt.GRW

Examples

## generate and plot two paleoTS objects
 y.rw <- sim.GRW(ns=20, ms=0.5, vs=0.1)  
 y.st <- sim.Stasis(ns=20)
 layout(1:2)
 plot(y.rw, col="red")
 plot(y.st, col="blue")
 layout(1)