sim.OU {paleoTS} | R Documentation |
Generates an evolutionary time-series according to an Orstein-Uhlenbeck (OU) model.
sim.OU(ns = 20, anc = 0, theta = 10, alpha = 0.3, vs = 0.1, vp = 1, nn = rep(20, ns), tt = 1:ns) ou.M(anc, theta, aa, tt) ou.V(vs, aa, tt)
ns |
number of samples in time-series |
anc |
ancestral phenotype at the start of the series |
theta |
phenotype of the evolutionary optimum |
alpha |
strength of the attracting force pulling the population to the optimum |
vs |
step variance of the random walk component of change |
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 |
aa |
strength of the attracting force pulling the population to the optimum (same as alpha ) |
See Hansen (1997) for a description of this model in a macroevolutionary context. This model also arises naturally in
microevolution as a finite population evolving in the vicinity of an optimum in the adaptive landscape; see Lande (1976)
and Estes & Arnold (2007).
Functions ou.M
and ou.V
are used internally by sim.OU
and add.OU.curves
to generate the
means and variances of an OU process.
A paleoTS
object for sim.OU
. For ou.M
and ou.V
, a vector of means or variances,
respectively, are generated.
Gene Hunt
Lande, R. 1976. Natural selection and random genetic drift in phenotypic evolution. Evolution 30:314-334.
Hansen, T. 1997. Stabilizing selection and the comparative analysis of adaptation. Evolution 51:1341-1351.
Estes, S. & Arnold, S. J. 2007. Resolving the paradox of stasis: models of stabilizing selection explain evolutionary divergence
on all timescales. American Naturalist 169:227-244.
Hunt, G., M. Bell & M. Travis. 2008. Evolution towards a new adaptive optimum: phenotypic evolution in a fossil stickleback lineage. Evolution in press.
x1<- sim.OU(ns=100, anc=0, theta=10, alpha=0.2, vs=0.1, vp=0.1, nn=rep(100, times=100), tt=0:99) plot(x1)