simulate (un)biased random walk (specimen level)

Simulates a (continuous time) random walk as a Brownian drift on specimen level. For mu = 0 the random walk is unbiased, otherwise it is biased.

Usage

random_walk_sl(
  t,
  sigma = 1,
  mu = 0,
  y0 = 0,
  intrapop_var = 1,
  n_per_sample = 10
)

Arguments

t

numeric vector with strictly increasing elements, can be heterodistant. Times at which the random walk is evaluated

sigma

positive number, variance parameter

mu

number, directionality parameter

y0

number, starting value (value of the random walk at the first entry of t)

intrapop_var

intrapopulation variance, determines how much specimens from the same population vary

n_per_sample

integer, number of specimens sampled per population/sampling locality

Value

an object of S3 class pre_paleoTS, inherits from timelist and list. The list has two elements: t, containing a vector of times of sampling, and vals, a list of trait values of the same length as t, with element containing trait values of individual specimens. This object can be transformed using apply_taphonomy, apply_niche or time_to_strat, and then reduced to a paleoTS object using reduce_to_paleoTS. This can then be used to test for different modes of evolution.

See also

• random_walk() for the equivalent function to simulate mean trait values

• reduce_to_paleoTS() to transform outputs into paleoTS format.

• stasis_sl(), strict_stasis_sl() and ornstein_uhlenbeck_sl() to simulate other modes of evolution

Examples

library("paleoTS")
x = random_walk_sl(1:5)
y = reduce_to_paleoTS(x) # turn into paleoTS format
plot(y) # plot using the paleoTS package

# see also
#vignette("paleoTS_functionality")
#for details and advanced usage