strict_stasis_sl {StratPal} | R Documentation |
simulate strict phenotypic stasis (specimen level)
Description
simulates strict stasis on the population level (Hunt et al. 2015). This means each population has the same mean trait value, and all deviations are due to the fact that specimens traits differ from this value due to randomness.
Usage
strict_stasis_sl(t, mean = 0, intrapop_var = 1, n_per_sample = 10)
Arguments
t |
times at which the traits are determined |
mean |
mean trait value |
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/time |
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.
References
Hunt, Gene, Melanie J. Hopkins, and Scott Lidgard. 2015. “Simple versus Complex Models of Trait Evolution and Stasis as a Response to Environmental Change.” Proceedings of the National Academy of Sciences of the United States of America 112 (16): 4885–90. https://doi.org/10.1073/pnas.1403662111.
See Also
-
stasis_sl()
for the (non-strict) equivalent -
reduce_to_paleoTS()
to transform outputs intopaleoTS
format -
random_walk_sl()
andornstein_uhlenbeck_sl()
for other modes of evolution
Examples
library("paleoTS")
x = strict_stasis_sl(1:5, mean = 2, intrapop_var = 2) # simulate strict stasis
y = reduce_to_paleoTS(x) # transform into paloeTS format
plot(y) # plot using paleoTS package
# see also
#vignette("paleoTS_functionality")
#for details and advanced usage