evoTS (version 1.0.3)

opt.joint.OUBM: Fit an Ornstein-Uhlenbeck model with an optimum that evolves according to a Unbiased Random Walk.

Description

Function to find maximum likelihood solutions to an Ornstein-Uhlenbeck model with an optimum that evolves according to a Unbiased Random Walk.

Usage

opt.joint.OUBM(
  y,
  pool = TRUE,
  meth = "L-BFGS-B",
  hess = FALSE,
  iterations = NULL,
  iter.sd = NULL,
  opt.anc = TRUE
)

Value

logL

the log-likelihood of the optimal solution

AICc

AIC with a correction for small sample sizes

parameters

parameter estimates

modelName

abbreviated model name

method

Joint consideration of all samples

K

number of parameters in the model

n

the number of observations/samples

Arguments

y

an univariate paleoTS object.

pool

logical indicating whether to pool variances across samples

meth

optimization method, passed to function optim. Default is "L-BFGS-B".

hess

logical, indicating whether to calculate standard errors from the Hessian matrix.

iterations

the number of times the optimization method is run from different starting points. Default is NULL, meaning the optimization is run once.

iter.sd

defines the standard deviation of the Gaussian distribution from which starting values for the optimization routine is run. Default is 1.

opt.anc

logical, indicating whether the the ancestral trait state is at the optimum.

Author

Kjetil Lysne Voje

References

Hansen, T. F., Pienaar, J. & Orzack, S. H. 2008. A Comparative Method for Studying Adaptation to a Randomly Evolving Environment. Evolution 62:1965–1977.

Examples

## Generate a paleoTS object by simulating a univariate evolutionary sequence
x <- paleoTS::sim.GRW(60)

## Fit the model
opt.joint.OUBM(x)