opt.GRW: Fit evolutionary model using "AD" parameterization

In paleoTS: Analyze Paleontological Time-Series

Fit evolutionary model using "AD" parameterization

Description

Fit evolutionary model using "AD" parameterization

Usage

opt.GRW(
  y,
  pool = TRUE,
  cl = list(fnscale = -1),
  meth = "L-BFGS-B",
  hess = FALSE
)

opt.URW(
  y,
  pool = TRUE,
  cl = list(fnscale = -1),
  meth = "L-BFGS-B",
  hess = FALSE
)

opt.Stasis(
  y,
  pool = TRUE,
  cl = list(fnscale = -1),
  meth = "L-BFGS-B",
  hess = FALSE
)

opt.StrictStasis(
  y,
  pool = TRUE,
  cl = list(fnscale = -1),
  meth = "L-BFGS-B",
  hess = FALSE
)

Arguments

y	a paleoTS object
pool	if TRUE, sample variances are substituted with their pooled estimate
cl	optional control list, passed to optim()
meth	optimization algorithm, passed to optim()
hess	if TRUE, return standard errors of parameter estimates from the hessian matrix

Details

These functions use differences between consecutive populations in the time series in order to remove temporal autocorrelation. This is referred to as the "Ancestor-Descendant" or "AD" parameterization by Hunt [2008], and it is a REML approach (like phylogenetic independent contrasts). A full ML approach, called "Joint" was found to have somewhat better performance (Hunt, 2008) and generally should be used instead.

Value

a paleoTSfit object with the model fitting results

Functions

• opt.URW(): fit the URW model by the AD parameterization

• opt.Stasis(): fit the Stasis model by the AD parameterization

• opt.StrictStasis(): fit the Strict Stasis model by the AD parameterization

Note

It is easier to use the convenience function fitSimple.

References

Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology 32(4): 578-601.

See Also

fitSimple, opt.joint.GRW

Examples

x <- sim.GRW(ns = 20, ms = 1)  # strong trend
plot(x)
w.grw <- opt.GRW(x)
w.urw <- opt.URW(x)
compareModels(w.grw, w.urw)

paleoTS documentation built on Aug. 9, 2022, 1:06 a.m.