opt.joint.GRW | R Documentation |
Fit evolutionary models using the "Joint" parameterization
opt.joint.GRW(
y,
pool = TRUE,
cl = list(fnscale = -1),
meth = "L-BFGS-B",
hess = FALSE
)
opt.joint.URW(
y,
pool = TRUE,
cl = list(fnscale = -1),
meth = "L-BFGS-B",
hess = FALSE
)
opt.joint.Stasis(
y,
pool = TRUE,
cl = list(fnscale = -1),
meth = "L-BFGS-B",
hess = FALSE
)
opt.joint.StrictStasis(y, pool = TRUE, cl = list(fnscale = -1), hess = FALSE)
y |
a |
pool |
if |
cl |
optional control list, passed to |
meth |
optimization algorithm, passed to |
hess |
if TRUE, return standard errors of parameter estimates from the hessian matrix |
These functions use the joint distribution of population means to fit models using a full maximum-likelihood approach. This approach was found to have somewhat better performance than the "AD" approach, especially for noisy trends (Hunt, 2008).
a paleoTSfit
object with the model fitting results
opt.joint.URW()
: fit the URW model by the Joint parameterization
opt.joint.Stasis()
: fit the Stasis model by the Joint parameterization
opt.joint.StrictStasis()
: fit the Strict Stasis model by the Joint parameterization
It is easier to use the convenience function fitSimple
.
Hunt, G., M. J. Hopkins and S. Lidgard. 2015. Simple versus complex models of trait evolution and stasis as a response to environmental change. Proc. Natl. Acad. Sci. USA 112(16): 4885-4890.
fitSimple
, opt.GRW
x <- sim.GRW(ns = 20, ms = 1) # strong trend
plot(x)
w.grw <- opt.joint.GRW(x)
w.urw <- opt.joint.URW(x)
compareModels(w.grw, w.urw)
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