opt.GRW.shift {paleoTS}

Fit random walk model with shift(s) in generating parameters

Description

Fit random walk model with shift(s) in generating parameters

Usage

opt.GRW.shift(y, ng = 2, minb = 7, model = 1, pool = TRUE, silent = FALSE)

Arguments

y

a paloeTS object

ng

number of segments in the sequence

minb

minimum number of populations in each segment

model

numeric, specifies exact evolutionary model; see Details

pool

if TRUE, sample variances are substituted with their pooled estimate

silent

logical, if TRUE, progress updates are suppressed

Details

Fits a model in which a sequence is divided into two or more segments and trait evolution proceeds as a general random walk, with each segment (potentially) getting its own generating parameters (mstep, vstep).

This function tests for shifts after each population, subject to the constraint that the number of populations in each segment is always >= minb. The shiftpoint yielding the highest log-likelihood is returned as the solution, along with the log-likelihoods (all.logl of all tested shift points (GG).

Different variants of the model can be specified by the model argument:

• model = 1: mstep is separate across segments; vstep is shared

• model = 2: mstep is shared across segments; vstep is separate

• model = 3: mstep is set to zero (unbiased random walk); vstep is separate across segments

• model = 4: mstep and vstep are both separate across segments

Value

a paleoTSfit object

See Also

sim.GRW.shift

Examples

x <- sim.GRW.shift(ns = c(15,15), ms = c(0, 1), vs = c(0.1,0.1))
w.sep <- opt.GRW.shift(x, ng = 2, model = 4)
w.sameVs <- opt.GRW.shift(x, ng = 2, model = 1)
compareModels(w.sep, w.sameVs)
plot(x)
abline(v = x$tt[16], lwd = 3)  # actual shift point
abline(v = x$tt[w.sameVs$par["shift1"]], lty = 3, col = "red", lwd = 2) # inferred shift point