opt.GRW.shift {paleoTS} | R Documentation |
Functions to simulate and to infer a model with random walk dynamics, with parameter values that shift at one or more points in the sequence.
opt.GRW.shift(y, ng = 2, minb = 5, model = 1, pool = TRUE, silent = FALSE)
y |
a paleoTS object |
ng |
the number of different segments in the sequence |
minb |
the minimum number of samples to consider as a segment |
model |
options for variants of random walk to fit (see Details ). |
pool |
if TRUE, pool phenotypic variances across samples |
silent |
if TRUE, do not print information on fitting to screen |
This model divides an evolutionary sequence into two or more non-overlapping parts
called segments, and then fits some version of the random walk model to each segment separately.
The model
argument has four options:
model=1
, general random walk model, step variance shared across segments
model=2
, general random walk model, step mean shared across segments
model=3
, unbiased random walk, separate step variance for each segment
model=4
, general random walk, speparate step mean and variance for each segment
A list including:
value |
the log-likelihood of the optimal solution |
par |
parameter estimates |
K |
number of parameters in the model |
n |
the number of observations, equal to the number of evoltuionary transistions |
shift.start |
the index of the initial samples of each segment |
AIC |
Akaike information criterion |
AICc |
modified Akaike information criterion |
BIC |
Bayes (or Schwarz) information criterion |
shift.start |
index of each sample that starts a new segment |
all.logl |
log-likelihoods for all tested partitions of the series into segments |
GG |
matrix of indices of initial samples of each tested segment configuration; each column of GG corresponds to the elements of all.logl |
Gene Hunt
Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology32:578–601.
Hunt, G. 2008. Gradual or pulsed evolution: when should punctuational explanations be preferred? Paleobiology34:In press.
sim.GRW.shift
, opt.GRW
, opt.RW.Mult
x<- sim.GRW.shift(ns=c(20,20), ms=c(0,1), vs=c(0.2, 0.2)) plot(x) w.shift<- opt.GRW.shift(x, ng=2, model=1) print (w.shift$par) print (w.shift$shift.start) # estimated first sample in second segment