fit.sgs {paleoTS} | R Documentation |
Functions required to fit evolutionary models with sampled puntuations, i.e., where the transitional period is represented by at least several sampled populations.
fit.sgs(y, minb = 5, oshare = TRUE, pool = TRUE, silent = FALSE, hess = FALSE, meth = "L-BFGS-B", model = "GRW") opt.sgs(y, gg, cl = list(fnscale = -1), meth = "L-BFGS-B", hess = FALSE, oshare = TRUE, model = "GRW") logL.sgs(p, y, gg, model = "GRW") logL.sgs.omega(p, y, gg, model = "GRW")
y |
a paleoTS object |
minb |
the minimum number of samples within a segment to consider |
oshare |
logical, if TRUE , the same variance (omega ) is assumed across the starting and ending Stasis segments. If FALSE , separate variances are assumed |
pool |
logical indicating whether to pool variances across samples |
silent |
if TRUE , less information is printed to the screen as the model is fit |
hess |
if TRUE , standard errors are computed from the Hessian matrix |
meth |
optimization method, to be passed to optim |
model |
either GRW or URW , indicating whether evolution during the transitional interval is directional (general random walk) or not (unbiased random walk) |
p |
parameters of the punctuation model for the log-likelihood functions |
gg |
numeric vector indicating membership of each sample in segments 1, 2, .. ng |
cl |
control list to be passed to optim |
These functions are used to fit a model with an sampled punctuation.
Formally, this is a three-segment model that starts as Stasis, transitions to a punctuation of directional evolution (general random walk) or
unconstrained (unbiased random walk). The name comes from an abbreviation of the three modes in the segments: Stasis - General Random Walk - Stasis,
bearing in mind that the general random walk can be changed to an unbiased random walk.
Users are likely only to use fit.sgs
, which will calls the other functions in order to find the best parameter
estimates and shift points for the segments.
The log-likelihood functions return the log-likelihood of the model for a given set of parameter values (p
),
assuming that the periods of Stasis have the same variance (logL.punc.omega
) or different variances (logL.punc
).
Functions fitGpunc
and opt.punc
return a list with the following elements:
par |
parameter estimates |
value |
the log-likelihood of the optimal solution |
counts |
returned by optim |
convergence |
returned by optim |
message |
returned by optim |
p0 |
initial guess for parameter values at start of optimization |
K |
number of parameters in the model |
n |
the number of observations, equal to the number of evoltuionary transistions |
AIC |
Akaike information criterion |
AICc |
modified Akaike information criterion |
BIC |
Bayes (or Schwarz) information criterion |
se |
standard errors for parameter estimates, computed from the curvature of the log-likelihood surface (only if hess = TRUE ) |
... |
other output from call to optim |
fit.sgs
also returns the following elements:
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.
x<- sim.sgs(ns=c(15, 10, 15), ms=0.5, vs=0.3) plot(x) # compare sampled punctuation to uniform models w1<- fit.sgs(x, minb=7, model="GRW") wu<- fit3models(x, silent=TRUE) aa<- akaike.wts(c(w1$AICc, wu$aicc)) names(aa)[1]<- "Samp.Punc" cat("Akaike Weights:\n") print(round(aa,5))