fitGpunc {paleoTS} | R Documentation |
Functions required to fit evolutionary models with puntuations that are rapid relative to the temporal spacing of samples (so-called unsampled punctuations).
fitGpunc(y, ng = 2, minb = 5, pool = TRUE, oshare = TRUE, silent = FALSE, hess=FALSE, ...) opt.punc(y, gg, cl = list(fnscale = -1), pool = TRUE, meth = "L-BFGS-B", hess = FALSE, oshare) logL.punc(p, y, gg) logL.punc.omega(p, y, gg)
y |
a paleoTS object |
ng |
the number of separate segments in the sequence |
minb |
the minimum number of samples within a segment to consider |
pool |
logical indicating whether to pool variances across samples |
oshare |
logical, if TRUE , the same variance (omega ) is assumed across all segments. If FAlSE , separate variances are assumd for each segment |
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 |
... |
other arguments to send to opt.punc |
p |
parameters of the punctuation model to be optimized |
gg |
numeric vector indicating membership of each sample in segments 1, 2, .. ng |
cl |
control list to be passed to optim |
meth |
optimization method, to be passed to optim |
These functions are used to fit a model with an unsampled punctuation.
It is equivalent to a Stasis model in which the optimum instantaneously shifts at one or more points in time; see refereces below for details.
Users are likely only to use fitGpunc
, 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 |
fitGpunc
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.punc(theta=c(0,5), ns=c(20,20), omega=c(1,1), vp=c(0.2,0.2)) plot(x) w<- fitGpunc(x, ng=2, minb=7, pool=TRUE, oshare=TRUE) print (w$par) ## add lines to show the solution segments(x$tt[1], w$par[1], x$tt[w$shift.start-1], w$par[1], lty=3, col="red", lwd=5) segments(x$tt[w$shift.start], w$par[2], x$tt[length(x$tt)], w$par[2], lty=3, col="red", lwd=5)