fitGpunc {paleoTS}R Documentation

Analyze evolutionary models with unsampled punctuations

Description

Functions required to fit evolutionary models with puntuations that are rapid relative to the temporal spacing of samples (so-called unsampled punctuations).

Usage

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)

Arguments

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

Details

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.

Value

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
In addition, function 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

Author(s)

Gene Hunt

References

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.

See Also

sim.punc, opt.GRW, fit.sgs

Examples

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)

[Package paleoTS version 0.3-1 Index]