opt.RW.Mult {paleoTS}R Documentation

Functions to analyze multiple time-series jointly

Description

These functions are used to estimate parameters of the general random walk over two or more sequences. Three different models may be fit: the same parameters are present in all time series (opt.RW.mult); the same mean step but different step variances in each series (opt.RW.SameMs); the same step variance but different mean steps in each series (opt.RW.SameVs).

Usage

logL.Mult(p, y, model = c("GRW", "URW"))
logL.SameMs(p, y)
logL.SameVs(p, y)
opt.RW.Mult(y, cl=list(fnscale=-1), model=c("GRW", "URW"), pool=TRUE, meth="L-BFGS-B", hess=FALSE)
opt.RW.SameMs(y, cl=list(fnscale=-1), pool=TRUE, meth="L-BFGS-B", hess=FALSE)
opt.RW.SameVs(y, cl=list(fnscale=-1), pool=TRUE, meth="L-BFGS-B", hess=FALSE)

Arguments

p a vector of parameter values
y a emphlist of paleoTS objects.
model RW for general random walk, RWu for unbiased random walk
pool logical, if TRUE variances are pooled across samples
cl optimization option, passed to optim
meth optimization option, passed to optim
hess optimization option, passed to optim

Details

These functions work just as their conterparts for the analysis of single sequences; see those help functions for more detail.

Value

Varies by function, see corresponding functions for the analysis of single sequences for more information.

Author(s)

Gene Hunt

References

Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology32:578–601.

See Also

logL.GRW, opt.GRW, fit3models

Examples

 ## create two sequences, with different parameter values
 y1<- sim.GRW(ns=20, ms=0, vs=1)
 y2<- sim.GRW(ns=20, ms=0, vs=0.2)

 ## fit some models with at least some shared dynamics across sequences
 m1<- opt.RW.Mult(list(y1,y2), model="GRW")
 m2<- opt.RW.SameMs(list(y1, y2))
 m3<- opt.RW.SameVs(list(y1,y2))

 ## fit separate models to each sequence
 msep1<- opt.GRW(y1)
 msep2<- opt.GRW(y2)
 sep.logL<- msep1$value + msep2$value   # total logL sums across two solutions
 sep.aic <- -2*sep.logL + 2*4     # AIC of separate solutions

 ## check out resulting log-likelihoods and AIC values
 mres<- array(dim=c(4,2))
 row.names(mres)<- c("m1", "m2", "m3", "msep")
 colnames(mres)<- c("logL", "AIC")
 mres[1,]<- c(m1$value, m1$AIC)
 mres[2,]<- c(m2$value, m2$AIC)
 mres[3,]<- c(m3$value, m3$AIC)
 mres[4,]<- c(sep.logL, sep.aic)
 print (mres)

[Package paleoTS version 0.3-1 Index]