paleoTS (version 0.5-1)

logL.joint.GRW: Log-likelihoods for evolutionary models (joint parameterization)

Description

Returns log-likelihood for general random walk (logL.joint.GRW), unbiased random walk (logL.joint.URW), stasis (logL.joint.Stasis) and OU (logL.joint.OU) models for the Joint parameterization.

Usage

logL.joint.GRW(p, y)
logL.joint.URW(p, y)
logL.joint.Stasis(p, y)
logL.joint.StrictStasis(p, y)
logL.joint.OU(p, y)

Arguments

p

a vector of parameters

y

a paleoTS object

Value

The log-likelihood of the parameter estimates (p), given the data (x).

Warning

Because these functions parameterize the models differently, their log-likelihoods are not comparable to those that do not use the joint parameterization.

Details

For the general random walk, p = c(anc, mstep, vstep); for an unbiased random walk, p = c(anc, vstep); for the stasis model, p = c(theta, omega), and for the OU model p = c(anc, vstep, theta, alpha). In general, users will not be access these functions directly, but instead use the optimization functions, which use these functions to find the best-supported parameter values.

References

Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology 32:578--601. Hunt, G., M. Bell & M. Travis. 2008. Evolution towards a new adaptive optimum: phenotypic evolution in a fossil stickleback lineage. Evolution 62:700-710. Hunt, G. 2008. Evolutionary patterns within fossil lineages: model-based assessment of modes, rates, punctuations and process.. In R.K. Bambach and P.H. Kelley, eds. From Evolution to Geobiology: Research Questions Driving Paleontology at the Start of a New Century:578--601.

See Also

opt.joint.GRW, logL.GRW

Examples

# NOT RUN {
x<- sim.GRW(ns=20, ms=0, vs=1)
L1<- logL.joint.GRW(p=c(0,0,1), x)	# actual parameters
L2<- logL.joint.GRW(p=c(0,10,1), x)	# should be a bad guess
cat(L1, L2, "\n")
# }