paleoTS (version 0.4-4)

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.

Usage

logL.joint.GRW(p, x)
logL.joint.URW(p, x)
logL.joint.Stasis(p, x)
logL.joint.OU(p, x)

Arguments

p

a vector of parameters

x

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

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, "")