paleoTS (version 0.5-1)

logL.GRW: Compute log-likelihoods for random walk and stasis models

Description

Returns log-likelihood for general random walk logL.GRW, unbiased random walk logL.URW, and stasis logL.Stasis models for the AD parameterization.

Usage

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

Arguments

p

vector of parameters

y

a paleoTS object

Value

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

Details

For the general random walk, p = c(mstep, vstep); for an unbiased random walk, p = vstep; for the stasis model, p = c(theta, omega). 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.

See Also

mle.GRW, opt.GRW

Examples

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