likelihood_t_DD function

Likelihood of a dataset under diversity-dependent models.

Computes the likelihood of a dataset under either the linear or exponential diversity dependent model with specified sigma2 and slope values.

likelihood_t_DD(phylo, data, par,model=c("DDlin","DDexp"))

Arguments

• phylo: an object of type 'phylo' (see ape documentation)
• data: a named vector of continuous data with names corresponding to phylo$tip.label
• par: a vector listing a value for log(sig2) (see Note) and either b (for the linear diversity dependent model) or r (for the exponential diversity dependent model), in that order.
• model: model chosen to fit trait data, "DDlin" is the diversity-dependent linear model, and "DDexp" is the diversity-dependent exponential model of Weir & Mursleen 2013.

Details

When specifying par, log(sig2) must be listed before the slope parameter (b or r).

Note

To stabilize optimization, this function exponentiates the input sig2 value, thus the user must input the log(sig2) value to compute the correct log likelihood (see example).

Returns

the negative log-likelihood value of the dataset (accordingly, the negative of the output should be recorded as the likelihood), given the phylogeny and sig2 and slope values

References

Drury, J., Clavel, J., Manceau, M., and Morlon, H. 2016. Estimating the effect of competition on trait evolution using maximum likelihood inference. Systematic Biology doi 10.1093/sysbio/syw020

Weir, J. & Mursleen, S. 2012. Diversity-dependent cladogenesis and trait evolution in the adaptive radiation of the auks (Aves: Alcidae). Evolution 67:403-416.

Author(s)

Jonathan Drury jonathan.p.drury@gmail.com

Julien Clavel

See Also

fit_t_comp

likelihood_t_DD_geog

Examples

data(Anolis.data)
phylo <- Anolis.data$phylo
pPC1 <- Anolis.data$data

# Compute the likelihood that the r value is twice the ML estimate for the DDexp model
par <- c(0.08148371, (2*-0.3223835))
lh <- -likelihood_t_DD(phylo,pPC1,par,model="DDexp")