likelihood_t_DD {RPANDA}

Likelihood of a dataset under diversity-dependent models.

Description

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

Usage

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).

Value

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

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).

Author(s)

Jonathan Drury [e-mail]

Julien Clavel

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.

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")