RPANDA (version 2.3)

likelihood_t_DD_geog: Likelihood of a dataset under diversity-dependent models with biogeography.

Description

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

Usage

likelihood_t_DD_geog(phylo, data, par,geo.object,model=c("DDlin","DDexp"),maxN=NA)

Value

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

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.

geo.object

a list of sympatry through time created using CreateGeoObject

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.

maxN

when fitting DDlin model, it is necessary to specify the maximum number of sympatric lineages to ensure that the rate returned does not correspond to negative sig2 values at any point in time (see Details).

Author

Jonathan Drury

Julien Clavel

Details

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

maxN can be calculated using maxN=max(vapply(geo.object$geography.object,function(x)max(rowSums(x)),1)), where geo.object is the output of CreateGeoObject

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 CreateGeoObject likelihood_t_DD

Examples

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

# Compute the likelihood with geography using ML parameters for fit without geography
par <- c(log(0.01153294),-0.0006692378)
maxN<-max(vapply(geography.object$geography.object,function(x)max(rowSums(x)),1))
lh <- -likelihood_t_DD_geog(phylo,pPC1,par,geography.object,model="DDlin",maxN=maxN)