| fit_t_comp {RPANDA} | R Documentation |
Fits models of trait evolution incorporating competitive interactions
Description
Fits matching competition (MC), diversity dependent linear (DDlin), or diversity dependent exponential (DDexp) models of trait evolution to a given dataset and phylogeny.
Usage
fit_t_comp(phylo, data, error=NULL, model=c("MC","DDexp","DDlin"), pars=NULL,
geography.object=NULL, regime.map=NULL)
Arguments
phylo |
an object of type 'phylo' (see ape documentation) |
data |
a named vector of trait values with names matching |
error |
A named vector with standard errors (SE) of trait values for each species (with names matching |
model |
model chosen to fit trait data, |
pars |
vector specifying starting parameter values for maximum likelihood optimization. If unspecified, default values are used (see Details) |
geography.object |
if incorporating biogeography, a list of sympatry through time created using |
regime.map |
if running two-regime versions of models, a stochastic map of the two regimes stored as a simmap object output from |
Details
Note: if including known measurement error, the model fit incorporates this known error and, in addition, estimates an unknown, nuisance contribution to measurement error. The current implementation does not differentiate between the two, so, for instance, it is not possible to estimate the nuisance measurement error without providing the known, intraspecific error values.
For single-regime fits without measurement error, par takes the default values of var(data)/max(nodeHeights(phylo)) for sig2 and 0 for either S for the matching competition model,
b for the linear diversity dependence model, or r for the exponential diversity dependence model. Values can be manually entered as a vector with the first element
equal to the desired starting value for sig2 and the second value equal to the desired starting value for either S, b, or r. Note: since likelihood optimization
uses sig rather than sig2, and since the starting value for is exponentiated to stabilize the likelihood search, if you input a par value, the first value specifying sig2
should be the log(sqrt()) of the desired sig2 starting value.
For two-regime fits without measurement error, the second and third values for par correspond to the first and second S, b, or r value (run trial fit to see which regime corresponds to each slope).
For fits including measurement error, the default starting value for sig2 is 0.95*var(data)/max(nodeHeights(phylo)), and nuisance values start at 0.05*var(data)/max(nodeHeights(phylo)).
In all cases, the nuisance parameter is the last in the par vector, with the order of other variables as described above.
For two-regime fits, particularly under the matching competition model, we recommend fitting with several different starting values.
Value
a list with the following elements:
LH |
maximum log-likelihood value |
aic |
Akaike Information Criterion value |
aicc |
AIC value corrected for small sample size |
free.parameters |
number of free parameters from the model |
sig2 |
maximum-likelihood estimate of |
S |
maximum-likelihood estimate of |
b |
maximum-likelihood estimate of |
r |
maximum-likelihood estimate of |
z0 |
maximum-likelihood estimate of |
nuisance |
maximum-likelihood estimate of |
convergence |
convergence diagnostics from |
Note
In current version, the S parameter is restricted to take on negative values in MC + geography ML optimization.
Author(s)
Jonathan Drury jonathan.p.drury@gmail.com
Julien Clavel
References
Drury, J., Clavel, J. Tobias, J., Rolland, J., Sheard, C., and Morlon, H. Tempo and mode of morphological evolution are decoupled from latitude in birds. PLOS Biology doi:10.1371/journal.pbio.3001270
Drury, J., Clavel, J., Manceau, M., and Morlon, H. 2016. Estimating the effect of competition on trait evolution using maximum likelihood inference. Systematic Biology 65:700-710
Nuismer, S. & Harmon, L. 2015. Predicting rates of interspecific interaction from phylogenetic trees. Ecology Letters 18:17-27.
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
sim_t_comp
CreateGeoObject
likelihood_t_MC
likelihood_t_MC_geog
likelihood_t_DD
likelihood_t_DD_geog
fit_t_comp_subgroup
Examples
data(Anolis.data)
geography.object<-Anolis.data$geography.object
pPC1<-Anolis.data$data
phylo<-Anolis.data$phylo
regime.map<-Anolis.data$regime.map
# Fit three models without biogeography to pPC1 data
MC.fit<-fit_t_comp(phylo, pPC1, model="MC")
DDlin.fit<-fit_t_comp(phylo, pPC1, model="DDlin")
DDexp.fit<-fit_t_comp(phylo, pPC1, model="DDexp")
# Now fit models that incorporate biogeography, NOTE these models take longer to fit
MC.geo.fit<-fit_t_comp(phylo, pPC1, model="MC", geography.object=geography.object)
DDlin.geo.fit<-fit_t_comp(phylo, pPC1,model="DDlin", geography.object=geography.object)
DDexp.geo.fit<-fit_t_comp(phylo, pPC1, model="DDexp", geography.object=geography.object)
# Now fit models that estimate parameters separately according to different 'regimes'
MC.two_regime.fit<-fit_t_comp(phylo, pPC1, model="MC", regime.map=regime.map)
DDlin.two_regime.fit<-fit_t_comp(phylo, pPC1,model="DDlin", regime.map=regime.map)
DDexp.two_regime.fit<-fit_t_comp(phylo, pPC1, model="DDexp", regime.map=regime.map)
# Now fit models that estimate parameters separately according to different 'regimes',
# including biogeography
MC.two_regime.geo.fit<-fit_t_comp(phylo, pPC1, model="MC",
geography.object=geography.object, regime.map=regime.map)
DDlin.two_regime.geo.fit<-fit_t_comp(phylo, pPC1,model="DDlin",
geography.object=geography.object, regime.map=regime.map)
DDexp.two_regime.geo.fit<-fit_t_comp(phylo, pPC1, model="DDexp",
geography.object=geography.object, regime.map=regime.map)