fit_t_standard: Fits standard models of trait evolution incorporating known...

In RPANDA: Phylogenetic ANalyses of DiversificAtion

Fits standard models of trait evolution incorporating known and nuisance measurement error

Description

Fits Brownian motion (BM), Ornstein-Uhlenbeck (OU), or early burst (EB) models of trait evolution to a given dataset and phylogeny.

Usage

fit_t_standard(phylo, data, model=c("BM","OU","EB"), error, two.regime=FALSE, 
		method="Nelder-Mead", echo=TRUE, ...)

Arguments

phylo	an object of type 'phylo' (see ape documentation); if two.regime=TRUE, this must be a simmap object from make.simmap with two regimes
data	a named vector of trait values with names matching phylo$tip.label
model	model chosen to fit trait data, "BM" is the Brownian motion model, "OU" is the Ornstein-Uhlenbeck model, and "EB" is the early burst model.
error	if including measurement error, a named vector of error values (the standard error of the [intraspecific] mean) with names matching phylo$tip.label
two.regime	if TRUE, fits a two-regime model
method	optimization method from link{optim}
echo	prints information to console during fit
...	Optional arguments. e.g. "upper=xx", "lower=xx" to specify bounds on the parameter search. "fixedRoot=TRUE" to use an OU model where the root state is assumed fixed (instead of sampled from the stationary distribution)

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.

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 sig2 parameter
alpha	maximum-likelihood estimate of alpha parameter of OU model (see Note)
r	maximum-likelihood estimate of the slope parameter of early burst model
z0	maximum-likelihood estimate of z0, the value at the root of the tree
nuisance	maximum-likelihood estimate of nuisance, the unknown, nuisance contribution to measurement error (see details)
convergence	convergence diagnostics from optim function (see optim documentation)

Author(s)

Jonathan Drury [e-mail]

Julien Clavel

See Also

fit_t_comp sim_t_tworegime

Examples

if(test){
data(Cetacea_clades)
data<-sim_t_tworegime(Cetacea_clades,pars=c(sig2=0.01,r1=-0.01,r2=-0.02),
	root.value=0,Nsegments=1000,model="EB")
error<-rep(0.05,length(Cetacea_clades$tip.label))
names(error)<-Cetacea_clades$tip.label

#Fit single-regime models
BM1.fit<-fit_t_standard(Cetacea_clades,data,model="BM",error,two.regime=FALSE)
OU1.fit<-fit_t_standard(Cetacea_clades,data,model="OU",error,two.regime=FALSE)
EB1.fit<-fit_t_standard(Cetacea_clades,data,model="EB",error,two.regime=FALSE)

#Now fit models that incorporate biogeography, NOTE these models take longer to fit
BM2.fit<-fit_t_standard(Cetacea_clades,data,model="BM",error,two.regime=TRUE)
OU2.fit<-fit_t_standard(Cetacea_clades,data,model="OU",error,two.regime=TRUE)
EB2.fit<-fit_t_standard(Cetacea_clades,data,model="EB",error,two.regime=TRUE)
  }

RPANDA documentation built on Oct. 24, 2022, 5:06 p.m.