Phylogenetic uncertainty - Trait Evolution Continuous Characters

Fits models for trait evolution of continuous characters, evaluating phylogenetic uncertainty.

tree_continuous(
  data,
  phy,
  n.tree = 10,
  model,
  bounds = list(),
  n.cores = NULL,
  track = TRUE,
  ...
)

Arguments

data	Data vector for a single continuous trait, with names matching tips in phy.
phy	Phylogenies (class 'multiPhylo', see ?ape).
n.tree	Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file. If NULL, n.tree = 10
model	The evolutionary model (see Details).
bounds	settings to constrain parameter estimates. See fitContinuous
n.cores	number of cores to use. If 'NULL', number of cores is detected.
track	Print a report tracking function progress (default = TRUE)
...	Further arguments to be passed to fitContinuous

Value

The function tree_continuous returns a list with the following components:

call: The function call

data: The original full data vector

sensi.estimates: (rate of evolution sigsq, root state z0 and where applicable optpar), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for each analysis with a different phylogenetic tree.

N.tree: Number of trees n.tree analysed

stats: Main statistics for model parameters, i.e. minimum, maximum, mean, median and sd-values

optpar: Evolutionary model used (e.g. lambda, kappa etc.)

Details

This function fits different models of continuous character evolution using fitContinuous to n trees, randomly picked in a multiPhylo file.

Different evolutionary models from fitContinuous can be used, i.e. BM,OU, EB, trend, lambda, kappa, delta and drift.

See fitContinuous for more details on character models and tree transformations.

Output can be visualised using sensi_plot.

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.

Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.

See also

fitContinuous

Examples

# \dontshow{
#Load data:
data("primates")
#Model trait evolution accounting for phylogenetic uncertainty
adultMass<-primates$data$adultMass
names(adultMass)<-rownames(primates$data)
tree_cont<-tree_continuous(data = adultMass,phy = primates$phy,
model = "OU",n.tree=1,n.cores = 2,track = TRUE)
#> 
  |                                                                            
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  |======================================================================| 100%
# }
if (FALSE) {
#Load data:
data("primates")
#Model trait evolution accounting for phylogenetic uncertainty
adultMass<-primates$data$adultMass
names(adultMass)<-rownames(primates$data)
tree_cont<-tree_continuous(data = adultMass,phy = primates$phy,
model = "OU",n.tree=30,n.cores = 2,track = TRUE)
#Print summary statistics
summary(tree_cont)
sensi_plot(tree_cont)
sensi_plot(tree_cont,graphs="sigsq")
sensi_plot(tree_cont,graphs="optpar")
#Use a different evolutionary model 
tree_cont2<-tree_continuous(data = adultMass,phy = primates$phy,
model = "delta",n.tree=30,n.cores = 2,track = TRUE)
summary(tree_cont2)
sensi_plot(tree_cont2)
}