tree_continuous {sensiPhy}

Phylogenetic uncertainty - Trait Evolution Continuous Characters

Description

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

Usage

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

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.

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.)

Author(s)

Gijsbert Werner & Caterina Penone

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

## Not run: 
#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)

## End(Not run)