R/tree_Discrete.R
tree_discrete.RdFits models for trait evolution of discrete (binary) characters, evaluating phylogenetic uncertainty.
tree_discrete( data, phy, n.tree = 10, model, transform = "none", bounds = list(), n.cores = NULL, track = TRUE, ... )
| data | Data vector for a single binary trait, with names matching tips in |
|---|---|
| phy | Phylogenies (class 'multiPhylo', see ? |
| n.tree | Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file. If NULL, |
| model | The Mkn model to use (see Details). |
| transform | The evolutionary model to transform the tree (see Details). Default is |
| bounds | settings to constrain parameter estimates. See |
| 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 |
The function tree_discrete returns a list with the following
components:
call: The function call
data: The original full data vector
sensi.estimates: Parameter estimates (transition rates q12 and q21),
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: Transformation parameter used (e.g. lambda, kappa etc.)
This function fits different models of discrete character evolution using fitDiscrete
to n trees, randomly picked in a multiPhylo file. Currently, only binary discrete traits are supported
Different character model from fitDiscrete can be used, including ER (equal-rates),
SYM (symmetric), ARD (all-rates-different) and meristic (stepwise fashion).
All transformations to the phylogenetic tree from fitDiscrete can be used, i.e. none,
EB, lambda, kappa anddelta.
See fitDiscrete for more details on character models and tree transformations.
Output can be visualised using sensi_plot.
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.
if (FALSE) { #Load data: data("primates") #Create a binary trait factor adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small") adultMass_binary<-as.factor(as.factor(adultMass_binary)) names(adultMass_binary)<-rownames(primates$data) #Model trait evolution accounting for phylogenetic uncertainty tree_binary<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "ARD",transform = "none",n.tree = 30,n.cores = 2,track = TRUE) #Print summary statistics summary(tree_binary) sensi_plot(tree_binary) sensi_plot(tree_binary,graphs="q12") sensi_plot(tree_binary,graphs="q21") #Use a different evolutionary model or transformation. tree_binary_lambda<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "SYM",transform = "lambda",n.tree = 30,n.cores = 2,track = TRUE) summary(tree_binary_lambda) #Using Pagel's Lambda sensi_plot(tree_binary_lambda) #Symmetrical rates, with an Early Burst (EB) model of trait evolution tree_binary_SYM_EB<-tree_discrete(data = adultMass_binary,phy = primates$phy, model = "SYM",transform = "EB",n.tree = 30,n.cores = 2,track = TRUE) summary(tree_binary_SYM_EB) sensi_plot(tree_binary_SYM_EB) sensi_plot(tree_binary_SYM_EB,graphs="optpar") }