influ_discrete {sensiPhy}

Influential Species Detection - Trait Evolution Discrete Characters

Description

Fits models for trait evolution of discrete (binary) characters, detecting influential species.

Usage

influ_discrete(
  data,
  phy,
  model,
  transform = "none",
  bounds = list(),
  cutoff = 2,
  n.cores = NULL,
  track = TRUE,
  ...
)

Arguments

data

Data vector for a single binary trait, with names matching tips in phy.

phy

A phylogeny (class 'phylo') matching data.

model

The Mkn model to use (see Details).

transform

The evolutionary model to transform the tree (see Details). Default is none.

bounds

settings to constrain parameter estimates. See fitDiscrete

cutoff

The cut-off parameter for influential species (see Details).

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 fitDiscrete

Details

This function sequentially removes one species at a time, fits a model of discrete character evolution using fitDiscrete, stores the results and calculates the effects on model parameters. Currently, only binary discrete traits are supported.

influ_discrete detects influential species based on the standardised difference in q12 or q21 when removing a given species compared to the full model including all species. Species with a standardised difference above the value of cutoff are identified as influential.

Different character model from fitDiscrete can be used, including ER (equal-rates), SYM (symmetric), ARD (all-rates-different) and meristic (stepwise fashion).

Different 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.

Value

The function tree_discrete returns a list with the following components:

call: The function call

cutoff: The value selected for cutoff

data: The original full data vector

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

full.model.estimates: Parameter estimates (transition rates q12 and q21), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for the full model.

influential_species: List of influential species, based on standardised difference in estimates for q12 and q21. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

sensi.estimates: Parameter estimates (transition rates q12 and q21),,(percentual) difference in parameter estimate compared to the full model (DIFq12, sigsq.q12,sDIFq12, DIFq21, optpar.q21,sDIFq21), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for each analysis with a species deleted.

Author(s)

Gijsbert Werner & Gustavo Paterno

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

fitDiscrete

Examples

## Not run: 
#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$data)
#Model trait evolution accounting for influential species
influ_binary<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
model = "SYM",transform = "none",cutoff = 2,n.cores = 2,track = TRUE)
#Print summary statistics
summary(influ_binary)
sensi_plot(influ_binary) #q12 and q21 are, as expected, exactly the same in symmetrical model. 
#Use a different evolutionary model. 
influ_binary2<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
model = "SYM",transform = "delta",n.cores = 2,track = TRUE)
summary(influ_binary2)
sensi_plot(influ_binary2)
#Or change the cutoff and transformation
influ_binary3<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
model = "ARD",transform = "none",cutoff = 1.2,n.cores = 2,track = TRUE)
summary(influ_binary3)
sensi_plot(influ_binary3) 

## End(Not run)