samp_continuous {sensiPhy}

Species Sampling uncertainty - Trait Evolution Continuous Characters

Description

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

Usage

samp_continuous(
  data,
  phy,
  n.sim = 30,
  breaks = seq(0.1, 0.5, 0.1),
  model,
  n.cores = NULL,
  bounds = list(),
  track = TRUE,
  ...
)

Arguments

data

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

phy

A phylogeny (class 'phylo') matching data.

n.sim

The number of times species are randomly deleted for each break.

breaks

A vector containing the percentages of species to remove.

model

The evolutionary model (see Details).

n.cores

number of cores to use. If 'NULL', number of cores is detected.

bounds

settings to constrain parameter estimates. See fitContinuous

track

Print a report tracking function progress (default = TRUE)

...

Further arguments to be passed to fitContinuous

Details

This function randomly removes a given percentage of species (controlled by breaks), fits different models of continuous character evolution using fitContinuous, repeats this this many times (controlled by n.sim), stores the results and calculates the effects on model parameters.

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

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

full.model.estimates: Parameter estimates (rate of evolution sigsq and where applicable optpar), root state z0, AICc for the full model without deleted species.

break.summary.tab: Summary per break of the mean and median effects of species removal on percentage and absolute change parameter estimates.

sensi.estimates: Parameter estimates (sigsq and optpar),(percentual) difference in parameter estimate compared to the full model (DIFsigsq, sigsq.perc,sDIFsigsq, DIFoptpar, optpar.perc,sDIFoptpar), AICc and z0 for each repeat with random species removed.

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

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.

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

See Also

fitContinuous

Examples

## Not run: 
#Load data:
data("primates")
#Model trait evolution accounting for sampling size 
adultMass<-primates$data$adultMass
names(adultMass)<-rownames(primates$data)
samp_cont<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
model = "OU",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2, track = TRUE)
#Print summary statistics
summary(samp_cont)
sensi_plot(samp_cont)
sensi_plot(samp_cont, graphs = 1)
#Use a different evolutionary model 
samp_cont2<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
model = "kappa",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE)
summary(samp_cont2)
sensi_plot(samp_cont2)
sensi_plot(samp_cont2, graphs = 2)
samp_cont3<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
model = "BM",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE)
summary(samp_cont3)

## End(Not run)