RRphylo (version 3.0.0)

overfitST: Testing search.trend overfit

Description

Testing the robustness of search.trend (Castiglione et al. 2019a) results to sampling effects and phylogenetic uncertainty.

Usage

overfitST(RR,y,oveRR,x1=NULL,x1.residuals=FALSE,node=NULL,cov=NULL,clus=0.5)

Value

The function returns a 'RRphyloList' object containing:

$ST.list a 'RRphyloList' including the results of each

search.trend performed within overfitST.

$trend.results a list including the percentage of simulations showing significant p-values for phenotypes versus age and absolute rates versus age regressions for the entire tree separated by slope sign ($tree). If one or more nodes are specified within

trend.args, the list also includes the same results at nodes ($node) and the results for comparison between nodes ($comparison). For each node the proportion of tested trees (i.e. where the clade identity was preserved; always 1 if no phylo.list is supplied) is also indicated.

The output always has an attribute "Call" which returns an unevaluated call to the function.

Arguments

RR

an object produced by RRphylo.

y

a named vector of phenotypes.

oveRR

an object produced by applying overfitRR on the object provided to the function as RR.

x1, node, cov

arguments as passed to overfitRR.

x1.residuals

as passed to search.trend. Default is FALSE.

clus

the proportion of clusters to be used in parallel computing. To run the single-threaded version of overfitST set clus = 0.

Author

Silvia Castiglione, Carmela Serio, Giorgia Girardi, Pasquale Raia

References

Castiglione, S., Tesone, G., Piccolo, M., Melchionna, M., Mondanaro, A., Serio, C., Di Febbraro, M., & Raia, P. (2018). A new method for testing evolutionary rate variation and shifts in phenotypic evolution. Methods in Ecology and Evolution, 9: 974-983.doi:10.1111/2041-210X.12954

Castiglione, S., Serio, C., Mondanaro, A., Di Febbraro, M., Profico, A., Girardi, G., & Raia, P. (2019a) Simultaneous detection of macroevolutionary patterns in phenotypic means and rate of change with and within phylogenetic trees including extinct species. PLoS ONE, 14: e0210101. https://doi.org/10.1371/journal.pone.0210101

See Also

overfitST vignette ; search.trend vignette ; Alternative-trees vignette

Examples

if (FALSE) {
cc<- 2/parallel::detectCores()
library(ape)

## Case 1
# load the RRphylo example dataset including Ornithodirans tree and data
data("DataOrnithodirans")
DataOrnithodirans$treedino->treedino
DataOrnithodirans$massdino->massdino
DataOrnithodirans$statedino->statedino

# extract Pterosaurs tree and data
extract.clade(treedino,746)->treeptero
massdino[match(treeptero$tip.label,names(massdino))]->massptero
massptero[match(treeptero$tip.label,names(massptero))]->massptero

# perform RRphylo and search.trend on body mass data
RRphylo(tree=treeptero,y=log(massptero),clus=cc)->RRptero
search.trend(RR=RRptero, y=log(massptero),node=143,clus=cc)->st2

## overfitST routine
# generate a list of subsampled and swapped phylogenies setting as node
# the clade under testing
treeptero.list<-resampleTree(RRptero$tree,s = 0.25,node=143,
                             swap.si = 0.1,swap.si2 = 0.1,nsim=10)

# test the robustness of search.trend
ofRRptero<-overfitRR(RR = RRptero,y=log(massptero),phylo.list=treeptero.list,clus=cc)
ofSTptero<-overfitST(RR=RRptero,y=log(massptero),oveRR=ofRRptero,node=143,clus=cc)


## Case 2
# load the RRphylo example dataset including Cetaceans tree and data
data("DataCetaceans")
DataCetaceans$treecet->treecet
DataCetaceans$masscet->masscet
DataCetaceans$brainmasscet->brainmasscet

# cross-reference the phylogenetic tree and body and brain mass data. Remove from
# both the tree and vector of body sizes the species whose brain size is missing
drop.tip(treecet,treecet$tip.label[-match(names(brainmasscet),
                                               treecet$tip.label)])->treecet.multi
masscet[match(treecet.multi$tip.label,names(masscet))]->masscet.multi

# peform RRphylo on the variable (body mass) to be used as additional predictor
RRphylo(tree=treecet.multi,y=masscet.multi,clus=cc)->RRmass.multi
RRmass.multi$aces[,1]->acemass.multi

# create the predictor vector: retrieve the ancestral character estimates
# of body size at internal nodes from the RR object ($aces) and collate them
# to the vector of species' body sizes to create
c(acemass.multi,masscet.multi)->x1.mass

# peform RRphylo and search.trend on brain mass by using body mass as additional predictor
RRphylo(tree=treecet.multi,y=brainmasscet,x1=x1.mass,clus=cc)->RRmulti
search.trend(RR=RRmulti, y=brainmasscet,x1=x1.mass,clus=cc)->STcet

## overfitST routine
# generate a list of subsampled and swapped phylogenies to test
treecet.list<-resampleTree(RRmulti$tree,s = 0.25,swap.si=0.1,swap.si2=0.1,nsim=10)

# test the robustness of search.trend with and without x1.residuals
ofRRcet<-overfitRR(RR = RRmulti,y=brainmasscet,phylo.list=treecet.list,clus=cc,x1 =x1.mass)
ofSTcet1<-overfitST(RR=RRmulti,y=brainmasscet,oveRR=ofRRcet,x1 =x1.mass,clus=cc)
ofSTcet2<-overfitST(RR=RRmulti,y=brainmasscet,oveRR=ofRRcet,x1 =x1.mass,x1.residuals = TRUE,clus=cc)
}