Searching for morphological convergence" In RRphylo: Phylogenetic Ridge Regression Methods for Comparative Studies

if (!requireNamespace("rmarkdown", quietly = TRUE) ||
!rmarkdown::pandoc_available()) {
knitr::knit_exit()
}

knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) require(RRphylo) require(rgl) options(knitr.kable.NA = '',mc.cores=2,rgl.useNULL=TRUE, rmarkdown.html_vignette.check_title = FALSE)  Index search.conv basics {#basics} Dealing with multivariate data, each species at the tree tips is represented by a phenotypic vector, including one entry value for each variable. Naming$A$and$B$the phenotypic vectors of a given pair of species in the tree, the angle$θ$between them is computed as the inverse cosine of the ratio between the dot product of$A$and$B$, and the product of vectors sizes: $$θ = arccos(\frac{A•B}{|A||B|})$$ The cosine of angle$θ$actually represents the correlation coefficient between the two vectors. As such, it exemplifies a measure of phenotypic resemblance. Possible$θ$values span from 0° to 180°. Small angles (i.e. close to 0˚) imply similar phenotypes. At around 90˚ the phenotypes are dissimilar, whereas towards 180˚ the two phenotypic vectors point in opposing directions (i.e. the two phenotypes have contrasting values for each variable). For a phenotype with$n$variables, the two vectors intersect at a vector of$n$zeros. require(RRphylo) require(RColorBrewer) require(rgl) require(ape) require(phytools) require(mvMORPH) theta<-function(a,b){ unitV <- function(x) { sum(x^2)^0.5 } deg2rad <- function(deg) { (deg * pi)/(180) } rad2deg <- function(rad) { (rad * 180)/(pi) } rad2deg(acos((a%*%b)/(unitV(a) *unitV(b))))->theta return(theta) } veclen <- function(v) sqrt(sum(v^2)) xprod <- function(v, w) c( v[2]*w[3] - v[3]*w[2], v[3]*w[1] - v[1]*w[3], v[1]*w[2] - v[2]*w[1] ) arc3d <- function(from, to, center, radius, n, circle = 50, base = 0, plot = TRUE, ...) { fixarg <- function(arg) { if (is.matrix(arg)) arg[, 1:3, drop = FALSE] else matrix(arg, 1, 3) } normalize <- function(v) v / veclen(v) getrow <- function(arg, i) { arg[1 + (i - 1) %% nrow(arg),] } from <- fixarg(from) to <- fixarg(to) center <- fixarg(center) m <- max(nrow(from), nrow(to), nrow(center), length(base)) base <- rep_len(base, m) result <- matrix(NA_real_, nrow = 1, ncol = 3) for (j in seq_len(m)) { from1 <- getrow(from, j) to1 <- getrow(to, j) center1 <- getrow(center, j) base1 <- base[j] logr1 <- log(veclen(from1 - center1)) logr2 <- log(veclen(to1 - center1)) A <- normalize(from1 - center1) B <- normalize(to1 - center1) steps <- if (base1 <= 0) 4*abs(base1) + 1 else 4*base1 - 1 for (k in seq_len(steps)) { if (k %% 2) { A1 <- A * (-1)^(k %/% 2) B1 <- B * (-1)^(k %/% 2 + (base1 > 0)) } else { A1 <- B * (-1)^(k %/% 2 + (base1 <= 0)) B1 <- A * (-1)^(k %/% 2) } theta <- acos(sum(A1*B1)) if (isTRUE(all.equal(theta, pi))) warning("Arc ", j, " points are opposite each other! Arc is not well defined.") if (missing(n)) n1 <- ceiling(circle*theta/(2*pi)) else n1 <- n if (missing(radius)) { pretheta <- (k %/% 2)*pi - (k %% 2 == 0)*theta if (k == 1) totaltheta <- (steps %/% 2)*pi - (steps %% 2 == 0)*theta + theta p1 <- pretheta/totaltheta p2 <- (pretheta + theta)/totaltheta radius1 <- exp(seq(from = (1 - p1)*logr1 + p1*logr2, to = (1 - p2)*logr1 + p2*logr2, length.out = n1 + 1)) } else radius1 <- rep_len(radius, n1) arc <- matrix(NA_real_, nrow = n1 + 1, ncol = 3) p <- seq(0, 1, length.out = n1 + 1) arc[1,] <- center1 + radius1[1]*A1 arc[n1 + 1,] <- center1 + radius1[n1 + 1]*B1 AB <- veclen(A1 - B1) for (i in seq_len(n1)[-1]) { ptheta <- p[i]*theta phi <- pi/2 + (0.5 - p[i])*theta q <- (sin(ptheta) / sin(phi))/AB D <- (1-q)*A1 + q*B1 arc[i,] <- center1 + radius1[i] * normalize(D) } if (k == 1) result <- rbind(result, arc) else result <- rbind(result[-nrow(result), ,drop = FALSE], arc) } result <- rbind(result, result[1,]) } if (plot) lines3d(result[c(-1, -nrow(result)), , drop = FALSE], ...) else result[c(-1, -nrow(result)), , drop = FALSE] } ### creating a phylogenetic tree with 100 species set.seed(14) rtree(50)->tree ### select, extract and then modify the clade to be duplicated dist.nodes(tree)[(Ntip(tree)+1),]->cd cd[cd<0.8*max(cd)]->cd cd[which(as.numeric(names(cd))>Ntip(tree))]->cd cdt<-array() for(e in 1:length(cd)) length(tips(tree,names(cd)[e]))->cdt[e] names(cd[-which(cdt<Ntip(tree)*.1|cdt>Ntip(tree)/4)])->cd cdd<-array() for(i in 1:length(cd)){ if((length(tips(tree,cd[i]))-2)>(length(tips(tree,cd[i]))+Ntip(tree)-2)*0.1) cdd[i]<-"ok" else cdd[i]<-"no" } if(length(which(cdd=="ok"))>0) cd[which(cdd=="ok")]->cd else cd->cd as.numeric(sample(cd,1))->n extract.clade(tree,n)->t1 max(nodeHeights(t1))->nH1 suppressWarnings(swapONE(t1)[[1]]->t1) drop.tip(t1,t1$tip.label[c(1,length(t1$tip.label))])->t1 # if(max(nodeHeights(t1))!=nH1) geiger:::rescale(t1,nH1,model = "depth")->t1 if(max(nodeHeights(t1))!=nH1) rescaleRR(t1,height=nH1)->t1 t1$root.edge<-data.frame(tree$edge,tree$edge.length)[which(
data.frame(tree$edge,tree$edge.length)[,2]==n),3]

### selecting the node where the new clade is to be binded
distNodes(tree,n)[1:Nnode(tree),]->dfN
dfN[-which(rownames(dfN)%in%c(n,getDescendants(tree,n))),]->dfN
distNodes(tree,(Ntip(tree)+1))[1:Nnode(tree),][-1,]->dR
dR[match(rownames(dfN),rownames(dR)),]->dR1
dfN[match(rownames(dR1)[which(dR1[,2]<max(dR[,2])*0.8)],rownames(dfN)),]->dfN
rownames(dfN)[which(dfN[,1]<=Ntip(tree)/10)]->bar
dfN[-which(dfN[,1]<=Ntip(tree)/10),]->dfn2

if(!inherits(dfn2,"matrix")){
t(as.matrix(dfn2))->dfn2
rownames(dfN)[-which(dfN[,1]<=Ntip(tree)/10)]->rownames(dfn2)
}

if(dim(dfn2)[1]==0){
rownames(dfN)[which.max(dfN[,1])]->nodN
dfN[which.max(dfN[,1]),1]->minD
} else{
dR[match(rownames(dfn2),rownames(dR)),]->dfn3
if(!inherits(dfn3,"matrix")){
t(as.matrix(dfn3))->dfn3
rownames(dR)[match(rownames(dfn2),rownames(dR))]->rownames(dfn3)
}
rownames(dfn3)[which.min(dfn3[,2])]->nodN
dfn2[rownames(dfn2)==nodN,1]->minD
}
nodN->tar

at<-tar
data.frame(tree$edge,tree$edge.length)[which(data.frame(tree$edge, tree$edge.length)[,2]==at),3]->pos
diff(dist.nodes(tree)[(Ntip(tree)+1),c(n,tar)])->dH
if(dH>0) {
#geiger:::rescale(t1,(abs(diff(c(max(nodeHeights(t1)),dH)))),model = "depth")->t1
rescaleRR(t1,height=(abs(diff(c(max(nodeHeights(t1)),dH)))))->t1
t1$root.edge+pos/2->t1$root.edge
}else{
# geiger:::rescale(t1,(max(nodeHeights(t1))+abs(dH)),model = "depth")->t1
rescaleRR(t1,height=(max(nodeHeights(t1))+abs(dH)))->t1
t1$root.edge+pos/2->t1$root.edge
}

tree->treeO
t1->t1O


However, it is important to note that with geometric morphometric data (PC scores) the origin coincides with the consensus shape (where all PC scores are 0), so that, for instance, a large $θ$ indicates the two species diverge from the consensus in opposite directions and the phenotypic vectors can be visualized in the PC space (see the figures below).

set.seed(93)
tree$tip.label<-paste("a",seq(1,Ntip(tree)),sep="") t1$tip.label<-paste("a",seq((Ntip(tree)+1),(Ntip(tree)+Ntip(t1))),sep="")
bind.tree(tree,t1,where=at,position=pos/2)->tree1
ntraits<-3
thetaX=c(0,0,0)
matrix(c(1,0.5,0.5,0.5,1,0.5,.5,.5,1),3,3)->sigma
mvSIM(tree1,param=list(ntraits=ntraits,theta=thetaX,sigma=sigma))->y
c(getMRCA(tree1,tree$tip.label[which(tree$tip.label%in%tips(tree,n))]),
getMRCA(tree1,t1$tip.label))->nod.par apply(y,2,function(x) fastAnc(tree1,x))->ant y[c(sample(tips(tree1,nod.par[1]),1),sample(tips(tree1,nod.par[2]),1)),]->y.vec match(rownames(y.vec),tree1$tip.label)->aa

apply(y[tips(tree1,nod.par[1]),],2,mean)->y.vec[1,]
apply(y[tips(tree1,nod.par[2]),],2,mean)->y.vec[2,]
matrix(c(0,0,0),1,3)->rV

rep("gray80",length(y))->coltips
coltips[match(tips(tree1,nod.par[1]),rownames(y))]<-"firebrick1"
coltips[match(tips(tree1,nod.par[2]),rownames(y))]<-"deepskyblue1"
plot3d(y,col=coltips,size=1,bbox=FALSE,axes=FALSE,box=FALSE,type="s")
box3d()
title3d(xlab="y[,1]",ylab="y[,2]",zlab="y[,3]")

segments3d(rbind(rV,matrix(ant[match(nod.par[1],rownames(ant)),],1,3)),col="red4",lwd=4)
segments3d(rbind(rV,matrix(ant[match(nod.par[2],rownames(ant)),],1,3)),col="blue3",lwd=4)
segments3d(rbind(rV,y.vec[2,]),col="deepskyblue1",lwd=4)
segments3d(rbind(rV,y.vec[1,]),col="firebrick1",lwd=4)

matrix(ant[match(nod.par[1],rownames(ant)),],1,3)->a
matrix(ant[match(nod.par[2],rownames(ant)),],1,3)->b
theta(c(a),c(b))->theta.ace
as.numeric(round(theta.ace,1))->theta.ace
veclen(a)->la
veclen(b)->lb
if(la>lb) (la/lb)*b->b else (lb/la)*a->a
if(la<.5*lb) segments3d(rbind(matrix(ant[match(nod.par[1],rownames(ant)),],1,3),(lb/la)*matrix(ant[match(nod.par[1],rownames(ant)),],1,3)))
if(la>.5*lb) segments3d(rbind(matrix(ant[match(nod.par[2],rownames(ant)),],1,3),(la/lb)*matrix(ant[match(nod.par[2],rownames(ant)),],1,3)))

veclen(a)->la

matrix(y.vec[1,],1,3)->at1
matrix(y.vec[2,],1,3)->at2
theta(c(at1),c(at2))->theta.real
as.numeric(round(theta.real,1))->theta.real
veclen(at1)->lat1
veclen(at2)->lat2
if(lat1>lat2) (lat1/lat2)*at2->at2 else (lat2/lat1)*at1->at1
if(lat1<.5*lat2) segments3d(rbind(matrix(y.vec[1,],1,3),(lat2/lat1)*matrix(y.vec[1,],1,3)))
if(lat2<.5*lat1) segments3d(rbind(matrix(y.vec[2,],1,3),(lat1/lat2)*matrix(y.vec[2,],1,3)))
veclen(at1)->lat1

range(y[,3])->ran
if(ran[1]<0) ran[1]*0.9->ran[1] else ran[1]*1.1->ran[1]
if(ran[2]<0) ran[2]*1.1->ran[2] else ran[2]*0.9->ran[2]

points3d(matrix(c(rep(range(y[,1])[2]*1.5,5),rep(range(y[,3])[1]*1.2,5),seq(ran[1],ran[2],length.out=5)),5,3,byrow=FALSE),
col=c("gray80","gray16","green","firebrick1","deepskyblue1","blue3","red4"),size=8)
text3d(matrix(c(rep(range(y[,1])[2]*1.6,5),rep(range(y[,3])[1]*1.2,5),seq(ran[1],ran[2],length.out=5)),5,3,byrow=FALSE),

rglwidget(elementId = "plot3drgl")


Under the Brownian Motion (BM) model of evolution, the phenotypic dissimilarity between any two species in the tree (hence the $θ$ angle between them) is expected to grow proportionally to their phylogenetic distance. In the figure above, the mean directions of phenotypic change from the consensus shape formed by the species in two distinct clades (in light colors) diverge by a large angle (represented by the blue arc). This angle is expected to be larger than the angle formed by the direction of phenotypic change calculated at the ancestors of the two clades (the red arc).

set.seed(14)
treeO->tree
t1O->t1
ntraits<-3
thetaX=c(0,0,0)
matrix(c(1,0.5,0.5,0.5,1,0.5,.5,.5,1),3,3)->sigma
mvSIM(tree,param=list(ntraits=ntraits,theta=thetaX,sigma=sigma))->y
y[match(tree$tip.label,rownames(y)),]->y y[match(tips(tree,n),rownames(y)),]->a apply(y,2,range)->m.a m.a[2,]*1.2->m.a a1<-matrix(ncol=dim(y)[2],nrow=dim(a)[1]) for(m in 1:dim(a)[1]) { v<-array() for(i in 1:length(m.a)) jitter(m.a[i],amount=(sd(a[,i])*1))->v[i] v->a1[m,] } rownames(a1)<-rownames(a) y[match(tips(tree,n),rownames(y)),]<-a1 apply(y[match(t1$tip.label,rownames(y)),],2,jitter)->y.t1

tree$tip.label<-paste("a",seq(1,Ntip(tree)),sep="") t1$tip.label<-paste("a",seq((Ntip(tree)+1),(Ntip(tree)+Ntip(t1))),sep="")
rownames(y)<-tree$tip.label rownames(y.t1)<-t1$tip.label
bind.tree(tree,t1,where=at,position=pos/2)->tree1
rbind(y,y.t1)->y

### nod.par is the node pair set to converge
c(getMRCA(tree1,tree$tip.label[which(tree$tip.label%in%tips(tree,n))]),
getMRCA(tree1,t1$tip.label))->nod.par apply(y,2,function(x) fastAnc(tree1,x))->ant y[c(sample(tips(tree1,nod.par[1]),1),sample(tips(tree1,nod.par[2]),1)),]->y.vec match(rownames(y.vec),tree1$tip.label)->aa

apply(y[tips(tree1,nod.par[1]),],2,mean)->y.vec[1,]
apply(y[tips(tree1,nod.par[2]),],2,mean)->y.vec[2,]
matrix(c(0,0,0),1,3)->rV

rep("gray80",length(y))->coltips
coltips[match(tips(tree1,nod.par[1]),rownames(y))]<-"firebrick1"
coltips[match(tips(tree1,nod.par[2]),rownames(y))]<-"deepskyblue1"
plot3d(y,col=coltips,size=1,bbox=FALSE,axes=FALSE,box=FALSE,type="s")
box3d()
title3d(xlab="y[,1]",ylab="y[,2]",zlab="y[,3]")

segments3d(rbind(rV,matrix(ant[match(nod.par[1],rownames(ant)),],1,3)),col="red4",lwd=4)
segments3d(rbind(rV,matrix(ant[match(nod.par[2],rownames(ant)),],1,3)),col="blue3",lwd=4)
segments3d(rbind(rV,y.vec[2,]),col="deepskyblue1",lwd=4)
segments3d(rbind(rV,y.vec[1,]),col="firebrick1",lwd=4)

matrix(ant[match(nod.par[1],rownames(ant)),],1,3)->a
matrix(ant[match(nod.par[2],rownames(ant)),],1,3)->b
theta(c(a),c(b))->theta.ace
as.numeric(round(theta.ace,1))->theta.ace
veclen(a)->la
veclen(b)->lb
if(la>lb) (la/lb)*b->b else (lb/la)*a->a
if(la<.5*lb) segments3d(rbind(matrix(ant[match(nod.par[1],rownames(ant)),],1,3),(lb/la)*matrix(ant[match(nod.par[1],rownames(ant)),],1,3)))
if(la>.5*lb) segments3d(rbind(matrix(ant[match(nod.par[2],rownames(ant)),],1,3),(la/lb)*matrix(ant[match(nod.par[2],rownames(ant)),],1,3)))

veclen(a)->la

matrix(y.vec[1,],1,3)->at1
matrix(y.vec[2,],1,3)->at2
theta(c(at1),c(at2))->theta.real
as.numeric(round(theta.real,1))->theta.real
veclen(at1)->lat1
veclen(at2)->lat2
if(lat1>lat2) (lat1/lat2)*at2->at2 else (lat2/lat1)*at1->at1
if(lat1<.5*lat2) segments3d(rbind(matrix(y.vec[1,],1,3),(lat2/lat1)*matrix(y.vec[1,],1,3)))
if(lat2<.5*lat1) segments3d(rbind(matrix(y.vec[2,],1,3),(lat1/lat2)*matrix(y.vec[2,],1,3)))
veclen(at1)->lat1

range(y[,3])->ran
if(ran[1]<0) ran[1]*0.9->ran[1] else ran[1]*1.1->ran[1]
if(ran[2]<0) ran[2]*1.1->ran[2] else ran[2]*0.9->ran[2]

points3d(matrix(c(rep(range(y[,1])[2]*1.5,5),rep(range(y[,3])[1]*1.2,5),seq(ran[1],ran[2],length.out=5)),5,3,byrow=FALSE),
col=c("gray80","gray16","green","firebrick1","deepskyblue1","blue3","red4"),size=8)
text3d(matrix(c(rep(range(y[,1])[2]*1.6,5),rep(range(y[,3])[1]*1.2,5),seq(ran[1],ran[2],length.out=5)),5,3,byrow=FALSE),
rglwidget(elementId = "plot3drgl1")


Under convergence, the expected positive relationship between phylogenetic and phenotypic distances is violated and the mean angle between the species of the two clades will be shallow.

set.seed(14)
treeO->tree
t1O->t1

ntraits<-3
thetaX=c(0,0,0)
matrix(c(1,0.5,0.5,0.5,1,0.5,.5,.5,1),3,3)->sigma
mvSIM(tree,param=list(ntraits=ntraits,theta=thetaX,sigma=sigma))->y
y[match(tree$tip.label,rownames(y)),]->y y[match(tips(tree,n),rownames(y)),]->a apply(y,2,range)->m.a sample(seq(.8,1.2,.1),1)->ff m.a[2,]*ff->m.a a1<-matrix(ncol=dim(y)[2],nrow=dim(a)[1]) for(m in 1:dim(a)[1]) { v<-array() for(i in 1:length(m.a)) jitter(m.a[i],amount=(sd(a[,i])*1))->v[i] v->a1[m,] } rownames(a1)<-rownames(a) y[match(tips(tree,n),rownames(y)),]<-a1 apply(y[match(t1$tip.label,rownames(y)),],2,jitter)->y.t1

tree$tip.label<-paste("a",seq(1,Ntip(tree)),sep="") t1$tip.label<-paste("a",seq((Ntip(tree)+1),(Ntip(tree)+Ntip(t1))),sep="")
rownames(y)<-tree$tip.label rownames(y.t1)<-t1$tip.label
bind.tree(tree,t1,where=at,position=pos/2)->tree1
rbind(y,y.t1)->y

c(getMRCA(tree1,tree$tip.label[which(tree$tip.label%in%tips(tree,n))]),
getMRCA(tree1,t1$tip.label))->nod.par apply(y,2,function(x) fastAnc(tree1,x))->ant ### sample one tip descending from each node set to converge y[c(sample(tips(tree1,nod.par[1]),1),sample(tips(tree1,nod.par[2]),1)),]->y.vec match(rownames(y.vec),tree1$tip.label)->aa

### modify the phenotypes at the mrcas (nod.par) as the median of the distribution of phenotypes of descending tips
apply(y[tips(tree1,nod.par[1]),],2,median)->mrca.1
ant[match(nod.par[1],rownames(ant)),]<-mrca.1
apply(y[tips(tree1,nod.par[2]),],2,median)->mrca.2
ant[match(nod.par[2],rownames(ant)),]<-mrca.2

apply(y[tips(tree1,nod.par[1]),],2,mean)->y.vec[1,]
apply(y[tips(tree1,nod.par[2]),],2,mean)->y.vec[2,]
matrix(c(0,0,0),1,3)->rV

rep("gray80",length(y))->coltips
coltips[match(tips(tree1,nod.par[1]),rownames(y))]<-"firebrick1"
coltips[match(tips(tree1,nod.par[2]),rownames(y))]<-"deepskyblue1"
plot3d(y,col=coltips,size=1
,bbox=FALSE,axes=FALSE,box=FALSE,type="s")
box3d()
title3d(xlab="y[,1]",ylab="y[,2]",zlab="y[,3]")

segments3d(rbind(rV,matrix(ant[match(nod.par[1],rownames(ant)),],1,3)),col="red4",lwd=4)
segments3d(rbind(rV,matrix(ant[match(nod.par[2],rownames(ant)),],1,3)),col="blue3",lwd=4)
segments3d(rbind(rV,y.vec[2,]),col="deepskyblue1",lwd=4)
segments3d(rbind(rV,y.vec[1,]),col="firebrick1",lwd=4)

matrix(ant[match(nod.par[1],rownames(ant)),],1,3)->a
matrix(ant[match(nod.par[2],rownames(ant)),],1,3)->b
theta(c(a),c(b))->theta.ace
as.numeric(round(theta.ace,1))->theta.ace
veclen(a)->la
veclen(b)->lb
if(la>lb) (la/lb)*b->b else (lb/la)*a->a
if(la<.5*lb) segments3d(rbind(matrix(ant[match(nod.par[1],rownames(ant)),],1,3),(lb/la)*matrix(ant[match(nod.par[1],rownames(ant)),],1,3)))
if(la>.5*lb) segments3d(rbind(matrix(ant[match(nod.par[2],rownames(ant)),],1,3),(la/lb)*matrix(ant[match(nod.par[2],rownames(ant)),],1,3)))

veclen(a)->la

matrix(y.vec[1,],1,3)->at1
matrix(y.vec[2,],1,3)->at2
theta(c(at1),c(at2))->theta.real
as.numeric(round(theta.real,1))->theta.real
veclen(at1)->lat1
veclen(at2)->lat2
if(lat1>lat2) (lat1/lat2)*at2->at2 else (lat2/lat1)*at1->at1
if(lat1<.5*lat2) segments3d(rbind(matrix(y.vec[1,],1,3),(lat2/lat1)*matrix(y.vec[1,],1,3)))
if(lat2<.5*lat1) segments3d(rbind(matrix(y.vec[2,],1,3),(lat1/lat2)*matrix(y.vec[2,],1,3)))
veclen(at1)->lat1

range(y[,3])->ran
if(ran[1]<0) ran[1]*0.9->ran[1] else ran[1]*1.1->ran[1]
if(ran[2]<0) ran[2]*1.1->ran[2] else ran[2]*0.9->ran[2]

points3d(matrix(c(rep(range(y[,1])[2]*1.5,5),rep(range(y[,3])[1]*1.2,5),seq(ran[1],ran[2],length.out=5)),5,3,byrow=FALSE),
col=c("gray80","gray16","green","firebrick1","deepskyblue1","blue3","red4"),size=8)
text3d(matrix(c(rep(range(y[,1])[2]*1.6,5),rep(range(y[,3])[1]*1.2,5),seq(ran[1],ran[2],length.out=5)),5,3,byrow=FALSE),
rglwidget(elementId = "plot3drgl2")


One particular case of convergence applies when species in the two clades start from similar ancestral phenotypes and tend to remain similar, on average, despite the passing of evolutionary time. These parallel trajectories are evident in the figure above, representing two clades evolving towards the same mean phenotype.

The function search.conv (Castiglione et al. 2019) is specifically meant to calculate $θ$ values and to test whether actual $θ$s between groups of species are smaller than expected by their phylogenetic distance. The function tests for convergence in either entire clades or species grouped under different evolutionary ‘states’.

When convergence between clades is tested, the user indicates the clade pair supposed to converge by setting the argument node. Otherwise, the function automatically scans the phylogeny searching for significant instance of convergent clades. In this case, the minimum distance (meant as either number of nodes or evolutionary time), and the maximum and minimum sizes (in term of number of tips) for the clades to be tested are pre-set within the function or indicated by the user through the arguments min.dist, max.dim, and min.dim, respectively.

Given two monophyletic clades (subtrees) $C1$ and $C2$, search.conv computes the mean angle $θ_{real}$ over all possible combinations of pairs of species taking one species per clade. This $θ_{real}$ is divided by the patristic (i.e. the sum of branch lengths) distance between the most recent common ancestors (mrcas) to $C1$ and $C2$, $mrcaC1$ and $mrcaC2$, respectively, to account for the fact that the mean angle (hence the phenotypic distance) is expected to increase, on average, with phylogenetic distance. To assess significance, search.conv randomly takes a pair of tips from the tree ($t1$ and $t2$), computes the angle $θ_{random}$ between their phenotypes and divides $θ_{random}$ by the distance between $t1$ and $t2$ respective immediate ancestors (i.e. the distance between the first node $N1$ above $t1$, and the first node $N2$ above $t2$). This procedure is repeated 1,000 times generating $θ_{random}$ per unit time values, directly from the tree and data. The $θ_{random}$ per unit time distribution is used to test whether $θ_{real}$ divided by the distance between $mrcaC1$ and $mrcaC2$ is statistically significant, meaning if it is smaller than 5% of $θ_{random}$ values the two clades are said to converge.

With seach.conv, it is also possible to test for the initiation of convergence. In fact, given a pair of candidate clades under testing, the phenotypes at $mrcaC1$ and $mrcaC2$ are estimated by RRphylo, and the angle between the ancestral states ($θ_{ace}$) is calculated. Then, $θ_{ace}$ is added to $θ_{real}$ and the resulting sum divided by the distance between $mrcaC1$ and $mrcaC2$. The sum $θ_{ace} + θ_{real}$ should be small for clades evolving from similar ancestors towards similar daughter phenotypes. Importantly, a small $θ_{ace}$ means similar phenotypes at the mrcas of the two clades, whereas a small $θ_{real}$ implies similar phenotypes between their descendants. It does not mean, though, that the mrcas have to be similar to their own descendants. Two clades might, in principle, start with certain phenotypes and both evolve towards a similar phenotype which is different from the initial shape. This means that the two clades literally evolve along parallel trajectories. Under search.conv, simple convergence is distinguished by such instances of convergence with parallel evolution. The former is tested by looking at the significance of $θ_{real}$. The latter is assessed by testing whether the quantity $θ_{ace} + θ_{real}$ is small (at alpha = 0.05) compared to the distribution of the same quantity generated by summing the $θ_{random}$ calculated for each randomly selected pair of species $t1$ and $t2$ plus the angle between the phenotypic estimates at their respective ancestors $N1$ and $N2$ divided by their distance.

rad2deg <- function(rad) (rad * 180)/(pi)
unitV <- function(x) sum(x^2)^0.5

set.seed(22)
rtree(14)->tree
getDescendants(tree,18)->des1
getDescendants(tree,24)->des2
c(18,getMommy(tree,18))->mom1
c(24,getMommy(tree,24))->mom2
mom1[-length(mom1)]->mom1
mom2[-length(mom2)]->mom2
colo<-rep("gray50",length(tree$edge.length)) colo[which(tree$edge[,2]%in%des1)]<-"deepskyblue1"
colo[which(tree$edge[,2]%in%des2)]<-"firebrick1" colo[which(tree$edge[,2]%in%c(mom1,mom2))]<-"black"

wid<-rep(3,length(tree$edge.length)) wid[which(tree$edge[,2]%in%mom1)]<-4
wid[which(tree$edge[,2]%in%mom2)]<-4 set.seed(14) fastBM(tree,nsim=3)->y apply(y[tips(tree,18)[1:3],],2,function(x) jitter(x,2))->y[tips(tree,24),] RRphylo(tree,y,clus=2/parallel::detectCores())->RR rbind(RR$aces,y)->phen
tree->tree1

c(18,24)->nod
dist.nodes(tree1)[nod[1],nod[2]]->nT
tips(tree1,nod[1])->tt1
tips(tree1,nod[2])->TT
expand.grid(tt1,TT)->ctt
aa<-array()
for(g in 1:dim(ctt)[1]){
phen[match(c(as.character(ctt[g,1]),as.character(ctt[g,2])),rownames(phen)),]->ppTT
as.matrix(ppTT)->ppTT
}
mean(aa)->ang.tip

RR$aces[which(rownames(RR$aces)%in%nod),]->ac

apply(ctt,2,as.character)->ctt
rbind(c("$\\theta_{real}$", "=",round(ang.tip,3)),c(paste("$\\theta_{real}$","+","$\\theta_{ace}$",sep=""),"=",round(ang.tip+ang.ac,3)),c("$distance_{mrcas}$","=",round(nT,3)))->abc
as.data.frame(rbind(cbind(ctt,round(aa,3)),c("mrcaC-18","mrca-24",round(ang.ac,3)),abc))->angs

col2hex <- function(col, alpha) rgb(t(col2rgb(col)), alpha=alpha, maxColorValue=255)
require(kableExtra)
knitr::kable(angs,digits=3,align="c") %>%
kable_styling(full_width = F, position = "float_right")  %>%
column_spec(1, color = col2hex("deepskyblue1"),bold=TRUE) %>%
column_spec(2, color = col2hex("firebrick1"),bold=TRUE) %>%
pack_rows("", 14, 16) %>%
row_spec(14:16, color = "black",bold=TRUE)

plot(tree,no.margin = TRUE,direction="downward",srt=90,adj=0.5,edge.color = colo,edge.width = wid)
lty=1,col=c("deepskyblue1","firebrick1","black"),lwd=c(3,3,4))


$$\frac{\theta_{real}}{dist_{mrcas}} = 17.286 ; \frac{\theta_{real}+\theta_{ace}}{dist_{mrcas}} = 31.242$$

Regardless of whether clades are indicated (by the argument node) or not (i.e. the function automatically locates convergent clades), search.conv returns the metrics (i.e. $θ_{real}$, $θ_{ace}$ and so on) and the relative significance level for each clade pair under testing ($node pairs). search.conv(RR=RR,y=y,min.dim=3,max.dim=4,nsim=100,rsim=100, clus=2/parallel::detectCores(),filename="convergence plot")->SC  pdf.options(useDingbats=FALSE) # search.conv(RR=RR,y=y,min.dim=3,max.dim=4,nsim=100,rsim=100, # clus=2/parallel::detectCores(),foldername=getwd())->SC RR=RR;y=y;min.dim=3;max.dim=4;min.dist=NULL;nsim=100;rsim=100;foldername=getwd() { require(ape) require(phytools) require(vegan) require(cluster) require(plotrix) require(geomorph) traitgram = function( x, phy, xaxt='s', underscore = FALSE, show.names = TRUE, show.xaxis.values = TRUE, method = c('ML','pic'), col=NULL, lwd=NULL, mgp=NULL,...) { method <- match.arg(method) Ntaxa = length(phy$tip.label)
Ntot = Ntaxa + phy$Nnode phy = picante::node.age(phy) ages = phy$ages[match(1:Ntot,phy$edge[,2])] ages[Ntaxa+1]=0 if (class(x) %in% c('matrix','array')) { xx = as.numeric(x) names(xx) = row.names(x) } else xx = x if (!is.null(names(xx))) { umar = 0.1 if (!all(names(xx) %in% phy$tip.label)) {
print('trait and phy names do not match')
return()
}
xx = xx[match(phy$tip.label,names(xx))] } else umar = 0.1 lmar = 0.2 if (xaxt=='s') if (show.xaxis.values) lmar = 1 else lmar = 0.5 xanc <- ape::ace(xx, phy, method=method)$ace
xall = c(xx,xanc)

a0 = ages[phy$edge[,1]] a1 = ages[phy$edge[,2]]
x0 = xall[phy$edge[,1]] x1 = xall[phy$edge[,2]]

if (show.names) {
maxNameLength = max(nchar(names(xx)))
ylim = c(min(ages),max(ages)*(1+maxNameLength/50))
if (!underscore) names(xx) = gsub('_',' ',names(xx))
} else ylim = range(ages)

if(is.null(mgp)) magp<-NULL else{
mgp->magp
}

if(is.null(col)) colo<-par("fg") else{
col->colo
colo[match(names(x1),names(colo))]->colo
data.frame(x0,x1,a0,a1,colo)->dato
dato[order(dato[,5],decreasing=TRUE),]->dato
dato[,1]->x0
dato[,2]->x1
dato[,3]->a0
dato[,4]->a1
as.character(dato[,5])->colo
rownames(dato)->names(x1)->names(x0)->names(a1)->names(a0)->names(colo)
}

par(mar = c(3, 2.5, 2, 1))
plot(range(c(a0,a1)),range(c(x0,x1)),

type='n',xaxt='n',yaxt='n',
xlab='',ylab='',bty='n',cex.axis=0.8)
if (xaxt=='s') if (show.xaxis.values) axis(1,labels=TRUE,mgp=magp) else axis(1,labels=FALSE,mgp=magp)

if(is.null(lwd)) linwd<-1 else{
lwd->linwd
linwd[match(names(x1),names(linwd))]->linwd
}

segments(a0,x0,a1,x1,col=colo,lwd=linwd)

if (show.names) {
text(max(ages),sort(xx),
labels = names(xx)[order(xx)],
srt=90,
cex=.3)
}

return(data.frame(a1,x1))
}

Plot_ConvexHull<-function(xcoord, ycoord, lcolor,lwd=NULL, lty=NULL,col.p=NULL){
hpts <- chull(x = xcoord, y = ycoord)
hpts <- c(hpts, hpts[1])
lines(xcoord[hpts], ycoord[hpts], col = lcolor,lwd=lwd, lty=lty)
polygon(xcoord[hpts], ycoord[hpts], col=col.p, border=NA)
}

RR$tree->tree1 RR$aces->RRaces

# y <- treedata(tree1, y, sort = TRUE)[[2]]
y <- treedataMatch(tree1, y)[[1]]

RR$tip.path->L RR$rates->betas
RR$node.path->L1 rbind(RRaces,y)->phen if(is.null(min.dim)) min.dim<-Ntip(tree1)*0.1 else min.dim<-min.dim if(is.null(min.dist)) { min.dist<-Ntip(tree1)*0.1 dist.type<-"node" }else{ if(any(grep("time",min.dist))){ min.dist<-as.numeric(gsub("time","",min.dist)) dist.type<-"time" }else{ min.dist<-as.numeric(gsub("node","",min.dist)) dist.type<-"node" } } if(is.null(max.dim)) max.dim<-Ntip(tree1)/5 else max.dim<-max.dim subtrees(tree1)->subT->subTN if(length(sapply(subT,Ntip)>max.dim)>0){ subT[-c(which(sapply(subT,Ntip)<min.dim),which(sapply(subT,Ntip)>max.dim))]->subT }else{ subT[-which(unlist(lapply(subT,Ntip))<min.dim)]->subT } sapply(subT,function(x) getMRCA(tree1,x$tip.label))->names(subT)
as.numeric(names(subT))->nod
c(nod,Ntip(tree1)+1)->nod
subT[[length(subT)+1]]<-tree1
names(subT)[length(subT)]<-Ntip(tree1)+1

RRaces[match(nod,rownames(RR$aces)),]->aces res<-list() # if(round((detectCores() * clus), 0)==0) cl<-makeCluster(1) else cl <- makeCluster(round((detectCores() * clus), 0)) # registerDoParallel(cl) # res <- foreach(i = 1:(length(nod)-1), # .packages = c("ape", "geiger", "phytools", "doParallel")) %dopar% # { # gc() for(i in 1:(length(nod)-1)){ nod[i]->sel1 tips(tree1,sel1)->tt1 if(length(which(nod%in%getDescendants(tree1,sel1)))>0) nod[-which(nod%in%getDescendants(tree1,sel1))]->mean.sel else nod->mean.sel if(length(which(mean.sel%in%getMommy(tree1,sel1)))>0) mean.sel[-which(mean.sel%in%getMommy(tree1,sel1))]->mean.sel else mean.sel->mean.sel mean.sel[-which(mean.sel==sel1)]->mean.sel if(dist.type=="time") { distNodes(tree1,sel1)[1:Nnode(tree1),]->distN distN[,2]->matDist distN[,1]->matN }else{ distNodes(tree1,sel1)[1:Nnode(tree1),1]->matDist } matDist->matNod if (length(which(mean.sel %in% names(matDist[which(matDist<min.dist)])))>0) mean.sel[-which(mean.sel %in% names(matDist[which(matDist<min.dist)]))]->mean.sel else mean.sel->mean.sel if(length(mean.sel)==0) { c(dir.diff=NULL,diff=NULL,ang=NULL)->diff.p nDD<-NULL nTT<-NULL }else{ nDD<-array() nTT<-array() diff.p<-matrix(ncol=6,nrow=length(mean.sel)) for(k in 1:length(mean.sel)){ if(dist.type=="time"){ matN[match(as.numeric(as.character(mean.sel[k])),names(matN))]->nD matDist[match(as.numeric(as.character(mean.sel[k])),names(matDist))]->nT }else{ matDist[match(as.numeric(as.character(mean.sel[k])),names(matDist))]->nD dist.nodes(tree1)[sel1,as.numeric(mean.sel[k])]->nT } nD->nDD[k] nT->nTT[k] tips(tree1,mean.sel[k])->TT expand.grid(tt1,TT)->ctt aa<-array() for(g in 1:dim(ctt)[1]){ phen[match(c(as.character(ctt[g,1]),as.character(ctt[g,2])),rownames(phen)),]->ppTT as.matrix(ppTT)->ppTT aa[g] <- rad2deg(acos((ppTT[1,]%*%ppTT[2,])/(unitV(ppTT[1,]) *unitV(ppTT[2,])))) } mean(aa)->ang.tip aces[which(rownames(aces)%in%c(sel1,mean.sel[k])),]->ac rad2deg(acos((ac[1,] %*% ac[2,])/(unitV(ac[1,]) *unitV(ac[2,]))))->ang.ac c(dir.diff=ang.tip/nT,diff=(ang.tip+ang.ac)/nT,ang=ang.ac,ang.tip=ang.tip,nD=nD,nT=nT)->diff.p[k,] } rownames(diff.p)<-mean.sel colnames(diff.p)<-c("ang.bydist.tip","ang.conv","ang.ace","ang.tip","nod.dist","time.dist") } diff.p->mean.diff res[[i]]<-list(matNod,mean.diff,mean.sel,nDD,nTT) } lapply(res,"[[",1)->matNod lapply(res,"[[",2)->mean.diff lapply(res,"[[",3)->mean.sel lapply(res,"[[",4)->nD.sel lapply(res,"[[",5)->nT.sel nod[1:(length(nod)-1)]->names(mean.diff)->names(matNod)->names(nD.sel)->names(nT.sel) sapply(mean.diff,is.null)->nulls if(length(which(nulls == TRUE))==length(mean.diff)) stop("There are no nodes distant enough to search convergence, consider using a smaller min.dist") if(length(which(nulls==TRUE)>0)) mean.diff[-which(nulls==TRUE)]->mean.diff if(length(which(nulls==TRUE)>0)) mean.sel[-which(nulls==TRUE)]->mean.sel if(length(which(nulls==TRUE)>0)) matNod[-which(nulls==TRUE)]->matNod if(length(which(nulls==TRUE)>0)) nD.sel[-which(nulls==TRUE)]->nD.sel if(length(which(nulls==TRUE)>0)) nT.sel[-which(nulls==TRUE)]->nT.sel mean.aRd<-array() AD<-matrix(ncol=5,nrow=rsim) suppressWarnings(for(h in 1:rsim){ tree1$tip.label[sample(seq(1:length(tree1$tip.label)),2)]->tsam phen[match(c(as.character(tsam[1]),as.character(tsam[2])),rownames(phen)),]->ppt tt <- rad2deg(acos((ppt[1,]%*%ppt[2,])/(unitV(ppt[1,]) *unitV(ppt[2,])))) getMommy(tree1,which(tree1$tip.label==as.character(tsam[1])))[1]->n1
getMommy(tree1,which(tree1$tip.label==as.character(tsam[2])))[1]->n2 phen[match(c(n1,n2),rownames(phen)),]->pp aa <- rad2deg(acos((pp[1,]%*%pp[2,])/(unitV(pp[1,]) *unitV(pp[2,])))) dist.nodes(tree1)[n1,n2]->dt paste(n1,n2,sep="/")->cb c(diff=(aa+tt)/dt,dist=dt,n1=n1,n2=n2,combo=cb)->AD[h,] tt/dt->mean.aRd[h] }) as.data.frame(AD)->AD as.numeric(as.character(AD[,1]))->AD[,1] as.numeric(as.character(AD[,2]))->AD[,2] if(length(which(is.na(AD[,1])))>0){ which(is.na(AD[,1]))->outs AD[-outs,]->AD mean.aRd[-outs]->mean.aRd } if(length(which(AD[,1]=="Inf"))>0){ which(AD[,1]=="Inf")->outs AD[-outs,]->AD mean.aRd[-outs]->mean.aRd } colnames(AD)<-c("ang.by.dist","dist","n1","n2","combo") res.ran <- list() # cl <- makeCluster(round((detectCores() * clus), 0)) # registerDoParallel(cl) # res.ran <- foreach(k = 1:nsim, # .packages = c("ape", "geiger", "phytools", "doParallel")) %dopar% # { # gc() for(k in 1:nsim){ mean.diffR<-list() for(u in 1:length(mean.diff)){ names(mean.diff)[[u]]->sel1 mean.sel[[u]]->msel nT.sel[[u]]->ntsel diff.pR<-list() for(j in 1:length(msel)){ msel[j]->sel2 as.numeric(names(L1[,which(colnames(L1)==sel1)][which(L1[,which(colnames(L1)==sel1)]!=0)]))[-1]->des ldes<-array() for(i in 1:length(des)){ length(which(des%in%getMommy(tree1,des[i])))->ldes[i] } des[which(ldes<=1)]->des1 as.numeric(names(L1[,which(colnames(L1)==sel2)][which(L1[,which(colnames(L1)==sel2)]!=0)]))[-1]->des ldes<-array() for(i in 1:length(des)){ length(which(des%in%getMommy(tree1,des[i])))->ldes[i] } des[which(ldes<=1)]->des2 expand.grid(c(getMommy(tree1,sel1)[1:2],des1,sel1),c(getMommy(tree1,sel2)[1:2],des2,sel2))->ee as.numeric(as.character(ee[,2]))->ee[,2] as.numeric(as.character(ee[,1]))->ee[,1] ee[,c(2,1)]->e2 colnames(e2)<-colnames(ee) rbind(ee,e2)->ex apply(ex,1, function(x) paste(x[1],x[2],sep="/"))->exx as.data.frame(exx)->exx if (length(which(AD$combo%in%exx[,1]))>0) AD[-which(AD$combo%in%exx[,1]),]->ADD else AD->ADD sample(seq(1:dim(ADD)[1]),1)->s mean.aRd[s]->rdiff ADD[s,1]->mdiff data.frame(dir.diff=rdiff,diff=mdiff)->diff.pR[[j]] } do.call(rbind,diff.pR)->mean.diffR[[u]] rownames(mean.diffR[[u]])<-names(msel) } names(mean.diffR)<-names(mean.diff) mean.diffR->res.ran[[k]] } diff.rank<-list() for(i in 1:length(mean.diff)){ rnk<-matrix(ncol=2,nrow=dim(mean.diff[[i]])[1]) for(k in 1:dim(mean.diff[[i]])[1]){ apply(rbind(mean.diff[[i]][k,c(1,2)],do.call(rbind,lapply(lapply(res.ran,"[[",i), function(x) x[k,c(1,2)]))[1:(nsim-1),]),2, function(x) rank(x)[1]/nsim )->rnk[k,] } data.frame(mean.diff[[i]],rnk)->diff.rank[[i]] colnames(diff.rank[[i]])[c(7,8)]<-c("p.ang.bydist","p.ang.conv") } names(mean.diff)->names(diff.rank) lapply(diff.rank,function(x) abs(x[order(abs(x[,1])),]))->diff.rank as.data.frame(do.call(rbind,lapply(diff.rank,function(x) cbind(rownames(x)[1],x[1,]))))->df colnames(df)[1]<-"node" df[order(df[,8],df[,7]),]->df ###################################################################### df->sc sc[which(sc$p.ang.bydist<=0.05 | sc$p.ang.conv<=0.05),]->sc.sel if(nrow(sc.sel)==0){ print("no candidate node pairs selected, no convergence found") sc->sc.sel i=1 while(i<nrow(sc.sel)){ rbind(data.frame(n1=rownames(sc.sel)[i],n2=sc.sel[i,1]),data.frame(n1=sc.sel[,1],n2=rownames(sc.sel)))->dat which(duplicated(dat))-1->out if(length(out>0))sc.sel[-out,]->sc.sel i<-i+1 } ### phenotypic vectors descending from node pairs resulting from search.conv ### phen2<-list() for(i in 1:nrow(sc.sel)){ c(rownames(sc.sel)[i],getDescendants(tree1,rownames(sc.sel)[i]),as.numeric(as.character(sc.sel[i,1])),getDescendants(tree1,as.numeric(as.character(sc.sel[i,1]))))->d2 d2[which(as.numeric(d2)<(Ntip(tree1)+1))]<-tree1$tip.label[as.numeric(d2[which(as.numeric(d2)<(Ntip(tree1)+1))])]
phen[which(rownames(phen)%in%d2),]->phen2[[i]]
}

sapply(phen2,nrow)->len
gg<-list()
for(i in 1:length(len)) rep(paste(rownames(sc.sel)[i],sc.sel[i,1],sep="/"),len[i])->gg[[i]]
unlist(gg)->gr

### perform betadisper and TukeyHSD ###
dist(do.call(rbind,phen2))->dis
data.frame(bd$group,dist=bd$distance)->M
sc.sel->x
data.frame(gr=paste(rownames(x),x[,1],sep="/"),ndist=x[,5],dtime=x[,6])->xdist
M[,2]/xdist[match(M[,1],xdist[,1]),3]->M[,3]
tapply(M[,3],M[,1],mean)->mean.dist
M[,2]/xdist[match(M[,1],xdist[,1]),3]->M[,3]
tapply(M[,3],M[,1],mean)->mean.dist

data.frame(sc.sel,
list(sc.sel,mean.dist)->res.tot
names(res.tot)<-c("node pairs","average distance from group centroids")

}else{

### remove duplicated couples
i=1
while(i<nrow(sc.sel)){
rbind(data.frame(n1=rownames(sc.sel)[i],n2=sc.sel[i,1]),data.frame(n1=sc.sel[,1],n2=rownames(sc.sel)))->dat
which(duplicated(dat))-1->out
if(length(out>0))sc.sel[-out,]->sc.sel
i<-i+1
}

if(length(which(sc.sel[,9]<=0.05))>0) print("parallel and convergent trajectories") else {
if(length(which(sc.sel[,4]/sc.sel[,5]>1.1))>0) print("convergent trajectories") else print("parallel and convergent trajectories")
}

### phenotypic vectors descending from node pairs resulting from search.conv ###
phen2<-list()
for(i in 1:nrow(sc.sel)){
c(rownames(sc.sel)[i],getDescendants(tree1,rownames(sc.sel)[i]),as.numeric(as.character(sc.sel[i,1])),getDescendants(tree1,as.numeric(as.character(sc.sel[i,1]))))->d2
d2[which(as.numeric(d2)<(Ntip(tree1)+1))]<-tree1$tip.label[as.numeric(d2[which(as.numeric(d2)<(Ntip(tree1)+1))])] phen[which(rownames(phen)%in%d2),]->phen2[[i]] } sapply(phen2,nrow)->len gg<-list() for(i in 1:length(len)) rep(paste(rownames(sc.sel)[i],sc.sel[i,1],sep="/"),len[i])->gg[[i]] unlist(gg)->gr ### perform betadisper and TukeyHSD ### dist(do.call(rbind,phen2))->dis vegan::betadisper(dis,gr)->bd data.frame(bd$group,dist=bd$distance)->M sc.sel->x data.frame(gr=paste(rownames(x),x[,1],sep="/"),ndist=x[,5],dtime=x[,6])->xdist M[,2]/xdist[match(M[,1],xdist[,1]),3]->M[,3] M[,2]/xdist[match(M[,1],xdist[,1]),3]->M[,3] tapply(M[,3],M[,1],mean)->mean.dist if(dim(x)[1]>1) { TukeyHSD(aov(M[,3]~M[,1]))[[1]]->BD } else { paste("no comparison is performed")->BD } data.frame(sc.sel, clade.size.n1=sapply(as.numeric(rownames(sc.sel)),function(x) length(tips(tree1,x))), clade.size.n2=sapply(as.numeric(as.character(sc.sel[,1])),function(x) length(tips(tree1,x))))->sc.sel list(sc.sel,BD,mean.dist)->res.tot names(res.tot)<-c("node pairs","node pairs comparison","average distance from group centroids") } ##################################################################### #### Plot preparation #### aceRR <- (L1 %*% betas[1:Nnode(tree1), ]) y.multi <- (L %*% betas) c(aceRR[,1],y.multi[,1])->phen1 rep("gray",Ntip(tree1)+Nnode(tree1))->colo strsplit(names(which.min(res.tot$average distance from group centroids)),"/")[[1]]->nn
as.numeric(nn)->nn
c(getMommy(tree1,nn[1]),getDescendants(tree1,nn[1]))->path1
path1[which(path1<(Ntip(tree1)+1))]<-tree1$tip.label[path1[which(path1<(Ntip(tree1)+1))]] c(getMommy(tree1,nn[2]),getDescendants(tree1,nn[2]))->path2 path2[which(path2<(Ntip(tree1)+1))]<-tree1$tip.label[path2[which(path2<(Ntip(tree1)+1))]]

colo[which(names(phen1)%in%c(nn[1],path1))]<-"#E7298A"
colo[which(names(phen1)%in%c(nn[2],path2))]<-"#91003F"

names(colo)<-names(phen1)
linwd<-rep(2,length(colo))
linwd[which(colo!="gray")]<-3
names(linwd)<-names(colo)

if(ncol(phen)>nrow(phen)) phen[,1:nrow(phen)]->phen
princomp(phen)->a
a$scores[order(a$scores[,1]),]->bb
suppressWarnings(bb[match(c(nn[1],path1[-which(as.numeric(path1)<=nn[1])]),rownames(bb)),]->a1)
suppressWarnings(bb[match(c(nn[2],path2[-which(as.numeric(path2)<=nn[2])]),rownames(bb)),]->a2)

dist(RRaces)->dist.ace
dist(y)->dist.Y
as.matrix(dist.Y)->dist.Y
as.matrix(dist.ace)->dist.ace
mean(dist.Y[match(tips(tree1,nn[1]),rownames(dist.Y)),match(tips(tree1,nn[2]),colnames(dist.Y))])->dist.Ynn
dist.ace[match(nn[1],colnames(dist.ace)),][which(names(dist.ace[match(nn[1],colnames(dist.ace)),])%in%nn[2])]->dist.nod

pdf(file = paste(foldername, "convergence plot.pdf",
sep = "/"))

layout(matrix(c(1,3,2,4),ncol=2,nrow=2, byrow = TRUE),widths = c(1,2))

par(mar = c(3, 1, 2, 1))
hist(dist.ace[lower.tri(dist.ace)],xlab="",ylab="",axes=FALSE,main="", col=rgb(58/255,137/255,255/255,1))
title(main="ace distances",line=0.5,cex.main=1.5)
axis(1,mgp=c(1,0.5,0))
abline(v=dist.nod,col=rgb(255/255,213/255,0/255,1),lwd=4)

par(mar = c(3, 1, 2, 1))
hist(dist.Y[lower.tri(dist.Y)],main="",xlab="",ylab="",axes=FALSE,col=rgb(58/255,137/255,255/255,1))
title(main="tip distances",line=0.5,cex.main=1.5)
axis(1,mgp=c(1,0.5,0))
abline(v=dist.Ynn,col=rgb(255/255,213/255,0/255,1),lwd=3)

par(mar = c(3, 2.5, 2, 1))
plot(bb[-match(nn, rownames(bb)),1:2], ylab="PC2",xlab="PC1",cex=1.5,mgp=c(1.5,0.5,0),font.lab=2,pch=21,col="black",bg="gray",asp=1)
Plot_ConvexHull(xcoord = a1[,1], ycoord = a1[,2], lcolor = "#E7298A",lwd=3,lty=2, col.p = rgb(212/255,185/255,218/255,0.5))
Plot_ConvexHull(xcoord = a2[,1], ycoord = a2[,2], lcolor = "#91003F",lwd=3,lty=2, col.p = rgb(212/255,185/255,218/255,0.5))
points(cluster::pam(a1, 1)$medoids,xlim=range(bb[,1]),ylim=range(bb[,2]),bg="#E7298A",type = "p",pch=21,col="black", cex=2.5) points(cluster::pam(a2, 1)$medoids,xlim=range(bb[,1]),ylim=range(bb[,2]),bg="#91003F",type = "p",pch=21,col="black", cex=2.5)
points(a1[1,1],a1[1,2],xlim=range(bb[,1]),ylim=range(bb[,2]),col="black",type = "p",pch=8, cex=1.5,lwd=2)
points(a2[1,1],a2[1,2],xlim=range(bb[,1]),ylim=range(bb[,2]),col="black",type = "p",pch=8, cex=1.5,lwd=2)

par(mar = c(2, 2.5, 2, 1))
suppressWarnings(traitgram(y.multi[,1],tree1,col=colo, method = 'ML',show.names = FALSE,lwd=linwd,mgp=c(0,0.5,0)))->traitres
points(traitres[match(nn,rownames(traitres)),],cex=1.5,col="black",pch=8,lwd=2)

dev.off()

res.tot->SC
}

paste(rownames(SC$node pairs),SC$node pairs[,1],sep="-")->SC$node pairs[,1] colnames(SC$node pairs)[c(1,6:11)]<-c("node.pair","node","time","ang.bydist","ang.conv","n1","n2")
rownames(SC$node pairs)<-NULL knitr::kable(SC$node pairs,digits=3,align="c") %>%
column_spec(1, bold=TRUE) %>%


Here, ang.bydist.tip and ang.conv correspond to $\frac{\theta_{real}}{dist_{mrcas}}$ and $\frac{\theta_{real}+\theta_{ace}}{dist_{mrcas}}$, respectively; ang.tip and ang.ace are $θ_{real}$ and $θ_{ace}$; the distance between the clades is computed both in terms of number of nodes (node) and time (time; N.B. this is $dist_{mrcas}$); p-values for ang.bydist and ang.conv are the significance levels for such metrics; clade size indicates the number of tips within the clades under testing.

The function also returns the $average distance from group centroids, that is the average phenotypic distance of each single species within the paired clades to the centroid of each pair (i.e. the mean phenotype for the pair as a whole) in multivariate space. Such distances are compared between significantly convergent pairs to identify the pair with the most similar phenotypes ($node pairs comparison).

knitr::kable(as.data.frame(cbind(SC$node pairs comparison, t(as.matrix(SC$average distance from group centroids)))),digits=3,align="c")%>%
kable_styling(full_width = F, position = "center")%>%


As for the example above, search.conv found two clade pairs under "convergence and parallelism" (which is also printed out in the console when the function ends running). In both cases, $\theta_{real}$ by time (ang.bydist.tip) is not significant (p.ang.bydist > 0.05) while $\theta_{real}+\theta_{ace}$ by time (ang.conv) is significantly different from random (p.ang.conv < 0.05). This means the clades within each pair started with similar phenotypes and evolved along parallel trajectories. Although not significantly different (p adj), the average distance from group centroid for the pair 18/24 is smaller than for 23/18, which means the former has less phenotypic variance.

The function also stores a pdf file in the directory specified by the argument filename.

require(pdftools)
ddpcr::quiet(pdf_convert(paste(getwd(),"/convergence plot.pdf",sep=""), format = "png",dpi=300))
ddpcr::quiet(file.remove(paste(getwd(),"/convergence plot.pdf",sep="")))
knitr::include_graphics("convergence plot_1.png")


The histograms on the left represent the euclidean distances between the phenotypic vectors of all possible pairs of aces/tips within the phylogeny. The yellow lines indicate the mean distance between aces/tips within the convergent clade pair. The PC plot at the top right is obtained by performing a PCA on the species phenotypes. Convergent clades are indicated by colored convex hulls. Large colored dots represent the mean phenotypes per clade (i.e. their group centroids). On bottom right, a modified traitgram plot highlights the branches of the clades found to converge. In both PCA and traitgram, asterisks represent the ancestral phenotypes of the individual clades. If more than one instance of convergence has been found, the figure represents the clade pair with the smallest $average distance from group centroids. Morphological convergence within/between categories {#state} The clade-wise approach we have described so far ignores instances of phenotypic convergence that occur at the level of species rather than clades. search.conv is also designed to deal with this case. To do that, the user must specify distinctive ‘states’ (by providing the argument state within the function) for the species presumed to converge. The function will test convergence within a single state or between any pair of given states. The species ascribed to a given state may belong anywhere on the tree or be grouped in two separate regions of it, in which case two states are indicated, one for each region. The former design facilitates testing questions such as whether all hypsodont ungulates converge on similar shapes, while latter aids in testing questions such as whether hypsodont artiodactyls converge on hypsodont perissodactyls. When searching convergence within/between states, search.conv first checks for phylogenetic clustering of species within categories and "declusterizes" them when appropriate. This is accomplished by randomly removing one species at time from the "clustered" category until such condition is not met (this feature can be escaped by setting declust = FALSE). Then, the function calculates the mean$θ_{real}$between all possible species pairs evolving under a given state (or between the species in the two states presumed to converge on each other). The$θ_{random}$angles are calculated by shuffling the states 1,000 times across the tree tips. Both$θ_{real}$and individual$θ_{random}$are divided by the distance between the respective tips. set.seed(22) fastBM(tree,nsim=3)->y apply(y,2,range)[2,]->m.a sample(seq(1:Ntip(tree)),3)->ff p1<-matrix(ncol=length(m.a),nrow=length(ff)) for(e in 1:length(ff)){ p0<-array() for(i in 1:length(m.a)) jitter(m.a[i],amount=sd(y[,i]))->p0[i] p0->p1[e,] } ### simulating the phenotypic state y[ff,]<-p1 rep("nostate",Ntip(tree))->state names(state)<-rownames(y) state[ff]<-"a" sample(seq(1:Ntip(tree))[-ff],4)->f2 p2<-matrix(ncol=length(m.a),nrow=length(f2)) for(e in 1:length(f2)){ p0<-array() for(i in 1:length(m.a)) jitter(m.a[i],amount=2*sd(y[,i]))->p0[i] p0->p2[e,] } y[f2,]<-p2 state[f2]<-"b" tree->tree1 state[match(rownames(y),names(state))]->state if("nostate"%in%state) state[-which(state=="nostate")]->state.real else state->state.real combn(unique(state.real),2)->stcomb1 if("nostate"%in%state) combn(unique(state),2)->stcomb else stcomb1->stcomb cophenetic.phylo(tree1)->cop i=1 y[which(state==stcomb1[1,i]),]->tt1 mean(apply(tt1,1,unitV))->vs1 y[which(state==stcomb1[2,i]),]->TT mean(apply(TT,1,unitV))->vs2 expand.grid(rownames(tt1),rownames(TT))->ctt aa<-array() dt<-array() for(g in 1:dim(ctt)[1]){ y[match(c(as.character(ctt[g,1]),as.character(ctt[g,2])),rownames(y)),]->ppTT as.matrix(ppTT)->ppTT aa[g] <- rad2deg(acos((ppTT[1,]%*%ppTT[2,])/(unitV(ppTT[1,]) *unitV(ppTT[2,])))) cop[match(as.character(ctt[g,1]),rownames(cop)),match(as.character(ctt[g,2]),rownames(cop))]->dt[g] } rbind(c(paste("mean","$\\theta_{real}$",sep=" "), "=",round(mean(aa),3)), c(paste("mean", "$\\frac{\\theta_{real}}{distance}",sep=" "),"=",round(mean(aa/dt),3)))->abc as.data.frame(rbind(cbind(as.matrix(ctt)[,2:1],round(aa,3),round(dt,3)),c("","","",""),c("","","",""),c("","","","")))->angs colnames(angs)<-c("state a","state b","angle","distance") col2hex <- function(col, alpha) rgb(t(col2rgb(col)), alpha=alpha, maxColorValue=255) require(kableExtra) knitr::kable(angs,digits=3,align="c") %>% kable_styling(full_width = F, position = "float_right") %>% column_spec(1, color = col2hex("deepskyblue1"),bold=TRUE) %>% column_spec(2, color = col2hex("firebrick1"),bold=TRUE)%>% pack_rows(paste(paste("mean","\\theta_{real}$",sep=" "), round(mean(aa),3),sep=" = "), 14,14, label_row_css = "",latex_gap_space = "0em") %>% pack_rows(paste(paste("mean", "$\\frac{\\theta_{real}}{distance}$",sep=" "),round(mean(aa/dt),3),sep=" = "), 15,15, label_row_css = "",latex_gap_space = "0em") colo<-rep("gray50",length(tree$tip.label))
colo[which(tree$tip.label%in%names(which(state=="a")))]<-"deepskyblue1" colo[which(tree$tip.label%in%names(which(state=="b")))]<-"firebrick1"

tiplabels(bg=colo,text=rep("",length(tree$tip.label)),frame="circle",adj=0.5,offset=0.05) legend("bottomright",legend=c("State a","State b","nostate"),pch=21,pt.cex=2, pt.bg=c("deepskyblue1","firebrick1","gray50"))  Under the "state" case, search.conv returns the mean$θ_{real}$within/between states (ang.state) and the same metric divided by time distance (ang.state.time), along with respective significance level (p.ang.state and p.ang.state.time). search.conv(tree=tree,y=y,state=state,nsim=100,clus=2/parallel::detectCores(), filename="convergence plot states")->SC  pdf.options(useDingbats=FALSE) # search.conv(tree=tree,y=y,state=state,nsim=100, # clus=2/parallel::detectCores(),foldername=getwd())->SC tree=tree;y=y;state=state;nsim=100 { phylo.run.test<-function(tree,state,st,nsim=100){ cophenetic.phylo(tree)->cop cop[which(state==st),which(state==st)]->subcop mean(subcop[upper.tri(subcop)])->mds dim(subcop)[1]->sl r.mds<-array() for(e in 1:nsim){ sample(tree$tip.label,sl)->test.tip
cop[test.tip,test.tip]->r.cop
mean(r.cop[upper.tri(r.cop)])->r.mds[e]
}
return(list(p=length(which(r.mds<mds))/nsim))
}
declusterize<-function(tree,state,st){
remT<-c()
length(which(state==st))->lenst
while(phylo.run.test(tree=tree,state=state,st=st)$p<0.05){ names(state)->nam as.numeric(as.factor(state))->rt #as.numeric(rt)->rt #names(rt)<-nam data.frame(state,rt)->def unique(def[which(def[,1]==st),2])->stN data.frame(V=rle(rt)$values,L=rle(rt)$lengths)->VL data.frame(VL,pos=cumsum(VL[,2]))->VL #subset(VL,V==stN)->vel VL[which(VL$V==stN),]->vel
vel[which.max(vel$L),]->hit tree$tip.label[(hit[,3]-hit[,2]+1):hit[,3]]->hitips
sample(hitips,0.5*length(hitips))
sample(hitips,ceiling(.5*length(hitips)))->remtips
drop.tip(tree,remtips)->tree
state[-which(names(state)%in%remtips)]->state
if(length(c(remtips,remT))>(lenst-3)) break else c(remtips,remT)->remT
}
if(length(remT)==0) remT<-NA
#return(list(tree=tree,state=state))
return(remT=remT)
}

tree->tree1
#if (inherits(y,"data.frame"))
# y <- treedata(tree1, y, sort = TRUE)[[2]]
y <- treedataMatch(tree1, y)[[1]]

state[match(rownames(y),names(state))]->state
if("nostate"%in%state) state[-which(state=="nostate")]->state.real else state->state.real
if(sort(table(state.real),decreasing = TRUE)[1]>Ntip(tree1)*0.5) warning("one or more states apply to a large portion of the tree, this might be inappropriate for testing convergence")

combn(unique(state.real),2)->stcomb1
if("nostate"%in%state) combn(unique(state),2)->stcomb else stcomb1->stcomb

state2decl<-list()
for(k in 1:ncol(stcomb1)){
if(any(table(state[which(state%in%stcomb1[,k])])<4)) state2decl[[k]]<-NA else{
replace(state,which(state%in%stcomb1[,k]),"A")->f
replace(f,which(!state%in%stcomb1[,k]),"B")->f

state2decl[[k]]<-f
}
}

sapply(state2decl,function(x){
if(!any(is.na(x))) phylo.run.test(tree1,x,"A")$p->ret else 1->ret return(ret) })->prt if(any(prt<=0.05)) for(q in which(prt<=0.05) ) print(paste("species in state", stcomb1[1,q],"and", stcomb1[2,q], "are phylogenetically clustered; consider running search.conv with declust=TRUE")) decl<-as.list(rep(NA,ncol(stcomb1))) y->yOri state->stateOri cophenetic.phylo(tree1)->cop ang.by.state<-matrix(ncol=4,nrow=ncol(stcomb1)) y.stcomb<-state.stcomb<-list() for(i in 1:ncol(stcomb1)){ if(!any(is.na(decl[[i]]))){ yOri[-match(decl[[i]],rownames(yOri)),]->y->y.stcomb[[i]] stateOri[-match(decl[[i]],names(stateOri))]->state->state.stcomb[[i]] }else{ yOri->y->y.stcomb[[i]] stateOri->state->state.stcomb[[i]] } y[which(state==stcomb1[1,i]),]->tt1 mean(apply(tt1,1,unitV))->vs1 y[which(state==stcomb1[2,i]),]->TT mean(apply(TT,1,unitV))->vs2 expand.grid(rownames(tt1),rownames(TT))->ctt aa<-array() dt<-array() for(g in 1:dim(ctt)[1]){ y[match(c(as.character(ctt[g,1]),as.character(ctt[g,2])),rownames(y)),]->ppTT as.matrix(ppTT)->ppTT aa[g] <- rad2deg(acos((ppTT[1,]%*%ppTT[2,])/(unitV(ppTT[1,]) *unitV(ppTT[2,])))) cop[match(as.character(ctt[g,1]),rownames(cop)),match(as.character(ctt[g,2]),rownames(cop))]->dt[g] } c(mean(aa),mean(aa/dt),vs1,vs2)->ang.by.state[i,] } data.frame(state1=t(stcomb1)[,1],state2=t(stcomb1)[,2],ang.state=ang.by.state[,1],ang.state.time=ang.by.state[,2],size.v1=ang.by.state[,3],size.v2=ang.by.state[,4])->ang2state ang2stateR <- list() # cl <- makeCluster(round((detectCores() * clus), 0)) # registerDoParallel(cl) # ang2stateR <- foreach(j = 1:nsim) %dopar%{ # gc() for(j in 1:nsim){ ang.by.stateR<-matrix(ncol=2,nrow=ncol(stcomb1)) for(i in 1:ncol(stcomb1)){ y.stcomb[[i]]->y state.stcomb[[i]]->state sample(state)->state y[which(state==stcomb1[1,i]),]->tt1 y[which(state==stcomb1[2,i]),]->TT expand.grid(rownames(tt1),rownames(TT))->ctt aa<-array() dt<-array() for(g in 1:dim(ctt)[1]){ y[match(c(as.character(ctt[g,1]),as.character(ctt[g,2])),rownames(y)),]->ppTT as.matrix(ppTT)->ppTT aa[g] <- rad2deg(acos((ppTT[1,]%*%ppTT[2,])/(unitV(ppTT[1,]) *unitV(ppTT[2,])))) cop[match(as.character(ctt[g,1]),rownames(cop)),match(as.character(ctt[g,2]),rownames(cop))]->dt[g] } c(mean(aa),mean(aa/dt))->ang.by.stateR[i,] } data.frame(state1=t(stcomb1)[,1],state2=t(stcomb1)[,2],ang.state=ang.by.stateR[,1],ang.state.time=ang.by.stateR[,2])->ang2stateR[[j]] } pval<-matrix(ncol=2,nrow=nrow(ang2state)) rlim<-matrix(ncol=2,nrow=nrow(ang2state)) for(k in 1:nrow(ang2state)){ apply(rbind(ang2state[k,3:4],do.call(rbind,lapply(ang2stateR,function(x) x[k,3:4]))[1:(nsim-1),]),2,rank)[1,]/nsim->pval[k,] quantile(do.call(rbind,lapply(ang2stateR,function(x) x[k,]))[,3],c(0.05,0.95))->rlim[k,] } data.frame(ang2state,p.ang.state=pval[,1],p.ang.state.time=pval[,2])->res.tot for(j in 1:nrow(res.tot)){ if(res.tot[j,8]<=0.05) print(paste("convergent trajectories between",res.tot[j,1], "and",res.tot[j,2])) } #### Plot preparation #### yOri->y stateOri->state data.frame(res.tot[,3],rlim=rlim[,1]*res.tot[,4]/res.tot[1,4],res.tot[,5:6])->bbb data.frame(bbb,l1=bbb[,1]/2,l2=360-(bbb[,1]/2),rlim1=bbb[,2]/2, rlim2=360-bbb[,2]/2,p=res.tot[,8])->ccc pdf(file = "convergence plot states.pdf") if(nrow(res.tot)==1) { mat<-matrix(c(1,2),ncol=1,nrow=2,byrow=TRUE) ht<-c(1,1) }else{ if(nrow(res.tot)%%2==0) matrix(ncol=nrow(res.tot)/2,nrow=3)->mat else matrix(ncol=(nrow(res.tot)/2+1),nrow=3)->mat mat[1,]<-rep(1,ncol(mat)) if(nrow(res.tot)%%2==0) mat[2:nrow(mat),]<-seq(2,(nrow(res.tot))+1,1) else mat[2:nrow(mat),]<-seq(2,(nrow(res.tot))+2,1) mat->mat ht<-(c(1.5,1,1)) } layout(mat,heights = ht) if("nostate"%in%state) c("nostate",names(sort(table(state.real), decreasing = TRUE)))->statetoplot else names(sort(table(state.real), decreasing = TRUE))->statetoplot if(ncol(y)>nrow(y)) y[,1:nrow(y)]->ypc else y->ypc princomp(ypc)->compy compy$scores[,1:2]->sco
#y[,1:2]->sco
sco[match(names(state),rownames(sco)),]->sco
RColorBrewer::brewer.pal(length(unique(state)),"Set2")->cols
par(mar=c(2.5,2.5,1,1))
plot(sco, ylab="PC2",xlab="PC1",cex=1.5,mgp=c(1.5,0.5,0),font.lab=2,xlim=c(range(sco[,1])[1]*1.1,range(sco[,1])[2]),col="white",asp=1)
for(h in 1:length(statetoplot)){
if("nostate"%in%statetoplot){
if(h==1){
points(sco[which(state==statetoplot[h]),],pch=21,bg="gray",cex=1.5)
Plot_ConvexHull(xcoord=sco[which(state==statetoplot[h]),1], ycoord=sco[which(state==statetoplot[h]),2], lcolor = "#bebebe",lwd=3,lty=2, col.p = paste("#bebebe","4D",sep=""))
}else{
points(sco[which(state==statetoplot[h]),],pch=21,bg=cols[h-1],cex=1.5)
Plot_ConvexHull(xcoord=sco[which(state==statetoplot[h]),1], ycoord=sco[which(state==statetoplot[h]),2], lcolor = cols[h-1],lwd=3,lty=2, col.p = paste(cols[h-1],"4D",sep=""))
}
}else{
points(sco[which(state==statetoplot[h]),],pch=21,bg=cols[h],cex=1.5)
Plot_ConvexHull(xcoord=sco[which(state==statetoplot[h]),1], ycoord=sco[which(state==statetoplot[h]),2], lcolor = cols[h],lwd=3,lty=2, col.p = paste(cols[h],"4D",sep=""))
}
}
if("nostate"%in%statetoplot){
legend(min(sco[,1])*1.1,max(sco[,2])*1.1, legend = statetoplot,fill = c("#bebebe",cols), bg = rgb(0, 0, 0, 0),
box.col = rgb(0,0, 0, 0), border = NA, x.intersp = 0.25,y.intersp=0.8)
}else{
legend(min(sco[,1])*1.1,max(sco[,2])*1.1, legend = statetoplot,fill = cols, bg = rgb(0, 0, 0, 0),
box.col = rgb(0,0, 0, 0), border = NA, x.intersp = 0.25,y.intersp=0.8)
}

if(nrow(res.tot)>6) {
lp<-seq(0,340,40)
lbs<-seq(0,340,40)
}else{
lp<-seq(0,340,20)
lbs<-seq(0,340,20)
}
for(i in 1:nrow(res.tot)){
par(cex.axis=.75)
plotrix::polar.plot(lengths=c(0,mean(unname(as.matrix(ccc)[i,c(3,4)])),mean(unname(as.matrix(ccc)[i,c(3,4)]))),
polar.pos = c(0,ccc[i,7],ccc[i,8]),rp.type="p",line.col=rgb(0,0,0,0.6),
title(main=paste(as.character(res.tot[i,1]),as.character(res.tot[i,2]),sep="-"),cex.main = 2)
plotrix::polar.plot(lengths=c(0,ccc[i,3],ccc[i,4]),polar.pos = c(0,ccc$l1[i],ccc$l2[i]),
text(paste("p-value = ",ccc[i,9]),x=0,y=-max(ccc[i,c(3,4)])/2.3,cex=1.5,col="red")
}
dev.off()

res.tot[,-c(5,6)]->SC

}
knitr::kable(SC,digits=3,align="c") %>%
column_spec(1:2, bold=TRUE)


The example above produced significant results for convergence between states regarding both mean $θ_{real}$ (p.ang.state < 0.05) and mean $θ_{real}$ by time (p.ang.state.time). Whether p.ang.state.time or p.ang.state should be inspected to assess significance depends on the study settings. Ideally, p.ang.state.time provides the most appropriate significance metric, however, for badly incomplete tree with clades pertaining to very distant parts of the tree of life (which is commonplace in studies of morphological convergence), the time distance could be highly uninformative and p.ang.state should be preferred.

As above, the function stores a pdf file in the folder indicated by the argument filename.

require(pdftools)
ddpcr::quiet(pdf_convert(paste(getwd(),"/convergence plot states.pdf",sep=""), format = "png",dpi=300))
ddpcr::quiet(file.remove(paste(getwd(),"/convergence plot states.pdf",sep="")))
knitr::include_graphics("convergence plot states_1.png")


The PC plot is obtained by performing a PCA on the species phenotypes. Species belonging to different states are indicated by colored convex hulls. The circular plot represents the mean $θ_{real}$ between states (blue lines) and the range of $θ_{random}$ (gray shaded area) around it. The p-value for the convergence test (p.ang.state.time) is printed within the circular plot.

Guided examples {#examples}

# load the RRphylo example dataset including Felids tree and data
data("DataFelids")
DataFelids$PCscoresfel->PCscoresfel # mandible shape data DataFelids$treefel->treefel # phylogenetic tree
DataFelids$statefel->statefel # conical-toothed ("nostate") or saber-toothed condition library(ape) plot(ladderize(treefel),show.tip.label = F,no.margin = T) colo<-rep("gray50",length(treefel$tip.label))
colo[match(names(which(statefel=="saber")),treefel\$tip.label)]<-"firebrick1"
tiplabels(text=rep("",Ntip(treefel)),bg=colo,frame="circle",cex=.4)
legend("bottomleft",legend=c("Sabertooths","nostate"),pch=21,pt.cex=1.5,
pt.bg=c("firebrick1","gray50"))

# perform RRphylo on Felids tree and data
RRphylo(tree=treefel,y=PCscoresfel)->RRfel

## Example 1: search for morphological convergence between clades (automatic mode)
## by setting 9 nodes as minimum distance between the clades to be tested
search.conv(RR=RRfel, y=PCscoresfel, min.dim=5, min.dist="node9",

## Example 2: search for morphological convergence within sabertoothed species
search.conv(tree=treefel, y=PCscoresfel, state=statefel,
filename = "sc state")->SC.state


References

Castiglione, S., Serio, C., Tamagnini, D., Melchionna, M., Mondanaro, A., Di Febbraro, M, Profico, A., Piras, P., Barattolo, F., & Raia, P. (2019). A new, fast method to search for morphological convergence with shape data. PloS one 14: e0226949. https://doi.org/10.1371/journal.pone.0226949

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RRphylo documentation built on May 9, 2022, 9:08 a.m.