if (!requireNamespace("rmarkdown", quietly = TRUE) || !rmarkdown::pandoc_available()) { warning(call. = FALSE, "Pandoc not found, the vignettes is not built") knitr::knit_exit() } knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) require(RRphylo) options(rmarkdown.html_vignette.check_title = FALSE) load("plotting-data.Rda")
Phenotypic and evolutionary rate values can be plotted onto the tree branches by means of plotRR
. The function takes as mandatory objects the result of RRphylo
, the vector/matrix of phenotypic values (y
) used to generate the RR
object, and a multivariate
argument indicating whether individual rates for each variable ("multiple.rates"
) or the norm2 vector of multivariate rates ("rates"
) should be plotted. The output includes two customized functions to chart phenotipic values/evolutionary rates along the tree branches: plotRRphen
and plotRRrates
. The plots generated by both these functions are completely customizable by providing a list of tree.args
, being all the arguments usually passed to the function plot.phylo
, color.pal
function to generate the color palette and a list of colorbar.args
to be passed to the function colorbar
. The only mandatory argument for plotRRphen
and plotRRrates
with multivariate data is the name or the number of the variable
to plot.
### load the RRphylo example dataset including Apes tree and shape data (multivariate) data("DataApes") DataApes$PCstage->PCstage DataApes$Tstage->Tstage # Perform RRphylo RRphylo(tree=Tstage,y=PCstage)->RR # Visualize phenotypic values and "multiple" evolutionary rates onto the # phylogenetic tree. plotRR(RR,y=PCstage,multivariate="multiple.rates")->pRRmulti # Visualize phenotypic values and the norm2 vector of multiple rates onto the # phylogenetic tree. plotRR(RR,y=PCstage,multivariate="rates")->pRR
# Using default options pRRmulti$plotRRphen(variable=1) pRRmulti$plotRRrates(variable=2)
# Using default options pRR$plotRRphen(variable=1) # this is the same as before of course pRR$plotRRrates()
# Customizing parameters pRRmulti$plotRRphen(variable=3,tree.args=list(edge.width=2,cex=0.7),color.pal=heat.colors, colorbar.args = list(x="topleft",direction="h",labs.adj=0.7,xpd=TRUE)) pRRmulti$plotRRrates(variable=4,tree.args=list(edge.width=2,direction="leftwards",cex=0.7), color.pal=terrain.colors,colorbar.args = list(x="topright",labs.adj=0.7,xpd=TRUE,tck.pos="out"))
Evolutionary rates computed by RRphylo for all the tree branches belonging to a specific clade can be visualized by means of plotRates
. The function takes as objects the result of RRphylo
and the focal node
number (located on the tree returned by RRphylo
) and returns two customized functions. The function plotHist
plots the histograms of rates (in ln absolute values) computed for the focal clade compared to rates computed for the rest of the tree. The plot is completely customizable by providing lists of hist.args
, being all the arguments usually passed to the function hist
, and legend.args
, usually passed to the function legend
. The function plotLollipop
returns a lollipop plot (see documentation for lollipoPlot
function in RRphylo package) of rate values for each branch in the clade. As well as plotHist
, this is customizable by indicating lists of lollipop.args
for the lollipops and line.args
for the vertical line representing the mean rate for the entire tree. Additionally, plotLollipop
returns the vector of evolutionary rates for each branch within the focal clade, sorted in the same order they are plotted, that is from the largest to the smallest value. In this way, the user could ideally set different graphical parameters for each species/node in the vector (see the example below).
### load the RRphylo example dataset including Cetaceans tree and ln body masses data("DataCetaceans") DataCetaceans$treecet->treecet DataCetaceans$masscet->masscet # Perform RRphylo RRphylo(treecet,masscet)->RRcet # Visualize evolutionary rates computed for the clade including extant Mysticetes plotRates(RRcet,node=131)->pRates
# Using default options par(mar=c(3,3,1,1),mgp=c(1.7,0.5,0)) pRates$plotHist() par(mar=c(3,12,1,1),mgp=c(1.7,0.5,0)) pRates$plotLollipop()->pLol
# Customizing parameters par(mar=c(3,3,1,1),mgp=c(1.7,0.5,0)) pRates$plotHist(hist.args=list(yaxt="n",ylab=NULL,col1="blue",col2="cyan"), legend.args = list(pch=21,pt.cex=2)) # Setting different pch and color for nodes and species colo<-rep("gray70",length(pLol)) colo[names(pLol)%in%treecet$tip.label]<-"cyan" pchc<-rep(22,length(pLol)) pchc[names(pLol)%in%treecet$tip.label]<-21 par(mar=c(3,12,1,1),mgp=c(1.7,0.5,0)) pRates$plotLollipop(lollipop.args = list(pch=pchc,bg=colo,col=colo,cex=2,lwd=2), line.args = list(col="deeppink2",lwd=4,lty=2))->pLol2
Results for phenotypic and evolutionary rates trends through time returned by the function search.trend can be visualized by means of plotTrend
. Such function takes as only object the output of search.trend
and returns customized functions to produce plots of phenotypes (plotSTphen
) and (rescaled) evolutionary rates (plotSTrates
) versus time regressions on the entire tree. The plots generated by both these functions are basically made by a scatterplot representing the distribution of phenotypes/rates versus time, a polygon depicting the 95% CI of slopes generated according to the Brownian Motion (see search.trend for further details) and the regression line. Each of these elements can be customized by the user by specifying the arguments plot.args
, polygon.args
, and line.args
. The only mandatory argument for plotSTphen
and plotSTrates
is the name or the number of the variable
to plot.
### load the RRphylo example dataset including Felids tree and 4 mandible shape variables (PCs) data("DataFelids") DataFelids$treefel->treefel DataFelids$PCscoresfel->PCscoresfel # Perform RRphylo and search.trend RRphylo(treefel,PCscoresfel)->RRfel search.trend(RRfel,PCscoresfel)->ST # Visualize search.trend results plotTrend(ST)->pTrend
# Using default options par(mfrow=c(1,2),mar=c(4,3,3,1),mgp=c(1.5,0.5,0)) pTrend$plotSTphen("PC1") pTrend$plotSTrates(1) # This is for PC1 as well ## Customizing parameters # Extracting nodes and tips descending from the most recent common ancestor of Felinae library(phytools) Felinae<-getDescendants(treefel,94) Felinae[which(Felinae<=Ntip(treefel))]<-treefel$tip.label[Felinae[which(Felinae<=Ntip(treefel))]] # Setting different pch for Felinae only pchc<-rep(21,nrow(ST$trend.data$phenotypeVStime)) pchc[rownames(ST$trend.data$phenotypeVStime)%in%Felinae]<-6 par(mfrow=c(1,2),mar=c(4,3,3,1),mgp=c(1.5,0.5,0)) pTrend$plotSTphen("PC2",plot.args = list(pch=pchc,cex=1.3), polygon.args = list(col="white",border="black",lwd=2,lty=3), line.args = list(lty=4,col="purple3")) # When multivariate data are used, a "rate" vector is calculated as the 2-norm # vector of rates computed for each individual variable. The "total rate" can be plotted: pTrend$plotSTrates("rate") # Equivalent to setting variable = 5
If the output of search.trend
also includes results for phenotypic and evolutionary rates trends trough time occurring at individual clades in the tree, plotTrend
returns two additional functions to generate plots for individual nodes (plotSTphenNode
and plotSTratesNode
). As above, these functions allow the user to customize the plot by setting the parameters plot.args
(for everything pertaining the scatterplot), lineTree.args
and lineNode.args
(for the regression lines for the entire tree and the clade, respectively), and node.palette
(including as many colors as the number of nodes the plot should be produced for). Mandatory arguments are the variable
and the indices or numbers of nodes to plot (up to 9 nodes at the same time). An argument peculiar to these functions (listed in plot.args
) is pch.node
, which can be used to set different pch for each node.
# Perform search.trend setting Smilodontini and Pantherini as individual clades search.trend(RRfel,PCscoresfel,node=c(129,154),clus=cc)->STclades # Visualize search.trend results plotTrend(STclades)->pTrend2
# Using default options par(mar=c(4,3,3,1),mgp=c(1.5,0.5,0)) pTrend2$plotSTphenNode("PC1",node=1:2) pTrend2$plotSTratesNode("PC1",node=c(154,129)) # This is the same as indicating node= 2:1 ## Customizing parameters par(mar=c(4,3,3,1),mgp=c(1.5,0.5,0)) pTrend2$plotSTphenNode("PC2",node=1:2, plot.args = list(pch.node=c(23,24),pch=1,col="gray70",cex=1.2), lineTree.args = list(col="black",lwd=3),lineNode.args = list(lwd=5), node.palette = c("orangered","chartreuse")) pTrend2$plotSTratesNode("rate",node=c(154,129), plot.args = list(pch.node=c(5,6),pch=16,col="gray70",cex=1.2,lwd=2), lineTree.args = list(col="gold",lwd=3,lty=4), lineNode.args = list(lwd=5), node.palette = c("deeppink","cyan2"))
Evolutionary rate shifts located on the tree by the function search.shift can be visualized by means of plotShift
. The function takes as mandatory objects the outputs of RRphylo
and search.shift
and returns customized functions to produce plots of evolutionary rates shifts occurring at clade level (plotClades
) or sparse across the tree (plotStates
), depending on the status.type
set when performing search.shift
. In the latter case (i.e. status.type = "sparse"
) a further state
argument must be provided to plotShift
.
plotClades
highlights the shifting clades onto the phylogenetic tree by drawing colored circles on their Most Recent Common Ancestors (MRCA). The radii of such circles are directly proportional to the absolute value of absolute rate difference (see search.shift for further details), so that a circle for a clade whose mean absolute rates "shifts" more from the mean absolute rates of the tree is larger than a circle for a clade with a smaller difference. Plots for the tree and the circles can be customized by the user by specifying the arguments tree.args
and symbols.args
, respectively. For example, the user can choose different color options than the standard blue/red for positive/negative shifts, by setting
symbols.args=list(fg=c(pos="color for positive shift",neg="color for negative shift"))
, or provide a vector of as many colors as the number of shifting clades. The same applies to the symbols
argument "bg".
### load the RRphylo example dataset including Ornithodirans tree, body mass and locomotory type data("DataOrnithodirans") DataOrnithodirans$treedino->treedino DataOrnithodirans$massdino->massdino DataOrnithodirans$statedino->statedino # Perform RRphylo and search.shift under status.type="clade" RRphylo(tree=treedino,y=massdino)->dinoRates search.shift(RR=dinoRates,status.type="clade")->SSauto # Visualize search.shift results plotShift(RR=dinoRates,SS=SSauto)->plotSS
# Using default options plotSS$plotClades() ## Customizing parameters # Setting different colors for positive and negative shift plotSS$plotClades(tree.args=list(no.margin=TRUE,type="fan"), symbols.args=list(lwd=2,fg=c(pos="gold",neg="green"), bg=scales::alpha("grey70",0.3))) # Setting different colors for each shifting clade plotSS$plotClades(tree.args=list(no.margin=TRUE), symbols.args=list(lwd=2,fg=NA,bg=scales::alpha(c("red","green","blue"),0.3)))
If search.shift
was performed under status.type = "sparse"
, the function plotStates
(returned by plotShift
) plots the states onto the phylogenetic tree and prints into the legend the direction (i.e. positive or negative) of significant shifts for each state. Plots for the tree, the points, and the legend can be customized by the user by specifying the arguments tree.args
, points.args
, and legend.args
respectively.
# Perform RRphylo and search.shift under status.type="sparse" search.shift(RR=dinoRates,status.type= "sparse",state=statedino)->SSstate # Visualize search.shift results plotShift(RR=dinoRates,SS=SSstate,state=statedino)->plotSS2
# Using default options plotSS2$plotStates() ## Customizing parameters # Setting customized colors and pch for different states and suppressing the legend plotSS2$plotStates(tree.args=list(no.margin=TRUE,type="phylogram"), points.args=list(bg=c("gold","forestgreen","royalblue","white"), col=c("black","black","black","orangered"), pch=c(21,22,24,11)),legend.args=NULL) # Setting customized colors and pch for different states and suppressing the legend plotSS2$plotStates(tree.args=list(no.margin=TRUE,type="phylogram"), points.args=list(bg=c("gold","forestgreen","royalblue","white"), col=c("black","black","black","orangered"), pch=c(21,22,24,11)),legend.args=list(pch=21))
Instances of phenotypic convergence detected on the tree by the function search.conv can be visualized by means of plotConv
. The function takes as mandatory objects the outputs of search.conv
, the multivariate phenotype (y
) used to perform search.conv
, and the index of result to plot (variable
), and returns customized functions to produce plots of convergence occurring at clade level or related to some discrete category scattered within the tree, depending on the way search.conv
was performed. In the latter case (convergence within/between states) a further state
argument must be provided to plotConv
.
When convergence between clades is inspected, four functions are returned. plotHistTips
and plotHistAces
generate histograms of the euclidean distances between phenotypic vectors of all possible pairs of tips/nodes within the phylogeny, and draw a vertical line representing the real distance between tips/nodes within convergent clade pair (it is the mean distance in the case of tips). Histogram and line characteristics can be customized by the user by specifying the arguments hist.args
and line.args
. The function plotPChull
generates a PC1/PC2 plot (obtained by performing a PCA of the species phenotypes) with convergent clades represented by colored convex hulls, and the mean phenotype and the ancestral characters for such clades indicated by customizable symbols. The arguments passed to plotPChull
allow to modify scatterplot elements (plot.args
), convex hull charateristics (chull.args
), and the points for mean phenotypes (means.args
) and ancestral characters (ace.args
). The function plotTraitgram
produces a modified traitgram plot (see package picante) where converging clades are highlighted by different colors. The colors for branches belonging to converging clades and for other branches can be customized by indicating the arguments colNodes
and colTree
, respectively.
### load the RRphylo example dataset including Felids tree, 4 mandible shape variables (PCs), ### and a category indicating whether or not the species shows sabertooth morphology data("DataFelids") DataFelids$PCscoresfel->PCscoresfel DataFelids$treefel->treefel DataFelids$statefel->statefel # Perform RRphylo and search.conv between a pair of clades cc<-2/parallel::detectCores() RRphylo(treefel,PCscoresfel,clus=cc)->RRfel search.conv(RR=RRfel, y=PCscoresfel, nodes=c(85,155),clus=cc)->sc_clade ## Visualize search.conv results # Set variable = 1 to see results for the pair "85/155" (the first element in # sc_clade$`average distance from group centroids`) plotConv(SC=sc_clade, y=PCscoresfel, variable=1, RR = RRfel)->plotSC
# Using default options par(mfrow=c(1,2)) plotSC$plotHistTips() plotSC$plotHistAces()
plotSC$plotPChull() plotSC$plotTraitgram()
## Customizing parameters # Set variable = 2 to see results for the pair "86/156" (the second element in # sc_clade$`average distance from group centroids`) plotConv(SC=sc_clade, y=PCscoresfel, variable=2, RR = RRfel)->plotSC
par(mfrow=c(1,2)) plotSC$plotHistTips(hist.args = list(col="gray80",yaxt="n",cex.axis=0.8,cex.main=1.5), line.args = list(lwd=3,lty=4,col="purple")) plotSC$plotHistAces(hist.args = list(col="gray80",cex.axis=0.8,cex.main=1.5), line.args = list(lwd=3,lty=4,col="gold"))
plotSC$plotPChull(chull.args = list(border=c("cyan","magenta"),lty=1), means.args = list(pch=c(21,22),cex=3,bg=c("cyan2","magenta2")), ace.args=list(pch=c(7,10)),legend.args = list(pch=c(24,11),bty="o",x="top")) plotSC$plotTraitgram(colTree = "gray70",colNodes = c("cyan","magenta"),yaxt="s")
When convergence between states is tested, plotConv
returns two functions. Similarly to the 'clade case', plotPChull
generates a PC1/PC2 plot (obtained by performing a PCA of the species phenotypes) with different states enclosed by colored convex hulls. The plot is entirely customizable by setting the arguments plot.args
,chull.args
, points.args
, and legend.args
. If the state vector includes a background state ("nostate"), the polygon for such category is always the first to be plotted, so that the first border
color or lty
in chull.args
is attributed to "nostate" (see the example below for clarification). The function plotPolar
generates a polar plot showing the mean angle within/between state/s contrasted to the 95% confidence interval of angles derived by randomization (see search.conv for further details). The circular plot area can be customized by providing a list of polar.args
to plotPolar
. The lines for the mean angle and the polygon for its 95% CI can be modified by setting the arguments line.args
(arguments passed to polar.plot
under rp.type="r"
) and polygon.args
(arguments passed to polar.plot
under rp.type="p"
), respectively.
# Perform search.conv within "saber" category search.conv(tree=treefel, y=PCscoresfel, state=statefel,declust=TRUE,clus=cc)->sc_state ## Visualize search.conv results # variable = 1 must be indicated also when a single row is output plotConv(SC=sc_state, y=PCscoresfel, variable=1, state=statefel)->plotSC_state
# Using default options plotSC_state$plotPChull() plotSC_state$plotPolar() ## Customizing parameters plotSC_state$plotPChull(chull.args=list(border=c("gold2","blue"),lty=3), points.args=list(pch=c(23,21),bg="gray"), legend.args=list(pch=c(23,21),x="top")) plotSC_state$plotPolar(polar.args=list(clockwise=TRUE,start=0,rad.col="black",grid.col="black"), polygon.args=list(line.col="green",poly.col=NA,lwd=2), line.args=list(line.col="deeppink",lty=2,lwd=3))
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