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
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
```

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
```

```
## 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
```

```
## 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
```