Using map.to.singleton to plot a stochastic character map tree using ape::plot.phylo

It occurred to me today that it is both possible & relatively straightforward to plot a tree with a stochastic map character using ape's S3 plot method for the "phylo" object class by merely first converting the "simmap" object to a "phylo" object with singleton (that is, unbranching) nodes (which can already be done using phytools::map.to.singleton).

The main advantage of this is ape::plot.phylo has some options & plot styles that are not represented in phytools::plot.simmap.

Let's see.

First, here's our tree:

library(phytools)
tree

## 
## Phylogenetic tree with 26 tips and 25 internal nodes.
## 
## Tip labels:
##  S, K, R, A, E, Z, ...
## 
## The tree includes a mapped, 2-state discrete character with states:
##  0, 1
## 
## Rooted; includes branch lengths.

colors<-setNames(c("blue","red"),0:1)
plot(tree,colors,lwd=3)

Now, let's turn it in to a "phylo" object with unbranching nodes:

singles<-map.to.singleton(tree)
singles

## 
## Phylogenetic tree with 26 tips and 35 internal nodes.
## 
## Tip labels:
##  S, K, R, A, E, Z, ...
## 
## Rooted; includes branch lengths.

plotTree.singletons(singles)

Finally, let's replot it using ape::plot.phylo:

plot(singles,edge.col=colors[names(singles$edge.length)],
    edge.width=3,no.margin=TRUE)

Likewise, we can plot our tree in any of the different styles that ape::plot.phylo implements. For instance, an unrooted tree:

plot(singles,edge.col=colors[names(singles$edge.length)],
    edge.width=1,type="unrooted",lab4ut="axial",
    cex=0.8,no.margin=TRUE)

Or, we can use other cool plot.phylo options, like plotting our different states with different line types instead of different colors:

par(lend=2)
types<-setNames(c("dotted","solid"),0:1)
greys<-setNames(c("black","grey"),0:1)
plot(singles,edge.lty=types[names(singles$edge.length)],
    edge.col=greys[names(singles$edge.length)],
    edge.width=4,no.margin=TRUE,label.offset=0.02)

That's kind of cool.

The tree & character history were simulated as follows:

set.seed(999)
Q<-0.5*matrix(c(-1,1,1,-1),2,2,dimnames=list(0:1,0:1))
tree<-sim.history(rtree(n=26,tip.label=LETTERS),Q)