Over the last couple of days
(1,
2),
I've posted about plotting fitted M*k* models in which we visualize rate
differences among transition types using different colors and arrow widths.

These posts combined both
pre-existing
and
totally new
functionality of the *phytools* package. Unfortunately, these plotting methods did not
make it into
my book with
Luke Harmon (I wish they had) – but, nonetheless!

After a few additional updates, here's a workflow that illustrates applying these new graphing methods to a phylogeny and dataset for squamate digit number evolution from Brandley et al. (2008) that can be obtained from our book website.

First, load packages. To follow along, readers will have to update *phytools* from
GitHub, which can be done most easily using
the CRAN package *devtools*.

```
library(phytools)
packageVersion("phytools")
```

```
## [1] '1.1.17'
```

Now let's read in our phylogeny and data from the book website.

```
## read data matrix
sqData<-read.csv("http://www.phytools.org/Rbook/6/squamate-data.csv",
row.names=1)
## read phylogenetic tree
sqTree<-read.nexus("http://www.phytools.org/Rbook/6/squamate.tre")
## name.check
chk<-geiger::name.check(sqTree,sqData)
## drop tips of tree that are missing from data matrix
sqTree.pruned<-drop.tip(sqTree,chk$tree_not_data)
## drop rows of matrix that are missing from tree
sqData.pruned<-sqData[!(rownames(sqData)%in%
chk$data_not_tree),,drop=FALSE]
## extract trait
toes<-setNames(as.factor(sqData.pruned[,"rear.toes"]),
rownames(sqData.pruned))
```

Terrific. Now we're ready to fit our models.

In the book we fit equal-rates (“ER”) and all-rates-different (“ARD”) models, but (spoiler alert) they don't explain our data very well.

Here, instead, I'm going to fit a series of more biologically-informed models: one of
*only* digit loss, with a single rate; a second with digit loss and gain, but only
between adjacent states, with one rate of loss and one of gain; and then the more
parameter-rich directional (loss-only) and ordered (gain-and-loss) models.

To fit each of these models, I first build a *design matrix* indicating which types
of transitions are permitted (and allowed to assume different rates). Then I pass this
design matrix to `phytools::fitMk`

to fit the model.

```
## loss-only (one-rate)
loss.model<-matrix(c(
0,0,0,0,0,0,
1,0,0,0,0,0,
0,1,0,0,0,0,
0,0,1,0,0,0,
0,0,0,1,0,0,
0,0,0,0,1,0),6,6,byrow=TRUE,
dimnames=list(0:5,0:5))
fitLoss<-fitMk(sqTree.pruned,toes,model=loss.model,pi="fitzjohn")
fitLoss
```

```
## Object of class "fitMk".
##
## Fitted (or set) value of Q:
## 0 1 2 3 4 5
## 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 1 0.013603 -0.013603 0.000000 0.000000 0.000000 0.000000
## 2 0.000000 0.013603 -0.013603 0.000000 0.000000 0.000000
## 3 0.000000 0.000000 0.013603 -0.013603 0.000000 0.000000
## 4 0.000000 0.000000 0.000000 0.013603 -0.013603 0.000000
## 5 0.000000 0.000000 0.000000 0.000000 0.013603 -0.013603
##
## Fitted (or set) value of pi:
## 0 1 2 3 4 5
## 0 0 0 0 0 1
## due to treating the root prior as (a) nuisance.
##
## Log-likelihood: -188.653866
##
## Optimization method used was "nlminb"
```

```
## loss/gain (two-rate)
loss_gain.model<-matrix(c(
0,1,0,0,0,0,
2,0,1,0,0,0,
0,2,0,1,0,0,
0,0,2,0,1,0,
0,0,0,2,0,1,
0,0,0,0,2,0),6,6,byrow=TRUE,
dimnames=list(0:5,0:5))
fitLossGain<-fitMk(sqTree.pruned,toes,model=loss_gain.model,
pi="fitzjohn")
fitLossGain
```

```
## Object of class "fitMk".
##
## Fitted (or set) value of Q:
## 0 1 2 3 4 5
## 0 -0.037551 0.037551 0.000000 0.000000 0.000000 0.000000
## 1 0.023253 -0.060803 0.037551 0.000000 0.000000 0.000000
## 2 0.000000 0.023253 -0.060803 0.037551 0.000000 0.000000
## 3 0.000000 0.000000 0.023253 -0.060803 0.037551 0.000000
## 4 0.000000 0.000000 0.000000 0.023253 -0.060803 0.037551
## 5 0.000000 0.000000 0.000000 0.000000 0.023253 -0.023253
##
## Fitted (or set) value of pi:
## 0 1 2 3 4 5
## 0.149488 0.176177 0.196163 0.186328 0.156696 0.135149
## due to treating the root prior as (a) nuisance.
##
## Log-likelihood: -164.205161
##
## Optimization method used was "nlminb"
```

```
## loss-only (different rates)
directional.model<-matrix(c(
0,0,0,0,0,0,
1,0,0,0,0,0,
0,2,0,0,0,0,
0,0,3,0,0,0,
0,0,0,4,0,0,
0,0,0,0,5,0),6,6,byrow=TRUE,
dimnames=list(0:5,0:5))
fitDirectional<-fitMk(sqTree.pruned,toes,model=directional.model,
pi="fitzjohn")
fitDirectional
```

```
## Object of class "fitMk".
##
## Fitted (or set) value of Q:
## 0 1 2 3 4 5
## 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 1 0.024241 -0.024241 0.000000 0.000000 0.000000 0.000000
## 2 0.000000 0.103141 -0.103141 0.000000 0.000000 0.000000
## 3 0.000000 0.000000 0.109233 -0.109233 0.000000 0.000000
## 4 0.000000 0.000000 0.000000 0.083290 -0.083290 0.000000
## 5 0.000000 0.000000 0.000000 0.000000 0.004369 -0.004369
##
## Fitted (or set) value of pi:
## 0 1 2 3 4 5
## 0 0 0 0 0 1
## due to treating the root prior as (a) nuisance.
##
## Log-likelihood: -118.387049
##
## Optimization method used was "nlminb"
```

```
## loss/gain (different rates)
ordered.model<-matrix(c(
0,1,0,0,0,0,
2,0,3,0,0,0,
0,4,0,5,0,0,
0,0,6,0,7,0,
0,0,0,8,0,9,
0,0,0,0,10,0),6,6,byrow=TRUE,
dimnames=list(0:5,0:5))
fitOrdered<-fitMk(sqTree.pruned,toes,model=ordered.model,
pi="fitzjohn")
fitOrdered
```

```
## Object of class "fitMk".
##
## Fitted (or set) value of Q:
## 0 1 2 3 4 5
## 0 0.000000 0.000000 0.0000 0.000000 0.000000 0.000000
## 1 0.022289 -0.022289 0.0000 0.000000 0.000000 0.000000
## 2 0.000000 0.066392 -321.0419 320.975537 0.000000 0.000000
## 3 0.000000 0.000000 397.9908 -397.990777 0.000000 0.000000
## 4 0.000000 0.000000 0.0000 0.026384 -0.066294 0.039910
## 5 0.000000 0.000000 0.0000 0.000000 0.005813 -0.005813
##
## Fitted (or set) value of pi:
## 0 1 2 3 4 5
## 0.000000 0.000000 0.000000 0.000000 0.275171 0.724829
## due to treating the root prior as (a) nuisance.
##
## Log-likelihood: -114.967278
##
## Optimization method used was "nlminb"
```

Just as I did recently for the
`"fitPagel"`

and
`"fit.bd"`

object classes, I have also added an `anova`

S3 method for the `"fitMk"`

object, so
we can use that here to compare our four fitted models.

```
anova(fitLoss,fitLossGain,fitDirectional,fitOrdered)
```

```
## log(L) d.f. AIC weight
## fitLoss -188.6539 1 379.3077 1.378370e-29
## fitLossGain -164.2052 2 332.4103 2.103820e-19
## fitDirectional -118.3870 5 246.7741 8.292369e-01
## fitOrdered -114.9673 10 249.9346 1.707631e-01
```

This shows us, pretty clearly, that the best-supported model (taking into account parameterization) is the directional, loss-only model – but in which different rates are allowed for each transition type.

Lastly, let's plot all four of our fitted models.

```
par(mfrow=c(2,2))
plot(fitLoss,color=TRUE,lwd=2,show.zeros=FALSE,
text=FALSE,signif=2)
mtext("a) fitted single-rate \"loss-only\" model",adj=0,
line=0)
plot(fitLossGain,color=TRUE,width=TRUE,show.zeros=FALSE,
max.lwd=5,text=FALSE,signif=2)
mtext("b) fitted two-rate \"loss/gain\" model",adj=0,
line=0)
plot(fitDirectional,color=TRUE,width=TRUE,show.zeros=FALSE,
max.lwd=5,text=FALSE,signif=2)
mtext("c) fitted multi-rate \"loss-only\" model",adj=0,
line=0)
plot(fitOrdered,color=TRUE,width=TRUE,show.zeros=FALSE,
max.lwd=5,text=FALSE,signif=2)
mtext("d) fitted multi-rate \"loss/gain\" model",adj=0,
line=0)
```

## No comments:

## Post a Comment

Note: due to the very large amount of spam, all comments are now automatically submitted for moderation.