Recently, a colleague contacted me to ask me about his analysis fitting the M*k* model with a tree
transformation - such as the λ transform of Pagel (1999). This can be done pretty easily
using *geiger*'s `fitDiscrete`

function.

For some reason, I've always looked at this endeavor with some degree of suspicion. This is because
λ, by some definitions, is a metric of phylogenetic signal - and thus it seemed logical to
assume that λ might be confounded with the transition rate between states, *q*.

In the limiting case in which *q* is very high relative to the tree length it would seem that
this would have to be true because at that point the data become random with respect to the
topology and thus would have the same probability under a low value of λ and a relatively
low *q*, as with high values of both parameters.

Nonetheless, I was curious enough to investigate whether or not it is possible to simultaneously
estimate *q* & λ - and under what conditions.

The first think I did (after loading my packages) was simulate a tree & discrete character under a modest transition rate between states:

```
library(phytools)
library(geiger)
tree<-pbtree(n=100,scale=1)
Q<-matrix(c(-1,1,1,-1),2,2,dimnames=list(0:1,0:1))
x<-sim.Mk(tree,Q)
```

Here's my simulated data:

```
plotTree(tree,ftype="off",type="fan",part=0.5,
mar=c(3.1,1.1,1.1,1.1),lwd=1)
axis(1,at=c(0,0.5,1))
cols<-setNames(palette()[2:3],levels(x))
tiplabels(pie=to.matrix(x,levels(x)),piecol=cols,
cex=0.3)
```

Next, let's fit a λ model

```
fit.er<-fitDiscrete(tree,x,model="ER")
fit.lambda<-fitDiscrete(tree,x,model="ER",
transform="lambda")
```

```
fit.er
```

```
## GEIGER-fitted comparative model of discrete data
## fitted Q matrix:
## 0 1
## 0 -1.118624 1.118624
## 1 1.118624 -1.118624
##
## model summary:
## log-likelihood = -45.181453
## AIC = 92.362907
## AICc = 92.403723
## free parameters = 1
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 0
## number of iterations with same best fit = 100
## frequency of best fit = 1.00
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates
```

```
fit.lambda
```

```
## GEIGER-fitted comparative model of discrete data
## fitted Q matrix:
## 0 1
## 0 -1.118624 1.118624
## 1 1.118624 -1.118624
##
## fitted 'lambda' model parameter:
## lambda = 1.000000
##
## model summary:
## log-likelihood = -45.181453
## AIC = 94.362907
## AICc = 94.486618
## free parameters = 2
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 41
## number of iterations with same best fit = NA
## frequency of best fit = NA
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates
```

At least in this case, the estimated value of λ is identical to its simulated value, and
thus does not affect the fit or model parameter estimates for our M*k* model.

Nonetheless, let's plot our likelihood surface to see how if they are correlated as I had suspected:

```
m<-60
q<-seq(0.1*fit.lambda$opt$q12,1.5*fit.lambda$opt$q12,
length.out=m)
lambda<-seq(0,phytools:::maxLambda(tree),
length.out=m)
logL<-matrix(NA,m,m)
for(i in 1:m) for(j in 1:m)
logL[i,j]<-logLik(fitMk(
phytools:::lambdaTree(tree,lambda[i]),
x,fixedQ=Q*q[j],pi="fitzjohn"))
contour(lambda,q,logL,nlevels=50,
xlab=expression(lambda),ylab="q",bty="l",
font.lab=3)
title(main=expression(paste("Likelihood surface for q and ",
lambda)))
points(fit.lambda$opt$lambda,fit.lambda$opt$q12,cex=1.5,
pch=19,col="blue")
ml.q<-sapply(lambda,function(lambda,tree,x)
fitMk(phytools:::lambdaTree(tree,lambda),
x,model="ER",pi="fitzjohn")$rates,tree=tree,
x=x)
lines(lambda,ml.q,col=make.transparent("blue",0.5),
lwd=3)
legend("topleft",c(expression(paste("ML(q,",lambda,")")),
expression(paste("ML(q|",lambda,")"))),
lwd=c(NA,3),pch=c(19,NA),
col=c("blue","blue"),
bg="white",pt.cex=1.5,pt.lwd=2)
```

The curved line gives the conditional ML value of *q* for each value of λ represented on
the surface.

Interestingly, *q* and λ seem to be somewhat correlated - but perhaps not as much as I'd
expected *a priori*

Now, for fun, let's try again but with a 10 × value of *q* - the transition rate between
states.

```
y<-sim.Mk(tree,10*Q)
plotTree(tree,ftype="off",type="fan",part=0.5,
mar=c(3.1,1.1,1.1,1.1),lwd=1)
axis(1,at=c(0,0.5,1))
cols<-setNames(palette()[5:6],levels(y))
tiplabels(pie=to.matrix(y,levels(y)),piecol=cols,
cex=0.3)
```

```
fit.er<-fitDiscrete(tree,y,model="ER")
fit.lambda<-fitDiscrete(tree,y,model="ER",
transform="lambda")
```

```
fit.er
```

```
## GEIGER-fitted comparative model of discrete data
## fitted Q matrix:
## 0 1
## 0 -9.308253 9.308253
## 1 9.308253 -9.308253
##
## model summary:
## log-likelihood = -65.758662
## AIC = 133.517325
## AICc = 133.558141
## free parameters = 1
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 0
## number of iterations with same best fit = 100
## frequency of best fit = 1.00
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates
```

```
fit.lambda
```

```
## GEIGER-fitted comparative model of discrete data
## fitted Q matrix:
## 0 1
## 0 -9.308253 9.308253
## 1 9.308253 -9.308253
##
## fitted 'lambda' model parameter:
## lambda = 1.000000
##
## model summary:
## log-likelihood = -65.758662
## AIC = 135.517325
## AICc = 135.641036
## free parameters = 2
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 41
## number of iterations with same best fit = NA
## frequency of best fit = NA
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates
```

Even though we increased the transition rate, *q*, quite a lot, our ML value of λ remains
totally unchanged.

Let's visualize the likelihood surface again. Having looked ahead (that is, tried this in advance of building the post), I know that our likelihood surface is going to look a bit weird, so I will only estimate it for 0.6 < λ < 1.

```
m<-60
q<-seq(0.1*fit.lambda$opt$q12,1.5*fit.lambda$opt$q12,
length.out=m)
lambda<-seq(0.6,phytools:::maxLambda(tree),
length.out=m)
logL<-matrix(NA,m,m)
for(i in 1:m) for(j in 1:m)
logL[i,j]<-logLik(fitMk(
phytools:::lambdaTree(tree,lambda[i]),
y,fixedQ=Q*q[j],pi="fitzjohn"))
contour(lambda,q,logL,nlevels=50,
xlab=expression(lambda),ylab="q",bty="l",
font.lab=3)
title(main=expression(paste("Likelihood surface for q and ",
lambda)))
points(fit.lambda$opt$lambda,fit.lambda$opt$q12,cex=1.5,
pch=19,col="blue")
ml.q<-sapply(lambda,function(lambda,tree,x)
fitMk(phytools:::lambdaTree(tree,lambda),
x,model="ER",pi="fitzjohn")$rates,tree=tree,
x=y)
lines(lambda,ml.q,col=make.transparent("blue",0.5),
lwd=3)
legend("topleft",c(expression(paste("ML(q,",lambda,")")),
expression(paste("ML(q|",lambda,")"))),
lwd=c(NA,3),pch=c(19,NA),
col=c("blue","blue"),
bg="white",pt.cex=1.5,pt.lwd=2)
```

Here, as before, we see a prominent ridge on the likelihood surface - but one that nonetheless leads up to λ = 1.

Lastly, let's simulate an intermediate value of λ - let's say, λ = 0.6 - and then see if it can be estimated from a simulated dataset for a discrete trait.

```
z<-sim.Mk(phytools:::lambdaTree(tree,0.6),Q)
plotTree(tree,ftype="off",type="fan",part=0.5,
mar=c(3.1,1.1,1.1,1.1),lwd=1)
axis(1,at=c(0,0.5,1))
cols<-setNames(palette()[5:6],levels(y))
tiplabels(pie=to.matrix(z,levels(z)),piecol=cols,
cex=0.3)
```

```
fit.er<-fitDiscrete(tree,z,model="ER")
fit.lambda<-fitDiscrete(tree,z,model="ER",
transform="lambda")
```

```
fit.er
```

```
## GEIGER-fitted comparative model of discrete data
## fitted Q matrix:
## 0 1
## 0 -2.552545 2.552545
## 1 2.552545 -2.552545
##
## model summary:
## log-likelihood = -64.272856
## AIC = 130.545712
## AICc = 130.586528
## free parameters = 1
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 0
## number of iterations with same best fit = 100
## frequency of best fit = 1.00
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates
```

```
fit.lambda
```

```
## GEIGER-fitted comparative model of discrete data
## fitted Q matrix:
## 0 1
## 0 -0.8316731 0.8316731
## 1 0.8316731 -0.8316731
##
## fitted 'lambda' model parameter:
## lambda = 0.765701
##
## model summary:
## log-likelihood = -59.455357
## AIC = 122.910714
## AICc = 123.034426
## free parameters = 2
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 42
## number of iterations with same best fit = NA
## frequency of best fit = NA
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates
```

```
m<-60
q<-seq(0.1*fit.lambda$opt$q12,1.5*fit.lambda$opt$q12,
length.out=m)
lambda<-seq(0,phytools:::maxLambda(tree),
length.out=m)
logL<-matrix(NA,m,m)
for(i in 1:m) for(j in 1:m)
logL[i,j]<-logLik(fitMk(
phytools:::lambdaTree(tree,lambda[i]),
z,fixedQ=Q*q[j],pi="fitzjohn"))
contour(lambda,q,logL,nlevels=50,
xlab=expression(lambda),ylab="q",bty="l",
font.lab=3)
title(main=expression(paste("Likelihood surface for q and ",
lambda)))
points(fit.lambda$opt$lambda,fit.lambda$opt$q12,cex=1.5,
pch=19,col="blue")
ml.q<-sapply(lambda,function(lambda,tree,x)
fitMk(phytools:::lambdaTree(tree,lambda),
x,model="ER",pi="fitzjohn")$rates,tree=tree,
x=z)
lines(lambda,ml.q,col=make.transparent("blue",0.5),
lwd=3)
legend("topleft",c(expression(paste("ML(q,",lambda,")")),
expression(paste("ML(q|",lambda,")"))),
lwd=c(NA,3),pch=c(19,NA),
col=c("blue","blue"),
bg="white",pt.cex=1.5,pt.lwd=2)
```

I'm not sure how far we can push *q* & have this continue to hold up - but here for even quite
large transition rates between states, it would seem that (contrary to my prediction) *q* and
λ are *not* totally confounded, and probably can be separately estimated from many
datasets.

The interpretation of λ…. That's another story!

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