Today I’ve been working on a multi-rate discrete character dependent trended Brownian motion continuous trait evolution model using the discretized diffusion approximation, which I recommend you read about in earlier posts to this blog (e.g., 1, 2, 3, 4, 5, and others).
Our multi-rate trended model will be one in which both the diffusion rate (\(\sigma^2\)) and the mean trend parameter (\(\mu\)) are free to vary depending on a discrete character that we model jointly with our continuous trait.
My function to fit this model to data using the discretized diffusion approximation is based on fitmultiBM (from prior posts and phytools) and looks as follows. (Please treat this as “bleeding edge,” of course!)
## function to fit a discrete-state-dependent multi-rate, multi-trend Brownian
## model using the discretized diffusion approximation of Boucher & Demery (2016)
fitmultiTrend<-function(tree,x,y=NULL,model="ER",ncat=1,...){
if(hasArg(logscale)) logscale<-list(...)$logscale
else logscale<-TRUE
if(hasArg(rand_start)) rand_start<-list(...)$rand_start
else rand_start<-TRUE
levs<-if(hasArg(levs)) list(...)$levs else 100
parallel<-if(hasArg(parallel)) list(...)$parallel else
FALSE
lik.func<-if(parallel) "parallel" else "pruning"
if(is.null(y)&&ncat==1) lik.func<-"eigen"
if(hasArg(opt.method)) opt.method<-list(...)$opt.method
else opt.method<-"nlminb"
null_model<-if(hasArg(null_model)) list(...)$null_model else
FALSE
ncores<-if(hasArg(ncores)) list(...)$ncores else
detectCores()-1
## continuous character
x<-x[tree$tip.label]
if(hasArg(lims)) lims<-list(...)$lims
else lims<-expand.range(x)
dd<-diff(lims)
delta<-dd/levs
tol<-1e-8*delta
bins<-cbind(seq(from=lims[1]-tol,by=(dd+2*tol)/levs,
length.out=levs),seq(to=lims[2]+tol,by=(dd+2*tol)/levs,
length.out=levs))
X<-to_binned(x,bins)
if(hasArg(wrapped)) wrapped<-list(...)$wrapped
else wrapped<-FALSE
## discrete character
if(!is.null(y)){
if(is.matrix(y)){
y<-y[tree$tip.label,]
m<-ncol(y)
states<-colnames(y)
} else {
y<-to.matrix(y,sort(unique(y)))
y<-y[tree$tip.label,]
m<-ncol(y)
states<-colnames(y)
}
} else {
y<-matrix(1,nrow(X),1,dimnames=list(rownames(X),"0"))
}
if(ncat>1){
y_tmp<-y
for(i in 2:ncat){
colnames(y_tmp)<-paste(colnames(y_tmp),"*",sep="")
y<-cbind(y,y_tmp)
}
y<-y[,order(colnames(y))]
}
## combine
nn<-x_by_y(colnames(y),colnames(X))
XX<-matrix(0,nrow=nrow(X),ncol=ncol(y)*ncol(X),
dimnames=list(rownames(X),nn))
for(i in 1:ncol(y)){
for(j in 1:nrow(X)){
XX[j,1:levs+(i-1)*levs]<-X[j,]*y[j,i]
}
}
## set pi
if(hasArg(root)) root=list(...)$root
else root<-"mle"
if(root=="nuisance") pi<-"fitzjohn"
else if(root=="mle") pi<-"mle"
else if(root=="flat") pi<-rep(1/ncol(XX),ncol(XX))
## build continuous model
cmodel<-matrix(0,nrow=ncol(XX),ncol=ncol(XX),
dimnames=list(nn,nn))
for(i in 1:(levs-1)){
for(j in 0:(ncol(y)-1)){
cmodel[i+j*levs,i+1+j*levs]<-(j+1)
cmodel[i+1+j*levs,i+j*levs]<-(j+1)+ncol(y)
}
}
if(wrapped){
for(i in 0:(ncol(y)-1)){
cmodel[1+i*levs,(i+1)*levs]<-(i+1)+ncol(y)
cmodel[(i+1)*levs,1+i*levs]<-(i+1)
}
}
state_ind<-setNames(1:ncol(y),colnames(y))
if(null_model){
if(ncat==1){
for(i in 2:ncol(cmodel)){
if(cmodel[i-1,i]>0) cmodel[i-1,i]<-1
if(cmodel[i,i-1]>0) cmodel[i,i-1]<-2
}
state_ind[]<-1
} else {
## allow hidden character to have different rates
hs<-strsplit(nn,"")
foo<-function(x){
a<-paste(x[x!="*"],collapse="")
b<-paste(x[x=="*"],collapse="")
return(c(a,b))
}
hs<-sapply(hs,foo)[2,]
un.hs<-sort(unique(hs))
ind<-which(cmodel>1,arr.ind=TRUE)
k<-1
for(i in 1:ncat){
ii<-which(hs==un.hs[i])
ii<-ii[!((ii%%levs)==0)]
state_ind[unique(cmodel[cbind(ii+1,ii)])]<-k
cmodel[cbind(ii+1,ii)]<-k
cmodel[cbind(ii,ii+1)]<-k+ncat
k<-k+1
}
}
}
## build discrete model
dmodel<-matrix(0,nrow=ncol(XX),ncol=ncol(XX),
dimnames=list(nn,nn))
qmodel<-matrix(0,nrow=ncol(y),ncol=ncol(y),
dimnames=list(colnames(y),colnames(y)))
dn<-strsplit(colnames(qmodel),"")
foo<-function(x){
a<-paste(x[x!="*"],collapse="")
b<-paste(x[x=="*"],collapse="")
return(c(a,b))
}
dn<-sapply(dn,foo)
q1<-matrix(0,length(unique(dn[1,])),
length(unique(dn[1,])),
dimnames=list(sort(unique(dn[1,])),
sort(unique(dn[1,]))))
if(is.matrix(model)){
cust_model<-model
model<-"custom"
}
if(model=="ER"){
q1[]<-1
diag(q1)<-0
} else if(model=="SYM"){
k<-1
for(i in 1:(nrow(q1)-1)){
for(j in (i+1):ncol(q1)){
q1[i,j]<-q1[j,i]<-k
k<-k+1
}
}
} else if(model=="ARD"){
k<-1
for(i in 1:nrow(q1)){
for(j in 1:ncol(q1)){
if(i!=j){
q1[i,j]<-k
k<-k+1
}
}
}
} else if(model=="custom"){
if(is.null(rownames(cust_model)))
colnames(cust_model)<-rownames(cust_model)<-rownames(q1)
q1[]<-cust_model[rownames(q1),colnames(q1)]
cat("This is the design of your custom discrete-trait model:\n")
print(q1)
}
q2<-matrix(0,length(unique(dn[2,])),
length(unique(dn[2,])),
dimnames=list(sort(unique(dn[2,])),
sort(unique(dn[2,]))))
if(ncat>1){
if(hasArg(model.hrm)) model.hrm<-list(...)$model.hrm
else model.hrm<-if(model%in%c("ER","SYM","ARD")) model else "SYM"
} else model.hrm<-NULL
if(!is.null(model.hrm)){
if(is.matrix(model.hrm)){
q2[]<-model.hrm
cat("This is the design of your custom hidden-trait model:\n")
print(q2)
} else if(model.hrm=="ER"){
q2[]<-max(q1)+1
diag(q2)<-0
} else if(model.hrm=="SYM"){
k<-max(q1)+1
for(i in 1:(nrow(q2)-1)){
for(j in (i+1):ncol(q2)){
q2[i,j]<-q2[j,i]<-k
k<-k+1
}
}
} else if(model.hrm=="ARD"){
k<-max(q1)+1
for(i in 1:nrow(q2)){
for(j in 1:ncol(q2)){
if(i!=j){
q2[i,j]<-k
k<-k+1
}
}
}
}
}
for(i in 1:nrow(qmodel)){
for(j in 1:ncol(qmodel)){
if(dn[1,i]!=dn[1,j]&&dn[2,i]==dn[2,j])
qmodel[i,j]<-q1[dn[1,i],dn[1,j]]
else if(dn[1,i]==dn[1,j]&&dn[2,i]!=dn[2,j])
qmodel[i,j]<-q2[which(rownames(q2)==dn[2,i]),
which(colnames(q2)==dn[2,j])]
}
}
dn<-strsplit(nn,",")
for(i in 1:ncol(dmodel)){
for(j in 1:ncol(dmodel)){
if(dn[[i]][2]==dn[[j]][2]){
dmodel[i,j]<-qmodel[dn[[i]][1],dn[[j]][1]]
}
}
}
dmodel[dmodel>0]<-dmodel[dmodel>0]+max(cmodel)
model<-cmodel+dmodel
plot_model<-if(hasArg(plot_model))
list(...)$plot_model else FALSE
## graph model (optional)
if(plot_model){
if(hasArg(asp)) asp<-list(...)$asp
else asp<-1
if(hasArg(mar)) mar<-list(...)$mar
else mar<-c(1.1,1.1,4.1,1.1)
if(hasArg(title)) title<-list(...)$title
else title<-TRUE
plot.new()
dev.hold()
par(mar=mar)
if(hasArg(cols)){
cols<-list(...)$cols
cols<-setNames(cols,0:max(model))
} else {
cols<-setNames(c("#f9f9f7",sample(rainbow(n=max(model)))),
0:max(model))
}
plot.window(xlim=c(-0.05*ncol(model),1.1*ncol(model)),
ylim=c(nrow(model),-0.05*nrow(model)),
asp=asp)
for(i in 1:nrow(model)){
for(j in 1:ncol(model)){
polygon(x=c(i-1,i,i,i-1),y=c(j-1,j-1,j,j),
border=FALSE,col=cols[as.character(model[i,j])])
}
}
polygon(c(0,ncol(model),ncol(model),0),
c(0,0,nrow(model),nrow(model)),border="black")
for(i in 1:ncol(qmodel)) for(j in 1:nrow(qmodel)){
polygon(c(0,levs,levs,0)+(i-1)*levs,
c(0,0,levs,levs)+(j-1)*levs,border="grey")
}
for(i in 1:ncol(qmodel)){
text(x=mean(c(0,levs)+(i-1)*levs),
y=-0.025*ncol(model),colnames(qmodel)[i])
text(x=-0.025*ncol(model),
y=mean(c(0,levs)+(i-1)*levs),
rownames(qmodel)[i],srt=90)
}
if(title) title("structure of discretized model",font.main=3)
lp<-legend(x=ncol(model)/2,y=1.05*nrow(model),
legend=names(cols),pch=15,col=cols,bty="n",xpd=TRUE,
horiz=TRUE,plot=FALSE,cex=0.8)
for(i in 1:ncol(qmodel)){
tmp<-paste("[",colnames(qmodel)[i],"] ",sep="")
if(i==1) nm<-bquote(sigma^2~.(tmp)~-mu~.(tmp))
else nm<-c(nm,bquote(sigma^2~.(tmp)~-mu~.(tmp)))
}
for(i in 1:nrow(qmodel)){
tmp<-paste("[",colnames(qmodel)[i],"] ",sep="")
nm<-c(nm,bquote(sigma^2~.(tmp)~+mu~.(tmp)))
}
if(max(qmodel)>=1) nm<-c(nm,paste("q[",1:max(qmodel),"]",sep=""))
legend(x=ncol(model),y=0,
legend=nm,pch=15,
col=cols[2:length(cols)],
bty="n",xpd=TRUE,horiz=FALSE,cex=0.8)
dev.flush()
}
## set initial values for optimization
qq<-if(ncol(y)>1) fitMk(tree,y,model="ER")$rates else
1/sum(tree$edge.length)
q.init<-c(rep((1/2)*mean(pic(x,multi2di(tree))^2)*(levs/dd)^2,
max(cmodel)),rep(qq,max(qmodel)))
if(rand_start) q.init<-q.init*runif(n=length(q.init),0,2)
max.q<-max(q.init)*1000
## optimize model
if(lik.func%in%c("pruning","parallel")){
fit<-fitMk(tree,XX,model=model,
lik.func=if(parallel) "parallel" else "pruning",
expm.method=if(isSymmetric(model)) "R_Eigen" else
"Higham08.b",
pi=pi,logscale=logscale,q.init=q.init,
opt.method=opt.method,max.q=max.q)
} else {
QQ<-model
diag(QQ)<--rowSums(QQ)
eQQ<-eigen(QQ)
pw<-reorder(tree,"postorder")
if(parallel){
if(hasArg(ncores)) ncores<-list(...)$ncores
else ncores<-min(nrow(tree$edge),detectCores()-1)
mc<-makeCluster(ncores,type="PSOCK")
registerDoParallel(cl=mc)
}
fit<-optimize(eigen_pruning,c(tol,10*q.init[1]),tree=pw,
x=X,eigenQ=eQQ,parallel=parallel,pi=pi,
maximum=TRUE)
fit<-list(
logLik=fit$objective,
rates=fit$maximum,
index.matrix=model,
states=colnames(XX),
pi=eigen_pruning(fit$maximum,pw,X,eQQ,pi=pi,
return="pi",parallel=parallel),
method="optimize",
root.prior=if(pi[1]=="fitzjohn") "nuisance" else pi,
opt_results=list(convergence=0),
data=XX,
tree=pw)
class(fit)<-"fitMk"
if(parallel) stopCluster(cl=mc)
}
sig2<-(fit$rates[1:(max(cmodel)/2)]+
fit$rates[1:(max(cmodel)/2)+max(cmodel)/2])*delta^2
mu<-(fit$rates[1:(max(cmodel)/2)]-
fit$rates[1:(max(cmodel)/2)+max(cmodel)/2])*delta
q<-fit$rates[1:max(qmodel)+max(cmodel)]
index.matrix<-qmodel
lnL<-logLik(fit)-Ntip(tree)*log(delta)
attr(lnL,"df")<-max(model)+1
## likelihood function (to export)
lfunc<-function(sig2,mu,q,x0="mle",...){
if(hasArg(lik.func)) lik.func<-list(...)$lik.func
else lik.func<-"pruning"
if(hasArg(parallel)) parallel<-list(...)$parallel
else parallel<-FALSE
delta<-dd/levs
jj<-1:(length(sig2)/2)
kk<-1:(length(sig2)/2)+length(sig2)/2
q<-c(sig2[jj]/(2*delta^2)+mu[jj]/(2*delta),
sig2[kk]/(2*delta^2)-mu[kk]/(2*delta),
q)
if(x0=="mle") pi<-"mle"
else if(x0=="nuisance") pi<-"fitzjohn"
else if(is.numeric(x0)) pi<-to_binned(x0,bins)[1,]
if(lik.func=="pruning"){
lnL<-pruning(q,tree,XX,model,pi=pi,
expm.method=if(isSymmetric(model)) "R_Eigen" else
"Higham08.b")-
Ntip(tree)*log(delta)
} else if(lik.func=="parallel"){
if(!exists("ncores")) ncores<-min(nrow(tree$edge),
detectCores()-1)
mc<-makeCluster(ncores,type="PSOCK")
registerDoParallel(cl=mc)
lnL<-parallel_pruning(q,tree,XX,model,pi=pi,
expm.method=if(isSymmetric(model)) "R_Eigen" else
"Higham08.b")-Ntip(tree)*log(delta)
stopCluster(cl=mc)
}
lnL
}
object<-list(
sigsq=sig2,
mu=mu,
state_ind=state_ind,
x0=sum(fit$pi*rowMeans(bins)),
bounds=lims,
rates=q,
index.matrix=qmodel,
states=colnames(y),
ncat=levs,
logLik=lnL,
opt_results=fit$opt_results,
mk_fit=fit,
lik=lfunc)
class(object)<-c("fitmultiTrend","fitmultiBM","fitMk")
object
}
print.fitmultiTrend<-function(x,digits=4,...){
cat(paste("Object of class \"fitmultiTrend\" based on\n",
" a discretization with k =",
x$ncat,"levels.\n\n"))
cat("Fitted multi-rate trend model parameters:\n")
cat(paste(" levels: [",
paste(names(x$state_ind),collapse=", "),
"]\n"))
cat(paste(" sigsq: [",
paste(round(x$sigsq[x$state_ind],digits),collapse=", "),
"]\n"))
cat(paste(" mu: [",
paste(round(x$mu[x$state_ind],digits),collapse=", "),
"]\n"))
cat(paste(" x0:",round(x$x0,digits),"\n\n"))
print(as.Qmatrix(x))
cat(paste("\nLog-likelihood:",round(x$logLik,digits),
"\n\n"))
if(x$opt_results$convergence == 0)
cat("R thinks it has found the ML solution.\n\n")
else cat(
"R thinks optimization may not have converged.\n\n")
}
This function is very slow at the moment, but I’ll soon be working on other solutions to speed up likelihood calculation.
Nonetheless, let’s try it out.
First, I’m going to simulate data with a trend but no discrete-state specific rate or trend variation.
We can fit this model using geiger::fitContinuous and compare it to our new function before we move on to more interesting models.
To start, let’s load packages and also pull to the namespace a number of phytools internals that our new function is going to need.
library(phytools)
library(parallel)
library(geiger)
expand.range<-phytools:::expand.range
to_binned<-phytools:::to_binned
x_by_y<-phytools:::x_by_y
(We won’t need to do this when the function gets incorporated into phytools.)
Next, we can simulate data for trended evolution of a continuous character as well as a separate discrete character that (in this case) has nothing to do with our continuous trait.
Note that I’m purposely simulating a tree with non-contemporaneous tips (e.g., a tip-dated tree). This is because conventional wisdom suggests that trend models will be non-identifiable for ultrametric trees, and I believe this to be correct. (Trend models can nonetheless be very useful for studying things like viral phylodynamics where the phylogeny has been measured in real-time.)
tree<-rtree(n=500)
Q<-matrix(c(-1,1,1,-1),2,2,
dimnames=list(letters[1:2],letters[1:2]))
x<-fastBM(tree,mu=-0.2)
y<-sim.Mk(tree,Q)
Let’s go ahead and fit our trended null model – that is, one in which the discrete state evolves via a simple Mk process, our continuous trait via trended Brownian motion, but neither has anything to do with the other.
fit_trended<-fitmultiTrend(tree,x,y,
levs=100,parallel=TRUE,
plot_model=TRUE,null_model=TRUE)
fit_trended
## Object of class "fitmultiTrend" based on
## a discretization with k = 100 levels.
##
## Fitted multi-rate trend model parameters:
## levels: [ a, b ]
## sigsq: [ 1.0735, 1.0735 ]
## mu: [ -0.1413, -0.1413 ]
## x0: -0.3227
##
## Estimated Q matrix:
## a b
## a -0.7916461 0.7916461
## b 0.7916461 -0.7916461
##
## Log-likelihood: -1099.3138
##
## R thinks it has found the ML solution.
Inspecting our fitted model object shows that our parameter estimates are quite close to their generating values, which is good.
In this case there is actually a closed form solution for the likelihood, since our processes are operating independently, which we can use to fit our model with both geiger::fitContinuous and phytools::fitMk. Let’s do that and compare our results.
fitc_trended<-fitContinuous(tree,x,
model="mean_trend")
fitc_trended
## GEIGER-fitted comparative model of continuous data
## fitted 'mean_trend' model parameters:
## sigsq = 1.058073
## drift = -0.146110
## z0 = -0.331782
##
## model summary:
## log-likelihood = -759.914195
## AIC = 1525.828391
## AICc = 1525.876778
## free parameters = 3
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 0
## number of iterations with same best fit = 28
## frequency of best fit = 0.280
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimates
mk_fit<-fitMk(tree,y,model="ER",pi="mle")
mk_fit
## Object of class "fitMk".
##
## Fitted (or set) value of Q:
## a b
## a -0.791646 0.791646
## b 0.791646 -0.791646
##
## Fitted (or set) value of pi:
## a b
## 0 1
## due to treating the root prior as (a) it's MLE.
##
## Log-likelihood: -337.158553
##
## Optimization method used was "nlminb"
##
## R thinks it has found the ML solution.
logLik(fitc_trended)+logLik(mk_fit)
## [1] -1097.073
## attr(,"df")
## [1] 3
(The two different likelihoods should eventually converge for increased levs, but it becomes more and more computationally onerous.)
This is very good. It tells us at least that our discretized diffusion approximation works properly under the null.
Before we can go and fit our new discrete character dependent trended Brownian model, we need a simulator function – because none exists.
Here’s one I just programmed.
sim.multiTrend<-function(tree,sig2=1,mu=0,x0=0,...){
if(hasArg(plot)) plot<-list(...)$plot
else plot<-TRUE
tt<-map.to.singleton(tree)
ss<-sort(unique(names(tt$edge.length)))
if(length(sig2)==1) sig2<-rep(sig2,length(ss))
if(length(mu)==1) mu<-rep(mu,length(ss))
if(is.null(names(sig2))) names(sig2)<-ss
if(is.null(names(mu))) names(mu)<-ss
sig2<-sig2[names(tt$edge.length)]
mu<-mu[names(tt$edge.length)]
delta<-rnorm(n=nrow(tt$edge),
mean=mu,sd=sqrt(tt$edge.length*sig2))
tt<-reorder(tt)
X<-matrix(x0,nrow(tt$edge),ncol(tt$edge))
for(i in 1:nrow(tt$edge)){
X[i,2]<-X[i,1]+delta[i]
ii<-which(tt$edge[,1]==tt$edge[i,2])
if(length(ii)>0) X[ii,1]<-X[i,2]
}
if(plot){
xx<-c(
sapply(1:Ntip(tt),
function(ii,ee,x) x[which(ee[,2]==ii),2],
ee=tt$edge,x=X),
X[1,1],
sapply(2:tt$Nnode+Ntip(tt),
function(ii,ee,x) x[which(ee[,2]==ii),2],
ee=tt$edge,x=X))
xx<-setNames(xx,c(tt$tip.label,
1:tt$Nnode+Ntip(tt)))
edge_col<-setNames(palette()[1:length(ss)],ss)
tt$maps<-as.list(tt$edge.length)
for(i in 1:length(tt$maps))
tt$maps[[i]]<-setNames(tt$maps[[i]],
names(tt$edge.length)[i])
phenogram(tt,xx,ftype="off",colors=edge_col)
}
setNames(
sapply(1:Ntip(tt),
function(ii,ee,x) x[which(ee[,2]==ii),2],
ee=tt$edge,x=X),
tt$tip.label)
}
Now let’s use it to simulate data for a constant \(\sigma^2 = 1\), but different values of \(\mu\) (the trend parameter) depending on the state of our discrete character. Our simulator has a handy plotter for visualizing the evolutionary process that has been generated.
Q<-matrix(c(-1,1,1,-1),2,2,
dimnames=list(letters[1:2],letters[1:2]))
tt<-sim.history(tree,Q)
## Done simulation(s).
x<-sim.multiTrend(tt,sig2=1,mu=c(-0.05,0.2))
legend("topleft",
c(expression(paste(mu," = -0.05")),
expression(paste(mu, " = 0.20"))),
lwd=3,col=palette()[1:2],
bty="n",cex=0.8)
## pull out the discrete character as a factor
y<-as.factor(getStates(tt,"tips"))
Last of all, let’s fit our new trended model.
fit_dependent<-fitmultiTrend(tree,x,y,
levs=100,parallel=TRUE,plot_model=TRUE)
fit_dependent
## Object of class "fitmultiTrend" based on
## a discretization with k = 100 levels.
##
## Fitted multi-rate trend model parameters:
## levels: [ a, b ]
## sigsq: [ 1.0075, 0.8932 ]
## mu: [ -0.0709, 0.438 ]
## x0: 0.6077
##
## Estimated Q matrix:
## a b
## a -1.074791 1.074791
## b 1.074791 -1.074791
##
## Log-likelihood: -1077.9299
##
## R thinks it has found the ML solution.
Seems like it pretty much works…. ¯\_(ツ)_/¯
That’s all folks!
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