Nothing
R/fitpolyMk.R
In phytools: Phylogenetic Tools for Comparative Biology (and Other Things)
Defines functions
graph.polyMk
inv.ncombn2
inv.ncombn
ncombn
plot.fitpolyMk
logLik.fitpolyMk
print.fitpolyMk
fitpolyMk
Combinations
as.Qmatrix.fitpolyMk
anova.fitpolyMk
Documented in
fitpolyMk
graph.polyMk
plot.fitpolyMk
## fitpolyMk
## fits several polymorphic discrete character evolution models
## written by Liam J. Revell 2019, 2020, 2022, 2023
anova.fitpolyMk<-function(object,...) anova.fitMk(object,...)
as.Qmatrix.fitpolyMk<-function(x,...){
class(x)<-"fitMk"
as.Qmatrix(x,...)
}
Combinations<-function(n,r,v=1:n){
if(n!=length(v)) stop("n and v should have the same length")
else return(t(combn(v,r)))
}
fitpolyMk<-function(tree,x,model="SYM",ordered=FALSE,...){
if(hasArg(return_matrix)) return_matrix<-list(...)$return_matrix
else return_matrix<-FALSE
if(return_matrix) quiet<-TRUE
else {
if(hasArg(quiet)) quiet<-list(...)$quiet
else quiet<-FALSE
}
if(is.factor(x)) x<-setNames(as.character(x),names(x))
if(is.matrix(x)) X<-strsplit(colnames(x),"+",fixed=TRUE)
else X<-strsplit(x,"+",fixed=TRUE)
ns<-sapply(X,length)
## get the states
states<-sort(unique(unlist(X)))
## check if ordered
if(ordered){
if(hasArg(max.poly)) max.poly<-list(...)$max.poly
else if(hasArg(max.states)){
max.states<-list(...)$max.states
max.poly<-max.states
} else max.poly<-max(ns)
## get any user-supplied ordering
if(hasArg(order)) order<-list(...)$order
else order<-NULL
if(!is.null(order)){
if(setequal(order,states)) states<-order
else cat("order & states do not match. using alphabetical order.\n")
}
}
if(all(ns==1)&&return_matrix==FALSE){
cat("No polymorphic species found. Use fitMk.\n\n")
object<-NULL
} else {
## fix the order of the input data
if(is.matrix(x)){
Levs<-sapply(X,function(x) paste(sort(x),collapse="+"))
colnames(x)<-Levs
} else x<-sapply(X,function(x) paste(sort(x),collapse="+"))
if(ordered){
ss<-vector()
for(i in 1:(length(states)-1)){
ss<-c(ss,states[i])
for(j in (i+1):length(states))
if((j-i)<max.poly) ss<-c(ss,paste(sort(states[i:j]),collapse="+"))
}
ss<-c(ss,states[i+1])
tmodel<-matrix(0,length(ss),length(ss),dimnames=list(ss,ss))
} else {
ss<-vector()
for(i in 1:length(states))
ss<-c(ss,apply(Combinations(length(states),i,states),
1,paste,collapse="+"))
tmodel<-matrix(0,length(ss),length(ss),dimnames=list(ss,ss))
}
poly<-strsplit(ss,"+",fixed=TRUE)
index<-0
for(i in 1:nrow(tmodel)){
for(j in i:ncol(tmodel)){
INT<-intersect(poly[[i]],poly[[j]])
SDij<-setdiff(poly[[i]],poly[[j]])
SDji<-setdiff(poly[[j]],poly[[i]])
if(length(INT)>0
&&(0%in%c(length(SDij),length(SDji)))
&&(1%in%c(length(SDij),length(SDji)))){
if(model=="ER"){
tmodel[i,j]<-tmodel[j,i]<-1
} else if(model%in%c("ARD","SYM")){
index<-index+1
tmodel[i,j]<-index
if(model=="SYM") tmodel[j,i]<-index
else {
tmodel[j,i]<-index+1
index<-index+1
}
} else if(model=="transient") {
if(length(poly[[i]])>length(poly[[j]])){
tmodel[i,j]<-1
tmodel[j,i]<-2
} else {
tmodel[i,j]<-2
tmodel[j,i]<-1
}
}
}
}
}
if(!quiet){
cat("\nThis is the design matrix of the fitted model.\nDoes it make sense?\n\n")
print(tmodel)
cat("\n")
flush.console()
}
if(is.matrix(x)){
X<-matrix(0,nrow(x),length(ss),dimnames=list(rownames(x),ss))
X[rownames(x),colnames(x)]<-x
} else X<-to.matrix(x,ss)
if(return_matrix) return(X)
object<-fitMk(tree,X,model=tmodel,...)
}
object$model<-model
object$ordered<-ordered
if(ordered) attr(object$ordered,"max.poly")<-max.poly
class(object)<-"fitpolyMk"
object
}
## print method for objects of class "fitpolyMk"
print.fitpolyMk<-function(x,digits=6,...){
cat("Object of class \"fitpolyMk\".\n\n")
if(x$ordered){
cat("Evolution was modeled as \'ordered\' (i.e., transitions are assumed\n")
cat(paste("to occur ",x$states[1]," <-> ",x$states[2]," <-> ",x$states[3],
" and so on) ",sep=""))
cat(paste("using the \"",x$model,"\" model.\n\n",sep=""))
} else {
cat("Evolution was modeled as \'unordered\' ")
cat(paste("using the \"",x$model,"\" model.\n\n",sep=""))
}
cat("Fitted (or set) value of Q:\n")
Q<-matrix(NA,length(x$states),length(x$states))
Q[]<-c(0,x$rates)[x$index.matrix+1]
diag(Q)<-0
diag(Q)<--rowSums(Q)
colnames(Q)<-rownames(Q)<-x$states
print(round(Q,digits))
cat("\nFitted (or set) value of pi:\n")
print(round(x$pi,digits))
cat(paste("\nLog-likelihood:",round(x$logLik,digits),"\n"))
cat(paste("\nOptimization method used was \"",x$method,"\"\n\n",sep=""))
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")
}
## logLik method for objects of class "fitpolyMk"
logLik.fitpolyMk<-function(object,...){
lik<-object$logLik
attr(lik,"df")<-length(object$rates)
lik
}
## S3 plot method for objects of class "fitpolyMk"
plot.fitpolyMk<-function(x,...){
if(hasArg(signif)) signif<-list(...)$signif
else signif<-3
if(hasArg(main)) main<-list(...)$main
else main<-NULL
if(hasArg(cex.main)) cex.main<-list(...)$cex.main
else cex.main<-1.2
if(hasArg(cex.traits)) cex.traits<-list(...)$cex.traits
else cex.traits<-1
if(hasArg(cex.rates)) cex.rates<-list(...)$cex.rates
else cex.rates<-0.6
if(hasArg(mar)) mar<-list(...)$mar
else mar<-c(1.1,1.1,3.1,1.1)
if(hasArg(lwd)) lwd<-list(...)$lwd
else lwd<-1
if(hasArg(asp)) asp<-list(...)$asp
else asp<-1
if(hasArg(add)) add<-list(...)$add
else add<-FALSE
if(hasArg(xlim)) xlim<-list(...)$xlim
else xlim<-c(-1.2,1.2)
if(hasArg(ylim)) ylim<-list(...)$ylim
else ylim<-c(-1.2,1.2)
if(hasArg(offset)) offset<-list(...)$offset
else offset<-1.5
if(hasArg(spacer)) spacer<-list(...)$spacer
else spacer<-0.1
Q<-matrix(NA,length(x$states),length(x$states))
Q[]<-c(0,x$rates)[x$index.matrix+1]
diag(Q)<-0
if(!add) plot.new()
par(mar=mar)
plot.window(xlim=xlim,ylim=ylim,asp=asp)
if(!is.null(main)) title(main=main,cex.main=cex.main)
nstates<-length(x$states)
if(x$ordered){
if(attr(x$ordered,"max.poly")==2){
step<-360/nstates
angles<-seq(-floor(nstates/2)*step,360-ceiling(nstates/2)*step,by=step)/180*pi
if(nstates==2) angles<-angles+pi/2
v.x<-sin(angles)
v.y<-cos(angles)
for(i in 1:nstates) for(j in 1:nstates){
if(if(!isSymmetric(Q)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(Q)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(Q)) 0 else shift.y)
if(x$index.matrix[i,j]!=0){
if(abs(diff(c(i,j)))==1||abs(diff(c(i,j)))==(nstates-1))
text(mean(c(s[1],e[1]))+offset*shift.x,
mean(c(s[2],e[2]))+offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
else
text(mean(c(s[1],e[1]))+0.3*diff(c(s[1],e[1]))+
offset*shift.x,
mean(c(s[2],e[2]))+0.3*diff(c(s[2],e[2]))+
offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(Q)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,x$states,cex=cex.traits,
col=make.transparent(par()$fg,0.7))
} else {
nlevs<-attr(x$ordered,"max.poly")
Ns<-inv.ncombn2(nstates,nlevs)
v.x<-v.y<-vector()
xx<-seq(-1,1,length.out=Ns)
for(i in 1:Ns){
for(j in 1:min(nlevs,Ns-i+1)){
v.x<-c(v.x,mean(xx[i:(i+j-1)]))
v.y<-c(v.y,1-2*(j-1)/(nlevs-1))
}
}
for(i in 1:nstates) for(j in 1:nstates){
if(if(!isSymmetric(Q)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(Q)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(Q)) 0 else shift.y)
if(x$index.matrix[i,j]!=0){
text(mean(c(s[1],e[1]))+offset*shift.x,
mean(c(s[2],e[2]))+offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(Q)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,x$states,cex=cex.traits,
col=make.transparent(par()$fg,0.7))
}
} else {
Ns<-inv.ncombn(nstates)
step.y<-2/(Ns-1)
v.x<-v.y<-vector()
for(i in 1:Ns){
nc<-ncombn(Ns,i)
v.x<-c(v.x,if(nc>1) seq(-1,1,by=2/(nc-1)) else 0)
v.y<-c(v.y,rep(1-rep((i-1)*step.y,nc)))
}
for(i in 1:nstates) for(j in 1:nstates)
if(if(!isSymmetric(Q)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(Q)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(Q)) 0 else shift.y)
if(x$index.matrix[i,j]!=0){
if(abs(diff(c(i,j)))==1||abs(diff(c(i,j)))==(nstates-1))
text(mean(c(s[1],e[1]))+offset*shift.x,
mean(c(s[2],e[2]))+offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
else
text(mean(c(s[1],e[1]))+0.3*diff(c(s[1],e[1]))+
offset*shift.x,
mean(c(s[2],e[2]))+0.3*diff(c(s[2],e[2]))+
offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(Q)) 3 else 2,lwd=lwd)
}
}
text(v.x,v.y,x$states,cex=cex.traits,
col=make.transparent(par()$fg,0.7))
}
}
## internally used functions to either calculate the number of combinations
## of r elements of n or calculate the number of elements n from the sum of
## all combinations from 1:r of n elements
ncombn<-function(n,r) factorial(n)/(factorial(n-r)*factorial(r))
inv.ncombn<-function(N){
n<-Nc<-1
while(Nc!=N){
Nc<-0
for(r in 1:n) Nc<-Nc+factorial(n)/(factorial(n-r)*factorial(r))
n<-n+1
}
return(n-1)
}
inv.ncombn2<-function(N,m){
n<-2
Nc<-0
while(Nc!=N){
Nc<-sum((n:1)[1:min(m,n)])
n<-n+1
}
return(n-1)
}
## maybe this should be plot.mkModel or something?
## then you create a model class & plot it?
graph.polyMk<-function(k=2,model="SYM",ordered=FALSE,...){
if(hasArg(states)) states<-list(...)$states
else states<-0:(k-1)
if(hasArg(max.poly)) max.poly<-list(...)$max.poly
else max.poly<-k
if(hasArg(main)) main<-list(...)$main
else main<-NULL
if(hasArg(cex.main)) cex.main<-list(...)$cex.main
else cex.main<-1.2
if(hasArg(cex.traits)) cex.traits<-list(...)$cex.traits
else cex.traits<-1
if(hasArg(mar)) mar<-list(...)$mar
else mar<-c(1.1,1.1,3.1,1.1)
if(hasArg(lwd)) lwd<-list(...)$lwd
else lwd<-1
if(hasArg(quiet)) quiet<-list(...)$quiet
else quiet<-FALSE
if(hasArg(xlim)) xlim<-list(...)$xlim
else xlim<-c(-1.2,1.2)
if(hasArg(ylim)) ylim<-list(...)$ylim
else ylim<-c(-1.2,1.2)
if(hasArg(plot)) plot<-list(...)$plot
else plot<-TRUE
if(hasArg(asp)) asp<-list(...)$asp
else asp<-1
if(ordered){
ss<-vector()
for(i in 1:(length(states)-1)){
ss<-c(ss,states[i])
for(j in (i+1):length(states))
if((j-i)<max.poly) ss<-c(ss,paste(states[i:j],collapse="+"))
}
ss<-c(ss,states[i+1])
tmodel<-matrix(0,length(ss),length(ss),dimnames=list(ss,ss))
} else {
ss<-vector()
for(i in 1:length(states))
ss<-c(ss,apply(Combinations(length(states),i,states),
1,paste,collapse="+"))
tmodel<-matrix(0,length(ss),length(ss),dimnames=list(ss,ss))
}
nstates<-length(ss)
poly<-strsplit(ss,"+",fixed=TRUE)
index<-0
for(i in 1:nrow(tmodel)){
for(j in i:ncol(tmodel)){
INT<-intersect(poly[[i]],poly[[j]])
SDij<-setdiff(poly[[i]],poly[[j]])
SDji<-setdiff(poly[[j]],poly[[i]])
if(length(INT)>0
&&(0%in%c(length(SDij),length(SDji)))
&&(1%in%c(length(SDij),length(SDji)))){
if(model=="ER"){
tmodel[i,j]<-tmodel[j,i]<-1
} else if(model%in%c("ARD","SYM")){
index<-index+1
tmodel[i,j]<-index
if(model=="SYM") tmodel[j,i]<-index
else {
tmodel[j,i]<-index+1
index<-index+1
}
} else if(model=="transient"){
if(length(poly[[i]])>length(poly[[j]])){
tmodel[i,j]<-1
tmodel[j,i]<-2
} else {
tmodel[i,j]<-2
tmodel[j,i]<-1
}
}
}
}
}
if(plot){
spacer<-if(hasArg(spacer)) list(...)$spacer else 0.1
plot.new()
par(mar=mar)
plot.window(xlim=xlim,ylim=ylim,asp=asp)
if(!is.null(main)) title(main=main,cex.main=cex.main)
if(ordered){
if(max.poly==2){
step<-360/nstates
angles<-seq(-floor(nstates/2)*step,360-ceiling(nstates/2)*step,by=step)/180*pi
if(k==2) angles<-angles+pi/2
v.x<-sin(angles)
v.y<-cos(angles)
for(i in 1:nstates) for(j in 1:nstates){
if(if(!isSymmetric(tmodel)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
if(tmodel[i,j]!=0){
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(tmodel)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,colnames(tmodel),cex=cex.traits,
col=make.transparent(par()$fg,0.7))
} else {
nlevs<-max.poly
Ns<-inv.ncombn2(nstates,nlevs)
v.x<-v.y<-vector()
xx<-seq(-1,1,length.out=Ns)
for(i in 1:k){
for(j in 1:min(nlevs,Ns-i+1)){
v.x<-c(v.x,mean(xx[i:(i+j-1)]))
v.y<-c(v.y,1-2*(j-1)/(nlevs-1))
}
}
for(i in 1:nstates) for(j in 1:nstates){
if(if(!isSymmetric(tmodel)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
if(tmodel[i,j]!=0){
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(tmodel)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,rownames(tmodel),cex=cex.traits,
col=make.transparent(par()$fg,0.7))
}
} else {
step.y<-2/(k-1)
v.x<-v.y<-vector()
for(i in 1:k){
nc<-ncombn(k,i)
v.x<-c(v.x,if(nc>1) seq(-1,1,by=2/(nc-1)) else 0)
v.y<-c(v.y,rep(1-rep((i-1)*step.y,nc)))
}
for(i in 1:ncol(tmodel)) for(j in 1:ncol(tmodel)){
if(if(!isSymmetric(tmodel)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
if(tmodel[i,j]!=0){
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(tmodel)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,rownames(tmodel),cex=cex.traits,
col=make.transparent(par()$fg,0.7))
}
}
invisible(tmodel)
}
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Defines functions graph.polyMk inv.ncombn2 inv.ncombn ncombn plot.fitpolyMk logLik.fitpolyMk print.fitpolyMk fitpolyMk Combinations as.Qmatrix.fitpolyMk anova.fitpolyMk
Documented in fitpolyMk graph.polyMk plot.fitpolyMk
## fitpolyMk
## fits several polymorphic discrete character evolution models
## written by Liam J. Revell 2019, 2020, 2022, 2023
anova.fitpolyMk<-function(object,...) anova.fitMk(object,...)
as.Qmatrix.fitpolyMk<-function(x,...){
class(x)<-"fitMk"
as.Qmatrix(x,...)
}
Combinations<-function(n,r,v=1:n){
if(n!=length(v)) stop("n and v should have the same length")
else return(t(combn(v,r)))
}
fitpolyMk<-function(tree,x,model="SYM",ordered=FALSE,...){
if(hasArg(return_matrix)) return_matrix<-list(...)$return_matrix
else return_matrix<-FALSE
if(return_matrix) quiet<-TRUE
else {
if(hasArg(quiet)) quiet<-list(...)$quiet
else quiet<-FALSE
}
if(is.factor(x)) x<-setNames(as.character(x),names(x))
if(is.matrix(x)) X<-strsplit(colnames(x),"+",fixed=TRUE)
else X<-strsplit(x,"+",fixed=TRUE)
ns<-sapply(X,length)
## get the states
states<-sort(unique(unlist(X)))
## check if ordered
if(ordered){
if(hasArg(max.poly)) max.poly<-list(...)$max.poly
else if(hasArg(max.states)){
max.states<-list(...)$max.states
max.poly<-max.states
} else max.poly<-max(ns)
## get any user-supplied ordering
if(hasArg(order)) order<-list(...)$order
else order<-NULL
if(!is.null(order)){
if(setequal(order,states)) states<-order
else cat("order & states do not match. using alphabetical order.\n")
}
}
if(all(ns==1)&&return_matrix==FALSE){
cat("No polymorphic species found. Use fitMk.\n\n")
object<-NULL
} else {
## fix the order of the input data
if(is.matrix(x)){
Levs<-sapply(X,function(x) paste(sort(x),collapse="+"))
colnames(x)<-Levs
} else x<-sapply(X,function(x) paste(sort(x),collapse="+"))
if(ordered){
ss<-vector()
for(i in 1:(length(states)-1)){
ss<-c(ss,states[i])
for(j in (i+1):length(states))
if((j-i)<max.poly) ss<-c(ss,paste(sort(states[i:j]),collapse="+"))
}
ss<-c(ss,states[i+1])
tmodel<-matrix(0,length(ss),length(ss),dimnames=list(ss,ss))
} else {
ss<-vector()
for(i in 1:length(states))
ss<-c(ss,apply(Combinations(length(states),i,states),
1,paste,collapse="+"))
tmodel<-matrix(0,length(ss),length(ss),dimnames=list(ss,ss))
}
poly<-strsplit(ss,"+",fixed=TRUE)
index<-0
for(i in 1:nrow(tmodel)){
for(j in i:ncol(tmodel)){
INT<-intersect(poly[[i]],poly[[j]])
SDij<-setdiff(poly[[i]],poly[[j]])
SDji<-setdiff(poly[[j]],poly[[i]])
if(length(INT)>0
&&(0%in%c(length(SDij),length(SDji)))
&&(1%in%c(length(SDij),length(SDji)))){
if(model=="ER"){
tmodel[i,j]<-tmodel[j,i]<-1
} else if(model%in%c("ARD","SYM")){
index<-index+1
tmodel[i,j]<-index
if(model=="SYM") tmodel[j,i]<-index
else {
tmodel[j,i]<-index+1
index<-index+1
}
} else if(model=="transient") {
if(length(poly[[i]])>length(poly[[j]])){
tmodel[i,j]<-1
tmodel[j,i]<-2
} else {
tmodel[i,j]<-2
tmodel[j,i]<-1
}
}
}
}
}
if(!quiet){
cat("\nThis is the design matrix of the fitted model.\nDoes it make sense?\n\n")
print(tmodel)
cat("\n")
flush.console()
}
if(is.matrix(x)){
X<-matrix(0,nrow(x),length(ss),dimnames=list(rownames(x),ss))
X[rownames(x),colnames(x)]<-x
} else X<-to.matrix(x,ss)
if(return_matrix) return(X)
object<-fitMk(tree,X,model=tmodel,...)
}
object$model<-model
object$ordered<-ordered
if(ordered) attr(object$ordered,"max.poly")<-max.poly
class(object)<-"fitpolyMk"
object
}
## print method for objects of class "fitpolyMk"
print.fitpolyMk<-function(x,digits=6,...){
cat("Object of class \"fitpolyMk\".\n\n")
if(x$ordered){
cat("Evolution was modeled as \'ordered\' (i.e., transitions are assumed\n")
cat(paste("to occur ",x$states[1]," <-> ",x$states[2]," <-> ",x$states[3],
" and so on) ",sep=""))
cat(paste("using the \"",x$model,"\" model.\n\n",sep=""))
} else {
cat("Evolution was modeled as \'unordered\' ")
cat(paste("using the \"",x$model,"\" model.\n\n",sep=""))
}
cat("Fitted (or set) value of Q:\n")
Q<-matrix(NA,length(x$states),length(x$states))
Q[]<-c(0,x$rates)[x$index.matrix+1]
diag(Q)<-0
diag(Q)<--rowSums(Q)
colnames(Q)<-rownames(Q)<-x$states
print(round(Q,digits))
cat("\nFitted (or set) value of pi:\n")
print(round(x$pi,digits))
cat(paste("\nLog-likelihood:",round(x$logLik,digits),"\n"))
cat(paste("\nOptimization method used was \"",x$method,"\"\n\n",sep=""))
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")
}
## logLik method for objects of class "fitpolyMk"
logLik.fitpolyMk<-function(object,...){
lik<-object$logLik
attr(lik,"df")<-length(object$rates)
lik
}
## S3 plot method for objects of class "fitpolyMk"
plot.fitpolyMk<-function(x,...){
if(hasArg(signif)) signif<-list(...)$signif
else signif<-3
if(hasArg(main)) main<-list(...)$main
else main<-NULL
if(hasArg(cex.main)) cex.main<-list(...)$cex.main
else cex.main<-1.2
if(hasArg(cex.traits)) cex.traits<-list(...)$cex.traits
else cex.traits<-1
if(hasArg(cex.rates)) cex.rates<-list(...)$cex.rates
else cex.rates<-0.6
if(hasArg(mar)) mar<-list(...)$mar
else mar<-c(1.1,1.1,3.1,1.1)
if(hasArg(lwd)) lwd<-list(...)$lwd
else lwd<-1
if(hasArg(asp)) asp<-list(...)$asp
else asp<-1
if(hasArg(add)) add<-list(...)$add
else add<-FALSE
if(hasArg(xlim)) xlim<-list(...)$xlim
else xlim<-c(-1.2,1.2)
if(hasArg(ylim)) ylim<-list(...)$ylim
else ylim<-c(-1.2,1.2)
if(hasArg(offset)) offset<-list(...)$offset
else offset<-1.5
if(hasArg(spacer)) spacer<-list(...)$spacer
else spacer<-0.1
Q<-matrix(NA,length(x$states),length(x$states))
Q[]<-c(0,x$rates)[x$index.matrix+1]
diag(Q)<-0
if(!add) plot.new()
par(mar=mar)
plot.window(xlim=xlim,ylim=ylim,asp=asp)
if(!is.null(main)) title(main=main,cex.main=cex.main)
nstates<-length(x$states)
if(x$ordered){
if(attr(x$ordered,"max.poly")==2){
step<-360/nstates
angles<-seq(-floor(nstates/2)*step,360-ceiling(nstates/2)*step,by=step)/180*pi
if(nstates==2) angles<-angles+pi/2
v.x<-sin(angles)
v.y<-cos(angles)
for(i in 1:nstates) for(j in 1:nstates){
if(if(!isSymmetric(Q)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(Q)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(Q)) 0 else shift.y)
if(x$index.matrix[i,j]!=0){
if(abs(diff(c(i,j)))==1||abs(diff(c(i,j)))==(nstates-1))
text(mean(c(s[1],e[1]))+offset*shift.x,
mean(c(s[2],e[2]))+offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
else
text(mean(c(s[1],e[1]))+0.3*diff(c(s[1],e[1]))+
offset*shift.x,
mean(c(s[2],e[2]))+0.3*diff(c(s[2],e[2]))+
offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(Q)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,x$states,cex=cex.traits,
col=make.transparent(par()$fg,0.7))
} else {
nlevs<-attr(x$ordered,"max.poly")
Ns<-inv.ncombn2(nstates,nlevs)
v.x<-v.y<-vector()
xx<-seq(-1,1,length.out=Ns)
for(i in 1:Ns){
for(j in 1:min(nlevs,Ns-i+1)){
v.x<-c(v.x,mean(xx[i:(i+j-1)]))
v.y<-c(v.y,1-2*(j-1)/(nlevs-1))
}
}
for(i in 1:nstates) for(j in 1:nstates){
if(if(!isSymmetric(Q)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(Q)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(Q)) 0 else shift.y)
if(x$index.matrix[i,j]!=0){
text(mean(c(s[1],e[1]))+offset*shift.x,
mean(c(s[2],e[2]))+offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(Q)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,x$states,cex=cex.traits,
col=make.transparent(par()$fg,0.7))
}
} else {
Ns<-inv.ncombn(nstates)
step.y<-2/(Ns-1)
v.x<-v.y<-vector()
for(i in 1:Ns){
nc<-ncombn(Ns,i)
v.x<-c(v.x,if(nc>1) seq(-1,1,by=2/(nc-1)) else 0)
v.y<-c(v.y,rep(1-rep((i-1)*step.y,nc)))
}
for(i in 1:nstates) for(j in 1:nstates)
if(if(!isSymmetric(Q)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(Q)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(Q)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(Q)) 0 else shift.y)
if(x$index.matrix[i,j]!=0){
if(abs(diff(c(i,j)))==1||abs(diff(c(i,j)))==(nstates-1))
text(mean(c(s[1],e[1]))+offset*shift.x,
mean(c(s[2],e[2]))+offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
else
text(mean(c(s[1],e[1]))+0.3*diff(c(s[1],e[1]))+
offset*shift.x,
mean(c(s[2],e[2]))+0.3*diff(c(s[2],e[2]))+
offset*shift.y,
round(Q[i,j],signif),cex=cex.rates,
srt=atan(asp*dy/dx)*180/pi)
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(Q)) 3 else 2,lwd=lwd)
}
}
text(v.x,v.y,x$states,cex=cex.traits,
col=make.transparent(par()$fg,0.7))
}
}
## internally used functions to either calculate the number of combinations
## of r elements of n or calculate the number of elements n from the sum of
## all combinations from 1:r of n elements
ncombn<-function(n,r) factorial(n)/(factorial(n-r)*factorial(r))
inv.ncombn<-function(N){
n<-Nc<-1
while(Nc!=N){
Nc<-0
for(r in 1:n) Nc<-Nc+factorial(n)/(factorial(n-r)*factorial(r))
n<-n+1
}
return(n-1)
}
inv.ncombn2<-function(N,m){
n<-2
Nc<-0
while(Nc!=N){
Nc<-sum((n:1)[1:min(m,n)])
n<-n+1
}
return(n-1)
}
## maybe this should be plot.mkModel or something?
## then you create a model class & plot it?
graph.polyMk<-function(k=2,model="SYM",ordered=FALSE,...){
if(hasArg(states)) states<-list(...)$states
else states<-0:(k-1)
if(hasArg(max.poly)) max.poly<-list(...)$max.poly
else max.poly<-k
if(hasArg(main)) main<-list(...)$main
else main<-NULL
if(hasArg(cex.main)) cex.main<-list(...)$cex.main
else cex.main<-1.2
if(hasArg(cex.traits)) cex.traits<-list(...)$cex.traits
else cex.traits<-1
if(hasArg(mar)) mar<-list(...)$mar
else mar<-c(1.1,1.1,3.1,1.1)
if(hasArg(lwd)) lwd<-list(...)$lwd
else lwd<-1
if(hasArg(quiet)) quiet<-list(...)$quiet
else quiet<-FALSE
if(hasArg(xlim)) xlim<-list(...)$xlim
else xlim<-c(-1.2,1.2)
if(hasArg(ylim)) ylim<-list(...)$ylim
else ylim<-c(-1.2,1.2)
if(hasArg(plot)) plot<-list(...)$plot
else plot<-TRUE
if(hasArg(asp)) asp<-list(...)$asp
else asp<-1
if(ordered){
ss<-vector()
for(i in 1:(length(states)-1)){
ss<-c(ss,states[i])
for(j in (i+1):length(states))
if((j-i)<max.poly) ss<-c(ss,paste(states[i:j],collapse="+"))
}
ss<-c(ss,states[i+1])
tmodel<-matrix(0,length(ss),length(ss),dimnames=list(ss,ss))
} else {
ss<-vector()
for(i in 1:length(states))
ss<-c(ss,apply(Combinations(length(states),i,states),
1,paste,collapse="+"))
tmodel<-matrix(0,length(ss),length(ss),dimnames=list(ss,ss))
}
nstates<-length(ss)
poly<-strsplit(ss,"+",fixed=TRUE)
index<-0
for(i in 1:nrow(tmodel)){
for(j in i:ncol(tmodel)){
INT<-intersect(poly[[i]],poly[[j]])
SDij<-setdiff(poly[[i]],poly[[j]])
SDji<-setdiff(poly[[j]],poly[[i]])
if(length(INT)>0
&&(0%in%c(length(SDij),length(SDji)))
&&(1%in%c(length(SDij),length(SDji)))){
if(model=="ER"){
tmodel[i,j]<-tmodel[j,i]<-1
} else if(model%in%c("ARD","SYM")){
index<-index+1
tmodel[i,j]<-index
if(model=="SYM") tmodel[j,i]<-index
else {
tmodel[j,i]<-index+1
index<-index+1
}
} else if(model=="transient"){
if(length(poly[[i]])>length(poly[[j]])){
tmodel[i,j]<-1
tmodel[j,i]<-2
} else {
tmodel[i,j]<-2
tmodel[j,i]<-1
}
}
}
}
}
if(plot){
spacer<-if(hasArg(spacer)) list(...)$spacer else 0.1
plot.new()
par(mar=mar)
plot.window(xlim=xlim,ylim=ylim,asp=asp)
if(!is.null(main)) title(main=main,cex.main=cex.main)
if(ordered){
if(max.poly==2){
step<-360/nstates
angles<-seq(-floor(nstates/2)*step,360-ceiling(nstates/2)*step,by=step)/180*pi
if(k==2) angles<-angles+pi/2
v.x<-sin(angles)
v.y<-cos(angles)
for(i in 1:nstates) for(j in 1:nstates){
if(if(!isSymmetric(tmodel)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
if(tmodel[i,j]!=0){
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(tmodel)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,colnames(tmodel),cex=cex.traits,
col=make.transparent(par()$fg,0.7))
} else {
nlevs<-max.poly
Ns<-inv.ncombn2(nstates,nlevs)
v.x<-v.y<-vector()
xx<-seq(-1,1,length.out=Ns)
for(i in 1:k){
for(j in 1:min(nlevs,Ns-i+1)){
v.x<-c(v.x,mean(xx[i:(i+j-1)]))
v.y<-c(v.y,1-2*(j-1)/(nlevs-1))
}
}
for(i in 1:nstates) for(j in 1:nstates){
if(if(!isSymmetric(tmodel)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
if(tmodel[i,j]!=0){
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(tmodel)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,rownames(tmodel),cex=cex.traits,
col=make.transparent(par()$fg,0.7))
}
} else {
step.y<-2/(k-1)
v.x<-v.y<-vector()
for(i in 1:k){
nc<-ncombn(k,i)
v.x<-c(v.x,if(nc>1) seq(-1,1,by=2/(nc-1)) else 0)
v.y<-c(v.y,rep(1-rep((i-1)*step.y,nc)))
}
for(i in 1:ncol(tmodel)) for(j in 1:ncol(tmodel)){
if(if(!isSymmetric(tmodel)) i!=j else i>j){
dx<-v.x[j]-v.x[i]
dy<-v.y[j]-v.y[i]
slope<-abs(dy/dx)
shift.x<-0.02*sin(atan(dy/dx))*sign(j-i)*if(dy/dx>0) 1 else -1
shift.y<-0.02*cos(atan(dy/dx))*sign(j-i)*if(dy/dx>0) -1 else 1
s<-c(v.x[i]+spacer*cos(atan(slope))*sign(dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[i]+spacer*sin(atan(slope))*sign(dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
e<-c(v.x[j]+spacer*cos(atan(slope))*sign(-dx)+
if(isSymmetric(tmodel)) 0 else shift.x,
v.y[j]+spacer*sin(atan(slope))*sign(-dy)+
if(isSymmetric(tmodel)) 0 else shift.y)
if(tmodel[i,j]!=0){
arrows(s[1],s[2],e[1],e[2],length=0.05,
code=if(isSymmetric(tmodel)) 3 else 2,lwd=lwd)
}
}
}
text(v.x,v.y,rownames(tmodel),cex=cex.traits,
col=make.transparent(par()$fg,0.7))
}
}
invisible(tmodel)
}
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