| Title: | Estimating Birth and Death Rates Based on Phylogenies |
|---|---|
| Description: | (i) For a given species phylogeny on present day data which is calibrated to calendar-time, a method for estimating maximum likelihood speciation and extinction processes is provided. The method allows for non-constant rates. Rates may change (1) as a function of time, i.e. rate shifts at specified times or mass extinction events (likelihood implemented as LikShifts(), optimization as bd.shifts.optim() and visualized as bd.shifts.plot()) or (2) as a function of the number of species, i.e. density-dependence (likelihood implemented as LikDD() and optimization as bd.densdep.optim()) or (3) extinction rate may be a function of species age (likelihood implemented as LikAge() and optimization as bd.age.optim.matlab()). Note that the methods take into account the whole phylogeny, in particular it accounts for the "pull of the present" effect. (1-3) can take into account incomplete species sampling, as long as each species has the same probability of being sampled. For a given phylogeny on higher taxa (i.e. all but one species per taxa are missing), where the number of species is known within each higher taxa, speciation and extinction rates can be estimated under model (1) (implemented within LikShifts() and bd.shifts.optim() with groups !=0). (ii) For a given phylogeny with sequentially sampled tips, e.g. a virus phylogeny, rates can be estimated under a model where rates vary across time using bdsky.stt.optim() based on likelihood LikShiftsSTT() (extending LikShifts() and bd.shifts.optim()). Furthermore, rates may vary as a function of host types using LikTypesSTT() (multitype branching process extending functions in R package diversitree). This function can furthermore calculate the likelihood under an epidemiological model where infected individuals are first exposed and then infectious. |
| Authors: | Tanja Stadler |
| Maintainer: | Tanja Stadler <[email protected]> |
| License: | GPL-2 |
| Version: | 3.3 |
| Built: | 2024-12-08 04:48:16 UTC |
| Source: | https://github.com/tanja819/TreePar |
(i) For a given species phylogeny on present day data which is calibrated to calendar-time, a method for estimating maximum likelihood speciation and extinction processes is provided. The method allows for non-constant rates. Rates may change (1) as a function of time, i.e. rate shifts at specified times or mass extinction events (likelihood implemented as LikShifts(), optimization as bd.shifts.optim() and visualized as bd.shifts.plot()) or (2) as a function of the number of species, i.e. density-dependence (likelihood implemented as LikDD() and optimization as bd.densdep.optim()) or (3) extinction rate may be a function of species age (likelihood implemented as LikAge() and optimization as bd.age.optim.matlab()). Note that the methods take into account the whole phylogeny, in particular it accounts for the "pull of the present" effect. (1-3) can take into account incomplete species sampling, as long as each species has the same probability of being sampled. For a given phylogeny on higher taxa (i.e. all but one species per taxa are missing), where the number of species is known within each higher taxa, speciation and extinction rates can be estimated under model (1) (implemented within LikShifts() and bd.shifts.optim() with groups !=0). (ii) For a given phylogeny with sequentially sampled tips, e.g. a virus phylogeny, rates can be estimated under a model where rates vary across time using bdsky.stt.optim() based on likelihood LikShiftsSTT() (extending LikShifts() and bd.shifts.optim()). Furthermore, rates may vary as a function of host types using LikTypesSTT() (multitype branching process extending functions in R package diversitree). This function can furthermore calculate the likelihood under an epidemiological model where infected individuals are first exposed and then infectious.
| Package: | TreePar |
| Type: | Package |
| Version: | 3.3 |
| Date: | 2015-01-02 |
| License: | GPL-2 |
| LazyLoad: | yes |
Tanja Stadler <http://www.bsse.ethz.ch/cEvo>
T. Stadler. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Nat. Acad. Sci., 108(15): 6187-6192, 2011.
ape
TreeSim
addroot adds a root edge ancestral to the first branching event.
addroot(tree,rootlength)addroot(tree,rootlength)
tree |
Tree of class phylo. |
rootlength |
Length of the root edge to be added. |
res |
Tree of class phylo with root edge added to $edge and $edge.length (alternative is to store root edge in $root.edge as done in ape). |
Tanja Stadler
set.seed(1) # Simulate a tree tree<-sim.bd.taxa(20,1,2,1,complete=FALSE,stochsampling=TRUE) # add the root edge to the vector tree$edge and tree$edge.length addroot(tree[[1]],tree[[1]]$root.edge)set.seed(1) # Simulate a tree tree<-sim.bd.taxa(20,1,2,1,complete=FALSE,stochsampling=TRUE) # add the root edge to the vector tree$edge and tree$edge.length addroot(tree[[1]],tree[[1]]$root.edge)
bd.age.optim.matlab estimates the maximum likelihood speciation and extinction rates in a (possibly incomplete sampled) phylogeny. Speciation rate is constant, time to extinction is assumed to be a Gamma distribution.
bd.age.optim.matlab(x, lambdainit=1, kinit=1, thetainit=2, sampling, model="G", root=1, inputformat=0,precision=4,numgridpts=500, path,matfilename="setup")bd.age.optim.matlab(x, lambdainit=1, kinit=1, thetainit=2, sampling, model="G", root=1, inputformat=0,precision=4,numgridpts=500, path,matfilename="setup")
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
lambdainit |
Speciation rate at the initial point from which optimization starts. |
kinit |
Shape parameter of the Gamma distribution modelling time to extinction at the initial point from which optimization starts. |
thetainit |
Scale parameter of the Gamma distribution modelling time to extinction at the initial point from which optimization starts. |
sampling |
Probability to sample a present day lineage. |
model |
Model assumed for time to extinction. "G": Gamma distribution, i.e. shape parameter is estimated. "E": Exponential distribution, i.e. shape parameter is set to 1. |
root |
If root=1, max(x) is the crown age and the likelihood is conditioned on the crown age. If root=0, max(x) is the stem age and the likelihood is conditioned on the stem age. |
inputformat |
If 0, then speciation time vector 'x' is supplied. If 1, then mat-file input (obtained by create.mat) in 'matfilename' is supplied, and 'x' is not considered. |
precision |
Number of decimal places returned from the numerical likelihood calculation. |
numgridpts |
Number of gridpoints (evenly spaced time points between zero and the tree age, max(x)) used in the numerical likelihood calculation. The higher this number, the more accurate the numerical results will be. |
path |
Path where your MCR (Matlab Compiler Runtime) is installed (for details see ?LikAge). |
matfilename |
Only relevant if inputformat=1; then matfilename="setup" if input file is setup.mat. |
res |
Vector of four numbers: negative log likelihood value and maximum likelihood estimate of lambda, k, theta. |
This function relies on compiled Matlab applications that need to be downloaded. For setting up the environment to run the function, please refer to 'Notes' in function 'LikAge'.
Helen Alexander, Tanja Stadler
H. Alexander, A. Lambert, T. Stadler. Quantifying Age-Dependent Extinction from Species Phylogenies. Submitted.
## You need MCR to run the example ## directory where MCR is installed # path<-"/Applications/MATLAB_R2014a_node.app/" ## location of folder TreePar_Matlab #locationMat<-"/Users/tstadler/Documents/Data/Uni/Research/R/TreeParProject/TreePar_Matlab" ## # setwd(locationMat) # x<-read.table("branchingtimes.txt") # numgridpts<-500 # lambda<-2 # k<-1 # theta<-1 # sampling<-1 # setwd(paste(locationMat,"/compiled",sep="")) # LikAge(x,lambda, k, theta, sampling, path=path) # outG <- bd.age.optim.matlab(x, lambda, k, theta, sampling, path=path) # outE <- bd.age.optim.matlab(x, lambda, k, theta, sampling, path=path, model="E") ## compare with bd.shifts.optim for exponenetial lifetime # outEcompare<-c(outE[1],1/(outE[2]*outE[3]),outE[2]-1/outE[3]) # outShifts <- bd.shifts.optim(x,sampling=c(1),survival=1)[[2]]## You need MCR to run the example ## directory where MCR is installed # path<-"/Applications/MATLAB_R2014a_node.app/" ## location of folder TreePar_Matlab #locationMat<-"/Users/tstadler/Documents/Data/Uni/Research/R/TreeParProject/TreePar_Matlab" ## # setwd(locationMat) # x<-read.table("branchingtimes.txt") # numgridpts<-500 # lambda<-2 # k<-1 # theta<-1 # sampling<-1 # setwd(paste(locationMat,"/compiled",sep="")) # LikAge(x,lambda, k, theta, sampling, path=path) # outG <- bd.age.optim.matlab(x, lambda, k, theta, sampling, path=path) # outE <- bd.age.optim.matlab(x, lambda, k, theta, sampling, path=path, model="E") ## compare with bd.shifts.optim for exponenetial lifetime # outEcompare<-c(outE[1],1/(outE[2]*outE[3]),outE[2]-1/outE[3]) # outShifts <- bd.shifts.optim(x,sampling=c(1),survival=1)[[2]]
bd.densdep.optim estimates the maximum likelihood speciation and extinction rates under a density-dependent speciation model. Speciation rate is a function of the number of species N, lambda(N) = max(0,lambda(1-N/K)), where K is the carying capacity and lambda the speciation rate when N<<K. Extinction rate is mu (constant). For a computationally much faster implementation, please optimize the likelihood function with runExpoTree in R package expoTree. In contrast to bd.densdep.optim, runExpoTree can handle trees with tips sampled sequentially through time.
bd.densdep.optim(x,minK,maxK,discrete=TRUE,continuous=FALSE,lambdainit=2, muinit=1,Kinit=0,Yule=FALSE,muset=0,rho=1,model=-1)bd.densdep.optim(x,minK,maxK,discrete=TRUE,continuous=FALSE,lambdainit=2, muinit=1,Kinit=0,Yule=FALSE,muset=0,rho=1,model=-1)
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
minK |
Minimal value of K (when discrete=TRUE). Default is minK = (number of species). |
maxK |
Maximal value of K (when discrete=TRUE). Default is maxK = 1.5(number of species). |
discrete |
If discrete=TRUE, the likelihood function is maximized with K being an integer and the minimal size being minK and the maximal size being maxK. |
continuous |
If continuous=TRUE, the likelihood function is maximized with K being a continuous parameter. The function subplex is used for optimization and sometimes gets stuck at a non-optimal K. Thus it is recommended to also calculate with discrete=TRUE. |
lambdainit |
Initial lambda value for optimization when K is continuous (default is 2). |
muinit |
Initial mu value for optimization when K is continuous (default is 1). |
Kinit |
Initial K value for optimization when K is continuous (default is Kinit=0 which automatically sets Kinit=(number of species)+1). |
Yule |
Yule=FALSE is default. Yule=TRUE fixes mu=0, i.e. no extinction. |
muset |
muset=0 (default) maximizes over the whole parameter range. muset>0 means that the optimization is done over all mu>muset. muset<0 fixes mu=-muset. |
rho |
rho=1 is default meaning all present-day species are sampled. 0<rho<1 assumes that the phylogeny is incomplete, and each present-day species is included with probability rho. rho=-1 means any number of present-day species N>=n has given rise to a sample of size n with probability 1. rho<-1 means that any number n,n+1,..,(-k) of present-day species may have given rise to a sample of size n with probability 1. rho>1 means that exactly rho>n present-day species gave rise to the sample n with probability 1. |
model |
model=-1 (default) is the density-dependent model. model=0 (only relevant for testing purposes) assumes that lambda is constant for number of species < K, and 0 for number of species >= K. model=0 is used for testing / comparing to constant rate model implemented in bd.shifts.optim. |
res |
Maximum likelihood speciation rate lambda and extinction rate mu and the saturation value K; the first entry, res[[1]], is the result when K is discrete (0 if discrete=FALSE) and the second entry, res[[2]], is the result when K is continuous (0 if continuous=FALSE). $par is the maximum likelihood estimate of (lambda,mu,K). $value is the -log likelihood value. The likelihood is calculated assuming there were two lineages at the time of the root. The likelihood is NOT conditioned on survival of the two lineages. Likelihood values from bd.shifts.optim are directly comparable (eg. using AIC) for survival = 0. Likelihood values from laser are directly comparable to those obtained by bd.densdep.optim and bd.shifts.optim for survival = 0 after the TreePar output $value is transformed to -$value+sum(log(2:length(x))). |
A faster version is now implemented in R package expoTree by Gabriel Leventhal. It is equivalent to the method here, see examples for function LikDD. Further, it can handle trees with sequentially sampled tips. bd.densdep.optim(x,Yule=TRUE,discrete=FALSE,continuous=TRUE) in TreePar and DDL(x) in Laser return the same results (up to transforming the -log likelihood ($value) from TreePar via -$value+sum(log(2:length(x))).
Tanja Stadler, Gabriel Leventhal
R.S. Etienne, B. Haegeman, T. Stadler, T. Aze, P.N. Pearson, A. Purvis, A.B. Phillimore. Diversity-dependence brings molecular phylogenies closer to agreement with the fossil record. Proc. Roy. Soc. B, 279: 1300-1309, 2012.
G. Leventhal, H. Guenthard, S. Bonhoeffer, T. Stadler. Using an epidemiological model for phylogenetic inference reveals density-dependence in HIV transmission. Mol. Biol. Evol., 31(1): 6-17, 2014.
set.seed(1) x<-c(10:1) bd.densdep.optim(x,discrete=FALSE,continuous=TRUE) # Laser returns same result for Yule model res <- -bd.densdep.optim(x,Yule=TRUE,discrete=FALSE,continuous=TRUE)[[2]]$value res<-res+ sum(log(2:length(x))) res # library(laser) # DDL(x)set.seed(1) x<-c(10:1) bd.densdep.optim(x,discrete=FALSE,continuous=TRUE) # Laser returns same result for Yule model res <- -bd.densdep.optim(x,Yule=TRUE,discrete=FALSE,continuous=TRUE)[[2]]$value res<-res+ sum(log(2:length(x))) res # library(laser) # DDL(x)
bd.shifts.optim estimates the maximum likelihood speciation and extinction rates together with the rate shift times t=(t[1],t[2] .., t[m]) in a (possibly incomplete sampled) phylogeny. At the times t, the rates are allowed to change and the species may undergo a mass extinction event.
bd.shifts.optim(x, sampling, grid, start, end, maxitk = 5, yule = FALSE, ME = FALSE, all = FALSE, posdiv = FALSE, miniall = c(0), survival = 1,groups=0)bd.shifts.optim(x, sampling, grid, start, end, maxitk = 5, yule = FALSE, ME = FALSE, all = FALSE, posdiv = FALSE, miniall = c(0), survival = 1,groups=0)
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
sampling |
Vector of length m. sampling[i] is the probability of a species surviving the mass extinction at time t[i]. sampling[1] is the probability of an extant species being sampled. sampling[1]=1 means that the considered phylogeny is complete. sampling[i]=1 (i>1) means that at time t[i], a rate shift may occur but no species go extinct. If ME=TRUE, all entries but sampling[1] will be discarded as they are estimated. However, you have to input a vector sampling of the appropriate length such that the program knows how many mass extinction events you want to allow for. |
grid, start, end
|
The model parameters are optimized for different fixed rate shift times. The fixed rate shift times are specified by being at (start, start+grid, start+2*grid .. end). I calculate the likelihood for the different rate shift times t instead of optimizing t with the function optim used for the other parameters, as the optimization performed poor for t (namely getting stuck in local optima). |
yule |
yule=TRUE sets the extinction rates to zero. |
maxitk |
Integer value defining how many iterations shall be done in the optimization. Default is 5, but needs to be increased if too many warnings "convergence problem" appear. |
ME |
ME=FALSE (default) uses the mass extinction fractions specified in sampling and does not estimate them. If ME=FALSE is used with sampling=c(1,1, .. , 1), no mass extinction events are considered. |
all |
Only relevant when ME=TRUE. all=FALSE (default and recommended) estimates one speciation and one extinction rate for the whole tree, and estimates the intensities sampling[i] (i>1) of mass extinction events. all=TRUE allows for varying speciation and extinction rates. Since the parameters might correlate, all=TRUE is not recommended. |
posdiv |
posdiv=FALSE (default) allows the (speciation - extinction) rate to be negative, i.e. allows for periods of declining diversity. posdiv=TRUE forces the (speciation - extinction) rate to be positive. |
miniall |
If you have run the bd.shifts.optim for k shifts, but you now want to have K>k shifts, then set for the subsequent analysis the following: update sampling, and set miniall=res[[2]] where res[[2]] is the output from the run with k shifts. |
survival |
If survival = 1, the likelihood is conditioned on survival of the process (recommended). Otherwise survival = 0. |
groups |
If groups != 0, the first column of groups indicates the age of higher taxa and the second column the number of species in the higher taxa (each row in groups corresponds to a leaf in the tree). |
res[[1]][[i]] |
List of maximum likelihood parameter estimates for each fixed t (t determined by start, end, grid) where i-1 shifts are allowed to occur (i in 1:m). The first i entries are the turnover (extinction/speciation) estimates, for the successive intervals going back in time. The next i entries are the diversification rate estimates (speciation-extinction). The next i-1 entries are the probabilities that the lineage survives the mass extinction event (if ME=TRUE). (Note: if ME=TRUE and all=FALSE, the first entry is the turnover, the second the diversification rate, followed by the mass extinction survival probability). |
res[[2]][[i]] |
Maximum likelihood parameter estimates for i-1 shifts (i in 1:m). First entry is the (-log likelihood) value. The next entries are the maximum likelihood parameter estimates (see res[[1]][[i]]). The last i-1 entries are the shift times. |
res[[3]] |
Vector of time points where the function was evaluated. |
res[[4]] |
Array specifying the time points when there was a convergence problem: a row of res[[4]] with entry (i,t[i]) means that when adding the i-th shift at time t[i], a convergence problem was encountered. |
The likelihood is calculated assuming there were two lineages at the time of the root. The likelihood is conditioned on survival of the two lineages if survival = 1. Likelihood values from bd.densdep.optim are directly comparable (eg. using AIC) for survival = 0. Likelihood values from package laser are comparable for survival = 0 after the TreePar output $value is transformed to -$value+sum(log(2:length(x))).
Tanja Stadler
T. Stadler. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Nat. Acad. Sci., 108(15): 6187-6192, 2011.
T. Stadler, F. Bokma. Estimating speciation and extinction rates for phylogenies of higher taxa. Syst. Biol., 62(2): 220-230, 2013. (for groups>0 but no rate shift).
A. Lambert, T. Stadler. Macro-evolutionary models and coalescent point processes: the shape and probability of reconstructed phylogenies. Theo. Pop. Biol., 90: 113-128, 2013. (for groups>0).
set.seed(1) # First we simulate a tree, and then estimate the parameters for the tree: # Number of species nspecies <- 20 # At time 1 and 2 in the past, we have a rate shift: time <- c(0,1,2) # Mass extinction intensities 0.5 at time 1 in past, 0.4 at time 2 in past. # Present day species are all sampled (rho[1]=1): rho <- c(1,0.5,0.4) # speciation rates (between t[i],t[i+1] we have speciation rate lambda[i]): lambda <- c(2,2,1) # extinction rates (between t[i],t[i+1] we have extinction rate mu[i]): mu <- c(1,1,0) # Simulation of a tree: tree<-sim.rateshift.taxa(nspecies,1,lambda,mu,frac=rho,times=time,complete=FALSE) # Extracting the speciation times x: x<-sort(getx(tree[[1]]),decreasing=TRUE) # When estimating the the rate shift times t based on branching times x, # we allow the shift times to be 0.6, 0.8, 1, 1.2, .. ,2.4: start <- 0.6 end <- 2.4 grid <- 0.2 # We fix rho and estimate time, lambda, mu: res <- bd.shifts.optim(x,rho,grid,start,end)[[2]] res # res[[2]] tells us about the maximum likelihood estimate given one rate shift: # - log lik = 17.330862988. # rate shift at time 2.2. # turnover (extinction/speciation) = 0.186301549 more recent than 2.2, # and = 0.939681843 more ancestral than 2.2. # net diversification (speciation-extinction) rate = 0.958947381 more recent than 2.2, # and = 0.000100009 more ancestral than 2.2. #test if i shifts explain the tree significantly better than i-1 shifts, here i=1: i<-1 test<-pchisq(2*(res[[i]][1]-res[[i+1]][1]),3) #if test>0.95 then i shifts is significantly better than i-1 shifts at a 5% error # We fix rho=1 and mu=0 and then estimate time, lambda: resyule <- bd.shifts.optim(x,rho,grid,start,end,yule=TRUE) resyule[[2]] # We estimate time, rho, lambda, mu: resrho <- bd.shifts.optim(x,rho,grid,start,end,ME=TRUE) resrho[[2]] # Data analysis in Stadler & Bokma, 2012: # Number of species in each order from Sibley and Monroe (1990) data(bird.orders) S <- c(10, 47, 69, 214, 161, 17, 355, 51, 56, 10, 39, 152, 6, 143, 358, 103, 319, 23, 291, 313, 196, 1027, 5712) groups<-get.groups(bird.orders,S,0) groupscut<-get.groups(bird.orders,S,96.43) x<-branching.times(bird.orders) # transforming molecular timescale into calendar timescale groups[,1]<-groups[,1]/0.207407 x<-x/0.207407 bd.shifts.optim(x,sampling=c(1),survival=1,groups=groups)[[2]] bd.shifts.optim(x,sampling=c(1),survival=1,groups=groupscut)[[2]] # allowing one shift in rates: bd.shifts.optim(x,sampling=c(1,1),grid=1,start=20,end=25,survival=1,groups=groupscut)[[2]]set.seed(1) # First we simulate a tree, and then estimate the parameters for the tree: # Number of species nspecies <- 20 # At time 1 and 2 in the past, we have a rate shift: time <- c(0,1,2) # Mass extinction intensities 0.5 at time 1 in past, 0.4 at time 2 in past. # Present day species are all sampled (rho[1]=1): rho <- c(1,0.5,0.4) # speciation rates (between t[i],t[i+1] we have speciation rate lambda[i]): lambda <- c(2,2,1) # extinction rates (between t[i],t[i+1] we have extinction rate mu[i]): mu <- c(1,1,0) # Simulation of a tree: tree<-sim.rateshift.taxa(nspecies,1,lambda,mu,frac=rho,times=time,complete=FALSE) # Extracting the speciation times x: x<-sort(getx(tree[[1]]),decreasing=TRUE) # When estimating the the rate shift times t based on branching times x, # we allow the shift times to be 0.6, 0.8, 1, 1.2, .. ,2.4: start <- 0.6 end <- 2.4 grid <- 0.2 # We fix rho and estimate time, lambda, mu: res <- bd.shifts.optim(x,rho,grid,start,end)[[2]] res # res[[2]] tells us about the maximum likelihood estimate given one rate shift: # - log lik = 17.330862988. # rate shift at time 2.2. # turnover (extinction/speciation) = 0.186301549 more recent than 2.2, # and = 0.939681843 more ancestral than 2.2. # net diversification (speciation-extinction) rate = 0.958947381 more recent than 2.2, # and = 0.000100009 more ancestral than 2.2. #test if i shifts explain the tree significantly better than i-1 shifts, here i=1: i<-1 test<-pchisq(2*(res[[i]][1]-res[[i+1]][1]),3) #if test>0.95 then i shifts is significantly better than i-1 shifts at a 5% error # We fix rho=1 and mu=0 and then estimate time, lambda: resyule <- bd.shifts.optim(x,rho,grid,start,end,yule=TRUE) resyule[[2]] # We estimate time, rho, lambda, mu: resrho <- bd.shifts.optim(x,rho,grid,start,end,ME=TRUE) resrho[[2]] # Data analysis in Stadler & Bokma, 2012: # Number of species in each order from Sibley and Monroe (1990) data(bird.orders) S <- c(10, 47, 69, 214, 161, 17, 355, 51, 56, 10, 39, 152, 6, 143, 358, 103, 319, 23, 291, 313, 196, 1027, 5712) groups<-get.groups(bird.orders,S,0) groupscut<-get.groups(bird.orders,S,96.43) x<-branching.times(bird.orders) # transforming molecular timescale into calendar timescale groups[,1]<-groups[,1]/0.207407 x<-x/0.207407 bd.shifts.optim(x,sampling=c(1),survival=1,groups=groups)[[2]] bd.shifts.optim(x,sampling=c(1),survival=1,groups=groupscut)[[2]] # allowing one shift in rates: bd.shifts.optim(x,sampling=c(1,1),grid=1,start=20,end=25,survival=1,groups=groupscut)[[2]]
bd.shifts.plot plots the diversification rate estimates obtained with the function bd.shifts.optim.
bd.shifts.plot(resall,shifts,timemax=100,ratemin=-1,ratemax=1,plotturnover=FALSE)bd.shifts.plot(resall,shifts,timemax=100,ratemin=-1,ratemax=1,plotturnover=FALSE)
resall |
When k trees were analyzed, a list of length k with entries being the component [[2]] of the output from bd.shifts.optim for each tree. |
shifts |
resall contains the maximum likelihood parameter estimates for 0...m shifts. shifts specifies for how many shifts you want to plot the estimated diversification rates (you can determine the number of significant shifts using a likelihood ratio test). |
timemax |
Specifies the upper end of the x-axis (time in past). Lower end is always 0. |
ratemin, ratemax
|
Specifies the upper and lower end of the y-axis (diversification rates). |
plotturnover |
The net diversification (speciation-extinction) rate is plotted. If plotturnover=TRUE, also turnover=extinction/speciation is plotted. |
Tanja Stadler
T. Stadler. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Nat. Acad. Sci., 108(15): 6187-6192, 2011.
set.seed(1) # First we simulate a tree, and then estimate the parameters for the tree: # Number of species nspecies <- 20 # At time 1 and 2 in the past, we have a rate shift: time <- c(0,1,2) # Mass extinction intensities 0.5 at time 1 in past, 0.4 at time 2 in past. # Present day species are all sampled (rho[1]=1): rho <- c(1,0.5,0.4) # speciation rates (between t[i],t[i+1] we have speciation rate lambda[i]): lambda <- c(2,2,1) # extinction rates (between t[i],t[i+1] we have extinction rate mu[i]): mu <- c(1,1,0) # Simulation of a tree: tree<-sim.rateshift.taxa(nspecies,1,lambda,mu,frac=rho,times=time,complete=FALSE) # Extracting the speciation times x: x<-sort(getx(tree[[1]]),decreasing=TRUE) # When estimating the shift times t for x, we allow the shift times to be 0.6, 0.8, 1, 1.2, .. ,2.4: start <- 0.6 end <- 2.4 grid <- 0.2 # We fix rho and estimate time, lambda, mu: res <- bd.shifts.optim(x,rho,grid,start,end) res[[2]] # We plot the result for 2 shifts: bd.shifts.plot(list(res[[2]]),2,3,0,2)set.seed(1) # First we simulate a tree, and then estimate the parameters for the tree: # Number of species nspecies <- 20 # At time 1 and 2 in the past, we have a rate shift: time <- c(0,1,2) # Mass extinction intensities 0.5 at time 1 in past, 0.4 at time 2 in past. # Present day species are all sampled (rho[1]=1): rho <- c(1,0.5,0.4) # speciation rates (between t[i],t[i+1] we have speciation rate lambda[i]): lambda <- c(2,2,1) # extinction rates (between t[i],t[i+1] we have extinction rate mu[i]): mu <- c(1,1,0) # Simulation of a tree: tree<-sim.rateshift.taxa(nspecies,1,lambda,mu,frac=rho,times=time,complete=FALSE) # Extracting the speciation times x: x<-sort(getx(tree[[1]]),decreasing=TRUE) # When estimating the shift times t for x, we allow the shift times to be 0.6, 0.8, 1, 1.2, .. ,2.4: start <- 0.6 end <- 2.4 grid <- 0.2 # We fix rho and estimate time, lambda, mu: res <- bd.shifts.optim(x,rho,grid,start,end) res[[2]] # We plot the result for 2 shifts: bd.shifts.plot(list(res[[2]]),2,3,0,2)
bdsky.stt.optim estimates the maximum likelihood birth and death rates together with the rate shift times t=(t[1],t[2] .., t[m]) for a given phylogenetic tree with sequentially sampled tips. At the times t, the rates are allowed to change.
bdsky.stt.optim(x,ttype=0,rho=0,sampprob=c(0),constdeath=0,root=0)bdsky.stt.optim(x,ttype=0,rho=0,sampprob=c(0),constdeath=0,root=0)
x |
Vector of branching and sampling times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE,sersampling=TRUE). |
ttype |
Vector of same length as x. If ttype[i]=0, then x[i] denotes a branching event. If ttype[i]=1, then x[i] denotes a sampling event. |
rho |
Probability of sampling individuals at present. rho=0 is default. |
sampprob |
Vector of length k where k is the number of different birth rates to be estimated. |
constdeath |
If constdeath=0 (default) then k death rates are estimated. If constdeath=1, then 1 death rate is estimated. |
root |
root=0 indicates that there is an edge above the root (mrca) in the tree. root=1 indicates that there is no edge above the root. |
out[[1]] |
Entry [[j]] are the maximum likelihood parameter estimates for j-1 shifts, j=1...k. The first entry of out[[1]][[j]] is the -log likelihood value, followed by the maximum likelihood parameter estimates. The parameters are stated in the following order: first the j turnover estimates (from recent to ancient), then the j diversification (speciation-extinction) rate estimates (from recent to ancient), then the j-1 shift times. |
out[[2]] |
Matrix where in each row, the first entry denotes the type of convergence problem, the second entry denotes the number of shifts in the problematic caluclation, and the third entry denotes in which interval it happened. |
bdsky.stt.optim extends the function bd.shifts.optim to trees with sequentially sampled tips.
Tanja Stadler
T. Stadler, D. Kuehnert, S. Bonhoeffer, A. Drummond. Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). Proc. Nat. Acad. Sci., 110(1): 228-233, 2013.
set.seed(1) # simulation of a tree with one rate shift at 0.5: lambda<-c(3,4) mu<-c(1,1) sampprob<-c(0.5,0.5) time<-c(0,0.5) n<-10 tree<- sim.bdsky.stt(n,lambda,mu,time,sampprob) tree2<-addroot(tree[[1]],tree[[1]]$root.edge) summary<-getx(tree2,sersampling=TRUE) times<-summary[,1] ttype<-summary[,2] # Maximum likelihood parameter estimation: out <- bdsky.stt.optim(x=times,ttype=ttype,sampprob=sampprob,root=0)set.seed(1) # simulation of a tree with one rate shift at 0.5: lambda<-c(3,4) mu<-c(1,1) sampprob<-c(0.5,0.5) time<-c(0,0.5) n<-10 tree<- sim.bdsky.stt(n,lambda,mu,time,sampprob) tree2<-addroot(tree[[1]],tree[[1]]$root.edge) summary<-getx(tree2,sersampling=TRUE) times<-summary[,1] ttype<-summary[,2] # Maximum likelihood parameter estimation: out <- bdsky.stt.optim(x=times,ttype=ttype,sampprob=sampprob,root=0)
create.mat generates input for LikAge and bd.age.optim.matlab.
create.mat(x, numgridpts=500, path, matfilename="setup")create.mat(x, numgridpts=500, path, matfilename="setup")
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
numgridpts |
Number of gridpoints to be used in the numerical likelihood calculation in LikAge or bd.age.optim.matlab. |
path |
Path where your MCR (Matlab Compiler Runtime) is installed (for details see ?LikAge). |
matfilename |
File matfilename.mat is created which contains input (speciation times) in a valid format for LikAge and bd.age.optim.matlab. |
res |
0 is returned. The file for downstream analysis is saved as matfilename.mat. |
This function relies on compiled Matlab applications that need to be downloaded. For setting up the environment to run the function, please refer to 'Notes' in function 'LikAge'. This function is useful to evaluate if LikAge or bd.age.optim.matlab are called several times for one tree. Then the input for the matlab functions is created once and used in LikAge or bd.age.optim.matlab through inputformat=1 and specifying matfilename.
Helen Alexander, Tanja Stadler
H. Alexander, A. Lambert, T. Stadler. Quantifying Age-Dependent Extinction from Species Phylogenies. Submitted.
## You need MCR to run the example ## directory where MCR is installed # path<-"/Applications/MATLAB_R2014a_node.app/" ## location of folder TreePar_Matlab containing compiled Matlab applications (see ?LikAge) # locationMat<-"/Users/tstadler/Documents/Data/Uni/Research/R/TreeParProject/TreePar_Matlab" ## # x<-1:20 # setwd(paste(locationMat,"/compiled",sep="")) # out<-create.mat(x,path=path)## You need MCR to run the example ## directory where MCR is installed # path<-"/Applications/MATLAB_R2014a_node.app/" ## location of folder TreePar_Matlab containing compiled Matlab applications (see ?LikAge) # locationMat<-"/Users/tstadler/Documents/Data/Uni/Research/R/TreeParProject/TreePar_Matlab" ## # x<-1:20 # setwd(paste(locationMat,"/compiled",sep="")) # out<-create.mat(x,path=path)
get.groups generates input for bd.shifts.optim if the phylogeny is not resolved on the species level (groups!=0).
get.groups(tree,S,xcut=0)get.groups(tree,S,xcut=0)
tree |
Phylogenetic tree to be analyzed with bd.shifts.optim. |
S |
S[i]: Number of species in tree represented by leaf i. |
xcut |
Age of the higher taxa. If xcut=0: age of higher taxa is the length of the edge corresponding to the higher taxa. If xcut>0: age of each higher taxa is xcut. |
Tanja Stadler
T. Stadler, F. Bokma. Estimating speciation and extinction rates for phylogenies of higher taxa. Syst. Biol., 62(2): 220-230, 2013.
# see manual of bd.shifts.optim()# see manual of bd.shifts.optim()
LikAge calculates the likelihood of speciation and extinction rates for an ultrametric phylogeny under an age-dependent extinction model conditioning on the age of the tree. Speciation rate is constant. Time to extinction is a Gamma distribution.
LikAge(x, lambda, k, theta, sampling, root=1, inputformat=0, precision=4, numgridpts=500, path, matfilename="setup")LikAge(x, lambda, k, theta, sampling, root=1, inputformat=0, precision=4, numgridpts=500, path, matfilename="setup")
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
lambda |
Speciation rate. |
k |
Shape parameter of the Gamma distribution modelling time to extinction. |
theta |
Scale parameter of the Gamma distribution modelling time to extinction. |
sampling |
Probability to sample a present day lineage. |
root |
If root=1, max(x) is the crown age and the likelihood is conditioned on the crown age. If root=0, max(x) is the stem age and the likelihood is conditioned on the stem age. |
inputformat |
If 0, then speciation time vector 'x' is supplied. If 1, then mat-file input (obtained by create.mat) in 'matfilename' is supplied, and 'x' is not considered. |
precision |
Number of decimal places returned from the numerical likelihood calculation. |
numgridpts |
Number of gridpoints (evenly spaced time points between zero and the tree age, max(x)) used in the numerical likelihood calculation. The higher this number, the more accurate the numerical results will be. |
path |
Path where your MCR (Matlab Compiler Runtime) is installed (for details see ?LikAge). |
matfilename |
Only relevant if inputformat=1; then matfilename="setup" if input file is setup.mat. |
res |
negative log likelihood of the parameters given the tree. The likelihood conditions on sampling of at least 1 extant tip (analog to survival=1 in other likelihood functions of TreePar). |
You do not need Matlab software to use the compiled Matlab applications. First you need to download the Matlab Compiler Runtime (MCR), freely available from Mathworks at: http://www.mathworks.com/products/compiler/mcr/. You should download the same version with which the applications were compiled: if you use the compiled code we provide, this is v8.3. Note the directory where you install MCR, it has to be inputted in LikAge as 'path'. When you install MCR, the Installer gives the message: "On the target computer, append the following to you DYLD_LIBRARY_PATH environment variable:.." then it gives a path name which should be copied and pasted before closing the installer. Second you have to download the folder TreePar_Matlab which is available on our github account: https://github.com/cevo-public. The compiled Matlab applications are in the subfolder TreePar_Matlab/compiled and should run on Mac OS version 10.7 and newer. If our compiled code does not run on your system, then you need to compile our Matlab code (m-files) in TreePar_Matlab/source to produce applications compatible with your system. This requires a licensed copy of the Matlab software (see http://www.mathworks.com/). Open Matlab. Change to directory TreePar_Matlab/source. Then execute the following commands to compile the m-files into stand-alone applications using the Matlab compiler MCC: >> mcc -m -R -nodisplay -R -nojvm EvalLFcn.m ; >> mcc -m -R -nodisplay -R -nojvm MaxLFcn.m ; >> mcc -m -R -nodisplay -R -nojvm ReadTreeFcn.m . Copy the three resulting .sh and .app files to the folder TreePar_Matlab/compiled. Note that if you have a licensed copy of Matlab and do not wish to use the R interface, the m-files we provide in TreePar_Matlab/source can also be used directly in Matlab for likelihood computations.
Helen Alexander, Tanja Stadler
H. Alexander, A. Lambert, T. Stadler. Quantifying Age-Dependent Extinction from Species Phylogenies. Submitted.
## You need MCR to run the example ## directory where MCR is installed # path<-"/Applications/MATLAB_R2014a_node.app/" ## location of folder TreePar_Matlab # locationMat<-"/Users/tstadler/Documents/Data/Uni/Research/R/TreeParProject/TreePar_Matlab" # # x<-1:20 # numgridpts<-500 # lambda<-2 # k<-1 # theta<-1 # sampling<-1 ## Provide path where the folder TreePar_Matlab is available # setwd(paste(locationMat,"/compiled",sep="")) # LikAge(x,lambda, k, theta, sampling, path=path) ## If the shape parameter is 1, we have a constant extinction rate, ## corresponding to the function LikShift. Then the following commands return the same result. # LikAge(x,1, 1, 2, sampling, numgridpts=1000, path=path) # LikShifts(x,c(0),c(1),c(1/2),c(1))## You need MCR to run the example ## directory where MCR is installed # path<-"/Applications/MATLAB_R2014a_node.app/" ## location of folder TreePar_Matlab # locationMat<-"/Users/tstadler/Documents/Data/Uni/Research/R/TreeParProject/TreePar_Matlab" # # x<-1:20 # numgridpts<-500 # lambda<-2 # k<-1 # theta<-1 # sampling<-1 ## Provide path where the folder TreePar_Matlab is available # setwd(paste(locationMat,"/compiled",sep="")) # LikAge(x,lambda, k, theta, sampling, path=path) ## If the shape parameter is 1, we have a constant extinction rate, ## corresponding to the function LikShift. Then the following commands return the same result. # LikAge(x,1, 1, 2, sampling, numgridpts=1000, path=path) # LikShifts(x,c(0),c(1),c(1/2),c(1))
LikConstant calculates the likelihood of constant speciation and extinction rates for a given phylogenetic tree, conditioning on the age of the tree.
LikConstant(lambda,mu,sampling,x,root=1,survival=1)LikConstant(lambda,mu,sampling,x,root=1,survival=1)
lambda, mu
|
Speciation and extinction rate. |
sampling |
Sampling is the probability of an extant species being sampled and included into the tree. |
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
root |
If root = 1 then max(x) is the mrca (crown age), if root = 0 then max(x) is the age of a branch ancestral to the mrca (stem age). |
survival |
If survival = 1: The likelihood is conditioned on survival of the process (recommended). Otherwise survival = 0. |
res |
-log likelihood of the birth and death rates given the tree. |
Tanja Stadler
T. Stadler. On incomplete sampling under birth-death models and connections to the sampling-based coalescent. Jour. Theo. Biol. 261: 58-66, 2009.
# see manual of LikShifts()# see manual of LikShifts()
LikConstantn calculates the likelihood of constant birth and death rates for a given phylogenetic tree, conditioning on the age of the tree and the number of tips.
LikConstantn(lambda,mu,sampling,x,root=1)LikConstantn(lambda,mu,sampling,x,root=1)
lambda, mu
|
Speciation and extinction rate. |
sampling |
Sampling is the probability of an extant species being sampled and included into the tree. |
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
root |
If root = 1 then max(x) is the mrca (crown age), if root = 0 then max(x) is the age of a branch ancestral to the mrca (stem age). |
res |
-log likelihood birth and death rate given the phylogeny. |
Tanja Stadler
T. Stadler. On incomplete sampling under birth-death models and connections to the sampling-based coalescent. Jour. Theo. Biol. 261: 58-66, 2009.
# see manual of LikShifts()# see manual of LikShifts()
LikDD calculates the likelihood of speciation and extinction rates for a phylogeny under a density-dependent speciation model. Speciation rate is a function of the number of species N, lambda(N) = max(0,lambda(1-N/K)), where K is the carying capacity and lambda the speciation rate when N<<K. Extinction rate is mu (constant). For a computationally much faster implementation, please use function runExpoTree in R package expoTree. In contrast to LikDD, runExpoTree can handle trees with tips sampled sequentially through time.
LikDD(par,model=-1,x,Ndec=-1,minN=0,psi=0,sampling=1,root=0,ki=0,muset=0,vec=0)LikDD(par,model=-1,x,Ndec=-1,minN=0,psi=0,sampling=1,root=0,ki=0,muset=0,vec=0)
par |
Parameters (lambda,mu,K) for which to calculate the likelihood: lambda is speciation rate, mu is extinction rate, and K is carrying capacity. |
model |
model = -1: speciation rate is lambda(N) = max(0,lambda(1-N/K)). model = 0: speciation rate is lambda(N) = lambda for N <= K, und lambda(N) = 0 for N>K. |
x |
Vector of speciation times and ancient lineage sampling times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
Ndec |
Help variable when optimizing. |
minN |
Help variable when optimizing. |
psi |
Rate for sampling lineages through time. |
sampling |
Probabiliy to sample a present day lineage. |
root |
If root = 1 then max(x) is the mrca (crown age), if root = 0 then max(x) is the age of a branch ancestral to the mrca (stem age). |
ki |
Help variable when optimizing. |
muset |
Help variable when optimizing. |
vec |
vec=0 returns likelihood of model parameters for a tree given 1 lineage at beginning. vec=1 returns vector of likelihoods given i lineages at beginning. |
res |
-log likelihood of the parameters given the tree. |
A faster version is now implemented in R package expoTree by Gabriel Leventhal. It is equivalent to the method here, see examples in LikShifts() manual. Further, expoTree can handle trees with sequentially sampled tips.
Tanja Stadler, Gabriel Leventhal
R.S. Etienne, B. Haegeman, T. Stadler, T. Aze, P.N. Pearson, A. Purvis, A.B. Phillimore. Diversity-dependence brings molecular phylogenies closer to agreement with the fossil record. Proc. Roy. Soc. B, 279: 1300-1309, 2012.
G. Leventhal, H. Guenthard, S. Bonhoeffer, T. Stadler. Using an epidemiological model for phylogenetic inference reveals density-dependence in HIV transmission. Mol. Biol. Evol., 31(1): 6-17, 2014.
# see manual of LikShifts()# see manual of LikShifts()
LikShifts calculates the likelihood of speciation and extinction rates and shift times for a given phylogenetic tree, conditioning on the age of the tree.
LikShifts(x, t, lambda, mu, sampling, posdiv=FALSE,survival=1,groups=0)LikShifts(x, t, lambda, mu, sampling, posdiv=FALSE,survival=1,groups=0)
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
t |
Vector of length m specifying the time of rate shifts (t[1]=0 is required, being the present). An entry in t may not coincide with an entry in x. |
lambda, mu
|
Vectors of the same length as t. l[i] (resp. mu[i]) specifies the speciation (resp. extinction rate) prior to t[i]. |
sampling |
Vector of same length as t. sampling[i] is the probability of a species surviving the mass extinction at time t[i]. sampling[1] is the probability of an extant species being sampled. sampling[1]=1 means that the considered phylogeny is complete. sampling[i]=1 (i>1) means that at time t[i], a rate shift may occur but no species go extinct. |
posdiv |
Not relevant when using LikShifts without optimizing (for bd.shifts.optim: posdiv=FALSE (default) allows the (speciation-extinction) rate to be negative, i.e. allows for periods of declining diversity. posdiv=TRUE forces the (speciation-extinction) rate to be positive). |
survival |
If survival = 1, the likelihood is conditioned on survival of the process (recommended). Otherwise survival = 0. |
groups |
If groups != 0: the first column of groups indicates the age of higher taxa and the second column the number of species in the higher taxa (each row in groups corresponds to a leaf in the tree). |
res |
-log likelihood of the model parameters for the given phylogenetic tree. The likelihood is calculated assuming there were two lineages at the time of the root. The likelihood is conditioned on survival of the two lineages if survival = 1. Likelihood values from bd.densdep.optim are directly comparable (eg. using AIC) for survival = 0. Likelihood values from laser are directly comparable to the TreePar output for survival = 0 after the TreePar output $value is transformed to -$value+sum(log(2:length(x))). |
Tanja Stadler
T. Stadler. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Nat. Acad. Sci., 108(15): 6187-6192, 2011.
timevec<-c(0,0.15,0.25) lambdavec<-c(2.5,2,3) muvec<-c(0.5,0.7,0.6) x<-c(0.3,0.19,0.1) x1<-c(x,max(x)*1.1) x2<-c(x,max(x)) sampling<-0.4 grouptime<- rep(min(x)*0.95,length(x)+1) group<- cbind(grouptime,grouptime*0+1) group2 <- group group2[1,2] <- 4 group2[2,2] <- 5 group2[3,2] <- 3 group3<-group group3[2,2]<-10 ### calculate likelihoods with root = 1 ## Shifts in speciation / extinction rates (Stadler, PNAS 2011; Smrckova & Stadler, Manuscript 2014) for (survival in c(0,1)) { print(LikShiftsPP(x,timevec,lambdavec,muvec,sampling,survival=survival)) print(LikShifts(x,timevec,lambdavec,muvec,c(sampling,1,1),survival= survival)) print(LikShifts(x,timevec,lambdavec,muvec,c(sampling,1,1),survival= survival,groups=group)) print(LikShiftsSTT(par=c(lambdavec,muvec,timevec[-1]),x,x*0+1,sprob=c(0,0,0), sampling=c(sampling,0,0),survival=survival,root=1)) print(" ") } ## Shifts in speciation / extinction rates with group sampling for (survival in c(0,1)) { print(LikShifts(x,timevec,lambdavec,muvec,c(sampling,1,1),survival= survival,groups=group2)) print(LikShifts(x,timevec,lambdavec,muvec,c(sampling,1,1),survival= survival,groups=group3)) print(" ") } ## Constant speciation and extinction rates # condition on age of tree x[1] and number of tips n LikShiftsPP(x,timevec[1],lambdavec[1],muvec[1],sampling,n=1) LikConstantn(lambdavec[1],muvec[1],sampling,x) print(" ") # condition on age of tree x[1] for (survival in c(0,1)) { print(LikConstant(lambdavec[1],muvec[1],sampling,x,root=1,survival=survival)) print(LikShiftsSTT(par=c(lambdavec[1],lambdavec[1],muvec[1],muvec[1],1),x,x*0+1, sprob=c(0,0),sampling=c(sampling,1),survival=survival,root=1)) print(LikShiftsPP(x,c(0),lambdavec[1],muvec[1],sampling,root=1,survival=survival)) print(LikShifts(x,c(0),lambdavec[1],muvec[1],c(sampling),survival=survival)) print(LikShifts(x,c(0),lambdavec[1],muvec[1],c(sampling),survival= survival,groups=group)) print(LikShifts(x,c(0),c(lambdavec[1],lambdavec[1],lambdavec[1]), c(muvec[1],muvec[1],muvec[1]),c(sampling,1,1),survival= survival)) if (survival == 0 ) { print(LikDD(c(lambdavec[1],muvec[1], 200), model=0 ,root=1, x=sort(x),sampling=sampling)[1]) } if (survival == 0 ) { print(LikDD(c(lambdavec[1],muvec[1], 300), model=-1 ,root=1, x=sort(x),sampling=sampling)[1]) } print(" ") } ## Diversity-dependent speciation rates # condition on age of tree x[1], survival = 0 N<-10 pars <- matrix(c(N,lambdavec[1],muvec[1],0,sampling),nrow=1) print(LikDD(c(lambdavec[1],muvec[1],N),x=sort(x),model=-1,root=1,sampling=sampling)[1]) ## If you have the R package expoTree, you can compare: # library(expoTree) # print(-runExpoTree(pars,sort(x2),rep(1,length(x2)),survival=0)+(length(x)-1)*log(2)) print(" ") ### calculate likelihoods with root = 0 ## Constant speciation and extinction rates # condition on age of tree x[1] and number of tips n print(LikShiftsPP(x,timevec[1],lambdavec[1],muvec[1],sampling,root=0,n=1)) print(LikConstantn(lambdavec[1],muvec[1],sampling,x,root=0)) print(" ") # condition on age of tree x[1] for (survival in c(0,1)){ print(LikShiftsPP(x,c(0),lambdavec[1],muvec[1],sampling,root=0,survival=survival)) print(LikConstant(lambdavec[1],muvec[1],sampling,x,root=0,survival=survival)) if (survival == 0 ) {print(LikDD(c(lambdavec[1],muvec[1], 200), model=0 ,root=0, x=sort(x),sampling=sampling)[1]) } if (survival == 0 ) {print(LikDD(c(lambdavec[1],muvec[1], 300), model=-1 ,root=0, x=sort(x),sampling=sampling)[1]) } print(" ") } ## Diversity-dependent speciation rates # condition on age of tree x[1], survival = 0 print(LikDD(c(lambdavec[1],muvec[1],N),x=sort(x),model=-1,root=0,sampling=sampling)[1]) ## If you have the R package expoTree, you can compare: # print(-runExpoTree(pars,sort(x),rep(1,length(x)),survival=0)+(length(x)-1)*log(2))timevec<-c(0,0.15,0.25) lambdavec<-c(2.5,2,3) muvec<-c(0.5,0.7,0.6) x<-c(0.3,0.19,0.1) x1<-c(x,max(x)*1.1) x2<-c(x,max(x)) sampling<-0.4 grouptime<- rep(min(x)*0.95,length(x)+1) group<- cbind(grouptime,grouptime*0+1) group2 <- group group2[1,2] <- 4 group2[2,2] <- 5 group2[3,2] <- 3 group3<-group group3[2,2]<-10 ### calculate likelihoods with root = 1 ## Shifts in speciation / extinction rates (Stadler, PNAS 2011; Smrckova & Stadler, Manuscript 2014) for (survival in c(0,1)) { print(LikShiftsPP(x,timevec,lambdavec,muvec,sampling,survival=survival)) print(LikShifts(x,timevec,lambdavec,muvec,c(sampling,1,1),survival= survival)) print(LikShifts(x,timevec,lambdavec,muvec,c(sampling,1,1),survival= survival,groups=group)) print(LikShiftsSTT(par=c(lambdavec,muvec,timevec[-1]),x,x*0+1,sprob=c(0,0,0), sampling=c(sampling,0,0),survival=survival,root=1)) print(" ") } ## Shifts in speciation / extinction rates with group sampling for (survival in c(0,1)) { print(LikShifts(x,timevec,lambdavec,muvec,c(sampling,1,1),survival= survival,groups=group2)) print(LikShifts(x,timevec,lambdavec,muvec,c(sampling,1,1),survival= survival,groups=group3)) print(" ") } ## Constant speciation and extinction rates # condition on age of tree x[1] and number of tips n LikShiftsPP(x,timevec[1],lambdavec[1],muvec[1],sampling,n=1) LikConstantn(lambdavec[1],muvec[1],sampling,x) print(" ") # condition on age of tree x[1] for (survival in c(0,1)) { print(LikConstant(lambdavec[1],muvec[1],sampling,x,root=1,survival=survival)) print(LikShiftsSTT(par=c(lambdavec[1],lambdavec[1],muvec[1],muvec[1],1),x,x*0+1, sprob=c(0,0),sampling=c(sampling,1),survival=survival,root=1)) print(LikShiftsPP(x,c(0),lambdavec[1],muvec[1],sampling,root=1,survival=survival)) print(LikShifts(x,c(0),lambdavec[1],muvec[1],c(sampling),survival=survival)) print(LikShifts(x,c(0),lambdavec[1],muvec[1],c(sampling),survival= survival,groups=group)) print(LikShifts(x,c(0),c(lambdavec[1],lambdavec[1],lambdavec[1]), c(muvec[1],muvec[1],muvec[1]),c(sampling,1,1),survival= survival)) if (survival == 0 ) { print(LikDD(c(lambdavec[1],muvec[1], 200), model=0 ,root=1, x=sort(x),sampling=sampling)[1]) } if (survival == 0 ) { print(LikDD(c(lambdavec[1],muvec[1], 300), model=-1 ,root=1, x=sort(x),sampling=sampling)[1]) } print(" ") } ## Diversity-dependent speciation rates # condition on age of tree x[1], survival = 0 N<-10 pars <- matrix(c(N,lambdavec[1],muvec[1],0,sampling),nrow=1) print(LikDD(c(lambdavec[1],muvec[1],N),x=sort(x),model=-1,root=1,sampling=sampling)[1]) ## If you have the R package expoTree, you can compare: # library(expoTree) # print(-runExpoTree(pars,sort(x2),rep(1,length(x2)),survival=0)+(length(x)-1)*log(2)) print(" ") ### calculate likelihoods with root = 0 ## Constant speciation and extinction rates # condition on age of tree x[1] and number of tips n print(LikShiftsPP(x,timevec[1],lambdavec[1],muvec[1],sampling,root=0,n=1)) print(LikConstantn(lambdavec[1],muvec[1],sampling,x,root=0)) print(" ") # condition on age of tree x[1] for (survival in c(0,1)){ print(LikShiftsPP(x,c(0),lambdavec[1],muvec[1],sampling,root=0,survival=survival)) print(LikConstant(lambdavec[1],muvec[1],sampling,x,root=0,survival=survival)) if (survival == 0 ) {print(LikDD(c(lambdavec[1],muvec[1], 200), model=0 ,root=0, x=sort(x),sampling=sampling)[1]) } if (survival == 0 ) {print(LikDD(c(lambdavec[1],muvec[1], 300), model=-1 ,root=0, x=sort(x),sampling=sampling)[1]) } print(" ") } ## Diversity-dependent speciation rates # condition on age of tree x[1], survival = 0 print(LikDD(c(lambdavec[1],muvec[1],N),x=sort(x),model=-1,root=0,sampling=sampling)[1]) ## If you have the R package expoTree, you can compare: # print(-runExpoTree(pars,sort(x),rep(1,length(x)),survival=0)+(length(x)-1)*log(2))
LikShiftsPP calculates the likelihood of speciation and extinction rates and shift times given a phylogenetic tree, conditioning on the age of the tree. This function uses the point process theory (Lambert and Stadler, 2013).
LikShiftsPP(x, t, lambda, mu, sampling, survival=1,root=1,n=0)LikShiftsPP(x, t, lambda, mu, sampling, survival=1,root=1,n=0)
x |
Vector of speciation times in the phylogeny. Time is measured increasing going into the past with the present being time 0. x can be obtained from a phylogenetic tree using getx(TREE). |
t |
The time of rate shifts (t[1]=0 is required, being the present). An entry in t may not coincide with an entry in x. |
lambda, mu
|
Vectors of the same length as t. l[i] (resp. mu[i]) specifies the speciation (resp. extinction rate) prior to t[i]. |
sampling |
Sampling is the probability of an extant species being sampled and included into the tree. |
survival |
If survival = 1: The likelihood is conditioned on survival of the process (recommended). Otherwise survival = 0. |
root |
If root = 1 then max(x) is the mrca (crown age), if root = 0 then max(x) is the age of a branch ancestral to the mrca (stem age). |
n |
If n != 0 the likelihood is conditioned on n extant sampled tips and tree age max(x). |
res |
-log likelihood of the model parameters given the phylogenetic tree. |
Tanja Stadler
T. Stadler. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Nat. Acad. Sci., 108(15): 6187-6192, 2011.
A. Lambert, T. Stadler. Macro-evolutionary models and coalescent point processes: the shape and probability of reconstructed phylogenies. Theo. Pop. Biol., 90: 113-128, 2013.
# see manual of LikShifts()# see manual of LikShifts()
LikShiftsSTT calculates likelihood of piecewise constant birth and death rates for a given phylogenetic tree with sequentially sampled tips, conditioning on the age of the tree.
LikShiftsSTT(par,times,ttype,numbd=0,tconst=-1,sampling=0, sprob,root=0,survival=1,tfixed=vector(),mint=0,maxt=0)LikShiftsSTT(par,times,ttype,numbd=0,tconst=-1,sampling=0, sprob,root=0,survival=1,tfixed=vector(),mint=0,maxt=0)
par |
First k entries are piecewise constant speciation rates backward in time, second k entries are piecewise constant extinction rates backward in time, the last k-1 entries are the times of the rate shift. |
times |
Vector of branching and sampling times in the phylogeny. Time is measured increasing going into the past with the present being time 0. times can be obtained from a phylogenetic tree using getx(TREE,sersampling=TRUE). An time of rate shift specified in par may not coincide with an entry in times. |
ttype |
If ttype[i]=0, then times[i] denotes a branching event. If ttype[i]=1, then times[i] denotes a sampling event. |
numbd |
Help variable when optimizing. |
tconst |
Help variable when optimizing. |
sampling |
Probability of sampling individuals at present. sampling=0 is default. |
sprob |
Vector of length k with the entries specifying the probability of a death event coinciding with sampling. In other words, sprob is the probability that an extinct node is sampled to appear in the observed phylogeny. |
root |
root=0 indicates that there is an edge above the root (mrca) in the tree. root=1 indicates that there is no edge above the root. |
survival |
If survival = 1, the likelihood is conditioned on survival of the process (recommended). Otherwise survival = 0. |
tfixed |
Help variable when optimizing. |
mint |
Help variable when optimizing. |
maxt |
Help variable when optimizing. |
res |
-log likelihood of the parameters for the given tree. |
LikShiftsSTT extends the function LikShifts to trees with sequentially sampled tips.
Tanja Stadler
T. Stadler, D. Kuehnert, S. Bonhoeffer, A. Drummond. Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). Proc. Nat. Acad. Sci., 110(1): 228-233, 2013.
################################################### # Generating a tree set.seed(1) rootlength<-1 test0<-read.tree(text="((3:1.5,4:0.5):1,(1:2,2:1):3);") test<-addroot(test0,rootlength) test$Nnode<-4 test$states<-rep(1,4) times<-getx(test,sersampling=1)[,1] ttype<-getx(test,sersampling=1)[,2] times0<-getx(test0,sersampling=1)[,1] ttype0<-getx(test0,sersampling=1)[,2] ################################################### # Likelihood calculation # Tree with root edge print(-LikShiftsSTT(c(2,1,0.8,0.5,1.5345), times,ttype,sampling=0,sprob=c(1/3,2/3),survival=1,root=0) ) # Tree without root edge print(-LikShiftsSTT(c(2,1,0.8,0.5,1.5345), times0,ttype0,sampling=0,sprob=c(1/3,2/3),survival=1,root=1) ) ################################################### # This little example shows that in the case of constant rates, # LikShiftsSTT and LikTypesSTT yield the same results. # In LikShiftsSTT root=0 or 1 allowed. In LikTypesSTT only root=0 possible. death2<-c(3/2) sampprob2<-c(1/3) lambda2<-c(2) t2<-c(0) par2<-c(lambda2,death2) #collapse to 1 state epsi<- 0 test$states<-rep(1,4) root<-0 for (survival in c(0,1)) { print(-LikShiftsSTT(par2,times,ttype,sampling=0,sprob=sampprob2,survival=survival,root=root) ) print(-LikShiftsSTT(c(lambda2,lambda2,death2,death2,(max(times)*0.2431)), times,ttype,sampling=0,sprob=c(sampprob2,sampprob2),survival=survival,root=root) ) print(-LikShiftsSTT(par2,times,ttype,sampling=0, sprob=c(sampprob2),root=root,survival=survival) ) print(-LikTypesSTT(c(lambda2,epsi,epsi,epsi,death2,epsi,0,0),test, sampfrac=c(sampprob2,0),survival=survival,rtol=10e-10,atol=10e-10,freq=1,migr=0)) print(-LikTypesSTT(c(lambda2,epsi,epsi,epsi,death2,epsi,0,0),test, sampfrac=c(sampprob2,0),survival=survival,rtol=10e-10,atol=10e-10,freq=1,migr=1)) print(" ") }################################################### # Generating a tree set.seed(1) rootlength<-1 test0<-read.tree(text="((3:1.5,4:0.5):1,(1:2,2:1):3);") test<-addroot(test0,rootlength) test$Nnode<-4 test$states<-rep(1,4) times<-getx(test,sersampling=1)[,1] ttype<-getx(test,sersampling=1)[,2] times0<-getx(test0,sersampling=1)[,1] ttype0<-getx(test0,sersampling=1)[,2] ################################################### # Likelihood calculation # Tree with root edge print(-LikShiftsSTT(c(2,1,0.8,0.5,1.5345), times,ttype,sampling=0,sprob=c(1/3,2/3),survival=1,root=0) ) # Tree without root edge print(-LikShiftsSTT(c(2,1,0.8,0.5,1.5345), times0,ttype0,sampling=0,sprob=c(1/3,2/3),survival=1,root=1) ) ################################################### # This little example shows that in the case of constant rates, # LikShiftsSTT and LikTypesSTT yield the same results. # In LikShiftsSTT root=0 or 1 allowed. In LikTypesSTT only root=0 possible. death2<-c(3/2) sampprob2<-c(1/3) lambda2<-c(2) t2<-c(0) par2<-c(lambda2,death2) #collapse to 1 state epsi<- 0 test$states<-rep(1,4) root<-0 for (survival in c(0,1)) { print(-LikShiftsSTT(par2,times,ttype,sampling=0,sprob=sampprob2,survival=survival,root=root) ) print(-LikShiftsSTT(c(lambda2,lambda2,death2,death2,(max(times)*0.2431)), times,ttype,sampling=0,sprob=c(sampprob2,sampprob2),survival=survival,root=root) ) print(-LikShiftsSTT(par2,times,ttype,sampling=0, sprob=c(sampprob2),root=root,survival=survival) ) print(-LikTypesSTT(c(lambda2,epsi,epsi,epsi,death2,epsi,0,0),test, sampfrac=c(sampprob2,0),survival=survival,rtol=10e-10,atol=10e-10,freq=1,migr=0)) print(-LikTypesSTT(c(lambda2,epsi,epsi,epsi,death2,epsi,0,0),test, sampfrac=c(sampprob2,0),survival=survival,rtol=10e-10,atol=10e-10,freq=1,migr=1)) print(" ") }
LikTypesSTT calculates the likelihood of the 2-type birth-death model parameters given a tree, conditioning on the age of the tree. For obtaining the maximum likelihood parameter estimates use the R function optim (see example below).
LikTypesSTT(par,phylo,fix=rbind(c(0,0),c(0,0)),sampfrac, survival=0,posR=0,unknownStates=FALSE,rtol=1e-12,atol=1e-12,migr=0,freq=0,cutoff=10^12)LikTypesSTT(par,phylo,fix=rbind(c(0,0),c(0,0)),sampfrac, survival=0,posR=0,unknownStates=FALSE,rtol=1e-12,atol=1e-12,migr=0,freq=0,cutoff=10^12)
par |
Parameters of the 2-type branching model in the order lambda11, lambda12, lambda21, lambda22, death1, death2, gamma12, gamma21. Currently only gamma=0 is possible. Note that it is possible to only include a subset of these 8 parameters in par, the remaining ones are specified in fix (using optim, only the parameters specified in par will be estimated). |
phylo |
Phylogenetic tree for which the likelihood of the parameters is calculated. |
fix |
Determines which parameters are constrained when optimizing is performed. First row of fix specifies the parameters being constrained (1 for lambda11, 2 for lambda12 etc). Second row of fix specifies the constrained parameters: (i) If entry [2,j] is non-negative, say x, then parameter [1,j] is fixed to x. (ii) If entry [2,j] is negative but not equal to -0.4, then parameter [1,j] is fixed to paramter -m times entry [3,j] (exception is m=-0.4: then the parameter lambda22 is fixed to lambda21*lambda12/lambda11, used in Stadler & Bonhoeffer (2013) full reference below). |
sampfrac |
Vector of length 2. sampfrac[j] denotes the probability of sampling an individual in state j upon death (i.e. include the individual into the tree). |
survival |
survival=1 conditions the likelihood on sampling at least one tip. survival=0 default. |
posR |
posR=1 constrains the parameters (when optimizing) on the basic reproductive number R0 = >1. R0 for two types is calculated using TreePar:::R0types. posR=0 default. |
unknownStates |
If unknownStates=FALSE (default), phylo$states are used for the analysis. If unknownStates=TRUE, then the likelihood is calculated ignoring the tip states (used e.g. for identifying superspreader dynamics). |
rtol |
Relative tolerance parsed to the differential equation solver lsoda from package deSolve. |
atol |
Absolute tolerance parsed to the differential equation solver lsoda from package deSolve. |
migr |
If migr=0 then rate changes only at branching events (i.e. across-state transmission); if migr=1 then rate changes only along lineages (i.e. migration); if migr=2 then we assume a model with exposed state (1) and infectious state (2) where an infected individual transmits with rate lambda21 giving rise to a new exposed individual, and an exposed individual moves to the infectious class with rate lambda12. |
freq |
Specifies the probability of the origin of the tree being of type 1. If freq=0 then the equilibrium frequency given by the parameters par is used (see Supplement in Stadler & Bonhoeffer (2013) for details). |
cutoff |
We assume that prior to time cutoff, the sampling probability is 0. This setting acknowledges no sampling effort prior to a certain time. Cutoff must be older than the most ancestral tip. Default 10^12 means sampling throughout the whole time. |
out |
-log probability density of the (oriented) tree given the parameters (i.e. - log likelihood of the parameters for a fixed tree). |
This likelihood function extends the likelihood framework in the R package diversitree to trees with sequentially sampled tips. Our Ebola 2014 analyses highly relied on the function LikTypesSTT. Scripts used for this Ebola analysis are provided on our webpage www.bsse.ethz.ch/cevo.
Tanja Stadler
T. Stadler, S. Bonhoeffer. Uncovering epidemiological dynamics in heterogeneous host populations using phylogenetic methods. Phil. Trans. Roy. Soc. B, 368 (1614): 20120198, 2013.
set.seed(1) lambda11<-15 lambda12<-3 lambda21<-1 lambda22<-3 death1<-4 death2<-4 sampprob1<-0.05 sampprob2<-0.05 l<-rbind(c(lambda11,lambda12),c(lambda21,lambda22)) d<-c(death1,death2) s<-c(sampprob1,sampprob2) n<-20 init<- -1 tree<-sim.bdtypes.stt.taxa(n,l,d,s,init) tree<-addroot(tree,tree$root.edge) # Calculate likelihood for lambda11=15,lambda12=lambda21=lambda22=mu1=mu2=2,gamma=0 LikTypesSTT(par=c(2,2,2,2),phylo=tree, fix=rbind(c(1,6,7,8),c(15,-5,0,0),c(1,1,1,1)),sampfrac=s,survival=0,posR=0) # Calculate maximum likelihood parameter estimates of lambda12,lambda21, # lambda22,mu1 constraining lambda11=15,mu2=mu1 and gamma=0. out<-try(optim(c(2,2,2,2),LikTypesSTT,phylo=tree,fix=rbind(c(1,6,7,8),c(15,-5,0,0),c(1,1,1,1)), sampfrac=s,survival=0,posR=0,control=list(maxit=10000))) ### Likelihood calculation assuming a model with exposed class (migr = 2) # Simulating a tree with exposed class set.seed(2) # simulate tree with expected incubation period of 14 days, # infectious period of 7 days, and R0 of 1.5: mu <- c(0,1/7) lambda <- rbind(c(0,1/14),c(1.5/7,0)) # sampling probability of infectious individuals is 0.35: sampprob <-c(0,0.35) # we stop once we have 20 samples: n <- 20 # we simulate one tree: numbsim<-1 # We mark first eliminate=10 tips such that we can easily drop them later # (if deleting these 10 tips, we mimic no sampling close to the outbreak) trees<-lapply(rep(n,numbsim),sim.bdtypes.stt.taxa,lambdavector=lambda,deathvector=mu, sampprobvector=sampprob,EI=TRUE,eliminate=10) tree<-trees[[1]] origin<-max(getx(tree,sersampling=1)[,1])+tree$root.edge # delete first eliminate=10 tips: droptip<-tree$tip.label[which(tree$states == 1)] phylo<-drop.tip(tree,droptip) br<-getx(phylo,sersampling=1) # we only sample after cutoff time: cutoff<-max(br[which(br[,2]==0),1])*1.01 # add time since origin: phylo<-addroot(phylo,origin-max(br)) # all tips were infectious: phylo$states<-rep(2,length(phylo$tip.label)) ## # Evaluate likelihood at parameters used for simulation: LikTypesSTT(c(0,lambda[1,2],lambda[2,1],0,0,mu[2],0,0),phylo, sampfrac=sampprob,migr=2,cutoff=cutoff,freq=1) ##################### #This little verifies the correctness of the implementation by permuting both states and rates ################################################### test<-read.tree(text="((C:1.5,D:0.5):1,(A:1,B:1):3);") test<-addroot(test,0.1) par1<-2 par2<-1 par3<-0.5 par4<-3 par5<-1 par6<-0.5 par7<-1/3 par8<-0.5 ################################################### for (survival in c(0,1)) { test$states<-c(2,1,2,1) print(-LikTypesSTT(c(par4,par3,par2,par1,par6,par5,0,0),test, sampfrac=c(par8,par7),survival=survival,rtol=10e-14,atol=10e-14,migr=0,freq=0.5)) test$states<-c(1,2,1,2) print(-LikTypesSTT(c(par1,par2,par3,par4,par5,par6,0,0),test, sampfrac=c(par7,par8),survival=survival,rtol=10e-14,atol=10e-14,migr=0,freq=0.5)) print(" ") test$states<-c(2,1,2,1) print(-LikTypesSTT(c(par4,par3,par2,par1,par6,par5,0,0),test, sampfrac=c(par8,par7),survival=survival,rtol=10e-14,atol=10e-14,migr=1,freq=0.5)) test$states<-c(1,2,1,2) print(-LikTypesSTT(c(par1,par2,par3,par4,par5,par6,0,0),test, sampfrac=c(par7,par8),survival=survival,rtol=10e-14,atol=10e-14,migr=1,freq=0.5)) print(" ") }set.seed(1) lambda11<-15 lambda12<-3 lambda21<-1 lambda22<-3 death1<-4 death2<-4 sampprob1<-0.05 sampprob2<-0.05 l<-rbind(c(lambda11,lambda12),c(lambda21,lambda22)) d<-c(death1,death2) s<-c(sampprob1,sampprob2) n<-20 init<- -1 tree<-sim.bdtypes.stt.taxa(n,l,d,s,init) tree<-addroot(tree,tree$root.edge) # Calculate likelihood for lambda11=15,lambda12=lambda21=lambda22=mu1=mu2=2,gamma=0 LikTypesSTT(par=c(2,2,2,2),phylo=tree, fix=rbind(c(1,6,7,8),c(15,-5,0,0),c(1,1,1,1)),sampfrac=s,survival=0,posR=0) # Calculate maximum likelihood parameter estimates of lambda12,lambda21, # lambda22,mu1 constraining lambda11=15,mu2=mu1 and gamma=0. out<-try(optim(c(2,2,2,2),LikTypesSTT,phylo=tree,fix=rbind(c(1,6,7,8),c(15,-5,0,0),c(1,1,1,1)), sampfrac=s,survival=0,posR=0,control=list(maxit=10000))) ### Likelihood calculation assuming a model with exposed class (migr = 2) # Simulating a tree with exposed class set.seed(2) # simulate tree with expected incubation period of 14 days, # infectious period of 7 days, and R0 of 1.5: mu <- c(0,1/7) lambda <- rbind(c(0,1/14),c(1.5/7,0)) # sampling probability of infectious individuals is 0.35: sampprob <-c(0,0.35) # we stop once we have 20 samples: n <- 20 # we simulate one tree: numbsim<-1 # We mark first eliminate=10 tips such that we can easily drop them later # (if deleting these 10 tips, we mimic no sampling close to the outbreak) trees<-lapply(rep(n,numbsim),sim.bdtypes.stt.taxa,lambdavector=lambda,deathvector=mu, sampprobvector=sampprob,EI=TRUE,eliminate=10) tree<-trees[[1]] origin<-max(getx(tree,sersampling=1)[,1])+tree$root.edge # delete first eliminate=10 tips: droptip<-tree$tip.label[which(tree$states == 1)] phylo<-drop.tip(tree,droptip) br<-getx(phylo,sersampling=1) # we only sample after cutoff time: cutoff<-max(br[which(br[,2]==0),1])*1.01 # add time since origin: phylo<-addroot(phylo,origin-max(br)) # all tips were infectious: phylo$states<-rep(2,length(phylo$tip.label)) ## # Evaluate likelihood at parameters used for simulation: LikTypesSTT(c(0,lambda[1,2],lambda[2,1],0,0,mu[2],0,0),phylo, sampfrac=sampprob,migr=2,cutoff=cutoff,freq=1) ##################### #This little verifies the correctness of the implementation by permuting both states and rates ################################################### test<-read.tree(text="((C:1.5,D:0.5):1,(A:1,B:1):3);") test<-addroot(test,0.1) par1<-2 par2<-1 par3<-0.5 par4<-3 par5<-1 par6<-0.5 par7<-1/3 par8<-0.5 ################################################### for (survival in c(0,1)) { test$states<-c(2,1,2,1) print(-LikTypesSTT(c(par4,par3,par2,par1,par6,par5,0,0),test, sampfrac=c(par8,par7),survival=survival,rtol=10e-14,atol=10e-14,migr=0,freq=0.5)) test$states<-c(1,2,1,2) print(-LikTypesSTT(c(par1,par2,par3,par4,par5,par6,0,0),test, sampfrac=c(par7,par8),survival=survival,rtol=10e-14,atol=10e-14,migr=0,freq=0.5)) print(" ") test$states<-c(2,1,2,1) print(-LikTypesSTT(c(par4,par3,par2,par1,par6,par5,0,0),test, sampfrac=c(par8,par7),survival=survival,rtol=10e-14,atol=10e-14,migr=1,freq=0.5)) test$states<-c(1,2,1,2) print(-LikTypesSTT(c(par1,par2,par3,par4,par5,par6,0,0),test, sampfrac=c(par7,par8),survival=survival,rtol=10e-14,atol=10e-14,migr=1,freq=0.5)) print(" ") }