generate_tree_hbds {castor}R Documentation

Generate a tree from a birth-death-sampling model in forward time.

Description

Generate a random timetree according to a homogenous birth-death-sampling model with arbitrary time-varying speciation/extinction/sampling rates. Lineages split (speciate) or die (go extinct) at Poissonian rates and independently of each other. Lineages are sampled continuously (i.e., at Poissonian rates) in time and/or during concentrated sampling attempts (i.e., at specific time points). Sampled lineages are assumed to continue in the pool of extant lineages at some given "retention probability". The final tree can be restricted to sampled lineages only, but may optionally include extant (non-sampled) as well as extinct lineages. Speciation, extinction and sampling rates as well as retention probabilities may depend on time. This function may be used to simulate trees commonly encountered in viral epidemiology, where sampled patients are assumed to exit the pool of infectious individuals.

Usage

generate_tree_hbds( max_sampled_tips             = NULL, 
                    max_sampled_nodes            = NULL, 
                    max_extant_tips              = NULL,
                    max_extinct_tips             = NULL,
                    max_tips                     = NULL,
                    max_time                     = NULL,
                    include_extant               = FALSE,
                    include_extinct              = FALSE,
                    as_generations               = FALSE,
                    time_grid                    = NULL,
                    lambda                       = NULL,
                    mu                           = NULL,
                    psi                          = NULL,
                    kappa                        = NULL,
                    splines_degree               = 1,
                    CSA_times                    = NULL,
                    CSA_probs                    = NULL,
                    CSA_kappas                   = NULL,
                    no_full_extinction           = FALSE,
                    max_runtime                  = NULL,
                    tip_basename                 = "",
                    node_basename                = NULL,
                    edge_basename                = NULL,
                    include_birth_times          = FALSE,
                    include_death_times          = FALSE)

Arguments

max_sampled_tips

Integer, maximum number of sampled tips. The simulation is halted once this number is reached. If NULL or <=0, this halting criterion is ignored.

max_sampled_nodes

Integer, maximum number of sampled nodes, i.e., of lineages that were sampled but kept in the pool of extant lineages. The simulation is halted once this number is reached. If NULL or <=0, this halting criterion is ignored.

max_extant_tips

Integer, maximum number of extant tips. The simulation is halted once the number of concurrently extant tips reaches this threshold. If NULL or <=0, this halting criterion is ignored.

max_extinct_tips

Integer, maximum number of extant tips. The simulation is halted once this number is reached. If NULL or <=0, this halting criterion is ignored.

max_tips

Integer, maximum number of tips (extant+extinct+sampled). The simulation is halted once this number is reached. If NULL or <=0, this halting criterion is ignored.

max_time

Numeric, maximum duration of the simulation. If NULL or <=0, this halting criterion is ignored.

include_extant

Logical, specifying whether to include extant tips (i.e., neither extinct nor sampled) in the final tree.

include_extinct

Logical, specifying whether to include extant tips (i.e., neither extant nor sampled) in the final tree.

as_generations

Logical, specifying whether edge lengths should correspond to generations. If FALSE, then edge lengths correspond to time. If TRUE, then the time between two subsequent events (speciation, extinction, sampling) is counted as "one generation".

time_grid

Numeric vector, specifying time points (in ascending order) on which the rates lambda, mu and psi are provided. Rates are interpolated polynomially between time grid points as needed (according to splines_degree). The time grid should generally cover the maximum possible simulation time, otherwise it will be polynomially extrapolated as needed.

lambda

Numeric vector, of the same size as time_grid (or size 1 if time_grid==NULL), listing per-lineage speciation (birth) rates (λ, in units 1/time) at the times listed in time_grid. Speciation rates must be non-negative, and are assumed to vary as a spline between grid points (see argument splines_degree). Can also be a single numeric, in which case λ is assumed to be constant over time.

mu

Numeric vector, of the same size as time_grid (or size 1 if time_grid==NULL), listing per-lineage extinction (death) rates (μ, in units 1/time) at the times listed in time_grid. Extinction rates must be non-negative, and are assumed to vary as a spline between grid points (see argument splines_degree). Can also be a single numeric, in which case μ is assumed to be constant over time. If omitted, the extinction rate is assumed to be zero.

psi

Numeric vector, of the same size as time_grid (or size 1 if time_grid==NULL), listing per-lineage sampling rates (ψ, in units 1/time) at the times listed in time_grid. Sampling rates must be non-negative, and are assumed to vary as a spline between grid points (see argument splines_degree). Can also be a single numeric, in which case ψ is assumed to be constant over time. If omitted, the continuous sampling rate is assumed to be zero.

kappa

Numeric vector, of the same size as time_grid (or size 1 if time_grid==NULL), listing retention probabilities (κ, unitless) of continuously (Poissonian) sampled lineages at the times listed in time_grid. Retention probabilities must be true probabilities (i.e., between 0 and 1), and are assumed to vary as a spline between grid points (see argument splines_degree). Can also be a single numeric, in which case κ is assumed to be constant over time. If omitted, the retention probability is assumed to be zero (a common assumption in epidemiology).

splines_degree

Integer, either 0,1,2 or 3, specifying the polynomial degree of the provided lambda, mu and psi between grid points in age_grid. For example, if splines_degree==1, then the provided lambda, mu and psi are interpreted as piecewise-linear curves; if splines_degree==2 the lambda, mu and psi are interpreted as quadratic splines; if splines_degree==3 the lambda, mu and psi is interpreted as cubic splines. If your age_grid is fine enough, then splines_degree=1 is usually sufficient.

CSA_times

Optional numeric vector, listing times of concentrated sampling attempts, in ascending order. Concentrated sampling is performed in addition to any continuous (Poissonian) sampling specified by psi.

CSA_probs

Optional numeric vector of the same size as CSA_times, listing sampling probabilities at each concentrated sampling time. Note that in contrast to the sampling rates psi, the CSA_probs are interpreted as probabilities and must thus be between 0 and 1. CSA_probs must be provided if and only if CSA_times is provided.

CSA_kappas

Optional numeric vector of the same size as CSA_times, listing sampling retention probabilities at each concentrated sampling time, i.e. the probability at which a sampled lineage is kept in the pool of extant lineages. Note that the CSA_kappas are probabilities and must thus be between 0 and 1. CSA_kappas must be provided if and only if CSA_times is provided.

no_full_extinction

Logical, specifying whether to prevent complete extinction of the tree. Full extinction is prevented by temporarily disabling extinctions whenever the number of extant tips is 1. Note that, strictly speaking, the trees generated do not exactly follow the proper probability distribution when no_full_extinction is TRUE.

max_runtime

Numeric, optional maximum computation time (in seconds) to allow for the simulation. Use this to avoid occasional explosions of runtimes, for example due to very large generated trees. Aborted simulations will return with the flag success=FALSE (i.e., no tree is returned at all).

tip_basename

Character. Prefix to be used for tip labels (e.g. "tip."). If empty (""), then tip labels will be integers "1", "2" and so on.

node_basename

Character. Prefix to be used for node labels (e.g. "node."). If NULL, no node labels will be included in the tree.

edge_basename

Character. Prefix to be used for edge labels (e.g. "edge."). Edge labels (if included) are stored in the character vector edge.label. If NULL, no edge labels will be included in the tree.

include_birth_times

Logical. If TRUE, then the times of speciation events (in order of occurrence) will also be returned.

include_death_times

Logical. If TRUE, then the times of extinction events (in order of occurrence) will also be returned.

Details

The simulation proceeds in forward time, starting with a single root. Speciation/extinction and continuous (Poissonian) sampling events are drawn at exponentially distributed time steps, according to the rates specified by lambda, mu and psi. Sampling also occurs at the optional CSA_times. Only extant lineages are sampled at any time point, and sampled lineages are removed from the pool of extant lineages at probability 1-kappa.

The simulation halts as soon as one of the halting criteria are met, as specified by the options max_sampled_tips, max_sampled_nodes, max_extant_tips, max_extinct_tips, max_tips and max_time, or if no extant tips remain, whichever occurs first. Note that in some scenarios (e.g., if extinction rates are very high) the simulation may halt too early and the generated tree may only contain a single tip (i.e., the root lineage); in that case, the simulation will return an error (see return value success).

The function returns a single generated tree, as well as supporting information such as which tips are extant, extinct or sampled.

Value

A named list with the following elements:

success

Logical, indicating whether the simulation was successful. If FALSE, then the returned list includes an additional 'error' element (character) providing a description of the error; all other return variables may be undefined.

tree

The generated timetree, of class "phylo". Note that this tree need not be ultrametric, for example if sampling occurs at multiple time points.

root_time

Numeric, giving the time at which the tree's root was first split during the simulation. Note that this may be greater than 0, i.e., if the tips of the final tree do not coalesce all the way back to the simulation's start.

final_time

Numeric, giving the final time at the end of the simulation.

root_age

Numeric, giving the age (time before present) at the tree's root. This is equal to final_time-root_time.

Nbirths

Integer, the total number of speciation (birth) events that occured during the simulation.

Ndeaths

Integer, the total number of extinction (death) events that occured during the simulation.

Nsamplings

Integer, the total number of sampling events that occured during the simulation.

Nretentions

Integer, the total number of sampling events that occured during the simulation and for which lineages were kept in the pool of extant lineages.

sampled_clades

Integer vector, specifying indices (from 1 to Ntips+Nnodes) of sampled tips and nodes in the final tree (regardless of whether their lineages were subsequently retained or removed from the pool).

retained_clades

Integer vector, specifying indices (from 1 to Ntips+Nnodes) of sampled tips and nodes in the final tree that were retained, i.e., not removed from the pool following sampling.

extant_tips

Integer vector, specifying indices (from 1 to Ntips) of extant (non-sampled and non-extinct) tips in the final tree. Will be empty if include_extant==FALSE.

extinct_tips

Integer vector, specifying indices (from 1 to Ntips) of extinct (non-sampled and non-extant) tips in the final tree. Will be empty if include_extinct==FALSE.

Author(s)

Stilianos Louca

References

T. Stadler (2010). Sampling-through-time in birth–death trees. Journal of Theoretical Biology. 267:396-404.

T. Stadler et al. (2013). Birth–death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). PNAS. 110:228-233.

See Also

generate_tree_hbd_reverse, generate_gene_tree_msc, generate_random_tree, fit_hbds_model_parametric, simulate_deterministic_hbds

Examples

# define time grid on which lambda, mu and psi will be specified
time_grid = seq(0,100,length.out=1000)

# specify the time-dependent extinction rate mu on the time-grid
mu_grid = 0.5*time_grid/(10+time_grid)

# define additional concentrated sampling attempts
CSA_times  = c(5,7,9)
CSA_probs  = c(0.5, 0.5, 0.5)
CSA_kappas = c(0.2, 0.1, 0.1)

# generate tree with a constant speciation & sampling rate,
# time-variable extinction rate and additional discrete sampling points
# assuming that all continuously sampled lineages are removed from the pool
simul = generate_tree_hbds( max_time        = 10,
                            include_extant  = FALSE,
                            include_extinct = FALSE,
                            time_grid       = time_grid,
                            lambda          = 1,
                            mu              = mu_grid,
                            psi             = 0.1,
                            kappa           = 0,
                            CSA_times       = CSA_times,
                            CSA_probs       = CSA_probs,
                            CSA_kappas      = CSA_kappas);
if(!simul$success){
    cat(sprintf("ERROR: Could not simulate tree: %s\n",simul$error))
}else{
    # simulation succeeded. print some basic info about the generated tree
    tree = simul$tree
    cat(sprintf("Generated tree has %d tips\n",length(tree$tip.label)))
}

[Package castor version 1.7.0 Index]