popsim {IBMPopSim}

Simulation of a model.

Description

This function simulates the random evolution of an IBM.

Usage

popsim(
  model,
  initial_population,
  events_bounds,
  parameters = NULL,
  age_max = Inf,
  time,
  multithreading = FALSE,
  num_threads = NULL,
  clean_step = NULL,
  clean_ratio = 0.1,
  seed = NULL,
  verbose = FALSE
)

Arguments

model

Model resulting from a call to the function mk_model.

initial_population

Object of population class representing the initial population.

events_bounds

Named vector of events bounds, with names corresponding to events names.

parameters

List of model parameters.

age_max

Maximum age of individuals in the population (Inf by default).

time

Final time (Numeric). Can be of length 1 or a vector of simulation discretized times.

multithreading

Logical for multithread activation, FALSE by default. Should be only activated for IBM simulation with no interactions.

num_threads

(Optional) Number of threads used for multithreading. Set by default to the number of concurrent threads supported by the available hardware implementation.

clean_step

(Optional) Optional parameter for improving simulation time. Time step for removing dead (or exited) individuals from the population. By default, equal to age_max.

clean_ratio

(Optional) Optional parameter for improving simulation time. 0.1 by default.

seed

(Optional) Random generator seed, random by default.

verbose

(Optional) Activate verbose output, FALSE by default.

Value

List composed of

arguments

Simulation inputs (initial population, parameters value, multithreading...)

logs

Simulation logs (algorithm duration, accepted/rejected events...).

population

If time is of length 1, population is an object of type population containing of all individuals who lived in the population in the time interval [0,time]. If time is a vector (time[1], ..., time[n]), population is a list of n objects of type population, each representing the state of the population at time time[i], for i = 1,..., n.

See Also

mk_model.

Examples

init_size <- 100000
pop_df <- data.frame(birth = rep(0, init_size), death = NA)
pop <- population(pop_df)

birth = mk_event_poisson(type = 'birth', intensity = 'lambda')
death = mk_event_poisson(type = 'death', intensity = 'mu')
params = list('lambda' = 100, 'mu' = 100)
birth_death <- mk_model(events = list(birth, death),
                       parameters = params)

sim_out <- popsim(model = birth_death,
                 initial_population = pop,
                 events_bounds = c('birth' = params$lambda, 'death' = params$mu),
                 parameters = params,
                 time = 10)