poems (version 1.3.1)

SimulationManager: R6 class representing a simulation manager.

Description

R6 class to represent a manager for running multiple model simulations and saving results.

Super classes

poems::GenericClass -> poems::GenericManager -> SimulationManager

Public fields

attached

A list of dynamically attached attributes (name-value pairs).

Active bindings

sample_data

A data frame of sampled parameters for each simulation/result.

model_template

A SimulationModel (or inherited class) object with parameters common to all simulations.

nested_model

A SimulationModel (or inherited class) object with empty sample parameters and a nested model template common to all simulations.

generators

A list of generators (Generator or inherited class) objects for generating simulation model values.

model_simulator

A ModelSimulator (or inherited class) object for running the simulations.

parallel_cores

Number of cores for running the simulations in parallel.

results_dir

Results directory path.

results_ext

Result file extension (default is .RData).

results_filename_attributes

A vector of: prefix (optional); attribute names (from the sample data frame); postfix (optional); utilized to construct results filenames.

error_messages

A vector of error messages encountered when setting model attributes.

warning_messages

A vector of warning messages encountered when setting model attributes.

Methods

Public methods

• SimulationManager$new()

• SimulationManager$run()

• SimulationManager$set_model_sample()

• SimulationManager$log_simulation()

• SimulationManager$clone()

Inherited methods

• poems::GenericClass$new_clone()
• poems::GenericManager$get_attribute()
• poems::GenericManager$get_message_sample()
• poems::GenericManager$get_results_filename()

---

Method new()

Initialization method sets any included attributes (sample_data, model_template, generators, model_simulator, parallel_cores, results_dir, results_filename_attributes) and attaches other attributes individually listed.

Usage

SimulationManager$new(model_template = NULL, ...)

Arguments

model_template

A SimulationModel (or inherited class) object with parameters common to all simulations.

...

Parameters listed individually.

---

Method run()

Runs the multiple population simulations (via the set function), stores the results, and creates/writes a simulation log.

Usage

SimulationManager$run(results_dir = NULL)

Arguments

results_dir

Results directory path (must be present if not already set within manager class object).

Returns

Simulator log as a list.

---

Method set_model_sample()

Sets the model sample attributes via the sample data frame and the generators.

Usage

SimulationManager$set_model_sample(model, sample_index)

Arguments

model

SimulationModel (or inherited class) object (clone) to receive sample attributes.

sample_index

Index of sample from data frame.

---

Method log_simulation()

Summarizes the simulation log generated within the run method and writes it to a text file in the results directory.

Usage

SimulationManager$log_simulation(simulation_log)

Arguments

simulation_log

Nested list of simulation log entries generated via the run method.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

SimulationManager$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # interactive()
# U Island example region
coordinates <- data.frame(
  x = rep(seq(177.01, 177.05, 0.01), 5),
  y = rep(seq(-18.01, -18.05, -0.01), each = 5)
)
template_raster <- Region$new(coordinates = coordinates)$region_raster # full extent
template_raster[][-c(7, 9, 12, 14, 17:19)] <- NA # make U Island
region <- Region$new(template_raster = template_raster)
raster::plot(region$region_raster,
  main = "Example region (indices)",
  xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
  colNA = "blue"
)
# Example population model template
model_template <- PopulationModel$new(
  region = region,
  time_steps = 10, # years
  populations = region$region_cells, # 7
  stage_matrix = 1
)
# Example generators for initial abundance and carrying capacity
hs_matrix <- c(0.5, 0.3, 0.7, 0.9, 0.6, 0.7, 0.8)
initial_gen <- Generator$new(
  description = "initial abundance",
  region = region,
  hs_matrix = hs_matrix, # template attached
  inputs = c("initial_n"),
  outputs = c("initial_abundance")
)
initial_gen$add_generative_requirements(list(initial_abundance = "function"))
initial_gen$add_function_template("initial_abundance",
  function_def = function(params) {
    stats::rmultinom(1,
      size = params$initial_n,
      prob = params$hs_matrix
    )[, 1]
  },
  call_params = c("initial_n", "hs_matrix")
)
capacity_gen <- Generator$new(
  description = "carrying capacity",
  region = region,
  hs_matrix = hs_matrix, # template attached
  inputs = c("density_max"),
  outputs = c("carrying_capacity")
)
capacity_gen$add_generative_requirements(list(carrying_capacity = "function"))
capacity_gen$add_function_template("carrying_capacity",
  function_def = function(params) {
    round(params$density_max * params$hs_matrix)
  },
  call_params = c("density_max", "hs_matrix")
)
# Sample input parameters
sample_data <- data.frame(initial_n = c(40, 60, 80), density_max = c(15, 20, 25))
# Simulation manager
sim_manager <- SimulationManager$new(
  sample_data = sample_data,
  model_template = model_template,
  generators = list(initial_gen, capacity_gen),
  parallel_cores = 2,
  results_dir = tempdir()
)
run_output <- sim_manager$run()
run_output$summary
dir(tempdir(), "*.RData") # includes 3 result files
for (i in 1:3) {
  print(paste("Run", i, "results:"))
  file_name <- paste0(sim_manager$get_results_filename(i), ".RData")
  print(readRDS(file.path(tempdir(), file_name)))
}
dir(tempdir(), "*.txt") # plus simulation log
}