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Infer Multiple FCMs in a List

Source: R/infer_and_simulate_fcm.R
infer_fcm_set.Rd

This function simulates a set of FCMs (Conventional, IVFN, and/or TFN) (whose edge weights were sampled using monte carlo methods) by repetitively calling the infer_fcm function for each adj. matrix in the set.

Usage

infer_fcm_set(
  adj_matrices = list(matrix()),
  initial_state_vector = c(),
  clamping_vector = c(),
  activation = c("kosko", "modified-kosko", "rescale"),
  squashing = c("sigmoid", "tanh"),
  lambda = 1,
  point_of_inference = c("peak", "final"),
  max_iter = 100L,
  min_error = 1e-05,
  parallel = TRUE,
  n_cores = 1L,
  show_progress = TRUE,
  include_sims_in_output = FALSE,
  silent = FALSE,
  skip_checks = FALSE
)

Arguments

adj_matrices

[list()]
A single adjacency matrix or a list of adjacency matrices (n x n) representing FCMs. Matrices can have conventional edge weights, IVFN edge weights or TFN edge weights.

initial_state_vector

[vector("double")]
A list of state values (one per node) at the start of an FCM simulation. In pulse simulations the initial_state_vector controls the scenario (i.e., a non-zero value is a transient perturbation). In clamped simulations all values in the initial_state_vector are set to 1.

clamping_vector

[vector("double")]
A list of values (one per node) that indicates whether clamped simulations will be performed. In clamped simulations the clamping_vector controls the scenario (nodes assigned non-zero values will remain at those values for the entire simulation). In pulse simulations all values in the clamping_vector are set to 0.

activation

[character(1)]
The activation function used. Must be one of the following: 'kosko', 'modified-kosko', or 'rescale'.

squashing

[character(1)]
The squashing function used. Must be one of the following: 'tanh' or 'sigmoid'.

lambda

[double(1)] Positive
A numeric value that defines the steepness of the squashing function's slope.

point_of_inference

[character(1)]
Definition of an inference. The metric used to calculate the response of each node to a scenario of interest from a simulation timeseries. Must be one of the following: 'peak' (the maximum value) or 'final' (the state at equilibrium).

max_iter

[integer(1) - Positive]
The maximum number of iterations to run (increase if the minimum error value is not achieved).

min_error

[double(1) - Positive]
The error past which a simulation has converged and no further iterations are necessary. Error equals the sum of the absolute value of the current state vector minus the previous state vector.

parallel

[logical(1)]
If TRUE, utilize parallel processing.

n_cores

[integer(1) - Positive]
The number of cores to use in parallel processing. If no input given, all available cores will be used. Should be a positive integer.

show_progress

[logical(1)]
If TRUE, show progress bars and print runtime updates in the console when performing FCM simulations.

include_sims_in_output

[logical(1)]
If TRUE, include simulations and inferences in output. Set to FALSE to reduce output size.

silent

[logical(1)]
If TRUE, suppress warning and error messages.

skip_checks

[logical(1)]
FOR DEVELOPER USE ONLY. If TRUE, skip call to check_simulation_inputs (internal function)

Value

[inference_of_fcm_set]
A list/data.frame of the inferences (and simulations if included) for each FCM in the set

Details

The show_progress and parallel inputs change how the function is run, but do NOT change the output! These are allowed to be toggled on/off to increase user control at runtime.

Examples


# Set of Conventional FCMs
conventional_fcm_set_inferences <- infer_fcm_set(
  adj_matrices = sample_fcms$simple_fcms$conventional_fcms[1:5],
  initial_state_vector = c(1, 1, 1, 1, 1, 1, 1),
  clamping_vector = c(1, 0, 0, 0, 0, 0, 0),
  activation = "modified-kosko",
  squashing = "sigmoid",
  lambda = 1.0,
  point_of_inference = "final",
  max_iter = 100L,
  min_error = 1e-5,
  parallel = FALSE,
  n_cores = 1L,
  show_progress = FALSE,
  include_sims_in_output = TRUE,
  silent = FALSE,
  skip_checks = FALSE
)

# Set of IVFN FCMs
ivfn_fcm_set_inferences <- infer_fcm_set(
  adj_matrices = sample_fcms$simple_fcms$ivfn_fcms[1:5],
  initial_state_vector = c(1, 1, 1, 1, 1, 1, 1),
  clamping_vector = c(1, 0, 0, 0, 0, 0, 0),
  activation = "modified-kosko",
  squashing = "sigmoid",
  lambda = 1.0,
  point_of_inference = "final",
  max_iter = 100L,
  min_error = 1e-5,
  parallel = FALSE,
  n_cores = 1L,
  show_progress = FALSE,
  include_sims_in_output = TRUE,
  silent = FALSE,
  skip_checks = FALSE
)

# Set of TFN FCMs
tfn_fcm_set_inferences <- infer_fcm_set(
  adj_matrices = sample_fcms$simple_fcms$tfn_fcms[1:5],
  initial_state_vector = c(1, 1, 1, 1, 1, 1, 1),
  clamping_vector = c(1, 0, 0, 0, 0, 0, 0),
  activation = "modified-kosko",
  squashing = "sigmoid",
  lambda = 1.0,
  point_of_inference = "final",
  max_iter = 100L,
  min_error = 1e-5,
  parallel = FALSE,
  n_cores = 1L,
  show_progress = FALSE,
  include_sims_in_output = TRUE,
  silent = FALSE,
  skip_checks = FALSE
)