Simulate TNA Network Objects

Simulate multiple TNA network objects (fitted models) for simulation studies. This function creates TNA model objects by simulating sequence data and fitting models. For generating raw sequence data with associated probabilities, use simulate_tna_datasets. For group TNA models, use simulate_group_tna_networks.

Usage

simulate_tna_networks(
  n_networks = 1,
  n_states = 8,
  n_sequences = 100,
  seq_length = 30,
  model_type = "tna",
  use_learning_states = TRUE,
  categories = NULL,
  seed = NULL,
  verbose = TRUE,
  num_sequences = NULL,
  max_seq_length = NULL,
  learning_categories = NULL,
  ...
)

generate_tna_networks(...)

Arguments

n_networks

Integer. Number of networks to generate. Default: 1.

n_states

Integer. Number of states/actions in each network. Default: 5.

n_sequences

Integer. Number of sequences to simulate per network. Default: 100.

seq_length

Integer. Maximum length of each sequence. Default: 30.

model_type

Character. Type of TNA model to fit: "tna", "ftna", "ctna", "atna". Default: "tna".

use_learning_states

Logical. If TRUE, uses learning action verbs as state names. Default: TRUE.

categories

Character vector or NULL. Which categories of learning verbs to use. Options: "metacognitive", "cognitive", "behavioral", "social", "motivational", "affective", "group_regulation", or "all". If NULL (default), randomly selects one category.

seed

Integer or NULL. Random seed for reproducibility. Default: NULL.

verbose

Logical. If TRUE, prints progress messages. Default: TRUE.

num_sequences

Deprecated. Use n_sequences instead.

max_seq_length

Deprecated. Use seq_length instead.

learning_categories

Deprecated. Use categories instead.

...

Arguments passed to simulate_tna_networks.

Value

A list of length n_networks containing fitted TNA model objects. Each element is a tna model object (class "tna").

Details

This function differs from simulate_tna_datasets in that it returns only the fitted model objects, not the underlying sequence data or generating probabilities. Use this when you need TNA network objects for simulation studies or method comparisons.

Random Category Selection: When categories = NULL and use_learning_states = TRUE, a random learning category is selected. Available categories: metacognitive, cognitive, behavioral, social, motivational, affective, group_regulation.

See also

simulate_group_tna_networks for group TNA models, simulate_tna_datasets for generating complete datasets with probabilities, fit_network_model for model fitting, get_learning_states for learning state verbs.

Examples

if (FALSE) { # \dontrun{
# Generate 5 TNA networks with random learning category
nets <- simulate_tna_networks(n_networks = 5, seed = 42)

# Generate networks with specific category
meta_nets <- simulate_tna_networks(
  n_networks = 3,
  n_states = 6,
  categories = "metacognitive",
  seed = 123
)

# Generate filtered TNA networks
ftna_nets <- simulate_tna_networks(
  n_networks = 5,
  model_type = "ftna",
  seed = 456
)

# Use the networks
plot(nets[[1]])
tna::centralities(nets[[1]])

# Old parameter names still work
nets <- simulate_tna_networks(
  n_networks = 3,
  num_sequences = 50,
  max_seq_length = 25,
  seed = 42
)
} # }