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Simulate TNA Transition Matrix with Node Groupings

Source: R/simulate_tna_networks.R
simulate_tna_matrix.Rd

Simulate a transition matrix with node groupings, compatible with tna::plot_htna() (hierarchical) and tna::plot_mlna() (multilevel) visualizations. By default creates a 25-node matrix (5 nodes x 5 types) using learning category names.

This is a convenience wrapper around simulate_htna.

Usage

simulate_tna_matrix(
  nodes_per_group = 5,
  group_names = c("Metacognitive", "Cognitive", "Behavioral", "Social", "Motivational"),
  n_groups = 5,
  edge_prob_range = c(0, 1),
  self_loops = FALSE,
  use_learning_states = TRUE,
  categories = c("metacognitive", "cognitive", "behavioral", "social", "motivational"),
  within_prob = 0.4,
  between_prob = 0.15,
  node_prefix = "N",
  seed = NULL,
  verbose = TRUE,
  learning_categories = NULL
)

generate_tna_matrix(...)

Arguments

nodes_per_group

Integer. Number of nodes per group. Default: 5.

group_names

Character vector. Names for each group. Default uses learning categories: "Metacognitive", "Cognitive", "Behavioral", "Social", "Motivational".

n_groups

Integer. Number of groups. Default: 5.

edge_prob_range

Numeric vector of length 2. Range for edge weights c(min, max). Default: c(0, 1).

self_loops

Logical. Allow self-loops (diagonal elements). Default: FALSE.

use_learning_states

Logical. Use learning state verbs as node names. Default: TRUE.

categories

Character vector. Categories for node names, one per group. Default: c("metacognitive", "cognitive", "behavioral", "social", "motivational").

within_prob

Numeric. Probability of edges within each group. Default: 0.4.

between_prob

Numeric. Probability of edges between groups. Default: 0.15.

node_prefix

Character. Prefix for node names when not using learning states. Default: "N".

seed

Integer or NULL. Random seed. Default: NULL.

verbose

Logical. Print progress messages. Default: TRUE.

learning_categories

Deprecated. Use categories instead.

...

Arguments passed to simulate_tna_matrix.

Value

A list with two elements:

matrix

Square transition matrix (rows sum to 1) with named rows/columns.

node_types

Named list mapping group names to node names. Use as node_types for plot_htna() or as layers for plot_mlna().

Details

This function generates a random transition matrix and node groupings. The output can be used with both hierarchical and multilevel TNA plots:

  • For plot_htna(): use net$node_types directly

  • For plot_mlna(): use layers = net$node_types

See also

simulate_htna for the underlying function, simulate_matrix for basic matrix simulation, simulate_tna_networks for TNA model objects.

Examples

if (FALSE) { # \dontrun{
# Default: 5 groups x 5 nodes = 25 node matrix
net <- simulate_tna_matrix(seed = 42)
net$matrix
net$node_types  # Metacognitive, Cognitive, Behavioral, Social, Motivational

# Use with plot_htna
plot_htna(net$matrix, net$node_types, layout = "polygon")

# Use with plot_mlna
plot_mlna(net$matrix, layers = net$node_types)

# Custom group names (3 groups)
net <- simulate_tna_matrix(
  nodes_per_group = 6,
  group_names = c("Macro", "Meso", "Micro"),
  seed = 42
)

# Custom categories per group
net <- simulate_tna_matrix(
  nodes_per_group = 4,
  group_names = c("Teacher", "Student", "System"),
  categories = c("metacognitive", "cognitive", "behavioral"),
  seed = 123
)
} # }