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Compare Model Estimation Across Simulations

Source: R/compare_estimation.R
compare_estimation.Rd

Run multiple simulations comparing how well different TNA model types recover the true transition structure, using tna::compare() for each comparison. Supports any model type available in the tna package.

Usage

compare_estimation(
  models = c("tna", "ftna"),
  n_simulations = 1000,
  n_sequences = 200,
  seq_length = 25,
  n_states = 6,
  na_range = c(0, 5),
  scaling = "minmax",
  seed = NULL,
  verbose = TRUE,
  parallel = FALSE,
  cores = parallel::detectCores() - 1
)

Arguments

models

Character vector. Model types to compare. Any model function exported by tna package works: "tna", "ftna", "ctna", "atna", "sna", "tsn". Default: c("tna", "ftna").

n_simulations

Integer. Number of simulations to run. Default: 1000.

n_sequences

Integer. Number of sequences per simulation. Default: 200.

seq_length

Integer. Maximum sequence length. Default: 25.

n_states

Integer. Number of states. Default: 6.

na_range

Integer vector of length 2. Range of NAs per sequence for varying lengths. Default: c(0, 5).

scaling

Character. Scaling for tna::compare(). Default: "minmax".

seed

Integer or NULL. Random seed. Default: NULL.

verbose

Logical. Print progress. Default: TRUE.

parallel

Logical. Use parallel processing. Default: FALSE.

cores

Integer. Number of cores for parallel. Default: detectCores() - 1.

Value

A list containing:

comparison

Side-by-side comparison of key metrics across models.

summary

Data frame with mean/sd of all metrics by model type.

raw_results

Data frame with all simulation results.

ranking

Models ranked by Pearson correlation (best to worst).

winner

Model with highest Pearson correlation.

params

Parameters used for the simulation.

Details

For each simulation:

  1. Generate random transition probabilities (ground truth)

  2. Simulate sequences with optional NAs (varying lengths)

  3. Fit all specified model types

  4. Compare each to ground truth using tna::compare()

  5. Collect metrics (Pearson, Spearman, Kendall, Euclidean, etc.)

Available model types (any tna package model):

  • tna: Standard transition network analysis (probabilities)

  • ftna: Frequency-based TNA (raw counts)

  • ctna: Concurrent TNA

  • atna: Absorbing TNA

  • sna: Sequential network analysis

  • tsn: Time-series network

  • Any other model function exported by tna package

Examples

if (FALSE) { # \dontrun{
# Compare 2 models (default: tna vs ftna)
results <- compare_estimation(n_simulations = 100, seed = 42)

# Compare 3 models
results <- compare_estimation(
  models = c("tna", "ftna", "ctna"),
  n_simulations = 100,
  seed = 42
)

# Compare all 4 models
results <- compare_estimation(
  models = c("tna", "ftna", "ctna", "atna"),
  n_simulations = 500,
  parallel = TRUE,
  seed = 42
)

# View results
results$comparison  # Side-by-side metrics
results$ranking     # Best to worst
results$winner      # Top performer
} # }