Compare Network Estimation Across TNA Model Types

Perform side-by-side comparison of multiple TNA model types (tna, atna, ctna, ftna) using comprehensive sampling analysis. Uses a statistically correct approach: comparing models built on independent data subsets (sample vs remaining).

Usage

compare_network_estimation(
  data,
  model_types = c("tna", "ftna"),
  model_scaling = NULL,
  sampling_percent = 0.3,
  iterations = 100,
  seed = NULL,
  verbose = TRUE
)

compare_tna_models(
  data,
  model_types = c("tna", "ftna"),
  model_scaling = NULL,
  sampling_percent = 0.3,
  iterations = 100,
  seed = NULL,
  verbose = TRUE
)

Arguments

data

A data frame containing sequence data.

model_types

Character vector of model types to compare. Valid types are "tna", "atna", "ctna", "ftna". Default: c("tna", "ftna").

model_scaling

A named list specifying scaling for each model type. Example: list(tna = "minmax", ftna = "skip"). If NULL, uses default scaling.

sampling_percent

Numeric value between 0 and 1 indicating the proportion of data to sample. Default: 0.3.

iterations

Integer specifying the number of sampling iterations for each model. Default: 100.

seed

Integer seed for reproducible random sampling. Default: NULL.

verbose

Logical. Print progress messages. Default: TRUE.

Value

A list containing:

individual

Data frame with all iteration results including model_type column.

aggregated

Data frame with aggregated metrics by model type.

ranking

Models ranked by mean Pearson correlation.

winner

Model with highest Pearson correlation.

params

Parameters used for the comparison.

Details

The function workflow:

1. For each model type, builds a model from the data.

2. Runs sampling analysis comparing sample vs remaining subsets.

3. Combines all results for comparison.

This approach is statistically correct because it compares models built on independent data subsets, providing a true measure of estimation stability.

Examples

if (FALSE) { # \dontrun{
library(tna)
data(group_regulation)

# Compare tna and ftna models
results <- compare_network_estimation(group_regulation)
results$ranking
results$winner

# Compare all model types
results <- compare_network_estimation(
  group_regulation,
  model_types = c("tna", "atna", "ctna", "ftna"),
  iterations = 100
)

# Plot results
plot(results)  # Default histogram
plot(results, metric_name = "Pearson")
} # }