mcmodule (version 1.2.0)

mcmodule_converg: Analyse Monte Carlo Simulation Convergence

Description

Analyses convergence in Monte Carlo simulations by computing standardised and raw differences between consecutive iterations to evaluate stability and convergence of statistical measures.

Usage

mcmodule_converg(
  mcmodule,
  from_quantile = 0.95,
  to_quantile = 1,
  conv_threshold = NULL,
  print_summary = TRUE,
  progress = FALSE
)

Value

A data frame with convergence statistics. Each row represents one node. Key columns:

• expression: Expression identifier.

• variate: Variate (data row) identifier.

• node: Node name.

• max_dif_scaled: Maximum standardised difference.

• max_dif: Maximum raw difference.

• conv_threshold: Convergence at custom threshold, if provided.

• conv_01, conv_025, conv_05: Convergence at 1%, 2.5%, 5% thresholds.

Arguments

mcmodule

(mcmodule object). Module containing simulation results.

from_quantile

(numeric). Lower bound quantile for analysis. Default: 0.95.

to_quantile

(numeric). Upper bound quantile for analysis. Default: 1.

conv_threshold

(numeric, optional). Custom convergence threshold for standardised differences. Default: NULL.

print_summary

(logical). If TRUE, print convergence analysis summary. Default: TRUE.

progress

(logical). If TRUE, print progress information. Default: FALSE.

Details

The function performs the following:

• Calculates convergence statistics for specified quantile range

• Generates diagnostic plots for standardized and raw differences

• Provides detailed convergence summary including:

  • Total nodes analyzed

  • Number and percentage of nodes converged at different thresholds

  • Maximum/minimum deviations

  • List of non-converged nodes (if any)

Examples

if (FALSE) {
results <- mcmodule_converg(mc_results)
results <- mcmodule_converg(mc_results, from_quantile = 0.90, conv_threshold = 0.01)
}