Prints a formatted summary of Monte Carlo simulation results.
Arguments
- mc_results
Output from
run_hurdle_monte_carlo.- digits
Number of digits to display. Default is 3.
Examples
# \donttest{
mc_results <- run_hurdle_monte_carlo(n_sim = 50, n_subjects = 100, seed = 123)
#> Running 50 Monte Carlo simulations...
#> Simulation 10/50
#> Simulation 20/50
#> Simulation 30/50
#> Simulation 40/50
#> Simulation 50/50
#> Done. 50/50 simulations converged (100.0%).
print_mc_summary(mc_results)
#>
#> Monte Carlo Simulation Summary
#> ==============================
#>
#> Simulations: 50 attempted, 50 converged (100.0%)
#>
#> Parameter True Mean_Est Bias Rel_Bias% Emp_SE Mean_SE SE_Ratio
#> beta0 -2.000 -1.997 0.003 0.1 0.179 0.204 1.14
#> beta1 1.000 0.838 -0.162 -16.2 0.187 0.321 1.71
#> log_q0 2.303 2.314 0.011 0.5 0.060 0.056 0.94
#> k 2.000 NA NA NA NA NA NA
#> alpha 0.500 NA NA NA NA NA NA
#> logsigma_a 0.000 -0.420 -0.420 NA 0.515 0.713 1.39
#> logsigma_b -0.693 -0.712 -0.019 -2.8 0.075 0.078 1.05
#> logsigma_e -1.204 -1.210 -0.006 -0.5 0.034 0.032 0.95
#> rho_ab_raw 0.310 1.055 0.745 240.8 2.117 168.570 79.63
#> Coverage_95% N
#> 98 50
#> 90 50
#> 92 50
#> NA 0
#> NA 0
#> 94 50
#> 98 50
#> 96 50
#> 100 50
#>
#> Interpretation:
#> - SE Ratio close to 1.0 indicates well-calibrated SEs
#> - Coverage close to 95% indicates valid confidence intervals
#> - Relative bias < 5% is generally acceptable
# }