Simulation-based power and design analysis for a two-group Gaussian design

Estimate power by repeatedly simulating from the specification and applying a two-sample t-test, alongside the Type S (sign) and Type M (magnitude) design-analysis errors of Gelman and Carlin (2014).

Usage

power_design(spec, n_sims = 1000, alpha = 0.05, workers = 1)

Arguments

spec: A design specification (path or list) for a two-group Gaussian design.
n_sims: Number of Monte Carlo replicates. A power estimate carries a Monte Carlo standard error of about sqrt(p * (1 - p) / n_sims), and type_s and type_m average over the significant replicates alone, so they settle more slowly still. At least 200 replicates are advisable for study planning.
alpha: Two-sided significance level.
workers: Number of local worker processes over which to spread the replicates. The default of 1 runs serially. Because every replicate seeds the shared RNG from its own index, any worker count returns results identical to a serial run.

Value

A list with elements n_sims, alpha, power, n_significant, true_effect, mean_estimate, type_s (sign-error rate among significant replicates), and type_m (mean exaggeration ratio among significant replicates).

References

Gelman, A. and Carlin, J. (2014). Beyond power calculations: Assessing Type S (sign) and Type M (magnitude) errors. Perspectives on Psychological Science, 9(6), 641-651. doi:10.1177/1745691614551642

Examples

spec <- build_spec(list(name = "d", seed = 1, design_kind = "between",
  factor_name = "group", lev1 = "a", lev2 = "b", n_subject = 64,
  intercept = 100, effect = 5, family = "gaussian", resp_name = "", sigma = 10))
# n_sims is small so the example runs quickly. Use 200 or more for real planning.
power_design(spec, n_sims = 50)
#> $n_sims
#> [1] 50
#> 
#> $alpha
#> [1] 0.05
#> 
#> $power
#> [1] 0.6
#> 
#> $n_significant
#> [1] 30
#> 
#> $true_effect
#> [1] 5
#> 
#> $mean_estimate
#> [1] 5.238285
#> 
#> $type_s
#> [1] 0
#> 
#> $type_m
#> [1] 1.361989
#>