Run the Power Study Under Several Mediation Patterns at Once

A convenience wrapper around pe_power_curve() that runs the size and power study under more than one mediation pattern in a single call and stacks the results, so a homogeneous and a contrasting curve (the two panels of a figure in the article) come back together. Each pattern is swept over the same signal grid.

Usage

pe_simulation_study(
  n,
  p,
  outcome = c("continuous", "binary", "count"),
  patterns = c("homogeneous", "contrasting"),
  c1_grid = seq(0, 1, by = 0.25),
  c2 = 0.5,
  n_rep = 100,
  alpha_level = 0.05,
  method = c("Bonferroni", "BH", "BY"),
  error_level = 0.05,
  lambda_grid = seq(0.05, 10, length.out = 100),
  cores = 1L,
  seed = NULL
)

Arguments

n, p

Observations and candidate mediators per simulated data set.

outcome

Outcome type: "continuous", "binary", or "count".

patterns

Character vector of mediation patterns to run, each named once. The default runs "homogeneous" and "contrasting".

c1_grid, c2, n_rep, alpha_level, method, error_level, lambda_grid, cores

Passed to pe_power_curve().

seed

Optional integer seed. When supplied it is set for the duration of the call and the caller's random number generator state is restored on exit. NULL (the default) sets no seed: the draws come from the session's random number stream, which the call advances as any random function does.

Value

A tidy data.frame (class poemed_tbl) with a leading pattern column and the pe_power_curve() columns (c1, rejection_hdmm, rejection_pe, n_valid, n_empty) for each pattern. The settings are recorded in the attributes outcome, n, p, n_rep, alpha_level, error_level, and lambda_grid.

Details

Everything pe_power_curve() says about Monte Carlo error, empty fits, and the tuning grid applies here; the article's logistic and Poisson figures fix c2 at 1 and 0.4. The patterns take their data from one continuing random stream, so no two patterns share their draws. With cores above 1 the points of c1_grid within a pattern do share them (see cores on pe_power_curve()).

How to Cite

If you use POEMED in published work, please cite Yu and Kelley (in press), the article that introduces its methods and the package. citation("POEMED") gives the full reference and a BibTeX entry.

See also

pe_power_curve() for a single pattern, plot.poemed_tbl() to draw one pattern's curves.

Other mediation simulation: guo_calibration, mediation_ar1_cov(), pe_identification_study(), pe_power_curve(), plot.poemed_tbl(), simulate_guo_mediation(), simulate_mediation_data(), ss_power_pe_mediation()

Author

Xiufan Yu and Ken Kelley

Examples

# n_rep = 5 keeps this example fast; a rate from five replications has a
# Monte Carlo standard error of up to 0.22, so read the shape, not the
# numbers. A reported study uses the article's design (n = 300, p = 500)
# and 1000 replications. A study is also where a shorter tuning grid
# pays: every replication searches the whole grid, so this one passes 20
# values from 0.05 to 2 instead of the default 100 values from 0.05 to 10
# (see the lambda_grid argument of ?pe_mediation).
set.seed(113)
pe_simulation_study(n = 120, p = 50, outcome = "continuous",
                    c1_grid = c(0, 1), n_rep = 5,
                    lambda_grid = seq(0.05, 2, length.out = 20))
#>  pattern     c1 rejection_hdmm rejection_pe n_valid n_empty
#>  homogeneous 0  0              0            5       0      
#>  homogeneous 1  0.6            1            5       0      
#>  contrasting 0  0              0            5       0      
#>  contrasting 1  0              1            5       0      
#> 
#> Outcome: continuous