Generate datasets for all 18 conditions in the simulation design

Iterates over the 2 scenarios x 3 separation levels x 3 sample sizes, generating n_rep independent replications per condition. Seeds are derived deterministically from base_seed so the entire experiment is reproducible from a single integer.

Usage

generate_all_conditions(
  n_rep = 500L,
  base_seed = 5262026L,
  params = bk2018_params,
  scenarios = c("covariate", "distal"),
  sep_levels = c("low", "mid", "high"),
  sample_sizes = c(500L, 1000L, 2000L),
  verbose = TRUE
)

Arguments

n_rep

Integer. Replications per condition (paper uses 500).

base_seed

Integer. Base seed for reproducibility.

params

Population parameters list. Defaults to bk2018_params.

scenarios

Character. Lists the scenario(s) ("covariate" and/or "distal") wanting to be simulated. Passed into generate_data().

sep_levels

Character. Lists the separation level(s) ("low", "mid", "high") wanting to be simulated. Passed into generate_data().

sample_sizes

Integer. Lists the sample size(s) wanting to be generated for each replication condition. Passed into generate_data().

verbose

Logical. If TRUE (default), display a live CLI progress bar with per-rep status and ETA.

Value

Nested list indexed as datasets[[scenario]][[separation]][[as.character(n)]], each element a list of n_rep data frames.

Examples

# \donttest{
# Generate 5 replicates for mid and high separation only
datasets <- generate_all_conditions(n_rep = 5L, base_seed = 1L,
                                    sep_levels = c("mid", "high"))
# Access a single replicate
head(datasets[["covariate"]][["high"]][["500"]][[1]])
#>   Y1 Y2 Y3 Y4 Y5 Y6 X Zp
#> 1  1  1  1  1  0  1 1  2
#> 2  0  1  1  1  0  0 1  3
#> 3  1  1  1  1  1  1 1  3
#> 4  1  1  1  1  1  1 1  3
#> 5  1  1  1  0  0  0 2  1
#> 6  0  1  1  0  0  0 2  2
# }