Wrapper function for Type 1 and Power simulations

Usage

run_ligof_sims(
  model_no = 1,
  nsim = 1000,
  samp_size = 1000,
  samp = c("srs", "wtd", "strat", "clust", "strcl", "strat2"),
  simtype = c("type1", "power"),
  starting_seed = 16423,
  ncores = parallel::detectCores() - 2,
  pop_Sigma = FALSE,
  Sigma2 = NULL,
  wt = NULL
)

Arguments

model_no: (integer) Choose from 1–5. See pkgdown articles for details.
nsim: (integer) The number of simulations to conduct.
samp_size: (integer) The exact sample size for SRS simulations or stratified sampling; otherwise the average sample size for the other complex sampling methods.
samp: (character) Choose the sampling method for the simulated data. One of srs, wtd, strat, clust or strcl.
simtype: (character) Whether this is a type1 simulation or power simulation.
starting_seed: (integer) The starting random seed.
ncores: (integer) The number of cores to use for parallelisation.
pop_Sigma: (boolean) Should the population value for the multinomial covariance matrix be used, and not estimated?
Sigma2: (for internal testing only) by default calculates (weighted) sample covariance
wt: (character) Character vector indicating the column name of the sampling weights to use. Defaults to NULL.

Value

A list of tibble()s with the output from all_tests().

Examples

if (FALSE) { # \dontrun{
library(tidyverse)
library(lavaan.bingof)
analysis_path <- dirname(rstudioapi::getSourceEditorContext()$path)

# Run all scenarios described in manuscript
for (sim_type in c("type1", "power")) {
  for (samp_method in c("srs", "strat", "clust", "strcl")) {
    for (the_samp_size in c(500, 1000, 2000, 3000)) {
      for (mod_no in 1:5) {
        sim_name <- paste0(samp_method, mod_no, "_n", the_samp_size, "_",
                           sim_type)
        cat("[", as.character(Sys.time()), "]", "Now running simulation",
            sim_name, "\n")
        sim <- run_ligof_sims(mod_no, samp_size = the_samp_size,
                              samp = samp_method, simtype = sim_type)
        invisible(list2env(setNames(list(sim), sim_name), envir = .GlobalEnv))
        save(list = sim_name, file = paste0(analysis_path, "/Rsave/",
                                            sim_name, ".RData"))
      }
    }
  }
}
} # }