Generate meta-analytic data

Generate meta-analytic correlated or correlated and hierarchical effects data with options to simulate selective outcome reporting

Usage

r_meta(
  mean_smd,
  tau,
  omega,
  m,
  cor_mu,
  cor_sd,
  censor_fun,
  n_ES_sim,
  m_multiplier = 2,
  id_start = 0L,
  paste_ids = TRUE,
  include_sel_prob = FALSE
)

Arguments

mean_smd

numeric value indicating the true mean effect size

tau

numeric value characterizing between-study heterogeneity in effects

omega

numeric value characterizing within-study heterogeneity in effects

m

numeric value of studies in the simulated meta-analysis

cor_mu

numeric value indicating the average correlation between outcomes

cor_sd

numeric value indicating standard deviation of correlation between outcomes

censor_fun

a function used to censor effects; this package provides functionals step_fun() and beta_fun() to censor effects based on step-function or beta-function models respectively.

n_ES_sim

a function used to simulate the distribution of primary study sample sizes and the number of effect sizes per study

m_multiplier

numeric value indicating a multiplier for buffer for the number of studies

id_start

integer indicating the starting value for study id

paste_ids

logical with TRUE (the default) indicating that the study id and effect size id should be pasted together

include_sel_prob

logical with TRUE indicating that the returned dataset should include a variable selection_prob reporting the true probability of selection given the observed p-value. Default of FALSE indicates that the selection_prob variable should be omitted.

Value

A data.frame containing the simulated meta-analytic dataset.

Examples

example_dat <- r_meta(
  mean_smd = 0,
  tau = .1, omega = .01,
  m = 50,
  cor_mu = .4, cor_sd = 0.001,
  censor_fun = step_fun(cut_vals = .025, weights = 0.4),
  n_ES_sim = n_ES_param(40, 3)
)