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Simulate From Data-Generating Mechanism

Source: R/dgm.R
simulate_dgm.Rd

This function provides a unified interface to various data-generating mechanisms for simulation studies. The specific DGM is determined by the first argument. See vignette("Adding_New_DGMs", package = "PublicationBiasBenchmark") for details of extending the package with new DGMs.

Usage

simulate_dgm(dgm_name, settings)

Arguments

dgm_name

Character string specifying the DGM type

settings

List containing the required parameters for the DGM or numeric condition_id

Value

A data frame containing the generated data with standardized structure

Output Structure

The returned data frame follows a standardized schema that downstream functions rely on. Across the currently implemented DGMs, the following columns are used:

  • yi (numeric): The effect size estimate.

  • sei (numeric): Standard error of yi.

  • ni (integer): Total sample size for the estimate (e.g., sum over groups where applicable).

  • es_type (character): Effect size type, used to disambiguate the scale of yi. Currently used values are "SMD" (standardized mean difference / Cohen's d), "logOR" (log odds ratio), and "none" (unspecified generic continuous coefficient).

  • study_id (integer/character, optional): Identifier of the primary study/cluster when a DGM yields multiple estimates per study (e.g., Alinaghi2018, PRE). If absent, each row is treated as an independent study.

See also

validate_dgm_setting(), dgm.Stanley2017(), dgm.Alinaghi2018(), dgm.Bom2019(), dgm.Carter2019()

Examples


simulate_dgm("Carter2019", 1)
#>              yi       sei  ni es_type
#> 1   0.007637488 0.4472152  20     SMD
#> 2   0.179673222 0.1829422 120     SMD
#> 3   0.093579427 0.4266347  22     SMD
#> 4   0.057188571 0.3923124  26     SMD
#> 5  -0.052892856 0.4472918  20     SMD
#> 6   0.040592673 0.1482651 182     SMD
#> 7   0.147353405 0.2890666  48     SMD
#> 8   0.029867798 0.0889158 506     SMD
#> 9  -0.064986328 0.3163112  40     SMD
#> 10 -0.129336528 0.3089292  42     SMD

simulate_dgm("Carter2019", list(mean_effect = 0, effect_heterogeneity = 0,
                       bias = "high", QRP = "high", n_studies = 10))
#>            yi       sei  ni es_type
#> 1  1.00556181 0.3781218  32     SMD
#> 2  0.08461967 0.2086119  92     SMD
#> 3  0.36956727 0.1594920 160     SMD
#> 4  1.12632130 0.5165476  18     SMD
#> 5  0.78400899 0.3294138  40     SMD
#> 6  0.08363017 0.2295215  76     SMD
#> 7  0.51244062 0.2191765  86     SMD
#> 8  0.99811889 0.3668715  34     SMD
#> 9  0.89897768 0.3733193  32     SMD
#> 10 0.54598650 0.2149387  90     SMD

simulate_dgm("Stanley2017", list(environment = "SMD", mean_effect = 0,
                        effect_heterogeneity = 0, bias = 0, n_studies = 5,
                        sample_sizes = c(32,64,125,250,500)))
#>             yi        sei   ni es_type
#> 1  0.360739876 0.25202513   64     SMD
#> 2 -0.688925385 0.18194498  128     SMD
#> 3 -0.194041430 0.12678842  250     SMD
#> 4  0.197469917 0.08966044  500     SMD
#> 5 -0.001791946 0.06324557 1000     SMD