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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  -0.36100456 0.3681106  30     SMD
#> 2  -0.32997749 0.2643940  58     SMD
#> 3   0.70533775 0.4858412  18     SMD
#> 4  -0.25994330 0.1255268 256     SMD
#> 5   0.02979852 0.1474501 184     SMD
#> 6   0.16323063 0.4721889  18     SMD
#> 7  -0.28402439 0.2639336  58     SMD
#> 8  -0.04741783 0.3015537  44     SMD
#> 9  -0.16656330 0.2239942  80     SMD
#> 10  0.29194225 0.3102463  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  0.78360290 0.3686681  32     SMD
#> 2  0.04228118 0.2182435  84     SMD
#> 3  0.47544911 0.2255153  81     SMD
#> 4  0.90066316 0.3178459  44     SMD
#> 5  0.72976778 0.3455482  36     SMD
#> 6  0.69439468 0.3116263  44     SMD
#> 7  1.18205518 0.5541036  16     SMD
#> 8  0.13864104 0.1049557 364     SMD
#> 9  0.80602118 0.4029696  27     SMD
#> 10 0.51675737 0.2121197  92     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  0.27645111 0.25119131   64     SMD
#> 2 -0.14731625 0.17701631  128     SMD
#> 3  0.25515327 0.12700475  250     SMD
#> 4 -0.07596428 0.08947497  500     SMD
#> 5 -0.03670826 0.06325088 1000     SMD