PublicationBiasBenchmark (version 0.1.3)

simulate_dgm: Simulate From Data-Generating Mechanism

Description

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)

Value

A data frame containing the generated data with standardized structure

Arguments

dgm_name

Character string specifying the DGM type

settings

List containing the required parameters for the DGM or numeric condition_id

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)

simulate_dgm("Carter2019", list(mean_effect = 0, effect_heterogeneity = 0,
                       bias = "high", QRP = "high", n_studies = 10))

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)))