Description Usage Arguments Value Examples

View source: R/create_ad_wrappers.R

This function generates artifact-distribution objects containing either interactive or Taylor series artifact distributions for dichotomous group-membership variables.
Use this to create objects that can be supplied to the `ma_r_ad`

and `ma_d_ad`

functions to apply psychometric corrections to barebones meta-analysis objects via artifact distribution methods.

Allows consolidation of observed and estimated artifact information by cross-correcting artifact distributions and forming weighted artifact summaries.

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`ad_type` |
Type of artifact distribution to be computed: Either "tsa" for Taylor series approximation or "int" for interactive. |

`rGg` |
Vector of incumbent reliability estimates. |

`n_rGg` |
Vector of sample sizes associated with the elements of |

`wt_rGg` |
Vector of weights associated with the elements of |

`pi` |
Vector of incumbent/sample proportions of members in one of the two groups being compared (one or both of |

`pa` |
Vector of applicant/population proportions of members in one of the two groups being compared (one or both of |

`n_pi` |
Vector of sample sizes associated with the elements in |

`n_pa` |
Vector of sample sizes associated with the elements in |

`wt_p` |
Vector of weights associated with the collective element pairs in |

`mean_rGg` |
Vector that can be used to supply the means of externally computed distributions of correlations between observed and latent group membership. |

`var_rGg` |
Vector that can be used to supply the variances of externally computed distributions of correlations between observed and latent group membership. |

`k_rGg` |
Vector that can be used to supply the number of studies included in externally computed distributions of correlations between observed and latent group membership. |

`mean_n_rGg` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of correlations between observed and latent group membership. |

`var_unbiased` |
Logical scalar determining whether variance should be unbiased ( |

`...` |
Further arguments. |

Artifact distribution object (matrix of artifact-distribution means and variances) for use in artifact-distribution meta-analyses.

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## Example artifact distribution for a dichotomous grouping variable:
create_ad_group(rGg = c(.8, .9, .95), n_rGg = c(100, 200, 250),
mean_rGg = .9, var_rGg = .05,
k_rGg = 5, mean_n_rGg = 100,
pi = c(.6, .55, .3), pa = .5, n_pi = c(100, 200, 250), n_pa = c(300, 300, 300),
var_unbiased = TRUE)
create_ad_group(ad_type = "int", rGg = c(.8, .9, .95), n_rGg = c(100, 200, 250),
mean_rGg = .9, var_rGg = .05,
k_rGg = 5, mean_n_rGg = 100,
pi = c(.6, .55, .3), pa = .5, n_pi = c(100, 200, 250), n_pa = c(300, 300, 300),
var_unbiased = TRUE)
``` |

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