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.
create_ad_group(
ad_type = c("tsa", "int"),
rGg = NULL,
n_rGg = NULL,
wt_rGg = n_rGg,
pi = NULL,
pa = NULL,
n_pi = NULL,
n_pa = NULL,
wt_p = n_pi,
mean_rGg = NULL,
var_rGg = NULL,
k_rGg = NULL,
mean_n_rGg = NULL,
var_unbiased = TRUE,
...
)Artifact distribution object (matrix of artifact-distribution means and variances) for use in artifact-distribution meta-analyses.
Type of artifact distribution to be computed: Either "tsa" for Taylor series approximation or "int" for interactive.
Vector of incumbent reliability estimates.
Vector of sample sizes associated with the elements of rGg.
Vector of weights associated with the elements of rGg (by default, sample sizes will be used as weights if provided).
Vector of incumbent/sample proportions of members in one of the two groups being compared (one or both of pi/pa can be vectors - if both are vectors, they must be of equal length).
Vector of applicant/population proportions of members in one of the two groups being compared (one or both of pi/pa can be vectors - if both are vectors, they must be of equal length).
Vector of sample sizes associated with the elements in pi.
Vector of sample sizes associated with the elements in pa.
Vector of weights associated with the collective element pairs in pi and pa.
Vector that can be used to supply the means of externally computed distributions of correlations between observed and latent group membership.
Vector that can be used to supply the variances of externally computed distributions of correlations between observed and latent group membership.
Vector that can be used to supply the number of studies included in externally computed distributions of correlations between observed and latent group membership.
Vector that can be used to supply the mean sample sizes of externally computed distributions of correlations between observed and latent group membership.
Logical scalar determining whether variance should be unbiased (TRUE) or maximum-likelihood (FALSE).
Further arguments.
## 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)
Run the code above in your browser using DataLab