View source: R/create_ad_wrappers.R

create_ad | R Documentation |

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

and `ma_r_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.

For u ratios, error variances can be computed for independent samples (i.e., settings in which the unrestricted standard deviation comes from an external study) or dependent samples (i.e., settings in which the range-restricted standard deviation comes from a sample that represents a subset of the applicant sample that provided the unrestricted standard deviation). The former circumstance is presumed to be more common, so error variances are computed for independent samples by default.

```
create_ad(
ad_type = c("tsa", "int"),
rxxi = NULL,
n_rxxi = NULL,
wt_rxxi = n_rxxi,
rxxi_type = rep("alpha", length(rxxi)),
k_items_rxxi = rep(NA, length(rxxi)),
rxxa = NULL,
n_rxxa = NULL,
wt_rxxa = n_rxxa,
rxxa_type = rep("alpha", length(rxxa)),
k_items_rxxa = rep(NA, length(rxxa)),
ux = NULL,
ni_ux = NULL,
na_ux = NULL,
wt_ux = ni_ux,
dep_sds_ux_obs = rep(FALSE, length(ux)),
ut = NULL,
ni_ut = NULL,
na_ut = NULL,
wt_ut = ni_ut,
dep_sds_ut_obs = rep(FALSE, length(ut)),
mean_qxi = NULL,
var_qxi = NULL,
k_qxi = NULL,
mean_n_qxi = NULL,
qxi_dist_type = rep("alpha", length(mean_qxi)),
mean_k_items_qxi = rep(NA, length(mean_qxi)),
mean_rxxi = NULL,
var_rxxi = NULL,
k_rxxi = NULL,
mean_n_rxxi = NULL,
rxxi_dist_type = rep("alpha", length(mean_rxxi)),
mean_k_items_rxxi = rep(NA, length(mean_rxxi)),
mean_qxa = NULL,
var_qxa = NULL,
k_qxa = NULL,
mean_n_qxa = NULL,
qxa_dist_type = rep("alpha", length(mean_qxa)),
mean_k_items_qxa = rep(NA, length(mean_qxa)),
mean_rxxa = NULL,
var_rxxa = NULL,
k_rxxa = NULL,
mean_n_rxxa = NULL,
rxxa_dist_type = rep("alpha", length(mean_rxxa)),
mean_k_items_rxxa = rep(NA, length(mean_rxxa)),
mean_ux = NULL,
var_ux = NULL,
k_ux = NULL,
mean_ni_ux = NULL,
mean_na_ux = rep(NA, length(mean_ux)),
dep_sds_ux_spec = rep(FALSE, length(mean_ux)),
mean_ut = NULL,
var_ut = NULL,
k_ut = NULL,
mean_ni_ut = NULL,
mean_na_ut = rep(NA, length(mean_ut)),
dep_sds_ut_spec = rep(FALSE, length(mean_ut)),
estimate_rxxa = TRUE,
estimate_rxxi = TRUE,
estimate_ux = TRUE,
estimate_ut = TRUE,
var_unbiased = TRUE,
...
)
```

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

`rxxi` |
Vector of incumbent reliability estimates. |

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

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

`rxxi_type` , `rxxa_type` , `qxi_dist_type` , `rxxi_dist_type` , `qxa_dist_type` , `rxxa_dist_type` |
String vector identifying the types of reliability estimates supplied (e.g., "alpha", "retest", "interrater_r", "splithalf"). See the documentation for |

`k_items_rxxi` , `mean_k_items_qxi` , `mean_k_items_rxxi` , `k_items_rxxa` , `mean_k_items_qxa` , `mean_k_items_rxxa` |
Numeric vector of the number of items in each scale (or mean number of items, for pre-specified distributions). |

`rxxa` |
Vector of applicant reliability estimates. |

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

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

`ux` |
Vector of observed-score u ratios. |

`ni_ux` |
Vector of incumbent sample sizes associated with the elements of |

`na_ux` |
Vector of applicant sample sizes that can be used in estimating the sampling error of supplied ux values. |

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

`dep_sds_ux_obs` |
Logical scalar or vector determining whether supplied ux values were computed using dependent samples ( |

`ut` |
Vector of true-score u ratios. |

`ni_ut` |
Vector of incumbent sample sizes associated with the elements of |

`na_ut` |
Vector of applicant sample sizes that can be used in estimating the sampling error of supplied ut values. |

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

`dep_sds_ut_obs` |
Logical scalar or vector determining whether supplied ut values were computed using dependent samples ( |

`mean_qxi` |
Vector that can be used to supply the means of externally computed distributions of incumbent square-root reliabilities. |

`var_qxi` |
Vector that can be used to supply the variances of externally computed distributions of incumbent square-root reliabilities. |

`k_qxi` |
Vector that can be used to supply the number of studies included in externally computed distributions of incumbent square-root reliabilities. |

`mean_n_qxi` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of incumbent square-root reliabilities. |

`mean_rxxi` |
Vector that can be used to supply the means of externally computed distributions of incumbent reliabilities. |

`var_rxxi` |
Vector that can be used to supply the variances of externally computed distributions of incumbent reliabilities. |

`k_rxxi` |
Vector that can be used to supply the number of studies included in externally computed distributions of incumbent reliabilities. |

`mean_n_rxxi` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of incumbent reliabilities. |

`mean_qxa` |
Vector that can be used to supply the means of externally computed distributions of applicant square-root reliabilities. |

`var_qxa` |
Vector that can be used to supply the variances of externally computed distributions of applicant square-root reliabilities. |

`k_qxa` |
Vector that can be used to supply the number of studies included in externally computed distributions of applicant square-root reliabilities. |

`mean_n_qxa` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of applicant square-root reliabilities. |

`mean_rxxa` |
Vector that can be used to supply the means of externally computed distributions of applicant reliabilities. |

`var_rxxa` |
Vector that can be used to supply the variances of externally computed distributions of applicant reliabilities. |

`k_rxxa` |
Vector that can be used to supply the number of studies included in externally computed distributions of applicant reliabilities. |

`mean_n_rxxa` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of applicant reliabilities. |

`mean_ux` |
Vector that can be used to supply the means of externally computed distributions of observed-score u ratios. |

`var_ux` |
Vector that can be used to supply the variances of externally computed distributions of observed-score u ratios. |

`k_ux` |
Vector that can be used to supply the number of studies included in externally computed distributions of observed-score u ratios. |

`mean_ni_ux` |
Vector that can be used to supply the mean incumbent sample sizes of externally computed distributions of observed-score u ratios. |

`mean_na_ux` |
Vector or scalar that can be used to supply the mean applicant sample size(s) of externally computed distributions of observed-score u ratios. |

`dep_sds_ux_spec` |
Logical scalar or vector determining whether externally computed ux distributions were computed using dependent samples ( |

`mean_ut` |
Vector that can be used to supply the means of externally computed distributions of true-score u ratios. |

`var_ut` |
Vector that can be used to supply the variances of externally computed distributions of true-score u ratios. |

`k_ut` |
Vector that can be used to supply the number of studies included in externally computed distributions of true-score u ratios. |

`mean_ni_ut` |
Vector that can be used to supply the mean sample sizes for of externally computed distributions of true-score u ratios. |

`mean_na_ut` |
Vector or scalar that can be used to supply the mean applicant sample size(s) of externally computed distributions of true-score u ratios. |

`dep_sds_ut_spec` |
Logical scalar or vector determining whether externally computed ut distributions were computed using dependent samples ( |

`estimate_rxxa` |
Logical argument to estimate rxxa values from other artifacts ( |

`estimate_rxxi` |
Logical argument to estimate rxxi values from other artifacts ( |

`estimate_ux` |
Logical argument to estimate ux values from other artifacts ( |

`estimate_ut` |
Logical argument to estimate ut values from other artifacts ( |

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

`...` |
Further arguments. |

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

```
## Example computed using observed values only:
create_ad(ad_type = "tsa", rxxa = c(.9, .8), n_rxxa = c(50, 150),
rxxi = c(.8, .7), n_rxxi = c(50, 150),
ux = c(.9, .8), ni_ux = c(50, 150))
create_ad(ad_type = "int", rxxa = c(.9, .8), n_rxxa = c(50, 150),
rxxi = c(.8, .7), n_rxxi = c(50, 150),
ux = c(.9, .8), ni_ux = c(50, 150))
## Example computed using all possible input arguments (arbitrary values):
rxxa <- rxxi <- ux <- ut <- c(.7, .8)
n_rxxa <- n_rxxi <- ni_ux <- ni_ut <- c(50, 100)
na_ux <- na_ut <- c(200, 200)
mean_qxa <- mean_qxi <- mean_ux <- mean_ut <- mean_rxxi <- mean_rxxa <- c(.7, .8)
var_qxa <- var_qxi <- var_ux <- var_ut <- var_rxxi <- var_rxxa <- c(.1, .05)
k_qxa <- k_qxi <- k_ux <- k_ut <- k_rxxa <- k_rxxi <- 2
mean_n_qxa <- mean_n_qxi <- mean_ni_ux <- mean_ni_ut <- mean_n_rxxa <- mean_n_rxxi <- c(100, 100)
dep_sds_ux_obs <- dep_sds_ux_spec <- dep_sds_ut_obs <- dep_sds_ut_spec <- FALSE
mean_na_ux <- mean_na_ut <- c(200, 200)
wt_rxxa <- n_rxxa
wt_rxxi <- n_rxxi
wt_ux <- ni_ux
wt_ut <- ni_ut
estimate_rxxa <- TRUE
estimate_rxxi <- TRUE
estimate_ux <- TRUE
estimate_ut <- TRUE
var_unbiased <- TRUE
create_ad(rxxa = rxxa, n_rxxa = n_rxxa, wt_rxxa = wt_rxxa,
mean_qxa = mean_qxa, var_qxa = var_qxa,
k_qxa = k_qxa, mean_n_qxa = mean_n_qxa,
mean_rxxa = mean_rxxa, var_rxxa = var_rxxa,
k_rxxa = k_rxxa, mean_n_rxxa = mean_n_rxxa,
rxxi = rxxi, n_rxxi = n_rxxi, wt_rxxi = wt_rxxi,
mean_qxi = mean_qxi, var_qxi = var_qxi,
k_qxi = k_qxi, mean_n_qxi = mean_n_qxi,
mean_rxxi = mean_rxxi, var_rxxi = var_rxxi,
k_rxxi = k_rxxi, mean_n_rxxi = mean_n_rxxi,
ux = ux, ni_ux = ni_ux, na_ux = na_ux, wt_ux = wt_ux,
dep_sds_ux_obs = dep_sds_ux_obs,
mean_ux = mean_ux, var_ux = var_ux, k_ux =
k_ux, mean_ni_ux = mean_ni_ux,
mean_na_ux = mean_na_ux, dep_sds_ux_spec = dep_sds_ux_spec,
ut = ut, ni_ut = ni_ut, na_ut = na_ut, wt_ut = wt_ut,
dep_sds_ut_obs = dep_sds_ut_obs,
mean_ut = mean_ut, var_ut = var_ut,
k_ut = k_ut, mean_ni_ut = mean_ni_ut,
mean_na_ut = mean_na_ut, dep_sds_ut_spec = dep_sds_ut_spec,
estimate_rxxa = estimate_rxxa, estimate_rxxi = estimate_rxxi,
estimate_ux = estimate_ux, estimate_ut = estimate_ut, var_unbiased = var_unbiased)
```

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