List packaged simulation datasets

List packaged simulation datasets

Usage

list_mfrmr_data(details = FALSE)

Arguments

details

If FALSE (default), return the backward-compatible character vector of dataset keys. If TRUE, return a catalog describing the intended teaching role and design of every dataset.

Value

A character vector of dataset keys accepted by load_mfrmr_data(), or, when details = TRUE, a data.frame with Key, Rows, Persons, Raters, Criteria, CountBasis, PrimaryUse, Design, and Empirical.

Details

Use this helper when you want to select packaged data programmatically (e.g., inside scripts, loops, or interactive-application wrappers).

Typical pattern:

1. call list_mfrmr_data() to see available keys.

2. pass one key to load_mfrmr_data().

Interpreting output

With details = FALSE, returned values are canonical dataset keys accepted by load_mfrmr_data(). With details = TRUE, use PrimaryUse and Design to distinguish the applied teaching example from idealized, planted-effect diagnostic, and larger sparse-design datasets. CountBasis states whether person/rater counts use raw labels or Study-prefixed labels. Every bundled dataset is synthetic rather than empirical.

Typical workflow

1. Capture keys in a script (keys <- list_mfrmr_data()).

2. Select one key by index or name.

3. Load data via load_mfrmr_data() and continue analysis. Treat a combined key as a design-review object, not as a direct-fit example.

See also

load_mfrmr_data(), ej2021_data

Examples

keys <- list_mfrmr_data()
keys
#> [1] "example_core"        "example_bias"        "example_operational"
#> [4] "study1"              "study2"              "combined"           
#> [7] "study1_itercal"      "study2_itercal"      "combined_itercal"   
list_mfrmr_data(details = TRUE)[, c(
  "Key", "PrimaryUse", "Design", "CountBasis"
)]
#>                   Key                                          PrimaryUse
#> 1        example_core                             Idealized fast examples
#> 2        example_bias    DFF and bias demonstrations with planted effects
#> 3 example_operational                           Beginner applied workflow
#> 4              study1               Unequal-workload sparse-design review
#> 5              study2                         Larger sparse-design review
#> 6            combined      Identity/linking design review; not direct fit
#> 7      study1_itercal                     Legacy synthetic variant review
#> 8      study2_itercal                     Legacy synthetic variant review
#> 9    combined_itercal Identity/linking sensitivity review; not direct fit
#>                                                                  Design
#> 1                               Complete crossing; no planned omissions
#> 2               Balanced two-rater assignment; planted non-null effects
#> 3                 Connected two-rater assignment; six planned omissions
#> 4                 Two raters per person; highly unequal rater workloads
#> 5                  Two raters per person; incomplete criterion coverage
#> 6 Overlapping IDs; requires explicit anchors/linking for a common scale
#> 7                    Legacy Study 1 variant; rows and scores can differ
#> 8                    Legacy Study 2 variant; rows and scores can differ
#> 9 Overlapping IDs; requires explicit anchors/linking for a common scale
#>                                                  CountBasis
#> 1                                             unique labels
#> 2                                             unique labels
#> 3                                             unique labels
#> 4                                             unique labels
#> 5                                             unique labels
#> 6 raw labels; 513 persons and 30 raters when Study-prefixed
#> 7                                             unique labels
#> 8                                             unique labels
#> 9 raw labels; 513 persons and 30 raters when Study-prefixed
d <- load_mfrmr_data("example_operational")
head(d)
#>                Study Person Rater    Criterion Score Group
#> 1 OperationalExample   P001   R01     Language     4     A
#> 2 OperationalExample   P001   R01 Organization     2     A
#> 3 OperationalExample   P001   R02      Content     4     A
#> 4 OperationalExample   P001   R02     Language     3     A
#> 5 OperationalExample   P001   R02 Organization     2     A
#> 6 OperationalExample   P002   R01      Content     3     A