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Load a packaged simulation dataset

Source: R/datasets.R
load_mfrmr_data.Rd

Load a packaged simulation dataset

Usage

load_mfrmr_data(
  name = c("example_core", "example_bias", "example_operational", "study1", "study2",
    "combined", "study1_itercal", "study2_itercal", "combined_itercal")
)

Arguments

name

Dataset key. One of the values from list_mfrmr_data(). If omitted, the backward-compatible default is "example_core"; new code should pass a key explicitly.

Value

A data.frame in long format.

Details

load_mfrmr_data("<key>") is the canonical loader for the packaged datasets and the entry point used across the package help and vignettes. The equivalent base-R alternative data("<object-name>", package = "mfrmr") remains available for users who prefer the full data() spelling; both paths return identical long-format data frames.

All returned datasets include the core long-format columns Study, Person, Rater, Criterion, and Score. Some datasets, such as the packaged documentation examples, also include auxiliary variables like Group for DIF/bias demonstrations.

Interpreting output

The return value is a plain long-format data.frame. The example and study-specific keys are ready for fit_mfrm() after checking role and score mappings. The combined keys are design-review objects: overlapping IDs or simple Study-based prefixes do not establish a common measurement scale, so an explicit identity, anchor, or linking design is required before a joint fit is interpretable.

Typical workflow

  1. list valid names with list_mfrmr_data().

  2. load one dataset key with load_mfrmr_data(name).

  3. fit a model with fit_mfrm() and inspect with summary() / plot().

See also

list_mfrmr_data(), ej2021_data

Examples

data("mfrmr_example_operational", package = "mfrmr")
head(mfrmr_example_operational)
#>                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

d <- load_mfrmr_data("example_operational")
table(d$Rater)
#> 
#> R01 R02 R03 R04 R05 R06 
#>  47  56  50  47  44  38 
table(d$Criterion, d$Score)
#>               
#>                 1  2  3  4
#>   Content      11 33 34 16
#>   Language     25 29 25 15
#>   Organization 26 34 19 15