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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", "study1", "study2", "combined",
    "study1_itercal", "study2_itercal", "combined_itercal")
)

Arguments

name

Dataset key. One of values from list_mfrmr_data().

Value

A data.frame in long format.

Details

This helper is useful in scripts/functions where you want to choose a dataset by string key instead of calling data() manually.

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, ready for direct use in fit_mfrm() without additional reshaping.

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_core", package = "mfrmr")
head(mfrmr_example_core)
#>         Study Person Rater Criterion Score Group
#> 1 ExampleCore   P001   R01   Content     3     A
#> 2 ExampleCore   P002   R01   Content     3     A
#> 3 ExampleCore   P003   R01   Content     4     A
#> 4 ExampleCore   P004   R01   Content     3     A
#> 5 ExampleCore   P005   R01   Content     2     A
#> 6 ExampleCore   P006   R01   Content     3     A

d <- load_mfrmr_data("example_core")
fit <- fit_mfrm(
  data = d,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  method = "JML",
  maxit = 25
)
summary(fit)
#> Many-Facet Rasch Model Summary
#>   Model: RSM | Method: JMLE
#>   N: 768 | Persons: 48 | Facets: 2 | Categories: 4
#>   LogLik: -820.949 | AIC: 1755.899 | BIC: 2020.595
#>   Converged: Yes | Iterations: 67
#> 
#> Facet overview
#>      Facet Levels MeanEstimate SDEstimate MinEstimate MaxEstimate  Span
#>  Criterion      4            0      0.287      -0.414       0.248 0.663
#>      Rater      4            0      0.313      -0.329       0.334 0.662
#> 
#> Person measure distribution
#>  Persons  Mean    SD Median   Min   Max  Span
#>       48 0.001 1.101  0.082 -2.18 2.686 4.866
#> 
#> Step parameter summary
#>  Steps    Min   Max  Span Monotonic
#>      3 -1.325 1.385 2.711      TRUE
#> 
#> Most extreme facet levels (|estimate|)
#>      Facet    Level Estimate
#>  Criterion  Content   -0.414
#>      Rater      R04    0.334
#>      Rater      R02   -0.329
#>  Criterion Accuracy    0.248
#>      Rater      R01   -0.196
#> 
#> Highest person measures
#>  Person Estimate
#>    P023    2.686
#>    P024    2.203
#>    P036    1.834
#>    P002    1.676
#>    P014    1.259
#> 
#> Lowest person measures
#>  Person Estimate
#>    P015   -2.180
#>    P045   -1.823
#>    P008   -1.669
#>    P006   -1.523
#>    P026   -1.523
#> 
#> Notes
#>  - No immediate warnings from fit-level summary checks.