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().
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("mfrmr_<key>", package = "mfrmr") remains available for users
who prefer the full data() spelling; both paths return identical
long-format data frames and are supported long-term.
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
list valid names with
list_mfrmr_data().load one dataset key with
load_mfrmr_data(name).fit a model with
fit_mfrm()and inspect withsummary()/plot().
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 = 30
)
summary(fit)
#> Many-Facet Rasch Model Summary
#> Model: RSM | Method: JML | N: 768 | Persons: 48 | Facets: 2 | Categories: 4
#>
#> Status
#> - Overall status: usable_fit
#> - Convergence: converged (severity: pass, sup-norm: 0.033)
#> - Estimation path: RSM / JML
#> - Reporting readiness: exploratory_fit_ready_for_diagnostics
#>
#> Key warnings
#> - None.
#>
#> Next actions
#> - If formal SE/CI or strict marginal diagnostics are needed, re-fit with `method = "MML"`.
#> - Run `diagnose_mfrm(fit, diagnostic_mode = "both")` for element-level fit review.
#> - Use `plot(fit, type = "wright", preset = "publication")` for targeting and scale review.
#> - After diagnostics, use `reporting_checklist(fit, diagnostics = diagnostics)` for reporting readiness.
#>
#> Fit overview
#> LogLik: -820.949 | AIC: 1753.898 | BIC: 2013.95
#> Converged: Yes | Status: converged | Basis: optimizer_gradient | Fn evals: 71 | Gr evals: 24
#> Terminal gradient: sup-norm = 0.033 | RMS = 0.008 | Review tol = 0
#> Optimization note: Optimizer returned convergence code 0.
#>
#> Population basis
#> PopulationModel PosteriorBasis Formula PersonRows DesignColumns
#> FALSE legacy_mml <NA> NA NA
#> CodingVariables ContrastVariables Policy ResidualVariance OmittedPersons
#> <NA> NA 0
#> OmittedRows
#> 0
#>
#> Facet overview
#> Facet Levels MeanEstimate SDEstimate MinEstimate MaxEstimate Span
#> Criterion 4 0 0.288 -0.415 0.249 0.664
#> Rater 4 0 0.313 -0.329 0.334 0.662
#>
#> Person measure distribution
#> Persons Mean SD Median Min Max Span
#> 48 0.001 1.1 0.082 -2.179 2.683 4.863
#>
#> Targeting (Person vs facet means; sum-to-zero ID makes Targeting = Person mean)
#> Facet PersonMean FacetMean Targeting PersonSD FacetSD SpreadRatio
#> Criterion 0.001 0 0.001 1.1 0.288 3.823
#> Rater 0.001 0 0.001 1.1 0.313 3.512
#>
#> Step parameter summary
#> Steps Min Max Span Monotonic
#> 3 -1.326 1.385 2.71 TRUE
#>
#> Estimation settings
#> StepFacet SlopeFacet NoncenterFacet WeightColumn QuadPoints RatingMin
#> <NA> <NA> Person <NA> 31 1
#> RatingMax RatingRangeSource RatingMinSource RatingMaxSource DummyFacets
#> 4 observed observed observed
#> PositiveFacets FacetInteractions UnusedScoreCategories
#>
#> UnusedScoreCategoryCount UnusedScoreCategoryType
#> 0 none
#>
#> Most extreme facet levels (|estimate|)
#> Facet Level Estimate
#> Criterion Content -0.415
#> Rater R04 0.334
#> Rater R02 -0.329
#> Criterion Accuracy 0.249
#> Rater R01 -0.196
#>
#> Highest person measures
#> Person Estimate SE Extreme
#> P023 2.683 NA none
#> P024 2.201 NA none
#> P036 1.832 NA none
#> P002 1.673 NA none
#> P003 1.258 NA none
#>
#> Lowest person measures
#> Person Estimate SE Extreme
#> P015 -2.179 NA none
#> P045 -1.821 NA none
#> P008 -1.665 NA none
#> P006 -1.524 NA none
#> P026 -1.524 NA none
#>
#> Paper reporting map
#> Area CoveredHere
#> Model identification / convergence yes
#> Data structure / missingness no
#> Reliability / fit / residual PCA no
#> Category functioning partial
#> Bias / DIF / interaction checks no
#> Draft reporting / checklist no
#> CompanionOutput
#> summary(fit)
#> summary(describe_mfrm_data(...))
#> summary(diagnose_mfrm(fit))
#> rating_scale_table() / category_structure_report() / category_curves_report()
#> summary(estimate_bias(...)) / analyze_dff() / related bundle summaries
#> reporting_checklist() / summary(build_apa_outputs(...))