Build a reproducibility manifest for an MFRM analysis

Build a reproducibility manifest for an MFRM analysis

Usage

build_mfrm_manifest(
  fit,
  diagnostics = NULL,
  bias_results = NULL,
  population_prediction = NULL,
  unit_prediction = NULL,
  plausible_values = NULL,
  include_person_anchors = FALSE
)

Arguments

fit

Output from fit_mfrm() or run_mfrm_facets().

diagnostics

Optional output from diagnose_mfrm(). When NULL, diagnostics are computed with residual_pca = "none".

bias_results

Optional output from estimate_bias() or a named list of bias bundles.

population_prediction

Optional output from predict_mfrm_population().

unit_prediction

Optional output from predict_mfrm_units().

plausible_values

Optional output from sample_mfrm_plausible_values().

include_person_anchors

If TRUE, include person measures in the exported anchor table.

Value

A named list with class mfrm_manifest.

Details

This helper captures the package-native equivalent of the Streamlit app's configuration export. It summarizes analysis settings, source columns, anchoring information, and which downstream outputs are currently available.

When to use this

Use build_mfrm_manifest() when you want a compact, machine-readable record of how an analysis was run. Compared with related helpers:

• export_mfrm() writes analysis tables only.

• build_mfrm_manifest() records settings and available outputs.

• build_mfrm_replay_script() creates an executable R script.

• export_mfrm_bundle() writes a shareable folder of files.

Output

The returned bundle has class mfrm_manifest and includes:

• summary: one-row analysis overview

• environment: package/R/platform metadata

• model_settings: key-value model settings table

• source_columns: key-value data-column table

• estimation_control: key-value optimizer settings table

• anchor_summary: facet-level anchor summary

• anchors: machine-readable anchor table

• available_outputs: availability table for diagnostics/bias/PCA/prediction outputs

• settings: manifest build settings

Interpreting output

The summary table is the quickest place to confirm that you are looking at the intended analysis. The model_settings, source_columns, and estimation_control tables are designed for audit trails and method write-up. The available_outputs table is especially useful before building bundles, because it tells you whether residual PCA, anchors, bias results, or prediction-side artifacts are already available. A practical reading order is summary first, available_outputs second, and anchors last when reproducibility depends on fixed constraints.

Typical workflow

1. Fit a model with fit_mfrm() or run_mfrm_facets().

2. Compute diagnostics once with diagnose_mfrm() if you want explicit control over residual PCA.

3. Build a manifest and inspect summary plus available_outputs.

4. If you need files on disk, pass the same objects to export_mfrm_bundle().

See also

export_mfrm_bundle(), build_mfrm_replay_script(), make_anchor_table(), reporting_checklist()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 25)
diag <- diagnose_mfrm(fit, residual_pca = "none")
manifest <- build_mfrm_manifest(fit, diagnostics = diag)
manifest$summary[, c("Model", "Method", "Observations", "Facets")]
#>   Model Method Observations Facets
#> 1   RSM    JML          768      2
manifest$available_outputs[, c("Component", "Available")]
#>               Component Available
#> 1    observed_residuals      TRUE
#> 2              measures      TRUE
#> 3           reliability      TRUE
#> 4          residual_pca     FALSE
#> 5          bias_results     FALSE
#> 6               anchors      TRUE
#> 7 population_prediction     FALSE
#> 8       unit_prediction     FALSE
#> 9      plausible_values     FALSE