| build_summary_table_bundle | R Documentation |
summary() outputsBuild a manuscript-oriented table bundle from summary() outputs
build_summary_table_bundle(
x,
which = NULL,
appendix_preset = NULL,
include_empty = FALSE,
digits = 3,
top_n = 10,
preview_chars = 160
)
x |
An |
which |
Optional character vector selecting a subset of named tables. |
appendix_preset |
Optional appendix-oriented table preset:
|
include_empty |
If |
digits |
Digits forwarded when |
top_n |
Row cap forwarded to compact |
preview_chars |
Character cap forwarded to
|
This helper turns the package's compact summary objects into a reproducible
table bundle for manuscript drafting, appendix handoff, or downstream
formatting. It does not replace apa_table(); instead, it provides a
consistent bridge from summary() to named data.frame components that can
later be rendered with apa_table() or exported directly.
The public entry point validates x and the summary-object contract up
front, so malformed summaries fail with a package-level message instead of
falling through to opaque downstream errors.
The function first normalizes x through the corresponding summary()
method when needed, then records a table_index describing every available
table and returns the selected tables in tables. Optional appendix presets
can be applied at bundle-construction time when you want a conservative
manuscript-facing subset before plotting or export.
An object of class mfrm_summary_table_bundle with:
overview
table_index
plot_index
tables
appendix_preset
notes
source_class
summary_class
fit_mfrm() or summary(fit)
diagnose_mfrm() or summary(diag)
precision_review_report() or summary(precision_review)
fit_measures_table() or summary(fit_measures)
facets_fit_review() or summary(facets_fit_review)
compute_person_fit_indices() or summary(person_fit)
describe_mfrm_data() or summary(ds)
reporting_checklist() or summary(chk)
build_apa_outputs() or summary(apa)
evaluate_mfrm_design() or summary(sim_eval)
evaluate_mfrm_signal_detection() or summary(sig_eval)
evaluate_mfrm_recovery() or summary(rec)
assess_mfrm_recovery() or summary(rec_assessment)
summary(validation) from recovery-validation.R
predict_mfrm_population() or summary(pred)
planning_schema$future_branch_active_branch or summary(...)
run_mfrm_facets() or summary(out)
estimate_bias() or summary(bias)
review_mfrm_anchors() or summary(review)
build_linking_review() or summary(review)
build_misfit_casebook() or summary(casebook)
build_weighting_review() or summary(review)
predict_mfrm_units() or summary(pred_units)
sample_mfrm_plausible_values() or summary(pv)
overview: one-row metadata about the source summary and table counts.
table_index: table names, dimensions, roles, and manuscript-oriented
descriptions.
plot_index: which returned tables contain numeric content and which
bundle-level plot types can use them directly.
tables: named data.frame objects ready for formatting or export.
appendix_preset: active appendix subset mode ("none" when not used).
notes: short guidance about omitted empty tables or source-level caveats.
fit-level caveats use the analysis_caveats role; pre-fit data
score-support caveats use the score_category_caveats role. Both roles are
classified as diagnostics and stay in recommended appendix subsets.
recovery-assessment and recovery-validation summaries expose
diagnostic_reporting_notes before diagnostic_review or
diagnostic_oc_summary so fit/separation caveats can be reported without
treating them as recovery or release gates.
recovery-validation summaries expose condition_reporting_notes before
condition_summary so GPCM generator stress and sparse score support are
not mistaken for recovery-metric failures.
precision-review summaries expose fit_separation_basis so fit,
ZSTD, separation/reliability/strata, and QC thresholds remain separate
reporting surfaces rather than implicit validation gates.
fit-measure and FACETS fit-review summaries expose df/ZSTD sensitivity tables under precision-review roles, keeping MnSq status, ZSTD standardization, and external FACETS matching distinct in appendix handoffs.
latent-regression fit summaries expose population_coding in the methods
appendix role so categorical levels, contrasts, and encoded columns can be
documented with the coefficient table.
Build a compact object with summary(...).
Convert it with build_summary_table_bundle(...).
Use bundle$tables[[...]] directly, or hand a selected table to
apa_table() for formatted manuscript output.
If you want a manuscript appendix subset up front, use a preset such as
appendix_preset = "recommended", "compact", or "diagnostics".
For recovery-assessment or recovery-validation summaries, inspect
bundle$tables$reading_order first when it is available.
For recovery-assessment or recovery-validation summaries with retained
diagnostics, read diagnostic_reporting_notes before the raw
diagnostic_review or diagnostic_oc_summary. Read
condition_reporting_notes before condition_review or
condition_summary when bounded GPCM generator stress is part of the
plan.
summary(), apa_table(), reporting_checklist(),
build_apa_outputs()
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
method = "JML", maxit = 30)
bundle <- build_summary_table_bundle(fit)
bundle$table_index
summary(bundle)$role_summary
# Recovery-validation output can be converted to appendix-ready tables.
## Not run:
source(system.file("validation", "recovery-validation.R", package = "mfrmr"))
validation <- mfrmr_run_recovery_validation(
case_ids = c("gpcm_slope_profile", "gpcm_high_dispersion_sparse"),
quick = TRUE,
seed = 20260525
)
validation_bundle <- build_summary_table_bundle(summary(validation))
validation_bundle$tables$reading_order
validation_bundle$tables$topline_release_decision
validation_bundle$tables$condition_reporting_notes
validation_bundle$tables$condition_summary
validation_bundle$tables$diagnostic_reporting_notes
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.