---
title: "mfrmr Reporting and APA"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{mfrmr Reporting and APA}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
is_cran_check <- !isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  eval = !is_cran_check
)
```

This vignette shows the package-native route from a fitted many-facet Rasch
model to manuscript-oriented prose, tables, figure notes, and revision checks.

The reporting stack in `mfrmr` is organized around four objects:

- `fit`: the fitted model from `fit_mfrm()`
- `diag`: diagnostics from `diagnose_mfrm()`
- `chk`: the revision guide from `reporting_checklist()`
- `apa`: structured manuscript outputs from `build_apa_outputs()`

For a broader workflow view, see `vignette("mfrmr-workflow", package = "mfrmr")`.
For a plot-first route, see
`vignette("mfrmr-visual-diagnostics", package = "mfrmr")`.

## Minimal setup

```{r setup}
library(mfrmr)

toy <- load_mfrmr_data("example_operational")

# The vignette uses compact quadrature so optional local execution stays fast.
# For final manuscript reporting, refit with the package default or a higher
# quadrature setting and record that setting in the analysis log.
fit <- fit_mfrm(
  toy,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  method = "MML",
  model = "RSM",
  quad_points = 7
)

diag <- diagnose_mfrm(fit, residual_pca = "none")
```

## 1. Start with the revision guide

Use `reporting_checklist()` first when the question is "what is still missing?"
rather than "how do I phrase the results?"

```{r checklist}
chk <- reporting_checklist(fit, diagnostics = diag)

head(
  chk$checklist[, c("Section", "Item", "DraftReady", "Priority", "NextAction")],
  10
)
```

Interpretation:

- `DraftReady` flags whether the current objects already support a section for
  drafting with the package's documented caveats.
- `Priority` shows what to resolve first.
- `NextAction` is the shortest package-native instruction for closing the gap.

## 2. Check the precision layer before strong claims

`mfrmr` intentionally distinguishes `model_based`, `hybrid`, and
`exploratory` precision tiers.

```{r precision}
prec <- precision_review_report(fit, diagnostics = diag)

prec$profile
prec$checks
prec$fit_separation_basis
```

Interpretation:

- Use stronger inferential phrasing only when the reported tier is
  `model_based`.
- Treat `hybrid` and `exploratory` outputs more conservatively, especially for
  SE-, CI-, and reliability-heavy prose.
- Read `fit_separation_basis` as a boundary table: MnSq fit, ZSTD
  standardization, Rasch/FACETS-style separation, and package QC thresholds
  have different source bases and should not be collapsed into a single
  validation pass/fail claim.

## 3. Build structured manuscript outputs

`build_apa_outputs()` is the writing engine. It returns report text plus a
section map, note map, and caption map that all share the same output
contract.

```{r apa}
apa <- build_apa_outputs(
  fit,
  diagnostics = diag,
  context = list(
    assessment = "Writing assessment",
    setting = "Local scoring study",
    scale_desc = "0-4 rubric scale",
    rater_facet = "Rater"
  )
)

cat(apa$report_text)
```

```{r section-map}
apa$section_map[, c("SectionId", "Heading", "Available")]
```

Interpretation:

- `report_text` is the compact narrative output.
- `section_map` is the machine-readable map of what text blocks are available.
- The same contract also feeds captions and notes, which reduces wording drift.

## Publication-readiness boundary

The APA route is strongest when it is used as a structured drafting and review
workflow. It is not a one-click manuscript generator. Before moving text into a
journal article, inspect the following objects together:

```{r publication-boundary}
res <- mfrm_results(fit, include = "publication")
report <- mfrm_report(res, style = "apa")

report$first_screen
report$claim_readiness
report$report_gaps
head(report$template_index[, c(
  "Area", "Topic", "BoundaryType", "ClaimStrength", "RecommendedUse"
)])
```

For a high-stakes manuscript, treat `build_apa_outputs()` and
`mfrm_report(style = "apa")` as a conservative drafting template. Stronger journal
claims still require a defensible study design, cited measurement rationale,
adequate precision evidence, linked or balanced design evidence where relevant,
and substantive interpretation written in the language of the target journal.
Do not report `DraftReady`, `ReadyForAPA`, or `ClaimStrength` as if they were
formal acceptance decisions; use them to decide what wording is currently safe
and which caveats must remain visible.

### Standards basis—and what the template cannot certify

The helpers are APA-oriented drafting aids, informed by the *Publication
Manual of the American Psychological Association* (7th ed.) and the
quantitative Journal Article Reporting Standards (JARS-Quant; Appelbaum et
al., 2018). MFRM-specific prompts are also tied to the measurement references
returned by `reporting_checklist(..., include_references = TRUE)`, including
Eckes, Myford and Wolfe, Linacre, Wright and Masters, and Muraki for GPCM.

This is not a certification of APA or JARS compliance. A fitted measurement
object cannot supply or verify hypotheses and their confirmatory/exploratory
status, recruitment and participant characteristics, ethics, sample-size
rationale, the missing-data mechanism and exclusions, multiplicity or
deviations from plan, or complete data/code availability statements. Complete
those study-level fields manually. Also review effect sizes and uncertainty,
statistic-specific rounding (especially *p* values and confidence intervals),
and the target journal's typography after exporting `apa_table()`.

Reference: Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu,
A. M., & Rao, S. M. (2018). Journal article reporting standards for
quantitative research in psychology. *American Psychologist, 73*(1), 3–25.
[https://doi.org/10.1037/amp0000191](https://doi.org/10.1037/amp0000191)

When the target is a local HTML/CSV/replay bundle rather than an interactive
review object, use `export_mfrm_bundle()` directly from the fitted object. The
result is a potentially identifying analysis archive, not a deidentified
sharing package:

```{r fit-to-html-bundle, eval=FALSE}
bundle <- export_mfrm_bundle(
  fit,
  diagnostics = diag,
  output_dir = "mfrmr-report-bundle",
  prefix = "analysis01",
  include = c(
    "core_tables", "checklist", "dashboard", "apa",
    "summary_tables", "manifest", "script", "html"
  ),
  overwrite = TRUE,
  acknowledge_sensitive = TRUE
)

bundle$written_files[bundle$written_files$Format == "html", ]
```

## 4. Build tables from the same contract

Use `apa_table()` when you want reproducible handoff tables without rebuilding
captions or notes by hand.

```{r apa-tables}
tbl_summary <- apa_table(fit, which = "summary")
tbl_reliability <- apa_table(fit, which = "reliability", diagnostics = diag)

tbl_summary$caption
tbl_reliability$note
```

The actual table data are stored in `tbl_summary$table` and
`tbl_reliability$table`. The `digits` argument applies uniform rounding to all
numeric columns; it is deliberately not a statistic-aware APA formatter.

## 5. Add figure-ready visual data

For reporting workflows, `build_visual_summaries()` is the bridge between
statistical results and figure-ready plot data.

```{r visuals}
vis <- build_visual_summaries(
  fit,
  diagnostics = diag,
  threshold_profile = "standard"
)

names(vis)
names(vis$warning_map)
```

## 6. Reporting route when interaction screening matters

When bias or local interaction screens matter, keep the wording conservative.
The package treats these outputs as screening-oriented unless the current
precision and design evidence justify stronger claims.

```{r bias-screen}
bias_df <- load_mfrmr_data("example_bias")

fit_bias <- fit_mfrm(
  bias_df,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  method = "MML",
  model = "RSM",
  quad_points = 7
)

diag_bias <- diagnose_mfrm(fit_bias, residual_pca = "none")
bias <- estimate_bias(fit_bias, diag_bias, facet_a = "Rater", facet_b = "Criterion")
apa_bias <- build_apa_outputs(fit_bias, diagnostics = diag_bias, bias_results = bias)

apa_bias$section_map[, c("SectionId", "Available", "Heading")]
```

## 7. Keep model comparison as a reporting review

When candidate models are fitted, separate same-data comparison from the
scoring interpretation. `compare_mfrm()` provides the fit-statistic table;
`build_model_choice_review()` attaches model roles, downstream route
boundaries, and cautious wording. Convert the review to a summary-table bundle
when the comparison needs to appear in an appendix or exported report. Fit all
candidates on the same q>=31 grid before automatic ranking; below q=31 the raw
criteria remain screening/review-only, and a close decision still requires a
prespecified denser common-grid check.

```{r model-choice-route, eval=FALSE}
cmp <- compare_mfrm(RSM = fit_rsm, PCM = fit_pcm, GPCM = fit_gpcm)
review <- build_model_choice_review(
  RSM = fit_rsm,
  PCM = fit_pcm,
  GPCM = fit_gpcm,
  run_weighting_review = TRUE
)
model_choice_tables <- build_summary_table_bundle(
  review,
  appendix_preset = "recommended"
)

cmp[, c("Model", "LogLik", "AIC", "BIC", "ICComparable")]
model_choice_tables$table_index
```

For bounded `GPCM`, report the fit as a slope-aware sensitivity model unless
the score interpretation explicitly justifies discrimination-based reweighting.
Do not use AIC/BIC alone as an operational-scoring decision.

## 8. Report the latent-regression population model

Latent-regression fits provide reportable results through the fit summary:
`population_overview`, `population_coefficients`, `population_coding`, and
`caveats`. Coefficients are conditional-normal population-model parameters,
not post-hoc regressions on EAP or MLE scores.

```{r latent-regression-reporting, eval=FALSE}
s_pop <- summary(fit_pop)
s_pop$population_overview
s_pop$population_coefficients
s_pop$population_coding
s_pop$caveats
```

Keep latent-regression claims within the documented
one-dimensional `MML` `RSM` / `PCM` route. Report the population formula,
coding/contrast handling, population policy, and any omitted-person or
omitted-row counts; do not imply multidimensional latent regression,
Wald-test inference, or posterior predictive checking from these tables alone.

## Recommended sequence

For a compact manuscript-oriented route:

1. `fit_mfrm()`
2. `diagnose_mfrm()`
3. `precision_review_report()`
4. `reporting_checklist()`
5. `build_apa_outputs()`
6. `apa_table()`
7. `build_visual_summaries()`
8. `compare_mfrm()` -> `build_model_choice_review()` when candidate-model
   comparisons are part of the manuscript

## Related help

- `help("mfrmr_reporting_and_apa", package = "mfrmr")`
- `help("mfrmr_reports_and_tables", package = "mfrmr")`
- `help("reporting_checklist", package = "mfrmr")`
- `help("build_apa_outputs", package = "mfrmr")`