---
title: "mfrmr quick reference"
author: "mfrmr package"
output:
  pdf_document:
    latex_engine: xelatex
    fig_width: 5
    fig_height: 3
geometry: landscape, margin=0.4in
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  eval = FALSE, echo = TRUE, message = FALSE, warning = FALSE,
  collapse = TRUE, comment = "#>"
)
```

# Data to fitted results

A compact reference for the data review / fit / summary / plot / diagnose /
report loop in mfrmr.
Every snippet assumes `library(mfrmr)` and a long-format
`data.frame` with `Person`, one or more facet columns, and `Score`.

## 1. Check data roles, score support, and missing codes

```{r data-review}
toy <- load_mfrmr_data("example_operational")

data_review <- describe_mfrm_data(
  toy, person = "Person", facets = c("Rater", "Criterion"),
  score = "Score", rating_min = 1, rating_max = 4
)
summary(data_review)

# Declare only codes that mean missing in the study.
dirty_review <- describe_mfrm_data(
  dirty_data, "Person", c("Rater", "Criterion"), "Score",
  missing_codes = c(99, 999, -1, "N/A", "")
)
```

Confirm retained and zero-count categories and inspect the score map. Use the
same `rating_min`, `rating_max`, `keep_original`, and `missing_codes` decisions
in `fit_mfrm()`.

\newpage

## 2. Fit

```{r fit}
fit <- fit_mfrm(
  toy,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score  = "Score",
  method = "MML",          # recommended starting method
  model  = "RSM",          # use PCM/GPCM only with a model rationale
  rating_min = 1,
  rating_max = 4
)
```

For PCM, set the step facet explicitly when its role is not unambiguous. Treat
bounded GPCM as a slope-aware sensitivity extension and read
`gpcm_capability_matrix()` before using downstream helpers.

## 3. Read summaries, then draw the required Wright map

```{r inspect}
fit_summary <- summary(fit, profile = "fit", detail = "brief")

# Comprehensive FACETS-organized review; mfrmr remains the estimator.
review <- summary(fit, profile = "facets", detail = "brief")
res <- review$results
diag <- res$diagnostics

# Primary final-scale display: all locations plus available facet uncertainty.
plot(res, type = "wright", renderer = "native",
     show_ci = TRUE, top_n = Inf, preset = "publication")
```

Read optimizer status and terminal-gradient guidance before interpreting
estimates. The native map is the primary result because it keeps facet
uncertainty and fitted step locations visible.

## 4. Add only the diagnostics needed for the question

```{r diagnose}
# Reuse review diagnostics; rerun only for explicit custom settings.
diag_pca <- diagnose_mfrm(fit, residual_pca = "both")

# Optional follow-up: x = Infit, y = measure. Persons are opt-in.
plot(fit, type = "fit_pathway", diagnostics = diag,
     fit_stat = "Infit", include_person = FALSE)
```

## 5. Report / APA

```{r report}
chk  <- reporting_checklist(fit, diagnostics = diag)
apa  <- build_apa_outputs(fit, diag)
cat(apa$report_text)

tbl  <- apa_table(fit, which = "facets", diagnostics = diag)
kab  <- as_kable(tbl)       # Markdown / HTML via kableExtra
ft   <- as_flextable(tbl)   # Word / PowerPoint via flextable
```

## 6. Bias / DFF / Equivalence

```{r bias}
bias <- estimate_bias(fit, diag, facet_a = "Rater", facet_b = "Criterion")
summary(bias)
plot_bias_interaction(bias, plot = "ranked", show_ci = TRUE)

# Requires a study data frame containing the named grouping column.
dff <- analyze_dff(fit, diagnostics = diag,
                   facet = "Rater", group = "Group", data = your_data)
plot_dif_summary(dff)

eq <- analyze_facet_equivalence(fit, facet = "Rater", ci_level = 0.95)
plot_facet_equivalence(eq, type = "forest")
```

## 7. Hierarchical structure and small-N review

```{r hier}
review <- facet_small_sample_review(fit)
summary(review)

icc <- compute_facet_icc(
  toy, facets = c("Rater", "Criterion"),
  score = "Score", person = "Person",
  ci_method = "profile"      # "boot" for parametric bootstrap CI
)

h <- analyze_hierarchical_structure(
  toy, facets = c("Rater", "Criterion"),
  ci_method = "profile"
)
```

## 8. Empirical-Bayes shrinkage for small-N facets

```{r eb}
# Integrated:
fit_eb <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                    method = "MML", facet_shrinkage = "empirical_bayes")
shrinkage_report(fit_eb)

# Post-hoc:
fit_eb2 <- apply_empirical_bayes_shrinkage(fit)
```

## 9. Plot surface (selected)

| Function | Purpose |
|---|---|
| `plot(fit)` | Wright map (default) |
| `plot_wright_unified(fit, renderer="facets")` | Optional FACETS-style `*` ruler; visual convention only |
| `plot(fit, type="fit_pathway", diagnostics=diag, include_person=TRUE)` | Infit-vs-measure pathway; person rows opt-in |
| `plot_threshold_ladder(fit)` | Category threshold ordering |
| `plot_person_fit(fit, diag)` | Per-person Infit/Outfit bubble |
| `plot_rater_severity_profile(fit, diag)` | Rater ranking + CI bands |
| `plot_bias_interaction(bias, plot="heatmap")` | Bias cell heatmap |
| `plot_displacement(fit, show_ci=TRUE)` | Anchor displacement lollipop |
| `plot_fair_average(fit, show_ci=TRUE)` | Fair averages; CIs for Rasch-family fits |
| `plot_anchor_drift(drift, type="forest")` | Wave-level anchor CI forest |
| `plot(chain, type="graph")` | Bipartite wave/anchor graph |

## 10. Reproducibility and export

```{r export}
# The default warning reminds you that this does not deidentify the files.
export_mfrm_bundle(fit, diagnostics = diag,
                   output_dir = "mfrmr_bundle/")
export_mfrm_results(res, output_dir = "mfrmr-results",
                    preset = "starter", overwrite = TRUE)
# Add acknowledge_sensitive = TRUE only after reviewing every intended file
# and confirming that the destination follows the study's handling policy.
manifest <- build_mfrm_manifest(fit, diagnostics = diag)
# manifest$environment, $dependencies, $input_hash, $session_info,
# $hierarchical_review, $missing_recoding, $shrinkage_review
replay_script <- build_mfrm_replay_script(fit, path = "replay_mfrmr.R")
```

## 11. FACETS and ConQuest users

- Keep the native Wright map with available facet uncertainty as the primary
  mfrmr display. Use
  `renderer = "facets"` when readers benefit from FACETS-style asterisks and
  ruler layout; this does not reproduce FACETS estimation.

```r
rubric_labels <- setNames(
  your_rubric_labels,
  fit$prep$score_map$OriginalScore
)
plot_wright_unified(
  fit, renderer = "facets", show_ci = FALSE,
  category_labels = rubric_labels
)
```

- Use `facets_feature_coverage()` for feature-by-feature alternatives.
- Use `build_conquest_overlap_bundle(fit_lr)` only after fitting the documented
  unidimensional MML comparison. Run the generated `.cqc` file in ConQuest,
  then pass its four CSV exports to `normalize_conquest_overlap_exports()` and
  `review_conquest_overlap()`.
- mfrmr does not execute FACETS or ConQuest and does not claim general
  numerical equivalence with either program.

## Package links

- Source and documentation: <https://github.com/Ryuya-dot-com/mfrmr>
- Citation:      `citation("mfrmr")`

*mfrmr quick reference*. Distributed under the MIT License.
