Migrating from FACETS to mfrmr

Mental model

The two stacks share the same psychometric framework but differ in operating model.

Before treating a legacy workflow as covered, inspect the public coverage boundary:

facets_feature_coverage()
facets_feature_coverage("not_implemented")

This matrix describes the availability of package-native output surfaces. implemented does not by itself mean that the two programs use the same estimand, conditioning, extreme-score rule, degrees of freedom, or numerical contract.

Visualize an existing FACETS analysis

read_facets_results() reads supported Scorefile exports without refitting. The included small synthetic example has one row per facet element, with fixed anchors and extreme scores. Inspect summary() for available measures and fit statistics; these counts do not assess the quality of the source analysis.

library(mfrmr)
score_directory <- system.file("extdata", "facets-example", package = "mfrmr")
facets_result <- read_facets_results(score_directory,
  facet_map = c("Person", "Rater", "Task"), source_version = "4.5.0")
summary(facets_result)
facets_result$table[, c("Facet", "ElementID", "Label", "Estimate", "SourceStatus")]

plot(facets_result, type = "measure", facets = "Person")

Squares identify FACETS Status=1, which can denote a direct anchor or a member of a group anchor. The import retains SourceStatus = "anchored", but this does not establish that the individual’s measure was fixed. A group member may be estimated around the constrained mean. GroupRaw can also be a reporting group; its value, the number of imported group members and a reported SE cannot identify the anchor subtype. Inspect the original Labels= for the anchor mode and Table 7 for row annotations. A Table 7 G annotation alone need not distinguish the G and X input modes. summary() therefore calls this count AnchorFlagged, replacing Anchored from earlier candidate versions. For saved imports, call plot() again to refresh the legend and notes; the source values and availability remain intact.

Extreme members of G/X groups can carry Status -2/-3 instead of 1, so AnchorFlagged is not a count of every member of an anchored group. Their triangles identify the source’s extreme-score status. A finite reported location does not establish which adjustment produced it: G and X can report different locations. The import preserves these locations and marginal SEs, while keeping their Infit/Outfit placeholders unavailable for fit plots.

type = "fit" shows measure versus Infit or Outfit; type = "infit_outfit" compares the two mean squares. Inspect the plotted rows and unavailable reasons before interpreting missing points:

fit_points <- plot_data(facets_result, component = "plot_data",
  type = "infit_outfit", facets = "Person")
fit_points[, c("Label", "Infit", "Outfit", "Available", "Reason")]
plot_data(facets_result, component = "notes")

For your own analysis, supply named comma/tab/semicolon files, a vector of per-facet exports, or a directory of score.N.txt files. Use the actual source encoding (for example encoding = "CP932" for an applicable Japanese Windows export). For FACETS text output, the following specifications retain the column headings and quoted labels needed by the reader:

Scorefile = score.txt
Heading lines = Yes
QM = Double
CSV = Semicolons

CSV = Commas or CSV = Tabs selects the other supported separators. Keep the generated facet heading line; the Scorefile reader finds the column header. Check that the files exist and that their labels are intact before importing. A successful process exit alone does not establish a successful export. The reader’s encoding converts an existing file; it neither changes FACETS input settings nor restores labels already lost in the external output.

You can keep FACETS’ exported IDs and assign local display names in R. The map uses the imported Facet and ElementID, not row positions or source labels. Store IDs as text to retain any leading zeros. Here only two Persons are renamed; other elements retain their original labels:

display_names <- data.frame(Facet = "Person", ElementID = c("1", "2"),
  DisplayLabel = c("受検者 甲", "受検者 乙"))
labelled <- plot(facets_result, facets = "Person",
  label_map = display_names, draw = FALSE)
labelled$data$plot_data[, c("ElementID", "Label", "DisplayLabel")]

The original result and its labels remain unchanged; labelled$data$label_map retains the supplied correspondence. Unknown or repeated keys are errors. Repeated display names are distinguished by element key. Only measure plots draw element labels; other element plots keep the names in their returned data. This map does not apply to Graphfile category labels.

Japanese text also needs a graphics device and font with the required glyphs. With Cairo support and Meiryo installed on Windows, save the plot as follows; choose an available Japanese font on another system. Check the saved file for missing glyphs. This does not require Japanese input to FACETS or change its results. See R’s Cairo device documentation.

png("facets-japanese.png", width = 7, height = 5, units = "in", res = 200,
  type = "cairo", family = "Meiryo")
plot(facets_result, facets = "Person", label_map = display_names)
dev.off()

cairo_pdf("facets-japanese.pdf", width = 7, height = 5, family = "Meiryo")
plot(facets_result, facets = "Person", label_map = display_names)
dev.off()

Fixed-width imports require the supported ASCII all-field layout. Element IDs distinguish duplicate labels. Source signs and reported values are preserved; no automatic recentering or sign reversal takes place. A point-only Wright map (type = "wright") additionally requires common_scale = TRUE at import after checking compatible units and origins in the original FACETS specification.

Extreme-score rows are flagged; their status does not identify the adjustment rule used by FACETS. Their fit values can be placeholders and are unavailable in fit plots. Source SEs are retained in the table; no confidence intervals are constructed. These descriptive results do not provide new-person scoring, threshold reconstruction or validation of the FACETS analysis. saveRDS(facets_result, ...) preserves the complete result; as.data.frame(facets_result) exports its element table. See ?read_facets_results and ?plot.mfrm_facets_results for the supported scope.

For category-probability and expected-score curves, export a FACETS Graphfile with headings. This separate synthetic PCM example contains one coordinate row per scale and reported measure; it is a different analysis from the binary score example above.

graph_path <- system.file("extdata", "facets-graph-example", "graph.txt",
  package = "mfrmr")
facets_curves <- read_facets_results(graph_file = graph_path,
  source_version = "4.5.0")
summary(facets_curves, component = "curves")

plot(facets_curves, type = "ccc", scales = 1)

Use type = "expected" for the exported expected-score curve. Inspect the actual coordinates and source-consistency checks with:

expected_points <- plot_data(facets_curves, "plot_data",
  type = "expected", scales = 1)
head(expected_points[, c("Scale", "Measure", "Value", "Available", "Reason")])
head(facets_curves$graph$diagnostics)

The reader preserves Graphfile scale numbers and category codes. A scale number identifies a Table 8 subtable; it does not automatically identify an item. Even binary exports may use category codes 1/2 instead of the original 0/1 responses. Check the source specification before interpreting expected scores. The graph Measure axis is separate from the element measure declarations; importing both files does not align them. When both exports belong to your analysis, supply file = ... and graph_file = ... in one call, after checking that correspondence.

Missing or invalid coordinates remain in as.data.frame(facets_curves, component = "curves") and break the plotted lines. Probability sums are checked using exported decimal precision, without renormalizing. These checks do not certify model fit; connecting segments do not calculate new model predictions.

Why results and decisions can differ

Match the statistical question before comparing numbers. A FACETS-style table name does not guarantee the same calculation. For an existing FACETS study, first align the response model, retained categories, orientation, anchors and centering, observations and replication weights, and extreme-score treatment. Compare unrounded logits before user scaling. Then compare response probabilities, mean squares, df and standardization separately. FACETS uses score-residual and measure-change convergence criteria; an optimizer’s success code alone does not establish agreement with them.

The corrected candidate has a bounded executed comparison against FACETS 4.5.0: complete binary ordinary JML data with unit weights and matched signs. Locations, SEs, MnSq, PTMEA and WHEXACT YES/NO agree within external report precision; bias estimates, baseline expected scores and the zero-bias summary also agree. This is not a drop-in equivalence claim for other models or options.

Definitions are given in the official WHEXACT, point-measure, Table 7, fair-average, and Table 13 documentation. The count-minus-1 observation above describes the executed version and example, not a correction to the external manual or a claim about every FACETS version.

Recompute diagnose_mfrm() from a saved fit, then recreate estimate_bias() and reports. Corrected native diagnostics and bias results carry calculation_version = 2; do not add that field manually to old values. These diagnostic changes do not require refitting the calibration.

For a negatively oriented rater, the model predictor contains -severity + bias, so its context-specific severity is severity - bias. The Table 14 examples make this direction explicit. Reversing the contrast leaves a two-sided tail area unchanged but reverses the substantive feedback. Pairwise df also needs its own version specification: the current t-statistics manual describes a 2025 modification alongside classical Welch. The executed FACETS 4.5.0 unequal-count comparison confirms the modified formula and integer truncation for the tail area. DFUnrounded and DFMethod retain that choice; df_method = "classical" reproduces the earlier reference.

Corrected pairwise tables carry CalculationVersion = 3. Recreate build_fixed_reports() and bias_pairwise_report() from the current bias bundle. First recreate old bias bundles from the saved fit and current diagnostics; no main calibration refit is needed. Historical report values are not automatically corrected by reloading them. Local and contrast SEs describe conditional cell uncertainty; calibration uncertainty and cross-cell covariance are not propagated. The earlier claim that these contrasts were generally conservative was incorrect. Check ScreenUnavailable in the pairwise summary before reading Flagged; the summary covers the full family even when top_n limits the table.

The Table 4 rule and its default threshold now match the native residual-only default. To retain the exploratory probability screen, request abs_z_min = 2, prob_max = .30, rule = "either". A fixed .30 cutoff has no universal interpretation: five equally likely categories each have probability .2, so it flags every response. For tied modes, MostLikely is missing, MostLikelyCount records the tie and CategoryGap uses the nearest mode. This ranking cannot decide which rater is unfair or which ratings to remove.

Category reports now distinguish the observed generating-measure average (AvgMeasure), model-expected generating-measure average (ExpectedMeasure), Person-only mean and expected response score. Category residual MnSq is descriptive: conditioning on the observed category removes the universal unit baseline, so category ZSTD and its flags are unavailable. This does not turn a missing fit flag into evidence that the category functions well.

In category_structure_report(), MedianThreshold now means a cumulative 50% boundary; AndrichThreshold retains the adjacent-category step. Recreate saved structure reports. Half-score crossings outside theta_range stay missing, and modal peaks describe only the requested grid. The random chi-square likewise cannot establish normality: with equal SEs and positive adjusted variance it is fixed by the number of eligible elements, regardless of shape. Chi-square summaries retain unavailable flags and use the full family before truncation.

For external files, fixed-field import requires validated all-field headings and element number after the statistics; other layouts should use named delimited columns. Native score exports contain observations, not the elements in a FACETS Scorefile. Native Tables 5 and 11 omit parts of FACETS’ response moments and variance reports. A single co-observation graph component also does not prove identification; inspect the fitted design audit. The historical conditional_probability plotting alias is ordinary P(k), not FACETS’ adjacent-conditional probability; use ccc to name the native quantity clearly.

Match denominators as well as cutoffs. FACETS Totalscore defaults to No, while fair_average_table() defaults to totalscore = TRUE. Native raw TotalScore/TotalCount and weighted WeightdScore/WeightdCount are distinct; ObservedAverage uses the weighted values. Scores 0 and 2 with weights 1 and 3 have raw total/count 2/2 but weighted total/count 6/4 and average 1.5. Keeping both counts is useful for describing the data, provided the comparison uses the same replication semantics and extreme-row inclusion. Weights do not by themselves establish the independence or sampling uncertainty of repeated ratings.

Ordinary JML, corrected JML and FACETS

FACETS’ estimation documentation states that it does not correct JMLE estimation bias. Accordingly, ordinary method = "JML" under matched RSM/PCM models, anchors and origin constraints is the relevant mfrmr comparison. Equal fitted values establish neither unbiased estimation nor calibrated intervals. More Persons alone need not remove the incidental-parameter bias when each Person still supplies few ratings.

These operations have different targets and should be recorded separately:

For the corrected route, fit_mfrm(model = "GPCM", method = "JML", jml_correction_order = ...) solves the adjusted equation described in ?fit_mfrm. This is an existing implementation, not an RSM/PCM correction switch. It currently excludes anchors and supplies local-root RootSE, not validated structural-truth confidence intervals. Its fixed/random-roster covariance describes repeated independent Person cohorts; the common-plan delta procedure instead holds each Person’s ability fixed across repeated responses. Passing that RootSE into the common-plan interval calculation would change both the estimator and the sampling target. Corrected JML is therefore not admitted by confint.mfrm_fair_average().

The benefit of retaining both estimators is that external compatibility and a possible reduction in incidental-parameter bias can be studied separately. The cost is a distinct mathematical, numerical and inference contract: correction order, remaining bias, variance and solver stability all require evaluation. FACETS agreement cannot select the correction order or qualify its uncertainty. Keep ordinary JML as the comparison/default route and select corrected JML explicitly within its documented scope. FACETS’ separate Bias interaction analysis also should not be confused with correction of an estimator’s bias.

Using TAM JML as a third comparison

TAM’s tam.jml() helps check the common RSM/PCM likelihood, but its default corrections differ from ordinary mfrmr JML. Match category support, cumulative difficulties, anchors and location constraints first. The TAM documentation describes its separate adjustment and bias arguments.

Executed TAM 4.3-25 comparisons support close ordinary-JML agreement for matched complete/sparse RSM/PCM designs and supported partial Person anchors. They do not establish general coverage, every FACETS anchoring convention, or a new JML import route: import_tam_fit() remains MML-only. Retain unavailable uncertainty instead of substituting WLE SEs or conditional information for it.

Inspect returned anchors too. In the checked TAM 4.3-25 version-1 path, theta.fixed with xsi.fixed = NULL still recenters every Person after an update, moving a supplied Person anchor. A free-intercept probe did not retain the anchor and reached its iteration limit. An empty fixed-item matrix prevented that recentering in this version, but the probe still did not reach the required score-equation accuracy within the same limit. This is a version-specific diagnostic observation, not a general workaround recommendation. The matching reference instead uses an independently fitted unanchored TAM solution translated to the specified Person origin, preserving every response probability. Keep that algebraic comparison separate from a successful direct anchored TAM execution.

A one-shot legacy-compatible call

If the goal is to translate a FACETS-style script with minimal R-side plumbing, use run_mfrm_facets() (alias mfrmRFacets()):

library(mfrmr)
data("mfrmr_example_operational", package = "mfrmr")

run <- run_mfrm_facets(
  data = mfrmr_example_operational,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  model = "RSM",
  method = "JML"
)

names(run)

The wrapper returns the same fit_mfrm() and diagnose_mfrm() objects that a step-by-step pipeline produces, plus the iteration log, fair-average table, and rating-scale table:

jml_status <- summary(run$fit, profile = "fit", detail = "brief")
jml_status$overview[, c(
  "Model", "Method", "Converged", "InferenceReady",
  "ConvergenceSeverity"
)]
jml_status$readiness
head(run$fair_average)

method = "JML" is shown here for a JMLE-oriented migration comparison. Do not infer readiness from Converged alone: require InferenceReady = TRUE for the numerical-readiness criteria and review the terminal-gradient guidance when severity is "review" or "fail". Numerical readiness does not override a Data, Design, or Stability hold; use the readiness table before interpreting or reporting the fit. Choose the estimator for the intended question. The default MML route integrates over a fixed N(0, 1) population and exposes Person posterior SDs; other supported population specifications must be selected explicitly. Its assumptions and shrinkage must be justified for the target population. A posterior SD and a JML sampling SE are different quantities.

Translating the specification file

The mapping below covers the most common FACETS specification keywords.

Facets = 3
Models = ?,?,?,R5
Labels =
  1, Examinee
    1 = P01
    ...
  2, Rater
    1 = R1
    ...
  3, Criterion
    1 = Content
    ...

fit_mfrm(
  data = examinee_long,
  person = "Examinee",
  facets = c("Rater", "Criterion"),
  score = "Score",
  rating_min = 1,
  rating_max = 5,
  model = "RSM"
)

Labels =
2, Rater, A
1 = R1, 0.0
2 = R2, 0.5
*

anchors <- data.frame(
  Facet = "Rater",
  Level = c("R1", "R2"),
  Anchor = c(0.0, 0.5),
  stringsAsFactors = FALSE
)
fit <- fit_mfrm(..., method = "JML", anchors = anchors,
                centering_scope = "anchor_origin", anchor_policy = "error")

ratings <- load_mfrmr_data("example_core")
rater_anchor <- data.frame(Facet = "Rater", Level = "R01", Anchor = 0.7)
anchored_fit <- fit_mfrm(
  ratings, person = "Person", facets = c("Rater", "Criterion"), score = "Score",
  method = "JML", anchors = rater_anchor, centering_scope = "anchor_origin"
)
anchored_fit$config$anchor_review$facet_summary

ratings <- load_mfrmr_data("example_core")
group <- data.frame(
  Facet = "Rater", Level = c("R01", "R02"), Group = "panel",
  GroupValue = 0.6
)
group_fit <- fit_mfrm(
  ratings, person = "Person", facets = c("Rater", "Criterion"), score = "Score",
  method = "JML", group_anchors = group, centering_scope = "anchor_origin",
  anchor_policy = "error"
)
group_fit$config$anchor_review$facet_summary
subset(group_fit$facets$others, Facet == "Rater")

diag <- diagnose_mfrm(fit)
bias <- estimate_bias(fit, diag,
                      facet_a = "Rater", facet_b = "Criterion")
summary(bias)

screens <- estimate_all_bias(fit, diagnostics = diag)
summary(screens)                     # Pair counts, availability and magnitude
head(summary(screens)$cells)         # Signed effects, SEs and observed counts
plot(screens)                       # All retained cells grouped by pair
plot(screens, pair = "Rater x Criterion")  # Individual heatmap

review <- summary(fit, profile = "facets", detail = "brief")
review$decision
res <- review$results

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

rubric_labels <- setNames(
  your_rubric_labels,
  fit$prep$score_map$OriginalScore
)
plot(res, type = "wright", renderer = "facets", show_ci = FALSE,
     category_labels = rubric_labels, preset = "publication")

plot(res, type = "fit_pathway", fit_stat = "Infit",
     include_person = TRUE, top_n_person = 12,
     person_labels = "none", facet_labels = "flagged")

diag <- diagnose_mfrm(fit, residual_pca = "none", fit_df_method = "both")
fm <- fit_measures_table(fit, diagnostics = diag,
                         facet = "Rater", fit_df_method = "both")

fm$facets_table
fm$df_sensitive
plot(fm, type = "df_sensitivity")

group_anchors <- data.frame(
  Facet = "Rater",
  Level = c("R1", "R2"),
  Group = c("PanelA", "PanelA"),
  GroupValue = c(0.0, 0.0),
  stringsAsFactors = FALSE
)
fit_g <- fit_mfrm(..., group_anchors = group_anchors, anchor_policy = "error")
diag_g <- diagnose_mfrm(fit_g)
dff <- analyze_dff(fit_g, diag_g, facet = "Criterion",
                   group = "FirstLanguage", data = ratings_with_language,
                   method = "refit")

Reviewing output contracts and fit tables

When migrating an existing study, facets_output_contract_review() checks whether the package-generated report components satisfy the FACETS-style output contract encoded in the package:

contract_review <- facets_output_contract_review(
  fit,
  diagnostics = diag,
  branch = "facets"
)
summary(contract_review)
contract_review$missing_preview
contract_review$metric_checks

The resulting object reviews column coverage and package-native metric checks. It is not a claim that mfrmr has reproduced FACETS estimates numerically. For external numerical comparison, use an exported FACETS fit table and facets_fit_review().

When that comparison involves an MML fit, remember that mfrmr evaluates residual-based fit statistics at shrunken EAP person measures while FACETS uses JMLE estimates, so MnSq differences can reflect the residual basis rather than a fit-computation difference; refit with method = "JML" before attributing such gaps. See facets_fit_df_guide() for this boundary and for the separate df/ZSTD standardization conventions.

If you already have a FACETS fit table on disk, read it first and then run the fit review. This does not run FACETS; it consumes an exported or otherwise harmonized table.

facets_fit <- read_facets_fit_table(
  "score.2.txt",
  facet_map = c("1" = "Person", "2" = "Rater", "3" = "Criterion")
)
review <- facets_fit_review(
  fit,
  diagnostics = diag,
  facets_fit = facets_fit,
  external_zstd_tolerance = 0.05
)

review$df_sensitivity
review$df_sensitive
review$external_table_quality
review$external_comparison
plot(review, type = "df_sensitivity")

Use external_comparison for the supplied FACETS table and df_sensitivity for the engine-vs-FACETS-style df convention check. This separation keeps external numerical differences distinct from ZSTD differences caused by df standardization. external_table_quality is the first place to look if the FACETS export only contains ZStd and T.Count columns, or if duplicate Facet x Level rows were supplied.

Producing FACETS-style output files

For traceability or downstream tools that expect FACETS output files, facets_output_file_bundle() writes a parallel set of fixed-width or CSV exports:

files <- facets_output_file_bundle(
  fit,
  diagnostics = diag,
  out_dir = tempdir(),
  include = c("graph", "score")
)

For RSM and PCM the score-side helpers are available. Under GPCM the score-side bundle is intentionally restricted; see ?gpcm_capability_matrix and the mfrmr-gpcm-scope vignette for the documented limitation.

Recommended next steps

For a Person comparison on the same scale exposure, supply reference_plan to fair_average_table(). Its reference_scores component uses each Person’s primary estimate with the actual fitted locations and scale profiles in that one weighted plan. This is a distinct expected-score target: PCM FairM/FairZ still retain each Person’s administered scale mixture. Score direction remains defined by the rubric; a higher standardized expected score is not automatically a judgment of better performance or fairness.

confint(fair, parm = ..., nsim = ..., seed = ...) provides an experimental ordinary-JML refit route for common-plan scores and named paired contrasts. When anchors were estimated from the same or overlapping samples, supply their anchor_source, a generating fit on the union of response events where needed, and explicit calibration/scoring row maps. The maps assert which observations are actually shared: equal labels alone do not establish event identity. Every replication re-estimates calibration and anchors before scoring; shared responses are generated once. Without the source and correspondence, estimated- anchor intervals are unavailable. anchor_uncertainty = "fixed" is an explicit conditional alternative, not a substitute for propagating estimated anchors.

Explicit generator_method = "jeffreys" offers an experimental refit option when extreme Person estimates prevent an ordinary generating model. It fits a canonical information-penalized model on the generating data, then simulates shared events once and reruns the ordinary calibration/anchor/scoring estimator. The original ordinary-JML points remain unchanged; extreme Persons retain limiting expected scores. Inspect attr(result, "generating_model") and attr(result, "generating_target") to distinguish the generator from the reported estimate. Declare the rubric and retain its full coding when fitting. This option is not available for delta or bounded intervals, and it does not resolve infinite estimated anchors or unqualified structural calibration.

A finite generator alone does not justify nominal coverage or score-bias reduction. The bias correction for canonical parameters does not automatically transfer to expected scores: even the binomial probability (S + .5)/(n + 1) has bias (.5 - p)/(n + 1). The option is separate from FACETS Xtreme and mfrmr’s corrected-GPCM estimator; it leaves CIEligible = FALSE.

An alternative is to select jml_penalty = "jeffreys" with method = "JML" in every original calibration, scoring and union fit. This changes the reported points as well as every replicate fit. The separate mfrm_jeffreys_fit result supplies Person, facet and step points and numerical checks; it does not inherit ordinary SEs or FACETS diagnostics. For its common-plan scores, call fair_average_table(fit, reference_plan = plan). The existing confint() interface propagates estimated anchors and shared events using the same penalized estimator throughout. Its generating method defaults to Jeffreys, using the supplied penalized union fit. Mixing ordinary and penalized fits across stages is refused. With method = "delta", the analytic derivative follows the penalized equations, including the change in the penalty when estimated anchors change. Shared response events contribute to both stages before computing covariance. This is a local model-based approximation, not inverse penalized curvature or a coverage guarantee. Inspect summary(fit)$checks and attr(result, "settings")$estimator to record which procedure was used. Declared empty categories are retained; unidentified designs and numerical failures are still refused. Finite-sample score bias, coverage and global optimization remain unqualified. This option is separate from FACETS Xtreme and corrected GPCM, and is not a FACETS-compatible default.

There is a concrete reason to retain the experimental label. With six binary responses and true success probability 0.5, exact enumeration gives 78.125% coverage for nominal 95% Jeffreys delta intervals. At probability 0.25, it is 78.4424%. The local variance formula is correct; the plug-in normal interval still fails to achieve its nominal coverage. More Persons do not necessarily provide more responses for the particular Person being scored. Use Available to check whether a result was computed, and CIEligible and the saved notes to assess its inferential status. Basic refit intervals also have counterexamples: with six responses and true probability 0.1, exact refit quantiles give coverage 45.2709%. An all-zero sample’s upper basic limit equals the fitted score 0.5/7, below the true probability. Even infinitely many refits do not fix this; Monte Carlo precision and sampling coverage are different requirements. The conservative bounded method retains its distinct assumptions and can be wide in sparse designs.

Read the interval table’s Status, Available, Unresolved and ExpectedTailDraws together. Numerical availability does not qualify coverage: the initial method is a pointwise fitted-model approximation, with no correction for JML incidental-parameter bias or guarantee under misspecification. Unresolved trials stay in the denominator and can make tail limits unavailable. The four LowerMCLower/LowerMCUpper and UpperMCLower/UpperMCUpper columns separately enclose the Monte Carlo error of the two endpoints, at mc_level (default .95). attr(result, "monte_carlo") retains the underlying binomial ranks. Infinite enclosures expose insufficient draws or unresolved refits; MCFinite = TRUE means finite, not necessarily precise enough. These diagnostics cover both endpoints for one fixed target under a fixed generating model and independent replications. They do not guarantee coverage across targets, adaptive reruns, or repeated samples of the original data. The plot still shows the empirical interval; its saved payload retains the Monte Carlo diagnostic for inspection. summary() and plot() reuse the saved result, and saveRDS() preserves its replications, checks and provenance. These intervals concern expected scores, not future observed ratings; they do not certify unfairness. The method’s help contains an executable example and the supported estimation/design restrictions.

confint(fair, parm = ..., method = "delta") evaluates a deterministic local approximation through the same estimator. Supply the same calibration fit, union fit and event maps when anchors are estimated. Omit nsim and seed. The result adds SE, Reason and attr(result, "covariance"); its covariance_components attribute separates direct scoring, propagation through calibration and the signed shared-event cross term. These terms retain the covariance needed for paired differences; a marginal anchor SE cannot replace them. The count columns for simulations are NA because this method does not run replications.

Delta limits use a local normal approximation centered on the observed estimate. They require finite estimates and positive information, and do not correct JML bias or establish coverage. Missing bounds retain their reasons; bounds outside the score support are not clipped. Compare the numerical approximation and availability under the intended design before using either method for decisions. The help for confint.mfrm_fair_average gives the derivative, uncertainty basis and references. These procedures require native fitted models; descriptive FACETS-result imports do not provide the source model or joint uncertainty.

confint(fair, parm = ..., method = "bounded") is a conservative alternative for the same true common-plan score or contrast. It uses scoring responses directly, so extreme Person estimates and the absence of an estimated-anchor covariance do not require a fitted generating model. Omit calibration/generator arguments and simulation controls for this method. Its finite-sample probability bound requires independent bounded responses, a fixed exogenous assignment, plan and contrast, and stable expected scores within each Person-plan cell. It is pointwise and does not justify selecting comparisons after seeing scores or claiming nominal coverage conditional on successful fitting. Declare both rating_min and rating_max from the rubric when fitting, and retain the full score coding with category_policy = "preserve". Observed sample extrema and internally collapsed categories are not accepted as known support. The interval result saves this basis in attr(result, "settings")$score_support. A retained empty category that prevents fitting is still a failure; changing the rubric to obtain an interval changes the inferential target.

The tradeoff is width: every unobserved plan cell keeps its full score range instead of borrowing a model prediction. Inspect Informative and attr(result, "support"); an available full-range bound is explicitly uninformative. Estimate retains the JML point, while DesignEstimate is a separate unbiased weighted mean only with complete plan support. The interval can be asymmetric around or exclude the JML point. It is not a bias correction, a FACETS interval convention, or a solution to dependent ratings. Failure to construct the prerequisite fitted result remains an end-to-end availability failure, even though the concentration inequality itself needs no fitted model.

After a FACETS-oriented package-native fit is in hand, the recommended mfrmr reporting workflow extends the analysis with:

• review_mfrm_anchors() before anchored fitting, and detect_anchor_drift() / plot() when common elements define a cross-form or cross-wave link.
• diagnose_mfrm(diagnostic_mode = "both") for the strict marginal screen alongside the residual stack.
• rating_scale_table(), category_structure_report(), and category_curves_report() for category-functioning evidence.
• fair_average_table() when FACETS Table 7 fair-average review is needed.
• plot(fit, type = "wright") or plot() for a variable-map view of targeting and threshold placement.
• estimate_bias(), bias_interaction_report(), and bias_pairwise_report() when FACETS Table 14-style local interaction screening is substantively relevant.
• reporting_checklist() for a manuscript-readiness summary.
• build_apa_outputs() for Method and Results paragraphs and APA tables.
• build_mfrm_manifest() and build_mfrm_replay_script() for the reproducibility bundle alongside the FACETS-style handoff out of the box.

The mfrmr-workflow vignette covers the full sequence end to end; the mfrmr-reporting-and-apa vignette focuses on manuscript preparation; the mfrmr-linking-and-dff vignette covers anchoring, drift, and DFF in detail.