Fit new data anchored to a baseline calibration

Re-estimates a many-facet Rasch model on new data while holding selected facet parameters fixed at the values from a previous (baseline) calibration. This is the standard workflow for placing new data onto an existing scale, linking test forms, or carrying a baseline calibration across administration windows.

Usage

anchor_to_baseline(
  new_data,
  baseline_fit,
  person,
  facets,
  score,
  anchor_facets = NULL,
  include_person = FALSE,
  weight = NULL,
  model = NULL,
  method = NULL,
  anchor_policy = "warn",
  ...
)

# S3 method for class 'mfrm_anchored_fit'
print(x, ...)

# S3 method for class 'mfrm_anchored_fit'
summary(object, ...)

# S3 method for class 'summary.mfrm_anchored_fit'
print(x, ...)

Arguments

new_data: Data frame in long format (one row per rating).
baseline_fit: An mfrm_fit object from a previous calibration.
person: Character column name for person/examinee.
facets: Character vector of facet column names.
score: Character column name for the rating score.
anchor_facets: Character vector of facets to anchor (default: all non-Person facets).
include_person: If TRUE, also anchor person estimates.
weight: Optional character column name for observation weights.
model: Scale model override; defaults to baseline model.
method: Estimation method override; defaults to baseline method.
anchor_policy: How to handle anchor issues: "warn", "error", "silent".
...: Ignored.
x: An mfrm_anchored_fit object.
object: An mfrm_anchored_fit object (for summary).

Value

Object of class mfrm_anchored_fit with components:

fit: The anchored mfrm_fit object.
diagnostics: Output of diagnose_mfrm() on the anchored fit.
baseline_anchors: Anchor table extracted from the baseline.
drift: Tibble of element-level drift statistics.

Details

This function automates the baseline-anchored calibration workflow:

Extracts anchor values from the baseline fit using make_anchor_table().
Re-estimates the model on new_data with those anchors fixed via fit_mfrm(..., anchors = anchor_table).
Runs diagnose_mfrm() on the anchored fit.
Computes element-level differences (new estimate minus baseline estimate) for every common element.

The model and method arguments default to the baseline fit's settings so the calibration framework remains consistent. Elements present in the anchor table but absent from the new data are handled according to anchor_policy: "warn" (default) emits a message, "error" stops execution, and "silent" ignores silently.

The returned drift table is best interpreted as an anchored consistency check. When a facet is fixed through anchor_facets, those anchored levels are constrained in the new run, so their reported differences are not an independent drift analysis. For genuine cross-wave drift monitoring, fit the waves separately and use detect_anchor_drift() on the resulting fits.

Element-level differences are calculated for every element that appears in both the baseline and the new calibration: $$\Delta_e = \hat{\delta}_{e,\text{new}} - \hat{\delta}_{e,\text{base}}$$ An element is flagged when $|\Delta_e| > 0.5$ logits or $|\Delta_e / SE_{\Delta_e}| > 2.0$, where $SE_{\Delta_e} = \sqrt{SE_{\mathrm{base}}^2 + SE_{\mathrm{new}}^2}$.

Which function should I use?

Use anchor_to_baseline() when you have one new dataset and want to place it directly on a baseline scale.
Use detect_anchor_drift() when you already have multiple fitted waves and want to compare their stability.
Use build_equating_chain() when you need cumulative offsets across an ordered series of waves.

Interpreting output

$drift: one row per common element with columns Facet, Level, Baseline, New, Drift, SE_Baseline, SE_New, SE_Diff, Drift_SE_Ratio, and Flag. Read this as an anchored consistency table. Small absolute differences indicate that the anchored re-fit stayed close to the baseline scale. Flagged rows warrant review, but they are not a substitute for a separate drift study on unanchored common elements.
$fit: the full anchored mfrm_fit object, usable with diagnose_mfrm(), measurable_summary_table(), etc.
$diagnostics: pre-computed diagnostics for the anchored calibration.
$baseline_anchors: the anchor table fed to fit_mfrm(), useful for auditing which elements were constrained.

Typical workflow

Fit the baseline model: fit1 <- fit_mfrm(...).
Collect new data (e.g., a later administration).
Call res <- anchor_to_baseline(new_data, fit1, ...).
Inspect summary(res) to confirm the anchored run remains close to the baseline scale.
For multi-wave drift monitoring, fit waves separately and pass the fits to detect_anchor_drift() or build_equating_chain().

Examples

d1 <- load_mfrmr_data("study1")
keep1 <- unique(d1$Person)[1:15]
d1 <- d1[d1$Person %in% keep1, , drop = FALSE]
fit1 <- fit_mfrm(d1, "Person", c("Rater", "Criterion"), "Score",
                 method = "JML", maxit = 15)
#> Warning: Optimizer did not fully converge (code = 1). Consider increasing maxit (current: 15) or relaxing reltol (current: 1e-06).
d2 <- load_mfrmr_data("study2")
keep2 <- unique(d2$Person)[1:15]
d2 <- d2[d2$Person %in% keep2, , drop = FALSE]
res <- anchor_to_baseline(d2, fit1, "Person",
                          c("Rater", "Criterion"), "Score",
                          anchor_facets = "Criterion")
#> Warning: Anchor audit detected 3 issue row(s): unknown_anchor_levels=3. Invalid rows were removed; duplicate keys keep the last row.
summary(res)
#> --- Anchored Fit Summary ---
#> Converged: TRUE 
#> Anchors used: 3 | Common elements: 0 | Flagged: 0 
#> 
head(res$drift[, c("Facet", "Level", "Drift", "Flag")])
#> # A tibble: 0 × 4
#> # ℹ 4 variables: Facet <chr>, Level <chr>, Drift <dbl>, Flag <lgl>
res$baseline_anchors[1:3, ]
#> # A tibble: 3 × 3
#>   Facet     Level                  Anchor
#>   <chr>     <chr>                   <dbl>
#> 1 Criterion Global_Impression      -1.07 
#> 2 Criterion Linguistic_Realization  0.585
#> 3 Criterion Task_Fulfillment        0.487