anchor_to_baseline: Fit new data anchored to a baseline calibration

In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

Fit new data anchored to a baseline calibration

Description

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).

Details

This function automates the baseline-anchored calibration workflow:

1. Extracts anchor values from the baseline fit using make_anchor_table().

2. Re-estimates the model on new_data with those anchors fixed via fit_mfrm(..., anchors = anchor_table).

3. Runs diagnose_mfrm() on the anchored fit.

4. 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}.

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.

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

1. Fit the baseline model: fit1 <- fit_mfrm(...).

2. Collect new data (e.g., a later administration).

3. Call res <- anchor_to_baseline(new_data, fit1, ...).

4. Inspect summary(res) to confirm the anchored run remains close to the baseline scale.

5. For multi-wave drift monitoring, fit waves separately and pass the fits to detect_anchor_drift() or build_equating_chain().

See Also

fit_mfrm(), make_anchor_table(), detect_anchor_drift(), diagnose_mfrm(), build_equating_chain(), mfrmr_linking_and_dff

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)
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")
summary(res)
head(res$drift[, c("Facet", "Level", "Drift", "Flag")])
res$baseline_anchors[1:3, ]

mfrmr documentation built on March 31, 2026, 1:06 a.m.