audit_mfrm_anchors: Audit and normalize anchor/group-anchor tables

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

Audit and normalize anchor/group-anchor tables

Description

Audit and normalize anchor/group-anchor tables

Usage

audit_mfrm_anchors(
  data,
  person,
  facets,
  score,
  anchors = NULL,
  group_anchors = NULL,
  weight = NULL,
  rating_min = NULL,
  rating_max = NULL,
  keep_original = FALSE,
  min_common_anchors = 5L,
  min_obs_per_element = 30,
  min_obs_per_category = 10,
  noncenter_facet = "Person",
  dummy_facets = NULL
)

Arguments

data	A data.frame in long format (one row per rating event).
person	Column name for person IDs.
facets	Character vector of facet column names.
score	Column name for observed score.
anchors	Optional anchor table (Facet, Level, Anchor).
group_anchors	Optional group-anchor table (Facet, Level, Group, GroupValue).
weight	Optional weight/frequency column name.
rating_min	Optional minimum category value.
rating_max	Optional maximum category value.
keep_original	Keep original category values.
min_common_anchors	Minimum anchored levels per linking facet used in recommendations (default 5).
min_obs_per_element	Minimum weighted observations per facet level used in recommendations (default 30).
min_obs_per_category	Minimum weighted observations per score category used in recommendations (default 10).
noncenter_facet	One facet to leave non-centered.
dummy_facets	Facets to fix at zero.

Details

Anchoring (also called "fixing" or scale linking) constrains selected parameter estimates to pre-specified values, placing the current analysis on a previously established scale. This is essential when comparing results across administrations, linking test forms, or monitoring rater drift over time.

This function applies the same preprocessing and key-resolution rules as fit_mfrm(), but returns an audit object so constraints can be checked before estimation. Running the audit first helps avoid estimation failures caused by misspecified or data-incompatible anchors.

Anchor types:

• Direct anchors fix individual element measures to specific logit values (e.g., Rater R1 anchored at 0.35 logits).

• Group anchors constrain the mean of a set of elements to a target value, allowing individual elements to vary freely around that mean.

• When both types overlap for the same element, the direct anchor takes precedence.

Design checks verify that each anchored element has at least min_obs_per_element weighted observations (default 30) and each score category has at least min_obs_per_category (default 10). These thresholds follow standard Rasch sample-size recommendations (Linacre, 1994).

Value

A list of class mfrm_anchor_audit with:

• anchors: cleaned anchor table used by estimation

• group_anchors: cleaned group-anchor table used by estimation

• facet_summary: counts of levels, constrained levels, and free levels

• design_checks: observation-count checks by level/category

• thresholds: active threshold settings used for recommendations

• issue_counts: issue-type counts

• issues: list of issue tables

• recommendations: package-native anchor guidance strings

Interpreting output

• issue_counts/issues: concrete data or specification problems.

• facet_summary: constraint coverage by facet.

• design_checks: whether anchor targets have enough observations.

• recommendations: action items before estimation.

Typical workflow

1. Build candidate anchors (e.g., with make_anchor_table()).

2. Run audit_mfrm_anchors(...).

3. Resolve issues, then fit with fit_mfrm().

See Also

fit_mfrm(), describe_mfrm_data(), make_anchor_table()

Examples

toy <- load_mfrmr_data("example_core")

anchors <- data.frame(
  Facet = c("Rater", "Rater"),
  Level = c("R1", "R1"),
  Anchor = c(0, 0.1),
  stringsAsFactors = FALSE
)
aud <- audit_mfrm_anchors(
  data = toy,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  anchors = anchors
)
aud$issue_counts
summary(aud)
p_aud <- plot(aud, draw = FALSE)
class(p_aud)

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