Compute displacement diagnostics for facet levels

Usage

displacement_table(
  fit,
  diagnostics = NULL,
  facets = NULL,
  anchored_only = FALSE,
  abs_displacement_warn = 0.5,
  abs_t_warn = 2,
  top_n = NULL
)

Arguments

fit: Output from fit_mfrm().
diagnostics: Optional output from diagnose_mfrm(). Must match this fitted analysis; see mfrmr_compatibility_layer for migration of saved diagnostics.
facets: Optional subset of facets.
anchored_only: If TRUE, keep only directly/group anchored levels.
abs_displacement_warn: Absolute displacement warning threshold.
abs_t_warn: Absolute displacement t-value warning threshold.
top_n: Optional maximum number of rows to keep after sorting.

Value

A named list with:

table: displacement diagnostics by level
summary: one-row summary
thresholds: applied thresholds

Details

Displacement is computed as a one-step Newton update: sum(residual) / sum(information) for each facet level. This approximates how much a level would move if constraints were relaxed.

Interpreting output

table: level-wise displacement and flag indicators.
summary: count/share of flagged levels.
thresholds: displacement and t-value cutoffs.

Large absolute displacement in anchored levels suggests potential instability in anchor assumptions.

Typical workflow

Run displacement_table(fit, anchored_only = TRUE) for anchor checks.
Inspect summary(disp) then detailed rows.
Visualize with plot(x, type = "displacement").

Output columns

The table data.frame contains:

Facet, Level: Facet name and element label.
Displacement: One-step Newton displacement estimate (logits).
DisplacementSE: Standard error of the displacement.
DisplacementT: Displacement / SE ratio.
Estimate, SE: Current measure estimate and its standard error.
N: Number of observations involving this level.
AnchorValue, AnchorStatus, AnchorType: Anchor metadata.
Flag: Logical; TRUE when displacement exceeds thresholds.

Saved-result reuse

The returned fit_identity records the fitted analysis for reuse with reports and exports. Recreate older results from the saved fit and matching diagnostics using the original screening options; no model refit is needed. See mfrmr_compatibility_layer for migration instructions.

Examples

# Supplementary workflow: run explicitly to keep routine checks brief.
# example("displacement_table", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
# Saved synthetic JML/RSM analysis of example_core.
# For your own data, fit a model first; see ?fit_mfrm.
example_fit <- readRDS(system.file("examples", "core-jml.rds", package = "mfrmr"))
fit <- example_fit$fit
disp <- displacement_table(fit, anchored_only = FALSE)
summary(disp)
p_disp <- plot(disp, draw = FALSE)
p_disp$data$plot
} # }