Build an estimation-iteration report (preferred alias)

Build an estimation-iteration report (preferred alias)

Usage

estimation_iteration_report(
  fit,
  max_iter = 20,
  reltol = NULL,
  include_prox = TRUE,
  include_fixed = FALSE
)

Arguments

fit

Output from fit_mfrm().

max_iter

Maximum replay iterations (excluding optional initial row).

reltol

Stopping tolerance for replayed max-logit change.

include_prox

If TRUE, include an initial pseudo-row labeled PROX.

include_fixed

If TRUE, include a legacy-compatible fixed-width text block.

Value

A named list with iteration-report components and, for GPCM, a gpcm_boundary table. Class: mfrm_iteration_report.

Details

summary(out) is supported through summary(). plot(out) is dispatched through plot() for class mfrm_iteration_report (type = "residual", "logit_change", "objective").

Interpreting output

• iterations: trajectory of convergence indicators by iteration.

• summary: final status and stopping diagnostics.

• optional PROX row: pseudo-initial reference point when enabled.

For GPCM, this helper replays slope-aware optimization steps from a reconstructed starting state. It is not the exact optimizer history from the fitted object and is not an additional convergence test. Use summary(fit, profile = "fit", detail = "brief") for the recorded convergence result, and read the returned gpcm_boundary before reporting the replay.

Typical workflow

1. Run estimation_iteration_report(fit).

2. Inspect plateau/stability patterns in summary/plot.

3. Adjust optimization settings if convergence looks weak.

See also

fit_mfrm(), specifications_report(), data_quality_report(), mfrmr_reports_and_tables, mfrmr_compatibility_layer

Examples

# Supplementary workflow: run explicitly to keep routine checks brief.
# example("estimation_iteration_report", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
toy <- load_mfrmr_data("example_operational")
fit <- fit_mfrm(
  toy, "Person", c("Rater", "Criterion"), "Score",
  method = "MML", quad_points = 7, maxit = 30
)
out <- estimation_iteration_report(fit, max_iter = 5)
summary(out)
p_iter <- plot(out, draw = FALSE)
p_iter$data$plot
} # }