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. 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.

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

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
out <- estimation_iteration_report(fit, max_iter = 5)
summary(out)
#> mfrmr Iteration Report Summary 
#>   Class: mfrm_iteration_report
#>   Components (3): table, summary, settings
#> 
#> Iteration overview
#>  FinalConverged FinalIterations ReplayRows ConnectedSubset
#>            TRUE              71          6            TRUE
#> 
#> Iteration rows: table
#>  Method Iteration MaxScoreResidualElements MaxScoreResidualPercent
#>    PROX         1                   44.000                1466.667
#>    JMLE         2                   32.372                1079.076
#>    JMLE         3                   17.345                 578.164
#>    JMLE         4                  -19.395                -646.496
#>    JMLE         5                   14.979                 499.290
#>    JMLE         6                   13.019                 433.962
#>  MaxScoreResidualCategories MaxLogitChangeElements MaxLogitChangeSteps
#>                     -39.726                     NA                  NA
#>                     -33.843                  0.226               0.155
#>                      38.014                  0.244               0.443
#>                     -40.558                  0.145               0.582
#>                     -20.843                  0.087               0.138
#>                      27.980                  0.120               0.364
#>  Objective
#>         NA
#>  -1021.913
#>   -982.631
#>   -959.201
#>   -948.673
#>   -930.036
#> 
#> Settings
#>        Setting Value
#>       max_iter     5
#>         reltol 1e-06
#>   include_prox  TRUE
#>    quad_points    31
#>  include_fixed FALSE
#> 
#> Notes
#>  - Legacy-compatible Table 3 replay of estimation iterations.
p_iter <- plot(out, draw = FALSE)
p_iter$data$plot
#> [1] "residual"