Build a facet statistics report (preferred alias)

Usage

facet_statistics_report(
  fit,
  diagnostics = NULL,
  metrics = c("Estimate", "Infit", "Outfit", "SE"),
  ruler_width = 41,
  distribution_basis = c("both", "sample", "population"),
  se_mode = c("both", "model", "fit_adjusted")
)

Arguments

fit: Output from fit_mfrm().
diagnostics: Optional output from diagnose_mfrm().
metrics: Numeric columns in diagnostics$measures to summarize.
ruler_width: Width of the fixed-width ruler used for M/S/Q/X marks.
distribution_basis: Which distribution basis to keep in the appended precision summary: "both" (default), "sample", or "population".
se_mode: Which standard-error mode to keep in the appended precision summary: "both" (default), "model", or "fit_adjusted".

Value

A named list with facet-statistics components. Class: mfrm_facet_statistics.

Details

summary(out) is supported through summary(). plot(out) is dispatched through plot() for class mfrm_facet_statistics (type = "means", "sds", "ranges").

Interpreting output

facet-level means/SD/ranges of selected metrics (Estimate, fit indices, SE).
fixed-width ruler rows (M/S/Q/X) for compact profile scanning.

Typical workflow

Run facet_statistics_report(fit).
Inspect summary/ranges for anomalous facets.
Cross-check flagged facets with fit and chi-square diagnostics. The returned bundle now includes:

precision_summary: facet precision/separation indices by DistributionBasis and SEMode
variability_tests: fixed/random variability tests by facet
se_modes: compact list of available SE modes by facet

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
out <- facet_statistics_report(fit)
summary(out)
#> mfrmr Facet Profile Summary 
#>   Class: mfrm_facet_statistics
#>   Components (6): table, ranges, settings, precision_summary, variability_tests, se_modes
#> 
#> Facet-profile overview
#>  Facets Rows Metrics PrecisionRows VariabilityRows
#>       3   12       4            12               3
#> 
#> Facet-profile rows: precision_summary
#>      Facet Levels DistributionBasis       SEMode SEColumn ObservedMean
#>  Criterion      4        population fit_adjusted   RealSE        0.000
#>  Criterion      4        population        model  ModelSE        0.000
#>  Criterion      4            sample fit_adjusted   RealSE        0.000
#>  Criterion      4            sample        model  ModelSE        0.000
#>     Person     48        population fit_adjusted   RealSE        0.001
#>     Person     48        population        model  ModelSE        0.001
#>     Person     48            sample fit_adjusted   RealSE        0.001
#>     Person     48            sample        model  ModelSE        0.001
#>      Rater      4        population fit_adjusted   RealSE        0.000
#>      Rater      4        population        model  ModelSE        0.000
#>  ObservedSD  RMSE TrueSD ObservedVariance ErrorVariance TrueVariance Separation
#>       0.249 0.099  0.228            0.062         0.010        0.052      2.310
#>       0.249 0.097  0.229            0.062         0.010        0.052      2.347
#>       0.287 0.099  0.270            0.082         0.010        0.073      2.729
#>       0.287 0.097  0.270            0.082         0.010        0.073      2.771
#>       1.089 0.365  1.026            1.186         0.133        1.053      2.811
#>       1.089 0.347  1.033            1.186         0.120        1.066      2.977
#>       1.101 0.365  1.038            1.212         0.133        1.078      2.845
#>       1.101 0.347  1.045            1.212         0.120        1.091      3.012
#>       0.271 0.099  0.253            0.074         0.010        0.064      2.563
#>       0.271 0.097  0.253            0.074         0.010        0.064      2.595
#>  Strata Reliability SEAvailable MeanSE MedianSE MeanInfit MeanOutfit FixedChiSq
#>   3.413       0.842           4  0.099    0.098     0.994      1.019     25.804
#>   3.462       0.846           4  0.097    0.097     0.994      1.019     25.804
#>   3.972       0.882           4  0.099    0.098     0.994      1.019     25.804
#>   4.028       0.885           4  0.097    0.097     0.994      1.019     25.804
#>   4.082       0.888          48  0.360    0.350     1.000      1.019    384.563
#>   4.303       0.899          48  0.344    0.330     1.000      1.019    384.563
#>   4.126       0.890          48  0.360    0.350     1.000      1.019    384.563
#>   4.350       0.901          48  0.344    0.330     1.000      1.019    384.563
#>   3.751       0.868           4  0.099    0.099     0.994      1.019     30.881
#>   3.794       0.871           4  0.097    0.097     0.994      1.019     30.881
#>  FixedDF FixedProb RandomVar RandomChiSq RandomDF RandomProb
#>        3         0     0.073       2.997        2      0.223
#>        3         0     0.073       2.997        2      0.223
#>        3         0     0.073       2.997        2      0.223
#>        3         0     0.073       2.997        2      0.223
#>       47         0     1.091      45.462       46      0.495
#>       47         0     1.091      45.462       46      0.495
#>       47         0     1.091      45.462       46      0.495
#>       47         0     1.091      45.462       46      0.495
#>        3         0     0.089       2.999        2      0.223
#>        3         0     0.089       2.999        2      0.223
#> 
#> Settings
#>             Setting                           Value
#>             metrics     Estimate, Infit, Outfit, SE
#>         ruler_width                              41
#>       marker_legend mean, +/-1 SD, +/-2 SD, +/-3 SD
#>  distribution_basis                            both
#>             se_mode                            both
#> 
#> Notes
#>  - Facet profile summary including distribution basis, SE mode, and variability tests.
p_fs <- plot(out, draw = FALSE)
class(p_fs)
#> [1] "mfrm_plot_data" "list"