Build an adjusted-score reference table bundle
Arguments
- fit
Output from
fit_mfrm().- diagnostics
Optional output from
diagnose_mfrm().- facets
Optional subset of facets.
- totalscore
Include all observations for score totals (
TRUE) or apply legacy extreme-row exclusion (FALSE).- umean
Additive score-to-report origin shift.
- uscale
Multiplicative score-to-report scale.
- udecimals
Rounding digits used in formatted output.
- reference
Which adjusted-score reference to keep in formatted outputs:
"both"(default),"mean", or"zero".- label_style
Column-label style for formatted outputs:
"both"(default),"native", or"legacy".- omit_unobserved
If
TRUE, remove unobserved levels.- xtreme
Extreme-score adjustment amount.
Value
A named list with:
by_facet: named list of formatted data.framesstacked: one stacked data.frame across facetsraw_by_facet: unformatted internal tablessettings: resolved options
Details
This function wraps the package's adjusted-score calculations and returns
both facet-wise and stacked tables. Historical display columns such as
Fair(M) Average and Fair(Z) Average are retained for compatibility, and
package-native aliases such as AdjustedAverage,
StandardizedAdjustedAverage, ModelBasedSE, and FitAdjustedSE are
appended to the formatted outputs.
Interpreting output
stacked: cross-facet table for global comparison.by_facet: per-facet formatted tables for reporting.raw_by_facet: unformatted values for custom analyses/plots.settings: scoring-transformation and filtering options used.
Larger observed-vs-fair gaps can indicate systematic scoring tendencies by specific facet levels.
Typical workflow
Run
fair_average_table(fit, ...).Inspect
summary(t12)andt12$stacked.Visualize with
plot_fair_average().
Output columns
The stacked data.frame contains:
- Facet
Facet name for this row.
- Level
Element label within the facet.
- Obsvd Average
Observed raw-score average.
- Fair(M) Average
Model-adjusted reference average on the reported score scale.
- Fair(Z) Average
Standardized adjusted reference average.
- ObservedAverage, AdjustedAverage, StandardizedAdjustedAverage
Package-native aliases for the three average columns above.
- Measure
Estimated logit measure for this level.
- SE
Compatibility alias for the model-based standard error.
- ModelBasedSE, FitAdjustedSE
Package-native aliases for
Model S.E.andReal S.E..- Infit MnSq, Outfit MnSq
Fit statistics for this level.
Examples
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
t12 <- fair_average_table(fit, udecimals = 2)
t12_native <- fair_average_table(fit, reference = "mean", label_style = "native")
summary(t12)
#> mfrmr Adjusted Score Summary
#> Class: mfrm_fair_average
#> Components (4): raw_by_facet, by_facet, stacked, settings
#>
#> Overview
#> Facets Levels MeanAbsObservedFairM
#> 3 56 0.012
#>
#> Facet-level adjusted-score rows: stacked
#> Facet Total Score Total Count Weightd Score Weightd Count Obsvd Average
#> Person 60 16 60 16 3.75
#> Person 58 16 58 16 3.62
#> Person 56 16 56 16 3.50
#> Person 55 16 55 16 3.44
#> Person 52 16 52 16 3.25
#> Person 52 16 52 16 3.25
#> Person 50 16 50 16 3.12
#> Person 49 16 49 16 3.06
#> Person 49 16 49 16 3.06
#> Person 49 16 49 16 3.06
#> Fair(M) Average Fair(Z) Average Measure Model S.E. Real S.E. Infit MnSq
#> 3.76 3.76 2.69 0.54 0.67 1.56
#> 3.64 3.64 2.20 0.45 0.45 0.75
#> 3.52 3.52 1.83 0.41 0.41 0.95
#> 3.45 3.45 1.68 0.39 0.39 1.00
#> 3.26 3.26 1.26 0.36 0.36 0.99
#> 3.26 3.26 1.26 0.36 0.36 0.80
#> 3.14 3.14 1.02 0.34 0.39 1.29
#> 3.07 3.07 0.90 0.33 0.39 1.33
#> 3.07 3.07 0.90 0.33 0.34 1.04
#> 3.07 3.07 0.90 0.33 0.37 1.20
#> Infit ZStd Outfit MnSq Outfit ZStd PtMea Corr Anch Status Element
#> 0.88 2.44 3.06 NA P023
#> -0.21 0.75 -0.65 NA P024
#> 0.10 1.00 0.12 NA P036
#> 0.18 1.03 0.21 NA P002
#> 0.14 0.99 0.08 NA P014
#> -0.27 0.75 -0.66 NA P003
#> 0.72 1.24 0.75 NA P019
#> 0.79 1.33 0.97 NA P004
#> 0.25 1.00 0.13 NA P025
#> 0.56 1.27 0.82 NA P005
#> ObservedAverage AdjustedAverage StandardizedAdjustedAverage ModelBasedSE
#> 3.75 3.76 3.76 0.54
#> 3.62 3.64 3.64 0.45
#> 3.50 3.52 3.52 0.41
#> 3.44 3.45 3.45 0.39
#> 3.25 3.26 3.26 0.36
#> 3.25 3.26 3.26 0.36
#> 3.12 3.14 3.14 0.34
#> 3.06 3.07 3.07 0.33
#> 3.06 3.07 3.07 0.33
#> 3.06 3.07 3.07 0.33
#> FitAdjustedSE
#> 0.67
#> 0.45
#> 0.41
#> 0.39
#> 0.36
#> 0.36
#> 0.39
#> 0.39
#> 0.34
#> 0.37
#>
#> Settings
#> Setting Value
#> facets NULL
#> totalscore TRUE
#> umean 0
#> uscale 1
#> udecimals 2
#> reference both
#> label_style both
#> omit_unobserved FALSE
#> xtreme 0
#>
#> Notes
#> - Adjusted-score reference summary by facet level.
p_t12 <- plot(t12, draw = FALSE)
class(p_t12)
#> [1] "mfrm_plot_data" "list"