Estimate abilities for people already included in a fitted RSM

Use the existing ratings and fitted model to estimate each person's ability. This is the common scoring entry for supported ordinary, shared-rater and testlet rating-scale models (RSMs). It reuses the fitted calibration, such as rater severity and category thresholds; it does not fit the model again.

Usage

score_mfrm_persons(fit, persons = NULL, level = 0.95, quad_points = NULL)

# S3 method for class 'mfrm_person_scores'
summary(object, ...)

# S3 method for class 'mfrm_person_scores'
print(x, ...)

# S3 method for class 'mfrm_person_scores'
plot(x, ...)

Arguments

fit: A numerically ready ordinary RSM MML, testlet or shared-rater fit.
persons: Distinct source Person IDs; NULL returns all source Persons, including Persons whose assigned scores are all missing. For a shared-rater fit, start with a few actual IDs: scoring everyone can be slow. Selecting IDs changes output rows, not the data used to account for shared raters.
level: Conditional equal-tail interval probability, between zero and one.
quad_points: Extension quadrature order; NULL uses its fit's order. Ordinary ability integration is continuous and does not use quadrature; leave this argument NULL for ordinary fits.
object, x: An ordinary mfrm_person_scores result.
...: Display controls passed to plot.mfrm_testlet_scores(); unused by print and summary.

Value

Ordinary fits return mfrm_person_scores with table, scoring_data, data usage, settings and source metadata. Extensions retain their existing scoring classes. Supply saved scores to compare_mfrm() as person_scores; extension scores also support model-aware maps through mfrm_results(). Saved plots/reports do not recompute scores.

Details

The estimate is the mean of the conditional ability distribution (EAP). Posterior SDs and continuous equal-tail intervals describe its uncertainty with calibration held fixed. Selecting persons changes the returned rows, not the ratings used to condition shared effects.

Ordinary fits use the bounded model/population specification of mfrm_response_diagnostics(): additive unanchored severity facets and known N(0,1) or an estimated intercept-only normal population. Both the fitted population mean and variance are retained. EAP, posterior SD and interval endpoints integrate the continuous conditional density; endpoints are not quadrature-grid quantiles. Ordinary plug-in scoring is unchanged.

For extensions this calls predict.mfrm_testlet() or score_mfrm_random_rater() on the complete source roster. Their numerical checks, approximation limits and uncertainty definitions apply unchanged. Use those functions directly for a different complete scoring roster.

Intervals condition on fitted calibration and the assumed normal population. They exclude calibration-estimation uncertainty, do not test Person differences and do not guarantee frequentist coverage for each fixed ability. Missing-only Persons remain prior_only; zero ability variance and failed integration return unavailable, not zero-width intervals. No missing scores are imputed.

Examples

# \donttest{
ratings <- load_mfrmr_data("example_core")
fit <- fit_mfrm(ratings, "Person", c("Rater", "Criterion"), "Score")
scores <- score_mfrm_persons(fit, persons = unique(ratings$Person)[1:4])
scores$table
#>   Person Observed  Estimate ConditionalSD      Lower    Upper
#> 1   P001       16 0.6011637     0.3029716 0.01607092 1.204810
#> 2   P002       16 1.4278211     0.3473444 0.77099827 2.133671
#> 3   P003       16 1.0902409     0.3249421 0.47028319 1.745150
#> 4   P004       16 0.7888096     0.3099718 0.19292475 1.409104
#>                  Status Reason
#> 1 available_conditional       
#> 2 available_conditional       
#> 3 available_conditional       
#> 4 available_conditional       
plot(scores)

# }