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Summarize posterior unit scoring output

Source: R/api-prediction.R
summary.mfrm_unit_prediction.Rd

Summarize posterior unit scoring output

Usage

# S3 method for class 'mfrm_unit_prediction'
summary(object, digits = 3, ...)

Arguments

object

Output from predict_mfrm_units().

digits

Number of digits used in numeric summaries.

...

Reserved for generic compatibility.

Value

An object of class summary.mfrm_unit_prediction with:

  • estimates: posterior summaries by person

  • audit: row-preparation audit

  • settings: scoring settings

  • notes: interpretation notes

See also

predict_mfrm_units()

Examples

toy <- load_mfrmr_data("example_core")
keep_people <- unique(toy$Person)[1:18]
toy_fit <- suppressWarnings(
  fit_mfrm(
    toy[toy$Person %in% keep_people, , drop = FALSE],
    "Person", c("Rater", "Criterion"), "Score",
    method = "MML",
    quad_points = 5,
    maxit = 15
  )
)
new_units <- data.frame(
  Person = c("NEW01", "NEW01"),
  Rater = unique(toy$Rater)[1],
  Criterion = unique(toy$Criterion)[1:2],
  Score = c(2, 3)
)
pred_units <- predict_mfrm_units(toy_fit, new_units)
summary(pred_units)
#> $estimates
#> # A tibble: 1 × 7
#>   Person Estimate    SD Lower Upper Observations WeightedN
#>   <chr>     <dbl> <dbl> <dbl> <dbl>        <dbl>     <dbl>
#> 1 NEW01    -0.097 0.648 -1.36  1.36            2         2
#> 
#> $audit
#> # A tibble: 1 × 7
#>   InputRows KeptRows DroppedRows DroppedMissing DroppedBadScore DroppedBadWeight
#>       <dbl>    <dbl>       <dbl>          <dbl>           <dbl>            <dbl>
#> 1         2        2           0              0               0                0
#> # ℹ 1 more variable: DroppedNonpositiveWeight <dbl>
#> 
#> $settings
#> $settings$interval_level
#> [1] 0.95
#> 
#> $settings$n_draws
#> [1] 0
#> 
#> $settings$quad_points
#> [1] 5
#> 
#> $settings$seed
#> NULL
#> 
#> $settings$method
#> [1] "MML"
#> 
#> $settings$source_columns
#> $settings$source_columns$person
#> [1] "Person"
#> 
#> $settings$source_columns$facets
#> [1] "Rater"     "Criterion"
#> 
#> $settings$source_columns$score
#> [1] "Score"
#> 
#> $settings$source_columns$weight
#> NULL
#> 
#> 
#> 
#> $notes
#> [1] "Posterior summaries are computed under the fixed fitted MML calibration."                                         
#> [2] "Non-person facets in `new_data` must already exist in the fitted calibration."                                    
#> [3] "Overlapping person IDs are treated as labels in `new_data`; the original fitted person estimates are not updated."
#> 
#> attr(,"class")
#> [1] "summary.mfrm_unit_prediction"