Skip to contents

Impute with a reference model's scores

Source: R/impute-missing-scores.R
impute_model_score.Rd

Strategy for impute_missing_scores() that fills missing scores with the scores of a specified reference model for the same target combination.

Usage

impute_model_score(model)

Arguments

model

Character string naming the reference model whose scores should be used for imputation. The reference model must have scores for all target combinations that need imputing; otherwise an error is raised.

Value

A strategy function for impute_missing_scores().

See also

impute_missing_scores()

Examples

scores <- example_quantile |>
  as_forecast_quantile() |>
  score()
#> ℹ Some rows containing NA values may be removed. This is fine if not
#>   unexpected.

impute_missing_scores(
  scores,
  strategy = impute_model_score("EuroCOVIDhub-baseline")
)
#> ℹ Imputing 137 missing score rows.
#> ℹ 2 model values affected.
#>       location target_end_date target_type location_name forecast_date
#>         <char>          <Date>      <char>        <char>        <Date>
#>    1:       DE      2021-05-08       Cases       Germany    2021-05-03
#>    2:       DE      2021-05-08       Cases       Germany    2021-05-03
#>    3:       DE      2021-05-08       Cases       Germany    2021-05-03
#>    4:       DE      2021-05-08      Deaths       Germany    2021-05-03
#>    5:       DE      2021-05-08      Deaths       Germany    2021-05-03
#>   ---                                                                 
#> 1020:       IT      2021-07-17       Cases         Italy    2021-06-28
#> 1021:       IT      2021-07-17       Cases         Italy    2021-07-05
#> 1022:       IT      2021-07-17       Cases         Italy    2021-07-12
#> 1023:       IT      2021-07-24       Cases         Italy    2021-07-05
#> 1024:       IT      2021-07-24       Cases         Italy    2021-07-12
#>                       model horizon         wis overprediction underprediction
#>                      <char>   <num>       <num>          <num>           <num>
#>    1: EuroCOVIDhub-ensemble       1  7990.85478    2549.869565       0.0000000
#>    2: EuroCOVIDhub-baseline       1 16925.04696   15275.826087       0.0000000
#>    3:  epiforecasts-EpiNow2       1 25395.96087   17222.260870       0.0000000
#>    4: EuroCOVIDhub-ensemble       1    53.88000       0.000000       0.6086957
#>    5: EuroCOVIDhub-baseline       1    46.79304       2.130435       0.0000000
#>   ---                                                                         
#> 1020:       UMass-MechBayes       3  4983.73043       0.000000    1708.5652174
#> 1021:       UMass-MechBayes       2  4871.79696       0.000000    2465.0869565
#> 1022:       UMass-MechBayes       1  4190.08739       0.000000    2884.7391304
#> 1023:       UMass-MechBayes       3 11374.06652       0.000000    8125.4347826
#> 1024:       UMass-MechBayes       2 11480.34913       0.000000    8971.0000000
#>       dispersion  bias interval_coverage_50 interval_coverage_90 ae_median
#>            <num> <num>               <lgcl>               <lgcl>     <num>
#>    1: 5440.98522  0.50                 TRUE                 TRUE     12271
#>    2: 1649.22087  0.95                FALSE                FALSE     25620
#>    3: 8173.70000  0.90                FALSE                 TRUE     44192
#>    4:   53.27130 -0.10                 TRUE                 TRUE        14
#>    5:   44.66261  0.30                 TRUE                 TRUE        15
#>   ---                                                                     
#> 1020: 3275.16522 -0.40                 TRUE                 TRUE      9253
#> 1021: 2406.71000 -0.50                 TRUE                 TRUE      9225
#> 1022: 1305.34826 -0.70                FALSE                 TRUE      7073
#> 1023: 3248.63174 -0.70                FALSE                 TRUE     23119
#> 1024: 2509.34913 -0.80                FALSE                 TRUE     20967
#>       .imputed
#>         <lgcl>
#>    1:    FALSE
#>    2:    FALSE
#>    3:    FALSE
#>    4:    FALSE
#>    5:    FALSE
#>   ---         
#> 1020:     TRUE
#> 1021:     TRUE
#> 1022:     TRUE
#> 1023:     TRUE
#> 1024:     TRUE