Strategy for impute_missing_scores() that fills each
missing metric with the worst (maximum) observed value for
that metric within the same target combination across all
values of the compare column. Target combinations with no
non-NA observations are filled with NA_real_.
Value
A strategy function for 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_worst_score())
#> 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 0.000000e+00
#> 2: EuroCOVIDhub-baseline 1 16925.04696 15275.826087 0.000000e+00
#> 3: epiforecasts-EpiNow2 1 25395.96087 17222.260870 0.000000e+00
#> 4: EuroCOVIDhub-ensemble 1 53.88000 0.000000 6.086957e-01
#> 5: EuroCOVIDhub-baseline 1 46.79304 2.130435 0.000000e+00
#> ---
#> 1020: UMass-MechBayes 3 12956.58826 0.000000 1.287265e+04
#> 1021: UMass-MechBayes 2 7242.90913 0.000000 6.685870e+03
#> 1022: UMass-MechBayes 1 4190.08739 8.478261 2.884739e+03
#> 1023: UMass-MechBayes 3 17401.27217 0.000000 1.647296e+04
#> 1024: UMass-MechBayes 2 11480.34913 341.000000 8.971000e+03
#> dispersion bias interval_coverage_50 interval_coverage_90 ae_median
#> <num> <num> <int> <int> <num>
#> 1: 5440.98522 0.50 1 1 12271
#> 2: 1649.22087 0.95 0 0 25620
#> 3: 8173.70000 0.90 0 1 44192
#> 4: 53.27130 -0.10 1 1 14
#> 5: 44.66261 0.30 1 1 15
#> ---
#> 1020: 3275.16522 -0.40 1 1 13259
#> 1021: 2406.71000 -0.50 1 1 10401
#> 1022: 1305.34826 0.10 1 1 7073
#> 1023: 3248.63174 -0.70 0 1 24208
#> 1024: 3331.34043 0.30 1 1 20967
#> .imputed
#> <lgcl>
#> 1: FALSE
#> 2: FALSE
#> 3: FALSE
#> 4: FALSE
#> 5: FALSE
#> ---
#> 1020: TRUE
#> 1021: TRUE
#> 1022: TRUE
#> 1023: TRUE
#> 1024: TRUE