Strategy for impute_missing_scores() that fills each
missing metric with the mean 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_mean_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 9429.09942 0.000000 8.184551e+03
#> 1021: UMass-MechBayes 2 5293.94435 0.000000 3.998116e+03
#> 1022: UMass-MechBayes 1 2527.43304 2.826087 1.608058e+03
#> 1023: UMass-MechBayes 3 12959.05783 0.000000 1.088126e+04
#> 1024: UMass-MechBayes 2 7690.73493 113.666667 5.053913e+03
#> dispersion bias interval_coverage_50 interval_coverage_90 ae_median
#> <num> <num> <num> <num> <num>
#> 1: 5440.98522 0.5000000 1.0000000 1.0000000 12271.000
#> 2: 1649.22087 0.9500000 0.0000000 0.0000000 25620.000
#> 3: 8173.70000 0.9000000 0.0000000 1.0000000 44192.000
#> 4: 53.27130 -0.1000000 1.0000000 1.0000000 14.000
#> 5: 44.66261 0.3000000 1.0000000 1.0000000 15.000
#> ---
#> 1020: 1244.54870 -0.8000000 0.3333333 0.3333333 11685.333
#> 1021: 1295.82841 -0.7600000 0.3333333 0.6666667 8902.667
#> 1022: 916.54899 -0.4666667 0.3333333 1.0000000 4051.667
#> 1023: 2077.79696 -0.8266667 0.0000000 0.6666667 21995.000
#> 1024: 2523.15522 -0.4666667 0.3333333 1.0000000 12982.333
#> .imputed
#> <lgcl>
#> 1: FALSE
#> 2: FALSE
#> 3: FALSE
#> 4: FALSE
#> 5: FALSE
#> ---
#> 1020: TRUE
#> 1021: TRUE
#> 1022: TRUE
#> 1023: TRUE
#> 1024: TRUE