Strategy for filter_scores() that restricts the kept
target combinations to those covered by every value listed
in include. With a single value this keeps only that
value's targets; with several values, the intersection of
their target sets is kept.
To use a proportion-based threshold over all compare values
instead, use filter_to_intersection().
Value
A strategy function for filter_scores(). Intended
to be passed to filter_scores() rather than called
directly — filter_scores() is where the scores class
and metrics attribute are preserved.
Examples
scores <- example_quantile |>
as_forecast_quantile() |>
score()
#> ℹ Some rows containing NA values may be removed. This is fine if not
#> unexpected.
filter_scores(
scores,
strategy = filter_to_include("EuroCOVIDhub-baseline")
)
#> ℹ No rows filtered. Returning scores unchanged.
#> 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
#> ---
#> 883: IT 2021-07-24 Deaths Italy 2021-07-12
#> 884: IT 2021-07-24 Deaths Italy 2021-07-05
#> 885: IT 2021-07-24 Deaths Italy 2021-07-12
#> 886: IT 2021-07-24 Deaths Italy 2021-07-05
#> 887: IT 2021-07-24 Deaths Italy 2021-07-12
#> model horizon wis overprediction underprediction
#> <char> <num> <num> <num> <num>
#> 1: EuroCOVIDhub-ensemble 1 7990.854783 2.549870e+03 0.0000000
#> 2: EuroCOVIDhub-baseline 1 16925.046957 1.527583e+04 0.0000000
#> 3: epiforecasts-EpiNow2 1 25395.960870 1.722226e+04 0.0000000
#> 4: EuroCOVIDhub-ensemble 1 53.880000 0.000000e+00 0.6086957
#> 5: EuroCOVIDhub-baseline 1 46.793043 2.130435e+00 0.0000000
#> ---
#> 883: EuroCOVIDhub-baseline 2 80.336957 3.608696e+00 0.0000000
#> 884: UMass-MechBayes 3 4.881739 4.347826e-02 0.0000000
#> 885: UMass-MechBayes 2 25.581739 1.782609e+01 0.0000000
#> 886: epiforecasts-EpiNow2 3 19.762609 5.478261e+00 0.0000000
#> 887: epiforecasts-EpiNow2 2 66.161739 4.060870e+01 0.0000000
#> dispersion bias interval_coverage_50 interval_coverage_90 ae_median
#> <num> <num> <lgcl> <lgcl> <num>
#> 1: 5440.985217 0.50 TRUE TRUE 12271
#> 2: 1649.220870 0.95 FALSE FALSE 25620
#> 3: 8173.700000 0.90 FALSE TRUE 44192
#> 4: 53.271304 -0.10 TRUE TRUE 14
#> 5: 44.662609 0.30 TRUE TRUE 15
#> ---
#> 883: 76.728261 0.20 TRUE TRUE 53
#> 884: 4.838261 0.10 TRUE TRUE 1
#> 885: 7.755652 0.80 FALSE TRUE 46
#> 886: 14.284348 0.50 TRUE TRUE 26
#> 887: 25.553043 0.90 FALSE TRUE 108