diff --git a/README.Rmd b/README.Rmd index 9221faa49..ff6da0e01 100644 --- a/README.Rmd +++ b/README.Rmd @@ -132,6 +132,7 @@ example_quantile %>% transform_forecasts(append = TRUE, fun = log_shift, offset = 1) %>% score %>% summarise_scores(by = c("model", "target_type", "scale")) %>% + summarise_scores(by = c("model", "target_type", "scale"), fun = signif, digits = 3) %>% head() ``` diff --git a/README.md b/README.md index 4b1e5afe9..e95d5ac61 100644 --- a/README.md +++ b/README.md @@ -171,33 +171,34 @@ example_quantile %>% transform_forecasts(append = TRUE, fun = log_shift, offset = 1) %>% score %>% summarise_scores(by = c("model", "target_type", "scale")) %>% + summarise_scores(by = c("model", "target_type", "scale"), fun = signif, digits = 3) %>% head() #> Some rows containing NA values may be removed. This is fine if not unexpected. #> Some rows containing NA values may be removed. This is fine if not unexpected. -#> model target_type scale wis overprediction -#> -#> 1: EuroCOVIDhub-ensemble Cases natural 11550.70664 3650.004755 -#> 2: EuroCOVIDhub-baseline Cases natural 22090.45747 7702.983696 -#> 3: epiforecasts-EpiNow2 Cases natural 14438.43943 5513.705842 -#> 4: EuroCOVIDhub-ensemble Deaths natural 41.42249 7.138247 -#> 5: EuroCOVIDhub-baseline Deaths natural 159.40387 65.899117 -#> 6: UMass-MechBayes Deaths natural 52.65195 8.978601 -#> underprediction dispersion bias interval_coverage_50 -#> -#> 1: 4237.177310 3663.52458 -0.05640625 0.3906250 -#> 2: 10284.972826 4102.50094 0.09726562 0.3281250 -#> 3: 3260.355639 5664.37795 -0.07890625 0.4687500 -#> 4: 4.103261 30.18099 0.07265625 0.8750000 -#> 5: 2.098505 91.40625 0.33906250 0.6640625 -#> 6: 16.800951 26.87239 -0.02234375 0.4609375 -#> interval_coverage_90 interval_coverage_deviation ae_median -#> -#> 1: 0.8046875 -0.10230114 17707.95312 -#> 2: 0.8203125 -0.11437500 32080.48438 -#> 3: 0.7890625 -0.06963068 21530.69531 -#> 4: 1.0000000 0.20380682 53.13281 -#> 5: 1.0000000 0.12142045 233.25781 -#> 6: 0.8750000 -0.02488636 78.47656 +#> model target_type scale wis overprediction +#> +#> 1: EuroCOVIDhub-ensemble Cases natural 11600.0 3650.00 +#> 2: EuroCOVIDhub-baseline Cases natural 22100.0 7700.00 +#> 3: epiforecasts-EpiNow2 Cases natural 14400.0 5510.00 +#> 4: EuroCOVIDhub-ensemble Deaths natural 41.4 7.14 +#> 5: EuroCOVIDhub-baseline Deaths natural 159.0 65.90 +#> 6: UMass-MechBayes Deaths natural 52.7 8.98 +#> underprediction dispersion bias interval_coverage_50 interval_coverage_90 +#> +#> 1: 4240.0 3660.0 -0.0564 0.391 0.805 +#> 2: 10300.0 4100.0 0.0973 0.328 0.820 +#> 3: 3260.0 5660.0 -0.0789 0.469 0.789 +#> 4: 4.1 30.2 0.0727 0.875 1.000 +#> 5: 2.1 91.4 0.3390 0.664 1.000 +#> 6: 16.8 26.9 -0.0223 0.461 0.875 +#> interval_coverage_deviation ae_median +#> +#> 1: -0.1020 17700.0 +#> 2: -0.1140 32100.0 +#> 3: -0.0696 21500.0 +#> 4: 0.2040 53.1 +#> 5: 0.1210 233.0 +#> 6: -0.0249 78.5 ``` ## Citation