From e613238e4796d71f6da6ba1193981725c75c3de9 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Sat, 16 Dec 2023 16:58:59 +0000 Subject: [PATCH] Automatic readme update --- README.md | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 3d28681ca..0158a960d 100644 --- a/README.md +++ b/README.md @@ -116,18 +116,20 @@ column names of the data into account. However, it is recommended to set the forecast unit manually using `set_forecast_unit()` as this may help to avoid errors, especially when scoringutils is used in automated pipelines. The function `set_forecast_unit()` will simply drop unneeded -columns. To verify everything is in order, the function `validate_forecast()` -should be used. The result of that check can then passed directly into -`score()`. `score()` returns unsummarised scores, which in most cases is -not what the user wants. Here we make use of additional functions from -`scoringutils` to add empirical coverage-levels (`add_coverage()`), and -scores relative to a baseline model (here chosen to be the -EuroCOVIDhub-ensemble model). See the getting started vignette for more -details. Finally we summarise these scores by model and target type. +columns. To verify everything is in order, the function +`validate_forecast()` should be used. The result of that check can then +passed directly into `score()`. `score()` returns unsummarised scores, +which in most cases is not what the user wants. Here we make use of +additional functions from `scoringutils` to add empirical +coverage-levels (`add_coverage()`), and scores relative to a baseline +model (here chosen to be the EuroCOVIDhub-ensemble model). See the +getting started vignette for more details. Finally we summarise these +scores by model and target type. ``` r example_quantile %>% set_forecast_unit(c("location", "target_end_date", "target_type", "horizon", "model")) %>% + as_forecast() %>% validate_forecast() %>% add_coverage() %>% score() %>%