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fix quantile/sample mixup in vignette #308

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nikosbosse merged 3 commits into from
Jul 25, 2023
Merged

fix quantile/sample mixup in vignette #308

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6be9f6c
fix quantile/sample mixup in vignette
sbfnk Jul 25, 2023
5d1216f
rename vignette
sbfnk Jul 25, 2023
e30db05
respond to lintr complaints
sbfnk Jul 25, 2023
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22 changes: 12 additions & 10 deletions vignettes/getting-started.Rmd → vignettes/scoringutils.Rmd
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Expand Up @@ -88,13 +88,15 @@ You can also visualise forecasts directly using the `plot_predictions()` functio

```{r, fig.width = 9, fig.height = 6}
example_quantile %>%
make_NA(what = "truth",
target_end_date >= "2021-07-15",
target_end_date < "2021-05-22"
make_NA(
what = "truth",
target_end_date >= "2021-07-15",
target_end_date < "2021-05-22"
) %>%
make_NA(what = "forecast",
model != "EuroCOVIDhub-ensemble",
forecast_date != "2021-06-28"
make_NA(
what = "forecast",
model != "EuroCOVIDhub-ensemble",
forecast_date != "2021-06-28"
) %>%
plot_predictions(
x = "target_end_date",
Expand Down Expand Up @@ -183,7 +185,7 @@ We can also summarise one particular metric across different categories using a
score(example_continuous) %>%
summarise_scores(by = c("model", "location", "target_type")) %>%
plot_heatmap(x = "location", metric = "bias") +
facet_wrap(~ target_type)
facet_wrap(~ target_type)
```

### Weighted interval score components
Expand Down Expand Up @@ -312,11 +314,11 @@ example_quantile %>%
facet_wrap(~ target_type, scales = "free")
```

## Tips and tricks - converting to sample-based forecasts
## Converting to quantile-based forecasts

Different metrics are available for different forecasting formats. In some cases, you may for example have forecasts in a sample-based format, but wish to make use of some of the functionality only available to quantile-based forecasts. For example, you may want to use the decomposition of the weighted interval score, or may like to compute interval coverage values.
Different metrics are available for different forecasting formats. In some cases, you may for example have forecasts in a sample-based format, but wish to make use of some of the functionality only available to quantile-based forecasts. For example, you may want to use the decomposition of the weighted interval score, or may like to compute interval coverage values.

You can convert your forecasts into a sample-based format using the function `sample_to_quantile()`. There is, however, one caveat: Quantiles will be calculated based on the predictive samples, which may introduce a bias if the number of available samples is small.
You can convert your sample-based forecasts into a quantile-based format using the function `sample_to_quantile()`. There is, however, one caveat: Quantiles will be calculated based on the predictive samples, which may introduce a bias if the number of available samples is small.

```{r}
example_integer %>%
Expand Down